diploma thesis - usf osnabrueck · diploma thesis “a participatory approach to policy assessment...
TRANSCRIPT
I
DIPLOMA THESIS
“A Participatory Approach to Policy Assessment in Complex Human-
Environment-Technology Systems
-
Application to Integrated Water Management in Cyprus”
by
Johannes Halbe
Supervisors:
1st: Prof. Dr. Gerd Förch
Research Institute for Water and Environment, University of Siegen
2nd: Prof. Dr. Claudia Pahl-Wostl
Institute for Environmental Systems Research, University of Osnabrueck
Case-Study in Cyprus: Dr. Jan Franklin Adamowski
Massachusetts Institute of Technology, Cambridge, USA
Submission Date: May 13, 2009
Revised: June 15, 2009
II
Task Description
Problem description
More and more technology-centered solutions for social needs and environmental problems are
mistrusted to be effective and efficient. Whereas often meeting short-term purposes, unanticipated
side-effects in terms of societal adaptation processes (e.g. water abundance leads to increasing water
use) or changing environmental impacts (e.g. climate change) lead to dysfunction. Hence, profound
management needs to consider these side-effects, and include them in their policies.
Taking the high uncertainties in human behavior and complex environmental processes into account,
prediction and analysis of the system can not be accomplished in all details. Consequently, the
contemporary command-and-control paradigm needs to be replaced by a learning paradigm that
acknowledges the inherent uncertainty of human-environment systems. Nevertheless, decision-makers
have to find ways to take action by dealing with this challenge constructively. The inclusion of
stakeholders in the decision-making and assessment of policies broadens the knowledge base and
foster commitment for the later implementation phase.
Theory
The theory of complex adaptive systems and the concepts of social learning and action research form
the foundation of the thesis. The panarchy metaphor exemplifies the challenges of management
approaches that try to cope with high uncertainties in evolutionary systems (Gunderson and Holling
2002). Therefore, changes can not be anticipated comprehensively as abrupt transitions might modify
the underlying structures and rules of the system. Consequently, these transformational shifts must
either be impeded or society has to find ways to adapt.
On the other hand, social learning processes support the exploration of mental models and their
adjustment to new circumstances. Social learning can also help to achieve convergence in the
perceptions and aspired management practices of water resources. Complexity has to be considered by
a broad and systemic perspective, particularly in situations where considerable gaps exist between
factual insights of the problem and mental models of the stakeholders (Pahl-Wostl and Hare 2004).
The concept of action research acknowledges the involvement of scientists in the change process of a
system. Action research assists the investigation of real-world problems as well as the evaluation and
implementation of policies. Participation of stakeholders is considered as a source of knowledge that is
essential in order to achieve transformation of the system (Checkland 1981).
Methodology
The thesis investigates the management of complex adaptive systems with a special contextual focus
on the role of technology. Human-environment-technology systems require interdisciplinary
approaches that reflect the interconnectedness of the underlying system. Issues with high uncertainties,
value judgments and conflicting interests preclude expert-driven solutions and require the participation
of stakeholders. Eventually, the need for participatory and systematic approaches also emerge from the
legislative demand for active involvement of stakeholders in water resource management (e.g. by the
European Water Framework Directive) and policy impact assessment (e.g. Impact Assessment
Guidelines of the EU).
In order to avoid excessive data collection and discussions, a scientific basis must guide this heuristic
III
process. Information and communication technology and computer-based simulation tools grounded in
complex-systems theory can help to conceptualize and explore complex systems. The ultimate goal is
the presentation of a framework that combines hard, expert-derived management with soft,
participatory approaches.
Technological solutions and social learning for the satisfaction of needs (e.g. water supply, waste
disposal, flood protection) can be considered as:
a) complementary: the technology has to be accepted and used appropriately by the stakeholders
b) substitutional: change in social behavior induced by continuous learning can be an alternative to
technological solutions (e.g. water demand management release the necessity for supply management;
designation of floodplains and adapted agricultural landuse instead of rigid dam construction)
The selection of the methods for social learning and action research are strongly dependent on the
context of the respective case study. Hence, the adequate methodology needs to be developed and
continuously readjusted in the course of the participatory process. In particular, the concept of group
model building is presented in the thesis which uses the system dynamics method to foster problem-
centered discussion. In this context, system dynamics models do not constitute the claim to reflect
real-world processes one-to-one. Rather it is a tool that supports learning in and about complex
systems (Sterman 1994). System dynamic models are virtual worlds that are employed to test mental
models of the real world. If these mental models turn out to be erroneous, they can be revised and
tested again. Hence, integrated policy assessment is an iterative process that strives for the
improvement of data collection, mental models, strategies and decisions (Sterman 2006).
Case study
The case study investigates social-technological options to mitigate drought-induced water shortages
in Cyprus at the national level. The study is integrated in a research project of the Cyprus Institute in
Nicosia about adaptive and collaborative water resources management. The implementation of a
participatory model building process structures and guides the participation of stakeholders.
A simplified water balance model of Cyprus is extended by social and environmental system elements
in order to enable an integrated policy assessment. Due to the complex nature of the system, different
perspectives are included derived from stakeholder interviews. In these individual interviews causal
loop diagrams are constructed and subsequently integrated in a holistic model. Follow-up interviews
and questionnaires ask the participants about their opinion in regard to the outcomes and insights
derived from the integrative conceptual model.
IV
Acknowledgements
This thesis would not have been feasible without the kind support of various people and
institutions. My thanks go to my supervisors Prof. Claudia Pahl-Wostl, Prof. Gerd Förch and
Dr. Jan Adamowski for their guidance and encouragement, and the members of the Institute of
Environmental Systems Research in Osnabrück for their assistance.
I am grateful for the financial support of the German Academic Exchange Service
(DAAD) which allowed me to visit the Division of Water Resources Engineering at Lund
University in Sweden. Special thanks to Prof. Peder Hjorth and Prof. Ronny Berndtsson for
mentoring me in my studies about the method of system dynamics and its application to
issues in water resource management.
I would also like to acknowledge the scholarship from the Ruhrverband, and the financial
and organizational aid of the Cyprus Institute in Nicosia for my case study in Cyprus. In
particular, many thanks to Prof. Manfred Lange, Dr. Pavlos Tsiartas and Mrs. Eroulla Cadd
for their multifaceted support.
V
Table of Contents
Task Description ………………………………………………………………………….…..…. II
Acknowledgements ……………………………………………………………………………… IV
List of Figures ………………………………………………………………………………..….. VIII
List of Tables…………………………………………………………………………………..…. XI
1 Introduction………………………………………………………………………………….....1
2 Theory: Management of Complex and Adaptive Systems………………………………….. 3
2.1 The role of science in the management of complex adaptive systems ………………. 4
2.1.1 Definition of tasks of science ……………………………………………... 4
2.1.2 Epistemology of action research ………………………………………...... 7
2.2 Systems theory ………………………………………………………………………. 8
2.3 Complex and adaptive systems theory……………………………………………….. 10
2.4 Participatory learning in complex systems ………………………………………….. 13
2.5 Integrated and Adaptive Water Resource Management ……………………………... 16
2.5.1 Integrated Water Resource Management ………………………………..… 16
2.5.2 Adaptive Water Resource Management ………………………………..…. 18
2.5.3 Synthesis ………………………………………………………………..…. 18
2.6 A participatory approach to policy assessment in complex systems ……………...…. 20
3 Methodology: Participatory Model Building by the Use of Systems Thinking
and System Dynamics………………………………………………………………………...…. 21
3.1 Problem definition………………………………………………………………….… 21
3.1.1 Problem framing…………………………………………………………… 22
3.1.2 The suitability of system dynamics………………………………….…….. 24
3.2 Stakeholder analysis …………………………………………………………….…… 25
3.2.1 Definition of 'stakeholder' …………………………………………….…… 26
3.2.2 Overall framework ………………………………………………………… 26
3.2.2.1 Stakeholder map ……………………………………...………… 27
3.2.2.2 Roles of stakeholders …………………………………………… 27
3.2.2.3 Power versus interest grid ………………………………………. 28
3.2.2.4 The dynamics of stakeholders …………………………….……. 29
3.2.3 Selecting the final stakeholder composition ……………….……... 30
3.3 Group Model Building ……………………………………………………...……….. 31
3.3.1 General features ………………………………………………….……….. 31
3.3.2 Proceeding of a group model building process …………………………… 32
VI
3.3.2.1 Step 1: Preparation ………………………………………….….. 32
3.3.2.1.1 Preliminary model ……………………………………. 32
3.3.2.1.2 Documents …………………………………………… 33
3.3.2.1.3 Personal interviews ………………………….…..…… 33
3.3.2.1.4 Workbook/Questionnaire ……………………….……. 35
3.3.2.2 Step 2: Workshops ……………………………………………… 35
3.3.2.3 Step 3: Follow-up ………………………………………...…….. 37
3.4 Systems analysis ……………………………………………………………..……… 37
3.4.1 Systems Thinking …………………………………………………...…… 38
3.4.1.1 Feedback loops …………………………………………………. 39
3.4.1.2 Time delays ……………………………………………...……… 41
3.4.1.3 Stocks and flows ………………………………………..……… 41
3.4.2 System Dynamics …………………………………………………………. 42
3.4.2.1 Formulation fundamentals of functional relationships …………. 43
3.4.2.1.1 Table functions ……………………………………….. 43
3.4.2.1.2 Calculation of stock and flows ………………………. 45
3.4.2.1.3 Delay Functions ……………………………………... 46
3.4.2.1.4 Smooth function …………………………………...… 47
3.4.2.2 Model testing ………………………………………………...…. 47
4 Case Study: Participative Assessment of Integrated Policies to Mitigate the
Effects of Water Scarcity in Cyprus……………………..…………………………………….. 49
4.1 The water scarcity problem in Cyprus ………………………………………………. 50
4.2 Stakeholder analysis ……………………………………………………………...….. 52
4.2.1 Application of techniques …………………………………………………. 53
4.2.2 Summary of the findings ………………………………………………….. 56
4.2.3 Participatory stakeholder analysis ……………………………………….... 57
4.3 Participatory model building ………………………………………………………… 57
4.3.1 Interviews ……………………………………………………………...….. 57
4.3.1.1 Personal model building from scratch………………………….. 57
4.3.1.2 Personal model building using a preliminary model……………. 59
4.3.1.3 Informal interview without personal model building………..….. 59
4.3.1.4 Success and problems in the interviews ………………………... 59
4.3.2 Questionnaire ……………………………………………………………… 59
4.3.2.1 The management sub-models ……………………………….….. 60
4.3.2.2 The social-environmental sub-model ……………………….…. 65
4.3.2.3 The policy sub-model ………………………………………. 69
4.3.2.4 Final remarks to the questionnaire results ……………………. 71
4.4 Quantitative simulation ……………………………………………………………… 71
4.4.1 System dynamics model …………………………………………………... 74
4.4.2 Hydrological system ………………………………………………………. 75
4.4.2.1 Hydrological model structure ………………………………...… 77
4.4.2.2 Surface depression ……………………………………………… 77
4.4.2.3 Soil water ………………………………………………………. 80
VII
4.4.2.4 Groundwater storages ……………………………………….….. 81
4.4.2.5 Surface water storage …………………………………………… 82
4.4.3 Water allocation system …………………………………………………… 83
4.4.3.1 Allocation rules …………………………………………………. 84
4.4.3.2 Satisfaction of potable and non-potable water demands ….…... 86
4.4.3.3 Domestic and agriculture water supply ………………………… 88
4.4.4 Calculation of the policy options ………………………………………..… 89
4.4.4.1 Desalination …………………………………………………….. 90
4.4.4.2 Wastewater recycling……………………………………………. 90
4.4.4.3 Water demand management ……………………………………. 92
4.4.4.3.1 Domestic water demand ……………………………… 92
4.4.4.3.2 Tourism water demand ………………………………. 100
4.4.4.3.3 Agriculture water demand…………………………….. 104
4.4.5 Model testing …………………………………………………………...…. 108
4.4.6 Scenario analysis………………………………………………………...… 110
4.4.7 Concluding comments …………………………………………………….. 118
4.5 Outlook for future research ………………………………………………………….. 119
5 Conclusions ……………….………………………………………………………………...…. 119
References………………………………………………………………………………………... XII
Appendix A: The different roles of the modeler
Appendix B: Causal loop diagrams from individual interviews
Appendix C: Project description
Appendix D: Example causal loop diagram which has been used in the interviews
Appendix E: Example for a causal loop model from a 1h-interview
Appendix F: Overall model structure of the system dynamics model
Appendix G: Model code for the system dynamics model
Appendix H: Examples for the compensation mechanism
Appendix I: Water balance of the Republic of Cyprus
Appendix J: Example for a decision rule of water rationing
Appendix K: Yearly Cyprus-wide precipitation rates
Appendix L: Reference modes of behavior
Appendix M: Example for a policy simulation interface
VIII
List of Figures
Figure 1: Problem-solving strategies for different problem attributes ............................................5
Figure 2: The cycle of action research in human situations ............................................................7
Figure 3: Caricature of nature ……………………………………………………………………. 10
Figure 4: The Adaptive Cycle.......................................................................................................... 11
Figure 5: 3-dimensional illustration of the adaptive cycle. ............................................................ 12
Figure 6: The feedback process of learning............................ ........................................................ 14
Figure 7: Conceptual framework for water resources management................................................ 15
Figure 8: General framework for IWRM ........................................................................................ 16
Figure 9: The interlaced connection between the economic, social, and environmental sphere…. 19
Figure 10: The overall approach that combines subjective perceptions with objective data.......... 20
Figure 11: Factors that should be considered in the problem definition ......................................... 23
Figure 12: Interrelation of the problem definition and stakeholder analysis................................... 25
Figure 13: Target Scheme to identify degree of involvement and type of stakeholder................... 28
Figure 14: Power versus interest grid.............................................................................................. 29
Figure 15: Stakeholder classes......................................................................................................... 30
Figure 16: Proceeding for of the construction of a causal loop diagram......................................... 34
Figure 17: Water Supply Management System……………............................................................ 38
Figure 18: Graph and causal structure of exponential growth......................................................... 40
Figure 19: Graph and causal structure of balancing behaviour........................................................ 40
Figure 20: Graph and causal structure of S-shaped growth............................................................. 41
Figure 21: Graph and causal structure of oscillation....................................................................... 41
Figure 22: Elements of Stock and Flow diagrams........................................................................... 42
Figure 23: Example for a table function ......................................................................................... 44
Figure 24: Pulse response of third-order delay by stage of processing........................................... 47
Figure 25: Response of higher order delays to a step input............................................................ 47
Figure 26: Mean annual precipitation Cyprus wide: 1901- 2002.................................................... 51
IX
Figure 27: Preliminary stakeholder list sorted by their respective role........................................... 53
Figure 28: Power versus Interest Diagram for stakeholders in Cyprus........................................... 54
Figure 29: Stakeholder classes belonging to the problem of water scarcity in Cyprus................... 55
Figure 30: Example of dam development loop from the questionnaire. ........................................ 60
Figure 31: Conceptual model structure of the ‘Water Scarcity’-system dynamics model. ............ 73
Figure 32: Graphical interface to implement data in the model. ............................ ....................... 74
Figure 33: Structure of the Continuous Soil-Moisture Accounting (SMA) Model......................... 76
Figure 34: Ration of actual to potential evaporation in the tension zone of the soil. .................... 76
Figure 35: Stock and flow structure of the hydrological model part1. ........................................... 77
Figure 36: Stock and flow structure of the hydrological model part2............................................. 81
Figure 37: Model structure of the surface water storage. ............................................................... 83
Figure 38: Stock and flow structure of the allocation model. ......................................................... 84
Figure 39: Assumed compensation ratio dependent on the capacity difference.............................. 85
Figure 40: Structure of the sub-model for groundwater extraction. ............................................... 86
Figure 41: Stock and flow structure of the wastewater treatment and reuse process. .................... 91
Figure 42: Water usage pattern in the domestic sector . ................................................................. 93
Figure 43: Water savings in a household in Cyprus ................................................................ 96
Figure 44: Stock and flow structure of the technological efficiency............................................... 96
Figure 45: Example of the implementation of efficiency improvements........................................ 97
Figure 46: Structure of the endogenous calculation of the domestic water demand. ..................... 98
Figure 47: Structure of the endogenous calculation of the tourism water demand. ....................... 100
Figure 48: Pattern of water use for a 3-star hotel ............................ .............................................. 102
Figure 49: Structure of the calculation of the agriculture water demand. ....................................... 105
Figure 50: Comparison of simulated and measured data................................................................. 108
Figure 51: Water scarcity indicators and the published water shortages......................................... 109
Figure 52: Annual precipitation levels and water scarcity indicators for scenario 1a. ................... 112
Figure 53: Water demands in scenario 1a. ............................ ......................................................... 113
Figure 54: Chosen capacities for desalination and wastewater recycling scenario 1b.................... 113
X
Figure 55: Water scarcity indicators for scenario 1b. ............................ ........................................ 114
Figure 56: Water demands with the application of water-saving technology in scenario 2a........... 115
Figure 57: Water scarcity indicators in scenario 2a. ....................................................................... 115
Figure 58: Water demands through application of demand management in scenario 2b ............... 116
Figure 59: Water scarcity indicators in scenario 2b ........................................................................ 116
Figure 60: Reduced annual capacities of non-conventional water sources in scenario 2c.............. 117
Figure 60: Water scarcity in scenario 2c.......................................................................................... 117
XI
List of Tables
Table 1: Calculation of the optimal efficiency in the domestic sector............................................. 94
Table 2: Calculation of the optimum demand in the tourism sector. .............................................. 103
Table 3: Defining reference of changes in planted crop types......................................................... 107
Table 4: Parameters from model testing........................................................................................... 110
Table 5: Assumed increases of technological and behavioral efficiencies...................................... 112
Table 6: Assumed increases in technological efficiencies............................................................... 114
Table 7: Assumed increases in behavioral efficiencies.................................................................... 116
1
1 Introduction
Up to today, the tremendous development of technology has led to an increase of opportunities and a
more comfortable life for many. Nevertheless, the number of problems does not seem to decline as a
consequence of more knowledge and capabilities. Water-related problems in particular are increasingly
complex and persistent as the needs of a multitude of people as well as the effects of measures on
processes in the ecosystem must be considered for a sustainable management. Major tasks of water
engineers like the supply of drinking water or flood protection demonstrate the resistance of complex
systems in respect to one-sided solutions. All the progress in technology of the past could not solve
these problems so far, as even prosperous nations still face water shortages, devastating floods and
water pollution. Especially the enduring miseries in many developing countries show that the flawless
operation of technologies is not guaranteed by sophisticated technical planning and implementation
alone, but also depends considerably on social and location-specific factors.
Usually unanticipated side-effects are the reasons for the failure of initially successful measures
and the emergence of new problems. Side-effects can emerge over long periods of time and also in
spatial areas that were not considered in the planning. For instance, the water management policies
that focused on the development of water supply by building dams, conveyance networks, or
extending groundwater exploitation have been quite successful in the short term. The unintended
effects of increasing water demands due to the perceived abundance of the resource have unfortunately
led to tremendous water scarcity problems in many countries though (e.g. Bagheri and Hjorth 2007).
The „Protect Landscape from the River‟ paradigm that aims at the avoidance of floods by riparian
embankments and river regulation serves as an example for spatial side-effects. The efforts to prevent
flooding encouraged the population to rely completely on the technical measures that had been taken.
This led to the settlement of former floodplains. By more and more preventing the natural dissipation,
floods became severe in volume and speed causing failure of dams, devastating destruction, and in
some cases even lasting stagnation of the economic and social life (Sendzimir et al. 2007, Green et al.
2000).
Side-effects can also emerge in other conceptual domains. Economic success for instance can
cause environmental and public health problems as can be seen in rapidly developing nations like
China (Wu et al. 1999). Other examples are the technical and economic difficulties of water utilities to
cope with decreasing water demands due to an increasing use of water-saving technologies (e.g. water-
efficient dish washers, or low-flush toilets). Here, a desirable development from the environmental
perspective leads to technical problems as the water supply infrastructure can hardly be downscaled.
In addition, the tariff structure of water prices is drawn on the volume of utilized water (cost/m³) so
that decreasing consumption causes monetary losses for water utilities as the major part of the costs
for water provision are fix costs (Tillman et al. 2005).
Profound and long-term oriented management has to anticipate and consider side-effects and
include them in their policies. Holistic approaches that illuminate the relevant social, environmental
and technical aspects of complex problems are needed. Besides the inclusion of various social and
environmental considerations, the demand to hear and include affected people in the decision-making
and planning process is growing for several reasons. First, public resistance can hinder the
implementation of measures by the forming of action groups or by taking legal proceedings. Second,
policies like water demand management depend considerably on the co-operation and information of
water users in order to influence their consumption behavior. Third, the knowledge of affected parties
based on the direct experiences with the problem or accompanied conflicts can nurture the finding of
effective and sustainable policies. Participation can therefore foster commitment and understanding,
2
simplify the implementation of measures, and offer immediate knowledge about the problem at hand.
However, the inclusion of interests and convictions of individuals and power groups makes the
planning even more complex and interminable, and does therefore often induce resistance of
responsible institutions towards participation. The engineer often resides in the focus of the conflict
domain as he has to acknowledge the various interests and demands and tries to find practical
solutions. The singularity of projects due to specific social-environmental and economic-legislative
circumstances avoids the application of simple decision rules. Consequently, a case-study approach is
needed that guides the decision-making process grounded on multidisciplinary knowledge about the
respective problem. The role of the civil engineer in this process is not the independent scientist who
stands outside the problem domain. The engineer should rather be an involved party that offers its
knowledge in order to find practical solutions together with interested and affected citizens.
These considerations are well-known and accepted by many scientists and engineers, but mostly
remain in the theoretical domain due to the absence of suitable approaches to organize and guide this
multidisciplinary and participatory process. Decision-makers are often satisfied with collecting
information instead of letting affected persons participate, and investigation of selected economic and
environmental aspects instead of a holistic inquiry.
Many universities have tried to find an answer to these gaps by adapting the education of civil
engineers to the growing complexity of their tasks. Courses in environmental science and business
economics have entered the curricula. Some universities even included ethics and the assessment of
technologies in their engineering education (Jischa 1999). Nevertheless, many scholars still attest the
lack of social and ecological sciences and general economics in the education which are needed to
embrace all relevant aspects in the planning in order to achieve a holistic management (Berndtsson et
al. 2005). The handling of conflicts and dealing with pressure groups is also usually not taught at
universities even though particularly water engineers need these interaction and conflict resolution
skills to communicate recommendations and proposals to the general public (Falkenmark and Folke
2003). Major reasons for the resistance of engineering towards the social sciences are the soft,
heuristic methodologies that stand in contradiction to the hard, mathematics-based approaches in civil
engineering. The demand of engineers to base their actions on reasonable and reproducible
calculations will certainly continue in the future. An approach for integrated and participatory
management should therefore combine „soft‟ and „hard‟ facts. At the same time, straightforward
methods are required that are amenable to all scientific fields and even to the non-academic world for
a transparent and participatory decision-making.
In this thesis, the approaches of system thinking and system dynamics are presented that comply
with these various requirements. System science can help to depict the underlying structure of the
problem in form of a computer model comprising the relevant social, economic, ecological and
technical processes, and derived dynamics. The participatory component will enter the process at the
construction phase by using the system thinking approach that allows the participation of interested
parties in model building. The system dynamics method enables the eventual simulation of the holistic
system. Scenarios demonstrate the behavior of the system in the future based on the assumptions and
knowledge of today. This allows profound and case-specific decision-making that considers the „big
picture‟ rather than cut and dried approaches.
The method is embedded in a new paradigm for water management that acknowledges the systems
that have to be managed as complex adaptive systems. Despite the command-control-paradigm that
strives for the reduction of uncertainties and optimization of measures, adaptive and integrated
management acknowledges incomplete information about the system by applying a learning paradigm.
Hence, the complexity of the system is illuminated by the participatory building of simulation models
3
that consider the various interconnected elements of the problem. The cooperation and discussion of
stakeholders simultaneously develop and strengthen their relationships that, in turn, increase the ability
of the group to manage the water resources jointly in the future. This expansion of „social capital‟ is
another gain of participatory approaches, besides the technical and problem-oriented outcomes.
Eventually, the implementation of policies is conceived as an experiment which needs to be monitored
in order to test and, if necessary, revise the model of the problem situation (Pahl-Wostl 2007).
The thesis is organized as follows: Chapter 2 contains the theoretical background of integrated and
participatory water resource management. The special role of the civil engineer is demonstrated by the
concept of post-normal science. The engineer is the suitable implementer of integrated studies due to
the case-study focus and the tight coupling of research and practice in engineering. The ambiguous
expression „complex systems‟ will be defined, and graphical metaphors illustrating different
perspectives on complexity are presented. The concept of the adaptive cycle is introduced that
provides an insight into the requirements for management frameworks in complex systems. The
importance of learning and participation are illuminated by the framework of social learning. This
leads to the concepts of adaptive and integrated water resource management that strives for the
integration of multi-disciplinary and participatory elements in water resource management.
The methods of system thinking and system dynamics that comply with the theoretical claims are
presented in detail in Chapter 3. The organization of the participatory process that comprises the
building, simulation and testing of the group model is also elaborated on.
Chapter 4 forms the main part of this thesis. It contains the application of the methodology to the
problem of water scarcity in Cyprus including a participatory model building process and a quantified
simulation model. The impediments of the application and the suitability of the method are discussed
on the basis of the experiences derived from the case study in Cyprus.
2 Theory: Management of Complex Adaptive Systems
The target of civil engineering projects is usually the provision of services for clients to meet such
basic needs as shelter (e.g. the construction of residential/commercial buildings or dikes), water (e.g.
the building of dams, waste water treatment plants or piped water distribution networks), energy (e.g.
transmission lines or power plants), or mobility (e.g. the creation of transportation networks or
bridges). The tight connection of engineering projects to environmental and social processes makes the
tasks challenging and interesting at the same time. The goals of the projects are often successfully met
by impressing technical solutions, for example huge concrete dams or miles long cable-stayed bridges.
However, there are numerous examples where technology-centered approaches turned out to be
shortsighted or completely failed to reach desired outcomes (see Chapter 1 for examples). In these
cases, unanticipated side-effects in the social or environmental sphere cause failure of technological
measures or outweigh positive short-term effects with unacceptable developments in the long run.
The introductory chapter marked the challenges that have to be mastered in order to achieve long-
lasting solutions for messy problems. The demand for multi-disciplinary and participatory approaches
does not only refer to the augmentation and refinement of methods in the various scientific fields. It
also stipulates a new paradigm of science itself by focusing on real-world problems and including
values and perceptions of affected parties rather than clinging to discipline-specific and reductionist
approaches. Students learn about the scientific method in the natural sciences as an objective approach
to generate knowledge in their first semesters at university. The influences of values, attitudes and
other personal factors are sought to be excluded from research and its outcomes. Testing hypotheses
4
by controlled and replicable experiments is very successful and has generated progress in natural and
applied sciences. Reductionism is another principle of science with which broad themes are divided
into smaller parts that, eventually, cause the emergence of more and more specialized scientific
disciplines (Checkland and Holwell 1998).
Despite the success to solve physical problems, the principles of science -reductionism,
repeatability and refutation- are not usable for phenomena that are “non-homogeneous through time
and, by extension, space” as Fontana (2006, p.167) quoted Keynes (1973) who discussed the
application of natural science methods in economics. Keynes concluded that in economics “any model
is historically and geographically determined” (Fontana 2006, p.167). Inquiries into systems that
include social elements require a different approach to knowledge generation. Hypotheses can not be
tested in the laboratory, as the outcomes are place-dependent (e.g. cultural aspects) and time-
dependent (solutions to today's problems might not work in ten years). Hence, the question arises how
to deal with path-dependent, evolutionary phenomena in science; or putting it differently: What are the
basic principles of a new kind of science that solves real-world problems?
This chapter presents the epistemological framework of post-normal science and action research
that points out the problems of core science to deal with complex problems and suggests an innovative
approach to acquiring knowledge. Subsequently, the definition of „systems‟ is given, followed by the
metaphor of the adaptive cycle that illustrates the features of „complexity and adaptivity‟. Sustainable
management in complex systems requires a learning paradigm that is discussed in this thesis with the
help of the concepts of double-loop and social learning. The approaches of adaptive and integrated
water resource management try to translate the demands of the theoretical considerations into practice.
After the exposition of these frameworks, their strengths and weaknesses are discussed. Finally, the
method of participatory group model building is considered to meet the requirements for integrated
and participatory management of water resources. Its theoretical background is sketched, before
Chapter 3 presents the approach in detail.
2.1 The role of science in the management of complex adaptive systems
Different problems require different approaches. Funtowicz and Ravetz (1993) bring forward the
argument that science itself is an evolving process that develops with its challenges. Thus, the
persistence of real world problems reveals the inefficiency of the reductionist, analytical approach
which strives for the reduction of uncertainty and control of nature. There is consequently the demand
for a new paradigm with a more holistic and systemic view which Funtowicz and Ravetz call „post-
normal science‟. Here, nature is perceived as an unpredictable dynamic and complex system whose
management has to include uncertainty, values and conflicts. Instead of attempting to reduce
uncertainty, the inherently uncertain future should be accepted and rather managed than avoided or
ignored. Values should be discussed explicitly in interactive dialogues instead of being presupposed.
Especially the quality of research needs to be assessed as outcomes can be interpreted differently
depending on interests or world views. Besides the „product‟ of research, also the „process‟, „persons‟
and „purposes‟ require an evaluation by an extended peer community which includes all stakeholders
of an issue (Funtowicz and Ravetz 1993).
2.1.1 Definition of tasks of science and suitable approaches for their solution
Nevertheless, analytical, reductionist approaches are still relevant and effective for many purposes.
Funtowicz and Ravetz (1993) therefore structure different approaches for knowledge generation and
problem solving in order to discriminate between the tasks of „post-normal science‟ and traditional
„core science‟ (see Figure 1).
5
The features of the scientific problem determine the most suitable approach for its solution. They
are specified by two dimensions: On the abscissa, system uncertainties that express the epistemic
aspects of the problem range from low uncertainties of standard problems and procedures up to high
uncertainties of messy and ambiguous problem situations. On the ordinate, the decision-stakes reflect
the conflict dimension of problems and the applied epistemology ranging from the accepted
epistemologies in conventional scientific research up to value-laden real world conflicts with
diverging interests, goals, points of views, and ethical considerations of stakeholders which preclude
standardized approaches.
The core, or basic, sciences that strive for the reduction of uncertainty and the elimination of values
and external interests from research by the paradigm of rationality and neutrality reside at the
intersection of the axes in Figure 1. Fundamental research is more curiosity-driven using an
analytical, “puzzle-solving approach” (Funtowicz and Ravetz 1993, p. 745) and is often funded by
public institutions in order to guarantee research that is not influenced by third parties, for example
businesses or lobbyists.
In applied science, system uncertainties reside at a technical level, and can be managed by
standard routines and procedures like statistical or stochastic approaches. This kind of research has
specified tasks that are usually connected to the demands of the target group, for example in the
economy or policy sector. Only limited external values and interests have to be considered except with
regard to the applicability of the research. For both, core science and applied science, peer reviews by
scientists or control by clients are appropriate methods for an internal quality assessment (Funtowicz
and Ravetz 1993). In this context, civil engineering uses the findings of core sciences like physics,
chemistry and biology, whereas applied sciences, like material science or hydrology, are traditional
fields of activity for civil engineers.
Professional consultancy has many similarities with the applied sciences. Professional consultants
are usually instructed by external clients and have to serve their interests and goals. Compared to the
applied sciences, encountered problems comprise a higher level of complexity and involve value
Figure 1: Problem-solving strategies for different problem attributes (after
Funtowicz and Ravetz 1993)
6
judgments. The methodology of applied science proves to be insufficient for these tasks and personal
decisions based on specialized skills are necessary. For instance, environmental impact assessment
projects merely preclude a statistical approach, as failure could enduringly affect ecosystems or even
human health. Hence, the personal judgment becomes important in contrast to the approach of the
applied sciences. Assigning the same tasks to distinct professional consultants could result in two
distinct proposals. Funtowicz and Ravetz (1993) appraise these ambiguous solutions as healthy and
inevitable, because the diverse outcomes and viewpoints reflect the nature of the problem. In this
context, civil engineers working in praxis and research are mainly professional consultants who deal
with projects that require the application of standard procedures on the one hand, and, on the other
hand, still demand some kind of intuition (Funtowicz and Ravetz call this “engineering judgement”
(1993, p. 748)). Especially hydraulic engineering projects can raise unique challenges without
textbook solutions.
Finally, post-normal science resides at the extremes in Figure 1. Contrary to core science, high
epistemological and ethical uncertainties as well as conflicts are involved here. As an example,
Funtowicz and Ravetz (1993) state the problem of climate change where long time-delays between
cause (e.g. CO2 emission, deforestation) and effect (e.g. sea-level rise, desertification) make
predictions difficult and lower the pressure to act. Although scientific forecasts can be very uncertain
decisions have to be made in the short-term to avoid possible devastating consequences of non-action.
The water scarcity of some Mediterranean islands serves as another example for complex problems.
Due to the absence of transboundary flows, these regions are heavily dependent on their local water
resources and precipitation events. Seawater intrusion further diminishes the already over-exploited
groundwater resources so that conflicts between the different water uses are prevalent (Lange and
MEDIS consortium 2004).
In these cases, the „hard‟ facts of science have to be relativized by „soft‟ measures like public
participation and ethical considerations. Funtowicz and Ravetz demand “an enriched systems theory,
deriving analytical rigour from it, and providing it with experience and insights” (1993, p. 751).
Legitimate participants like affected citizens play a central role by supplementing the peer-review of
the traditional sciences by challenging the underlying assumptions and valuations.
The description of post-normal problems is similar to the features of tasks that civil engineers in
water management are facing more and more often. Instead of focusing on the design of technologies,
projects demand the consideration of social and environmental factors as well as the inclusion of the
interests of affected citizens and organizations. Environmental impact assessments for large projects
have become the standard and encounter the limitations of the scientific method as predictions are
highly uncertain and assailable. In consequence, litigations initiated by interest groups can cause a halt
and postponement of the whole project (Kamphuis 2005). In this field, the engineer often has the role
of the communicator to the public who explains the planning and technical specifications. The
engineer also has to receive concerns, complaints and special requests of stakeholders and has to try to
consider them in the technical planning. These tasks are based on nearly all problem-solving strategies
depicted in the post-normal framework (see Figure 1). Findings of basic science, applied science as
well as the approaches of professional consultancy are utilized in order to find suitable solutions. To
fulfill these tasks, civil engineering has always been a practical and problem focuses field that
incorporated many subjects from chemistry to ecology in the course of time. As today's problems
demand a new approach which includes social science and participatory elements, it can be anticipated
that civil engineers will use the findings of post-normal science in the future for the solution of
contemporary complex real-world problems.
7
2.1.2 Epistemology of action research
The preceding explanations might be sufficient from a pragmatist's view. The success of a project and
the promise and usefulness of the post-normal approach can usually be measured and evaluated in
practice. Even if physical, chemical or biological indicators are not straightforward (e.g. in order to
quantify the water pollution in case of projects that focus on water quality improvement), surveys can
help to elicit qualitative indicators (e.g. for projects that aim at the satisfaction of customers). But an
important question emerges from a scientific perspective: How can new knowledge be acquired and
evaluated in the process of participative post-normal approaches? The realization of this kind of
research in the real world, instead of closed laboratories, and the inclusion of interests and perceptions
of stakeholders as well as the dealing with complex problems instead of neatly defined research
questions make generalizations of findings and comparison of cases difficult. In addition, the
imperatives of reductionism, repeatability and refutation can not be met due to the singularity of
problem situations. Nothing is sure in this process except for change itself (Checkland, Holwell 1998).
The framework of action research answers these epistemological questions and specifies the
preconditions for high-quality research in the realm of post-normal science. McKernan defines the
essential points of action research as: “first, action research is rigorous, systematic inquiry through
scientific procedures; and second, participants have critical-reflective ownership of the process and the
results” (McKernan 1996, p.5). The definition shows that systemic methods are mandatory for
rigorous research in real world situations. The scientist acts as the facilitator of a systemic inquiry that
is conducted by the stakeholders of a problem.
The case-dependency requires a new quality criterion of science that Checkland and Holwell
(1998) find in the „recoverability‟ of research, meaning that the conclusions that are drawn by the
scientist have to be traceable. Still, there can be disagreement about the valuation and heuristics
applied by the researcher. Scientific work should nevertheless contain the pieces of information,
theories, and methods upon which conclusions are drawn. Figure 2 shows this iterative process of
theory and methodology definition, and active involvement in problem solving.
A recoverable action research process requires the prior definition of the theories and methodologies
that are applied to the problem at hand. By doing so, the scientist has to clarify the epistemological
Figure 2: The cycle of action research in human situations (after Checkland, Holwell 1998)
8
question, i.e. what specifically counts as knowledge in the research process, first. Then, the theory and
methodology can be tested by entering the real-world situation. The experience from this endeavor
might lead to findings that help to achieve the aspired goals, or might pose new questions and research
topics. This is how the applicability and suitability of theories and methods can be tested, and
conclusions can be drawn for similar problem situations. In summary, the framework of post-normal
science and action research serves the necessary epistemology to improve real world situations.
Instead of research in the laboratory, recoverable and systemic action research supports the finding of
circumstances, prerequisites and trajectories to solve real-world problems.
The introductory chapter already presented the difficulties that are inherent to the management of
complex adaptive systems, and in particular to water resource management. Hence, a theory and
methodology for a systemic water resource management are needed that guide the participatory
process in order to avoid pitfalls as unintended side-effects and time-consuming litigations, and,
additionally, help to find generalizations that allow the comparison of findings. As this thesis focuses
on the assessment of policies in complex water resource systems, emphasis is put on participatory and
location-specific management of water resources (a post-normal approach) rather than on the
generation of universal knowledge about management and change processes. However, the interplay
of both case-specific and generalizing research are considered to be necessary in order to achieve a
sustainable management of resources.1
With respect to the demands of the epistemological theories of post-normal science and especially
action research, the theories that are considered to be suitable for the solution of water management
issues are presented in the following. Thus, systems theory forms the theoretical basis of the demanded
systemic approach, while the framework of social learning serves the theory for participatory
processes. Finally, the concepts of integrated and adaptive water management specify the tasks and
concepts in the management of water resources.
2.2 Systems theory
The system approach is a meta-theory as it is not exclusively used for the inquiry of systems but also
for knowledge generation in scientific disciplines like physics, biology or engineering. Here,
Descartes's assumption is opposed that the division of a scientific problem into smaller parts will not
distort the original phenomenon (Checkland, Holwell 1998). Reasonable for many physical issues,
reductionist approaches are inadequate for complex social-environmental problems. Here, the
interdisciplinary processes form a problem situation so that the system demands a holistic view
because “the whole is more than the sum of its parts” (Aristotle after Makin 2006). This chapter
presents the meta-theoretical aspects of complex adaptive systems before Chapter 3 elaborates on the
application of systems research by the use of specific methods.
The definition of systems shows that not any object can be a system, but that it requires certain
features. First of all, the object must have a special purpose that can be noticed by an observer.
Second, the object must consist of system elements that are connected by causal links which form the
system‟s structure. Third, the object must have a system identity that would be destroyed if parts of the
system structure were separated. Based on this definition, a chair is a system as it has a purpose
(sitting), consists of a system structure (chair legs and back, sitting plate), and would lose its integrity
if an element was removed. In contrast, a sand heap is not a system despite a purpose (e.g. storage of
1For approaches which investigate general patterns in case-study research see the Management Transition
Framework (MTF) for the systemic analysis of transformations in water management regimes (Pahl-Wostl et
al. 2008), and the Institutional Analysis and Development (IAD) framework for the inquiry of institutional
and policy processes (Ostrom 1994).
9
sand) and a system structure (merged grains). A removal of sand would not destroy the system identity
of the sand heap, so that the third mandatory feature of a system is not met (Bossel 2004).
The application of this definition to the concept of system research shows that the purpose of the
systems approach is always the explanation of the misbehavior of the problem variable(s) of the issue
at hand. The system structure is therefore extracted by the use of methods from system science (see
Chapter 3). Redundant structures are avoided by meeting the standard of simplicity that Albert
Einstein formulated as the goal of scientific theory: “It can scarcely be denied that the supreme goal of
all theory is to make the irreducible basic elements as simple and as few as possible without having to
surrender the adequate representation of a single datum of experience“ (1934). Consequently,
simplicity in the modeling of a system is necessary to create and sustain the systemic identity.
The previous explanations examined the theory of systems without relation to the special attribute
that water resource systems feature: complexity. However, a correct understanding of complexity is
required to derive the appropriate management paradigm and the related policy framework.
Complexity can be understood as „combinatory complexity‟ in problems with numerous solutions
where the optimal one needs to be discovered. The screening of the best mix of compounds in
pharmaceutical products is an example for these “needle-in-the-haystack” problems. However,
complex adaptive systems have the feature of „dynamic complexity‟ that induces counterintuitive
behavior of the system (Sterman 2006). This kind of complexity requires a precise definition as
different perceptions of the meaning can be encountered. Here, complex systems are considered to be
(after Pahl-Wostl 2007):
(1) evolutionary: the systemic processes depend on the historical context. Consequently, entirely
new system states can emerge in the future.
(2) adaptive: control of the complex system is not possible, as it adapts to interventions by
changing its structure
(3) non-linear: non-linearities make transitions of the system state possible that can not be
analyzed by probability
(4) ambiguous: personal perceptions on morals and risks bias the perspectives of stakeholders on
the issue
Hence, the future in all its aspects is unpredictable as the evolutionary process renders the system
states of the real world system to be unique. Optimization and command-and-control approaches are
therefore ineffective in the long-term as they depend on the knowledge about the development of the
system state.
Due to the inherently complex nature of reality, humans have always established models that
simplify their environment and problems in order to plan and anticipate the future. Ideologies,
traditions and scientific theory illustrate reality in a simplified way and can offer orientation in a fast
and complex world (Bossel 2004). How information is selected and meaning is created depends on the
mental model of the respective person. Problems are defined and decisions are made based on these
mental models. Related to systems, mental models determine the causes and effects, the boundary and
the time-horizon of the system (Sterman 2000). Pahl-Wostl (2007) adds frames as another concept that
influences the construction of reality. Frames refer to the context in which mental models are stated
and from which their meaning can be derived. People have therefore different mental models as they
frame reality by taking different roles, interests, or viewpoints, for example as a decision-maker,
entrepreneur, affected citizen, or scientist. Hence, multiple frames about a system can cause a
controversial discussion where people seem to talk about the same object, but, in fact, talk about
different system representations. For instance, in case of sustainable water resource management, a
10
politician might express the legislative standpoint based on the EU water framework directive, an
environmentalist might stress the importance of water quality for the ecosystem, while an entrepreneur
might talk about the industrial development of the region that maintains jobs for the inhabitants. All
these perceptions are „true‟ from a certain viewpoint. The resolution of conflicts requires the elicitation
of mental models and frames of stakeholders in order to allow a rational discussion.
An in-depth presentation of the role of perceptions and mental models in the construction of
reality is given in Chapter 2.4. The following chapter deals with an integrative theory about complex
systems that explains the dynamics of ecological, social and organizational change. Besides serving as
a mental model of complex systems, it helps to derive strategies for the management of these systems
that have to be tested and validated by their application in practice.
2.3 Complex and adaptive systems theory
Concepts of adaptive and complex systems can guide the search process to the most important
processes that determine the behavior of complex systems. In order to illustrate the different notions of
complexity and complicatedness, Holling et al. (2002) created a caricature of the mental model of
complicated systems that underlie the application of the command-and-control policies (see Figure 3).
Here, the system has a fixed structure that can be discovered by research, graphically presented by a
landscape with hills and caverns. The state of the system (illustrated by the small ball in the middle) is
amenable to navigation through the fixed system environment. There are stable states (the caverns)
and instable states (the hills) that can be targeted depending on the goals of management. In the system
above, there are two stable states available while „a‟ would be preferred due to the higher target value
in comparison to „b‟. In such a world, the system structure of a problem can be completely analyzed,
and an optimal policy can be defined on this basis.
Holling et al. call this world view “not wrong-just incomplete” (2002, p. 13) since such a stable
landscape is possible and allows policy setting at the optimum. Nevertheless, Holling et al. (2002)
emphasize that these stability domains usually exist only in the short-term. Over time, the non-linear
behavior of complex systems can cause a transformation of the system's structure so that policies that
have been optimal before turn out to be erroneous in the present situation. The different view on the
existence of the transformability of the system structure is considered as the basic distinction between
optimizing and complex adaptive management approaches.
Abrupt, transformational changes are attributes of evolutionary systems. According to research,
three properties are central for the disposition of a system towards these structural shifts: its potential,
connectedness, and resilience (Gundersson et al. 1995b). The potential of the system indicates the
stored energy and material in a system and, thus, the availability of options for change in the future.
The connectedness of the system elements represents the strengths and amount of relations that affect
trajectory Phase space
trajectory
metaphor
trajectory
trajectory Phase space
trajectory
metaphor
trajectory
Figure 3: Caricature of nature that underlies the perceptions of the command-and-control paradigm. It
is depicted: On the left, the caricatures of the paradigm; in the center, the phase diagram; and on the
right, the trajectory of the system (Holling et al. 2002)
11
the controllability of the system‟s behavior. The resilience specifies the resistance or adaptive capacity
of the system structure in respect to exogenous shocks. Thus, a highly connected system with a low
resilience and a high potential is susceptible to transformation, contrary to a system with low
connectivity of the elements, high resilience towards shocks, and low potential.
In order to visualize these systemic transformations, Holling (1986) created the concept of the
„adaptive cycle‟ (see Figure 4) that can explain aspects of the evolutionary development of systems in
the environmental and social sphere.
The illustration of the adaptive cycle in Figure 4 depicts only two dimensions of the transformability
of systems, their connectedness and potential. The case of low connectedness and potential is called
the „r‟-function of the system. Examples of this „exploitation mode‟ might be an ecosystem where a
species becomes dominant and starts to exploit the resources. In the economy, an innovative enterprise
stands up to its competitors and earns more and more market share. The elements of the systems are
not arranged in a fixed order but slowly start to create and strengthen their connections and networks.
Plants colonize a disturbed area and new kinds of businesses flourish. In this system state, the
development is rapid and connectedness and potential increase more and more. The system enters the
conservation phase (K) where high amounts of energy and material are stored and a certain form of
organization prevails. In ecosystems for instance, the growth rate has slowed down and a balanced set
of species from flora and fauna has emerged. In the economic sphere, the growth has also declined and
bureaucratic hierarchies and regulations have replaced the aggressive and competitive market
mechanisms (Holling et al. 2002).
But from experience it is known that the conservation phase is not the final point of development. In
the ecosystem example, the matured forest that has grown from the burned area is the fuel for the next
forest fire. Or, economically speaking, a rigid production system fails when it can not adapt to
changing circumstances. The transformation of the Soviet Union from a centrally planned economy to
a market economy might serve as an example (Levin et al. 1998). Based on the adaptive cycle, the
system slips in a release phase due to rigidity and high potential. The order and functioning of the
system brakes down and releases its intrinsic potential. This rather fast process is triggered by a crisis
like a forest fire or a revolt. In the Ω -phase, the system elements are disconnected more and more, and
innovation and restructuring of previously suppressed elements takes place. In the sphere of the
Pote
nti
al
Connectedness
Figure 4: The Adaptive Cycle (Holling 2001)
12
economy, Schumpeter's concept of 'creative destruction' (1943) denotes this process in which mature
technologies do not fit anymore to familiar surroundings and slip into a crisis.
In the 𝛼-phase, different solutions co-exist and develop independently, until the system
consolidates and a particular organization of the system emerges (r-phase). In ecology, a vegetation
type becomes dominant, or, in economy, the best-fitting innovations develop, earn higher profits and
thereby replace suboptimal ones (Holling et al. 2002).
The third property of systems, its resilience, determines the susceptibility of the structure towards
transformation, and renders the adaptive cycle three-dimensional (see Figure 5). The resilience of a
system represents its adaptive capacity, i.e. the property of the system to counter external forces
without losing its integrity.
In the conservation phase, the resilience is low as the system has become rigid. Conservative forces
hamper adaptation towards new challenges as they would induce a reorganization of the system. The
failure costs of innovations are high as earned potential could be lost and the success of the new
organizational structure is not guaranteed. But in the reorganization phase the resilience is maximal,
because low failure costs and absent constrains allow creative experimentations of novelty and the
adaptation to changes in the system environment is possible (Holling 2001).
In contrast to biological or physical processes, human systems have developed mechanisms to
avoid the breakdown of institutions or society (phase K → Ω). Management can anticipate the release
phase and take countermeasures. The economic system is also organized in a way that steadily creates
innovations that adapt economy and society to changing circumstances. Increasing resource
exploitation, pollution of the environment and lasting poverty have also initiated a social discussion
about the sustainability of the western market economy system (e.g. Meadows et al. 1972). Related to
the adaptive cycle, the western countries have accumulated wealth and potential which have brought
them to the conservation phase K. A further extension of the contemporary economic system by
developing nations like China or India might exceed the carrying capacity of nature and lead to
collapse. As a reaction, new structures and organizational measures are discussed in order to avoid
social and environmental crises (e.g. Yunus 2009). In summary, the capacity of society to adapt to
constraints by proactive policies helps to avoid the shift into the release phase.
The presentation of the adaptive cycle shows that optimal solutions to resource problems as water
Figure 5: 3-dimensional illustration of the adaptive cycle. The dimension of resilience is added to
the dimensions of connectedness and potential (Gunderson and Holling 2002)
13
scarcity are problematic if they are too rigid. The notion of a fixed world turns out to be wrong in
complex systems. Management should rather strive for a high adaptive capacity of the system in order
to be able to react to unanticipated future changes that can not be avoided. The understanding of the
term „sustainable development‟ based on these insights can be stated as follows: “Sustainability is the
capacity to create, test, and maintain adaptive capability. Development is the process of creating,
testing, and maintaining opportunity. The phrase that combines the two, 'sustainable development',
therefore refers to the goal of fostering adaptive capabilities while simultaneously creating
opportunities” (Holling 2001, p. 399). The transformation of the command-and-control management
approach to a learning paradigm in particular should enhance the adaptive capacity of water
management regimes so that future challenges can be met more effectively. In this new paradigm,
“policies are really questions masquerading as answers” (Gundersson 1999). Success in the real world
has to be monitored and compared to the expectations of the particular situation.
In this context, civil engineering applies the demanded case study approach, meaning that projects
are designed for unique situations guided by a methodological proceeding which is based on the
respective standards. These standards are based on empirical inquiries, for example in materials
science, or accumulated experience in practice over time. Whereas empirical findings belong mainly to
physical aspects, the impact of experience can even include social processes like human behavior or
preferences. For instance, standards of job safety anticipate dangerous tasks on construction sites and
the potential misjudgment of risks by employees (see for example DIN EN 12811 for working
scaffolds). Safety regulations of tools are also partly built on experiences of accidents, as the full range
of handling errors is hardly predictable (see for example EN ISO 12100 for the construction of safe
machines). Consequently, a learning process is already applied in engineering that even includes
behavioral aspects by learning from experience.
Gundersson (1999) acknowledges this by stating that “some learning occurs regardless of the
management approach”. Nevertheless, the learning paradigm aims at speeding up that process by
enhancing the institutional learning capabilities. In the past, institutions and approaches as well as
technical standards adapted to problems slowly by cycles of success and failure (Wesley 1995).
Adaptive management aspires more flexibility towards reform in order to seek opportunities and
relinquish ineffective practices (Gundersson 1999). Flexibility and learning are central attributes that
preserve or enhance resilience of the institutional, social and economical subsystems so that
disturbances and unexpected challenges can be met by adaptation.
But how can learning processes be practically implemented in water resource management?
Learning of individuals and organizations has been a research topic for a long time. However, learning
of multiple organizations and individuals about a complex system makes the process more challenging
due to different interests and backgrounds of stakeholders. The next chapter therefore presents the
theory about learning processes in complex systems by the concept of social learning.
2.4 Participatory learning in complex systems
The integrated and sustainable management of water resources requires the incorporation of social,
economic and environmental effects in the planning process that are perceived to be important by
stakeholders. The demanded participation compounds this challenge as diverging interests and
perspectives enter the process and make its outcome unpredictable.
These new impediments for a smooth and quick management process are, however, necessary due
to the complex and adaptive nature of the resource system. Rather than accepting ambiguous, delayed
and biased decisions, new ways of management can help to structure the process and speed it up to the
point of more intelligent and sustainable solutions.
14
2
3
1
Sterman (2006) demands a double-loop learning process in order to acquire insight into complex
behaviors due to an unknown system structure, time delays, and non-linearities. Single-loop learning
describes the common learning process in real world situations where our senses detect information
that is processed by our mental models (loop 1 in Figure 6). If the actual situation is perceived to be in
contrast to our goals, decision rules initiate a reaction that changes the real world accordingly (loop 2).
The outcomes of the action are again detected and evaluated until the desired situation is achieved.
Sterman (2006) gives the example of steering a car, where the goal of driving the car in the middle of
the lane is approached iteratively by turning the steering wheel. Single loop learning takes place
through fixed decision-rules or policies by institutional structures, organizational strategies, cultural
traditions, or other frameworks that define how things have to be done. Even though this learning
process is straightforward and quick, it is ineffective in complex systems where policies have to adapt
to the actual situation without having the opportunity to resort to well-known approaches.
Therefore, a learning process is needed that changes the decision rules, mental models and frames
according to the circumstances. The alteration of mental models and frames in particular is required in
situations where diverging interests and perspectives clash and new ideas for mediation and
collaboration are needed. Consequently, the information feedbacks that arise from the real world have
to induce a rethinking of the mental models and frames that are connected to habitual expectations,
conditions and perceptions of the system (see loop 3 in Figure 6). The policy setters and participants
that are interested in a suitable perception of a problem situation have to ask themselves: What are our
mental models? Are they correct and based on reliable information? Which are the ways to approve or
challenge my perception? By revising the mental models, new strategies and decisions as well as
research questions that should be investigated can emerge. This double-loop learning process
continuously adapts to real world challenges. In order to be successful, the learning process “must be
able to cycle around the loops faster than changes in the real world render existing knowledge
obsolete” (Sterman 2006).
Let alone the challenges for individuals to become more flexible and avoid adherence to fixed
ways of thinking due to, for example, education and cultural traditions, the difficulties of this concept
of learning increases significantly in the case of groups. This special learning environment has been
analyzed for the case of organizations like firms or public agencies where reality is experienced by
setting collective meaning through practice, rituals and heuristics (Argyris and Schön 1978, Wenger
1998). These concepts are still different though from the situation in water resource management
where decision stakes, ambiguity and institutional variety are high. Here, actors from the local level
have to collaborate with national entities, and environmental interest groups have to have discussions
with industrial stakeholders. For these multi-party and multilevel situations, the HarmoniCOP
Figure 6: Conceptualization of a learning process about the integrated and dynamic resource system
(after Sterman 2000)
15
(Harmonizing Collaborative Planning) project developed a social learning framework specifically for
river basin management (see Figure 7).
The process of social learning is separated into three interconnected stages. The context stage
comprises the type of governance, institutions, actors, and culture as well as the natural environment
and the technology belonging to the respective management issue. These circumstances determine the
starting point and the environment of the social learning process. The process stage of social learning
relates to the factual content of the issue and the social environment that pertain to the stakeholders
and their relationships. In order to improve the resource management practice, both aspects have to be
developed, the content-specific as well as relational aspects. This double-tracked process is
implemented by relational practices that aim at the improvement of specific goals of resource
management (e.g. the reliable and cost-recovering provision of drinking water) and, at the same time,
the improvement of the stakeholder relations (Pahl-Wostl et al. 2007). Whereas the content-centered
process refers to the contemporary outcome-oriented approach that is measurable by quantifiable
indicators, the more socially oriented task strives for the establishment of the capacity of stakeholders
to manage by collaboration. Relational tasks refer to the framing of the problem, the organization of
the learning process and the sharing of responsibility in the later implementation phase. The outcome
of the social process can be regarded as accumulation of social capital that is defined as the ability of
organizations to achieve collaboration and coordination (Putnam 2000). Both, social capital and the
ability to generate knowledge, are features that describe the adaptive capacity of social networks.
Joint practices of participants strengthen and change the relationships and improve the ability of
the group to solve future problems. As expressed by the feedback arrow in Figure 7, the outcomes of
this process again influence the context of resource management by changing institutional
responsibilities or revising policies. In doing so, the process iteratively strives for the achievement of
technical indicators as well as the capacity of stakeholders to jointly manage the system (Pahl-Wostl et
al. 2007). The framework of social learning about complex systems forms the theoretical basis for
participatory management. It points to the imperative of inclusion of stakeholders in order to achieve a
sustainable and integrated water resource management.
Figure 7: Conceptual framework for water resources management (Pahl-Wostl et al. 2007)
16
2.5 Integrated and Adaptive Water Resource Management
Two other well-known frameworks strive for the inclusion of the necessary participatory and
integrated elements in the practice of water resource management: Integrated Water Resource
Management (IWRM) and Adaptive Management (AM). These two concepts are presented in the
following and their effectiveness is evaluated on the background of the theories of social learning and
complex adaptive systems.
2.5.1 Integrated Water Resource Management
The concept of Integrated Water Resource Management tries to embrace the tasks of holistic and
participatory water management. Due to the multiple dimensions and academic fields that are involved
in this endeavor, the concept is not interpreted uniformly. The Global Water Partnership Technical
Advisory Committee (GWP) phrased a widely used and accepted definition (GWP TAC 2000, p.22):
“IWRM is a process which promotes the co-ordinated development and management of water, land
and related resources, in order to maximize the resultant economic and social welfare in an equitable
manner without compromising the sustainability of vital ecosystems”. This definition underlines the
process-character of the approach that strives for an iterative management by balancing trade-offs in
the economic, social and environmental sphere and tries to find win-win situations. The overall goals
are the sustainability of ecosystems and social equity that are promoted by an integrative, cross-
sectoral and participative water management (cp. Jønch-Clausen and Fungl 2001).
Consequently, sustainability in the IWRM concept stands on the three pillars: Environmental, social
and economic sustainability (see Figure 8). This follows the tradition of the Brundtland Commission
(WCED, 1978) which defined sustainability as the balance between development and environmental
concerns. In particular, the commission perceived sustainable development as „„development that
meets the needs of the present without compromising the ability of future generations to meet their
own needs‟‟ (WCED, 1987). IWRM presents practical approaches to achieve the goal of sustainability
in the respective realm of the three pillars. Economically, water should be used as efficiently as
possible. This refers mainly to the reduction of leakages and application of water-saving technology,
Figure 8: General framework for IWRM (GWP TAC 2000, p.31)
17
the cost-recovering provision of water to the users, and the fostering of conscious consumption
behavior. The pillar of social equity expresses the human right for adequate water supply in quality
and quantity to satisfy basic needs. Here, the government is perceived to play an important role as
controller and regulator, whereas private service providers should be responsible for the operation and
finance of measures where possible. The ecological pillar demands the preservation of the life-support
system for the well-being of future generations, for instance by including environmental costs in the
valuation of water (GWP TAC 2000). Inside the triangle in Figure 8, three components are depicted
that are considered to be important by the GWP TAC (2000) for a successful implementation of
IWRM. First, there has to be a legislative and policy environment that facilitates the process and sets
the rules and mechanisms of participation, decision-making and enforcement. Second, governance
needs a functioning network of local/national and public/private institutions that is adapted to the side-
specific cultural, geographical and environmental conditions. Third, a tool box containing scientific
methods of knowledge generation, information management and data-processing should help the
decision-makers to assess reasonable and effective measures.
The decision-making process should proceed as follows. In the first step, the status of the water
resource issue has to be defined and prioritized by taking progresses in the IWRM framework and
international developments into account. This should be done in a participatory way, and should
involve stakeholders from the highest political level to the local users in order to foster commitment
and willingness to reform. Based on this analysis, the gaps towards a sustainable water management
are assessed and potentials and constraints of the IWRM process are defined. This leads to the
preparation of an action plan where measures, institutional roles and financial considerations are
specified. Intense stakeholder involvement is required to build up commitment of actors. Eventually,
the resulting framework is implemented and the outcomes are monitored, so that a new cycle begins
with a revision of the management status (Jønch-Clausen 2005).
Medema et al. (2008) conduct an evaluation of the IWRM concept and its outcome in the course
of time and arrive at a sobering diagnosis. For them, the definition is still ambiguous despite the recent
efforts of the GWP aiming at clarification (see GWP TAC 2000). Beside the theoretical framework,
Medema et al. (2008) call for specifications in respect to what is meant by coordination and
integration of knowledge and decision-making, and which institutions should participate. Further
complaints point to the non-comparability of case-specific studies due to different physical, economic,
social, cultural and legal conditions (Biswas 2004). In fact, evidence about the benefits of IWRM are
generally put in question, as scientific publications lack specific outcomes and are poorly reported
(Jeffrey and Gearey 2006).
The criticism due to the absence of comparability of results refers to the issue of knowledge
generation that is discussed in Chapter 2.1 of this thesis. The concept of IWRM belongs to the realm
of post-normal science and action research by dealing with complex problem situations. Thus, a
replicability of results is impossible with respect to case-specific processes and organization of
research. Instead, Checkland and Holwell (1998) demand recoverability of research in order to
preserve the quality of scientific work. Hence, also science on the background of IWRM needs to
define the knowledge basis of their research, which aspects they specifically want to investigate and
which outcomes count as knowledge. Furthermore, a continuous and precise documentation of the
research process is mandatory to achieve recoverability.
Similarly to IWRM, the framework of adaptive water management also aims at a sustainable
management of water resources. This concept however responds more specifically to epistemological
issues. In the next passage, the concept and findings of adaptive management are presented in detail.
2.5.2 Adaptive Water Resource Management
18
Whereas IWRM focuses more on the integration of knowledge across scientific disciplines, sectors
and space, adaptive management (AM) stresses the role of uncertainties in the planning process.
Similar to the IWRM framework, AM also demands an integrated and multidisciplinary approach to
reduce surprising side-effects and unintended outcomes, but assumes instantaneously that surprises
and uncertainties are inevitable due to the adaptive behavior of the environment (Holling 1978).
Although AM has various origins, the quest for a concordant definition seems to be advanced
compared to the concept of IWRM since its development was promoted by a smaller group of people
from the ecological sciences. Holling, who considerably contributed to the concept of AM, describes
the approach as “an integrated, multidisciplinary and systematic approach to improving management
and accommodating change by learning from the outcomes of management policies and practices”
(1978). Or as expressed more concisely by Bormann et al. (1993): “adaptive management is learning
to manage by manage to learn”. The command-and-control management of water resources is
considered to be a concept that replaces the inherent uncertainty of resource issues by the certainty of
a process that can be of legal (e.g. regulations or standards) or institutional (e.g. expert committees)
nature (Gunderson 1999). The security of these rigid approaches is perceived as illusory because it is
based on the notion of a stable resource system that allows continuous policy making supported by
fixed rules.
The research process of AM starts with the setting of alternative hypotheses of the system
behavior and the internal causal structure. These hypotheses are subsequently translated into action
plans that define the needed interventions in order to improve the state of the system and inspect the
research questions. Monitoring and evaluation of the implementation and operation processes
determine the accuracy of the hypotheses and the lessons that were learned. This can finally lead to
new hypotheses that trigger a new policy circle (Walters 1986, Medema et al. 2008). This
epistemological framework of AM is case specific, but still makes generalizations possible. By
applying a systemic approach, the structure of the system is elicited, tested and verified in the course
of the research process. In the end, the iterative systemic approach helps to improve the model of the
system and the derived policy options. The research process distills and sorts the voluminous
information about the specific case in order to acquire knowledge about the system at hand. Thus,
models are abstractions and, therefore, simplifications of the complex system that enables the
comparison of case-studies. Lessons that have been learned can inspire and support the management in
similar systems at other locations.
Medema et al. (2008) discuss the evidence and success of AM and detect various impediments for
the process that are reported in the literature. Similar to the concept of IWRM, the implementation of
AM is criticized as not well planned and reported. Major obstacles for success are the resistance of
decision-makers to give up some of their operational power in favor of a participatory process, and the
unwillingness of stakeholders to commit themselves to a time-consuming process, and potentially
costly and risky experimentation (Lee 1999). In respect to the AM framework, there is the danger of
sticking too close to the modeling process in order to construct a perfect and presentable model instead
of focusing on the improvement of the resource management (Walters 1997). The problems and
obstacles of AM are therefore comparable to the ones of IWRM as the concepts have similar elements
and goals. The following paragraph concentrates on the similarities and differences, and tries to bring
them together.
2.5.3 Synthesis
The two concepts of IWRM and AM have the same overall goal in the achievement of sustainable
management of the scarce and precious resource water. Both approaches stress the importance of
19
integrated management and learning processes of stakeholders. The definitions of the two frameworks
are sometimes blurred and the expression “integrated and adaptive water management” (e.g. Garrido
and Dinar 2009) shows that the different concepts do not necessarily imply two competing
frameworks, but that coherence is possible.
Nevertheless, the two frameworks have two different origins with AM coming from ecological
science, and IWRM originating in engineering science. AM has a more profound theoretical
background, whereas IWRM serves more practical approaches (Pahl-Wostl and Senzimir 2005). The
combination of the two concepts in the future can help to foster the required discussion and
cooperation between humanities and engineering, decision-makers and affected parties, as well as
theory and practice. AM could lift the IWRM approach on a more theoretical basis in order to guide its
implementation. For instance, the three pillar concept of IWRM suggests the trade-off between social,
environmental and economic sphere. This practical concept certainly helps to find consensus in
conflicts by requesting allowances from all parties, but can be inconsistent with the preservation
imperative of the ecosystem. The theory of complex systems rejects the notion of three equitable
pillars as a functioning environment is the prerequisite of a functioning society that, in turn, is required
for economic activity as depicted in Figure 9. As a consequence of this perspective, research needs to
define the „Achilles heels‟ of the environment; the processes that could trigger detrimental shifts in the
ecosystem and cause social and economic problems (Steffen et al. 2004).
The IWRM concept itself could contribute to the practical and technical dimension of AM. It would be
interesting to see how concepts like resilience can be translated into the technical design of buildings
or infrastructure. For instance, their reconstructability could come more into focus in order to be able
to remove constructions in case of inefficiencies due to system shifts.
In this thesis, both theoretical concepts of AM and IWRM are considered to be helpful in order to
find an effective approach for learning about the integrated nature of resource management issues by
implementing a systematic and structured participatory learning process. In particular, the concept of
participatory model building is considered to meet the demands of the aforementioned theories. The
next chapter sketches the chosen approach and discusses the challenges that arise from the joining of
knowledge from the humanities and natural sciences.
Figure 9: The interlaced connection between the economic, social, and environmental sphere
20
2.6 A Participatory approach to policy assessment in complex systems
How can the required learning process practically combine soft, relational with hard, factual
knowledge? In this thesis, the approach of group model building is presented that can structure and
facilitate the participatory process. Prior to the process, the participants should agree on the application
of the method. Its prospects and limitations in particular should be discussed in order to decide
whether the approach is helpful to solve the respective problem. The ways in which knowledge is
generated and its evaluated soundness are additional points that should be clarified. Facts can be
determined and validated by observation (e.g. field visits), expert consultation, scientific inquiry, or
systemic modeling. Also, the ways of considering values, interests and preferences in the process
should be acknowledged.
Figure 10 shows the conceptual framework of a group model building process that applies systems
science in a participatory process.
A model of the issue in question is built by the participants that have been selected by a prior analysis
of the actor network. The process helps to structure and frame the problem, and give the diverse
stakeholders the opportunity to include their subjective perceptions. Empirical data and facts can also
be added to the model so that the final structure combines soft, subjective as well as hard, empirical
data. The simulation of the model structure produces scenarios of the system that can be compared to
observed real-world behaviors and future development of the system. Eventually, the detection of gaps
between simulated and observed system behavior as well as the recognition of future trajectories can
initiate a rethinking and learning process.
Today, improving computer technology makes the simulation of ever larger simulation models
possible. Models can however never reflect all processes of reality. Simplifications of complexity are
necessary and should be evaluated by their usefulness (Sterman 2000). Sterman emphasizes that “all
models are wrong” (Sterman 2000, p. 846) and validation in the sense of declaring models as „true‟
representation of reality is impossible. Rather, model evaluation and testing are based on quality
agreements in the respective scientific community. Scientific theory as such also simplifies reality and
helps to see the most significant and important processes that underlie the system of interest. The
application of theory in the praxis is determined by its usefulness, while its limits should be considered
at the same time. Newtonian physics, even though refuted by the general theory of relativity, is
therefore effective for many applications in material science or engineering. Similarly, models that
Figure 10: The overall approach that combines subjective perceptions with objective data by applying a
group model building process. Parentheses indicate the blurred distinction between soft and hard
knowledge elements (Pahl-Wostl 2007)
21
should be applied by decision-makers, for example in the political or economic sphere, should be
assessed with regard to their usefulness to the tasks they are designed for. The model builder and the
client have to come to an agreement about the purpose of the model comprising the model‟s boundary,
time-horizon, and level of aggregation (Sterman 2000).
In participatory model building processes, the purpose of the model is usually the structuring and
guidance of the discussion and learning about the system (cp. Brugnach and Pahl-Wostl 2007). The
model building process forces the participants to state their ideas and perceptions in a very clear way.
Rhetorical speech and ornate language are largely avoided when the discussion is guided by a
participatory model building. Unclear statements and point of views can be discovered by depicting
the underlying system structure of the explanations. The approach serves therefore both, the
improvement of relational and also of outcome oriented aspects in social learning processes. The
methods of system thinking and system dynamics are considered to be particularly suitable for
participatory model building. These methods are described in the following chapter, combined with
the participatory modeling framework in which they are applied.
3 Methodology: Participatory Model Building by the Use of Systems Thinking and System
Dynamics
The previous chapter presented the theoretical basis of the management of complex systems and the
assessment of policies. Despite the planning of measures on the drawing table, adaptive and integrated
management embraces systems thinking, participation of stakeholders and the need of learning
through experience. Chapter 3 builds on these considerations by introducing methods for analyzing
complex systems and their malfunctions as well as approaches to take action and improve the adaptive
capacity of societal networks. Hence, the following chapter is conceived as an outline of an
assessment and management process in complex systems with imperfect information mainly
encountered in environmental and social contexts.
First, approaches for problem framing (Chapter 3.1) and stakeholder analysis (Chapter 3.2) are
presented that help to specify the attributes of problems that needs to be managed, and the related
individuals and groups that should be included in the participatory process. Second, the framework of
group model building is introduced with an emphasis on the modeling process and responsibilities of
the modeler. Finally, the methods of system thinking and system dynamics are presented as suitable
approaches to guide the discussion and analysis of the system.
3.1 Problem definition
System dynamics is not the remedy for all problems that can be faced in this world. In many instances
other methodologies are more appropriate. In order to define the suitability of system dynamics, two
questions have to be answered. First, what are the features and the right frame concerning the problem
we face? And second, is system dynamics the appropriate method to find solutions or at least a
measure to improve the situation? Taking the high importance of the problem into account, the danger
arises to concentrate on the wrong problem definition and “finding the right solution to the wrong
problem” (Eden 1994, p. 257). The first paragraph of this chapter concentrates on concepts and
approaches that help to find an appropriate frame for the problem at stake. The following section will
discuss the strengths and weaknesses of the system dynamics methodology in order to be able to
define the suitability of its application.
22
3.1.1 Problem framing
In the center of the analysis of complex systems resides the problem that should be tackled. Instead of
modeling a complete system (e.g. a national economy, an ecosystem), action research tries to find
comprehensive high-leverage policies to improve a situation or prevent detrimental influences. This
focus simplifies the model since only problem-relevant elements and connections are considered. But
which is the adequate problem to tackle in order to improve the situation? Even though the answer of
this question might be simple on the first look, it requires some precise considerations in order to
avoid time and money consuming inquiries on symptoms rather than real problems.
An impediment for clear problem definitions is the selective perception of information by
individuals. Convictions, believes and ideas are seeking for affirmation while controversial aspects to
the respective world view are neglected (Vennix 1996). As already described in Chapter 2, the
learning cycle is hampered by the resistance of people to see, accept and apply new frames in order to
revise their mental models. In case of water resource issues, the perceptions cohere with the different
uses, ranging from recreational and cultural to economic and legal claims. Actors select relevant
aspects, connect them to a personal viewpoint and, thus, draw a line around what is perceived to be a
problem as well as its solution (Dewulf et al. 2004). Therefore, research about integrated management
has to acknowledge the multiple aspects of realities and find ways to handle them.
The theoretical approaches to the framing concept are separated into three branches (Putnam and
Holmer 1992). First, the 'cognitive heuristics' approach describes the bias of perceptions due to
underlying sense of events as gains or losses. Thus, a frame refers to the person's believe system and
the associated needs and goals. For instance, people could strive for the avoidance of losses by exhibit
more defensive routines in interaction with others. Second, the 'frame categories' perspective refers to
a more experience-derived framing. Incoming information is organized and decoded according to
schemata which have been acquired in the past. Third, the 'issue development' approach denies internal
states and structures of frames. Moreover, the frames depend on the linguistic choice that is refined in
controversial discussions where participants highlight different aspects of an issue. By this
communicative reframing process, new considerations emerge and make innovative solutions to
problems more likely (Drake Donuhue 1996).
Since participatory model building focuses on the structuring of discussion between stakeholders,
particularly the communicative framing is central for this method. Based on Wehr's (1979) conflict
map, Drake and Donuhue (1996) distinguish four communicative frame types that are factual, interest,
value and relational frames. Factual frames are unbiased appraisal of past or actual reality that can be
underpinned by evidence. Interests are related to future events and are expressed by desires or
aspirations. Value-laden frames comprise questions of right or wrong and can be grounded in moral as
well as rational considerations. Eventually, relational frames concern inter-subjective relation and
include emotions, trust or control. Consequently framing approaches have to relate to these different
types of frames in order to find an appropriate problem definition. Group model building includes all
of frame types. The model building supports the factual discussion of the problem, and stresses the
different values and interests of stakeholders. The transparent and participative process shall foster the
building of social capital by relational practices, and thereby, also changes the relational frames of
participants.
Whereas the previous described framing processes are facilitated through the application of the
group model building approach, further attributes of the respective problem should be considered
before the actual participatory process starts. Thus, the initiators of the model building have to define a
preliminary problem definition upon which stakeholders are selected and invited to the participatory
process. With reference to the goal of action research to tackle real world problems, it has to be taken
23
into account that the searching for solutions and their eventual implementations are social activities in
the sense that the modeler is often not the person that can bring change. In fact, the model building
should influence stakeholders who have the power to take action (Eden 1987). Consequently, the
feasibility of interventions by different actors should also influence the problem definition, model
building and the proposed solutions (Eden 1994). In summary, Eden suggests to define the problem
while keeping the social order in mind that defines the feasibility of solutions. For instance, a model
for the assessment of technological policy options for water demand management on the national level
would require the inclusion of non-governmental stakeholders like farmers and water users since their
behaviors have to be changed in order to be successful.
Besides these sociological implications, Sterman (2000) points to a further aspect that should
influence the problem definition. The participants‟ needs, capabilities and skills have to be in the focus
of the model in order to maintain motivation and commitment to the modeling process. Therefore, the
problem definition should be focused “on the problems that keep the clients up at night” (Sterman
2000, p.85). However, including special interests in the model is a mixed blessing as it could lead to a
biased perception that primarily serves to affirm the client's opinions. Hence, the balance has to be
found between challenging of the stakeholders to foster learning processes, and the inclusions of
special perceptions and requests. In the end, this aspect refers to the ethical responsibility of the
modeler to speak truth and defeat distorted descriptions of reality (Sterman 2000).
At the start of the modeling process, the reference modes of behavior has to be defined that
includes graphs or data that describes the development of the problem over time (Sterman 2000).
Therefore, the stakeholders have to detect the most important variables that serve as indicators of the
situation's evolution. Hence, the discussion needs to be very precise and, possibly, reveals diverging
problem perceptions that must be clarified before the model process can continue. At this point, the
participants have to agree on the time horizon of the problem so that phenomena that are irrelevant to
the chosen time frame can be ignored. The time horizon should include the processes in the past that
caused the problem as well as those in the future that represent the delayed and indirect effects
(Sterman 2000). The choice of the time horizon is fundamental in order to determine the problem
definition and the adequate model structure, and, eventually, find the right policies. Figure 11
summarizes the issues involved in the framing of the problem.
Figure 11: Factors that should be considered in the problem definition
24
Thus, the problem to be modeled has to be adapted to the respective situation. The ownership of the
problem highlights that eventually the group itself has to decide the frame of the problem by a
communicative framing process. Also the feasibility of solutions is related to the participants as their
capacities to take action are investigated instead passing responsibility to external parties. The
perceived urgency of the problem needs to be taken into account as the process of participatory
modeling is time, effort and cost-intensive, and should rather be applied for major and complex
problems than for issues that have a minor importance and can be solved by other more standardized
approaches. In particular, the reference mode of behavior and the time horizon are fundamental for the
application of the system dynamics method.
3.1.2 The suitability of system dynamics
Linstone (1978, p.275) remind the researcher to “suit the method to the problem, and not the problem
to the method”. Although this statement sounds quite simple, it might be hard to follow in practice.
Vennix (1996) gives two reasons for this: First, researchers are often acquainted with a limited number
of tools and methods that causes the “'child with the hammer' syndrome” (p.104). All problems that
are approached wit the same 'hammer' even when there is not a „nail‟ involved. To overcome this
danger, scientists can achieve proficiency in different methods in order to have a fitting approach in
his or her sleeve for a variety of tasks. Another approach is the blending of methods in order to
overcome the weaknesses of a singular method by applying a complementary one that seems to correct
this lack. However, Vennix (1996) points to the diverging theoretical backgrounds of methods that
could impede the simultaneous application.
A second reason for the difficult choice of the right method can be an insufficient defined problem
that shall be tackled by research. In particular, complex problems have inherent uncertainties and
incomplete information so that personal interests and scientific background can considerably influence
the framing. For instance, water scarcity can be a perceived as insufficient water supply, elevated
demand, or the cause of environmental processes like seawater intrusion or desertification.
In the end, the strengths and limits of methods should be well-known so that the applicability to
the problem at stake is guaranteed. In particular in interdisciplinary research, the scientist should not
cling to a specific method but should have an open attitude towards other disciplinary research in this
field. Based upon this, hidden assumption can be stated and reasons for the respective focus given that
allow the classification in the interdisciplinary discussion.
For the application of system dynamics, the problem should exhibit specific features. First, it
should be dynamically complex meaning that side-effects are expected over time and non-linearities
are inherent to the system‟s structure. Hence, the task of finding optimal solutions for a given point in
time makes system dynamics not applicable. However, static problems as the choice for a waste
disposal side can be formulated to a dynamic task by taking long-term effects into account. Second, a
long time-horizon is another central feature of system dynamics studies as already indicated above.
Instead of quick fixes of a problem, system dynamics investigates long-term effects that could render
highly effective politics for the short term to be ineffective in the long run. Third, the problem under
study should have a reference point of behavior that can be traced into the past and future. Even
though these reference time series can also be hypothetical (e.g. in case of the absence of historical
data), they have to be producible in a reasonable way. Thus, optimization tasks as well as legal and
design issues are not appropriate for system dynamics. Rather, complex problems that have been tried
to tackle in the past without success, and require the incorporation of various perspectives are suitable
for the system dynamics method. Or as Jac Vennix (1996, p.107) formulates: “system dynamics is
primarily a diagnostic and impact assessment method: finding what the problem is, what structural
25
causes are responsible for it, and which policies prove robust to tackle the problem”.
3.2 Stakeholder Analysis
Another preparatory task for the participatory process is the definition of the relevant stakeholders.
Intra-organizational model building processes where the problem and action space are comprised by
the respective organization (e.g. a firm, international organization, government) often imply a
predetermined group of participants (e.g. employees, or personnel of a certain department). Instead,
inter-organizational issues where the problem requires concerted actions of independent actors usually
needs a deliberated composition of invited stakeholders that is not specified at the outset of the
process. In the latter case, the different factors that define the problem formulation also influence the
choice for stakeholders as the problem serves as the reference point for the selection process. Problem
definition and stakeholder choice are highly interdependent. Whereas the definition of the respective
issue leads to a first selection of participating actors, these actors can bring different frames into the
process and modify the initial problem formulation that in turn could necessitate the consideration of
new stakeholder groups, and so forth. Figure 12 depicts this circular process that is initiated by a
facilitation team of researchers and continued by the participants.
The participatory stakeholder selection is not just an implication of the chosen modeling method but is
necessary in general, as outcomes of the analytical categorization of stakeholders are dependent on the
knowledge and experiences of the facilitation group. Besides formal institutional settings that can be
extracted from literature review, there could also be informal shadow networks that play a central role
(Sendzimir et al. 2007). Also cultural specifics or on-the-ground experiences of actors can result in
peculiar perspectives. Thus, the participation of stakeholders is required to yield a composition of the
group that is adjusted to the nature of the respective problem.
However, the planning team has to find a reasonable initial stakeholder composition for the first
workshop. This choice should be guided by theoretical and empirically tested frameworks in order to
avoid the ignorance of key stakeholders that would imply time-consuming re-adjustments of the group
Figure 12: Interrelation of the problem definition and stakeholder analysis in the context of a
participatory model building process
26
composition in the course of the model building process. Hence, the guidelines presented in this
section constitute a structured approach towards a reasonable participant composition for the first steps
in the process. Different techniques are introduced that illuminate the potential stakeholder group from
different perspectives. In the end, the parties considered to be important from a certain point of view
are invited to participate.
There are practical considerations that can induce conflicts with methodical requirements. On the one
hand, the stakeholder analysis might demand the inclusion of various stakeholders from several levels
and institutions in order to become various perspectives on the problem and foster constructive
communication. On the other, the amount of group members is limited due to financial and
manageability restrictions of the chosen approach. Whereas the restricting factors belongs mainly to
the methodological outset (e.g. costs, efforts and manageability of the process), the factors that expand
the involvement particularly concern the effectiveness of the group model building (e.g. inclusion of
powerful actors, horizontal and vertical integration in the governance network). The quest for a fair
balance between the capacity of the applied method and the requirements derived from the respective
social issue is a non-trivial task. The outcomes of the stakeholder analysis require the subsequent
adjustment to the methodological and practical demands. For instance, the merging of separate
stakeholders (individuals or organizations) to representative cluster could be a practical approach to
minimize the size of the group. Also a prioritization of stakeholders for the process helps to select less
important parties.
In summary, this section provides a framework to identify stakeholders and select the most salient
ones in order to organize a reasonable and effective participatory modeling process that is adapted to
the respective issue‟s requirements and practical limitations.
3.2.1 Definition of 'Stakeholder'
The term 'stakeholder' is not used uniformly and therefore requires a definition for its usage in the
following chapters. Exclusive definitions focus on possible impacts of stakeholders on the area of
interest and comprise “people and small groups with the power to respond to, negotiate with, and
change the strategic future of the organization” (Eden and Ackermann 1998, p. 117). Broader
definitions use the term „stakeholder‟ synonymous to 'interested' or „affected party‟. Hence,
stakeholders are perceived to be any “person, group or organisation with an interest or „stake‟ in an
issue, either because they will be directly affected or because they may have some influence on its
outcome. „Interested party‟ also includes members of the public who are not yet aware that they will
be affected” (HarmoniCOP 2005). This more general definition includes the concepts of democratic
and social justice as even powerless people should be considered (Bryson 2003). Primarily, the
exclusive definition refers to intra-organizational processes and is directed towards helping managers
to pursue the firm's interests (Mitchell et al. 1997). The inclusive definition focuses more on inter-
organizational issues where the problem is not exclusively located in the organizations borderline but
belongs also to external parties. As this thesis concentrates on the inter-organizational process-type
and strives for the application of stakeholder analysis methods to policy impact assessment, the term
'stakeholder' is in the following used synonymous to 'interested' or 'affected party'.
3.2.2 Overall framework
The following framework describes a structured selection process based on theory in order to define
the most important parties at the forefront of the participatory model process. There are numerous
criteria for stakeholder selection named in literature: the relation of parties to the water management
issue; the level and context of the actor‟s actions and the institution he/she represents; the role or
27
involvement in the issue (e.g. expert, victim, user, governor, executor); their capacity and motivation
for engagement (i.e. what can the stakeholder offer: knowledge, power, contacts) (EC 2003). Bakker et
al. (1999) propose additional categories including, amongst others: aggregation (ranging from an
individual to a collective); the concerned time-horizon (point in time or the historical evolution of the
stakeholder network); thematic networks (e.g. water supplier, farmers), and policy networks (e.g.
farmer unions or industrial lobbies).
The presented framework in this thesis is geared to the work of Elias et al. (2002) as it combines
different approaches and was successfully applied in case study research. In addition to the framework
of Elias et al., the functional dimension of stakeholders in relation to the issue is included as proposed
by the Common Implementation Strategy for the Water Framework Directive (EC 2003).
The following steps are implemented:
(1) Brainstorming of a stakeholder list/map
(2) Identifying the roles and function of the stakeholder in the issue
(3) Construction of a power versus interest grid
(4) Analysis of the dynamics of stakeholders
The first step has the purpose of defining possible stakeholders that belong to the respective issue.
Hence, in spite of a selection of the most crucial participants, the process is more divergent in nature
and aims at the inclusion of even apparently marginal stakeholders. The following steps (2) – (4)
structure the multitude of detected parties in order to define the most important ones that should be
invited to the first group session.
3.2.2.1 Stakeholder map
In the first step, a 'Stakeholder Map' is constructed for the issue of interest. The perceived problem is
set in the middle of a sheet of paper and the possible stakeholders are noted all-around. The finding of
adequate interest groups starts with a brainstorming session conducted by the facilitation group. Also
expert advice or existing contacts to local people can guide the preliminary selection process.
The different typologies of stakeholders comprise: professionals (e.g. from public and private
sector organizations or NGOs), authorities/elected people (e.g. from government departments,
municipalities, local authorities), non-professional local groups, separated in communities centered on
place (e.g. resident associations) and communities centered on interest (e.g. farmers unions,
fishermen), and individual citizens (EC 2003, p.16). In addition, van den Belt (2004, p.65) demands
the inclusion of scientists to the stakeholder list, as this group has specific information, knowledge and
skills that can contribute to the discussion. In particular, scientists can communicate a larger picture on
the problem as well as inform about the uncertainty of data.
3.2.2.2 Roles and functions of stakeholders
The Common Implementation Strategy for the Water Framework Directive (EC 2003) proposes the
application of a target scheme to detect the function of stakeholders in the process stage and
additionally in respect to the issue at hand (see Figure 13). The degree of involvement of the
stakeholder determines the position in the circular areas whereas its role specifies the classification in
the rectangular fields. Similar to the three different degrees of involvement (namely information
supply, consultation, and active involvement) the guideline defines as the possible functions of
stakeholders in the process: (1) co-operating/co-working: active participation and contribution to the
process; (2) co-thinking: source of knowledge about the content of the process like experts; (3) co-
knowing: no active participation, but gets informed about the process (EC 2003).
28
Furthermore, four different roles of actors in regard to the resource issue are distinguished: (a)
decision makers, who have the power to decide; (b) users, who are affected by the outcomes; (c)
implementers/executives, who are responsible and have the power to implement the policies; (d)
experts/suppliers, who offers their knowledge, information, expertise or resources. In the planning for
each stage of the project the desired involvement of stakeholders have to be defined and
communicated to them in order to achieve a goal-oriented process and avoid frustration.
Different stages in the process require different degrees of involvement. In the case of group model
building, the initial stakeholder analysis will be centered at the co-operation circle as participants are
actively involved in the modeling process. However, in the later stage also co-thinking stakeholders
can become more relevant if the group decides to obtain expert advice or specific information. At the
end of the model building the dissemination process comes to the fore so that co-knowing parties are
involved. Distribution channels could be the participants who circulate gained knowledge to their
environment, or medial instruments like newspapers, internet (e.g. via management-games) or
television. This exemplifies the importance of the stage of the project on the focus of the stakeholder
groups in the scheme.
Finally, the several stakeholders that have been selected in the brainstorming session are arranged
in the target scheme. A visual examination proves the distribution of the participant groups and, in
particular, looks for gaps in the scheme that would indicate the omission of relevant parties.
3.2.2.3 Power versus interest grid
The second step of the analysis is the creation of a power versus interest diagram (Elias et al. 2002).
The interest dimension reflects the willingness of the stakeholder to become active in the issue at
hand. The power dimension refers to the stakeholder‟s ability to affect the issue. Figure 14 shows a
two-by-two matrix containing these two dimensions. The stakeholders are grouped in the distinct
fields as players (power + interest), subjects (interest + limited power), context setters (power + little
interest), or crowd (little interest and limited power).
The power versus interest grid highlights the stakeholders who need to be included in the project (the
'players') and which coalitions can be encouraged. Furthermore, the diagram could also point to the
power dimension of the respective problem. For instance, lock-in situations for stakeholder groups
could emerge in consequence of missing power to change their situation independently (cp. Bryson
2003).
Figure 13: Target Scheme to identify degree of involvement
and type of stakeholder (EC 2003)
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3.2.2.4 The dynamics of stakeholders
Over time, the attributes of stakeholders and their salience for the process might change. Thus, the
dynamics of the stakeholders are of interest in order to anticipate variations in the group‟s
composition. Mitchell et al. (1997) provide a pragmatic approach grounded in a theory of stakeholder
identification and salience that help to manage dynamics. First, three central attributes are assigned to
the stakeholders: (1) power to influence the process, (2) legitimacy to influence and (3) the perceived
urgency. Accordingly, a stakeholder has power if he has access to coercive, utilitarian or normative
means to achieve his interests. Coercive means are physical resources to apply force or violence
whereas utilitarian means are material or financial assets that can be used to acquire goods and
services. Finally, normative means consists of symbolic resources like prestige or acceptance. The
definition of legitimacy is based on the work of Suchman (1995) to be “the generalized perception or
assumption that the actions of an entity are desirable, proper, or appropriate within some socially
constructed system of norms, values, beliefs and definitions” (p.574). This broad definition aims at a
socially constructed reality that exceeds the individual or organization-centered attitudes. The
definition of urgency points to the necessity for immediate action that involves the perceived existence
of time-sensitivity and criticality. Thus, an issue is time-sensitive if a delay in attendance to the claim
is not acceptable. Criticality means the degree of importance for the stakeholder.
Figure 15 depicts the classes in which stakeholders are sorted depending on the assigned
attributes. Groups which possess only one attribute are called „Latent Stakeholders‟, and have minor
importance for the participatory process. Dormant stakeholders (no.1) have power but don‟t use it as
they see no urgency and are not legitimate to do so. Discretionary stakeholders (2) have only
legitimate claims without urgency, whereas demanding parties (3) see their interests to be urgent, but
can not realize them due to lack in legitimacy or power.
The relative importance of stakeholders with two attribute are accordingly higher. These parties have
some interests and expectation in the respective problem and, therefore, are called „Expectant
Stakeholders‟. They should be considered in the planning of the participatory process by inviting
them, or, at least, by keeping their interest in mind for the assessment of policies. The dominant
stakeholders (4) have power and legitimacy, but see no immediate urgency to act. Dangerous
stakeholders (5) are those with power and urgency, but without legitimacy. They might try to achieve
Figure 14: Power versus interest grid (Bryson 2003)
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their interest through coercive force or other illegitimate means. Stakeholders with legitimacy and
urgency are called dependent (6) as they rely on the others due to the lack of power.
Finally, definitive stakeholders (7) have all attributes of
the scheme in Figure 15. They possess power,
legitimacy, and urgency. This group should definitively
participate as the name suggests.
The dynamics of stakeholders enter the framework of
Mitchell et al. (1997) by considering the changes of
attributes over time. Thus, a dependent party with
urgency and legitimacy can change to a definitive
stakeholder if this group acquires power. This can be
achieved by the individual efforts of the group, or by the
creation of coalitions with more powerful stakeholders.
Also, dominant stakeholders could enter the definitive
category as soon as problems get worse, affect the
respective party directly, and thereby increases the
perceived urgency.
3.2.3 Selecting the final stakeholder composition
The different techniques presented above serve multiple perspectives on the relative importance of
stakeholders that could imply diverging outcomes. For instance, the investigation of the role in step 2
can reveal the importance of stakeholders that prove to be marginal from the power perspective in step
3. Hence, the final stakeholder list encompasses the parties who are considered to be relevant from at
least one applied technique. Thus, all roles should be represented (step 1), as well as the „players‟ (step
2), and „definitive‟ stakeholders (step 3) included. In addition, changes in the stakeholder attributes
should be considered in order to detect potential participants. Also the inclusion of expectant
stakeholders might be appropriate. For instance, dependent groups should participate for the reason of
social justice. A comparison of the diverging outcomes of the methods above can provide crucial
insights as they might point to important parties that have been neglected in the past. Nevertheless the
applied techniques are to a large degree dependent on the knowledge of the implementer and the
available information from the literature. External experts or local residents that have experience in the
cultural and institutional environment can help to improve the accurateness of the analysis and,
eventually, the starting phase of the group modeling.
As addressed already before, the stakeholder group has to be adjusted to the requirements of the
method of group model building. A minimum number of group members (about 5-10) should be
achieved in order to foster creativity, a broad knowledge base, and a sufficient social network to
induce change. The upper limit sets the number of people that can be facilitated efficiently (about 30-
40). The optimum size of the group is particularly determined by the level of conflict, as a contentious
atmosphere renders small groups to be more effective, whereas uncontroversial issues might allow
higher numbers (van den Belt 2004).
In the end, there are no receipts for appropriate choices of stakeholder as the social system where
they are included is itself a complex system and requires a “certain amount of collective wisdom”
(Bryson 2003, p.13) in order to be managed successfully. A compromise has to be found between the
benefits of diverse and large groups and the manageability of the participatory process.
6
1 2
3
4
5
7
POWER LEGITIMACY
URGENCY Figure 15: Stakeholder classes after
Mitchell et al. (1997)
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3.3 Group Model Building
Group model building serves a systematic framework to facilitate the participation of stakeholders in
decision-making and research. Unlike expert-based approaches, group modeling fosters democratic
processes as affected persons or groups can express their views. Besides element of social justice,
group modeling supports the discussion by providing a definite language and impeding ineffective
communication. Diverging views are depicted on paper through the use of causal maps. In doing so,
eloquent hiding behind language or digressing emotional explanations are impeded as the facilitator of
a group modeling session can intervene and push for reference to the causal loop diagram. According
to van den Belt (2006), a modeling process can have different purposes: it can increase the common
understanding, build consensus about the system structure of a complex topic, provide a systematic
and strategy for discussions, and can serve as a tool to disseminate results.
There is a growing literature about practical insights from past group modeling processes that can
help to avoid pitfalls. Of course, every case is unique as this approach is usually used in unique messy
problem situations with several unique stakeholders involved. Hence, generalizations are hard to
accomplish. However, the following chapter presents general experiences from participatory modeling
processes that have been published. These lessons pertain to the role of the modeler in the process, the
content and structure of the group modeling sessions, and the insights that can be generated.
3.3.1 General features
In general, the particular difficulty of group modeling is its unpredictability. Therefore, scripts for
group processes (e.g. Andersen and Richardson 1997) should be regarded with attention as the
composition and experience of the support group is unique as well as the supported groups. However,
the study of general experiences derived from executed group processes expands the possible course
of action as the facilitator has ideas and concepts in mind that can be used in a flexible and creative
way. The following section introduces some general considerations that have been proven helpful.
As reality is not a given entity that cannot be changed, the worldview of the facilitator influences
the group model process in a fundamental way. Unconscious mental models about the issue at stake,
the participants and the social reality at such need to be revealed and revised in order to avoid
impediments for the group process. Vennix explores the issue of power games in organizations and
concludes that the best approach to tackle hampering group hierarchies or tensions is to “concentrate
more on the group task or problem” (1996, p.144). Hence, the facilitator should exhibit an exemplary
behavior as “the facilitator's behaviour fosters a different social reality in the group”(Vennix 1996,
p.145).
According to Vennix (1996) good behavior of the facilitator has the following features: a helping
attitude, authenticity and integrity, as well as an attitude of inquiry and neutrality. The 'helping attitude'
refers to an equitable discussion atmosphere where opinions and statements can be given without
deprecatory and arrogant reactions. 'Authenticity and integrity' help to create confidence in the group.
Power games or impression management counteract this development as well as tricks applied by the
facilitator. By 'attitude of inquiry' Vennix means the efforts of the facilitator and the group members to
understand each other. More than often, people try to give answers instead of asking questions in order
to persuade the others of their standpoint. Eventually, 'neutrality' means retaining of facilitator to voice
his opinion. Vennix (1996) underlines the responsibility of the facilitator for the process and
procedures, and the mandatory attitude of neutrality regarding the content. Personal preferences,
convictions and evaluations would hinder the group discussion. Nevertheless, if the facilitator wants to
contribute an idea or statement to the group, it should point out clearly that the facilitator's role is
32
abandoned for a while.2
3.3.2 Proceeding of a group model building process
The proceeding and structure of the group model process depends considerably on the respective issue
(e.g. its complexity, inherent conflicts), the number and composition of stakeholders (e.g. intra- or
inter-institutional) as well as the resources that are available (e.g. financial aspects, the composition
and experience of the supporter group). In the following, the participatory process is divided in three
stages, the preparatory, workshop and follow-up stage (after van den Belt 2004).
3.3.2.1 Step 1: Preparation
A careful preparation of the group model process is the foundation for its later success (Vennix 1996).
As already mentioned above, there may be different starting points depending on the client (e.g. a
firm, an organization, a government) and the issue at stake (environmental or financial problem,
composition of stakeholders). In some cases, the client could even be missing as the problem has no
real “prosecutor” but is derived from a scientific investigation or is concealed from the awareness of
concerned people. Also, a concrete problem could be lacking as a client faces only symptoms (e.g.
decreasing sales in a firm, exceeded pollution thresholds of a lake) or just have the feeling that
“something is wrong”, but does not know the reason. The sequence of the different steps needs to be
adapted to the respective situation. The following section presumes the preliminary definition of the
problem and the belonging stakeholders using the methods described in Chapter 3.1 and 3.2.
3.3.2.1.1 Preliminary Model
Jac Vennix (1996) places a major weight on the choice whether to use a preliminary model. This kind
of model is built previous to the workshops and is based on documents filled by the participants (e.g.
via email, regular mail) or personal interviews. The purpose of the preliminary model is its application
in the group model session as a starting point of the discussion. The group members can approve the
entire model (which would be a great leap in the process), accept it partly, or disapprove it in general.
However, even in the case of complete refusal the group has become a general idea of the method and
the objective of system dynamics. Also, the modeler had the opportunity to build up a personal relation
to the stakeholders and got a first impression about the conflict potential and controversial points. On
the other side, a preliminary model could narrow the discussion as people orientate themselves to the
chosen model structure. This could inhibit an open discussion and induce a premature agreement as
well as the ignorance of the negotiation of social order (cp. Eden 1994). Also the ownership of the
group could be lacking that would result in low commitment to the outcomes of the modeling process
(Akkermans 1995). In addition, the criticism of a very subtle preliminary model could overstrain the
group and might also lead to a defensive behavior of the modeler (Vennix 1996).
The omission of a preliminary model building might speed up the process at the preparatory stage,
but means a start from scratch in the first group session. Hence, the proceeding in the group becomes
more unpredictable, particularly if also previous interviews have been omitted. Hidden conflicts
between the participants or negative attitudes concerning the applied method of system dynamics
could surprise the modeler and necessitated flexible and competent reactions. In addition, the
facilitator has to translate the oral discussion into the system dynamics concept directly, without
having the possibility to refer to a preliminary model. Hence, only experienced modelers should omit
2 The different roles of the facilitator are specified in Appendix A. In the following, the term project team,
facilitator and modeler are used synonymous.
33
preparatory steps in order to speed up the process in case of homogeneous groups (e.g. within a firm or
department) where the conflict potential is low or if limited resources prohibit a long-term
commitment to the project.
The following paragraphs introduce methods to build a preliminary model, first, by means of
documents (especially appropriate for large group numbers), second, by personal interviews and, third,
by questionnaires and workbooks.
3.3.2.1.2 Documents
The building of a preliminary models based on documents requires the application of content analysis.
A disadvantage in the use of documents is the missing confirmation of the originator. Hence, the
outcome from such an interpretative analysis of a text depends considerably on the person who
conducts this approach. Often, concepts in the texts have to be modified or key words need to be
interpreted in order to be compatible to the system dynamics model. There are different methods
available in order to identify causal relationships from texts in an objective and systematic way (e.g.
see Axelrod 1976). A cause map can be derived from a text by pursuing the following steps. First, the
document is read entirely in order to get a comprehensive conception of its content. Second, the text is
read again sentence-by-sentence, and causal relationships are drawn simultaneously on a separate
sheet of paper. Finally, the resulting causal loop diagram is checked again to the content of the text.
More often than not, „white spots‟ can be detected in the causal diagram as texts are usually not written
for systemic investigations. In this case, the missing processes needs to be filled by the project team
(Vennix 1996).
Forrester considers written data as “an excellent source of information about system structure and
the reasons for decisions” (1980, p. 557). In particular, he points to daily and weekly, business and
public press which illuminates the background story of events and highlights the singularity and
immediacy of decisions in business and politics. Therefore, models should contain information about
how decisions are made and not how decisions should be made in an ideal state. Hence, besides
scientific texts, also newspapers and other written data that reflects the real world system and decision-
making are suitable for system dynamics modeling.
3.3.2.1.3 Personal Interviews
Another approach to build a preliminary model is the use of personal interviews in advance of the
workshops. Thereby the methodology can be explained to the participant, general questions answered
concerning the process and a first rapport established. Hence, these interviews function as a
preparation to the workshops and will save time in the first model session.
There are four different types of interviews (Patton 1980): First, there are 'informal interviews'
where questions are not prepared in advance but are intuitively raised in the course of the
conversation. Second, 'guided interviews' are structured by topics but their sequence and formulation
are optional. Third, the previous determination of questions and topics is called the 'standardized open-
end interview'. While the questions are standardized, the answers can be phrased in the respondent's
own words. And fourth, the 'closed, fixed field response interview' provides specified answers to the
interviewee. Vennix (1996) proposes the application of the first two types for the building of a system
dynamics model. Whereas the informal interview is particularly helpful to come acquainted with the
participants, guided interviews might be more appropriate for the modeling purpose as the
conversation is more centered and less arbitrarily.
The interview process starts with the contacting of the participants for the assignation of individual
appointments. Presumably the participant wants to know in advance about the purpose and proceeding
34
of the model building. Thus, the facilitator should have conducted the tasks of preliminary problem
framing and stakeholder analysis previously. Even a provisional model of the problem at stake can
help to clarify the objective and serves as an entry point for discussion. In the actual meeting, the
interviewer first describes the overall purpose and objectives of the project. This could be connected
with a short introduction to the system dynamics methodology and the answering of general questions.
At best, the interviewee concurs with the application of a tape recorder so that the interviewer can
concentrate in the conversation instead of being distracted by the necessity of taking notes. In
particular for the building of the causal loop diagram, the taped oral explanation for causal links can
help the modeler to reconstruct the original meaning of statements (Vennix 1996). According to Patton
(1980) the questions should be open-ended, neutral, singular and clear. In the end, the respondent
shouldn't be influenced or confused by the formulation of the question. The type of questions can aim
at the feelings, opinions/values, behavior and knowledge of the interviewee. Vennix (1996) regards
information about opinions/values and knowledge to be particularly important for model building.
Furthermore, the description of feelings by the participant helps to detect conflicts that could arise in
the later workshop phase. Information about the behavior of the system can be interesting in order to
compare them with the later model simulation results. The most important questions for system
dynamics models are 'why'-questions as they reveal the causal relationship belonging to the
respondent's mental model. Negative reactions of the interviewee due to the repeated inquiry of his or
her assumptions can be faced by a previous explanation of the sense of the 'why'-questions for the
method or the consideration and pointing to the inherent difficulty in describing the causal
relationships (Vennix 1996). Figure 16 depicts four steps towards a causal loop diagram that represents
the mental model of the interviewee.
Figure 16: Proceeding for of the construction of a causal loop diagram (after Vennix 1996)
35
The interviewer might initially describe the purpose and objectives of the study as well as the
presumed problem definition. This definition should be broad enough to encompass the field of the
problem despite of being too specific and constrain the participant in his creativity or trigger
deprecatory reactions. Subsequently the interviewee will announce his or her opinion about the
appropriateness of the assumptions and, due to the generality of the problem definition, might propose
a more specific problem variable. The second step comprises the addition of the causes for the defined
problem. Here, the interviewer asks the participant about the expected 'first order causes' of the
problem that are these causes which are directly linked to the problem variable. After this, the 'second
order causes' are requested that determine the first order causes, and so forth. Eventually, the
interviewee decides when the cause-side of the causal loop diagram is exhausted. In general, it might
be recommended to interrupt the conceptual modeling of the causes at the third order. In step 3, the
consequences of the problem are studied, again, beginning with the first order consequences. Finally,
in step 4, the interviewee is encouraged to find feedback loops meaning circular connections between
the consequences and the causes. These feedback structures are the major reason for the dynamic
behavior of the modeled system (cp. Chapter 3.4.1).
3.3.2.1.4 Workbook/Questionnaires
In the cases of a large number of participants, far-scattered stakeholders or limited resources that
inhibit a personal interview, workbooks and questionnaires are helpful to attain information from the
participant in the forefront of the workshops. They can be disseminated several times via email or
regular mail, so that the modeler can react to the evolving group process. However, a problem of this
approach might be the low response rate for mailed questionnaires. Hence, the stakeholders need to be
highly motivated or urged by their superior (Vennix 1996). Furthermore the questionnaires have to be
very precise and thought-out as the modeler is not able to answer clarification questions directly.
Therefore, the questionnaires should be tested several times to detect drawbacks and points for
potential improvements. Also the questions should be short so that the answers can be filled in quickly
as well as arranged by the level of complexity, beginning with easy ones. The questions should be
neutral, singular and clear in order to avoid external influences on the respondent. Closed questions
should be used if the purpose of the questionnaire lies in asking for consensus, for instance pertaining
to a conceptual model or specific causal links. The open-ended type is appropriate for questions which
are targeted at the generation of information from the participant. This information could contain
proposals for variables or linkages, opinions about a specific topic as well as ranking of different
variables by importance. As knowledge generation might be particularly important at the outset of a
participatory process, closed questions could be more used in later stages to obtain approval for
preliminary results (Vennix 1996).
As questionnaires are limited in size, a workbook can be useful to combine questions with explanation
passages. Figures and diagrams are presented to clarify certain aspects. Workbooks are especially
helpful in-between workshops in order to summarize findings of the previous meeting and prepare for
the next one. A high complexity of the model and a large size of the group are additional arguments for
the application of workbooks (Vennix 1996).
3.3.2.2 Step 2: Workshops
Before the group meets for the first time, the location, room layout and equipment need to be planned
by the project team. If possible, a neutral location should be chosen so that participants are not
interrupted by colleagues and can concentrate on the group tasks. In addition, the availability of
equipment needs to be checked as flip charts, or beamers. In any case, the room should suit to the size
36
of the group and allow the arrangement of tables and chairs in a semi-circle that is opened to the side
where the projection screen or flip chart is positioned (Vennix 1996).
After van den Belt (2004), the sequences of workshops can follow the sequence below:
1. Introduction
2. Problem definition
3. Qualitative Model Building
4. Quantitative Model Building
5. Simulation
The session starts with an introduction of the project team and their different roles. Subsequently, the
general agenda of the group meeting should be disclosed. In case of preceding interviews and the
construction of a preliminary model, the group model process can start quickly. Stakeholders are
acquainted with the method, and the preliminary model functions as an entry point for discussion after
its presentation by the modeler. Also the results of questionnaires can be reported so that possible
discussion points are revealed. If these preparatory tasks could not be accomplishes for the reasons of
time, or restrictions in the budget, an introduction of the system dynamics method and the possible
outcomes of a participatory model building have to be delivered. Andersen and Richardson (1997)
suggest the clarification of the final product that is expected from the model process. This could be a
causal-loop diagram, a stock-and-flow diagram, or a running simulation model (all these methods are
presented below in Chapter 3.4).
The project team should have thought thoroughly about the agenda prior to the session. This
includes the actual purpose and desired outcomes of the meeting. Scripts that are “fairly sophisticated
pieces of small group process” (Andersen and Richardson 1997, p.107) could facilitate the setting of
the agenda as they base on experiences from other group modeling processes. However, there is the
danger to cling too strictly to them, and thereby, become inflexible for spontaneous developments or
needs in the group (Vennix 1996). Van den Belt (2004) urges to consider cultural and historical
characteristics in the design of the agenda. For instance, the political history could imply mistrust in
authorities or lack in experience with democratic principles. Culture could also involve a goal-oriented
attitude that rather supports quick fixes of problems than long-lasting discussions. By considering
these aspects of the problem, the project team minimizes the probability to face surprises in the first
workshop sessions.
The model building itself proceeds similar to the individual model building in interviews. Thus,
the group first discusses about the appropriate problem frame. The result from this process is the
definition of a problem variable that is used for the later qualitative or quantitative system analysis.
Also, the boundary of the system the time horizon and the reference modes can be the outcomes of this
stage. Causal loop diagrams are a powerful tool for the qualitative investigation of a problem, and can
help to depict the problem structure in a clear and comprehensive way. For their construction the same
proceeding can be applied as in the individual interviews (see Figure 16). Feedback loops can be
located and their behavior analyzed. Another tool for investigation of the system‟s dynamics is the
stock and flow diagram that discriminates between stock, flow, and auxiliary variables (see Chapter
3.4.1.3). The quantitative simulation grounds on the qualitative analysis. Mathematical equations are
inserted and allow the computation of the system behavior. Scenarios assume changing circumstances
in the future and can help to assess polices and measures to solve the problem. Both, qualitative and
quantitative approaches are presented in Chapter 3.4 in detail.
37
3.3.2.3 Step 3: Follow-up
After the session, the outcomes and proceeding of the model building needs to be organized and
documented. This could be done in the form of a research report that presents the model and
conclusions that have been drawn in the respective workshop. The modeler should restructure the
model in order to highlight systemic components as feedback loops. Of course, the model structure
itself should not be changed. However, the modeler can have a critical look on the outcome and
identify issues that should be discussed in the next meeting. In order to speed up the process, a
workbook could be send to the participants that contains the report about the last workshop and
follow-up questions. If the participants are able to send the document back at the forefront of the next
meeting, the planning group of the workshop can prepare the session more easily and focus the tasks
and discussion to topics that turned out to be important from the workbooks.
When does the participatory process end? In the actual concept of participatory model building, the
process should develop continuously. Ideally, the policy maker refers to the simulation results and
defines measures accordingly. The effectiveness and side-effects of the implementation have to be
compared to these of the model, and gaps should lead to a revision. But the outcomes of the model
building should also affect other parties than the initiator or central decision-maker. In addition, the
commitment of other participating organizations to change their behavior or support the
implementation of policies should be stipulated. Thus, the model building facilitates the concerted
action of all parties. Consequently, the modeling group should meet from time to time to assess the
outcomes of the measures and discuss difficulties or resistance that might have been faced. These post-
modeling meetings can lead again to a discussion that is structured and guided by the group model
building approach.
3.4 Systems analysis
In this chapter, the methods of systems thinking and system dynamics are presented. The methods help
to connect hard and soft system elements and depict the problem-centered system structure including
social, environmental or technical links. Systems thinking is a qualitative approach that investigates
the structure of systems in order to investigate malfunctions and infer high-leverage policies for
solution. System dynamics bases on the structural findings of the qualitative research. Quantitative
simulation helps to discover the inherent dynamics of systems and makes the testing of measures
possible (Forrester 1994). There is disagreement between scholars concerning the relationship of the
methods. Some regard system thinking as being an independent methodology (Coyle 2000). They
particularly emphasize the great uncertainties of qualitative linkages that render the outcomes of
quantified models “becoming plausible nonsense” (Nuthmann 1994). Others consider soft system
analysis just to be a component of a thorough investigation of dynamic behaviors of systems and
quantification of conceptual models a mandatory step (Homer and Oliva 2001). In the case of huge
uncertainty of relationships, a system dynamics simulation could help to at least reveal data
requirements and identify areas for further research. Homer and Oliva (2001) conclude that simulation
of models almost always adds value to the outcomes of research and should only be omitted if a model
building would be too time consuming or costly. The modeling of uncertain and qualitative linkages
and variables is even seen as a peculiar strength of the system dynamics method (Forrester 1980).
Often sensitivity testing reveals that the model behavior is not affected by high uncertainties so that
even the use of estimated data is reasonable. Furthermore, the omission of uncertain and empirically
untested relationships would imply the denial of their influences, or as Forrester formulates: “To omit
such variables is equivalent to saying they have zero effect - probably the only value that is known to
be wrong!” (1961, p. 57).
38
In the following, systems thinking is perceived as a required step towards a quantified system analysis.
The decision to simulate or to draw from qualitative inquiry should be made by a careful consideration
of the added value of quantification and the required cost and time. The quantification of systems
facilitates the precise inquiry of problems, as relations between variables needs to be estimated even in
the absence of data. Hence, system dynamics reveals the gaps in knowledge and forces the model
builder to disclose underlying perceptions of systemic relationships. Furthermore, in case of intricate
causal loop structures, the qualitative analysis of the actual behavior of the system is hard to extract.
The case study of this thesis generated this kind of causal loop model on the basis of a participatory
group model building (see Appendix B). The inference of the system‟s behavior from the resulting
nine comprehensive sub-models would require a massive simplification of the model structure in order
to reveal the interplay of reinforcing and balancing loops. In this case, quantification is considered to
be more straightforward, particularly as the interest of stakeholders in such a simulation model was
high. In the following, the two methods of systems thinking and system dynamics are presented in
detail.
3.4.1 Systems Thinking
System Thinking is a methodology for the qualitative analysis of systems and their dynamic behavior
through time. Causal Loop Diagrams (CLD) are used to depict the system's structure and mark time
delays that are often responsible for difficulties in controlling inherent dynamics. In these diagrams,
elements of the system are connected by arrows which together form causal chains (see Figure 17).
The functional polarity is expressed using negative or positive signs. A positive link means that in the
case of an increase in the causing variable, the effected variable would also increase. In Figure 17, the
link between 'Scarcity in Utilized Water' and 'Perception of Water Shortage' is positive. If water
scarcity increases, the user's perception of water shortage would also increase, above what it would
Figure 17: Water supply management system, including social adaptation mechanisms that lead
to increasing water demand (from Bagheri and Hjorth 2007)
Scarcity in UtilizedWater (Problem)
Perception ofWater Shortage
Efforts for WaterProvision
+
+
Water Provision
Provided Water
+
+
-
Utility Perception due toWater Abundance in the
City
Water ConsumptionBehavior
Total WaterDemand
Water Supply
+
+
R
B
+
+
+
SupplyManagement Loop
Social AdaptationLoop
Acceptable Level ofWater Scarcity (Goal)
39
otherwise have been. Contrarily, a decrease in scarcity would imply a decrease in the perceived water
shortage, below hat it otherwise would have been. A negative link implies a reverse relationship
between cause and effect. For instance, if the variable 'Provided Water' increases due to supply-
management efforts (e.g. by pumping of groundwater or water transfer from distant aquifers), the
initial problem of water scarcity would decrease (see Figure 17), below what it otherwise would have
been. The subset „above/below what it otherwise would have been‟ clarifies that the polarity of the
links does not describe the actual behavior of the variables, but the consequences of an alteration in
one variable, assuming other influence factors to be constant. The final behavior is determined by the
systemic context that requires further analysis. For instance, the increase in ‟Provided Water‟ need not
induce lower „Water Scarcity‟ in all cases. The limiting effects of more provided water could be
exceeded by rising effects from a higher water demand or more ambitious goals for water scarcity
mitigation. Thus, the problem variable „water scarcity‟ could even increase despite more „provided
water‟.
The supply management loop in Figure 17 displays the approach of tackling water scarcity by the
development of the water supply. The introductory chapter already presented the limits of this policy
(see Chapter 1.1). Hence, this system perspective is too narrow and side-effects need to be included.
Therefore, Figure 17 shows the water supply management system extended by a sociological
adaptation mechanism. The fixation of a larger amount of provided water not only alleviates the
problem of water scarcity but also increases the utility perception of the users (the orthogonal lines
mark a delay in the process which is explained in the next chapter). As water is more abundant, the
application of this resource widens and water is used for more purposes than before (e.g. irrigation of
gardens, extended squandering). The added water consumption lifts the overall water demand that, in
turn, push pressure on water authorities to provide more supply. Eventually, the initial problem of
water scarcity is made worse by these social side-effects.
Besides these primarily structural considerations, the dynamics of these systems are the major
reason for peoples‟ problems to understand the behavior of complex systems. The system dynamics
methodology investigates the dynamics by the concepts of feedback loops, time delays, and stock and
flows (Sterman 2006). In the following, these dynamic elements are presented.
3.4.1.1 Feedback loops
A central concept in system dynamics is the elaboration of feedback loops. Two different sorts of
feedback loops exist: the self-correcting 'balancing loop' and the self-amplifying 'reinforcing loop'.
The balancing relationships imply a balancing behavior meaning that the state of the system converges
to equilibrium. In Figure 17, this goal-seeking behavior is represented by the 'Supply Management
Loop'. The system state of interest is the problem-variable, namely 'Scarcity in Utilized Water', which
is the reference point of the overall system. By conducting supply-extension measures, the problem is
alleviated. Hence, the internal dynamic will dampen the strength of the balancing loop as soon as an
acceptable level is achieved. A reinforcing loop produces exponential growth of the system state
variable. In our example, the 'Social Adaptation Loop' produces increasing water consumption which,
in turn, is leading to a continuous aggravation of water scarcity.
In order to define the specific polarity of loops, different methods can be used. A simple method is
the counting the numbers of negative variables. Uneven numbers indicate a balancing loop whereas
even numbers denote a reinforcing loop. In addition, tracing the effects of a change around the loop
refers to the polarity. Irrespective of a starting point, a variable is altered in mind and the effects are
pursued along the links. For instance, if the initial variable is increased and a rising tendency obtained
after passing the loop, a reinforcing behavior can be attested (Sterman 2000).
40
Net IncreaseRate
State of theSystem
+
+
R
In general, positive feedback generates exponential growth (see Figure 18). As time proceeds, the net
increase rate is growing. Examples are population growth or compound interest earnings. Positive
feedback can also generate accelerating decline that can at present be observed at the stock market in
consequence of the burst of the housing and credit bubble in the USA. Falling stock prices erode the
confidence of investors, leading to further sales (Sterman 2000).
Another common mode of dynamic systems is the goal-seeking behavior of balancing loops. Here, the
state of the system strives for a balance. Forces that move the system away from its goal are
counteracted in order to regain the desired state. Figure 19 show the course of the state variable
towards the goal as well as the system structure underlying this behavior. Examples for a balancing
loop are basic human needs as nutrition and sleep that require a specific level. Differences in the
desired and the actual level are reduced by physiological reactions, in this case hunger and tiredness
respectively. Also, a firm's stock keeping system represents a balancing feedback. When inventory
comes under a desired value, new utilities are ordered until the reservoir returns to the desired state.
Combining the two modes of feedback, reinforcing and balancing loops, implies a behavior that can
often be observed in nature. S-shaped growth is connected to a carrying capacity of the system that
restrains positive feedback processes if the state variable approaches a specific value. Figure 20 shows
the exponential growth in the initial phase where the positive feedback considerably outbalances the
balancing mechanism. The increasing system state goes along with a decline in the resource adequacy
and fractional net increase rate. Thus, the net increase rate diminishes with time as the balancing loop
gains momentum until the carrying capacity is reached. Standard examples for this sort of behavior are
population dynamics (Sterman 2000).
Figure 18: Graph and underlying causal structure of exponential growth
Figure 19: Graph and underlying causal structure of balancing behaviour
State of theSystem
Discrepancy
Corrective Action
Goal (Desired Stateof the System)
B
41
3.4.1.2 Time delays
Beside the level-off behavior of the state variable in Figure 20 there are further progressions
imaginable. Referring to the conceptualization of supply management (Figure 17), the influence of the
two opposed loops is changing with time. Hence, measures of supply management push the state
variable 'water scarcity' down towards the desired level. While balancing the undesired situation, the
reinforcing 'Social Adaptation Loop' foils the success after a while. If these counteracting process
proceeded in parallel, the rate of change of the state variable 'water scarcity' would be composed of
damping tendencies of supply management and the deteriorating effects of changing consumption
behavior. However, in this case the adaptation to heightened water demand appears with delay. This
fact is marked by the orthogonal lines on the causal arrow between the system elements 'Provided
Water' and 'Utility Perception due to Water Abundance in the City'. In this example, economic
development slowly enhances the water usage and the affordability of water. This slow process causes
a steady but slow increase of demand, in contrast to the relatively quick fixes of supply management.
Hence, the water policy might reach their goal in the short-term while from a long term perspective
water scarcity will come full circle. The behavior of the system is called oscillation and is the third
mode of behavior in dynamic systems and is presented in Figure 21 in more detail. Oscillation is
caused by negative feedback loops that induce corrective actions to a goal, but tends to overshoot this
goal variable due to time-delays. There are different reasons for delays, including measurement,
reporting and perception delays (see loop no. 1 in Figure 6), administrative and decision-making
delays (no. 2) as well as action delays (no.3) (Sterman 2000). In Chapter 3.4.2.1.3 the different reasons
for delays are explained in more detail.
3.4.1.3 Stocks and flows
Besides feedback loops, the stock and flow structure of systems is another central concept for the
analysis of systems behavior. Stocks are states of the system, like inventories of firms, or the water
State of theSystem
Goal (Desired Stateof the System)
Discrepancy
Corrective Action
-
+
++
Delay
Delay
Delay
1
2
3
Figure 20: Graph and underlying causal structure of S-shaped growth
Figure 21: Graph and underlying causal structure of oscillation
Net IncreaseRate
State of theSystem
ResourceAdequacy
Fractional NetIncrease Rate
Carrying Capacity
+
-
+
++
R
B
42
level of a dam. They are calculated by the integration of inflows and outflows of the respective stock.
Thereby, stocks accumulate inflows and cause a delay in the outflows. Consequently, disequilibrium
dynamics like oscillation can be caused by the stock and flow structure. The distinction of stock and
flow in systems alone can reveal causes for malfunction of the system or policy resistance.
Figure 22 depicts the general elements of stock and flow diagrams. Stocks are depicted by
rectangular boxes, inflows are pipes that point into the stock, whereas outflows point out of the stock
variable. Valves control the flow variables, and sources and sinks are the system boundaries
representing stocks from which flows enter the system (sources), and stocks which are filled from
flows out of the system (sinks). Finally, information linkages depict causal relationships between
variables. On the right side of Figure 22, an example of a stock and flow structure is depicted. The
diagrams are similar to causal loop diagrams except the specification of stock and flow variables.
Hence, the construction of stock and flow diagrams can follow the development of causal diagrams in
order to allow in-depth qualitative research beside the analysis of feedback structures
Auxiliary variables are dependent variables that can contain stocks, constant or exogenous data as
independent variables. In Figure 22, the food per capita variable is such an auxiliary that depends on
the population number (stock) and the available food (exogenous variable). In this model, the net birth
rate increases if the population growths which causes exponential growth in the stock variable.
However, as far as the food supply is not enhanced, the food per capita auxiliary decreases with
increasing population so that the fractional birth rate decreases accordingly. The fractional birth rate
can be perceived as the fertility of the population. Thus, with a decreasing fractional birth rate, also the
net birth rate decreases so that finally the system population growth is restrained by the food
availability. This goal seeking behavior is equivalent to the causal loop diagram in Figure 19.
Nevertheless, the stock and flow structure makes a more sophisticated analysis possible, where the
stock as the reason of delays is included in the model structure. The following chapter deals with the
mathematical expression of the qualitative relationships and features that have been explained above.
3.4.2 System Dynamics
The system dynamics method allows for the quantitative investigation of dynamics in systems. Below,
Figure 22: Elements of Stock and Flow diagrams (after Sterman 2000, p. 193)
s
Flow
Stock
Flow
Valve
Source/Sink
StockInflow Outflow
ss ssss Information Link
General Structure Example
Population
Net Birth Rate
Food per Capita
Food
Fractional BirthRate
+
-
+
+
R
B
j
43
the different steps of the modeling process are defined which, on the one hand, should be
accomplished in the participatory process, and additionally be considered in a later report and
scientific publication about the outcomes.
The first task belongs to the problem articulation and framing. Hence, the key variables and the
time horizon of the problem have to be identified as well as the set of graphs that represent the
reference mode of behavior. The next task is the formation of a dynamic hypothesis that looks for
systemic reasons for the observed behavior. For this purpose, current theories of problematic behavior
are analyzed. Subsequently, the problematic processes should be endogenously described by the
systems structure. At this stage the tools of causal loop and stock and flow diagrams can be used.
Third, a simulation model is built upon the qualitative systemic analysis. As a first step the structure of
the model as well as decision rules for involved decision-making processes are defined. Then, the
parameters, behavioral relationships and initial conditions are specified. The fourth step comprises
various testing procedures in order to test the model structure and outputs. For instance, simulated data
is compared to the behavior of the reference modes in the past that caused the problem situation.
Moreover, the robustness of the model can be checked by assuming extreme values of parameters.
Also, uncertain parameters should be varied in order to test the model‟s sensitivity. As soon as the
model is considered to be reliable, policies can be implemented and tested as well as the
appropriateness evaluated by model results. For this purpose, scenarios can be specified that assume
different developments in the environment. For instance, in the water balance model in Chapter 4.4,
one scenario could comprise the assumption of stable rainfall rates, whereas another could assume
decreasing precipitation in future. Based on these different scenarios, policies can be designed to
improve the problem situation. In particular, the systemic effects of measures should be studied in
respect of synergies or detrimental interactions of policies.
However, these distinct steps should not be considered as a receipt that should be followed in
order to achieve a successful modeling process. Rather, modeling is an iterative process that requires
alternation between questioning, testing and refinement. Furthermore, the modeling process should be
seen in a broader learning cycle so that the insight from the model building revises mental models of
stakeholders and induce a new decisions or behavior in the real world (see Figure 6 for the double loop
learning process).
3.4.2.1 Formulation fundamentals of functional relationships
As problem articulation and qualitative analysis have been topic in the preceding chapters, the next
chapter presents selected mathematical formulations and fundamentals for the creation of a system
dynamics simulation models.
The system dynamics approach enables the application of all analytical functions. Nevertheless,
concepts for the straightforward formulation of functional relationships are necessary in the
communication with stakeholders without a strong mathematical background. Hence, system
dynamics supports the application of table as well as analytical functions. In addition, the procedures
for the calculation of stock and flow processes and delays are specific for system dynamics.
3.4.2.1.1 Table functions
Beside the analytical formulation of dynamics, lookup or table functions are another approach to
describe the relation between independent and dependent variables. Figure 23 shows a lookup-
function for the relationship between the Education of Water Consumption Behavior (abscissa) and the
Conscious Consumption Behavior (ordinate). In contrast to analytical functions, look-up functions are
defined by a graphical interface or a table. Thus, the independent variable „Expenses for Education‟
44
enters the table function, which assigns the related value of the dependent variable „Conscious
Consumption‟. Values between specified points are calculated by linear interpolation.
The function 𝑌 = 𝑓 𝑋 (𝑌 ≜ 𝐶𝑜𝑛𝑠𝑐𝑖𝑜𝑢𝑠 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛; 𝑋 ≜ 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠 𝑓𝑜𝑟 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛) in Figure
23, could be expressed analytically by an exponential function 𝑓 𝑋 = 𝑋𝑎 . Nevertheless, in case of
controversial relationships where empirical data is not available, table functions facilitate the
transparent and participatory model building as stakeholders can modify the shape of the function
easily. Scenarios can help to test the effect of altered graphs and the sensitivity of the model behavior.
In order to arrive at a plausible and straightforward table function, different steps are required.
First, the independent and the dependent variables should be normalized. Instead of the formulation
𝑌 = 𝑓(𝑋), reference values 𝑋∗ and 𝑌∗ are used to transform the function to a normalized version
𝑌 = 𝑌∗ ∙ 𝑓 𝑋
𝑋∗ . The functions must pass the reference point 1,1 where 𝑋 = 𝑋∗ and 𝑌 = 𝑌∗. For
instance, the reference points could be a point in time for which data is available. Subsequently,
reference policies can be inserted in the diagram in order to depict infeasible regions. A reference
policy could be the 45° line that expresses the relation of 1% increase in X causing 1% increase in Y.
The function should be checked for the plausibility of extreme values (e.g. −∞, 0, +∞). Therefore, the
range of the variables needs to be discussed that comprises the values in normal situations and these in
extreme conditions. The function has to be adapted to the available data and knowledge. In particular,
inflection points should be justified, and the increments between the steps examined. Finally, the
behavior of the formulation and the sensitivity have to be tested by running the model (Sterman 2000).
By the use of table functions, all functional relationships can be expressed. Nevertheless, the
application of analytical functions can have advantages as they are smooth and differentiable and are
often defined for entire domain of real numbers. Instead, table functions are only piecewise continuous
and, therefore, can produce kinks in the simulated model variables. In return, table functions can be
specified and changed easily without extensive knowledge of mathematics. Thus, the choice for the
use of analytical or table functions depend on the purpose and character of the modeling process.
Standard analytical functions for exponential, goal-seeking, and S-shaped behavior are listed
below. Exponential growth can be expressed by basic exponential functions 𝑦 = 𝑥𝑎 where 𝑎 > 1,
𝑥 ∈ ℝ, and 𝑦 = 𝐸𝑋𝑃(𝑥) = 𝑒𝑥 where 𝑥 ∈ ℝ.
Balancing or goal-reaching behavior can be computed by the following functions:
𝑦 = 𝑥𝑎 where 0 < 𝑎 < 1, 𝑥 ∈ ℝ and 𝑦 = log(𝑥) where 𝑥 ∈ ℝ > 0.
For the generation of S-shaped growth the following functional expression can be chosen:
𝑦 =𝑦𝑚𝑎𝑥
1+𝑒−𝑎 𝑥−𝑏 where 𝑦𝑚𝑎𝑥 = goal variable; 𝑎 = parameter which specifies the slope; b = determines
the x value of the inflection point.
0
0,5
1
1,5
2
2,5
0 1 2 3 4
Conscious Consumption
Expenses for Education
Figure 23: Example for a table function
45
3.4.2.1.2 Calculation of stock and flows
Stocks are the state of the system. The net change of stocks is the sum of the inflows minus the
outflows. Hence, changing stock levels mean that the system is in disequilibrium whereas unchanging
stock levels represent a system in equilibrium. In a stable equilibrium all flows amount to zero
whereas in a dynamic equilibrium inflows equal outflows. The order of the system is defined by the
numbers of stocks involved. Thus, a first-order system points to an inclusion of one stock in the
systems structure. In case of a direct proportionality of the rate equations to the stock levels, the
system is denoted as “linear”:
𝐼𝑛𝑓𝑙𝑜𝑤 − 𝑂𝑢𝑡𝑓𝑙𝑜𝑤 = 𝑁𝑒𝑡 𝐼𝑛𝑓𝑙𝑜𝑤 = 𝑔𝑆 where g = proportionality factor (1)
The proportionality factor g is the fractional growth rate of the stock (cp. Figure 22). First-order
systems can not oscillate regardless of linearity or non-linearity. For oscillation the net increase rate
would have to change from negative to positive values (vice versa) and thereby pass zero. If this
happens, the systems remain in equilibrium. Hence, a system requires at least two stocks in order to
generate oscillating behavior.
The stock and flow structure in Figure 22 is a graphical representation of the mathematical
operation of integration of inflows minus outflows over time. Consequently, the following integral
equation expresses the calculation of the stock level at time t:
𝑆𝑡𝑜𝑐𝑘𝑡 = 𝐼𝑛𝑓𝑙𝑜𝑤 − 𝑂𝑢𝑡𝑓𝑙𝑜𝑤 𝑡
𝑡0𝑑𝑡 + 𝑆𝑡𝑜𝑐𝑘𝑡0
(2)
In the system dynamics simulation programs usually the following expression is used:
𝑆𝑡𝑜𝑐𝑘 = 𝐼𝑁𝑇𝐸𝐺𝑅𝐴𝐿(𝐼𝑛𝑓𝑙𝑜𝑤 − 𝑂𝑢𝑡𝑓𝑙𝑜𝑤, 𝑆𝑡𝑜𝑐𝑘𝑡0) (3)
Hence, in the following the notation of equation 2 is chosen. A screenshot of the interface that is used
for entering functions in the simulation program VenSimPLE is depicted in figure 32.
How does the simulation program calculate the stocks and flows? Both, stock and flow variables, are
dependent on time and could even be interdependent as the state of the stock could affect the flow
variable (compare to the example of population dynamics in Figure 22). Below, the integration
problem is formulated mathematically:
𝑆𝑡 = 𝐼𝑁𝑇𝐸𝐺𝑅𝐴𝐿(𝐼𝑡 −𝑂𝑡 , 𝑆𝑡0) (4)
and
𝐼𝑡 = 𝑓 𝑆𝑡 ,𝑈𝑡 ,𝐶 ; 𝑂𝑡 = 𝑓 𝑆𝑡 ,𝑈𝑡 ,𝐶 (5)
Where: 𝑆𝑡 = Stock at time t; 𝑆𝑡0 = initial value of the stock; 𝐼𝑡 ,𝑂𝑡 = inflow, outflow at time t; 𝑈𝑡
exogenous variable U at time t; C = constant
Since system dynamic models can be composed of various non-linear differential equations, analytical
solutions are often not possible. Hence, numerical integration methods are applied in order to compute
the system. The most basic numerical integration technique is the Euler integration after the
mathematician Leonard Euler (Sterman 2000). Here, the rates are assumed to be constant between
today (t) and tomorrow (t+∆𝑡), and the stock is calculated as follows:
𝑆𝑡+1 = 𝑆𝑡 + ∆𝑡 ∙ 𝐼𝑡 − 𝑂𝑡 (6)
The assumption of constant flows between the time step ∆𝑡 might be accurate for slow dynamics of
the system. The accuracy of the Euler integration method could be improved maximally if the time
step approaches zero:
46
lim𝑑𝑡→∞
𝑆𝑡+𝑑𝑡−𝑆𝑡
𝑑𝑡 =
𝑑𝑆
𝑑𝑡= 𝐼𝑡 −𝑂𝑡 (7)
According to Sterman (2000, pp.907f) the choice for a time step should be grounded on the following
considerations. First, the time step should be evenly divisible into the chosen unit of output data (e.g.
daily, monthly, yearly). Second, the time step should be small in order to minimize the integration
error of the Euler integration method which assumes constant flows between ∆𝑡. However, third, the
time step should not be too small in order to avoid long simulation times. Additionally, the smaller ∆𝑡
the larger the truncation errors of the computer model. Thus, there is a trade-off between integration
and rounding errors. A rule of thump is the selection of the time step between one-fourth and one-tenth
of the smallest time constant in the model. A sensitivity analysis should in any case prove the impact
of the time step on the simulation result.
Another numerical integration method that is more precise is the Runge-Kutta method which is not
presented in detail in this thesis. Interested readers can consult Lambert (1991) for an introduction to
the method.
3.4.2.1.3 Delay functions
Besides the retarding effects of stocks and flows, delays can be inserted through a specific delay
function. Delays can pertain to materials but also to information. An example for a material delay is
the supply chain of a factory. Information delay can be caused by the dissemination of information or
the resistance of mental models to change. In order to specify the mathematical expression of the
delay, two questions have to be answered. First, what is the average delay time between input and
output, and, second, what is the distribution of output around this average delay time.
There are two extreme types of material delays. The pipeline delay has a fixed delay time and the
outflow has the same order as the inflow. Mathematically, this can be expressed as follows:
𝑂𝑢𝑡𝑓𝑙𝑜𝑤 𝑡 = 𝐼𝑛𝑓𝑙𝑜𝑤(𝑡 − 𝐷) where D represents the delay in a unit of time (8)
In contrast, a first order material delay stresses the metaphor of a sink from which water is taken.
Here, the order of the inflow is irrelevant to the order of entry. If the water entered the sink before 1
hour or two weeks does not affect the probability of discharge as molecules are mixing perfectly.
Equation 9 formulates this kind of delay mathematically:
𝑂𝑢𝑡𝑓𝑙𝑜𝑤 𝑡 =𝑆(𝑡)
𝐷 (9)
Thus, the outflow of water from the sink depends not on the time of inflow, but on the location in the
stock. However, between these two extremes of no mixing and perfect mixing are many intermediate
situations where the material order is mixed slightly. Multiple processing of material could induce in-
between delays. For example, the procession of letters in a post office proceeds not successively
(letters are mixed in the post box), but also depends on sequences of operation (post boxes are cleared
regularly). Delays for these processes that comprise different steps can be calculated by a higher order
material delay. The order of the delay determines the distribution of the outflow around the average
delay time:
𝑂𝑢𝑡𝑓𝑙𝑜𝑤 = 𝐷𝐸𝐿𝐴𝑌𝑛(𝐼𝑛𝑓𝑙𝑜𝑤,𝐷) (10)
Hence, a delay of the n-th order simulates the flow of material through n stocks each with first order
delays. Figure 24 shows the response of the output to a higher order delay function to a step input.
47
3.4.2.1.4 Smooth function
Information flows are differently from material flows as the inflows are not conserved. Incoming
information has to be processed, and based upon this, perceptions adapt until the outflow represents
the delayed reaction to the received information. Therefore, the calculation of information delays (e.g.
perceptions or forecasts) applies an additional functional expression different from the functions for
material delays: the SMOOTH-function.
The simplest form is the first-order exponential smoothing that is the gradual adjustment of the
belief to the actual variable. Similar to material delays, also information flows can involve the
adjustments over multiple stages. Thus, the information input initially causes no immediate reactions
in the output. With time, the outflow gradually starts, and approaches the perceived value. Again, a n-
th order smoothing is the succession of first-order exponential delays over n stages. The functional
expression is as follows.
𝑂𝑢𝑡𝑝𝑢𝑡 = 𝑆𝑀𝑂𝑂𝑇𝐻𝑛(𝐼𝑛𝑝𝑢𝑡,𝐷) (11)
Figure 25 shows the reaction of higher order delay outputs for a step input.
3.4.2.2 Model testing
“All models are wrong” (Sterman 2000, p. 846). This sentence has been already stated in Chapter 1
and underlines that a verified and valid model is not possible in reality. Verification means the
Inflow
Figure 24: Pulse response of third-order delay by stage of processing (Sterman 2000)
Figure 25: Response of higher order delays to a step input (Sterman 2000)
48
reflection of truth and reality, whereas validation is the correct derivation of conclusions from
objective and true premises (see Baki 1995 for an overview of validation, verification, and testing). A
model can never reflect reality in all aspects and is therefore always a simplification which usefulness
has to be assessed by its purpose and its target audience (Sterman 2000).
In case of participatory group model building, the model is focused on the respective problem
under consideration, and the purpose is the structuring and rationalization of the discussion. The
participatory model is the result of the discussion and simulates the dynamic behavior of the system
structure that stakeholders have considered to be important. This shall foster confidence in the model
by the participants as they understand the underlying principles and could contribute to the final
product. In contrast, simulation models without the participation of their users in the modeling process
have to convince practioners and demonstrate their accuracy by sophisticated testing procedures. In
the participatory modeling, rather the model building process resides in the center of interest than the
final outcome. However, methods of model testing are important as well in order to base decision-
making on the best-available model for the respective situation. However, these tests should point to
the limitations and flaws of the model instead convince about the validity of the output. Whereas the
first leads to improvement of the model, the latter is more conceived as the end point and ideal of
model building (Sterman 2000).
There are various methods which test the usefulness of models. These tests mainly pertain to the
steps of the modeling process that have been described in Chapter 3.4.2. There are qualitative and
quantitative procedures to prove the adequacy of the problem frame, the dynamics hypothesis, and of
the structural and functional elements of the model. In the following, some examples are given for
model testing methods.
First, the boundary adequacy tests the appropriateness of the model boundary. Processes that are
important for the problem at stake should be endogenously included in the model structure in order to
consider feedback processes. A model boundary chart reveals the endogenous, exogenous and
excluded variables of a model. For this purpose, a table is established that explicitly lists the different
types of variables. Furthermore, a subsystem diagram shows the overall structure of the model and the
connections between different subsystems. Subsystems could be organizational entities (e.g. firms,
individuals), or processes (e.g. markets, hydrological system). This diagram should be as simple as
possible as it should provide an overview of the interconnected sub-systems as well as exogenous and
endogenous processes and variables.
Second, the extreme conditions test investigates the model‟s robustness. Even in cases of extreme
conditions, the model should produce reasonable output, meaning that storage variables (e.g.
inventories, water level) should not fall below zero, or outflows only occur if stocks are filled. The
robustness can be tested by inspection of the model or directly by assuming extreme conditions and
policies (e.g. precipitation levels approaching zero, or extreme population growth). Implausible
simulation results should lead to a revision of equations and the model structure.
Third, parameter assessment is central for the system dynamics method. In case of availability of
numerical data, regression techniques can be applied like Maximum Likelihood and Generalized Least
Squares methods. However, often numerical data is not at hand, so that parameters have to be
estimated on the basis of expert opinion, archival materials, or direct experience. In the cases of
qualitative and case-specific parameters, the meaning of the parameters should be defined clearly.
Sometimes, parameters can be assessed more easily by cutting feedback loops and digest the key
structure of the process in which the parameter is involved. Especially, in case of large models, the
inquiry of sub-models can help to find reasonable parameter values.
Fourth, the behavior reproduction test compares simulated data with measured data. This test is
49
particularly required for the comparison of the reference modes of behavior with time series from the
past. Again, the graphs can be compared qualitatively by stakeholders and experts, and thereupon, the
appropriateness of the results is defined. Descriptive statistics can help to evaluate the fitness of data
numerically. Standard procedures as R², or Mean Square Error tests can be applied. Nevertheless, these
methods can not distinguish systematic and unsystematic errors which point to flaws in the model and
random noise in exogenous data. Other statistical test, like the Theil inequality statistics, can
discriminate between systematic und unsystematic errors. However, behavior reproduction test do not
measure the correctness or reliability as many models with completely different structure can simulate
the same results. Hence, not the fitness of modeling results should be emphasized in discussions with
clients or the stakeholder group, but the inconsistencies that point to flaws in the model structure and
facilitates a revision of the model (Sterman 2000).
4 Case Study: Participative Assessment of Integrated Policies to Mitigate the Effects of Water
Scarcity in Cyprus
The case study about integrated water management in Cyprus takes up the major part of this thesis.
The chosen approach combines hard and soft modeling by integrating systemic processes derived from
participatory modeling sessions into a system dynamics model that illustrates the hydrological system.
While the hydrological model describes the replenishment of the ground- and surface water storages,
the participatory model explains the allocation and policy mechanisms that manage the scarce resource
water. The underlying assumption of this approach perceives the hydrological processes as
uncontroversial because they reflect mainly meteorological, physical, biological or chemical facts. It is
also unlikely that interviewees will explain hydrological processes in detail, although variables like
„precipitation‟ or „groundwater storage‟ might be mentioned. The target group of the model is
primarily the decision-maker who has to define the future strategy of water management. As decision-
making requires a certain degree of accuracy in the quantification of water flows, an adequate
hydrological model makes reasonable quantitative simulations possible that can, in turn, assist the
search for management strategies. Interested stakeholders that have become acquainted with the
qualitative system dynamics concepts (i.e. causal loops, and stocks and flows) are able to relate to the
hydrological model and challenge processes that are questionable for them by applying the system
dynamics method. The model helps to gather information, to consult stakeholders, and to integrate
considerations about the effectiveness and side-effects of measures that are aimed at a sustainable
resource management.
The participatory process took place from January to February 2009. Causal models that reflect
the mental models of stakeholders about the water scarcity problem in Cyprus were constructed in
individual interviews. They contain the political, economic, social and environmental processes that
are regarded as causes or consequences of water scarcity. Eight institutions and pressure groups with
diverging interests participated in the study: Water Development Department, Agriculture Research
Institute, Environment Service, Department of Agriculture, Cyprus Tourism Organization, Fassouri
Producers‟ Group (Farmers Union), Water Board of Limassol, as well as a Hotel Manager from
Limassol. A step-by-step guideline has been used in the interviews to structure the creation of the
causal loop diagrams. The process will be explained in detail below. The individual models have
subsequently been merged into a comprehensive model and presented to the participants by using a
questionnaire. The stakeholders did thus identify other perceptions and ideas about the water problem,
and had the opportunity to add comments and criticism.
Since the governmental decision-makers are the target group of the model, the aggregation level of
50
the study was chosen to be national. Certainly, this aggregated level is challenging as the collected
data is more imprecise than on regional or local levels. Also, the generation of required representative
information, which could be gathered with the help of surveys or field studies, might be more time-
consuming. Nevertheless, the national decision-makers face the same challenges and have to base their
decisions on information that is available. The participatory model building by using system dynamics
is therefore considered to be a pragmatic approach. The model helps to base immediate decisions on
the data that is available, and helps to clarify uncertainties and gaps, knowing that further research is
still required.
The outcomes of the study comprise a qualitative and quantitative analysis of the models. First, the
content of the causal diagrams is analyzed in connection with a description of the different points of
view found in the questionnaires. Second, selected processes from participatory models are added to
the hydrological model in order to simulate policies like seawater desalination or wastewater
recycling. Some simulation results are presented that show the potential of the method to analyze
policies in complex systems. Due to temporal and spatial restrictions, an inclusion of all processes into
the simulation model was impossible. Additionally, the autonomous quantification of the qualitative
model would stand in contrast to the participatory nature of its creation. The purpose of the model is
therefore the illustration of the potential and possible outcomes of a group model building process. As
a continuation of the study is planned due to the interest of various participants, the system dynamics
model can serve as a preliminary model for the first group meeting.
The organization of this chapter is as follows: First, the problem situation of water scarcity in
Cyprus is described in order to explain the challenges and motivation for this study. Subsequently, the
stakeholder analysis is presented that formed the basis for the choice of the initial group composition
for the participatory modeling. The proceedings of the interviews and the resulting model are
described in connection with an analysis of the questionnaires afterwards in Chapter 4.3. Especially
controversial comments with respect to the model structure are discussed. Chapter 4.4 introduces the
structure of the hydrological model and the inserted processes from the participatory model building.
Simulation results are presented and future steps and research efforts as well as their possible
outcomes are described that could result from a continuation of the study.
4.1 The water scarcity problem in Cyprus
Cyprus is the third largest island in the Mediterranean Sea after Sicily and Sardinia. It covers an area
of 9,251 km² and expands 241 km latitudinally and 97 km longitudinally. It is located in the Eastern
Mediterranean about 70 km south of Turkey, and 100 west of Syria and Lebanon. The topography is
heterogeneous with one third of the island being covered by the Troodos range in the southern central
with the Olympos having the highest elevation of 1,951m a.s.l. In the north of the Troodos mountains
is the central Mesaoria Plain which is northwards confined by the Kyrenia mountains, whereas the
center comprises the Cyprian capital Nicosia and the city Famagusta at the eastern coast. In the south
and east of the Troodos mountains, a narrow coastal shore line integrates the further major cities
Limassol, Paphos and Larnaka. Due to the topography one can find different microclimates, ranging
from tropical to temperate, that make the cultivation of a variety of fruits and vegetables possible. The
overall climate is Mediterranean with hot and dry summers from May to September, and rainy winters
from November until March (Katsikides et al. 2005).
The Cyprian island is divided into the Republic of Cyprus and the Turkish Republic of Northern
Cyprus (TRNC). The latter is only recognized by the Republic of Turkey but forms an independent
political entity. Nicosia is the capital of both republics, divided by a border with two open border
crossings at present. The Republic of Cyprus has approximately 690,000 inhabitants with 625,000
51
Cypriots and 65,000 foreigners. 87,600 Turkish Cypriots and more than 115,000 Turkish settlers live
in the north. The tension arising from the division and the uncertain future of the political structure is a
particular challenge. The accession of the Republic of Cyprus to the EU in the year 2004 is connected
to the hope for a medium-term solution for this conflict. Even though first steps have been made
towards a relaxation of the political situation, a final re-unification is still not in sight (Katsikides et al.
2005).
Keeping the political problems in mind, the tackling of water resource issues becomes the special
complexity that goes along with transnational watershed issues. However, the decreasing and unstable
precipitation quantities are the central source of concern for both water users and authorities. The
national mean annual precipitation data (see Figure 26) shows a high inter-annual variability of rainfall
and an overall decreasing trend by 14% from 560 to 480 mm in the last century.
The mean precipitation for the Mesaoria Plain (Nicosia) has even diminished by 20% from 380 to
300mm. Furthermore, five periods of droughts lasting for three consecutive years could be detected in
the last century (Katsikides et al. 2005). Since Cyprus has no transboundary water inflow, rainfall is
the only renewable water source. Hence, the decrease in the last 100 years also means a reduction of
the renewable water resources which amounts in average to 2670 Mm3 for the area of the Republic of
Cyprus. According to a calculation of the water balance by the Water Development Department for the
year 2000, about 86% of the rainwater evaporates, so that just 370 Mm3 remain on the island from
which 235 Mm3 are surface water and 135 Mm3 replenish the aquifers. 51% of the surface runoff is
diverted to dams, while the remaining water flows into the sea, evaporates, or is diverted from the
perennial rivers for irrigation purposes (Katsikides et al. 2005).
Summarizing the available renewable water resources from dams (127 Mm³), river diversions (15
Mm³), and groundwater (139 Mm³), and subtracting the overpumping of aquifers (29 Mm³), the
average volume of supply between 1971 and 2000 was 252 Mm³ (WDD 2009). In 2000, this supply
was opposed to an estimated water demand of 265.9 Mm³ (Savvides et al. 2002). The agricultural
sector‟s share of the overall water demand constituted 70% whereas the domestic sector took 20%, the
touristic 5%, the industrial 1%, and environmental flows 5% respectively. The projected total water
demand in Cyprus for the year 2020 is expected to increase by 18% up to 313.7 Mm3, under the
assumption that the agriculture demand remains stable. The touristic demand will double in 20 years,
Figure 26: Mean annual precipitation Cyprus wide: 1901- 2002 (Katsikides et al. 2005)
52
and the water use of inhabitants will rise by nearly 40% (Savvides et al. 2001).
The overexploitation of groundwater resources is a particular problem in Cyprus. The intensified
exploitation of the aquifers started after the Second World War by drilling deep boreholes and applying
high capacity pumps. Thus, agriculture became more independent from rainfalls and farmers could
extend the cultivation of irrigated crops. Already a study in the 1969s revealed groundwater
exploitation above the replenishment rate of 448.9 Mm3 by 42 Mm3 (≅ 9 %) (Katsikides et al. 2005).
Due to the decrease in precipitation water, the utilization of the groundwater reservoirs has increased
and became particularly bad in the 1990s with an overexploitation of 40%. In order to protect the
resources and manage them in a sustainable way, Georgiou (2002) recommends a maximal extraction
of 82 Mm3. Besides the decreasing amount of water that is stored in the aquifers, seawater intrusion
deteriorates the water quality and consequently the quantity of usable water. Pollution is also caused
by agrochemicals, domestic sewage, animal husbandry and industrial discharge. The government
strives for the reduction of environmental degradation caused by polluted effluents by the construction
of wastewater treatment plants, awareness campaigns, designation of water protection areas, and
expansion of central sewage systems even to rural areas (Katsikides et al. 2005).
In the 1980s, the water policy of the Cyprian Government was focused on the building of dams,
conveyors and irrigation networks in order to cushion the vulnerability caused by the variations in
rainfall, and to minimize the loss of surface water to the sea (Katsikides et al. 2005). Due to these
measures, the storage capacity increased from 6 Mm3 in 1960 to 307.5 Mm3 in 2003 (WDD 2003).
Based on pre-1970 hydrological data, the annual yield of the dams was estimated to be above 200
Mm3, but due to decreasing precipitation the annual yield has fallen to 127 Mm3 (Katsikides et al.
2005). The increase in water demand by a simultaneous deterioration of natural water resources
induced a shift in the water policy to the development of non-conventional water sources, namely
recycled and desalinated water. The capacity of the two desalination plants in operation amounts to
91000 m³/d that is exclusively used for drinking water supply. Future plans contain the construction of
two additional plants with a combined capacity of 30000 m³/d (Donta et al. 2005). The power for
operation of the reverse osmosis plants is obtained from oil-fueled power stations so that a future
increase in the oil price would render the operation even more costly (Koroneos et al. 2005). Recycled
water from wastewater treatment plants is usually employed for irrigation purposes or recharge of
aquifers. The estimated amount of recycled water adds up to 15.7 Mm3 in 2005, and is estimated to
rise until 2025 to 85 Mm3 (Yiannakou 2008). Besides the “maximum potential exploitation of non-
conventional water resources” (WDD 2009), water conservation and programs that increase the
consciousness of water consumption are also central in governmental policies. Institutional and
legislative changes in the water sector are discussed as well, in particular the consolidation of
executive power in a central water authority. All these efforts are prompted or at least influenced by
the EU Directives with which all reforms have to comply (WDD and FAO 2002).
This study can help to systematically depict and estimate the effects of the different supply and
demand management strategies that are planned by governmental agencies. Side-effects like costs and
expected social and environmental effects can also be included to get a holistic picture of the problem
situation. The participatory model building can support a rational discussion between stakeholders as
the effects of proposed solutions can be estimated and the systemic consequences simulated.
4.2 Stakeholder analysis
Before starting with the participatory modeling process, an in-depth stakeholder analysis is needed in
order to define the stakeholder groups that should be included in the participatory modeling process.
The importance of stakeholders for the process has different dimensions, e.g. their power, the urgency
53
of their needs, or their specific role. Hence, the stakeholder analysis should approach the subject from
different angles in order to capture the multi-faceted relations of individuals and institutions to the
problem at stake. As described in Chapter 4.2.3, the following steps are applied to work out a
deliberate stakeholder composition for the participatory modeling process in Cyprus: Brainstorming of
a stakeholder groups, identification of the stakeholder‟s role and function in the issue, construction of
a power versus interest grid, and analysis of the stakeholder dynamics
Stakeholders who are detected to be important with regard to the problem by at least one of the
methods above will enter the participants list. The resulting stakeholder composition is still considered
to be preliminary, as the participants are asked to suggest further people or institutions that they
consider important. The outcome of the stakeholder analysis is therefore only a preliminary group
composition that is grounded on a selection that offers a promising starting position for the
participatory process.
4.2.1 Application of techniques
First, a brainstorming session based on literature review provides a list of various individuals and
institution belonging to the problem of water scarcity in Cyprus (step 1) that are subsequently sorted
into categories (step 2): decision-makers, implementers, users, and experts (see Figure 27).
Some stakeholders belong to more than one category due to multiple features and responsibilities (e.g.
the Water Development Department is the subordinate implementer of government's policies and can
simultaneously decide independently on various water issues). Furthermore, it has to be noted that the
classification is based on the problem definition and preliminary framing of the problem. Although the
irrigation divisions have decision-making power on the local level, the divisions are perceived as the
implementers of policies without direct influence as the study concentrates on the national level.
Whereas the functions as decision-maker, implementer and user are represented by multiple
organizations or individuals, the expert group is formed by two entities only, namely the Agricultural
Research Institute, and the Cyprus Institute. Consequently, at least one of these organizations should
attend the participatory modeling workshops. The diagram will be used again in the last step of the
stakeholder analysis in order to proof the existence of every stakeholder group from Figure 27 in the
final group composition.
Figure 27: Preliminary stakeholder list sorted by their respective role
54
The interest-power diagram (step 3) helps to narrow down the number of participants and prioritize
them with regard to the importance for the participatory model process. Figure 28 depicts the
positions of detected stakeholders in the dimensions of power (x-axis) and interest (y-axis). The power
dimension reflects the ability and leverage of the stakeholder towards changing the status quo. The
interest dimension comprises the willingness and motivation of an actor to be engaged in the problem
of water scarcity and to take action.
The power and interests of an organizational stakeholder are mainly described by the purpose and task
of the respective organization. This information about institutions in the water sector is mainly
extracted from institutional analyses in the FAO-report (WDD and FAO 2002) and the MEDIS project
(Dörflinger 2004). Four group types can be specified by their location in the diagram. The 'player'-
stakeholders are the most important ones as they have the power and interest with respect to the issue
of water scarcity. The Water Development Department (WDD) is considered to be the most powerful
party in the realm of water management in Cyprus. Other sub-divisions of the Ministry of Agriculture,
Natural Resources and Environment (MANRE) like the Department of Agriculture or the Environment
Service belong to this category, too. In addition, the water boards are considered to be players, as they
are responsible for the distribution of potable water between domestic and industrial sector. On the
other side of the spectrum, the 'crowd'-parties can be neglected from the interest-power perspective as
they have low interest in the issue and no considerable power to induce change. The Hotelier‟s
Organization and the Commerce and Industry Chamber of Cyprus belong to this category. There are
four „context setters‟ in the depicted interest-power diagram: the House of Representatives, the
Council of Ministers, the Ministry of Interior and the Town Sewage Boards. All these stakeholders
have power with respect to water management, but presumably low interest to participate in the study.
The Town Sewage Boards for instance has limited power on the local and regional level but only
moderate interest in the issue of water scarcity due to its responsibility for water quality issues rather
than water quantity. The Ministry of Interior primarily co-ordinates, plans and supervises all district
administrations in Cyprus. Further responsibilities are related to urban development, town planning
Figure 28: Power versus Interest Diagram for stakeholder belonging to the issue of water scarcity in
Cyprus
55
and housing, land surveying, migration, civil defense and information policy (Republic of Cyprus
2008). Hence, merely a minor share of its tasks is water-related. This is reflected by the assumption of
a moderate degree of interest in the diagram. Most of the stakeholders reside in the subject field by
having interest in the issue but no considerable power to induce change. Research institutes like the
Agriculture Research Institute or the Cyprus Institute belong to this category. In addition, interest
groups from the agricultural sector (Farmers Unions, Citrus Farmers Association, Agricultural
Businesses), tourism (Cyprus Tourism Organization), domestic sector (Consumer Association), and
local authorities (e.g. irrigation divisions and associations) are members of this group. The media is
considered to have interest and some leverage to take action, too. The noticeable distribution with
most of the stakeholders located in the upper half of the scheme can be explained by the conflicting
and urgent issue of water scarcity in Cyprus that determines high priority and interest. The power
versus interest diagram clarifies that the Water Development Department, the Ministry of Agriculture,
Natural Resources and Environment, and Water Boards should participate in the study.
Finally the stakeholders‟ dynamics are analyzed (step 4) using the concept of Mitchell et al. (1997).
The attributes „power‟, „legitimacy‟ and „urgency‟ are assigned to stakeholders, resulting in a
categorization into eight stakeholder typologies. The availability of physical resources that can be used
for force, violence or restraint is subsumed in the power dimension. Legitimacy is socially attributed
through norms, values and believes, whereas urgency expresses that a delay of the stakeholders‟ claim
is not acceptable. The relative importance of stakeholders is defined by the number of the assigned
attributes, ranging from latent (one attribute) and expectant (two attributes), up to definitive
stakeholders (three attributes). The outcome of the framework for the issue of water scarcity in Cyprus
is depicted in Figure 29.
Figure 29: Stakeholder classes belonging to the problem of water scarcity in Cyprus
Cyprus.
1 2
3
4
5 6 7
POWER LEGITIMACY
URGENCY
2: Discretionary Stakeholder
Cyprus Institute
Agricultural Office
Land Surveys Department
Town Sewage Boards
Geological Survey Department
Industry
6: Dependent Stakeholder
Irrigation Division
Irrigation Association
Part-time Farmers
Full-time Farmers
Agricultural Businesses
Domestic Consumer
7: Definitive Stakeholder
Water Development Department (WDD)
Ministry of Agriculture, Natural
Resources and Environment
Water Boards (Towns, Municipalities,
Community)
Farmer Unions
Citrus Farmers Association (or other
crop types)
4: Dominant Stakeholder
Advisory Committee
Consumer Association
Cyprus Tourism
Organization + Hotelier's
Organization
Commerce and Industry
Chamber of Cyprus
District Officer
House of Representatives
Council of Ministers
56
Most of the parties that are listed in the typology of „Definitive Stakeholders‟ (see Figure 29) have
been proved to be important in the preceding steps of the stakeholder analysis. Additional stakeholders
are the Farmer Unions and crop-specific Farmers Associations that form representations of farmers on
the national level. Their inclusion would also meet the demand for the representation of the interests of
the dependent stakeholders from the agricultural sector. From the dynamical perspective, the diagram
clarifies that domestic and industrial stakeholders might shift from the dominant to the definitive type,
if urgency would rise in future, e.g. induced by a continuing increase of the water price or legislative
constraints on water usage or sewage discharge.
4.2.2 Summary of the findings
The application of the techniques above results in the initial stakeholder composition for the
participatory modeling workshops. The analysis of the roles of stakeholders as experts, decision-
makers, implementers and users revealed the limited number of experts. The Agricultural Research
Institute or the Cyprus Institute should therefore be represented in this study. The Power vs. Interest
Diagram determines the following parties to be „players‟ and therefore crucial participants: Water
Development Department (WDD), Water Boards (Towns, Municipalities, Communities), Ministry of
Agriculture, Natural Resources and Environment. Finally, the analysis of stakeholder dynamics added
the national representation of farmers to the list of key participants, namely Farmer Unions and crop-
specific Farmers Associations. It also highlights that the domestic and industrial sector might
participate in the future if the situation worsens.
The comparison of the different outcomes reveals a homogeneous group composition as all role-
categories derived from the target scheme in step 2 are represented. The final group composition is as
follows:
National government level: Ministry of Agriculture, Natural Resources and Environment;
Water Development Department
National non-governmental level: Farmer Unions; Farmers Associations
Regional/local level: Water Boards
Others: Agricultural Research Institute; Cyprus Institute
4.2.3 Participatory stakeholder analysis
In the participatory model building, the stakeholder list above was presented to the participants in
connection with a request for further suggestions. Most respondents considered the stakeholder list to
be sufficient. The inclusion of governmental organs like the House of Representatives, Council of
Ministers and District Officers was perceived to be unnecessary by some stakeholders as the Water
Development Department was regarded as the central water authority in Cyprus. In the interviews,
mainly the Sewage Boards and the Hotelier‟s Organization were suggested for future inclusion. The
questionnaires that are discussed in Chapter 4.3.2 in detail also contain a section in which institutions
could be proposed for future research. Thus, NGO‟s and organizations or individuals that support the
contemporary development strategy of the government should be asked to participate.
The groups that turned out to be important after the analysis were contacted by email or phone.3 In
particular, the contacts and high efforts of the Cyprus Institute have been valuable in order to approach
the interviewees. Interviews have been conducted with the following organizations: Ministry of
Agriculture, Natural Resources and Environment (represented by the Department of Agriculture and
the Environment Service), Water Development Department, Farmers Union (represented by the
3
A short project description has been sent to the participants via email (see appendix C).
57
Fassouri Producers‟ Group), Water Board of Limassol, and Agriculture Research Institute. Due to the
special interest of a Hotel Manager, an additional interview was organized with an individual
representative of the hotel-sector.
4.3 Participatory model building
This chapter presents the proceedings and outcomes of the participatory model building process in
Cyprus. Within two months, about ten interviews were conducted in Nicosia and Limassol. Causal
loop diagrams were created in eight interviews. After the interviews, the diagrams were merged,
thematically sorted and included into a questionnaire. The chosen approach in the interviews as well as
the results of the model building and questionnaires are presented below.
4.3.1 Interviews
The interviews were conducted in English and German depending on the participant. The proceeding
of the interviews had to be adapted to the respective time constraints. It turned out that the building of
a personal model required at least one hour. Therefore, three types of interviews have been applied:
informal interviews without the construction of a model by the participant, if the available time was
less than 30 minutes; interviews in which a preliminary model of the water scarcity problem was
presented and modified by the interviewee if he was available for 30 to 60 minutes; and a complete
participatory model building from scratch, if the participant was available for at least 60 minutes.
In the end, ten interviews were conducted, with seven model building processes from scratch, one
by the use of a preliminary model, and two informal interviews. Even though the major concern of the
study was the independent model constructions by the participants, the other types of interviews
turned out to be valuable as well, as the participants got acquainted with the method and study,
delivered interesting information about processes, and were interested in filling in the questionnaire in
most cases. In the following, the proceedings of the different types of interviews are presented in
detail.
4.3.1.1 Personal model building from scratch
Causal Loop models were constructed by the Environment Service, Water Development Department
(3 models), the Farmers Union, the Water Board of Limassol, the Agriculture Research Institute and
the Hotel Manager. A complete interview session with the construction of a personal model started
with a presentation of the study topic and its goals. The reasons for the participatory approach were
highlighted, namely the inclusion of conflicting points of view and interests, the collection of
knowledge about the problem from local, regional or national stakeholders, and the eventual
participatory development of strategies. Afterwards, the method of system dynamics was presented by
using a sample causal loop model about the problem of traffic congestion (Sterman 2000, pp. 181ff,
and Appendix D). The topic of road congestion has been chosen as it is straightforward and unrelated
to water problems so that influences on the participant‟s own model building were ruled out. Three
loops from the congestion-model were introduced to explain the concepts of link polarity, as well as
balancing and reinforcing loops. A step-by-step framework was presented to the participant afterwards
in order to guide and structure the individual model building process. The proceeding was already
presented in Chapter 3.3.2.1.3 (see Figure 16). According to the framework in Figure 16, the
construction starts with the definition of the problem variable that was written on a post-it® note by the
participant. Most participants chose the variable „water scarcity‟, while others used „water shortage‟.
At this stage, it was interesting to hear about the personal definitions of the broad term water scarcity.
Participants understood the term either in the sense of water shortage due to insufficient water supply
58
that impedes the satisfaction of demand, or as a problem of increased demand that has exceeded the
natural water supply capacity. One interviewee suggested to change the problem variable from „water
scarcity‟ to „fast economic development‟ at a later stage of the model building process, as he thought
that was the basic problem. In this case, a new model structure was constructed around the new
problem variable that incorporated the initial „water scarcity model‟. The discussions that
accompanied the definition of the problem variable point to the different frames that stakeholders have
from the problem situation (cp. Chapter 2.4). A future group model building would reveal the
differences in a more explicit way as participants have to discuss the problem variable before the
actual model building starts.
The second step of the framework depicted in Figure 16 comprises the adding of causes of water
scarcity (in the following explanation „water scarcity‟ will be used as problem variable) and the
connection of the cause variable to the problem variable. Again, the variable names were written on
post-it® notes by the participants. The arrows were drawn with a pencil in order to make later changes
possible. The link polarity was also defined by the participants. Starting with the direct causes of water
scarcity, the stakeholders continued by adding indirect causes until he or she considered the cause-side
to be sufficient for the time being. At this stage, the different perspectives of the stakeholders played a
more significant role. Some participants detected diminishing rainfall rates as the main cause of water
scarcity, whereas others considered the high water consumption of user groups as most important. The
latter aspect was not assessed uniformly, as some people thought that the agricultural sector was a
major cause for water scarcity due to its high share in the overall water consumption. Others however
emphasized the stable consumption of agriculture in the past, and suggested the tourism sector as the
main initiator due to the recent growth in demand.
The third stage of the model building process was entered by adding the perceived consequences
of water scarcity. The direct and indirect consequences were included until the participants were
satisfied with the model. At this stage, the participants concentrated also on different aspects, ranging
from economic and legislative consequences to environmental and social issues. The model structure
was finally analyzed in order to find feedback loops that connect the consequences with the cause side.
Chapter 4.3.2 presents the detected feedback processes in detail.
The participants have created their models independently. Questions were asked mainly with
respect to the application of the method. Special attention was given by the interviewer to avoid
exertion of influence regarding the content of the model. An additional question that proved to
facilitate the model building was the following: “What do you think are the policies that can help to
mitigate the effects of water scarcity?”. The participants were thus asked to include their ideas and
proposals for solutions in the model structure. The next question was related to the expectations of the
participants regarding the policies they stated: “Do you think these policies will be successful in
solving the problem of water scarcity?”. If this question was denied, a further question was asked in
order to elicit the obstacles of these policies: “What do you think are the impediments for the success
of these policies?”. Again, the interviewee was encouraged to include these impediments into the
model structure.
The outcomes of these model building sessions are some comprehensive and multi-faceted
models. The participants were mostly satisfied with their models and believed that they reflected their
point of view in a comprehensive way. Some expressed their surprise about the outcome and the
ability of causal loop diagrams to depict the various aspects of complex problems in such a clear way.
Another stakeholder commented on the model building in this way: “This is very interesting – and I
think this should be done in front of a group with different interests. Because one of the problems is
that everyone sees their own problems and thinks that their problems are the most important ones, and
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everybody else‟s problems have lower priorities. But with this approach you can see the big picture
and how everything is mixed together”. This statement is remarkable as it points to the strengths of the
system dynamics method even though the stakeholder got acquainted to the method merely one hour
before. Also, it shows that the study has the potential to be continued in the future as various
stakeholders uttered similar remarks. One exemplary model is depicted in Appendix E. For reasons of
confidentiality, the model is shown without the specification of names or institutions.
4.3.1.2 Personal model building using a preliminary model
In one case, the interview time was restricted to 45 minutes so that a complete model building process
was impossible. Rather than setting the model building completely aside, a preliminary causal model
based on previous interviews including economic, environmental and political aspects (i.e.
desalination, wastewater recycling, and demand management) was constructed. The purpose of this
interview was the presentation of the study and method, the discussion of the preliminary model, and
the correction or extension of the model structure according to the participant‟s opinion. The process
worked out well and the limited time was utilized effectively. After the correction of some links, the
participant went on by adding legislative issues that had been omitted by the previous interviewees.
4.3.1.3 Informal interviews without personal model building
Informal interviews were conducted in two cases. The interviews started with a presentation of the
study and its anticipated outcomes. The participant was then asked to explain his or her point of view
verbally. It was announced that the explanations were taped by a voice recorder and, subsequently,
included into the model. If possible, the approval of these causal loop structures was obtained by short
follow-up meetings where the model was presented and discussed (this was possible in one case).
Nonetheless, explicitly stated relationships were included in the merged model and thereby disclosed
to all participants.
4.3.1.4 Success and problems that were faced in the interviews
All the interviewees were willing to support the study and were open-minded regarding the applied
method of system dynamics. Even though most of the participants had no prior experiences with the
building of simulation models, not to mention causal loop diagrams, they built their personal model
independently. Technical terms like „feedback loops„ were also used naturally by the participants
during the building process.
The problems were mainly related to a limited amount of time, so that the participants had to build
the model quickly. Presumably, some additional processes would have been included or aspects would
have been depicted in more detail, if more time had been available. With respect to the concepts of
causal loop diagrams, the concept of „link polarity‟ turned out to be quite counterintuitive to the
participants. Instead of understanding links as structural elements of the system that inform about what
would happen if a variable was changed, many interviewees inferred the actual behavior of the system
from the polarities. For instance, a positive link was considered to cause an increase in the effect
variable. Repeated explanation of the concept was often required in order to clarify this point.
All in all, the interviews were successful in presenting the method of system dynamics, stimulating
its independent application by the participants, and gathering knowledge from different stakeholder
groups.
4.3.2 Questionnaire
The questionnaire starts with a motivation chapter where the strengths of the system dynamics method
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and the possible outcomes of the study are presented. Afterwards, a short description of the concepts
of link polarity as well as balancing and feedback loops is provided. It is explained that the holistic
model was constructed by merging the individual models of the participants. The mode of presentation
is also described as the management, social-environmental, and political processes are introduced
successively by using nine models. In order to prevent the impression that these models are
independent from each other, it is underlined that the nine models are intertwined and just presented in
this way for clarity. The merging and structuring of the individual qualitative models still required a
degree of heuristics. Despite omitting certain aspects in order to minimize the volume of the
document, all linkages and variables have been included. By doing so, personal interferences on the
model structure are attempted to be reduced. The analysis and marking of the balancing and
reinforcing loops was not accomplished in the interviews but has been added subsequently. The major
reason is the time-restriction in the interviews. Nevertheless, it was attempted to reflect the
participants‟ explanations correctly by listening to taped conversations, or asking questions via email
or telephone.
The participants were requested to correct the causal loop models directly, by renaming variables,
and adding or crossing out arrows. Questions on every emerging loop were also asked. The loops are
therefore named and marked by numbers in ascending order. Every loop was explained verbally
including the involved variables and the dynamics by alternating one variable and tracing the behavior
around the loop. Figure 30 shows the explanation and questions concerning the dam development
loop:
The first question is semi-structured as the interviewee is free to utter criticism in his or her own
words. Questions two and three are structured as they allow only predefined answers. The
Figure 30: Example of dam development loop from the questionnaire
Cyprus.
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questionnaire contains 54 questions of this type as well as six general questions that ask for the
participant‟s opinion on method and modeling process. The questionnaire is not part of this thesis due
to its high volume (75 pages), but will be enclosed as an electronic copy. Ten questionnaires were sent
to the participants and six participants found time to complete them. This return rate is considered to
be very high, especially when taking the length of the document into account.
Similar to the presentation in the questionnaires, the resulting merged model from the interviews is
presented successively in this chapter by dividing it into nine sub-models for clarity reasons. These
models are sorted into three categories, namely the management (4 models), social-environmental (3
models), and policy sphere (2 models). These spheres express only the emphasis of the respective
model and contain different kinds of variables (economic, social, environmental, political ones) that
are needed to explain the processes in question. The model structures are depicted in Appendix B on
DIN A3-paper and can be unfolded for consultation. Feedback loops in particular are highlighted as
they form the basis of the dynamic behavior of the system. Due to the complexity and the high number
of the loops, analytical inferences from the qualitative system structure are not straightforward.
Possible dynamics that can be interfered by the interaction of balancing and reinforcing loops are
however explained. After the presentation of the respective sub-model, the results from the
questionnaire are described, particularly conflicts and divergences in the points of view.
The questionnaires are evaluated anonymously. Controversial perspectives are not attributed to the
respective party for privacy reasons. Moreover, the survey is by no means representative in a
quantitative way and can not be utilized for generalizations. The results rather deliver an impression
for the different frames of the problem situation and issues that need further inquiry. A future group
model building could help to discuss the controversial perspectives and fathom the underlying causes
of the respective point of view. The results are presented in the order of the questionnaire, starting with
the management sub-models, and ending with the policy sub-models and general questions. Moreover,
the participants that filled in the questionnaires are called 'stakeholders' or 'respondents' in the
following evaluation.
4.3.2.1 The Management Sub-Models
The management sub-models show the different management measures that aim at the mitigation of
water scarcity by enhancing the water supply and opportunities for their funding. The following
measures are considered: the enhancement of the dam capacity, the application of desalination plants,
domestic wastewater recycling, water import by tankers, rainwater collection in cities, and reduction
of leakage.
Description of the Management Sub-Model part 1
Loops 1 to 6 show the balancing mechanisms of the proposed supply policies. Due to enhanced water
scarcity, more funding is given to countermeasures so that the capacities of desalination or wastewater
recycling will eventually rise. Consequently, the surface water supply and the potable water supply
respectively increase, thus easing the problem of water scarcity. The investment costs for building
dams or desalination plants are expressed by positive links from public finance to the respective
measure. The realization of the projects is thus dependent on the public budget. Loops 7 and 8 express
the finance mechanisms for the surface water supply. If the costs of non-potable water supply increase
due to the operation of costly technical devices as sewage treatment plants, the government could
decide to charge cost-covering surface water fees which would flow to public finance. Another option
is the subsidization of surface water that would strain the public budget, but relieve water users, e.g.
from the agricultural sector (the different sectors are included in the next sub-model). Loops 9 and 10
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show the same processes for the potable water supply. Here, the application of technologies like
desalination would increase the costs for potable water provision that can be refinanced by subsidies
or charging of water users, e.g. domestic households.
This model suggests the charging of water users to be the panacea to avoid a decreasing public
budget. There are nevertheless detrimental effects of the charging policy on the economic development
of the sectors. Thus, straining the economic sector hampers their development and might even restrain
their competitiveness in worldwide economy. In the end, public finances would diminish in case of an
economic downturn. These processes are included in the next sub-model.
Results of the Management Sub-Model part 1
In the questionnaire, the respondents mentioned the low potential of further dam development in the
future. Even though dams are important for the present situation to store rainfall, the policy of „no
drop to the sea‟ that aims at the extensive construction of dams has come to its end. Decreasing rainfall
trends already cause overcapacities in the existing dam stock. Most participants consider the recycling
of wastewater to be important, and become even more important in the future. The policy of
desalination also shows some consensus. The importance is evaluated as high or very high, whereas
the future development is considered to stay stable or increase. The water tanker loop revealed
different reactions. Some stakeholders completely reject the loop as a possible option in the future due
to the high costs. Others accept water tankers in case of emergency, but unanimously attest small
importance today and decreasing importance in the future. This shows that the supply by foreign
potable water import is rejected as a viable solution. The limiting of water losses from the conveyance
network is evaluated as considerably important or very important with an increasing tendency in the
future. The idea for an urban rainwater collection system causes differing reactions. Three of the
questionnaires attribute low, or small importance to this system and no increase in importance in
future, e.g. due to little prospect to capture the water and convert it to the dams. The other three
participants envision a high potential for this measure. The issue of subsidizing water versus charging
cost-recovering prices is also controversial. In case of non-potable water, some participants see the
importance of these economical loops, but anticipate no major changes in the future. Hence, the price
and subsidy level for non-potable water would stay stable. Others recognize both, lower subsidies and
increases in water prices, to be necessary to finance the future water supply. Another participant urges
for stable or decreasing price levels for agriculture as further charging of water would lead to a
collapse of the sector as the profitability is already low. The case of subsidies for potable water reveals
a more consistent picture with considerable up to high importance of subsidized water prices and
stable to increasing trends in the future. However, the importance of selling potable water is
challenged by some participants as the revenue would constitute only a small part of public finance.
All in all, the future trend of the loop significance is thought to be stable or even decreasing.
Description of the Management Sub-Model part 2
The second model describes the development process in the different economic sectors in more detail.
The sectors and users that have been considered important by the participants are real estate,
commerce, tourism, education, industry, and agriculture. Some sectors are interconnected, the industry
sector, for example, depends substantially on agricultural production (subsequent processing of
products), and the commerce sector is connected to real estate and the tourism sector (e.g. stores that
rely on the buying power of tourists). All the different sectors have a direct effect on the economic
development in Cyprus, but to different extents. The variables in the model that influence the
development in the distinct sectors are water price and water rationing, although there are of course
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more economic factors that determine economic progress and success. However, as this model focuses
on the problem of water scarcity, only problem-related variables were included. Loop 11 expresses the
necessity of economic development for the funding of costly supply management measures (e.g. dams,
water import by tankers, wastewater recycling, and desalination). By funding these facilities, the non-
potable and potable water supply increases so that rationing is needed to a lower extend. Less
rationing, in turn, makes more economic development possible. The loop consequently reinforces
itself, with economic success laying the foundations for even more economic success by the funding
of water supply measures.. Loop number 12 and 13 describe the other side of economic growth. Thus,
growth in the water intensive economic sectors (i.e. agriculture, tourism, real estate and industry)
would enhance the water demand, so that more rationing (loop 12) or higher water prices (loop 13)
would be implemented which would than hinder economic development. In the real system, these two
loops interact, whereas the increase in supply might come to its limit in the future so that the latter
loop gains more importance. The Double-Loss Mechanism (No. 14) that highlights the important role
of the agriculture sector in Cyprus is mentioned by several interviewees. Thus, a downturn of the
agriculture sector would mean less economic development and less job opportunities similar to the
other sectors and, would, additionally, render food imports necessary which would then cause higher
living expenses.
Results of the Management Sub-Model part 2
All stakeholders consider the reinforcing mechanism of development that makes supply management
necessary important. The process is estimated to stay at the same level or even increase in the future.
The development-impeding nature of water rationing and water prices is thought to be considerably
important by most participants. One stakeholder approves only of the detrimental effects of water
rationing on the economic development but denies considerable effects of water prices. Would the
downturn of the agriculture sector cause „double losses‟ namely, the loss of sectoral GDP, and higher
expenditures due to the higher import rates of agricultural products? Two respondents approve of this
mechanism whereas two deny higher costs due to agriculture import (one abstention). Another
participant does not consider any losses from a downturn of the agriculture sector as decreases in
agriculture income would drive framers to more profitable enterprises.
Description of the Management Sub-Model part 3
The third economic model shows the agriculture sector in more detail. Some participants explain the
decision–making process that underlies the choice of crop type and irrigated area due to changes in the
water price and the limitation of irrigation water by rationing. It turns out that an increase in the water
price would not necessarily lead to a decrease in the agriculture water demand by forcing the
implementation of more water-efficient crop types or irrigation techniques. In fact, farmers could also
change to more profitable crops that do not have to be more water efficient, or enhance their irrigation
area in order to achieve more revenue that balances the losses from the increased water costs. The
explanation of the processes in the model starts at the variable „Water Costs of Agriculture‟ that might
rise due to a reduction in subsidies for irrigation water or an enhancement of water prices. This could
be a consequence of higher costs of water provision that are passed to the consumer, or political
decisions in order to reduce water consumption. This finally causes a decrease in the actual revenue
and hampers the development of the agriculture sector. Consequently, the need for more profit rises
that can be met by several measures that are presented below.
Loop 15 shows the first option that comprises the implementation of water saving irrigation techniques
that enhance the irrigation efficiency and, eventually, reduce the agriculture water demand and water-
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related expenditures. Also, farmers could change their crop type to more water efficient ones (e.g.
planting olives instead of oranges) in order to reduce the water requirements (loop 16). This policy
could of course imply a delay of several years as new trees have to be planted and the specific
machineries in operation have to be adapted. While the previous measures reduce the water demand,
loop 17 and 18 present reactions of farmers that could even enhance water requirements. First, the
„Choose Economic Optimization„- Loop represents the option of crop changes to more profitable
crops in order to balance cuts in the revenue which do not have to be more water efficient.4 As
depicted in loop 18, the farmers could also maintain the actual crop type and vary the area of the
Irrigated Land in order to increase the revenue: the farmer will therefore increase irrigated land and
water consumption, if the additional yield exceeds the additional water costs. Otherwise, the farmer
will reduce the irrigated land, if the saved water costs are higher than the losses from the diminished
yield. The water demand of agriculture thus increases in the first case (more irrigated land) and
decreases in the second (reduction of irrigated land). Loop 19 („Water Rationing Agriculture‟ -Loop)
illuminates the system‟s reaction to water rationing. Thus, the cut-off in irrigation water delivery can
hardly be met in the short term by other measures than reducing the irrigation area and accepting crop
failures. Water demand will automatically be reduced unless switching to other sources is possible,
e.g. to groundwater. These evasive movements to groundwater resources are described in the next
model in more detail.
Results Management Sub-Model part 3
Nearly all the respondents regard the improvement of irrigation efficiency as considerably up to highly
important, and estimate future increases in importance. However, one respondent denied the
correctness of the loop as water efficient measures had already been taken. In contrast, he proposes the
adaptation of crop types. The farmers‟ choice of adapted crops in order to meet the problem of water
scarcity is thought to be substantially to highly important by the majority. However, one stakeholder
denies the correctness of this loop as market prices would determine the choice of crops.
Consequently, from this point of view, farmers do not have free choice about crop patterns. The
opportunity to increase the profit by planting economically more profitable crops or varying the
planted area have small to considerable importance today. The future estimations are judged
ambiguously, ranging from decreasing up to increasing trends. Is rationing of irrigation water effective
in order to urge farmers to invest in irrigation efficiency and choose adapted crop types? All
stakeholders approve of this loop with an importance ranging from considerable to high. The majority
anticipates an increasing importance of the instrument of water rationing in the future.
Description of the Management Sub-Model part 4
The fourth part of the management model analyzes the economic causes and consequences of the
problem of groundwater over-exploitation. Loops 20 and 21 underline the relevance of groundwater as
a source for the agriculture and domestic water demand. Hence, reduced groundwater availability
would have detrimental effects on agriculture, and also on tourism and real estate. The model contains
opposed loops for the effect of water pricing and water rationing on water demand. First, the pricing
and rationing policies depicted by loops 22 and 23 would urge the different sectors to reduce their
water use (this effect is ambiguous in the case of water pricing in the agriculture sector).
Consequently, the demand for potable and non-potable water would decrease and less water would be
4 For instance, the water requirement of colocasia amount to about 21000 m³/ha/year (Savvides et al. 2002) with a gross
margin of 27429 €/ha/year (Department of Agriculture 2008). In comparison: one ha of potatoes requires about 3500
m³/ha/year water with a gross margin of 7613 €/ha/year.
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abstracted from the aquifers. Besides reducing the water demand, unintended consequences of a higher
water price and rationing policies for water from public agencies is mentioned as these measures are
mainly limited to the metered potable and irrigation water (e.g. from dams, or groundwater). The
major factor that causes the overuse of groundwater is the incomplete metering of wells. Water users
might consequently increase the use of unmetered groundwater resources as they are usually free and
the volume is not restricted. Hence, the demand management for metered potable and irrigation water
increases the attractiveness of unmetered groundwater use. These processes are depicted for both the
potable and non-potable water price by loop 24. Loop 25 shows the same mechanisms for water
rationing. Several stakeholders propose the nation-wide comprehensive metering of wells as the
solution for the groundwater problem. Loop 26 shows the direct effect of constrained consumption by
metering on the groundwater abstraction. Finally, loop 27 expresses the option to set a price for
groundwater in order to reduce the consumption.
Results Management Sub-Model part 4
Is groundwater depletion a limiting factor for economic growth in the agriculture, tourism and real
estate sectors? All participants conclude that this is a major problem that will even increase in the
future as groundwater abstraction is at its limits and will cease to be available for agriculture soon if
no countermeasures are taken. For this reason, two stakeholders even deny the existence of the loop as
little amounts of groundwater are available anymore. Pricing and rationing of groundwater is
considered important, the future development however is not anticipated uniformly. Rationing is
preferred only by some stakeholders as similar cases in the world show that water prices can drop
water demand only temporarily. One participant doubts that rationing is feasible as control is not
possible. The loop that expresses the particular attractiveness of groundwater exploitation as the only
water source that is not comprehensively metered is assessed differently. The stakeholders estimate the
importance of this mechanism as low up to considerable. The future trend is anticipated differently
from decreasing to increasing. The measure of installing water meters nationwide is also assessed in
completely different ways. For two stakeholders, the importance of this policy is very high and will
increase in the future, others do not find this policy important, and also do not anticipate any major
changes. One participant states impediment for the installation of meters would be the required
changes in legislation. Two participants deny the correctness of this loop as no groundwater would be
available for water supply anyway, which would render metering unnecessary. The metering and
pricing is perceived considerable up to highly important today by most stakeholders. Again, one
stakeholder denies the correctness of links as the opportunity for metering is not seen in the future.
4.3.2.2 The Social-Environmental Sub-Model
Whereas the effects of the economy on the water resources and their development have been depicted
in the management sub-model, the social-environmental sub-model expresses social and
environmental aspects of the problem. Here, qualitative aspects as the „Quality of Life‟ or
„Attractiveness of Land‟ are integrated as they have been stated to be closely connected to water
scarcity in Cyprus.
Description of the social-environmental sub-model part 1
The first part expresses the social processes that are related to the tourism and real estate sectors. Both
sectors cause an increase in the population number of Cyprus. The causes of a higher population
number are more traffic that induces crowded roads, and more crime. Both processes mean a lower
quality of life that, in turn, forms the basis of the tourism and real estate industry. Consequently, the
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attractiveness for tourists to visit Cyprus as well as the attractiveness for foreigners to invest in real
estate will diminish, which leads to a downturn in the two sectors. Hence, the growth of the tourism
and real estate sector will balance itself once a certain population level is reached. Loops 3 and 4
depict the social effects of water rationing and increase in water price. Water rationing induces more
consumer dissatisfaction and conflict amongst users as certain demands are not satisfied (e.g. cut-off
of irrigation water supply). In the end, the quality of life decreases due to these conflicts and restraints.
An increase in the price for potable water implies more water costs of the households, which puts
pressure on their economic situation. If the economic situation gets worse, the standard of living also
decreases, causing less quality of life and a lesser extend of economic development due to less buying
power. The effects of employment on the economy of households and the quality of life are also
included in the model. The last loop (no. 5) of the model considers the migration of people from
densely populated areas to rural places due to the diminished quality of life. This loop is a reinforcing
loop as the settlements in rural areas induce a reduction in the pleasantness of the landscape that again
leads to a decrease in the quality of life.
Results of the social-environmental sub-model part 1
The problem of higher variable and residential population in Cyprus in connection with congestion
and crime is evaluated differently by the participants. One participant criticizes the loop as incorrect,
because a higher population number would not imply more crime. Others find this issue very
important, whereas a third part of participants perceive merely a small importance. The future
development is also anticipated ambiguously.
The majority of stakeholders think of the link of water rationing to the standard of living in Cyprus
as considerably to highly important with an increasing future tendency. Two participants however
anticipates relieve in the near future by the operation of new desalination plants so that rationing
would not be necessary anymore.
Does the water price have an effect on the standard of living? Three participants perceive only a
small importance as the water price constitutes only a small part of living costs. The others detect a
considerable up to high importance with an increasing tendency. The issue of migration of people
from urban areas to the countryside is also controversial. Whereas one participant completely denies
this relationship, the others saw a considerable importance with a stable up to increasing tendency.
One stakeholder approves the problem of diminishing quality of life, but challenges the cause of
migration. Rather the growth of overall population would cause settlement of rural areas.
Description of the social-environmental sub-model part 2
The second social-environmental model illuminates the options and effects of demand management
measures, especially for the domestic water supply: water pricing, water saving technologies,
incentives for water saving behavior, and awareness campaigns or information policy respectively.
Loop 6 shows the balancing effects of an „Increase the Price‟ - policy on the problem of water
scarcity. Higher prices cause a more conscious consumption behavior of the population, due to, for
example, the omission of water intensive activities like watering the garden. Some participants believe
this policy to be inefficient in the long term as people might get accustomed to a higher price level
and, the consciousness of water use will fall back to the initial level after a while (see loop 7). The real
behavior of the system is again determined by the interplay of a balancing loop and a reinforcing loop.
Loop 8 depicts a further policy proposal, namely the subsidization of water saving equipment in order
to increase the efficiency of domestic water use. Some specific examples for water saving equipment
are stated:
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A gray water recycling system that collects and prepares water from showers or washbasins in
the household, schools, public buildings, e.g. for flushing the toilet
Water saving toilets where the volume of water for flushing can be varied
Water efficient fittings for showers or water taps
The promotion of awareness is seen as a central policy of demand management. Loops 9 and 10
express the mechanisms of awareness campaigns conducted by public agencies (e.g. Water Boards). A
water consumption education is conducted with public participation that leads to more conscious water
use and, eventually, less water demand. In addition, the „Consumer Education Loop‟ expresses the
reinforcing process of exemplified water saving behavior that animates and urges others to participate.
The education of children has often been said to be central, as it can encourage parents to adapt their
consumption behavior, too. One participant suggests a water wastage hotline (loop no. 12). Observed
water wastage in times of drought could thus be reported to an institution that is authorized to impose
fines. Citizens would have more leverage to become active. Loop 13 is the last loop of this model and
illustrates the incentives to reduce water consumption voluntarily by major user groups due to public
pressure. Some stakeholders point to awareness campaigns that have been conducted by the tourism
sector. Win-win measures that save hotels water and therefore also money are particularly promoted.
The water demand decreases as a consequence of these efforts.
Results of the social-environmental sub-model part 2
Is pricing the adequate policy to reduce water demand? This topic is not answered uniformly by the
stakeholders. The majority is convinced that water prices have a high up to a very high importance
today. The future significance is assessed to be stable up to increasing. However, one respondent
approves of this relationship but perceives only a small importance for the control of water demand.
The importance of this instrument would, in his opinion, even decrease in the future. One stakeholder
states that economic development only has an effect on the consumption in early development stages.
Others believe that development has considerable effects on the customization of the water price today
and in the future.
Should the government subsidize water saving equipment like gray water treatment plants or water
efficient toilets and taps? Most stakeholders approve of this and even see an increasing importance of
this measure in the future. Only one participant thinks that this measure has a low significance at
present. In turn, the effectiveness of awareness campaigns and consumer education programs is more
debatable. Two stakeholders see a high or very high importance with increasing tendencies in the
future, whereas the others two see only a small importance today, with stable and increasing trends
(one abstention). The unconventional idea of a water wastage hotline is considered helpful by nearly
all respondents. One stakeholder denied the correctness as law enforcement would be problematic.
Self-initiative of water user groups on the other hand is evaluated differently to have small up to high
importance. However, the majority anticipated an increasing importance in future.
Description of the social-environmental sub-model part 3
The third part of the social-environmental model explains the connection of environmental quality
issues with the problem of water scarcity in general, and to the consequences for society and economy
in particular. Two loops appear from the model structure which describe the process of water
pollution. The first loop (no. 13) explains the effects of water pollution by an untreated discharge of
wastewater (from the domestic and industry sectors), and pollution in the course of agriculture
processes (e.g. by using chemical fertilizers). As a consequence, the water quality and the quality of
the environment diminish so that the quality of life eventually decreases. This also has an effect on the
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economy due to the deterioration of the attractiveness for tourism and real estate (these processes have
been explained in the first social-environmental model). While this loop constrains the development of
the sectors by reducing one of their fundamental resource, i.e. the attractiveness of the land, loop14
balances the pollution by considering purification of sewage by treatment plants in order to maintain a
desirable water quality. Its reinforcing behavior allows a continued development of the tourism and
real estate sectors from the perspective of water quality. Loops 15 and 16 express environmental
processes that determine the degradation of the environment in case of water pollution. Some
stakeholders mention the influences of water pollution on the carrying capacity of the ecosystem as a
major factor. Thus, a contaminated environment might collapse if the carrying capacity is exceeded. In
addition, the extinction of species would lower the environmental capacity to confront stress. All these
considerations are inserted by reinforcing loop 15 as a decreasing environmental quality lowers the
carrying capacity which again leads to a collapse of the ecosystem if it is exceeded. Loop no. 16
integrates the ability of the environment for self-purification that depends on the sort and volume of
water pollution. Marginally polluted water is naturally purified by ecological decomposition processes
– also known as natural attenuation. Loop 17 shows implications of water quality on the development
of the economy beyond the land attractiveness issues of tourism and real estate. Thus, the
contamination of groundwater or surface water decreases the available volume of water for drinking or
irrigation. The water scarcity problem getting worse could in turn lead to the application of measures
like rationing and price increases to a more aggravated extend than without the occurrence of water
pollution. The detrimental effects of rationing on the economy have been described in the 2nd
management sub-model. Finally, the model includes environmental processes that are external factors
as they are not directly affected by other variables.
Climate change in particular is the underlying cause of all the mechanisms described below:
First, an increase in the ambient temperature leads to more transpiration from the surface. Thus, the
volume of collected water in dams decreases so that the water quality deteriorates. A higher
temperature also causes higher evapotranspiration of plants that leads to a higher agriculture water
demand, and, eventually, desertification of the landscape. Second, climate change leads to less rainfall
quantities in Cyprus which decreases the collected water in dams and increases the desertification.
Besides the surface water storage, the aquifer recharge also diminishes, thus leading to a lower overall
water quality.
Results of the social-environmental sub-model part 3
The loop which links pollution to the quality of life is assessed differently, ranging from small to high
importance. One respondent completely denied a considerable balancing effect of pollution on
economic growth, as pollution levels would not change even if the tourism and real estate sectors
declined. Another stakeholder also denies the mechanism as wastewater from towns will be
completely treated soon and pollution from agriculture is already reduced. The majority of participants
think that the importance of sewage plants to reduce the detrimental effects of economic growth is
considerably important with an increasing tendency. However, one stakeholder sees only a small
importance of this loop with a stable trend. The reinforcing effect of the quality of the environment on
the carrying capacity is considered to be highly important by most respondents. The environmental
purification loop is thought to be small up to considerably important with no major changes in the
future.
Does a declining water quality also have effects on the development potential? Two respondents deny
this loop as water from dams and desalination would not be affected by water pollution. Others
approve of the loop and see considerable up to high importance in it, with stable to increasing future
69
trends. The various links of climate change to water quality and water quantity issues are accepted by
nearly all respondents. Only one stakeholder does not approve of the link between higher transpiration
and reduced water quality in dams.
4.3.2.3 The Policy Sub-Model
The participants of the study also state various political reasons for the contemporary water shortages
on the island, e.g. the fragmentation of decision-making, lack of strategic policy implementation and
planning, and the policy of the government that is focused on fast economic development.
Description of the policy sub-model part 1
The first part of the Policy Sub-Model expresses statements as well as solutions that might emerge
from the public pressure for action due to water scarcity, or the implementation of the EU Water
Framework Directive. First, the problem of water scarcity will increase the pressure on the
management of the involved institutions and could thus limit their egoism. This leads to more pressure
on reform institutions. The fragmentation of the water sector might decrease, which could eventually
lead to a central water entity. Second, a higher pressure on the management could also lead to more
studies to get a 'Holistic Picture' that would decrease the lack of strategic policy implementation and
planning. Third, the pressure to do better management might lead to demand management which
would then support policies that have been mentioned before, e.g. rationing, price increase, consumer
water saving (e.g. by subsidies or awareness campaigns). This reduces the problem of water scarcity
(here, the mechanisms are not analyzed here as this has already been done in the management sub-
model). These processes are further stimulated by the pressure that arises from the need of compliance
with the EU legislation (Water Framework Directive) that urges high public participation and supports
demand management. The EU-legislation also requests the reduction of the fragmentation in the water
sector.
The processes described before might together lead to the establishment of a central water
authority. Several participants express that a central decision maker might help to tackle problems that
arise from the fragmentation of the water policy sector, namely the issues of groundwater permissions
and metering of quantity and quality as well as the funding and enforcement of maintenance and pipe
replacement. The two following loops express these relationships, starting with loop 1 that refers to
the comprehensive metering of water quantities from wells. This would reduce the abstraction of
groundwater (compare to the 4th management sub model) and, eventually, reduce water scarcity. Loop
2 expresses the monitoring of water quality (especially for groundwater) that would increase water
quality. Less polluted water means bigger available water quantities, so that the water scarcity problem
would decline. Another consequence that has been stated in loop 3 is the ability of a unified water
entity to impose regulations (e.g. for water saving technologies) in order to improve the efficiency of
water consumption in the different sectors. Some participants state that the funding of maintenance
and pipe replacement is problematical due to institutional fragmentation. Therefore, a unified water
entity could tackle these problems in a more efficient way as depicted in loop 4.
As already mentioned in the presentation of the management model, an interviewee regards the
economic development, especially in the tourism and real estate sectors, as the most important reason
for water scarcity. Loop 5 reflects the reinforcing mechanism of economic growth on the development
policy. Some participants say that the contemporary development policy of the government of Cyprus
focuses on fast economic growth. The economic policy is therefore perceived to be successful by the
citizens if the GDP really grows. In case of success the contemporary development strategy is
reinforced. Loop 6 depicts the reinforcing process of allocation of water to powerful users due to
70
lobbying. As these users gain the relative advantage in the water supply, their relative power
heightens, so that they can increase their lobbying activities even further. Loop 7 shows that the
misallocation of water will finally decrease the effectiveness of water use and increase the problem of
water scarcity. Water scarcity, in turn, will hamper economic development thus implying a shift in the
policy paradigm and the allocation scheme.
Result of the policy sub-model part 1
The structure of processes in consequence of public and legislative pressures is approved of by all
participants. The effect of a unified water entity on the implementation of groundwater metering is
accepted by every respondent and the evaluated importance is set to high up to very high with
increasing significance in the future. The majority of respondents judge the regulation of the
application of water saving technologies as important. One stakeholder regards this option to have
small significance today, but an increasing importance in the future. The effect of a unified water
authority on the funding and maintenance of the water infrastructure is not assessed uniformly, with
importance from low to high levels. The reinforcing loop of economic policy success is approved of
by all participants and is evaluated as small to considerably important without a clear future trend. The
„water means power‟ loop is also accepted by all respondents, however with different very degrees of
importance. No major changes are anticipated in the significance of this loop. The balancing loop of
economic development due to misallocation of water to powerful users is considered to have a small to
considerable importance in Cyprus.
Description of the policy sub-model part 2
The second policy sub-model deals with the energy sector and the problem of land availability in
Cyprus. Many participants believe the problem of high energy demands to be a consequence of the
application of seawater desalination. The energy supply needs to be increased for the development of
the desalination capacity, which can be done by the extension of conventional (fossil fuel-driven)
power plants (loop 8) or alternative energy carriers as wind or solar energy (loop 9). Some
stakeholders see the conventional energy generation critical due to the environmental effects caused
by carbon dioxide emissions (loop 13). They also anticipate a rising oil price and the restraint to buy
emission certificates that would render the use of fossil fuel costly in the future, and was therefore
included into the model in loops 10 and 12. Hence, several participants favor the use of regenerative
energy, in particular solar power plants which have higher short-term, but far lower long-term costs
(loop 11). Subsidies could support the renewable energies by lowering the energy price and avoiding
detrimental effects on the economy. Particularly the long sunshine duration in Cyprus speaks in favor
of renewable energies. The low land availability is thought to be a great obstacle for large solar or
wind parks. Loop 14 expresses the reasons for the land shortage that are mainly connected to the
demand of the real estate sector for pleasant landscapes that preclude solar parks or sewage plants.
Inadequate planning is also stated to be a major reason, as all areas in Cyprus are potential land for
building except natural parks or forests. As a consequence of this, the construction of extensive solar
fields or sewage plants would decrease the attractiveness and value of the land so that the resistance
towards these measures is high.
Result of the policy sub-model part 2
Most participants consider the extension of the conventional energy capacity to be necessary in the
future. The majority considers regenerative energy sources highly important today with an overall
increasing future trend. However, two respondents question the correctness of this loop as the energy
71
price of conventional energy would make the investments in renewable energy ineffective. One
stakeholder considers the investment costs as the major impediment of renewable energy, as the
operational costs are less expensive. The issue of emission rights is perceived differently by attributing
low to high importance. The climate change loop has small importance for the participants or is denied
altogether, as the impacts of CO2 emissions would not be relevant for short-term policy assessments.
Finally, the issue of land availability is assessed ambiguous to have low up to high importance.
4.3.2.4 Final remarks about the questionnaire results
The questionnaire exemplifies the high density of information in causal loop diagrams. Although
merely nine sub-models were necessary to express the point of view of various stakeholders, the
conversion of the links into written data caused a large volume of the document with 75 pages. The
spectrum of relevant factors, ranging from land availability issues to aspects concerning the energy
production in Cyprus turned out to be surprising and underline the need for interdisciplinary research
in order to solve complex problems.
The outcome of the questionnaires clearly shows diverging opinions of stakeholders on various
issues. These differences should be clarified in order to define efficient solutions to the problem of
water scarcity in Cyprus. For instance, the question whether water pricing reduces the water demand
in a sustainable way, or if this instrument is only effective in the short term, determines the proposed
solutions. The role of the agriculture sector also needs further analysis, as some stakeholders propose
further price increases and less subsidies, whereas others point to the already difficult situation of
agriculture and the important role of the sector for food supply. The results of the questionnaire
promise interesting group model building discussions if the study is continued.
The questionnaire contains the questions related to the models, but also a general part where
questions about the method and future research opportunities are asked. Four respondents consider the
method of systems thinking efficient and helpful to depict a problem comprehensively and gather
information (one abstention). One participant emphasizes that numbers will be needed in order to draw
conclusions.
Have the participants learned something new by attending the process up to now? Four
stakeholders state that they have not acquired any new insights by the participatory model building
and the completion of the questionnaire. Two respondents say that the knowledge about the
methodology is new to them. Finally, four out of six participants are interested in the simulation of a
system dynamics model about the water scarcity problem in Cyprus, and would like to attend a group
model building workshop in the future.
4.4 Quantitative simulation
The qualitative model from the participatory model building process depicts the various
interconnected aspects of the problem of water scarcity in Cyprus that are perceived by the
stakeholders. Qualitative analysis of the causal loop diagrams can reveal dynamic behavior that is
produced by the system structure. Some remarkable processes in which balancing and reinforcing
loops are intertwined and cause unintended side-effects have been extracted in the preceding chapter.
Nevertheless, the final behavior of the system, and, particularly, the development of the problem of
water scarcity and its magnitude can not be derived. The quantification of the qualitative model in a
simulation model is therefore helpful to acquire a feeling for the behavior of the system and the
effectiveness of possible solutions.
The implementation of a system dynamics model is itself a learning process. Structures and
functional relationships are tested and the outcomes are compared to existing data. Surprising results
72
induce a rethinking of the model structure or the chosen analytical or table functions. Surprising
effects could also point to unanticipated relationships that have been ignored in the past. Thus, the
iterative revision process helps to improve the model performance as well as the understanding of the
real world system. Participants should therefore be involved in the model building, as the construction
process induces trust and ownership of the model. Gaps between observed and simulated data should
inspire discussions between the stakeholders about the model structure and the underlying functions.
As soon as the model is thought to be coherent and reliable, different policies can be tested with
respect to their effectiveness in tackling the problem of water scarcity in Cyprus. The outcomes of the
scenario analysis should again lead to a discussion among the stakeholders about the results and
possible conclusions. Finally, the decision-makers and interest groups can propose a certain set of
policies together, or the decision-makers can explain and justify their decision in connection with an
estimation of the outcomes of these measures based on the simulation results.
Beside the effects on water quantity, other consequences and interrelations of measures can also be
included. The qualitative model highlights the importance of various economic, environmental and
social processes for an integrated policy assessment. These and other links can be added to the model,
if stakeholders consider them important. Differences between the valuation of measures and the effects
will of course not be solved and a unanimous agreement on the future policies is unlikely. Still,
differing interests and values might result in the pledge for a particular policy and can cause conflicts
and disagreement between participants. Nevertheless, the model building serves as a guideline for
these necessary discussions and navigates the conversation to a more rational talk. Emotional or
eloquent speeches that might distract from the substance of the problem can be refocused by referring
to the model. In these cases, the stakeholder has to reveal his or her point in a language that is
understandable for everybody and in a way that can be related to the „big picture‟ of the problem. By a
participatory model building, the decision-makers can extract useful information from other
stakeholders in a very effective way, and can thus ground the later decision on a more profound basis.
The expected outcomes of measures can also be presented and discussed more clearly with other
participants. The involvement of stakeholders even in the decision-making process generates trust and
makes future co-operations based on the understanding of side-effects and a clear vision of the future
systemic development more likely.
As the participation of stakeholders in the modeling process is necessary in order to achieve trust
into modeling outcomes, a complete and autonomously constructed simulation model would not be
appropriate, although a preliminary model that incorporates the most important processes in a
comprehensive way can help to speed up the group model process. In Chapter 3.3, the benefits and
disadvantages of a preliminary model have been discussed shortly. Hence, a preliminary model
motivates by clarifying the possible outcomes of a participatory model building process and can serve
as a starting point for discussions. The simulation model presented below is considered as a
preliminary model that demonstrates the potential and method of system dynamics modeling. Hence,
the purpose of the model is not the provision of definite recommendations for action. It should rather
demonstrate a possible starting point for a group model building by the exemplification of a model
structure and functional relationships.
The approach chosen for the case study in Cyprus is a special approach as it comprises a physical
model that represents the hydrological and allocation characteristics of the water balance and a
participative model that contains the social-environmental processes and policies (see Figure 31).
Even though the hydrological model is based on the findings of applied science, a high value is set on
the comprehensibility of the processes for the participants. The theoretical and structural specifications
of the hydrological processes are therefore translated into a system dynamics model. Instead of
73
coupling a prefabricated „black box‟- model, interested stakeholders are able to immerse themselves in
the model by drawing on their knowledge of the system dynamics method. This guarantees the
transparency of the process as well as the output of usable hydrological data for decision-making. An
allocation model that describes the withdrawal of water from natural and non-conventional water
sources and the conveyance to the various usages is constructed, too. This allocation model is steered
by variables that are specified by the participatory model component. It can thus be regarded as a
network of pipes and bathtubs which faucets are steered by the participatory model. In summary, both
the hydrological model and the allocation model are considered uncontroversial by participants and
form the basis of the participatory model. It might even be possible to adapt and apply the
hydrological and allocation model to other problem situations that deal with large-scale water
management issues.
The selected processes of the participatory model building that are included in the preliminary
simulation model are:
The effect of decreasing rainfall on the water balance (cp. management sub-model, part 1;
social-environmental sub-model, part 3)
The effects of economic development on the water demand, as one stakeholder even regards
fast economic development as the underlying problem of water scarcity (cp. management sub-
model, part 2)
The application of unconventional water resources like wastewater recycling and desalination
(cp. economic sub-model, part 1)
Investment in technological efficiency in the domestic, tourism and agriculture sectors (cp.
management sub-model, part 3; social-environmental sub-model, part 2)
The effects of conscious consumption in the different sectors on the water demand (cp.
management sub-model, part 3; social-environmental sub-model, part 2)
For simplicity reasons, the model only simulates the effectiveness of measures and not the specific
processes that are needed for the implementation. For instance, the technological efficiency in the
domestic sector can be varied without considering the ways how people can be encouraged to invest in
water saving taps or toilets (e.g. by subsidies, or standard settings). Economical, social and
environmental side effects are also largely omitted as they would have rendered the preliminary model
Figure 31: Conceptual model structure of the ‘Water Scarcity’-system dynamics model
Cyprus.
74
too complex. The model capability is hence limited to the simulation of the effectiveness of measures
by relating strategies to their consequences in the water balance. The model shows how ambiguous
variables like „conscious water consumption‟ can be defined and pragmatic functional relationships
can be chosen for processes that have not been the topic of research yet, and for which information is
scarce or even not available. This shows how available information can be gathered and structured,
and, based upon that, reasonable and best-possible decisions can be made in situations of imperfect
information that are omnipresent in real-world decision-making.
4.4.1 System dynamics model
The system dynamics model consists of two sub-model structures in connection with the participatory
model. First, the natural water system encompasses the meteorological and hydrological processes that
divide the precipitation water into surface water, groundwater, and outflows due to evapotranspiration
or drainage to the ocean. Second, the allocation model represents the allocation of the usable water
resources from aquifers, dams or rivers to the different user groups. Third, the integrated social-
economic-environmental processes affect the hydrology and direct allocation mechanisms.
The model uses monthly data where possible and appropriate. According to Sterman (2000,
pp.907f), the time step of the model should be set between one-fourth and one-tenth of the smallest
time constant in the model. Hence, the time step is chosen to be 0.125 months. As future scenarios
shall be tested, the time period of the model begins in the year 1975 and ends in 2050. The model
structure can therefore be calibrated by comparing the model results to data from the past. The system
dynamics software in use is VensimPLE due to the user-friendly handling and, in particular, the
uncomplicated creation of causal loop diagrams. Furthermore, the software is available on the Internet
for free, so that interested participants can explore the system dynamics method themselves.5
Scenarios can be tested by assuming future developments of key variables as water demand or
precipitation rates. The graphical interface of VensimPLE enables the straightforward variation of
values in the course of personal meetings with participants by the use of table functions. Figure 32
shows the graphical representation of yearly precipitation rates. The data can be included either by
entering the values in the table on the left hand side of the Figure, or alternatively by clicking and
dragging the data points in the middle.
5 http://www.vensim.com/venple.html
Figure 32: Graphical interface to implement data in the model
Cyprus.
75
In the following chapter, the sub-systems of the water balance model are introduced in more detail. For
limitations of space, only the most important aspects are highlighted. Appendix F shows the overall
model structures and can be used to gain an overview on the interconnected elements that are
presented below. Moreover, Appendix G contains the equations from the models.
For this thesis, most equations are given in mathematical functions as it is assumed that the readers
have a strong mathematical background. Nevertheless, most functions can also be defined by the use
of the graphical interface of VensimPLE in order to discuss the equations with the stakeholders more
easily.
4.4.2 Hydrological system
As the model is perceived as a decision-tool for policy-makers, a hydrological model component is
important in order to quantify the replenishment of aquifers and inflows to dams. The purpose of the
model as a starting point for a group model building makes the application of a sophisticated and
calibrated hydrological model inappropriate, as the participants are free to adopt the proposal or insist
on model building from scratch. Hence, the hydrological model should reflect the hydrological
processes in its structure and simulate water flows qualitatively in order to demonstrate the
appropriateness of the approach. If the group approves of the model, parameters and functional
relationships can be discussed and refined. In case of denial, the concept of the hydrological model has
to be revised.
The choice of a hydrological model framework was based on various criteria that reflect its
usefulness for the tasks of the study. First, the basic attributes of the hydrological model should be
compatible to the system dynamics method. Thus, it should be a deterministic, continuous and
spatially lumped hydrological model. Second, the model should reflect the physical processes that
underlie the conversion of precipitation to runoff-generation and groundwater recharge. Third, the
model should incorporate various environmental processes that participants might mention in the
interviews, e.g. crop patterns or vegetation cover. These „docking-stations‟ permit the closing of
feedback loops between the hydrological and the participatory model. Fourth, the availability of a
detailed documentation of the model should allow an adequate translation into the system dynamics
concept of stocks and flows.
Cundelik (2003) lists widespread lumped semi-distributed and distributed hydrological models and
compares them by using various indicators. This assessment was done in the context of a research
project on the integrated assessment of drought and flood management practices in a Canadian river
basin by using the system dynamics method (Prodanovic and Simonovic 2007). Due to the similar
outline to the case study, the report about the search process for an adequate hydrological model was
helpful to determine the best suitable model for the study in Cyprus. In the end, the Hydrologic
Modeling System HEC-HMS from the US Army Corps of Engineers (2000) was chosen. It consists of
multiple methods that can be applied depending on the specifications of the tasks. For the Cyprus
case-study, the framework of the Continuous Soil-Moisture Accounting (SMA) Model was selected as
the basis of the hydrological sub-model. The underlying concept of the SMA is taken from the
Precipitation-Runoff-Modeling System of Leavesley (1984).
Figure 33 shows the conceptual scheme of the SMA model consisting of various storages and
interflows. Precipitation water is first stored in the canopy interception at rates defined by the
vegetation cover and evaporates at the potential evapotranspiration rate. Hence, the shares of the
respective vegetative cover need to be included in the model to gain the volume of the overall
interception storage. The growth cycle of the vegetation also has an impact on the interception as, for
example, high temperatures and limited rainfalls in the summer could dry up the vegetation and limit
76
the interception. Excess water flows through the canopies and reaches the surface depression storage
that encompasses all shallow water that is held in surface depressions. The water evaporates here at the
potential evaporation rate, infiltrates the soil profile, or drains to rivers or dams. The soil storage
comprises water near to the surface that is amenable to the evapotranspiration process. Infiltrated
water fills the tension zone storage and the upper zone storage and leaves the soil by
evapotranspiration or percolation to the groundwater storage. Water in the tension zone is attached to
soil particles, whereas water in the upper zone fills the pores of the soil. Upper-zone water evaporates
at the potential infiltration rate and drains into the groundwater. Tension-zone water can only
evapotranspirate, but at a reduced rate for lower storage levels as can be seen in Figure 34.
Figure 33: Structure of the Continuous Soil-Moisture Accounting (SMA) Model (Army Corps of
Engineers 2000)
Figure 34: Ratio of actual to potential evaporation in the tension zone
of the soil (Army Corps of Engineers 2000)
77
Percolated water fills the groundwater store that can consist of several sub-units. The outflows of these
storages are groundwater flows or percolation to deeper groundwater layers.
4.4.2.1 Hydrological model structure
The preliminary nature of the model due to the participatory character of the modeling process and
limited data availability made the simplification of the hydrological sub-model necessary. Some parts
of the HEC-HMS model are therefore omitted, simplified, or replaced by empirical relationships from
the literature. Nevertheless, the application of the basic HEC-HMS framework makes the
straightforward improvement of the hydrological model possible and is proposed for future research in
order to allow policy assessment on a more precise data base. Appendix F shows the overall model
structure of the system dynamics model. Below, the calculation of the storages and water flows are
presented ordered by the related stock variables of „surface depression‟, „soil water‟, „surface water
storage‟, and „groundwater layer‟.
4.4.2.2 Surface depression
The stock for the canopy interception storage is left out as the simulation of the dynamic vegetation
cover would have been beyond the time budget of the Diploma thesis. Thus, the canopy interception
and surface depression storages are merged to the „Surface Depression‟ stock. Figure 35 shows the
stock-and-flow structure of the hydrological model, except the percolation process to the deep aquifer
that will be introduced in Chapter 4.4.2.4.
Figure 35: Stock and flow structure of the hydrological model
Precipitation FlowSurface Depression
Actual Evapotranspiration I
Years
Area Cyprus
Annual
Precipitation Data
Runoff
Surface Water
Storage
Infiltration
Monthly
Precipitation
Monthly
Annual Distribution
of Rainfall
Annual Distribution of
Evapotranspiration
<Monthly>
Maximum Soil Percolation Rate
Soil Storage
Capacity
Soil Water
Soil Percolation
Potentential
Infiltration Rate
Maximum
Infiltration Rate
<Area Cyprus>
Actual
Evapotranspiration II
Potential
Evapotranspiration
Potential Soil
Percolation Rate
Reduction Factor for
Tension Zone
Groundwater Layer I
<Runoff>
<Infiltration>
<Soil Storage
Capacity>
<Soil Storage
Capacity>
<Groundwater Layer I
Storage Capacity>
Interception
<Interception>
<Interception>
<Interception>
78
Rectangular boxes specify stock variables, and broad arrows with valves indicate water flows. Blue
arrows are information streams that are required for calculation (cp. Chapter 3.4.1.3). Variables with
brackets (<…>) are shadow variables which add an existing model variable to the sketch view without
adding its causes (Ventana Systems 2007). For instance, the variable „Soil Storage Capacity‟ is
repeatedly included in the system structure depicted in Figure 35. All the shadow variables refer to the
initial soil storage capacity variable which is located in the middle right of the figure. Omitting the
shadow variables would make the inclusion of numerous links necessary and would reduce the clarity.
Annual rainfall rates (𝑃𝑟𝑒𝑐𝑖𝑎𝑛𝑛𝑢𝑎𝑙 [mm]) from the years i (1975 ≤i ≤2050) are inserted by
exogenous data from the Meteorological Service (2005) for past rates, whereas future precipitation is
estimated.6 These values are multiplied by the annual distribution of rainfall (𝜑𝑎𝑛𝑛𝑢𝑎𝑙
𝑝𝑟𝑒𝑐) (from Rossel
2002, p. 14) in order to compute the amount of monthly precipitation 𝑃𝑟𝑒𝑐𝑡𝑚𝑜𝑛𝑡 𝑙𝑦
[mm]) at month t,
with t ranging from 1 (January 1975) to 912 (December 2050):
𝑃𝑟𝑒𝑐𝑡𝑚𝑜𝑛𝑡 𝑙𝑦
= 𝑃𝑟𝑒𝑐𝑖𝑎𝑛𝑛𝑢𝑎𝑙 ∙ 𝜑𝑎𝑛𝑛𝑢𝑎𝑙
𝑝𝑟𝑒𝑐 (11)
The „Precipitation Flow‟ variable (𝑃𝑟𝑒𝑐𝑡𝑓𝑙𝑜𝑤
in Mm³) in month t represents the volume of precipitation
water in the area of the Republic of Cyprus in Mm³, and is calculated by the multiplication of the
monthly precipitation (in mm/m2) with the area of the Republic of Cyprus (𝐴𝐶𝑦𝑝𝑟𝑢𝑠 =5800 km²):
𝑃𝑟𝑒𝑐𝑡𝑓𝑙𝑜𝑤
= 𝑃𝑟𝑒𝑐𝑡𝑚𝑜𝑛𝑡 𝑙𝑦
∙ 𝐴𝐶𝑦𝑝𝑟𝑢𝑠 ∙ 0.001 (12)
Where
0.001 = conversion factor7
The „Precipitation Flow‟ enters the „Surface Depression‟ stock (𝑆𝐷𝑡 [Mm³]). The interception is
represented by a flow of water that directly leaves the system. The volume of the flow is determined
by the vegetation cover with individual interception storages that have been taken from the Flood-
Runoff Analysis Manual of the US Army Corps of Engineers (1994, p. 6-7). Monthly growth rates of
plants are not considered in this version. Interception losses have been calculated for forests,
grassland, agriculture (divided into cereals, tree crops, fodder crops and vegetables), and urban areas,
and set to 16.50 Mm³ per year. Hence, the monthly actual evapotranspiration from the interception
storage is calculated as the minimum of the surface storage level at time t, and the monthly potential
flow of 1.375 Mm³:
𝐼𝑛𝑡𝑡 = 𝑀𝐼𝑁(16.5
12, 𝑆𝐷𝑡) (13)
It is assumed that water falling on the ground first infiltrates until the soil storage capacity is reached.
Then the residual water volume either drains above the surface to rivers, ponds, and dams, or
evaporates at the potential evapotranspiration rate. The potential soil infiltration rate (𝐼𝑛𝑓𝑖𝑙𝑡𝑝𝑜𝑡
[Mm³])
depends on the maximum infiltration rate (𝐼𝑛𝑓𝑖𝑙𝑚𝑎𝑥 [Mm³/month]), the level of the soil storage
6 The concrete estimations are delivered in Chapter 4.4.6 in conjunction with the description of the scenario
hhhanalyses. 7 Conversion of the units: 1mm/m2=1l/m2=1dm3/m2= 1/1000 m3/m2; 1km2=1.000.000m2; thus: 1mm/m2 *
1 km2= 1/1000m3/m2 * 1.000.000m2= 1000m³
79
(𝑆𝑜𝑖𝑙𝑡−1 [Mm³]), and the soil storage capacity (𝑆𝑜𝑖𝑙𝑚𝑎𝑥 [Mm³]). 𝐼𝑛𝑓𝑖𝑙𝑡𝑝𝑜𝑡
is computed using the
equation from the SMA-model:
𝐼𝑛𝑓𝑖𝑙𝑡𝑝𝑜𝑡
= 𝐼𝑛𝑓𝑖𝑙𝑚𝑎𝑥 −𝑆𝑜𝑖𝑙 𝑡−1
𝑆𝑜𝑖𝑙 𝑚𝑎𝑥 ∙ 𝐼𝑛𝑓𝑖𝑙𝑚𝑎𝑥 (14)
The storage capacity 𝑆𝑜𝑖𝑙𝑚𝑎𝑥 is a function of overall residual saturation (θr [cm³/cm³]) of the soil,
overall porosity (θ [cm³/cm³]) and average depth (d [cm]) (see US Army Corps of Engineers 1994, p.
6-14):
𝑆𝑜𝑖𝑙𝑚𝑎𝑥 = θ − θr ∙ d (15)
The values for the variables above have been estimated by using tabular values from the Flood-Runoff
Analysis manual (US Army Corps of Engineers 1994, p. 6-13). The average soil texture class of sandy
clay loam has been chosen with an overall total porosity of 0.398 cm³/cm³ and an overall residual
saturation of 0.068 cm³/cm³. With an estimated average depth of the upper soil layer of 50 cm, a
storage capacity of 165 mm (equivalent depth of pore space in the surface layer) of the soil has been
estimated (≡ 957 Mm³). The maximum infiltration rate is received through calibration of the
hydrological model as calculation procedures in the literature are related to hourly maximum
infiltration rates (see Chapter 4.4.5 for details about the procedure). To arrive at the volume of water
that infiltrates into the soil at time t (𝐼𝑛𝑓𝑖𝑙𝑡∆𝑡 [Mm³]), the minimum of the surface depression storage
minus the interception losses at time t and potential infiltration rate is computed, as formulated in
equation 16:
𝐼𝑛𝑓𝑖𝑙𝑡∆𝑡 = 𝑀𝐼𝑁(𝐼𝑛𝑓𝑖𝑙𝑡𝑝𝑜𝑡 ∆𝑡, 𝑆𝑜𝑖𝑙𝑡 − 𝐼𝑛𝑡𝑡 ) (16)
Water that does not infiltrate or remains in the canopy is converted to runoff (𝑅𝑡 [Mm³/month]) or
actual evapotranspiration (𝐸𝐴𝑡1 [Mm³/month]). Runoff is calculated as a fixed share of the Surface
Storage (𝑠𝑟𝑢𝑛𝑜𝑓𝑓 ) that is defined in the model calibration process:
𝑅𝑡∆𝑡 = 𝑆𝐷𝑡 − 𝐼𝑛𝑡𝑡∆𝑡 − 𝐼𝑛𝑓𝑖𝑙𝑡∆𝑡 ∙ 𝑠𝑟𝑢𝑛𝑜𝑓𝑓 (17)
For future research, the more sophisticated calculation of runoff is strongly recommended, e.g. by the
application of a unit hydrograph model (US Army Corps of Engineers 2000).
The residual water is exposed to the evapotranspiration process. Evaporation is the conversation of
liquid water into a gaseous state from land or water surface. If the transpiration of plants is considered
additionally, the process is referred to as evapotranspiration. Transpiration of a plant occurs through
photosynthesis and respiration and is controlled by the opening and closing of the stomata. Hence,
evapotranspiration is dependent on the soil and vegetation characteristics of the land as well as the
availability of energy and water. The term potential evapotranspiration expresses the maximum
evapotranspiration, i.e. in case of abundant water and maximal side-specific radiation. The values can
be obtained by measurement using evaporation pans or lysimeters, or by the application of
mathematical estimation techniques (Davie 2008).
For the values of monthly potential evapotranspiration (𝐸𝑃𝑂𝑇𝑡 [Mm³/month]), a fixed value for
the annual potential evapotranspiration of 1750 mm is assumed, derived from the 30-year recordings
of class A evapotranspiration (Rossel 2002, p.17). The annual distribution of the potential evaporation
is also extracted from Rossel (2002, p. 18) by averaging over the distributions of several recording
80
stations. The actual volume of evapotranspiration (𝐸𝐴𝑡1∆𝑡 [Mm³]) is calculated as the minimum of
potential evapotranspiration minus interception losses, and the surface depression level that is reduced
by infiltration and runoff. Consequently, water evaporates at the maximum rate as long as enough
water on the surface is available. Equation 18 expresses the previous relationships mathematically:
𝐸𝐴𝑡1∆𝑡 = 𝑀𝐼𝑁 𝑆𝐷𝑡 − 𝐼𝑛𝑓𝑖𝑙𝑡∆𝑡 − 𝑅𝑡∆𝑡,𝐸𝑃𝑂𝑇𝑡 − 𝐼𝑛𝑡𝑡 (18)
4.4.2.3 Soil Water
The soil water storage represents the soil layer that is amenable to evapotranspiration processes (see
Figure 33). As described in the introductory chapter, the storage is divided into the upper zone and the
tension zone. A fixed share (chosen value 30%) of the soil storage capacity is devoted to the tension
zone whose water doesn't percolate to the groundwater. Similar to the processes in the surface layer, it
is assumed that the water in the soil storage first percolates at the actual percolation rate (𝑃𝑒𝑟𝑐𝑡𝑠𝑜𝑖𝑙
[Mm³/month]). The remaining water in the soil is portioned into evapotranspiration either at the
potential evapotranspiration rate from the upper soil storage or at a reduced rate from the tension
storage (cp. Figure 34). The percolation soil potential (𝑃𝑒𝑟𝑐𝑡𝑆𝑜𝑖𝑙 ,𝑝𝑜𝑡
[Mm³]) is affected by the constant
percolation rate, soil storage, soil storage capacity, capacity of the groundwater layer I (𝐺𝐿𝐼𝑚𝑎𝑥
[Mm³]), and the level of the groundwater storage I (𝐺𝐿𝐼𝑡 [Mm³]). The following equation determines
the potential soil percolation and is taken from the SMA-model (US Army Corps of Engineers 2000,
p.47):
𝑃𝑒𝑟𝑐𝑡𝑝𝑜𝑡 = 𝑃𝑒𝑟𝑐𝑚𝑎𝑥
𝑆𝑜𝑖𝑙 𝑡
𝑆𝑜𝑖𝑙 𝑚𝑎𝑥 1 −
𝐺𝐿I𝑡
𝐺𝐿I𝑚𝑎𝑥 (19)
The calculation of the current groundwater store is described in detail in Chapter 4.4.2.4. The inclusion
of an additional groundwater layer besides the aquifer storage makes the simulation of delays in the
percolation flow to deeper aquifers and baseflow possible. Thus, the groundwater storage capacity is
used for calculative purpose and has been estimated to be similar to the soil storage capacity and
amounts to 1000 Mm³. However, the amount is arbitrarily, so that this assumption has to be considered
particularly in the later sensitivity analysis. If sufficient water is located in the upper soil storage, the
potential soil percolation rate is abstracted. Otherwise, the available water volume in the soil storage
flows out. It is assumed that 70% of the actual soil water fills the pores and therefore belongs to the
upper zone. Thus, for the calculation of the soil percolation rate (𝑃𝑒𝑟𝑐𝑡𝑠𝑜𝑖𝑙
[Mm³/month]), the actual
level of the soil storage is subtracted by the water in the tension zone that is quantified by 30% of the
soil storage capacity, as water in the tension zone is not able to percolate due to adhesion forces that
attach them to the soil particles. Equation 20 expresses this relationship:
𝑃𝑒𝑟𝑐𝑡𝑠𝑜𝑖𝑙 ∆𝑡 = 𝑀𝐼𝑁(𝑃𝑒𝑟𝑐𝑡
𝑠𝑜𝑖𝑙 ,𝑝𝑜𝑡 ∆𝑡, 𝑆𝑜𝑖𝑙𝑡 − 0.3 ∙ 𝑆𝑜𝑖𝑙𝑚𝑎𝑥 ) (20)
Eventually, residual water is available to the evapotranspiration process from the soil (𝐸𝐴𝑡2
[Mm³/month]). In order to calculate this flow, the model tests if the sum of the actual
evapotranspiration flow I (𝐸𝐴𝑡 1 ∆𝑡) and the interception flow from the previous storage satisfies the
potential rate. If there is further potential and the upper zone storage is filled, water is abstracted at the
residual potential evapotranspiration rate. If the soil storage does not suffice to serve this amount, the
available volume of water in the storage is abstracted. When the upper zone storage is empty, the
81
tension zone is tapped so that the extraction rate is further diminished (see Figure 34 above to specify
the reduction factor 𝑅𝐹). The actual evapotranspiration flow from the soil storage at time t is
calculated using the following equation:
𝐸𝐴𝑡2∆t= 𝑀𝐼𝑁(𝑆𝑜𝑖𝑙𝑊𝑎𝑡𝑒𝑟𝑡 − 𝑃𝑒𝑟𝑐𝑡
𝑠𝑜𝑖𝑙 ,𝑝𝑜𝑡∆𝑡, 𝐸𝑃𝑂𝑇𝑡 − 𝐸𝐴𝑡
1 − 𝐼𝑛𝑡𝑡 ∗ 𝑅𝐹
0 , 𝑖𝑓 𝐸𝑃𝑂𝑇𝑡 − 𝐸𝐴𝑡
1 − 𝐼𝑛𝑡𝑡 > 0
, 𝑖𝑓 𝐸𝑃𝑂𝑇𝑡 − 𝐸𝐴𝑡1 − 𝐼𝑛𝑡𝑡 ≤ 0
(21)
4.4.2.4 Groundwater Storages
Figure 36 depicts the systems structure that underlies the simulation of the groundwater flows. For the
water balance model of Cyprus, the SMA model is simplified by assuming only two groundwater
storages.
Figure 36: Stock and flow structure of the hydrological model, part2
The groundwater layer I storage capacity (𝐺𝐿𝐼𝑚𝑎𝑥 [Mm³]) as well as the current storage level
GLIt[Mm³]), the maximum aquifer percolation rate (𝑃𝑒𝑟𝑐𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ,𝑚𝑎𝑥 [Mm³/month]), the aquifer
capacity (𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑚𝑎𝑥 [Mm³]), and the current level of the aquifer storage (𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑡[Mm³])
determine the potential aquifer percolation rate (𝑃𝑒𝑟𝑐𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ,𝑝𝑜𝑡 [Mm³/month]):
𝑃𝑒𝑟𝑐𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ,𝑝𝑜𝑡 = 𝑃𝑒𝑟𝑐𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ,𝑚𝑎𝑥
GLI t
𝐺𝐿𝐼𝑚𝑎𝑥 1 −
𝐴𝑞𝑢𝑖𝑓𝑒𝑟 𝑡
𝐴𝑞𝑢𝑖𝑓𝑒𝑟 𝑚𝑎𝑥 (22)
The actual percolation rate to the aquifer is computed as the minimum of potential percolation and the
available water in the layer:
𝑃𝑒𝑟𝑐𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟
∆t = 𝑀𝐼𝑁(𝑃𝑒𝑟𝑐𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ,𝑝𝑜𝑡
∆t, GLIt ) (23)
The baseflow is a share of the groundwater layer I storage. The flows are decelerated by the
application of a DELAY-function with a delay time of 2 months in order to simulate the prolongated
Groundwater
Surface Water
Storage
Groundwater to Sea
Aquifer CapacitySaturation Effect
GW
Soil Water
Percolation I
Groundwater
Layer 1
Percolation II
Maximum Groundwater
Percolation Rate
Potential Groundwater
Percolation RateGroundwater Layer 1
Storage Capacity
Baseflow
<Percolation II><Aquifer
Capacity>
<Environmental
Flow GW>
82
nature of percolation processes through multiple groundwater layers (cp. Rossel 2002, p.22). Equation
14 presents the computation of baseflow from groundwater layer 1 to the surface water storage:
𝐵𝑡 = 𝐷𝐸𝐿𝐴𝑌 𝐹𝐼𝑋𝐸𝐷 GLIt ∙ 𝑠𝑏𝑎𝑠𝑒𝑓𝑙𝑜𝑤 , 2,0 (24)
The percolation rates accumulate in the aquifer storage. From this stock, environmental flows (𝐸𝐹𝑡𝐺𝑊
[Mm³/month]) are abstracted at rates that are determined by exogenous data from Savvides et al.
(2001).8 „Groundwater to Sea‟ is another outflow that represents the 'saturation effect' of the
groundwater storage level that is approaching its capacity as well as a fixed share of percolated water
that drains out. The overflow of the aquifer in case of levels near the capacity is controlled by the
variable „Saturation Effect GW‟:
𝑆𝐸𝑡𝐺𝑊 =
𝐴𝑞𝑢𝑖𝑓𝑒𝑟 𝑡
𝐴𝑞𝑢𝑖𝑓𝑒𝑟 𝑚𝑎𝑥
15
(25)
The exponent determines the smooth convergence of the saturation function if the aquifer approaches
its maximum as well as marginal effects for low storage levels (𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑡 ≪ 𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑚𝑎𝑥 ).
𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑚𝑎𝑥 has been set to 4000 Mm³ after estimations from the United Nations Development
Programme basing on a survey in 1964-1969 (Katsikides et al. 2005).
The percolation rate to the ocean is computed by the sum of partial flows. First, a fixed share
𝑠𝑃𝑒𝑟𝑐𝑂𝑐𝑒𝑎𝑛 of the percolated water as well as a fixed share 𝑠𝐴𝑞𝑢𝑖𝑓𝑒𝑟 of the aquifer drains to the ocean.
Additionally, the saturation effect variable is multiplied with the percolation flow and reduced by the
factor 1 − 𝑠𝑃𝑒𝑟𝑐𝑂𝑐𝑒𝑎𝑛 so that in case of completely filled aquifers 100% of the percolation drains to
the sea:
𝑃𝑒𝑟𝑐𝑡𝑂𝑒𝑐𝑎𝑛 = 𝑃𝑒𝑟𝑐𝑡
𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ∙ 𝑠𝑃𝑒𝑟𝑐𝑂𝑐𝑒𝑎𝑛 + 𝑃𝑒𝑟𝑐𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ∙ 1 − 𝑠𝑃𝑒𝑟𝑐𝑂𝑐𝑒𝑎𝑛 ∙ 𝑆𝐸𝑡
𝐺𝑊 + 𝑠𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ∙
𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑡 (26)
Finally, the aquifer level is computed as follows:
𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑡 = INTEGRAL 𝑃𝑒𝑟𝑐𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟
− 𝐸𝐹𝑡𝑆𝑊 ,𝐴𝑞𝑢𝑖𝑓𝑒𝑟0 (27)
The calculation of the aquifer level is preliminary, as further inflows and outflows will be added as
soon as the allocation model is introduced. These flows will be described in detail in Chapter 4.4.3.
4.4.2.5 Surface water storage
Finally, runoff 𝑅𝑡 and baseflow 𝐵𝑡 are stored in dams, lakes or rivers and accumulate in the stock
variable 'surface water storage' (see Figure 37). Outflows are environmental flows
(𝐸𝐹𝑡𝑆𝑊 [Mm³/month]) and surface water that drains to the ocean (𝑅𝑡
𝑂𝑐𝑒𝑎𝑛 [Mm³/month]), so that the
surface water level at time t (𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑊𝑎𝑡𝑒𝑟𝑡 [Mm³]) is the integral above these flows 9:
𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑊𝑎𝑡𝑒𝑟𝑡 = INTEGRAL 𝑅𝑡 + 𝐵𝑡 − 𝐸𝐹𝑡𝑆𝑊 − 𝑅𝑡
𝑂𝑐𝑒𝑎𝑛 , 𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑊𝑎𝑡𝑒𝑟0 (28)
The capacity of the surface water stock is determined by the sum of the Natural Storage Capacity
which belongs to natural surface waters as rivers or lakes, and the Storage Capacity which refers to
artificial ponds and dams. Yearly numbers about the dam capacity derived from publicized data are
implemented in the model (WDD 2001).
8 See appendix G for the specific values 9 Again, further flows will be added to the surface water storage at a later stage.
83
Figure 37: Model structure of the surface water storage
Similar to the mechanism in the groundwater storage, the stock of the water storage is regulated by the
variable saturation dam:
𝑆𝐸𝑡𝑆𝑊 =
𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝑊𝑎𝑡𝑒𝑟 𝑡
𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑊𝑎𝑡𝑒𝑟 𝑑𝑎𝑚𝑠𝑚𝑎𝑥 +𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑊𝑎𝑡𝑒𝑟 𝑛𝑎𝑡𝑢𝑟𝑎𝑙
𝑚𝑎𝑥 15
(29)
Fixed shares of runoff and baseflow are routed to the ocean as well as a percentage of the surface
water storage drains by-and-by. If the capacity of the water storage is approached, the rate of runoff
and baseflow that flows to the sea increases exponentially. In case of full saturation, all inflows are
directly diverted to the ocean. The following equation is applied:
𝑅𝑡𝑂𝑐𝑒𝑎𝑛 = 𝑅𝑡 ∙ 𝑠
𝑅𝑡𝑜𝑆𝑒𝑎 + 𝐵𝑡 ∙ 𝑠𝐵𝑡𝑜𝑆𝑒𝑎 + 1 − 𝑠𝑅𝑡𝑜𝑆𝑒𝑎 ∙ 𝑅𝑡 + 1 − 𝑠𝐵𝑡𝑜𝑆𝑒𝑎 ∙ 𝐵𝑡 ∙ 𝑆𝐸𝑡
𝑆𝑊 +
𝑠𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝑊𝑎𝑡𝑒𝑟 ∙ 𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝑊𝑎𝑡𝑒𝑟𝑡 (30)
4.4.3 Water Allocation System
The purpose of the water allocation model is the simulation of the allocation mechanisms that diverts
water from storages (i.e. groundwater or surface storages) to the demanding sectors. Therefore,
allocation model uses the surface and groundwater storage variables that are compiled by the
hydrological sub-model. Additional supply devices stem from desalination plants and recycled
wastewater. The water demand of the sectors is compared to the available water resources. In case of
abundant water, the demand is satisfied, whereas in case of insufficient water storage, the extracted
amount is diminished and merely the available water amount is abstracted. The difference between
water demand and eventually supplied water for usage is defined as the indicator for water scarcity.
Figure 38 depicts merely the stock and flow structure for clarification reasons. The overall model
structure of the allocation system can be seen in Figure 38. It contains just four stocks, namely Non-
Potable Water Supply, Potable Water Supply, Wastewater and Agriculture. Water for agriculture,
landscaping&amenities, and industry is diverted to the non-potable water stock, whereas drinking
water for the domestic and tourism sector enters the potable-water stock. Used water from the industy,
tourism and domestic sectors enter the wastewater stock. Here, the treated sewage is diverted to the
Surface RunoffSurface Water
Storage
Storage Capacity
<Years>
Surface Water to
Ocean
Saturation Dam
NaturalStorageCapacity
Groundwater
Layer 1
Baseflow
<Runoff>
<Baseflow>
<Environmental
Flow SW>
Depression
84
non-potable water supply stock for reuse as irrigation water, or percolated to the aquifer. Untreated
water leaves the system boundary as it is not usable.
As the withdrawal of water from the storages is considered to be steered by the actual water needs,
usually no excess water accumulates in the stocks. Rather they serve as branching points that manage
the allocation of incoming water to the sectors. In the following chapters the implementation of the
system is explained in detail.
4.4.3.1 Allocation rules
The first allocation rule refers to the sequence of water supply sources that are stressed in order to
satisfy demand. In the past, ground and surface water were predominantly used in the domestic and
agriculture sector, before the prolonged growth of the sectoral demand and diminishing rainfall made
the application of non-conventional water sources necessary. Thus, desalination plants and wastewater
treatment have been developed and their capacity adapted to the level of water scarcity. In the model,
it is assumed that the installed capacity is utilized fully. Prolonged overcapacities of non-conventional
water sources would lead to reduction of produced water from desalination and sewage plants
depending on the costs per m³ water. The model allows the detection of unnecessary capacities that
can be eliminated accordingly. In reality, only delayed adaptation of capacities is possible since the
government of Cyprus is bound by contract to sell minimum quantities of desalinated water from
private operators (Koutsakos et al. 2005).
The second allocation rule deals with the ratio of water demand that is satisfied by groundwater
and surface water respectively. In the model, the percental extraction from ground or surface water of
the different sectors is endogenous, meaning that shortages in the groundwater storage would be
compensated by surface water. Nevertheless, initial shares are inserted exogenously that determine
how much water is extracted in case of sufficient storage levels. These shares are taken from the FAO
report 2002 where it is estimated that the 44% of the non-potable water stems from surface water
(𝑅𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 ) and 56% from groundwater reserves (𝑅𝑁𝑜𝑛−𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 ) in the year 2000 (Savvides et al.
Aquifer
Surface WaterStorage
Non-PotableWater Supply
Potable WaterSupply
Withdrawal for Non-Potable Water Supply
Pumping for Non-Potable Water Supply
Withdrawal forDomestic Use
Wastewater
UnusedDischarge
Reuse forIrrigation
Desalination
Potable Water Use
Irrigation WaterUse
Pumping forDomestic Sector
Surface Water toOcean
Effluent toAquifer
Agriculture
OutflowPercolation to
GWSoil Water
Landscaping&A
menities
Industry WaterUse
<EnvironmentalFlow SW>
EFSW
Figure 38: Stock and flow structure of the allocation model
85
Compensation Ratio
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 ∞
D Capacity
2001, p.12). These shares base on the following equations:
𝑅𝑁𝑜𝑛−𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 = 0.44 =
𝐷𝑁𝑜𝑛 −𝑝𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊
𝐷𝑁𝑜𝑛 −𝑝𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 +𝐷𝑁𝑜𝑛 −𝑝𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 (31)
𝑅𝑁𝑜𝑛−𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 = 0.56 =
𝐷𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒𝑆𝑊
𝐷𝑁𝑜𝑛 −𝑝𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 +𝐷𝑁𝑜𝑛 −𝑝𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 (32)
The domestic sector is assumed to get 26.8% of its water from surface sources, 23.1% from
groundwater and 50% from desalination plants. As desalinated and recycled water are utilized to their
respective capacities, only the ratio of groundwater to surface water use is implemented exogenously.
Therefore, 47.5% of the domestic water demand that belongs to the natural water supplies belongs to
surface water (𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 ) and 52.5% to groundwater (𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 ):
𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 = 0.475 =
𝐷𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊
𝐷𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 +𝐷𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 (33)
𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 = 0.525 =
𝐷𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊
𝐷𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 +𝐷𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 (34)
The third allocation rule is connected to the previous one, and is concerned with the compensation
supplies in case of water shortages in one of the natural storages (groundwater and surface water). For
instance, the ratio of groundwater pumping for the domestic sector is assumed to increase, if the
surface storage runs dry and can not offer the required amount anymore. The compensation
mechanism starts if one of the water sources can satisfy the demand only by 50 percent. The
underlying assumption of this rule considers the compensation mechanism to be costly and time-
consuming. Thus, compensation is realized just in state of emergency that is reached in the model
when only half of the demand is satisfied from a source. After that, the model proves whether the other
source can balance this shortage, which is the case if the difference between the ratios of demand to
supply of the distinct sources is greater than 20%. This rule considers that the withdrawal of a source
will be increased only if its capacity is considerably higher. Figure 39 shows the link between the
difference in capacity (x-axis) and the compensation ratio (y-axis).
The capacities of the groundwater 𝐶𝑡𝐺𝑊and surface storage 𝐶𝑡
𝑆𝑊 are calculated by dividing the
respective natural water storage (𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑡 , and 𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑊𝑎𝑡𝑒𝑟𝑡) by the water demand of the
agriculture, industry, tourism, and domestic sector from groundwater 𝐷𝑡𝐺𝑊 [Mm³/month], or surface
water 𝐷𝑡𝑆𝑊 [Mm³/month]:
𝐶𝑡𝐺𝑊 =
𝐴𝑞𝑢𝑖𝑓𝑒𝑟 𝑡
𝐷𝑡𝐺𝑊 , where 𝐷𝑡
𝐺𝑊 = 𝐷𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐𝐺𝑊 + 𝐷𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
𝐺𝑊 (35)
Figure 39: Assumed compensation ratio dependent on the capacity difference
86
𝐶𝑡𝑆𝑊 =
𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑊𝑎𝑡𝑒𝑟 𝑡
𝐷𝑡𝑆𝑊 , where 𝐷𝑡
𝑆𝑊 = 𝐷𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐𝑆𝑊 + 𝐷𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
𝑆𝑊 (36)
The capacity difference ∆𝐶𝑡 is calculated as follows:
∆𝐶𝑡 = 𝐶𝑡𝐺𝑊 − 𝐶𝑡
𝑆𝑊 (37)
For ∆𝐶𝑡 smaller than 20% the balancing mechanism is not applied. It is not until a spread of 40% that
75% of the shortage is attained from alternative storages. The limitation of the compensation flows to
75% of the shortage expresses that some water uses can not switch to another source, e.g. due to the
geographical position that prevent conveyance of water. The compensation ratio is computed by the
variable 𝐶𝑅𝑡 = 𝑓(∆𝐶𝑡). The functional relationship is depicted in Figure 39. In Appendix H different
examples are given for the compensation mechanism.
4.4.3.2 Satisfaction of the potable and non-potable water demands
Chapter 4.4.2 presented the hydrological processes that cause the inflow to the ground and surface
water storages (i.e. runoff, baseflow, and percolation), and the outflows to the ocean if the storage
capacities are approached as well as extractions for the environment. This chapter explains the in- and
outflows that are caused by human water demand. As the extraction mechanism for surface and
groundwater storages follows the same approach, only the groundwater exploitation process is
described in this chapter. Figure 40 depicts the structure of the model for the simulation of
groundwater withdrawal. Outflows from the groundwater storage are destined for the potable and non-
Aquifer
Non-Potable
Water Supply
Potable Water Supply
Pumping for
Non-Potable
Water Supply
Wastewater
Reuse for
Irrigation
Desalination
Potable Water Use
Irrigation Water
Use
Pumping for
Domestic Sector
<Weight Groundwater
Pumping>
<Ratio GW/Water
Need Domestic>
<Ratio GW/Water
Need Irrigation>
<Irrigation Water
Demand>
Water ScarcityAgriculture
Water ScarcityDomestic
Desalination
Capacity
<Desalination>
<Years>
Agriculture
Virtual Water
Percolation to GW
<Irrigation Water
Demand>
Soil Water
Landscaping
&Amenities
<Landscaping &Amenities Water
Demand>
<Potable Water
Demand>
<Potable Water
Demand>
<Landscapi
ng&Ameniti
es>
<Reuse for
Irrigation>
<Weight Groundwater
Pumping>
<Compensation
GW for SW><Compensation
GW for SW> <Losses of Potable
Water Supply>
Industry Water
Use
<Years>
Industry Water
Demand
<Industry Water
Use>
Figure 40: Structure of the sub-model for groundwater extraction
87
potable supply storages. Return flows are caused by infiltration of irrigation water and subsequent
percolation. The rates of Pumping for Non-Potable Water Supply and Pumping for Potable Water
Supply are determined by the respective water demands for industry, agriculture, and
landscaping&amenities (𝐷𝑡𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒 [Mm³/month]) as well as domestic and tourism
(𝐷𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 [Mm³/month]). The model tests if enough water in the aquifer is available to satisfy the
needs from the sectors by the variable Weight Groundwater Pumping (𝑊𝑡𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔
). Therefore, the
current water storage level of the aquifer (𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑡[Mm³]) is related to the potable
(𝐷𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 [Mm³/month]) and non-potable (𝐷𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 [Mm³/month]) water demands that shall be
abstracted. It is assumed that only 80% of the aquifers are reachable by pumping. Therefore, the
aquifer level at time t is multiplied by 0.8.10
Subsequently, the water demands are reduced by the
wastewater recycling (𝑊𝑅𝑡𝐼𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛
[Mm³/month]) for irrigation and desalination capacity (𝐷𝑆𝑡 )
respectively (allocation rule 1). The final demand that shall be satisfied by groundwater is calculated
by the multiplication of the groundwater ratios of the potable (𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 [Mm³/month]) and non-potable
(𝑅𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 [Mm³/month]) water supply:
𝑊𝑡𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔
=0.8∙𝐴𝑞𝑢𝑖𝑓𝑒𝑟 𝑡
𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 ∙ 𝐷𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 −𝐷𝑆𝑡 +𝑅𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 𝐷𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 −𝑊𝑅𝑡𝐼𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛
(38)
Hence, 𝑊𝑡𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔
= 1 if enough water is available in the aquifer in order to satisfy the potable and
non-potable water supply. On the other hand, in case of insufficient storage levels, 𝑊𝑡𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔
is
smaller than one and specifies the share by which the demands are delivered. The same ratio is
calculated for the surface water storage, and is called 𝑊𝑡𝑆𝑊𝑊𝑖𝑡 𝑑𝑟𝑎𝑤𝑎𝑙 .
11
In order to calculate the final abstraction rates from the aquifer, the compensation ratio has to be
added. Therefore, the groundwater extraction is increased by the compensation factor 𝐶𝐹𝐺𝑊𝑆𝑊 :
𝐶𝐹𝐺𝑊𝑆𝑊 =
1 + 1 −𝑊𝑡𝑆𝑊𝑊𝑖𝑡 𝑑𝑟𝑎𝑤𝑎𝑙 ∙ 𝐶𝑅𝑡
1 , 𝑖𝑓 𝑊𝑡
𝑆𝑊𝑊𝑖𝑡 𝑑𝑟𝑎𝑤𝑎𝑙 ≤ 0.5 𝑎𝑛𝑑 𝑊𝑡𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔
≥ 𝑊𝑡𝑆𝑊𝑊𝑖𝑡 𝑑𝑟𝑎𝑤𝑎𝑙 + 0.2
𝑜𝑡𝑒𝑟𝑤𝑖𝑠𝑒
(39)
The conditions reflect the allocation rules that have been described in Chapter 4.4.3.1. The
compensation factor that expresses the increase of surface water abstraction due to groundwater
shortages (𝐶𝐹𝑆𝑊𝐺𝑊 ) is omitted due to space restrictions.
Finally, the following equation is applied to compute the groundwater flow that is pumped for the
potable water supply:
𝑃𝑈𝑀𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 =
𝑀𝐼𝑁(𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 ∙ 𝐷𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 − 𝐷𝑆𝑡 ∙ 𝐶𝐹𝐺𝑊𝑆𝑊 ,𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 ∙ 𝐷𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 − 𝐷𝑆𝑡 ∙ 𝑊𝑡
𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔) (40)
Consequently, the potable water demand from the groundwater resource is satisfied as long as enough
water is available and, therefore, 𝑊𝑡𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔
≥ 1. The compensation factor 𝐶𝐹𝐺𝑊𝑆𝑊 in equation 39
would be greater than one if groundwater is used to compensate a shortage in surface waters
10 There is no published data about this variable available. Hence, this parameter has to be discussed with
stakeholders and the sensitivity needs to be tested. 11 Unlike the calculation of 𝑊𝑡
𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔, the surface storage is assumed to be reachable by 90%. Therefore, the
surface storage 𝑆𝑢𝑟𝑓𝑎𝑐𝑒𝑡 is multiplied by 0.9 instead of 0.8.
88
(allocation rule 3). If the aquifer level is insufficient, a diminished amount is abstracted, and, of
course, no groundwater is abstracted for compensation of shortages in the surface water storage.
The other outflow from the groundwater storage to the non-potable water supply (𝑃𝑈𝑀𝑡𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒
[Mm³/month]) for the agriculture and industry sector as well as Lanscaping&Amenities is calculated
in a similar way. Here, the non-potable water demand 𝐷𝑡𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒 is reduced by recycled water
(𝑊𝑅𝑡 [Mm]) and adapted to the water availability of the aquifer:
𝑃𝑈𝑀𝑡𝑁𝑜𝑛−𝑃𝑜𝑡𝑎𝑏𝑙𝑒 = 𝑀𝐼𝑁(𝑅𝑁𝑜𝑛−𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝐺𝑊 ∙ 𝐷𝑡𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒 −𝑊𝑅𝑡 ∙ 𝐶𝐹𝐺𝑊
𝑆𝑊 ,𝑅𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝐺𝑊 ∙
𝐷𝑡𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒 −𝑊𝑅𝑡 ∙ 𝑊𝑡
𝐺𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔) (41)
The withdrawal of water from the surface water storage to the potable and non-potable water stock is
similar to the calculation of groundwater pumping. Therefore, only the equations are given:
𝑊𝐼𝑇𝐻𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 = 𝑀𝐼𝑁(𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝑆𝑊 ∙ 𝐷𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 − 𝐷𝑆𝑡 ∙ 𝐶𝐹𝑆𝑊
𝐺𝑊 ,𝑅𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 ∙ 𝐷𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 −𝐷𝑆𝑡 ∙
𝑊𝑡𝑆𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔
) (42)
and
𝑊𝐼𝑇𝐻𝑡𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒 = 𝑀𝐼𝑁(𝑅𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝑆𝑊 ∙ 𝐷𝑡𝑁𝑜𝑛−𝑃𝑜𝑡𝑎𝑏𝑙𝑒 −𝑊𝑅𝑡 ∙ 𝐶𝐹𝑆𝑊
𝐺𝑊 ,𝑅𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒𝑆𝑊 ∙
𝐷𝑡𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒 −𝑊𝑅𝑡 ∙ 𝑊𝑡
𝑆𝑊𝑃𝑢𝑚𝑝𝑖𝑛𝑔) (43)
Where:
𝑊𝐼𝑇𝐻𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 = Withdrawal for the potable water supply at time t in Mm/month; 𝑊𝐼𝑇𝐻𝑡
𝑁𝑜𝑛 −𝑃𝑜𝑡𝑎𝑏𝑙𝑒 =
Withdrawal for the non-potable water supply at time t in Mm/month.
4.4.3.3 Domestic and Agriculture Water Supply
In this chapter, the allocation of water that reaches the supply-storages is described in more detail. The
potable water supply is satisfied by several water sources that comprise water from groundwater
resources, surface water, and desalination plants (see Figure 40). The computation and allocation of
the ground- and surface waters flows to the potable Water Supply storage have been explained in the
preceding chapter. Additionally to the pumped and withdrawn water, desalinated water is the third
inflow to the potable water stock. The development of the desalination capacity is inserted by
exogenous data that comprise installed desalination plants as well as future projects (see Chapter
4.4.5.1 for details). The Potable Water Use (𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 [Mm³/month]) is the minimum of the potable
water demand (𝐷𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 ) and the potable water storage (𝑃𝑆𝑡 [Mm³]):
𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 = 𝑀𝐼𝑁 𝐷𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 ,𝑃𝑆𝑡 (44)
The potable water storage is eventually computed by the integration of inflows and outflows:
𝑃𝑆𝑡 = INTEGRAL 𝑊𝐼𝑇𝐻𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝑃𝑈𝑀𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝐷𝑆𝑡 − 𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 ,𝑃𝑆0 (45)
The used water is further converted to the wastewater stock and allocated to reuse in irrigation, or,
alternatively, it is percolated to aquifers, or discharged to the ocean. The calculation of the wastewater
flows is presented in Chapter 4.4.5.2 in detail.
The domestic and touristic Water Scarcity indicator (𝑊𝑆𝑡𝐷𝑜𝑚 +𝑇𝑜𝑢𝑟 ) represents the quotient of
potable Water Use 𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 to Domestic Water demand 𝐷𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 (both in Mm³/month). By subtracting
89
this value from one, the degree of water scarcity is computed ranging from zero, if the complete
demand can be satisfied, to 1, if no water can be delivered to the domestic sector. The first-order delay
function damps the variations of the water scarcity indicator and is considered as a first policy
mechanism which enters the model. Thus, the policy maker will not wait until the water storages are
empty, but smooth the water shortages over time. Further decision rules which could be applied in the
model are presented in Appendix J. The decision-rules have to be discussed with the decision-maker in
order to improve the adequacy of the model. Equation 46 expresses the preliminary decision-rule that
is chosen in this version of the model:
𝑊𝑆𝑡𝐷𝑜𝑚 +𝑇𝑜𝑢𝑟 = DELAY1 1 −
𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒
𝐷𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 , 8 (46)
The agriculture water demand is managed in a similar way to the domestic one. The Non-potable
Water Supply (𝑁𝑃𝑆𝑡 [Mm³]) has three inflows comprising the pumping water from groundwater
resources, water from surface storages, and recycled water from treated domestic effluent. The
outflows comprise the water uses for agriculture (𝑈𝑖𝐴𝑔𝑟𝑖
), and industry (𝑈𝑖𝐼𝑛𝑑 ) in Mm³/month. The
latter flow is inserted by exogenous time series, whereas the agriculture water usage is calculated by
the minimum of the water demand for agriculture and amenities (𝐷𝑡𝐴𝑔𝑟𝑖 &𝐴𝑚𝑒𝑛
) and the non-potable
supply storage (𝑁𝑃𝑆𝑡).
𝑈𝑡𝐴𝑔𝑟𝑖
= 𝑀𝐼𝑁 𝐷𝑡𝐴𝑔𝑟𝑖 &𝐴𝑚𝑒𝑛
,𝑁𝑃𝑆𝑡 (47)
The non-potable water supply at time t is calculated by integration of the in- and outflows:
𝑁𝑃𝑆𝑡 = INTEGRAL 𝑊𝐼𝑇𝐻𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝑃𝑈𝑀𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝑊𝑅𝑡 −𝑈𝑡𝐼𝑛𝑑 −𝑈𝑡
𝐴𝑔𝑟𝑖,𝑁𝑃𝑆0 (48)
Utilized water percolates to the groundwater layer, is assimilated by plants or gets lost in the
agricultural production process (e.g. through pollution). Flows that leave the system by agricultural
practices are denoted Virtual Water after the concept of Tony Allan (1993) that links commodities to
their water requirements in the production process. At present, the diversion is realized by assuming
fixed shares (see Appendix G). At a later stage, the hydrological model will be attached and variables
like land cover and pasture patterns will determine the diversion. The stock „Agriculture‟ is used for
allocation of irrigation water.
Water Scarcity Agriculture 𝑊𝑆𝑡𝐴𝑔𝑟𝑖
is defined similar to the domestic sector by the following equation:
𝑊𝑆𝑡𝐴𝑔𝑟𝑖
= DELAY 1 −𝑈𝑡𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
𝐷𝑡𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒 (49)
Where; 𝑈𝑡𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
= utilized water in the agriculture sector in Mm³/month; 𝐷𝑡𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
= Demand
of the agriculture sector also in Mm³/month.
4.4.5 Calculating the policy options
Various policy options for the mitigation of the detrimental effects of water scarcity in Cyprus have
been stated: the building of desalination plants, the recycling of wastewater, improvement in the
maintenance of the conveyance network, and application of water demand management. In this
chapter, the inclusion of these measures in the model structure is described. Besides the effects of
these policies on the water balance, economic, social and environmental effects have to be considered
in order to make an integrative policy assessment possible. Due to time constrains of the diploma
90
thesis, only the effectiveness of the different policies are simulated. Future research can link cost,
pollution, or energy requirements to the measures in order to illustrate side-effects of policies.
However, prior to this in-depth analysis of the policy options, stakeholders and decision-makers
should be consulted if the assumptions in water balance model are appropriate and the simulation
results are reasonable. Also functional relations have to be discussed, before further efforts in
refinements in the model are straightforward.
4.4.5.1 Desalination
Desalination is applied for the generation of drinking water by the use of reverse osmosis. Therefore,
the sea water is pretreated in order to remove micro-organisms and suspended solids. Then, the water
is pressed through the membranes with a pressure range from 54-80 atmospheres so that salt is
removed from the liquid. At the post-treatment stage, the water is prepared for the distribution by
removal of gases and the adjustment of the pH and hardness (WDD 1999). Energy from the electric
power grid is used for the pressure generation. In Cyprus, the electric energy production depends to 90
% on oil products, 4.5% on coal, and 4.5% on solar energy (Koroneous et al. 2005). The desalination
process requires 5.3 KWh/m³ in the Dhekelia, and 4.4 KWh/m³ in the Larnaca plant (Sallangos 2004;
Koutsakos et al. 2005). The difference in energy consumption illustrates the rapid technical
development of the desalination methods. The desalination plants are financed by a BOOT (Build,
Own, Operate, Tranfer) contract that allows a private contractor to build the plant with its own
financial resources and operate it for 10 years. After the 10–year period the ownership of the plant is
transferred to the government without additional costs. The government has the right to conduct this
transfer before connected with monetary payments (Tsiortis 2001). Due to the BOOT contract, the
investment costs for the construction and installation of the desalination plant do not stress the public
budget. However, the government has to sell a minimum quantity of water for the costs of 54 cents/m³
for the Dhekalia, and 39,9 cents/m³ for the Larnaca plant (WDD 2009). The price difference again
point up to the improvements of the technology.
In the model, the desalination capacity is inserted by exogenous data up to the year 2009. In 1997,
Dhekalia desalination plant was installed with a capacity of 20000 m³/d that was enhanced to 40000
m³/d shortly after (WDD 1999). The Larnaca plant started its operation in 2001 with a nominal
capacity of 52000 m³/d (Koroneous et al. 2005). Two further plants are constructed in Limassol and
Paralimni with capacities of 20000 m³/d and 10000 m³/d respectively (Katsikides et al. 2005). By the
application of a lookup-function the future desalination capacity can be varied and different policies
tested. In the interviews, other aspects of desalination that have been stated were the high energy
consumption, and the connected emission of CO2. These aspects are proposed for inclusion in a later
version of the simulation model.
4.4.5.2 Wastewater Recycling
Another policy option that has been stated in the interviews was the application of recycling of
domestic and industrial wastewater for reuse in agriculture and watering of urban green spaces. In
1995, a first sewage plant started to operate in Limassol with a capacity of 4Mm³ per year. Further
plants have been established in all major cities with a capacity of 13Mm³ in 2005. Up to 2030, the
volume of treated effluent will be increased up to 30 Mm³ (Katsikides et al. 2005). More recent
publications reveal more ambitious plans that consider 59Mm³ treated effluent in 2012 by municipal
and rural plants, 65Mm³ in 2015 and 85Mm³ in 2025 (Yiannakou 2008). Wastewater treatment plants
have been built for rural communities, refugee housing estates, public hospitals, and military camps so
that the grand total capacity amounts to 21.28 Mm³ in 2005. In 2004 about 79% of the treated water
91
was reused in agriculture, 9% percolated for aquifer recharge, and 12% discharged to the sea
(Yiannakou 2008).
In the model, the capacities of waste water treatment plants are inserted by exogenous time series
based on present data and future plans published by Yiannakou (2008). The diversion of the recycled
water is arranged by the shares Recycling Rate Agriculture (set to 0.79), Recycling Rate aquifer (0.09)
and Wastewater to Sea Rate (0.12). These values can be varied by the use of a table function. Figure
41 depicts the stock and flow structure of the wastewater recycling system.
The sewage of the domestic, tourism and industry sectors enters the wastewater stock. Here, the flow
is divided in the Reuse for Irrigation Flow, the Effluent to Aquifer Flow, and the Unused Discharge
Flow. The Effluent to Aquifers Flow also comprises water that percolates without prior treatment to
the groundwater layers. Treated water that is discharged to the sea or unusable due to pollution and not
treated in wastewater plants are considered by the Unused Discharged Flow. The recycling rate (𝑅𝑅𝑡 )
specifies the rate of water from the industry, tourism and domestic usage that is recycled. Therefore,
the quotient of monthly wastewater capacity (𝑊𝐶𝑡 [Mm³/year]) and the used water in the sectors is
computed:
𝑅𝑅𝑡 =𝑊𝐶𝑡
12∙
1
𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 +𝑈𝑡
𝐼𝑛𝑑 (50)
It is assumed that maximally 80% of the industrial, tourism, and domestic water can be recycled as
some water is polluted or not treatable, e.g. for technical or economical reasons. Therefore, the real
recycling rate 𝑅𝑅𝑡𝑟𝑒𝑎𝑙 is calculated by equation 51:
𝑅𝑅𝑡𝑟𝑒𝑎𝑙 = 𝑀𝐼𝑁 𝑅𝑅𝑡 , 0.8 (51)
The agriculture recycling rate (𝑅𝑅𝑡𝐴𝑔𝑟𝑖
in %) is inserted by exogenous data. The wastewater reuse for
Aquifer
Non-Potable
Water Supply
Potable Water Supply
Wastewater
Unused
Discharge
Reuse for
Irrigation
Potable Water Use
Effluent to Aquifer
Water ScarcityDomestic+Tourism
Recycling Rate
Agriculture
Recycling Rate
Aquifer
Recycling Rate
real<Years>
<Recycling Rate
real>
<Recycling Rate
real>
<Potable Water
Use>
<Potable Water
Use>
<Recycling Rate
Aquifer>
<Recycling Rate
Agriculture>
<Recycling Rate
real>
<Potable Water
Demand>
Annual Capacity for
Wastewater Treatment
<Years>
Recycling Rate to
the Sea
<Years>
Recycling rate
Industry Water
Use
<Years>
Industry Water
Demand
Figure 41: Stock and flow structure of the wastewater treatment and reuse process
92
irrigation flow (𝑊𝑅𝑡𝐼𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛
[Mm³/month]) is calculated as follows:
𝑊𝑅𝑡𝐼𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛
= 𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝑈𝑡
𝐼𝑛𝑑 ∙ 𝑅𝑅𝑡𝐴𝑔𝑟𝑖
∙ 𝑅𝑅𝑡𝑟𝑒𝑎𝑙 (52)
The flow of effluent to the aquifer (𝑊𝑅𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟
[Mm³/month]) is computed in a similar way by the
usage of the exogenous aquifer recycling rate (𝑅𝑅𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟
[%]):
𝑊𝑅𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟
= 𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝑈𝑡
𝐼𝑛𝑑 ∙ 𝑅𝑅𝑡𝐴𝑞𝑢𝑖𝑓𝑒𝑟
∙ 𝑅𝑅𝑡𝑟𝑒𝑎𝑙 (53)
Water that is not treated and overcapacities leave the water balance system by the unused discharge
flow. The unused discharge recycling rate (in %) is generated as follows:
𝑅𝑅𝑡𝑈𝑛𝑢𝑠𝑒𝑑 = 100 − 𝑅𝑅𝑡
𝐴𝑞𝑢𝑖𝑓𝑒𝑟 − 𝑅𝑅𝑡𝐴𝑔𝑟𝑖
(54)
Finally, the unused discharge flow is calculated by equation 55:
𝑊𝑅𝑡𝑈𝑛𝑢𝑠𝑒𝑑 = 1 − 𝑅𝑅𝑡
𝑟𝑒𝑎𝑙 ∙ 𝑈𝑡𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝑈𝑡
𝐼𝑛𝑑 + 𝑅𝑅𝑡𝑈𝑛𝑢𝑠𝑒𝑑 ∙ 𝑈𝑡
𝑃𝑜𝑡𝑎𝑏𝑙𝑒 + 𝑈𝑡𝐼𝑛𝑑 ∙ 𝑅𝑅𝑡
𝑟𝑒𝑎𝑙
(55)
4.4.4.3 Water Demand Management
The water demands of the sectors are calculated partly by endogenous simulation. These calculation
procedures are presented below for the domestic, tourism, and agricultural sectors. The industrial
water demand has been considered to have minor importance for the problem of water scarcity by the
majority of the interviewees and is therefore not simulated endogenously but included by exogenous
time series. Different factors have been stated in the participatory model building that influence the
water demand in the sectors. In general, the economic development, technological efficiencies and
conscious consumption were considered as the major influence factors. Thus, the growing economic
sectors imply an increasing sectoral water demand. Also the per capita water demand in the domestic
sector depends on the economic development as affluence causes new usages of water consumption,
like garden irrigation or car-wash. The technological efficiencies comprise conveyance as well as
application efficiencies in the different sectors. Conscious consumption aims more at the behavioral
efficiency in water usage by avoidance of water wastage and the water-saving application of
technology. The following chapter describes the model structure of the water demand calculations for
the different sectors. Whereas the measures and influence factors have been defined in the
participatory model building, the implementation to the system dynamics simulation program has been
done without the involvement of stakeholders. Therefore, the following sub-model should be
considered as a preliminary model that has to be approved and discussed by the participants. Besides
the structure, also the various functional expressions need to be revised as many links are not
straightforward. For instance, the computation of the link between GDP and water demand is not easy
to estimate, as even most of the economic growth models do not include resource consumption (see
Ayres and Kneese 1969).
4.4.4.3 Domestic Water Demand
The domestic water demand at time t (𝐷𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 [𝑚³]) depends on the current number of households
(𝐻𝑡 ) and the per capita demand 𝐶𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 [𝑙/𝑑]), multiplied by the average number of days per month:
93
𝐷𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = 𝐻𝑡 ∙ 𝐶𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ∙365
12∙
1
1000 (56)
where: 1
1000 is the conversion factor from liter to m³.
The number of households is calculated by dividing the population by the average number of people
per household. The relation of the water demand to the household is chosen as many devices for water
usages (e.g. dish-washer) are rather used collectively in the household than individually. The
population number itself is inserted by exogenous data (Statistical Service 2007). Projections for the
future are considered that estimate an population increase from 705,539 in 2002 to 851,810 in 2032
until the population number decreases to 822,069 in 2052 (Statistical Service 2004). The average
household size decreased from 3.23 in 1992 to 3.06 in 2001 (Statistical Service 2003).
The household demand depends on the technological efficiency of the domestic water devices
(𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 [%]), the behavioral efficiency (𝐵𝐸𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 [%]) that pertains to the consciousness of
water consumption, and finally the wealth that is reflected by the national annual real GDP (Statistical
Service 2009). The reference per capita demand is calculated by the use of data from Savvides et al.
2001 and set to 209.75 l/d for the year 2000.12
In order to make the quantifications of technological
and behavioral efficiency gains possible, the different usages of the household demand are
investigated and the reference technology defined that is the most efficient technology for the today‟s
pattern of water use. Thus, the per capita water demand in the year 2000 is taken as the reference
demand [l/d] and multiplied with the average size of a household in Cyprus. The various purposes of
the standard domestic water consumption are depicted in Figure 42.
Therefore, the highest share of water is used for the toilet flushing and bathing. By multiplying these
shares with the average daily water demand in the domestic sector of Cyprus of 642 liter/household,
the quantities for the different water uses are calculated. These water quantities can be compared to the
values from literature that are obtained after the application of water efficient water technologies.
12 Annual domestic water demand for the year 2000: 53.4 Mm³ (Savvides et al. 2001, p. 42). Divided by the
population number of 697500 and by 365 days per year results in 209.75 l/d.
Figure 42: Water usage pattern in the domestic sector (WDD 2002)
94
Table 1: Calculation of the optimal technological and behavioral efficiency in the domestic sector
95
However, the distinction between technological and behavioral water saving potentials is not
straightforward as data for these variables is often not available. For the case study in Cyprus, the
following approach is chosen in order to calculate the technological and behavioral water saving
potential (see Table 1). First, the quantities for the different daily water uses in Cyprus are listed.
Second, the data about the most efficient daily water consumption of a household are provided from
research. Then, the technological water saving potential for the Cyprus water uses is estimated and the
saved quantities are calculated. Finally, the technically optimum daily water consumption per
household is presented for the complete implementation of water efficient technology. The same
procedure is done for water saving through changes in behavior. First, the percental water savings
related to the current water use are estimated and, subsequently, the volume quantified. Eventually, the
daily water consumption of a household for 100% conscious water usage is calculated.
Combining technical and behavioral measures, the most efficient consumption for Cyprus is
computed. These calculation bases mainly on the Ecologic EU Water saving potential report (2007).
Table 1 shows the calculation procedure. In column A, the shares from Figure 42 are multiplied by the
average daily water demand per household in Cyprus of 642 liter. Column B shows the most efficient
values from literature. The technologies are specified in the footnotes for every water usage, combined
with the respective bibliographical reference. Columns C and F determine the potential percental water
saving options for technology and behavioral changes respectively. Columns D and G contain the
daily savings in liter per household. Eventually, the columns E and H show the daily consumption of a
household with the application of 100% water saving technology and 100% conscious consumption
behavior. These values pertain to the selected technological options that are considered to be
applicable and realistic for nation-wide installation in Cyprus. Realistic means that the technological
and the behavioral changes are affordable for households and are adapted to the cultural and traditional
characteristics in Cyprus. Also, the overall identity of the water supply and sewage system should be
maintained. For instance, the water consumption for toilets could be reduced to zero by the usage of
vacuum toilets. However, the costs for transformation of the system as well as potential resistance of
the population in regard to this technology lead to a rejection of this option in this model version.
Nevertheless, the assumptions that are summarized in Table 1 can be varied in order to test the
effectiveness of other technologies or include future technological options that can not be anticipated
today. Another technical option that has been stated in the participatory interviews is the application of
grey water recycling in the households. A grey water recycling system could render the usage of
potable water for toilet flushing and garden irrigation unnecessary. Therefore, water from the kitchen
(13%), bath (21%), washbasins (8%) and washing machine (7%) are cleaned (in sum 49%) treated and
stored for the later usage (WDD 2002).As can be seen in Table 1, the most efficient daily water
consumption for a household without a grey water recycling device has been calculated to 250.6 liter.
Applying a grey water treatment plant would reduce the volume to 175.5 liter that is a reduction of
about 30 % (this value corresponds with the savings of 33% that have been stated of the WDD
(2002)).
Figure 43 depict a summary of the possible saving potentials for the domestic sector in Cyprus.
The reference technological efficiency in the year 2000 is calculated by the quotient of the demand at
the technological optimum and the monthly reference demand in the year 2000:
𝑅𝑇𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 =
𝑇𝑜𝑝𝑡𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐
𝐷2000𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 (57)
Hence, the average technological efficiency for the year 2000 amounts to 419.4
642= 65.3 %.
96
100% technological efficiency would imply water savings of 222.8 liter per household per day (see
Figure 43).
The potential water savings through technology (𝑆𝑇𝑝𝑜𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 [l/d/HH]) are computed for every water
usage separately by the subtraction of the reference water demand in the year 2000 with the optimal
technological demand of the respective Usagei:
𝑆𝑇𝑝𝑜𝑡𝑈𝑠𝑎𝑔𝑒 𝑖 = 𝑅𝐷2000
𝑈𝑠𝑎𝑔𝑒 𝑖 − 𝑇𝐷𝑜𝑝𝑡𝑈𝑠𝑎𝑔𝑒 𝑖 (58)
Hence, the actual exploitation of these savings at month t ( 𝑆𝐵𝑡𝑈𝑠𝑎𝑔𝑒 𝑖 ) are calculated as follows:
𝑆𝑇𝑡𝑈𝑠𝑎𝑔𝑒 𝑖 = 𝑆𝑇𝑝𝑜𝑡
𝑈𝑠𝑎𝑔𝑒 𝑖 ∙ 𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 (59)
Where 𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 is the technological efficiency at time t in %.
𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 has to be zero in the reference year 2000 as at this time the reference daily water demand
of 642 l per household is not reduced by water savings. However, if all the technological measures are
implemented, 𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 must equal one. The concept of the calculation of the technological
efficiency at time t is depicted in Figure 44:
Figure 44: Stock and flow structure that underlies the calculation of the technological efficiency
By the variable Choice Households, the sequence and magnitude of the efficiency improvements over
time can be inserted by the model user. For instance, the model users could assume the improvement
Technology Efficiency
Domestic
Choice
Households
Investment in water
saving technology
domestic
<Years>
CF2
ProportionTechnological Efficiency
Domestic
<Reference Technological
Efficiency Domestic 2000>
Figure 43: Potential water savings of a household in Cyprus
97
of the technological efficiency from about 70% in the year 2000 to 90 % within 30 years. The model
does not specify the appropriate measures that would lead to the investment in technology (e.g. water
pricing, subsides, standards) but simulates the causes and effectiveness on the overall water balance.
The sequence of the improvements can be inserted by a table function (see Figure 45). Here, the
investments in technological efficiency grow gradually with a maximum in the middle of the policy
period. However, other developments like major investment in the first half of the policy period can be
inserted. The cumulative growth function of Figure 45 is differentiated in order to calculate the
fractional improvement rate per month. The fraction of households that invest at time t (𝐼𝐻𝑡 ) is
generated by deriving the probability distribution function from the cumulative distribution function.
This is done by the following equation:
𝐼𝐻𝑡 = 𝐶𝐻𝑡 − 𝐶𝐻𝑡−1 (59)
Where: 𝐶𝐻𝑡 is the share of households that have chosen to invest until time t; 𝐶𝐻𝑡−1 is the share of
households that have had invested until time t-1
The stepwise progress accumulates in the Domestic Technology Efficiency stock until the goal (70%
efficiency in the example is reached):
𝑇𝐸𝑆𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = INTEGRAL(𝐼𝐻𝑡 , 0) (60)
The eventual technological efficiency (𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ) is calculated by a function that links the
accumulated technological efficiency stock (𝑇𝐸𝑆𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ) with the reference technological efficiency
in the year 2000 (𝑅𝑇𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ):
𝑇𝐸𝑡𝐷𝑜𝑚𝑒 𝑠𝑡𝑖𝑐 = 𝑇𝐸𝑆𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 − 𝑅𝑇𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ∙
100
100−𝑅𝑇𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 (61)
Hence, the 𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 equals zero (i.e. no water savings), if the cumulative efficiency gains
𝑇𝐸𝑆𝑇𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 equal the reference value 𝑅𝑇𝐸2000
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 in the year 2000. In case of a policy that strives
for 100% efficiency 𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 would amount to:
𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = 𝑇𝐸𝑆𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 –𝑅𝑇𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ∙
100
100−𝑅𝑇𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = 100 − 65.3 ∙
100
100−65.3= 1
(62)
Figure 45: Example of the implementation of efficiency improvements. In
this case an S-shaped approaching of the goal is chosen
98
Domestic Water
Demand
Tourism Water
Demand
Potable Water
Demand
<Bath act>
<Taps act>
<Toilet actual>
<Shower act>
<Dish Washer
act>
<Washing
Mashine act>
<Cleaning act>
<Households>
Income Elasticity of
Water Consumption
<Economic
Development>
Effect of BehavioralEfficiency on Income
Elasticity
<Proportion Behavioral
Efficiency Domestic>Per Household Daily
Water Demand
On the other hand, 𝑇𝐸𝑆𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 < 𝑅𝑇𝐸2000
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 would result in negative values, e.g. for lower
efficiencies prior to the year 2000. For instance, assume 𝑇𝐸𝑆1990𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = 60:
𝑇𝐸1990𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = 60 − 65.3 ∙
100
100−65.3= −5.3 ∙
100
100−65.3= −15.27 (63)
In this case, the water savings in the year 1990 are negative that induces an increase of the per capita
demand that is calculated with the following formula for the actual daily shower water demand per
household (𝐻𝐷𝑡𝑆𝑜𝑤𝑒𝑟 [l/d/HH]):
𝐻𝐷𝑡𝑆𝑜𝑤𝑒𝑟 = 𝑅𝐷2000
𝑆𝑜𝑤𝑒𝑟 − 𝑆𝑇𝑝𝑜𝑡𝑆𝑜𝑤𝑒𝑟 ∙ 𝑇𝐸𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 − 𝑆𝐵𝑝𝑜𝑡𝑆𝑜𝑤𝑒𝑟 ∙ 𝐵𝐸𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 (64)
The reference water demand for shower usage 𝑅𝐷2000𝑆𝑜𝑤𝑒𝑟 is reduced by savings by the technological
efficiency 𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 and behavioral 𝐵𝐸𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 efficiency. Therefore, the potential technological
savings 𝑆𝑇𝑝𝑜𝑡𝑆𝑜𝑤𝑒𝑟 and the potential behavioral savings 𝑆𝐵𝑝𝑜𝑡
𝑆𝑜𝑤𝑒𝑟 are multiplied by the respective
efficiency values at month t. The calculation of 𝑆𝐵𝑝𝑜𝑡𝑈𝑠𝑎𝑔𝑒 𝑖 and 𝐵𝐸𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 are analog to the
technological counterparts:
𝑆𝐵𝑝𝑜𝑡𝑈𝑠𝑎𝑔𝑒 𝑖 = 𝐵𝐷2000
𝑈𝑠𝑎𝑔𝑒 𝑖 −𝐵𝐷𝑜𝑝𝑡𝑈𝑠𝑎𝑔𝑒 𝑖 (65)
The behavioral efficiency at time t is computed simultaneously:
𝐵𝐸𝑡𝐷𝑜𝑚𝑒𝑠 𝑡𝑖𝑐 = 𝐵𝐸𝑆𝑇
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 − 𝑅𝐵𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ∙
100
100−𝐵𝑇𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 (66)
Where 𝑅𝐵𝐸2000𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 =
𝐵𝑜𝑝𝑡𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐
𝐷2000𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 (67)
Consequently, the calculated reference average behavioral efficiency for the year 2000 amounts to 473 .4
642= 73.7%.
The depicted causal structure in Figure 46 underlies the calculation of the final domestic
water demand at month t:
Figure 46: Structure of the endogenous calculation of the domestic water demand
99
All the per-household water demands for the different usages that have been adjusted by technological
and behavioral efficiencies are inserted. Additionally, the effect of the economic development
influences the daily water demand of the household. Equation 68, shows the functional expression that
underlies the calculation:
𝐻𝐷𝑡𝑡𝑜𝑡𝑎𝑙 = 𝐻𝐷𝑡
𝑏𝑎𝑡 + 𝐻𝐷𝑡𝑡𝑜𝑖𝑙𝑒𝑡 + 𝐻𝐷𝑡
𝑑𝑖𝑠 + 𝐻𝐷𝑡𝑠𝑜𝑤𝑒𝑟 + 𝐻𝐷𝑡
𝑤𝑎𝑠 + 𝐻𝐷𝑡𝑡𝑎𝑝𝑠 + 𝐻𝐷𝑡
𝑐𝑙𝑒𝑎𝑛 ∙
1 + 𝐷𝐸𝐹𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 − 1 ∙ 1 − 𝐸𝐵𝐷𝑡 (68)
Where: 𝐷𝐸𝐹𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = development effect on the per capita demand, and 𝐸𝐵𝐷𝑡 = effect of behavior on
the development effect; these variables are described below.
The development effect is influenced by the overall economic development of Cyprus. The abundance
of households has been stated as an important factor that determines the water demand and is inserted
in the model as a behavioral factor. Therefore, increasing wealth creates new usages of water in the
domestic sector (e.g. gardening, car-washing) and water prices have reduced leverage in steering the
water consumption. Besides the wealth of consumers, also their consciousness about the value and
scarcity of the resource water is important and can lead to water saving behavior. Awareness
campaigns or education at schools aim at the consciousness of water consumption. Absence of
consciousness can even counterbalance the positive effects of water saving technologies by
inappropriate usage, e.g. as dish washers could be filled half-full or the garden could be inefficiently
irrigated in midday heat. Therefore, the total real gross domestic product GDPttotal is used as a proxy
for the economic development. It is the sum of the sectoral GDP of agriculture (GDPtAgri
), tourism
(GDPtTourism ) and other sectors (GDPt
Others ) in bn€:
GDPttotal = GDPt
Agri+ GDPt
Tourism + GDPtOthers (69)
The sectoral GDP values are inserted by exogenous time series. The future development of the GDP is
assumed as follows: For the tourism sector an annual growth rate of 1.5% is assumed, whereas for the
agricultural sector the real GDP is estimated to be constant until the year 2050. This means that the
nominal GDP values of the agriculture sector can increase, but the nominal growth rates are equal to
the increase in the inflation rate (cp. Blanchard 2006, p. 31). For the total real GDP an annual growth
rate of 2% is assumed. The development effect on the per capita demand (DEFtDomestic ) is computed
as follows. The values of the total real GDP of Cyprus at time t are normalized with the level in the
year 2000 GDP2000total and serve as input to lookup-function that delineates the 45° line. Consequently,
for the year 2000 DEFtDomestic equals 1 and the per capita demand is not affected (compare equation
68). If the real GDP grows in the following years, also the development effect reflects the same
growth rate. For instance, a 1% increase of real GDP implies a DEFtDomestic of 1.01. This relationship
is expressed in equation 70:
DEFtDomestic =
GDP ttotal
GDP 2000total (70)
The variable „Effect of Behavior on the Development Effect‟ (EBDt ) depends on the behavioral
efficiency in the domestic sector at time t (BEtDomestic ). In the year 2000, this values equals zero, so
that the difference 1 − EBDt in equation 68 equals 1. It is assumed that in case of 100% behavioral
efficiency the level of the economic development has no impact on the per capita water consumption.
Increases that would be induced by the development effect (DEFtDomestic > 1) would be disposed by
100
the behavioral effect EBDt that would equal 1. EBDt is calculated as follows:
EBDt =BE t
Domestic
100 (71)
For BEtDomestic < 0 for the years prior to 2000, the difference 1 − EBDt becomes greater than 1, so
that the behavior component even reinforces the development effect.
The total daily water demand (HDttotal ) is multiplied with the number of households and the average
number of days per month in order to compute the total domestic water demand (Dtdomestic [Mm³]):
Dtdomestic = HDt
total ∙ Ht ∙365
12∙ 10−9 (72)
Where: the factor 10−9 converts the units from liter into Mm³.
4.4.4.2 Tourism Water Demand
The tourism water demand (DtTourism [Mm³/month]) is conceptualized in a similar way as the
domestic demand. It is influenced by the number of tourists ( PtVariable [cap]), the yearly distribution
of tourist arrivals (φannualTourism ), the length of their stay in Cyprus (dt
stay[d]), and the reference daily
capita demand of a tourist (Cttourism [
l
cap ∙d]):
𝐷𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = 𝑃𝑡
𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 ∙ φannualTourism ∙ 𝑑𝑡
𝑠𝑡𝑎𝑦∙ 𝐶𝑡
𝑡𝑜𝑢𝑟𝑖𝑠𝑚 ∙365
12∙ 10−9 (73)
The per capita demand of tourists depends on the sectoral income of tourism (𝐺𝐷𝑃𝑡𝑡𝑜𝑢𝑟𝑖𝑠𝑚 [bn€]), the
ratio of sectoral GDP per tourist as a proxy for the touristic strategy, e.g. mass or cultural tourism
(𝑆𝑡𝑡𝑜𝑢𝑟𝑖𝑠𝑚 [€/𝑐𝑎𝑝]), the technological efficiency of water devices in hotels, apartments, guesthouses
and camping sides (𝑇𝐸𝑡𝑡𝑜𝑢𝑟𝑖𝑠𝑚 [%]), and the behavioral efficiency of the water users in the tourism
sector, i.e. tourists and hotel employees (𝐵𝐸𝑡𝑡𝑜𝑢𝑟𝑖𝑠𝑚 [%]):
𝐶𝑡𝑡𝑜𝑢𝑟𝑖𝑠𝑚 = 𝑓 𝐺𝐷𝑃𝑡
𝑡𝑜𝑢𝑟𝑖𝑠𝑚 , 𝑆𝑡𝑡𝑜𝑢𝑟𝑖𝑠𝑚 ,𝑇𝐸𝑡
𝑡𝑜𝑢𝑟𝑖𝑠𝑚 ,𝐵𝐸𝑡𝑡𝑜𝑢𝑟𝑖𝑠𝑚 (74)
Figure 47 shows the conceptual framework of the calculation for the touristic water demand:
Figure 47: Structure of the endogenous calculation of the tourism water demand
TourismSector
Tourism Water
Demand
<Years>
Yearly Variation of
Tourists
<Monthly>
Variable
Population
<Years>
Lenght of Stay
Per Capita Demand
Tourism
<Years>
Tourism Demand
Optimum tec
Tourism Demand
Optimum beh
Ration GDPTourism
per Capita
Effect of GDPTourism
on per Capita Demand
<Greywater
Treatment>
Effect of Behavioral
Efficiency on GDP Effect
Reference Tourism per
Capita Demand 2000
<Behavioral
Efficiency Tourism>
<Behavioral
Efficiency Tourism>
<Technological
Efficiency Tourism>
101
The 𝐺𝐷𝑃𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 affects the reference per capita water demand 𝑅𝐷2000
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 in the year 2000 by the
variable „Development Effect Tourism‟ (𝐷𝐸𝐹𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ) as along with the development of the touristic
sector also the standard of accommodations and touristic attractions rise that implies a rising standard
of per capita water consumption. This could be cushioned by conscious consumption behavior of
tourists which, however, is more difficult to change than the consumption behavior of the permanent
population. Discomfort due to the promotion of water saving behavior would result in diminishing
tourist arrivals as tourists choose other locations. The effect of the behavioral efficiency on the
development effect is calculated by the correspondent variable 𝐸𝐵𝐷𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 . The sectoral GDP
determines the number of the variable population that is required to generate the desired sectoral
output together with the Ratio of GDP per Tourist (𝐺𝑇𝑅𝑡 ). Hence, in the later scenario analysis, a
desired future growth of the GDP is assumed upon which the required number of tourists is calculated:
𝑃𝑡𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 =
𝐺𝐷𝑃𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚
𝑆𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 (75)
The ratio of GDP per tourists reflects strategies of tourism in Cyprus. Low-price mass tourism means
low rates, whereas tourism striving more for niches and in-imitatable touristic attraction, e.g. by
focusing on cultural or environmental amenities of Cyprus, would induce high incomes per tourist.
Further influence factors on the touristic per capita demand are the technological and behavioral
efficiency of the tourism sector (𝑇𝐸𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 , 𝐵𝐸𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ) that reflect the applied technology for water
use and the degree of conscious consumption respectively. Finally, grey water recycling is considered
by the variable 𝐺𝑇𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 as the share of tourist accommodations that have installed a grey water
treatment plant.
The following function expresses the calculation of the per capita demand of a tourist:
𝐶𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = 𝑅𝐷2000
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 − 𝑅𝐷2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 − 𝑇𝐷𝑜𝑝𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ∙ 𝑇𝐸𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 𝑅𝐷2000
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 −𝐵𝐷𝑜𝑝𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ∙
𝐵𝐸𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚−𝑅𝐷2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚∙𝑆𝐺𝑝𝑜𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚∙𝐺𝑇𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚∙1+𝐷𝐸𝐹𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚−1∙1−𝐸𝐵𝐷𝑡𝑇𝑜𝑢𝑟
𝑖𝑠𝑚 (76)
Where:
𝑇𝐷𝑜𝑝𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = optimal technological water demand in l/cap/d; 𝐵𝐷𝑜𝑝𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 optimal behavioral water
demand in l/cap/d; 𝑆𝐺𝑝𝑜𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = potential savings that can be reached by the application of grey water
treatment in l/cap/d. All these values are specified below.
Equation 76 reduces the reference per capita water demand from the year 2000 by the technological
(𝑆𝑇𝑝𝑜𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = 𝑅𝐷2000
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 − 𝑇𝐷𝑜𝑝𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ), behavioral (𝑆𝐵𝑝𝑜𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = 𝑅𝐷2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 − 𝐵𝐷𝑜𝑝𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ), or grey
water (𝑆𝐺𝑝𝑜𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ) saving potential multiplied with the respective implementation rates. These values
are adjusted by the development effect, and the hampering or reinforcing effect of the behavior on the
development effect.
𝐷𝐸𝐹𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 and 𝐸𝐵𝐷𝑡
𝑇𝑜𝑢𝑟𝑖𝑠 𝑚 are calculated by functional expressions corresponding to the domestic
sector:
𝐷𝐸𝐹𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 =
𝐺𝐷𝑃𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚
𝐺𝐷𝑃2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 (77)
𝐸𝐵𝐷𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 =
𝐵𝐸𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚
100 (78)
The optimal technological (𝑇𝐷𝑜𝑝𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ) and behavioral demands (𝐵𝐷𝑜𝑝𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ) are determined by
values from literature research that are adapted to the specific circumstances in Cyprus. The specific
calculation of the optima is presented below.
The number of the optimal technological water consumption expresses the water consumption in
102
l/cap/d that would be achieved if 100% of the applied technical devices in the touristic sector have
optimal water efficiencies. Likewise, the optimal behavioral water consumption expresses the amount
of water which would be used if 100% of touristic water would be consumed in a most conscious way.
The saving potential in the tourism sector comprises the installation of water saving measures, the
conduction of awareness campaigns to influence the water use behavior of tourists, and the application
of grey water recycling. Therefore, the measures are comparable to the domestic sector even though
differences in the usage of water have to be considered. Figure 48 shows the different components of
the touristic water consumptions for a 3-star hotel.
Figure 48: Pattern of water use for a 3-star hotel (Ecologic 2008)
The biggest share is consumed in the rooms (37%) that comprises toilet, bath, shower and taps usage.
The next largest partition belongs to the water use in the kitchen (21%) for cooking and dish-washing.
Washbasins and toilet usage in public toilets amounts to 17%, and the laundry requires 12% of the
total water consumption. In Cyprus about 58% of the beds for accommodation pertain to five to one
star hotels whereas the remaining 42% comprise other options like apartments, guesthouses or
camping sides. Average water consumption in Europe per tourist is estimated to 300-350 liter per night
for hotels, 250-300 l in holiday houses, and 150-200 l in camping sites (Ecologic 2007). Based on
these numbers, the standard water demand in Cyprus would amount to about 310 l per night (assuming
350 l for 1-5 star hotels and 250 l for others). Savvides et al. (2001) estimate the daily water demand
of a tourist in the year 2000 to 465 liter per night. The gap between the standard value of about 350
l/p/d and the current of 465 l/p/d shows the high potential of water savings in the tourist sector in
Cyprus. Best practices show even lower water consumption of 213 l per overnight stay for hotels, 133
l for bed and breakfast, and 96 l for camping sides (Hamele and Eckardt 2006). Thus, for this Cyprus-
related study, the benchmark water demand is set to 180 l per overnight stay (assuming 213 l for 1-5
star hotels and 113 l for others). However, the eventual optimal value for water consumption is
enhanced slightly as the average touristic water demand in Mediterranean countries with 300-880 liter
per day is higher than the average demand for whole Europe (Plan Bleu 2004). Therefore, the optimal
water usage for Cyprus is set between 310 l (the European average from (Ecologic 2007)) and 180 l
(the European benchmark after Plan Bleu 2004) to 250 liter. Consequently, the water saving potential
amounts to 215 liter. It is estimated that 70% of this potential can be achieved by technological and
30% by behavioral measures. Behind this assumption lies the idea that the behavior of tourists can be
less affected in the short period of their holidays than domestic residents. Nevertheless, the water
saving technologies like dual flush toilets as well as widely applied awareness campaigns that aim at
103
the reduced laundering of towels require the participation of tourists. Eventually, these assumptions
imply a daily water consumption of 314.5 liter with 100% technical efficiency, and 400.5 liter with
100% conscious consumption. Of course, these values have to be discussed with stakeholders and
decision-makers. Nevertheless, the estimations are considered to be reasonable and realistic.
The installation of grey water could further reduce the water demand. Grey water can be used for toilet
flush or garden irrigation. If only the potential use of grey water in toilets is considered, about 24% of
the water demand of a type of hotel that is depicted in Figure 48 could be replaced.13
Cyprus-wide, this
share is estimated to amount to 15% of the total touristic water demand. The option of the installation
of grey water treatment plants is considered in the calculation of the tourism water demand.14
Table 2
shows the calculations of the optimal water demand and the current technological and behavioral
efficiencies.
Table 2: Calculation of the optimum technological and behavioral water demand in the tourism sector
Contemporary
daily water demand
per tourist
Average European
daily water demand
per tourist
Benchmark daily
water demand per
tourist
Cyprus specific
optimal daily
water demand per
tourist
Water saving
potential per tourist
per day
[liter] [liter] [liter] [liter] [liter]
465 310 180 255 210
Daily water
consumption with
100% technical
efficiency
Water savings at
100% conscious
consumption
Demand that can
be satisfied by
recycled water
Cyprus specific
optimal water
consumption
inclusive grey water
recycling
[liter] [liter] [liter] [liter]
318 402 38.25 216.75
Thus, the optimal technological water demand 𝑇𝐷𝑜𝑝𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 is set to 318 l/d/cap, and the optimal
behavioral demand 𝐵𝐷𝑜𝑝𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 to 402 l/d/cap. The technological reference efficiency for the year 2000
is computed by the following equation:
𝑅𝑇𝐸2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 =
𝑇𝑜𝑝𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚
𝐶2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 =
318
465= 68.4% (79)
Analogous the reference behavioral efficiency:
𝑅𝐵𝐸2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 =
𝐵𝑜𝑝𝑡𝑇𝑜𝑢𝑟 𝑖𝑠𝑚
𝐶2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 =
402
465= 86.5% (80)
13 For the calculation, it is assumed that in the rooms water is used in shower, toilets, baths and washbasins; in
the public toilets water is used for the toilet and washbasins. Including the shares of water usages in the
domestic sector deliver a share of 28% of water usage for toilets in rooms, and 80% in washrooms. 14 This aspect was omitted in equation 76 for clarity reasons. Thus the per capita demand including grey water
recycling 𝐶𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 +𝐺𝑅 is calculated as follows: 𝐶𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 +𝐺𝑅 = 𝐶𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 −0.15 ∙ 𝑅𝐷2000
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 ∙𝐺𝑇𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚/100
104
The behavioral efficiency at time t 𝐵𝐸𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 and the technological efficiency 𝑇𝐸𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚 are
computed correspondingly to the domestic sector (see Figure 44). For shortness reasons, the
presentation of the calculation is omitted. Treated grey water could also be used for irrigation of
touristic green areas or gulf courses. These water requirements are not included in the touristic potable
water demand as usually rather treated sewage or water from wells are used for these purposes
(Savvides et al. 2001). Therefore, in contrast to the domestic sector where drinking water is used for
garden irrigation, the touristic potable water demand would not decrease if water for irrigation is
received from grey water plants. Thus, the non-potable water demand of the tourism sector is
separately presented in the subsequent paragraph.
Savvides et al. (2001) specify the water demand for landscaping (𝐷2000𝐿𝑎𝑛𝑑 &𝐴𝑚𝑒𝑛 ) to 8.5 Mm³ (excluding
drinking water used for irrigation).15
The water is used in house gardens, municipal landscape areas,
hotels, and playgrounds. Due to lack of data, it is assumed that 40% of this water amount is consumed
by the domestic sector (e.g. gardening, public green spaces), whereas 60% is related to the touristic
activities (e.g. golf courses, or amenities). Therefore, the GDP for the Tourism sector 𝐺𝐷𝑃𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 and
the total economy𝐺𝐷𝑃𝑡𝑇𝑜𝑡𝑎𝑙 are normalized with the respective values in the year 2000. The formula
appears as follows:
𝐷𝑡𝐿𝑎𝑛𝑑 &𝐴𝑚𝑒𝑛 = 0.6 ∙ 𝐷2000
𝐿𝑎𝑛𝑑 &𝐴𝑚𝑒𝑛 ∙𝐺𝐷𝑃𝑡
𝑇𝑜𝑢𝑟𝑖𝑠𝑚
𝐺𝐷𝑃2000𝑇𝑜𝑢𝑟𝑖𝑠𝑚 + 0.4 ∙ 𝐷2000
𝐿𝑎𝑛𝑑 &𝐴𝑚𝑒𝑛 ∙𝐺𝐷𝑃𝑡
𝑇𝑜𝑡𝑎𝑙
𝐺𝐷𝑃2000𝑇𝑜𝑡𝑎𝑙 (81)
Thus, as the tourism sector growths also the irrigation water demand growth at the same rate. The
function values are normalized to GDP and water requirement for the year 2000 respectively. The
domestic influence is measured by the growth of the total GDP. Higher wealth implies an increase in
the demand for amenities like public green space or flower beds.
The water demand for landscaping & amenities is added to the agricultural water demand in order to
calculate the overall irrigation water demand:
𝐷𝑡𝑙𝑎𝑛𝑑𝑠𝑐𝑎𝑝𝑖𝑛𝑔 &𝑎𝑚𝑒𝑛𝑖𝑡𝑖𝑒𝑠
+ 𝐷𝑡𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
= 𝐷𝑡𝑖𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛
(82)
The rate of recycled water usage in the touristic sector is merged with that of the agriculture sector.
The calculation is presented in Chapter 4.4.5.2 in detail.
4.4.4.3.3 Agriculture Water Demand
The agriculture sector is the major water consumer in Cyprus with a demand of 183.4 Mm³ in the year
2002. This is a share of 69 % of the overall water demand (Savvides et al. 2001). The conceptual
model structure of the agricultural sector is reminiscent of the one of domestic and tourism sector (see
Figure 49). The reference water demand of agriculture 𝑅𝐷2000𝑎𝑔𝑟𝑖
[𝑚3
𝑎∙𝑦𝑒𝑎𝑟]) is taken from the FAO report
(Savvides et a. 2001) in the year 2000 and amounts to 5948 m³ per ha. Changes in this value are
mainly dependent on the crop types that are planted. Therefore, in the Agriculture sector the behavioral
efficiency (𝐵𝐸𝑡𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
[%]) pertains mainly to the planted crop type and the efficient usage of the
irrigation devices. Farmers can adapt to water scarcity by changing their crops to less water intensive
ones, and apply irrigation techniques in an optimal way. The technological efficiency comprises the
15 Actually 14.1 Mm³ are stated as the water demand for landscape irrigation. However, 5.5 Mm³ has been
already included in the value for the domestic water demand (see Savvides et al. 2001, p. 56).
105
conveyance and field application efficiency (𝑇𝐸𝑡𝐴𝑔𝑟𝑖
[%]). Rising GDP values for the sector
(𝐺𝐷𝑃𝑡𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
[bn€]), are connected with the planting of profitable crop types (expressed through
0000000
Figure 49: Structure of the calculation of the agriculture water demand
the variable 𝑃𝑃𝐶𝑡 ) that are not necessarily the most water-efficient ones. This aspect was also stated in
the participatory model building. The technological efficiency embraces the applied irrigation and
conveyance technology. The sectoral GDP influences the planted area at time t (𝐴𝑡 , [ha]), in
interaction with the ratio of GDP per ton output (𝑃𝑇𝑡 [€/ha]).
The per ha water demand 𝐷𝑡𝑎 is calculated by the following formula:
𝐷𝑡𝑎 = 𝑅𝐷𝑡
𝑎𝑔𝑟𝑖 𝑅𝐷2000
𝐴𝑔𝑟𝑖 − 𝑅𝐷2000
𝐴𝑔𝑟𝑖− 𝑇𝐷𝑜𝑝𝑡
𝐴𝑔𝑟𝑖 ∙ 𝑇𝐸𝑡
𝐴𝑔𝑟𝑖− 𝑅𝐷2000
𝐴𝑔𝑟𝑖 −𝐵𝐷𝑜𝑝𝑡
𝐴𝑔𝑟𝑖 ∙ 𝐵𝐸𝑡
𝐴𝑔𝑟𝑖 ∙
1 + 𝑃𝑃𝐶𝑡 − 1 ∙ 1 − 𝐶𝐴𝐶𝑡 (83)
Where: 𝑇𝐷𝑜𝑝𝑡𝐴𝑔𝑟𝑖
= water demand with optimal technology in m³/ha; 𝐵𝐷𝑜𝑝𝑡𝐴𝑔𝑟𝑖
= water demand with
optimal behavioral efficiency in m³/ha that is reflected in the planting of adapted crop types; 𝑃𝑃𝐶𝑡=
Planting Profitable Crops at time t reflects the factor by which 𝑅𝐷2000𝐴𝑔𝑟𝑖
is increased through economic
growth of the agriculture sector; 𝐶𝐴𝐶𝑡 = Choice of Adapted Crop types at time t is influenced by the
behavioral efficiency 𝐵𝐸𝑡𝐴𝑔𝑟𝑖
and countervails the variable 𝑃𝑃𝐶𝑡 .
The optimal water demand per ha with optimal application of technology and choice of crop types is
calculated as follows. The conveyance efficiency in Cyprus amounts to 90-95% and the field
application efficiency to 80-90% (EEA 2001). These high water use efficiencies are caused by high
efforts of the government which promoted micro-irrigation systems in the past by information
campaign, as well as subsidies and low-interest loans for investments in irrigation technologies.
Eventually, the irrigated agriculture land with inefficient surface irrigation systems decreased from
13400 ha in 1974 to less than 2000 ha in 1995, whereas the area of micro-irrigation increased from
2700 ha to almost 35 600 ha in the same period (EEA 2001).
AgricultureSector
Agriculture Water
Demand
BIP per Area
Per ha Water
Demand AgricultureTechnical Efficiency
Agriculture
Optimal Behaviroral
Efficiency Agriculture
Optimal Technical
Efficiency Agriculture
<Years>Effective Area
Planting of
Profitable Crops
<Behavioral Efficiency
Agriculture>
Animal Husbandry
<Years>
Reference WaterDemand Agriculture
2000
Choice of Adapted
Crop Types
106
The technological potential for the agriculture sector to save water is set to 20% in the model.16
In
contrast to the domestic and tourism sector, the recycling of wastewater is not considered as a
reduction of water demand, but as a separate water source. This is due to the fact that wastewater
treatment in tourism and households happens within the sectors. Grey water recycling plants are
installed in the households or hotels and lead, consequently, to a reduced demand for potable water
from the water boards. In the agriculture sector, the wastewater recycling is not endogenous, but
exogenous, as the effluents stems from the domestic sector and has to be treated in sewage plants.
Therefore, the technological water saving potential pertains solely to conveyance and field application
efficiency in the agriculture sector.
Changes in the planted crop types have additional potential for water saving besides the
technological measures. In the participatory model building, traditions and specialization of farmers
have been stated as the main impediments for changes in the planted crops. The farming technologies
are adjusted to the respective crops and realignment would require extensive investment costs. Also,
the changes require stepwise restructuration as many crops are not profitable for several years after
planting. For instance, the planting of olives is rentable after 9 years, if only the variable costs are
considered (Agriculture Service 2008). Besides these economical, also traditional aspects influence the
choice of crops as the producers‟ families or work-lives could bear relation and knowledge to a
specific plant type.
Thus, the behavioral efficiency variable is related to the planting of adapted crop types as social
processes are a major impediment to their implementation. The economic costs of restructuration can
be inserted in a later version of the model and is highly recommended for future research.
Nevertheless, an optimum crop pattern depends on many factors as the market price of agriculture
products or the properties of soils that narrow down potential alternatives. However, the participatory
model revealed that especially the planting of oranges, bananas and potatoes has been considered as
problematic, due to the high water demand of Oranges with 7326 m³ per ha and bananas of 11035 m³
per ha (calculated by numbers from Savvides et al. 2001), and the high export share of potatoes with
about 70% of the total production (Statistical Service 2005). The criticism of the export of water
intensive crops from semi-arid or arid countries is connected to the concept of Virtual Water from
Tony Allan (1993) that links commodities to their water requirements in the production process. Thus,
exported crops can be referred to the water that is required for their production. By exporting potatoes
and citrus products (about 50% of the Citrus production is exported), Cyprus uses high quantities of
the extremely scarce resource water to produce for water-rich countries like the United Kingdom or
Germany. For instance in the year 2005, 25% of the potatoes exports were destined to the UK and
Germany respectively.
In the model, the most optimal behavioral water consumption is assumed for a crop pattern in
which the area of Citrus and banana production has declined by 40% and are replaced by Olive trees
as the conversion of citrus and banana to olives plantations has been proposed by several participants
and in literature (cp. Socratous 2005). Conversions in the potatoes area is not considered as this would
imply a reduction of the added value as exports are highly rentable. The resulting water savings can be
examined in Table 3. Thus, the optimal behavioral demand 𝐵𝐷𝑜𝑝𝑡𝐴𝑔𝑟𝑖
amounts to 146.8 Mm³/year. The
behavioral reference efficiency is computed by the following equation
16 The Ecologic – Report (2007, p.184) shows a 23.7%-potential for water saving in the agriculture sector by
combining different technical measures that are not specified in detail.
107
𝑅𝐵𝐸2000𝐴𝑔𝑟𝑖
=𝐵𝐷𝑜𝑝𝑡
𝐴𝑔𝑟𝑖
𝑅𝐷2000𝐴𝑔𝑟𝑖 =
146 .8
161 .3= 91.0% (84)
Table 3: Defining reference of changes in planted crop types
Crops
Water
Demand
(2000) Area (2000)
Water
demand
2000 per ha
Area after
the assumed
conversion
Resulting
water demand
[Mm³] [ha] [m³/ha] [ha] [Mm³]
A B E D F
Citrus 51.9 7083.9 7326 4250.34 31.14
Olives 8.5 1984.5 4283 4934 21.13
Bananas 3.21 290.9 11035 174.54 1.93
Vegetables 38.4 6418.1 5983 3850.86 23.04
Potatoes 12.8 4269.8 2998 4269.8 12.80
Greenhouses 2.9 320.8 9040 320.8 2.90
Others 43.59 6752.2 6456 6752.2 43.59
Substitute Vegetables: 4000 2567 10.27
SUM 161.3 27120.2 5948 27120.2 146.80
The technological reference efficiency is set to 80%:
𝑅𝑇𝐸2000𝐴𝑔𝑟𝑖
=𝑇𝐷𝑜𝑝𝑡
𝐴𝑔𝑟𝑖
𝑅𝐷2000𝐴𝑔𝑟𝑖 = 80.0% (85)
Thus, the optimal technological water demand 𝑇𝐷𝑜𝑝𝑡𝐴𝑔𝑟𝑖
= 0.8 ∙ 161.3 = 129.04 Mm³/year.
For restrictions in the scope of the diploma thesis, the calculation of the behavioral efficiency at time t
𝐵𝐸𝑡𝐴𝑔𝑟𝑖
and the technological efficiency 𝑇𝐸𝑡𝐴𝑔𝑟𝑖
are not specified. They are computed analogously to
the domestic sector (see Figure 44).
Of course, other suggestions for potential water savings through changes in crop patterns or behavior
of farmers can be inserted and tested if they are proposed by stakeholders. The values in table 3 are
therefore considered as a possible option out of many. Also, a scenario could be tested where the water
demand per ha is varied in order to calculate the reduction in water consumptions that is needed for the
avoidance of water scarcity in future. However, as various stakeholders stated in the interviews, the
choice of planted crops are dependent on market prices and costs of production. For instance, the
replacement of citrus by olive trees is not profitable due to the higher variable costs of olive planting.
Especially labor costs are about 2.5 time higher for olives. In comparison to bananas, the gross
revenue per ha of olives is about 40% less (Agriculture Service 2008). Hence, many participants
regarded the rationing of water more efficient than economical measures like increases in price.
Finally, the agriculture water demand is computed as follows:
𝐷𝑡𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒
= 𝐷𝑡𝑎 ∙ 𝐴𝑡 ∙ 10−6 + 𝐷𝑡
𝐴𝐻 (86)
Where: 10−6 = conversion factor from m³ to Mm³; 𝐷𝑡𝐴𝐻= water demand of animal husbandry in
Mm³/month. This value is inserted by exogenous data. In the year 2000, the water demand was
assumed to be 8 Mm³/year (Savvides et al. 2001).
108
4.4.5 Model testing
The model was tested by different methods (see Chapter 3.4.2.2). First, an extreme conditions test (i.e.
no precipitation) was conducted in order to assure that stock and flows do not fall below zero. Second,
parameters were assessed by comparison of model outputs and measured data. However, nationwide
data for runoff, baseflow, or percolation processes are seldom published and also connected with high
uncertainties. Available data series were therefore gathered and qualitatively compared to simulation
results. Statistical tests like R² are considered to be inapplicable as the measured data itself is highly
uncertain. In the end, the stakeholder group has to decide which data is considered to be reliable and
whether the simulation results are reasonable.
For the parameter assessment, the Cyprus-wide water balance was used for the calibration
procedure (see Appendix I for a graphical representation). An annual mean precipitation of 480 mm
accordingly causes about 2300 Mm³ evapotranspiraton, 190 Mm³ inflow to the surface water storage,
and 180 Mm³ inflow to the aquifer.17
The capacities and maximal flows in the hydrological model
were adapted to these values (see Table 4). For this model test, the hydrological system was separated
from the allocation and participatory model to allow the straightforward assessment of hydrological
parameters.
In addition to the conformity to the water balance data, annual mean runoff data from Rossel
(2002) was compared to the sum of simulated baseflow and runoff. The inter-annual runoff
distribution was therefore multiplied by the annual runoff data and depicted along with the sum of the
simulated values for runoff and baseflow. Figure 50 depicts the result:
Figure 50: Comparison of simulated (blue graph), and measured data (red graph)
The graph shows that substantial further efforts are needed in order to achieve the fit of the simulation
results with the measured data. A qualitative correlation can however be attested. This rather
subjective judgment was discussed with modelers at the Institute of Environmental Systems Research
at the University of Osnabrück. The next step would be the questioning of experts at the Water
Development Department in Cyprus. These subjective expert opinions are an accepted procedure for
model testing (cp, Sterman 2000, Sargent 1998). In the end, the participants of a future group model
17
The aquifer recharge flow of 45 Mm³ from surface waters has been assigned directly to the groundwater
inflow.
VALIDATION RUNOFF
120
120
90
90
60
60
30
30
0
0
1975 1980 1985 1990 1995 2000
Years
Runoff plus baseflow : 7
Validation Runoff : 7
109
Water Scarcity Indicators + Published Water Shortage
1
1
1
1
0.5
0.5
0.5
0.5
0
0
0
0
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Years
Water Shortage Dams : run4
Water Scarcity Agri : run4
"Water Scarcity Dom+Tour" : run4
Water Scarcity Agriculture Rationing : run4
building have to decide if a more precise hydrological model is needed, or if the results are sufficient
for the purpose of the model. The water scarcity indicators are central results from the interplay of the
hydrological and allocation model. The comparison of the water scarcity indicators to publicized data
on water shortages in Cyprus in the past is not easy. The model indicates insufficient water volumes in
the natural water storages so that the demand can not be satisfied. The decision-makers at the WDD
would anticipate real water shortages in dams and induce countermeasures like water rationing early
enough in order to avoid the drying out of the surface water storage. Hence, the real water scarcity
levels would be increased artificially in order to maintain a desired level in the water storages and
avoid a total breakdown of water supply.
A reasonable water balance model should indicate droughts in case of dry periods which have
been experienced in the past. The water scarcity indicators in the current model version therefore
consider the balancing effect of the interventions of decision makers by a delay function which
balances the water scarcity indicator and avoids wild fluctuations (see Chapter 4.4.3.3). A more refined
decision rule that could be implemented in a future version is the rationing of water. Thus, the
decision-maker withholds available water in order to avoid the depletion of water storages. A simple
rule was tested in the model in which the rationing in the domestic sector is constrained to 15% of the
water demand whereas the agriculture sector is rationed at far higher rates (see Appendix J).18
This
example shall merely demonstrate a possible approach to include decision-making processes. More
sophisticated decision-rules that underlie the management of droughts in Cyprus are not included in
the model for the time being, but could be a future improvement.
In Figure 51, published water shortages in the Governmental water supply projects are depicted
from 1989 to 1999 in conjunction with the model results (Tsiortis 2001). The gray graph represents the
simple rationing decision-rule which is presented in Appendix J and underlines the possibility to
reproduce measured water scarcity levels in the past in a later model version.
The results show again a qualitative correlation between simulated and experienced water shortages in
the past and are therefore considered sufficient for a preliminary model.
18 The maximal water rationing level of 15 % has been stated in the interviews. This upper value reflect technical
restrictions as well as public health considerations.
Figure 51: Water scarcity indicators and the published water shortages for Governmental water supply projects
110
Finally, the parameters have been set based on the results of the model testing procedures:
4.4.6 Scenario Analysis
Five scenarios are implemented in which the measures for desalination, wastewater recycling,
technological efficiency and conscious consumption are tested. Future precipitation data can be
included from results of climate models, or set arbitrarily by the use of a table function. Regional
precipitation levels from 2000 to 2045 are presently computed by the PRECIS (Providing Regional
Climates for Impact Studies) Regional Climate Model and will be available soon. The PRECIS model
simulates climatic data at high spatial resolutions of 25x25 km grids and can therefore provide data on
the regional level (for a description of the model, see Jones et al. 2004). Cyprus is covered by 14 grid
boxes so that the calculation of regional and national climatic data is possible (Hadjinicolaou et al.
2004). Precipitation data is currently available for the period from 1980-2000, 2040-2059 and 2080-
2099. Cyprus-wide annual precipitation rates from 2040 to 2050 under the A1B scenario are included
in the water balance model which amounts to 363 mm on average.19
The A1 storyline assumes a rapid
economic development with the fast implementation of new and more efficient technologies, and a
peak of the global population around 2050. A1B further assumes the balance of fossil fuel and non-
fossil energy resources (A1B) (IPCC 2007). Hence, the precipitation rates for the time period from
2010 to 2040 have to be estimated. It is assumed that the annual average rainfall decreases to 420 mm
over the period with a inter-annual variability that has been experienced in the past. The rainfall
pattern from 1975 to 2005 has therefore been multiplied by a reduction factor and projected onto the
future time period from 2010 to 2040 (see Appendix K for the specific precipitation rates). Other
precipitation patterns and levels can be easily tested by variation of the table function. At this point, a
future improvement beside the input of data from climate models could be the inclusion of a weather
generator model which simulates the climatic processes based on local and global climate models (cp.
Sharif and Burn (2004), and Prodanović and Simonović (2007)). For the time being the reduced
historical rainfall patterns are considered an adequate estimation, as stakeholders can relate to
experienced climatic conditions easily. 20
As explained in Chapter 3, the reference mode of behavior is central to system dynamics
simulations and denotes a set of graphs that describes the problem situation and the underlying
dynamic behavior of the system. The following reference modes, consisting of four sets of graphs,
were chosen for modeling the water scarcity problem in Cyprus. The first diagram delineates the water
scarcity indicators for the domestic, tourism, and agriculture sectors in connection with the annual
19
The data were kindly provided by Panos Hadjinicolaou from the Cyprus Institute in Nicosia. 20
A related question could be: „What would be the consequences of a recurrence of the experienced drought in
the year 2000 in about 30 years?‟, or „Would measures that we plan to implement prevent water scarcity
levels that we have faced in the past?‟
Maximal Flows:
𝐼𝑛𝑓𝑖𝑙𝑚𝑎𝑥 = 600 Mm³/month
𝑃𝑒𝑟𝑐𝑆𝑜𝑖𝑙 ,𝑚𝑎𝑥 = 150 Mm³/month
𝑃𝑒𝑟𝑐𝐴𝑞𝑢𝑖𝑓𝑒𝑟 ,𝑚𝑎𝑥 = 400 Mm³/month
Storage capacities:
𝑆𝑜𝑖𝑙𝑚𝑎𝑥 = 957 Mm³
𝐺𝐿𝐼𝑚𝑎𝑥 = 1000 Mm³
𝐴𝑞𝑢𝑖𝑓𝑒𝑟𝑚𝑎𝑥 = 4600 Mm³
Flow ratios:
𝑠𝑃𝑒𝑟𝑐𝑂𝑐𝑒𝑎𝑛 = 0.35
𝑠𝑏𝑎𝑠𝑒𝑓𝑙𝑜𝑤 = 0.13
𝑠𝐴𝑞𝑢𝑖𝑓𝑒𝑟 = 0.02
𝑠𝑅𝑡𝑜 𝑆𝑒𝑎 = 0.05
𝑠𝐵𝑡𝑜𝑆𝑒𝑎 = 0.05
𝑠𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝑊𝑎𝑡𝑒𝑟 = 0.13
Table 4: Parameters from model testing
111
precipitation levels. The second diagram contains the water storage levels for natural water sources,
which are aquifer and surface water storages. The third set of graphs marks the technological measures
for the increase in water supply that are the desalination and wastewater capacities. It also shows the
real recycling rate, which is the share of sewage from industry, tourism and domestic sector that is
recycled. Thus, an overcapacity of wastewater treatment plants can be detected as it is assumed that
only 80% of the total sewage volume can be recycled. The water demands of the sectors are finally
depicted in the fourth diagram. They are partly the results of investments into technological and
behavioral efficiency, or increases in per capita and per ha consumption due to economic growth.
All scenarios assume a future annual growth rate of the total real GDP of 2%, an annual growth in
the tourism sector of 1.5%, and a stable agriculture sector. This means that nominal GDP values of
agriculture are still increasing, but in the range of the inflation rate. For the past, GDP data is inserted
from the Statistical Service (2005 and 2009) and the International Monetary Fund (1999).21
The scenario 1a implements the measures that are planned in the future or have already been
implemented and are assumed to be constant. Thus, the desalination capacity of 44.52 Mm³ per year in
2009 remains constant until 2050, and the wastewater recycling capacity is increased to 85Mm³ in
2025 and stays stable until 2050 (Yiannakou 2008). The behavioral and technological efficiencies of
the agriculture, domestic and tourism sectors show only slow improvements until 2050. They are
assumed to develop linearly over the covered period of time (the concrete values are specified below).
Hence, no peculiar efforts are devoted to the improvement of efficiency in water usage. Furthermore,
the recycling rates for the agriculture sector increase linearly from 71% in 2000 to 80% in 2050.
Scenario 1b builds upon these results and tries to solve the future water scarcity by supplying
management measures like wastewater recycling and desalination. The wastewater recycling capacity
and the desalination capacity are therefore increased until future agricultural and domestic water
scarcity is dissolved. This scenario tries to answer the question how much desalination and wastewater
recycling is necessary to avoid water scarcity in the future.
Scenario 2a applies improvements in the technological efficiency for the agriculture, domestic and
tourism sectors. However, the behavioral efficiency is assumed to proceed to the same values as in
scenario 1. Hence, no extra efforts are devoted to conscious consumption. The capacities for non-
conventional sources are the same as in scenario 1a.
Scenario 2b builds upon scenario 2a and assumes improvements in the behavioral efficiency and
technological efficiency together. Therefore, awareness campaigns and consumer education are
conducted, and incentives to invest in water-saving devices are given. Finally in scenario 2c, the
desalination and wastewater capacities are adapted to the estimated water scarcity situation in the
future.
In the remainder of the chapter, the results and specifications of the scenarios are presented in
detail. The complete reference modes of behavior for each scenario are depicted in Appendix L. Only
the most important graphs are shown below.
Scenario 1a
For scenario 1a, the technological and behavioral efficiencies are set to the values depicted in Table 5.
21
The calculation procedure is as follows: On the basis of the National Accounts figures of the Statistical
Service (2009) from 1995-2008, GDP values are calculated by the use of past growth rates of real GDP (IMF
1999). The sectoral GDP for the agriculture sector is computed by the uses of data from the Agriculture
Statistics. The tourism sector is assumed to follow the same growth rates as the total GDP as specific data are
not published for the period prior to 1995.
112
Table 5: Assumed increases of technological and behavioral efficiencies
An even increase of the values is assumed, because outdated technology is replaced by more modern
and water-efficient devices. Gray water treatment plants are implemented at low rates in the domestic
and tourism sector, too. Figure 52 depicts the resulting water demand of agriculture, domestic sector
and tourism.
Thus, the domestic water demand increases to the maximum value of 8.4 Mm³ per month in the year
2050. The agriculture sector demand stays stable and finishes at 13.1 Mm³/month in 2050. The tourism
sector demand increases exponentially and ends with a maximum demand of 4.1 Mm³/month in
summer 2050.
The desalination and wastewater treatment capacities are set to the values that are estimated for
the future and have been extracted from literature research. In Appendix L, the capacities are depicted
over time. Are these measures sufficient to prevent water scarcity in the future? Figure 53 shows the
water scarcity indicators from 1975 to 2050 for the domestic and agriculture sector in connection with
annual mean rainfall rates. Both the potable and non-potable water demand still faces water shortages
until 2050 despite all supply management measures. However, the water scarcity indicators for the
agriculture sector and for the potable water supply show a decreasing tendency due to the application
of recycled water and desalination until 2030. Abrupt declines in the precipitation rate in the period
from 1949 to 2050 induce a high water scarcity up to 75% in the agriculture and 50% in the domestic
sector. The remaining water scarcity in the future is caused by depleted groundwater storages,
decreasing rainfall levels that do not fill the dams to their capacity, and increasing demand of tourism
and the domestic sector (see Appendix L for the graphs of the ground- and surface water storage).
Year 𝐵𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 TEt
Domestic BEtTourism TEt
Tourism BEtAgri 𝑇𝐸𝑡
𝐴𝑔𝑟𝑖 𝑅𝑅𝑡
𝐴𝑔𝑟𝑖 𝐺𝑇𝑡
𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐺𝑇𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚
2000
[%] 73.7 65.3 86.5 68.4 91 80 71 1 2
2050
[%] 80 72 88 75 93 85 80 5 10
Figure 52: Water demands in scenario 1a
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
113
Supply Management
150 Mm³/Year
150 Mm³/Year
1
112.5 Mm³/Year
112.5 Mm³/Year
0.75
75 Mm³/Year
75 Mm³/Year
0.5
37.5 Mm³/Year
37.5 Mm³/Year
0.25
0 Mm³/Year
0 Mm³/Year
0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Desalination Capacity Mm³/Year
Wastwater Capacity Mm³/Year
Recycling Rate real : run1
Scenario 1b
In scenario 1b, the wastewater and desalination capacities are lifted to levels that prevent water
shortages from 2015 onwards. This scenario is based on the policy of major investments in
unconventional water sources. Figure 54 shows the final desalination capacities that would deliver
sufficient potable water for the domestic and non-potable water to the agricultural sector.
In 2019, the desalination capacity is increased by 10 Mm³ to a total capacity of 55 Mm³ per year.
Major increases in capacity are implemented in 2037 to 85 Mm³/year and in 2047 to 110 Mm³/year. By
doing this, water scarcity in the domestic sector is avoided. The wastewater treatment capacity is
simultaneously increased from 85 to 105 Mm³ per year in 2032. In 2040, the capacity grows further to
120 Mm³ per year. The potential volume for water recycling is limited to 80% of the used water from
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run1
"Water Scarcity Domestic + Tourism" : run1
Annual Precipitation Data : run1 mm/Year
Figure 53: Annual precipitation levels and water scarcity indicators for scenario 1a
Figure 54: Annual capacities of non-conventional water sources in scenario 1b
114
Table 6: Assumed increases in
technological efficiencies
tourism, the domestic sector and the industrial sector in this model. 20% of the wastewater is assumed
to be polluted, technically not recoverable or economically unprofitable for agricultural reuse. The
planned capacity is thus already at the maximal amount, as the recycling rate graph illustrates. The
annual variations of sewage due to touristic fluctuations are almost nonexistent after 2018. This points
to a capacity which is approaching its maximum.
These supply-centered measures are effective for the avoidance of water scarcity as can be seen in
figure 55.
Scenario 2a
In scenario 2a, demand management becomes more important, due to major investments into
technological developments in the domestic, tourism and agriculture sectors. However, the
technological optimums that have been described in Chapter 4.4.4.3 are not fully utilized. More
realistic values are assumed here, as 100% technological efficiency is hard to realize due to the life
cycles of older technologies that are awaited until investments into water-saving devices are taken (see
Table 6). Major investments like gray water treatment plants are
also not affordable for every household even though the
government could support them by subsidies or low-interest
loans. The resulting water demand based on theses values is
depicted in Figure 56. The domestic and touristic water demands
are still increasing due to the reinforcing effect of economic
development on the per capita demand, and increasing numbers
of both residential and short-term population. The touristic water
demand increases to 3.4 Mm³/month in 2050 (scenario 1a
𝐷𝑚𝑎𝑥𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = 4.1), the domestic demand has its peak in 2050 with
7.0 Mm³ (scenario 1a 𝐷𝑚𝑎𝑥𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 = 8.4), and the agriculture
sector shows a slight decline to 11.8Mm³/month in 2050 (in
scenario 1a 𝐷2050𝐴𝑔𝑟𝑖
= 13.1)) .
Year 2000 2050
[%] [%]
𝑇𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 65.3 85
𝑇𝐸𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 68.4 90
𝑇𝐸𝑡𝐴𝑔𝑟𝑖
80 95
𝑅𝑅𝑡𝐴𝑔𝑟𝑖
71 90
𝐺𝑇𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐
1 30
𝐺𝑇𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚
2 50
Figure 55: Water scarcity indicators for scenario 1b
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run1
"Water Scarcity Domestic + Tourism" : run1
Annual Precipitation Data : run1 mm/Year
115
Are these investments into technological efficiency useful to avoid water scarcity? The graph in Figure
57 shows that there is essentially no water scarcity between 2015 and 2040. In the severe drought in
the 2040s however, the water scarcity indicator depicts significant water shortages of 70% in the
agriculture and 35% in the domestic and tourism sector.
Scenario 2b Scenario 2b uses the same figures for the development of the technological efficiencies as scenario 2a,
but complements them with measures to foster conscious consumption in the tourism, domestic and
agriculture sectors. Optimal behavioral efficiencies are again not reached, as perfect implementation
by awareness campaigns, or water pricing is considered unrealistic. The chosen values are depicted in
Table 7.
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run4
"Water Scarcity Domestic + Tourism" : run4
Annual Precipitation Data : run4 mm/Year
Figure 56: Water demands with the application of water-saving technology in scenario 2a
Figure 57: Water scarcity indicators for scenario 2a
116
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run4
"Water Scarcity Domestic + Tourism" : run4
Annual Precipitation Data : run4 mm/Year
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
The total water demands of the domestic and agriculture sectors
are slightly decreasing, whereas the tourism sector still faces an
increasing trend (see Figure 58). The maximum water demand of
the domestic sector is reached in 2015 and decreases to 3.14
Mm³ per month in 2050 (compare to scenario 1a: 𝐷2050𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 =
8.4 ). These major savings are devoted to both, the efficient
application of technology and the decoupling of water
consumption from economic development. The tourism sector
water demand is still increasing steadily due to the growth in
tourist numbers, and the assumption that tourists are harder to persuade to save water than locals
because they are only staying in Cyprus for a very short period of time. One finds the maximal
monthly demand in 2050 with 2.8 Mm³ (compare scenario 1a, 𝐷𝑚𝑎𝑥𝑇𝑜𝑢𝑟𝑖𝑠𝑚 = 4.1 Mm³/month). The
agriculture water demand eventually decreases to an amount of 11.2 Mm³/month (compare to scenario
1a, D2050Agri
= 13.1 Mm³/month).
Year 2000 2050
[%] [%]
𝐵𝐸𝑡𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 77.9 90
𝐵𝐸𝑡𝑇𝑜𝑢𝑟𝑖𝑠𝑚 86.5 92
𝐵𝐸𝑡𝐴𝑔𝑟𝑖
91 98
Figure 59: Water scarcity indicators for scenario 2b
Figure 58: Water demands through application of demand management in scenario 2b
Table 7: Assumed increases in
behavioral efficiencies
117
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run2
"Water Scarcity Domestic + Tourism" : run2
Annual Precipitation Data : run2 mm/Year
These major water savings can still not impede water scarcity in all sectors with the desalination and
wastewater treatment capacities as they are estimated today (see Figure 59). Especially the agriculture
sector faces devastating water shortages up to 50% of the current demand. The domestic sector also
shows a water shortage of maximally 7%. The capacities of today would therefore not be sufficient to
avoid water rationing in the future.
Scenario 2c
Again, the model allows to test the adaptation of the desalination and recycling capacity to the water
scarcity indicators. Figure 60 shows the result of the model run in scenario 2c with minimum
capacities of non-conventional water sources for a sufficient water supply. Thus, the annual
desalination capacity is increased in 2038 from about 44.5 to 51 Mm³.
Supply Management
150 Mm³/Year
150
1
112.5 Mm³/Year
112.5
0.75
75 Mm³/Year
75
0.5
37.5 Mm³/Year
37.5
0.25
0 Mm³/Year
0
0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Desalination Capacity Mm³/Year
Wastwater Capacity
Recycling Rate real : run2
Figure 61: Water scarcity in scenario 2c
Figure 60: Reduced annual capacities of non-conventional water sources in scenario 2c
118
The wastewater recycling is limited as further increases would not yield additional quantities.
Decreasing water demands in the domestic and tourist sectors cause lower sewage volumes so that
even the wastewater recycling capacity of 85 Mm³/year turns out to be exaggerated. The recycling
capacity is thus reduced to 70 Mm³ which still implies the treatment of the full sewage volume from
the domestic, tourism and industry sectors. Figure 61 shows the effectiveness of these policies. Water
scarcity in the domestic and tourism sector can be avoided by a small extension of the desalination
capacity. Even in the extreme droughts between 2040 and 2050, the potable water supply is met. Only
the agriculture sector still encounters major shortages of up to 50%. The scope of action to sustain the
agriculture supply is limited as increases in the wastewater recycling capacity would not induce higher
recycling volumes.
4.4.7 Concluding comments
The results discussed in the previous sections of this chapter are useful for a wide variety of reasons.
Although the numbers obtained via the model are not exact predictions of the future water balance in
Cyprus, they reflect the systemic behavior of the water system in Cyprus in a qualitative way. In this
way, the model depicts the potential of system dynamic models to simulate intricate systemic
connections, as well as the outcome of a group model building process.
There are many important issues that can be analyzed by the system dynamics model developed in
this study. Examples include investigating extreme declines in precipitation levels, or long-lasting
droughts. As well, the sensitivity of „optimal‟ policies can be tested by varying uncertain variables like
annual precipitation or the growth rate of the economy. Such sensitivity analyses were not explored in
this research due to time and space restrictions, as well as the preliminary nature of the developed
model. However, this would be a useful area to explore in future studies.
The model that was developed in this study also allows for the exploration of policy
interconnections. For example, the connection between wastewater recycling and water demand
management serves as an example where policies that aim at different aspects (wastewater treatment
increases the supply side; while demand management tries to reduce water demand) are tightly
connected both in reality and in the model. In this example, decreasing the production of domestic and
tourist effluent in the scenario 2c also limits the maximum amount of wastewater recycling to 70 Mm³
per year. Also the synergies of desalination and water demand management are obvious. Whereas in
scenario 1b the desalination capacity of 110 Mm³/year is needed in order to avoid water scarcity, in
scenario 2c merely a small increase to 51 Mm³/year is needed. Consequently, the model helps to detect
the synergetic and inhibiting behavior of the interplay of policies in order to achieve a set of balanced
measures.
Besides assessing the effectiveness of water scarcity measures, other issues could be explored such
as costs or environmental externalities. The model could also be extended by including concrete
measures for fostering investments in technological efficiency, such as subsidies or water price
increases. Ultimately, however, it is the stakeholders who have to decide which processes they
consider to be important. The water balance model that was developed in this study is a „first attempt‟
at jointly exploring the effectiveness of different policies ranging from supply-centered measures of
desalination and wastewater recycling, to demand management with measures aimed at water saving
and technological efficiency.
Stakeholders might be irritated due to the model‟s complexity and mistrust the results. The stock
and flow structure with its underlying equations as well as the system dynamics method itself is not
straightforward initially and requires the active engagement of participants in order to achieve trust
and convenience. Without this knowledge, the independent testing of policies and variation of the
119
systems is hard to accomplish. This underlines the necessity to include stakeholders in the model
building process. As soon as confidence in the model is built, the sensitivity of policies can be tested
by qualitative and quantitative methods. In particular variables can be detected which decide on the
effectiveness of policies and the behavior of the system. In addition, uncertain variables like future
precipitation rates can be varied and so the effectiveness of measures in different problem situations
tested. Furthermore, the quantification of the model points to gaps in knowledge which could lead to
concentrated research efforts of scientific institutions in Cyprus.
An interface which allows the simple implementation of policies could be a further step of model
improvement. An example of such an interface for a management system dynamics model from a
participatory study in the United States is depicted in Appendix N (Stave 2003).
Eventually, the purpose of the model has been the demonstration of a possible outcome from a
participatory model building. Hence, the model requires the concerted efforts of stakeholders in a
participatory model building in order to develop a tool for water management and policy assessment in
Cyprus.
4.5 Outlook for future research
The preceding chapter presented preparatory steps which pave the way for a participative group model
building. The interviews and the qualitative and a quantitative model can serve as an entry point for a
future group meeting where the involved stakeholders meet and investigate the opportunity for a long
lasting participatory process. The facilitation of this process could be provided within the scope of a
Ph.D. thesis.
Before the actual beginning of the workshops, a report about the study will be established and
disseminated to all participants and other interested parties. In addition, follow-up interviews are
planned in order to discuss the outcomes and future plans with stakeholders. This could also comprise
the enlargement of the stakeholder group to organizations which have been stated to be important in
the questionnaires.
In the first workshop, the group has to decide if they want to utilize the preliminary system
dynamics model or if they want to start from scratch. The employment of a sub-model (e.g.
hydrological model, allocation model) is another option in order to find a starting point. For instance,
the group could decide to use the hydrological component, revise the allocation and extend the
participatory model. Refinements of the hydrological model could be commissioned to an expert
group since the processes are likely to be not controversial. Thus, the group could concentrate on the
more challenging task of structuring and quantifying the social, environmental, technological and
economic processes which underlie the problem of water scarcity in Cyprus. In the end, innovative
policies can be tested and a reasonable set of measures defined for all participating parties that, in sum,
show the way for a sustainable management.
5 Conclusions
Today the opportunities for sustainable management of water resources are better than ever. Extensive
knowledge and data about hydrological, environmental, economic and social processes in combination
with sophisticated technologies pave the way for concerted policies. However, the human factor in
particular impedes the application of optimal strategies and measures due to conflicting interests and
values in the economy and society. Additionally, the long-term assessment of measures is often
omitted since high uncertainties preclude definitive predictions. Therefore, policies aim more at short-
term success and avoid the consideration of future social or environmental adaptation processes.
120
Standard approaches like cost-benefit analysis try to find a common denominator for the different
aspects of a problem in order to select an optimal solution (cp. Maniak 2001). In order to gain
certainty in knowledge and general validity, ambiguous and complex processes are simplified through
disciplinary and abstract approaches. Thereby the complexity of the problem is replaced by the
certainty of standardized methods (Gunderson 1999). Case-dependent inquiries of complex tasks (e.g.
for environmental impact assessments) often cause significant delays in projects as findings can be
questioned and attacked easily. In addition, the knowledge that is used in decision making processes is
usually limited to findings from science and the experiences of decision-makers and related
institutions. Moreover, the real underlying problems of water resource issues are debatable as the
perspective of decision-makers can diverge considerably from these of water users. Hence, decisions
are made without the direct inclusion of interests, values and point of views by stakeholders.
The precondition for sustainable water resource management is an integrated and adaptive
approach which investigates the relevant social, economic, technical and environmental processes. In
addition, the participation of stakeholders is required in order to enhance the knowledge base about the
system and achieve collaboration. The theories and methods that are discussed and used in this thesis
are in accordance with the principles of holistic and participatory water resources management.
Theories about complex and adaptive systems define the nature of the central subject of the thesis.
The optimization of policies is not possible in complex adaptive systems as the prediction of effects
cannot be achieved with absolute certainty. Transformation of the system structure could change the
situation completely and render optimal measures into ineffective ones. Hence, a learning paradigm
has to be implemented which makes the management of water resources flexible enough to react to
unique and sometimes unanticipated problem situations. Adaptive management aims at the facilitation
of learning organizations which comprise the decision-maker and other stakeholders of the problem
situation. Participation is needed to make concerted action possible and generate the maximum amount
of available knowledge for the assessment and implementation of appropriate policies. Therefore,
problem frames are adapted to their respective situations by a communicative reframing process
(Drake Donuhue 1996). In addition, systemic methods help to elicit the underlying causes of the
situation and possible high-leverage policies for their improvement. Besides this more content-focused
outcome, participatory processes also enhance the social capital of the stakeholder group which
denotes the ability to solve problems by cooperation (Pahl-Wostl et al. 2007).
The task of integrating the knowledge and participation of stakeholders requires a framework
which structures and guides the process. The concept of post-normal science specifies the
epistemological challenges and approaches for case-specific management of complex problem
situations (Funtowicz and Ravetz 1993). Disciplinary and uncertainty-avoiding approaches of the
natural sciences are not suitable for complex problems such as those that are often encountered in
water resources management. In fact, participatory methods based on systems science are required to
solve problems with high uncertainties and diverging stakeholder interests. Systems theory serves as
an appropriate meta-theory as it is not limited by a discipline and field of application. Rather, it allows
for problem-centered and systematic investigations of causes and effects (Checkland, Holwell 1998).
Participatory model building is considered to be particularly suitable to guide participatory
processes for water management issues. Besides the gain in social capital, the decision-maker gets a
concrete outcome in form of a simulation model. The purpose of the model building is the facilitation
of a problem-focused discussion of stakeholders. Therefore, the system is qualitatively depicted by the
use of causal loop diagrams, which can be converted into a quantified simulation model at a later
stage. Policies are tested and outcomes assessed in a model structure which builds on the mental
models of participants. Interdisciplinary processes can be included in the model which induces a
121
holistic perspective on the problem at stake (Vennix 1996).
Systems thinking and system dynamics are applied in this participatory process, as these methods
are accessible to anyone regardless of their level of education due to their user-friendly and intuitive
concepts. Whereas the systems thinking method pertains to qualitative systems approaches, system
dynamics is viewed as the inquiry of systems by quantified simulations. The building of a simulation
model requires only basic mathematical knowledge as relationships can be entered by a graphical
interface. Concepts like exponential growth can also be included through the structural stock and flow
designs of the system. The concept of feedback loops is another tool for inquiry of the dynamic
behavior of systems. Balancing loops strive to equilibrium, whereas reinforcing loops lead to
instability. Ultimately, the interplay of feedback loops and the stock and flow structure determine the
behavior of the system. Quantitative models base on the best-available data and knowledge of the
system. The outputs of simulations are compared to the reference modes of behavior which are
measured or estimated data and graphs from the real world system. Gaps between simulated and
experienced system behavior lead to a revision of the model structure. This stimulates a learning
process since the quest for reasons of the differences challenges the initial problem frame and mental
model (Sterman 2000).
The participatory model building process has to be organized with respect to case-specific
requirements. However, the process generally consists of three steps: 1) preparation; 2) workshops;
and 3) follow-up (van den Belt, 2004). The preparatory phase can comprise interviews in order to
become acquainted with stakeholders and problem frames as well as to present the method. Also, a
preliminary model of the problem can be constructed in order to serve as an entry point for discussion
and to clarify the potential outcome of the participatory process. The group model building process is
conducted in consecutive workshops. Generally, the first workshop starts with a presentation of the
methods of system dynamics and systems thinking. If available, the preliminary model can be
demonstrated and participants can state their opinions. The group has to decide if the model building is
to start from scratch, or if the preliminary model is utilized. A complete model building process begins
with the definition of the problem variable. At this point, the different problem frames of group
members are discussed. Subsequently, the causal structure of the system is created by the use of causal
loop or stock and flow diagrams. Qualitative investigations guided by the method of systems thinking
can yield initial insights into the causes of system problems (Senge 1990). The final step comprises the
simulation of the model and the examination of scenarios. The quantification process is also done by
the stakeholders, so that group members know about the background and outline of the systems
structure. Unknown relationships lead to new research questions which can lead to concerted research
efforts. Policies are tested in the model and uncertainties and sensitivity of the measures are discussed.
Based upon this, the group decides on the best-available set of polices in order to solve the problem.
Instead of delegating responsibility to external parties, group model building should foster concerted
actions of all stakeholder groups (Vennix 1996). It is anticipated that in this manner innovative policies
and strategies can be implemented which would not be possible in centralized decision-making
structures. The consolidation of knowledge from stakeholders as well as their commitment to the
process should induce cooperation even beyond the modeling process (cp. Pahl-Wostl et al. 2007).
The case study in Cyprus demonstrated the potential and applicability of the participatory model
building approach. Cyprus has faced the issue of water scarcity for decades. Major causes are
decreasing precipitation levels and increasing demands from the agriculture, domestic and tourism
sectors. In particular, the potable water demand has increased in recent years due to population growth
and increasing tourism. At present, the agriculture sector has the largest share of the total water
demand with about 70%, followed by the domestic sector with 20% and the tourism sector with 5% of
122
the demand (Savvides et al. 2001). In the past, extensive dam construction was initiated by a „no drop
of water to the sea‟ policy. However, the decreasing trend in rainfall induces levels of dams below their
storage capacities. Recently, strategies have been readjusted to increase the development of
desalination and sewage treatment capacities.
The case study in Cyprus investigates the systemic effects and the interrelated side effects of
different policies, as well as the diverging frames of stakeholders with respect to the problem of water
scarcity in Cyprus. In light of all of these issues, the method of participatory model building was
chosen. The present study is planned to be a starting point for a complete group model building
process where stakeholders meet personally and construct a model through group discussion.
The following was accomplished in this study:
a stakeholder analysis
construction of causal loop diagrams during individual interviews with stakeholders
qualitative analysis of the causal loop diagrams and detection of feedback loops
merging of the individual diagrams into a holistic model
presentation of the holistic model to participants in the form of a workbook/questionnaire, in
connection with an inquiry about the approval and criticism of the proposed model structure
the building of a quantitative preliminary simulation model based on the outcomes of the
qualitative research which contains the hydrological processes, water-allocation mechanisms,
social processes that determine the sectoral water demands, and policy options of
desalination, wastewater recycling as well as demand management in the form of
technological efficiency improvements, and measures for conscious water consumption
scenario analyses for selected policies
The interviews were conducted in Cyprus from January until February 2009. The hosting institution
was the Energy, Environment and Water Research Center (EEWRC) of the Cyprus Institute in Nicosia,
which established stakeholder contacts and provided advice on cultural and water-related topics. Prior
to the stay in Cyprus a stakeholder analysis was conducted based on a literature review. The
suggestions of interviewees expanded the list so that, eventually, ten interviews were conducted with
eight different institutions, namely: Water Development Department, Agriculture Research Institute,
Environment Service, Department of Agriculture, Cyprus Tourism Organization, Fassouri Producers‟
Group (Farmers Union), Water Board of Limassol, and a Hotel Manager from Limassol. The
proceeding and content of the interviews had to be adapted to meet the time constraints of the
participants. In the end, seven individual causal loop diagrams were constructed from scratch, whereas
one interview comprised the extension of a preliminary causal loop diagram. Furthermore, two
interviews were conducted without the building of a model. The construction of the causal diagrams
turned out to be accessible for all participants even though the majority had no experience in model
building. After a short introduction to the method of systems thinking, the interviewee built their
models independently. Questions concerning recommended polices and the impediments for their
realization stimulated the design of comprehensive models. All the participants were satisfied with the
method and asked that the final questionnaire be forwarded to them.
After the interviews, the individual models were merged into a holistic model and sent to the
participants in the form of a workbook. In this document, the merged model was depicted and
questions asked for every feedback process which had been revealed in the qualitative analysis. The
participants had to assess the correctness of each proposed loop and explain their criticism if they
deemed the loop to be incorrect. Subsequently, the importance of the loop in the present problem
situation was assessed by the interviewee, with possible responses ranging from „no importance at all‟
123
up to „very high importance‟. The last questions pertained to the future importance of the loop which
could be estimated as „decreasing‟, „stays stable‟, and „increasing‟.
Six out of ten questionnaires were completed and demonstrated different interests, points of views,
and problem frames. As the questionnaires were anonymous, the diverging answers cannot be linked
to the respective stakeholder groups. Unanimity was limited to a few discussion points. All
respondents are confident of the success of the application of seawater desalination. In addition, a high
importance is anticipated for the usage of recycled wastewater for the agriculture sector. However,
many points indicate differing opinions. For instance, the metering of groundwater is regarded to have
no importance today as well as in the future by the majority of stakeholders due to the impossibility of
implementation. Contrarily, for two stakeholders the importance of this policy is very high and will
even increase in the future. Also, the effects and appropriateness of the pricing of non-potable water
was considered differently. Two stakeholders do not anticipate changes in the price and subsidy level
in the future, whereas two respondents proposed increases in the non-potable water and lower
subsidies. Another two stakeholders urged for decreasing price levels and higher subsidies and pointed
to the low profitability of agriculture. Higher water costs would lead to a substantial downturn in the
agriculture sector.
These examples show the potential conflicts between different stakeholder groups which would be
discussed frankly in the course of a group model building process. Furthermore, the results of the
questionnaire suggest diverging proposed strategies for solutions developed by stakeholder groups
which could be tested and assessed transparently by a system dynamics simulation model.
Based on the participatory model building process, a preliminary simulation model was
constructed. The hydrological and allocation sub-models were prepared in advance of the interviews
as their contents are considered to be not controversial between stakeholder groups. Whereas the
former simulates the hydrological processes that determine the replenishment of surface and
groundwater resources, the latter describes the conveyance of water from the natural sources to the
different consumers. For the stock and flow structure of the hydrological model the framework of the
Hydrologic Modeling System HEC-HMS from the US Army Corps of Engineers (USACE) (2000)
was chosen. By referring to a well-known and widely-used model, it is expected that the potential for
future improvements as well as the acceptance of decision-makers will be high. The participatory sub-
model comprises the endogenous simulation of the sectoral water demands and allows for the
application of demand management measures that aim at technological efficiency (e.g. water saving
devices, or irrigation techniques) and behavioral efficiency (i.e. proper application of technologies,
avoidance of water wastage). Changes in crop types are considered in the „behavioral efficiency‟
variable of the agriculture sector as traditions and crop-specific knowledge has been stated as major
impediments for changes in crop patterns. The sectoral water demand is dependent on:
the economic development in the sector
the technological efficiency in the sector
the behavioral efficiency or conscious consumption in the sector
in the case of the domestic and tourism sectors: growth of population and tourist numbers
The calculation procedures were based on literature reviews (Ecologic 2007), and, if sufficient data
was not available, were estimated on the basis of the best-available information.
Four scenarios are implemented from 2010 to 2050. For all scenarios, yearly average precipitation
data from the PRECIS Regional Climate Model is inserted for the time period 2040 to 2050. Due to
lack of data, annual rainfalls from 2010-2040 are estimated to amount to an average of 420 mm while
the inter-annual variations follow measured rainfall data from the past (i.e. the time period between
1975 and 2005). The first scenario (1a) assumes desalination and wastewater recycling capacities at
124
levels that are currently applied or planned. The second scenario (1b) investigates the capacities of
desalination and sewage treatment that are needed in order to avoid water scarcity in the future. The
third scenario (2a) explores the adoption of major investments in technological efficiency so that the
potential of water saving technology is approached in the future. The fourth scenario (2b)
complements improvements in technological efficiency with changes in the conscious consumption
behavior in the different sectors. Finally, the fifth scenario (2c) investigates the needed level of
wastewater and desalination capacities in case of high technological and behavioral efficiencies in
order to avoid water scarcity.
The results of the scenario runs show the interconnectedness of water-related policies. The
desalination and wastewater recycling capacities have only to be increased slightly in the future (to 51
Mm³/year and 70 Mm³/year respectively) if demand is limited due to major investments in
technological and behavioral efficiencies (scenario 2c). In the case of scenario 1b, where water
consumption increases due to economic development, the capacities of both desalination and sewage
treatment have to be enhanced considerably (to 110 Mm³/year and 120 Mm³/year respectively).
Another example of the tight connection of policies is the link between wastewater recycling and
demand management. In scenario 2c, water scarcity in the agricultural sector cannot be combated by
increases in the capacity of treatment plants since the volume of sewage will have decreased
considerably over time due to demand management efforts.
The outcomes of the model have to be regarded qualitatively as many variables (for example
precipitation levels) as well as their functional connections (for example the effect of GDP on
domestic water demand) are highly uncertain. In its present state, the model reflects a possible
outcome of a group model building process which can serve as a motivation for stakeholders to
participate. Parameters and sub-models should be refined once a longer period of study makes in-
depth investigations possible.
Besides the usage of the system dynamics model for the management of water resources in
Cyprus, the hydrological and allocation sub-models can also be applied in other geographical domains
since the simulated processes are universal. Together, the models could serve as starting point for other
participatory processes.
The qualitative outcomes of the case study comprising the stakeholder analysis, causal loop
models, and questionnaire results show the ability of the group modeling framework to effectively
structure and guide a participatory process. The diverging frames and perspectives on the water
scarcity problem on Cyprus underline the necessity of the involvement of stakeholders in order to
achieve progress towards a sustainable water management.
The active participation and the versatile results should allow for an interesting and valuable group
model building process in the future. In particular, the openness and interest of the participants with
respect to the method and the study highlight the demand and need for integrated and participative
approaches in water resources management, as well as the relevance and applicability of the group
model building approach.
XI
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1
Appendix A: The different roles of the modeler
The facilitator of a group model building process has different tasks to accomplish in order to gain
helpful insights, reach consensus on a topic, or manage data-requirements and conflicts. Whereas these
roles can be handled by an individual for relatively small groups, group-modeling processes for issues
including larger stakeholder networks could require the subdivision of the responsibilities to several
persons in charge. Richardson and Andersen (1995) detect five essential roles: the facilitator, the
modeler/reflector, the process coach, the recorder and the gate-keeper. In the following these different
roles are introduced in detail.
The „facilitator‟ is mostly involved in direct working with the group. The function of this role is
the facilitation of the group discussion and the elicitation of the gained knowledge and insights. The
facilitator prepares the modeling sessions and summarizes them predominantly by means of a system
dynamics or conceptual model (van den Belt 2004). Vennix (1996) adds that the facilitator doesn't
need to have extensive knowledge of the problem being discussed. In fact a thorough understanding of
the issue would hinder the free development of the model by the group. On the other hand, the
facilitator must be able to follow the discussion and, hence, requires knowledge of particular technical
terms and fundamentals. Vennix (1996) goes more into detail of the required abilities and calls for
different skills that a facilitator should have. As a matter of course, experience in system dynamics
modeling is the central skill and a prerequisite to an efficient group model building. Besides there are
further skills that are supportively to the process: First, proficiency in the structuring of a process
particularly in the case of larger groups. Second, the facilitator should also have conflict handling
skills to be able to mediate and facilitate problem-centered discussion instead of personal conflict.
Communication skills imply the consciousness of the facilitator about the significant role of an open
communication process. Therefore, the facilitator should avoid jargon (e.g. technical terms) and
explain tasks as easy as possible. Additionally, the facilitator has to keep the group discussion on the
track in direction of the model process to finally solve the respective problem. The concentration
skills are required whenever the discussion runs the risk of getting lost in details or personal conflicts.
The formation of a 'we'-feeling increases the coercion in the group and lays the foundation for a
communicative atmosphere. The team-building skills of the facilitator can support this atmosphere
by paying attention to the possibilities for every individual to participate in the discussion. The skills
previously mentioned are connected to the following, namely the skills to build consensus and
commitment. The facilitator can achieve consensus by the encouragement of every group member to
participate. Vennix (1996) points out that a preferable situation would be taking over of the facilitator's
task by a group member. Thereby, the group has learned to manage their problem alone which is the
most desirable outcome of a participatory approach. It can be assumed that the group will be able to
solve future issues endogenously which indicates an increase in its adaptive capacity. Last but not
least, intervention skills are required to intervene in detrimental situations, in particular when an
individual behavior impedes the group performance. Krueger (1988) defines three types of
problematic persons: the dominant talker who monopolizes the model building, the shy person who
refrains from participation, and the rambler who impedes the process by unnecessary statements.
The‟ modeler/reflector‟ requires deep understanding in system dynamics as he or she is concerned
with the model building and the correct application of systems thinking. Hence, the role includes the
input of thought-provoking impulses, guidance in the use of the model, and elicitation of structural
assumptions and opinions about system behaviour from participants. In general the modeler/reflector
2
serves the facilitator and the group to bring the discussion forward.
The „process coach‟ focuses more on the group dynamics than on the progress made on the issue at
stake. Usually the coach stays in close contact to the facilitator and gives advice on the social
dimension of the discussion. This could also include the mediation and moderation of conflicts, e.g.
arising from differences in culture or language (van den Belt 2004). Sometimes these group processes
can be the focus of the group modeling if social relationships need to be changed in order to improve
the situation. Eden (1994, p.259) calls this process 'negotiated social order' where not physical but
more social entities need to be rearranged to arrive at a solution. The „recorder‟ has to write down or
sketch the discussion of the group in order to make the process traceable and reproducible as the
situation or the manner how a statement was given plays a significant role to identify the real meaning.
This effort is supported by the drawings or scripts of the modeler/reflector and the notes of the
facilitator. The recorder must have experience in system dynamics in order to be able to select the
relevant information.
The „gatekeeper‟ represents the connection between the modeling team and the client/stakeholder
group and has two functions: First, to keep the contact with the participating actors by communicating
to them on behalf of the modeling team. Second, to inform the modeling team about the stakeholders‟
needs, motivation, concerns, or suggestions. Hence, the position of the gatekeeper is between the two
parties and functions like an advocate or middleman. Van den Belt (2004) even calls this person 'the
champion' as the participatory projects would not materialize without an initiator and promoter for
human and technical aspects.
Richardson and Andersen (1995, p.115) hypothesize, first, “that all five roles or functions must be
present for effective group support” and, second, “that group modeling efforts can be significantly
accelerated by explicitly recognizing the five roles and deliberately assigning them to different skilled
practitioners”. Van den Belt (2004) agrees to the first hypothesis but challenges the second one. In
particular she prefers the competency of all team members having facilitation and modeling skills as
they are closely intertwined. Hence, the learning process would be more facilitated if support team
members have “combined and balanced skills of facilitation and modeling” (van den Belt, p. 50).
Therefore, the role of the facilitator, modeler and reflector could be merged as well as the roles of the
process coach and the recorder. Besides the advantage for the cooperation with the group, a small team
would also minimize the time to reach agreement on issue like the specific competencies of the roles
in the group process or organizational topics. In the end, a duo consisting of a gatekeeper and a
modeler has the ability to initiate a group model process, and can subsequently expand the team if it is
feasible (e.g. because of limited resources) and helpful. On the other hand, an experienced and tested
team could outbalance the advantages of a smaller support group. In the following, the roles are not
distinguished explicitly. Hence, the term „facilitator‟ or „modeler‟ is used synonymous for „project
team‟ and can be an individual as well as a team.
1
Appendix B: Causal Loop Diagrams from individual interviews
B1: Management Sub-Model - Part 1
B2: Management Sub-Model - Part 2
B3: Management Sub-Model - Part 3
B4: Management Sub-Model - Part 4
B5: Social-Environmental Sub-Model - Part 1
B6: Social-Environmental Sub-Model - Part 2
B7: Social-Environmental Sub-Model - Part 3
B8: Policy Sub-Model – Part 1
B9: Policy Sub-Model - Part 2
Water Scarci ty
Funding ofDesali nati on Plants
Funding of WaterImport
Funding of DamDevelopment
+
+
+
Funding of Mai ntenance& Pi pe Replacement
Publi c Finance
Dam Capaci ty
Surface WaterSupply
Funding ofSewage Plants
Sewage TreatmentPlants
+
++
+
+
+
Potabl e WaterSupply
+
+
-
-
Water Tankers
+
+
Desali nati onCapaci ty
+
Mai ntenance of WaterInfrastructure
Replacement ofAging Network
Water Losses
+
+
-
-
+
+
+
Costs of Potabl eWater Suppl y
Chargi ng PotableWater Fees
+
+
+ +
Costs of Non-PotableWater Suppl y
+
Chargi ng Non-Potabl eWater Fees
+
+
B
Water Import Loop
B
Desalination Loop
B
Limiting WaterLosses Loop
B
Dam DevelopmentLoop
+
B
WastewaterTreatment Loop
R
Charging of PotableWater Supply Costs
Loop
R
Charging of SurfaceWater Costs Loop
Subsi di es forPotabl e Water
+
-
Subsi di es forNon-Potable Water
+
-
B
Subsidize SurfaceWater Loop
B
Subsidize PotableWater Loop
+
1
2
3
4
5
78
9
10
Water Puri fi cation
+
+
6
Urban RainwaterColl ecti on
+
+
B
Urban RainwaterCollection Loop
+
Potabl e Water Pri ce
Non-PotableWater Pri ce
+
+
Management Sub-Model - Part 1:
Supply Management Policies and their Effect on the Public Finance Appendix B1
2
CommerceSector Touri sm Sector
Real EstateEstate
EducationSector
Agri cul ture SectorIndustry Sector
Landscaping &Ameni ti es
Economic Development(Income)+
+
+
++
Water Rati oning
-
-
- -
-
Chargi ng PotableWater Fees
-
+
-
Chargi ng Non-Potabl eWater Fees
-
-
-
Publi c Finance
+
Touri sm WaterDemand
Domestic WaterDemand
+
Households
++
+
Industry WaterDemand
Agri cul ture WaterDemand
Non-Potable WaterDemand
++
+
+
+
Surface WaterSupply
-
Potabl e WaterSupply-
Water Scarci ty
- -
Funding of Desali nati onand Water Import
+
Funding of DamDevelopment &
Wastewater Recycl ing
+
+
+
+
-
+
Desali nati on & WaterTankers & Water
Puri fi cati on
Dam Capaci ty &Sewage Treatment
Plants
+
+
+
+
+
R
Economic Development -Funding of Supply
Management - Less RationingLoop
B
Economic Development -Higher Demand - More
Rationing Loop
+
Economic Development -Higher Demand - Charging of
Water Supply Costs Loop
B
+
11
13
12
+
Taxes & FeesRevenue
+
+
Import of Agri culturalProducts
-
- 14Double-LossMechanism
Empl oyment
+
+
++
Management Sub-Model - Part 2:
The Interrelations of Finance, Economic Development and Water Scarcity
3
1
Appendix B2
Agri cul tureSector
Need for moreProfit
Crop Type with highEconomicYiel d
-
+I rrigati on Effi ci ency
Agri cul tureYiel d
I rrigated Land
Actual RevenueAgri cul ture
+
Water Costs ofAgri cul ture
+
Water Savi ngI rrigati on Techni ques
Cultivati on of adaptedCrop Types
+
+
+
+
Agri cul ture WaterDemand
-
+
+ -
+
+
Choose Extention orReduction of Cultivation
Loop
B
Choose EconomicOptimization Loop
B
Choose Irrigation Efficiency Enhancement
Loop
B
Choose AdaptedCrops Loop
Chargi ng ofNon-Potable Water
Fees
+
Subsi dies forNon-Potable Water
-
B
+
+
15
16
18
17
-
Water Rati oning
Surface WaterSupply
Water Scarci ty
-
+
-
Funding of Dams,Wastewater Recycl ing
+
++
Avail abi l ity ofI rrigati on Water
+
+
Water RationingAgriculture Loop
B19
Management Sub-Model - Part 3:
A Closer Look at the Impacts of Variations in the Water Price in the
Agriculture Sector
4
1
Appendix B3
Abstraction ofGroundwater
Economic Attractiveness ofGroundwater Development
Storage Level i nAquifers
Capaci ty of Aqui fers
Water Rati oning
Non-PotableWater Pri ce
Potabl e Water Pri ce
-
+
+
Potabl e WaterDemand
Non-Potable WaterDemand
+
+
Meteri ng ofGroundwater
-
Pri ce forGroundwaterDevelopment
+-
Water Scarci ty
+
EconomicDevelopment -
+
Touri sm & RealEstate
++
Agri cul ture
+
+
+
+
-
R
R
Tourism & RealEstate Growth Loop
AgricultureGrowth Loop
-
B
Rationing -Groundwater
Abstraction Loop
+
+
+
Constrai n GroundwaterExtraction
+-
Seawater Intrus ion
-
-
B
Pricing - GroundwaterAbstraction Loop 1
-
-
B
Pricing - GroundwaterAbstraction Loop 2
R
Water Price -Groundwater
Attractiveness Loop
+
R
Rationing -Groundwater
Attractiveness Loop
B
Metering -Groundwater Pricing
Loop
Metering - ConstrainExtraction Loop
B
20
21 25
27
26
23
24
22
22
uu
Groundwater Supply
+
-
Management Sub-Model - Part 4:
The Problem of Groundwater Over-exploitation
5
1
Appendix B4
Touri sm
Real Estate
Population
Cri me
Congestion Traffi c
Quali ty of Li fe
+
++
+
+
-
-
Attractiveness forPeople to Cyprus
Attractiveness forTouri s ts
+
+
+
+
Attractiveness of theCountrys i de
People Movi ng toRural Areas
+
-
Rati oning of Water
Water Scarci ty
+
Potabl e WaterDemand
Potabl e WaterSupply
-
-
ConsumerDi ssatis faction
Confl ict AmongstUsers
+
+
-
-
+
Standard of Living+
Economic Si tuation ofHouseholds
+
Potabl e Water Pri ce
+
Quality of Life - RealEstate Loop
B
Quality of Life -Tourism Loop
B
Migration Loop
R
-
-
+
+
B
Rationing - Standardof Living Loop
+
+
B
Water Price - Standardof Living Mechanism
EconomicDevelopment
+
+
1
2
3
4
5
Empl oyment
+
+
+
Social-Environmental Sub-Model - Part 1: The Importance of
Quality of Life as the Basis of Economic Development
6
1
Appendix B5
Potabl e WaterDemand
Water Scarci ty
Save Water Pol icy
Subsi dise WaterSavi ng Equipment
Poli cy "Increase thePri ce of Water"
Pri ce of PotableWater
Customi zati on toPri ce Level
Affordabi li ty ofWaterConsci ous Consumption
Behavi or
+
+
+
-
+
+-
+
Economic Si tuati onof Househol ds
+
+
Publi c Finance
-
Effici ency ofDomestic Water Use
-
Awareness ofEconomical Wi n-Win
Situati ons
Incentives for WaterSavi ng Behavi or
+
+Pressure on Major
User Groups+
+
Publi c AwarenessCampaigns
+
Publi c Participation
+
+
+
EconomicDevelopment
+
+
B
"Increase thePrice"-Policy Loop
R
CustomizationLoop
B
AwarenessCampaign Loop
B
Subsidize WaterSaving Equipment
B
Self-Initiative of WaterUser Groups Loop
Programs to ReduceWater Consumpti on
++
Appli cati on of WaterSavi ng Technol ogy
+
+
+
+Supply
Management++
Water Wastage -Hotli ne+
+
Water-Consumpti onEducation
++
R
ConsumerEducation Loop
Hotline Mechanism
6
11
9
12
7
8
10
Social-Environmental Sub-Model - Part 2: A closer Look at
Water Demand Management in the Domestic Sector
7
1
Appendix B6
Water Scarci ty
Cli mate Change
Ambi entTemperature
Rainfall
+
-
Desertification
Quali ty of theEnvi ronment
Envi ronmentalSelf-Purificti on
Quali ty of Water
+
-
+
+
-
Poll uti onSewage
Treatment Plants
Industry
Agri cul tureDomestic Sewage
-
+
+
+Quali ty of Li fe
+
Real Estate
Touri sm
+
++
Aquifer Recharge
Coll ected Water i nDams
+
-
+
+
Evapotranspi rationof Plants
Agri cul ture WaterDemand
+
+
Surface WaterSupply
+
+
+
+
+
Envi ronmentalFlows
-
++
+
+
Health of Population+
+
B
Pollution - Qualityof Life Loop
R
Pollution - WaterTreatment Loop
Carryi ng Capaci ty+
+
Water Pri ci ng &Rati oning
+
-
-
-
-
R
CarryingCapacity Loop
B
Water Quality -Development Loop
R
EnvironmentalPurification Loop
-
+
Households
+
14
13
15
16
17
Social-Environmental Sub-Model - Part 3: The Importance of
Environmental Quality as the Basis of Economic Development
8
1
Appendix B7
EconomicDevelopment
ContemporaryDevelopment Poli cy
Lobbyi sm
Mi sall ocation to mostPowerful User
Power of supportedUser Groups
Pressure to Do BetterManagement
Water Scarci ty
+
Perceived Success ofEconomic Poli cy
+
+
+
Pressure to ReformInstituti ons
+
Fragmentati on ofWater Sector
Ignorance of "Bi gPicture"
-
+
Regulations
Self-Interest ofInstituti ons
-
Central WaterEnti ty
-
Compl iance wi th EULegi s lation (Water
Framework Di rective)
-
Quanti ty Monitori ngof Water Bodi es
Pri vate Dril l ing andUnmetered Use of
Borehol es
-
+
Abstraction ofGroundwater
Storage Level s inAquifers
+
-
-
Water Quali ty
-
Publi c Participation
Pressure to doDemand
Management
Lack of Strategi c Poli cyImplementati on and
Planni ng
+
+
Quali ty - Monitori ngof Water Bodi es+
+
Funding of Mai ntenance& Pi pe Replacement
+
Water Losses
-
+Studi es to get a'Hol is tic Pi cture'
+
-
+
-
- +
+
+
+
Irrigati on Effi ci encyAgri cul ture
-
-
Appli cati on of WaterSavi ng Technol ogy
+
+
+
+
+
-
-
R
Economic Success-Policy Loop
R
Water meansPower Loop
B
Policy - Water UseEfficiency Loop
+
Rati oning, Pri ce, ConsumerWater Savi ng (Subsedies ,Awareness Campai gns)
-
B
MeteringGroundwater Loop
B
QualityMonitoring Loop
B
Regulations Loop
B
Maintenance Loop
+
1
3
2
4
5
6
7
Policy Sub-Model – Part 1: The Political and Legislative Issues
around the Problem of Water Scarcity in Cyprus
9
1
Appendix B8
Cli mate Change
Overal l EnergyDemand
Actual EnergySupply
-CO2 Emi ssi ons
Sunshi ne Durati on i nCyprus Potential for
Renewable Energy
Land Avai l abi li ty
+
+
+
Rainfall
-
Water Scarci ty
-
Desali nati on Plants+
+
+
R
Climate ChangeLoop
Buying of Emi ssi onRights
+
Publi c Finance
o
Sewage TreatmentPlants
Attractiveness ofLand
Value of Land
-
+
-
Real EstateHouses for Sal e +
Pri ce of Real Estate
-
+
Population
+
Touri sm
+
+ Industry+
Solar Power Plantsand Wind Parks
+
Energy Pri ce
-
-
-
RenewableEnergy Supply
+
+
+
-
-
+
+
+
Demand for RealEstate in Cyprus
+
+
Funding of Solar PowerPlants and Wind Parks +
Funding ofSewage Plants
+
B
RenewableEnergy Loop
ConventionalEnergy Loop
B
R
Land Availbility forSewage and Solar Power
Plants
Funding ofConventional Power
Plants+
Costs forConventional Energy
+
Costs forRenewable Energy
+
Oi l Price+
-
ConventionalPower Plants
+
+
-
+
ConventionalEnergy Supply
+
+
+
B
Emission RightLoop
B
ConventionalEnergy Costs Loop
B
Renewable EnergyCosts Loop
Commerce -+
+
Energy Pri ceSubsi dies
+8 9
14
13
12
11
10
+
-
Policy Sub-Model - Part 2: The Energy Sector and the Problem of
Land Availability in Cyprus
10
1
Appendix B9
“Participative assessment of integrated policies to mitigate the effects of water scarcity in Cyprus”
Purpose: The study investigates social-technological options to mitigate the effects of water scarcity in Cyprus at the national level. Furthermore, it implements a participatory model building framework to structure participatory processes, as for example prescribed by the EU Water Framework Directive. The study is conducted in the context of a Master's thesis about policy assessment in complex social-technical systems by Johannes Halbe, student at the University of Siegen and the Institute of Environmental Systems Research, Germany. Supervisor of the case study is Dr. Jan Franklin Adamowski, Post Doctoral Fellow at the Massachusetts Institute of Technology, USA (Cyprus Energy, Environment and Water program). Approach:
The problem of water scarcity in Cyprus is a very complex issue, including various interests and interrelations. Hence, an integrated assessment study has to incorporate both an integrated social-environment-technical systemic view and the controversial perspectives of actors. This study uses a simplified water balance model describing the water flows in Cyprus to represent the physical system of the issue. The social and environmental system elements are included by stakeholder interviews in the context of a participatory model building process. In the individual interviews, causal diagrams are constructed to depict the perspective of the respondent. These individual models are subsequently merged and translated into a comprehensive system dynamics model. The applied method of system dynamics is an innovative approach to investigate policy options in complex and dynamic systems, as proposed by the Impact Assessment Guidelines of the EU. Content of the Interview 1) Individual model building: First, a short introduction to the system dynamics methodology and the research project is provided. Subsequently, the structure of a preliminary causal model of the problem of water scarcity is presented and discussed. The interviewee decides either to accept the preliminary model and expand it to his or her point of view, or to start a new causal loop model from scratch. Therefore, an adequate problem variable is defined, and, subsequently, the surrounding system is depicted (consisting of variables and connection arrows) using a stepwise guideline. The main focus of the method is the detection of causal loops (so-called feedback loops) in order to avoid thinking in linear causal-chains. These loops are important for the understanding and simulation of the system's behavior. 2) Discussion of the blended model structure: After the interviews have been completed, the individual causal diagrams are merged to a comprehensive model, including all different perspectives. Thereupon, a questionnaire is sent to all participants (in the mid of February) including the personal causal loop diagram and the holistic system structure, with the request for approval and criticism respectively. The editing time will be about 20 minutes. Participating Institutions:
Institute of Environmental Systems
Research
Appendix C: Project Description
Appendix D: Example causal loop diagram which has been used in the interviews
Desired TravelTime
Travel Time
Pressure toReduce
Congestion
RoadConstruction
HighwayCapacity
Traffic Volume
-
+
+
-
+
+
Attractiveness ofDrivingTrips per Day
-
+
+
Public TransitRidership
Public TransitRevenue
Public TransitDeficit
Public TransitNetwork
Adequacy ofPublic Transit
-
+ -
-
+
-
Figure1: Causal loop model about the problem of traffic congestion (Sterman 2000, pp. 181ff)
Appendix 3
Problem of
Water Scarcity
Quality of Water
Water Demand
Rainfall
AmbientTemperature
Climate Change
+
-
+
-
+
-
Cost of Water
Standard ofLiving
ConsumerDissatisfaction
ConflictAmongst Users
WaterQuantities
Environment Development ofFurther Sources
Reuse
Desalination
Treated DomesticEffluent
Greywater WithinHousehold
+
+
+
+
-
+-
+
+
+-
-
+
WaterConservation
-
DemandManagement
Leakage
+
-
Public Participation(to reduce wastage)
+
Institutional Problems(Fragmentation of Water
Sector)
Pressure fromUsers
Lack of Strategic PolicyImplementation and
Planning
EnvironmentalThreats -
-
Lack ofIncentives
Lack of Proper Controland Accountability of
Water Utilities
--
Water Use inDomestic/Agriculture/Industry/Tourism
Sector
+
--
-- -
-
+
Prioritization ofSectors
+
+
-+
+
-
-
Subsidies
+
R
B
EnvironmentalDegradation Loop
Supply -Environment Loop
B
DesalinationSupply Loop
B
Reuse SupplyLoop
B
DemandManagement Loop
R
DemandManagement
Adaptation Loop
+
B
Cost Loop
B
Participation -Environment Loop
B
Participation -Conservation Loop
Appendix E: Example for a causal loop model from a 1h-interview
F1 Stock and flow structure of the hydrological and allocation model
F2: Calculation of weights for dam and groundwater withdrawal; calculation of compensation flows
F3: Economic development, and the calculation of the water demand for landscaping&amenities
F4: Calculation of the domestic water demand
F4.1: Calculation of Reference Technological and Behavioral Water Demands in the Domestic
Sector
F4.2: Calculation of the Water Demand for different Usages in the Domestic Sector
F5: Calculation of the Agriculture Water Demand
F6: Calculation of Tourism Water Demand
F7: Demand Management by technological and behavioral measures in the Agriculture Sector
F8: Demand management by technological and behavioral measures in the domestic sector
F9: Demand Management by technological and behavioral measures in the Tourism Sector
Appendix F: Overall model structure of the system dynamics model
1
Precipitation Flow
Surface Water
Actual
Evapotranspiration I
Years
Area Cyprus
Annual
Precipitation Data
Aquifer
Runoff
Surface Water Storage
Non-Potable Water
Supply
Potable Water Supply
Withdrawal for Non-Potable Water Supply
Pumping forNon-Potable Water
Supply
Withdrawal for
Domestic Use
Wastewater
Unused
Discharge
Reuse for
Irrigation
Desalination
Potable Water Use
Irrigation Water
Use
Pumping for
Domestic Sector
Infiltration
Storage Capacity
<Years>
Surface Water to
Ocean
Groundwater to Sea
Effluent to Aquifer
<Weight Groundwater
Pumping>
<Desalination>
<Weight Dams
Withdrawal>
<Ratio GW/Water
Need Domestic>Aquifer Capacity
Saturation Effect
GW
Saturation Dam
NaturalStorageCapacity
<Ratio SW/Water
Need Irrigation>
<Ratio SW/Water
Need Domestic>
<Ratio GW/Water
Need Irrigation>
<Irrigation Water
Demand>
Water ScarcityAgriculture
Water ScarcityDomestic + Tourism
Desalination
Capacity
Recycling Rate
Agriculture
Recycling Rate
Aquifer
Recycling Rate
real
<Years>
<Weight Dams
Withdrawal>
<Recycling Rate
real>
<Recycling Rate
real>
<Potable Water
Use>
<Potable Water
Use>
<Desalination>
<Years>
Agriculture
Virtual Water
Percolation to GW
<Compensation SW
for GW>
<Recycling Rate
Aquifer>
Monthly
Precipitation
Monthly
Annual Distribution
of Rainfall
<Irrigation Water
Demand>
Annual Distribution of
Evapotranspiration
<Monthly>
Maximum Soil Percolation Rate
Soil Storage
Capacity
Soil Water
Percolation I
Potentential
Infiltration Rate
Maximum
Infiltration Rate
<Area Cyprus>
Actual
Evapotranspiration 2
Potential
Evapotranspiration
Potential Soil
Percolation Rate
Reduction Factor for
Tension Zone
Landscaping
&Amenities
<Landscaping &Amenities Water
Demand>
<Recycling Rate
Agriculture>
<Recycling Rate
real>
<Potable Water
Demand>
<Potable Water
Demand>
<Potable Water
Demand>
<Landscapi
ng&Ameniti
es>
<Landscapi
ng&Ameniti
es>
Groundwater
Layer 1
Percolation II
Maximum Aquifer
Percolation Rate
Potential Aquifer
Percolation RateGroundwater Layer 1
Storage Capacity
Baseflow
<Runoff>
<Infiltration>
<Runoff>
<Soil Storage
Capacity>
<Soil Storage
Capacity>
<Percolation II>
<Reuse for
Irrigation>
<Weight Groundwater
Pumping>
<Reuse for
Irrigation>
<Groundwater Layer 1
Storage Capacity>
<Aquifer
Capacity>
<Baseflow>
<Compensation SW
for GW>
<Compensation
GW for SW>
<Compensation
GW for SW>
Annual Capacity for
Wastewater Treatment
<Years>Recycling Rate to
the Sea
<Years>
Recycling rate
Industry Water
Use
<Years>
Industry Water
Demand<Industry Water
Use>
<Industry Water
Use>
<Environmental
Flow SW>
<Environmental
Flow GW>
<Industry Water
Use>
<Industry Water
Use>
<Industry Water
Use>Wastewater
Capacity
EFSW
EFGW
Appendix F1: Stock and flow structure of the hydrological and allocation model
2
Weight Dams
Withdrawal
<Surface Water
Storage>
Weight Groundwater
Pumping
<Aquifer>
<Desalination>
Ratio SW/Water
Need Domestic
<Ratio SW/Water
Need Domestic>
<Desalination>
<Reuse for
Irrigation>
Ratio SW/Water
Need IrrigationRatio GW/Water
Need Irrigation
Ratio GW/Water
Need Domestic
<Ratio GW/Water
Need Domestic>
<Ratio GW/Water
Need Irrigation>
<Reuse for
Irrigation>
<Irrigation Water
Demand>
<Irrigation Water
Demand>
<Domestic Water
Demand>
<Weight Dams
Withdrawal>
<Weight Groundwater
Pumping>
Compensation GW
for SWCompensation SW
for GW
Adjustment
<Potable Water
Demand>
<Potable Water
Demand>
Appendix F2: Calculation of Weights for Dam and Groundwater Withdrawal; Calculation of Compensation Flows
3
Irrigation Water
Demand
Economic
Development
Landscaping &Amenities Water
Demand
Non-Potable Water
Demand
<AgricultureSector>
<Agriculture Water
Demand>
<TourismSector>
Other Sectors
Effect of Tourism onLandscaping & Amenities
Water Demand
Domestic influence onLandscaping & Amenities
Water Demand
<Years>
<Economic
Development>
Appendix F3: Economic development, and the calculation of the water demand for
landscaping&amenities
4
Domestic Water
Demand
DomesticWater
Demand (Monthly)
<Bath act>
<Taps act>
<Toilet actual>
<Shower act>
<Dish Washer
act>
<Washing
Mashine act>
<Cleaning act>
<Households>
Development Effect
Domestic
<Economic
Development>
Effect of BehavioralEfficiency on Development
Effect
<Behavioral
Efficiency Domestic>
Per Household Daily
Water Demand
Appendix F4: Calculation of Domestic Water Demand
5
Reference Behavorial
Efficiency Domestic 2000Reference Technological
Efficiency Domestic 2000
<Taps Standard><Taps optimum
tec><Dish Washer
Optimum beh><Dish Washer
Optimum tec>
<Dish Washer
Standard>
<Washing Mashine
Optimum beh>
<Washing Mashine
Optimum tec>
<Washing Mashine
Standard>
<Shower
optimum beh>
<Shower
optimum tec><Shower
Standard>
<Toilet optimum
beh>
<Toilet optimum
tec>
<Toilet Standard>
<Bath optimum
beh>
<Bath Standard>
<Cleaning
Optimum beh>
<Cleaning
Optimum tec>
<Cleaning
Standard>
<Garden Irrigation
optimum beh>
<Garden Irrigation
optimum tec>
<Garden Irrigation
Standard>
<Bath optimum
tec>
<Garden Irrigation
Standard> <Dish Washer
Standard>
<Cleaning
Standard>
<Taps Standard>
<Bath Standard>
<Washing Mashine
Standard>
<Toilet Standard><Shower
Standard>
<Taps optimum
beh>
Appendix F4.1: Calculation of Reference Technological and Behavioral Water Demands in the Domestic Sector
6
Toilet Standard
Toilet optimum tec
Toilet actual
Shower Standard
Shower optimum
tec Shower act
Taps act
Taps Standard
Taps optimum tec
Bath act
Bath Standard
Bath optimum tec
Washing
Mashine act
Washing Mashine
Standard
Washing Mashine
Optimum tec
Dish Washer act
Dish Washer
Optimum tec
Dish Washer
Standard
Garden Irrigation
optimum tec
Garden Irrigation
Standard
Garden Irrigation
actCleaning act
Cleaning Standard
Cleaning
Optimum tec
Toilet optimum
beh
Garden Irrigation
optimum beh
Cleaning
Optimum beh
Shower optimum
beh
Dish Washer
Optimum beh
Washing Mashine
Optimum beh
Taps optimum beh
Bath optimum beh
<Greywater
Recycling Domestic>
<Behavioral
Efficiency Domestic>
<Behavioral
Efficiency Domestic>
<Behavioral
Efficiency Domestic>
<Behavioral
Efficiency Domestic>
<Behavioral
Efficiency Domestic>
<Behavioral
Efficiency Domestic>
<Behavioral
Efficiency Domestic>
<Behavioral
Efficiency Domestic>
<Technological
Efficiency>
Appendix F4.2: Calculation of the Water Demand for different Usages in the Domestic Sector
7
AgricultureSector
Agriculture Water
Demand
BIP per Area
Per ha Water
Demand Agriculture
Optimal Behaviroral
Efficiency Agriculture
Optimal Technical
Efficiency Agriculture
<Years>Effective Area
Planting of
Profitable Crops
Animal Husbandry
<Years>
Reference WaterDemand Agriculture
2000
Planting of
Adapted Crops
<Technological
Efficiency Agriculture>
<Behavioral Efficiency
Agriculture>
<Years>
Appendix F5: Calculation of the Agriculture Water Demand
8
TourismSector
Tourism Water
Demand
<Years>
Yearly Variation of
Tourists
<Monthly>
Variable
Population
<Years>
Lenght of Stay
Per Capita Demand
Tourism
<Years>
Tourism Demand
Optimum tec
Tourism Demand
Optimum beh
Ration GDPTourism
per Capita
Effect of GDPTourism
on per Capita Demand
<Greywater
Treatment>
Effect of Behavioral
Efficiency on GDP Effect
Reference Tourism per
Capita Demand 2000<Behavioral
Efficiency Tourism>
<Technological
Efficiency Tourism>
Appendix F6: Calculation of Tourism Water Demand
9
Water Saving
Irrigation
Techniques
<Years>
Investment in watersaving technology
agriculture
Choice Farmers
<Years>
CF4
BehavioralEfficiencyAgriculture
Stock
Policy Conscious
Consumption Agriculture
Investment in Behavioral
Efficiency Agriculture
CF7
Reference Technological
Efficiency Agriculture 2000
Reference Behavorial
Efficiency Agriculture 2000
Technological
Efficiency Agriculture
Behavioral Efficiency
Agriculture
<Reference WaterDemand Agriculture
2000>
<Optimal Behaviroral
Efficiency Agriculture>
<Optimal Technical
Efficiency Agriculture>
<Reference WaterDemand Agriculture
2000>
Appendix F7: Demand Management by technological and behavioral measures in the
Agriculture Sector
10
<Years>
Technology Efficiency
Domestic Stock
Choice
Households
Investment in water
saving technology
domestic
Greywater Recycling
Domestic
BehavioralEfficiencyDomestic
Stock
<Years>
CF2
Policy Conscious
Consumption Domestic
Investment in Behavioral
Efficiency Domestic
CF5
Investment in grey water
recycling domestic
Choice Domestic
Grey WaterCF8
<Years>
Behavioral
Efficiency Domestic
Technological
Efficiency
<Reference Technological
Efficiency Domestic 2000>
<Reference Behavorial
Efficiency Domestic 2000>
Appendix F8: Demand management by technological and behavioral measures in the domestic
sector
11
Technology Efficiency
Tourism Stock
Choice Tourism
Investment in water
saving technology
tourism
<Years>
CF
<Years>
Behavioral
Efficiency
Tourism Stock
Greywater
Treatment
Policy Conscious
Consumption Tourism
Investment in Behavioral
Efficiency Tourism
CF6
Investment in grey
water recycling tourism
Choice Tourism
Grey Water CF9
<Years>
Reference Behavorial
Efficiency Tourism 2000
Reference Technological
Efficiency Tourism 2000
Technological
Efficiency Tourism
Behavioral
Efficiency Tourism
<Tourism Demand
Optimum beh>
<Tourism Demand
Optimum tec>
<Reference Tourism per
Capita Demand 2000>
<Reference Tourism per
Capita Demand 2000>
Appendix F9: Demand Management by technological and behavioral measures in the Tourism
Sector
12
1
Appendix G: Model code for the system dynamics model
This appendix contains all equations, parameter settings, and units in alphabetical order for
the Vensim model described in this thesis. The equations were generated by the Vensim
documenting tool.
(001) {UTF-8}
Units: **undefined** (002) {UTF-8}
Units: **undefined**
(003) Actual evapotranspiration=
Actual Evapotranspiration 2+Actual Evapotranspiration I Units: Mm³/Month
(004) Actual Evapotranspiration 2=
MAX(IF THEN ELSE(Potential Evapotranspiration-Actual Evapotranspiration I >0,MIN(0.68*Soil Water*Reduction Factor for Tension Zone, Potential Evapotranspiration-
Actual Evapotranspiration I),0),0)
Units: Mm³/Month
(005) Actual Evapotranspiration I= MAX(SMOOTH(MIN(Surface Water-Infiltration-Runoff,Potential Evapotranspiration
),0.25) ,0)
Units: Mm³/Month (006) Adjustment = WITH LOOKUP (Weight Dams Withdrawal-Weight Groundwater Pumping,
([(-1,0)-(1,1)],(-1000,0.75),(-0.4,0.75),(-0.3,0.2),(-.2,0),(0.2,0),(0.3,0.2),(0.4,0.75),(1000,0.75)
)) Units: **undefined**
Weight Groundwater Pumping-Weight Dams Withdrawal>=0.3,\!\!\!
(007) Agriculture= INTEG ( Irrigation Water Use-Percolation to GW-Virtual Water*0,0)
Units: Mm³ (008) Agriculture Sector = WITH LOOKUP ( Years, ([(1975,0)-
(2050,2000)],(1975,336.3),(1976,402.9),(1977,394.1),(1978,352.9
),(1979,367.2),(1980,362.9),(1981,353.5),(1982,378.7),(1983,345.7),(1984,421.3 ),(1985,374.8),(1986,374.3),(1987,411.2),(1988,428.3),(1989,450.1),(1990,476.3
),(1991,425),(1992,437.3),(1993,432.9),(1994,403.7),(1995,338.9),(1996,331.9
),(1997,285.4),(1998,305.3),(1999,338.8),(2000,310.6),(2001,323.1),(2002,343.1
),(2003,318),(2004,302.7),(2005,294.2),(2006,260.7),(2007,251.9),(2008,246.1 ),(2009,300),(2010,300),(2011,300),(2012,300),(2013,300),(2014,300),(2015,
300),(2016,300),(2017,300),(2018,300),(2019,300),(2020,300),(2021,300),(2022
,300),(2023,300),(2024,300),(2025,300),(2026,300),(2027,300),(2028,300),(2029 ,300),(2030,300),(2031,300),(2032,300),(2033,300),(2034,300),(2035,300),(2036
,300),(2037,300),(2038,300),(2039,300),(2040,300),(2041,300),(2042,300),(2043
,300),(2044,300),(2045,300),(2046,300),(2047,300),(2048,300),(2049,300),(2050 ,300),(2051,300) ))
Units: m€
(009) Agriculture Water Demand= Per ha Water Demand Agriculture/12*Effective
Area/1e+006+Animal Husbandry/12 Units: Mm³/Month
(010) Animal Husbandry = WITH LOOKUP (Years, ([(1975,0)-
(2050,10)],(1975,6),(2000,7.98),(2050,9) )) Units: Mm³
(011) Annual Capacity for Wastewater Treatment = WITH LOOKUP (Years,
([(1975,0)-(2050,180)],(1975,0.1),(1993.35,0.438596),(2004,20.57),(2005, 21.28),(2006,22.19),(2007,27.74),(2010.55,48.9474),(2012,59),(2015,65),(2021.79
,83.6842),(2023.55,84),(2025,85),(2050.46,85),(2068.65,85),(2088.53,85),(2101.15
2
,85),(2101.15,85),(2101.15,85) ))
Units: Mm³/Year
(012) Annual Distribution of Evapotranspiration = WITH LOOKUP ( Monthly,
([(1,0)-(12,1)],(1.5,2.5),(2.5,3.5),(3.5,5),(4.5,8),(5.5,11.5),(6.5,14), (7.5,16),(8.5,14),(9.5,11),(10.5,8),(11.5,4),(12.5,2.5) ))
Units: **undefined**
(013) Annual Distribution of Rainfall = WITH LOOKUP ( Monthly, ([(1,0)-(12,0.3)],(1,0.216),(1.25,0.213),(1.5,0.21),(1.75,0.198),(2,0.185
),(2.25,0.1725),(2.5,0.16),(2.75,0.148),(3,0.135),(3.25,0.1225),(3.5,0.11)
,(3.75,0.098),(4,0.085),(4.25,0.0725),(4.5,0.06),(4.75,0.055),(5,0.05),(5.25 ,0.045),(5.5,0.04),(5.75,0.038),(6,0.035),(6.25,0.0325),(6.5,0.03),(6.75,0.025
),(6.95413,0.0184211),(7.1896,0.0131579),(7.49235,0.00921053),(7.75,0.013)
,(8,0.015),(8.25,0.0175),(8.5,0.02),(8.75,0.02),(9,0.02),(9.25,0.02),(9.5,
0.02),(9.77982,0.0263158),(10,0.035),(10.25,0.0425),(10.5,0.05),(10.75,0.058 ),(11,0.065),(11.2936,0.0710526),(11.5,0.08),(11.7645,0.114474),(12,0.145)
,(12.25,0.1775),(12.5,0.21),(12.75,0.213),(13,0.216) ))
Units: **undefined** (014) Annual Distribution of Runoff = WITH LOOKUP (Monthly, ([(1,0)-
(20,25)],(1,0.8),(1.5,1.5),(2.5,4.6),(3.5,11),(4.5,19),(5.5,24),
(6.5,22.1),(7.5,9),(8.5,4.1),(9.5,2.8),(10.5,1),(11.5,0.7),(12.5,0.2),(13, 0.8) ))
Units: **undefined**
(015) Annual Precipitation Data = WITH LOOKUP ( Years, ([(1975,0)-
(2050,900)],(1975,563),(1975.99,563),(1976,471),(1976.99,471) ,(1977,549),(1977.99,549),(1978,439),(1978.99,439),(1979,582),(1979.99,582
),(1980,574),(1980.99,574),(1981,425),(1981.99,425),(1982,437),(1982.99,437
),(1983,448),(1983.99,448),(1984,498),(1984.99,498),(1985,438),(1985.99,438 ),(1986,520),(1986.99,520),(1987,625),(1987.99,625),(1988,481),(1988.99,481
),(1989,363),(1989.99,363),(1990,282),(1990.99,282),(1991,637),(1991.99,637
),(1992,509),(1992.99,509),(1993,417),(1993.99,417),(1994,493),(1994.99,493
),(1995,383),(1995.99,383),(1996,399),(1996.99,399),(1997,388),(1997.99,388 ),(1998,473),(1998.99,473),(1999,363),(1999.99,363),(2000,468),(2000.99,468
),(2001,604),(2001.99,604),(2002,561),(2002.99,561),(2003,545),(2003.99,545
),(2004,412),(2004.99,412),(2008,372),(2008.99,372),(2010,400),(2010.99,400 ),(2011,445),(2011.99,445),(2012,518),(2012.99,518),(2013,415),(2013.99,415
),(2014,550),(2014.99,550),(2015,542),(2015.99,542),(2016,401),(2016.99,401
),(2017,413),(2017.99,413),(2018,423),(2018.99,423),(2019,470),(2019.99,470 ),(2020,414),(2020.99,414),(2021,491),(2021.99,491),(2022,590),(2022.99,590
),(2023,454),(2023.99,454),(2024,343),(2024.99,343),(2025,266),(2025.99,266
),(2026,550),(2026.99,550),(2027,481),(2027.99,481),(2028,394),(2028.99,394
),(2029,466),(2029.99,466),(2030,362),(2030.99,362),(2031,377),(2031.99,377 ),(2032,366),(2032.99,366),(2033,447),(2033.99,447),(2034,343),(2034.99,343
),(2035,442),(2035.99,442),(2036,570),(2036.99,570),(2037,530),(2037.99,530
),(2038,515),(2038.99,515),(2039,389),(2039.99,389),(2040,340.49),(2040.99 ,340.49),(2041,455),(2041.99,455),(2042,439),(2042.99,439),(2043,386),(2043.99
,386),(2044,264),(2044.99,264),(2045,320),(2045.99,320),(2046,504),(2046.99
,504),(2047,303),(2047.99,303),(2048,241),(2048.99,241),(2049,318),(2049.99 ,318),(2050.69,425),(2050.99,425) ))
Units: mm/Year
(016) Annual Runoff = WITH LOOKUP (Years,([(1975,0)-(2000,500)],(1975,270),(1975.99,270),
(1976,175),(1976.99,175),(1977,390),(1977.99,390),(1978,140),(1978.99,140),(1979,375), (1979.99,375),(1980,420),(1980.99,420),(1981,130),(1981.99,130),(1982,185),(1982.99,185),
(1983,142),(1983.99,142),(1984,220),(1984.99,220),(1985,85),(1985.99,85),(1986,320),
(1986.99,320),(1987,430),(1987.99,430),(1988,320),(1988.99,320),(1989,76),(1989.99,76), (1990,28),(1990.99,28),(1991,350),(1991.99,350),(1992,300),(1992.99,300),(1993,120),
(1993.99,120),(1994,225),(1994.99,225),(1995,68),(1995.99,68),(1996,48),(1996.99,48),
3
(1997,45),(1997.99,45),(1998,100),(1998.99,100),(1999,50),(1999.99,50)))
Units:**undefined**
(017) Aquifer=INTEG(
EffluenttoAquifer+PercolationII-EFGW-GroundwatertoSea-PumpingforDomesticSector -"PumpingforNon-PotableWaterSupply",
2500)
Units:Mm³ (018) AquiferCapacity=4600
Units:mcm
(019) AreaCyprus=5800 Units:km2
(020) AuxiliarlyUnusedDischarge=MAX(PotableWaterUse-EffluenttoAquifer-ReuseforIrrigation,0)
Units:**undefined**
(021) AverageNumberofPersonsperHousehold=WITHLOOKUP(Years, ([(1975,0)-(2050,10)],(1975,3.8),(1982,3.51),(1992,3.23),(2001,3.06),(2015,3),(2050,3)))
Units:**undefined**
(022) Baseflow= DELAYFIXED(MAX(GroundwaterLayer1*0.13,0),2,0.5)
Units:Mm³/Month
(023) Bathact= BathStandard-(BathStandard-Bathoptimumtec)*TechnologicalEfficiency/100-(BathStandard-
Bathoptimumbeh)*BehavioralEfficiencyDomestic/100Units:l/hh
(024) Bathoptimumbeh=49.6
Units:l/hh (025) Bathoptimumtec=49.6
Units:l/hh
(026) BathStandard=49.6 Units:l/hh
(027) BehavioralEfficiencyAgriculture=
(BehavioralEfficiencyAgricultureStock-ReferenceBehavorialEfficiencyAgriculture2000
)*100/(100-ReferenceBehavorialEfficiencyAgriculture2000) Units:**undefined**
(028) BehavioralEfficiencyAgricultureStock=INTEG(InvestmentinBehavioralEfficiencyAgriculture,
0) Units:**undefined**
(029) BehavioralEfficiencyDomestic=
(BehavioralEfficiencyDomesticStock-ReferenceBehavorialEfficiencyDomestic2000 )*100/(100-ReferenceBehavorialEfficiencyDomestic2000)
Units:**undefined**
(030) BehavioralEfficiencyDomesticStock=INTEG(InvestmentinBehavioralEfficiencyDomestic,
0) Units:**undefined**
(031) BehavioralEfficiencyTourism=
(BehavioralEfficiencyTourismStock-ReferenceBehavorialEfficiencyTourism2000)*100/(100-ReferenceBehavorialEfficiencyTourism2000)
Units:**undefined**
(032) BehavioralEfficiencyTourismStock=INTEG(InvestmentinBehavioralEfficiencyTourism, 1)
Units:**undefined**
(033)BIPperArea=WITHLOOKUP(Years,
([(1975,8000)-(2050,20000)],(1975,12400.4),(1976,14856.2),(1977,14531.7) ,(1978,13012.5),(1979,13539.8),(1980,13381.3),(1981,13034.7),(1982,13963.9
),(1983,12747),(1984,15534.7),(1985,13820.1),(1986,13801.6),(1987,15162.2)
,(1988,15792.8),(1989,16596.6),(1990,17562.7),(1991,15671.1),(1992,16124.6 ),(1993,15962.4),(1994,14885.7),(1995,12496.3),(1996,12238.2),(1997,10523.6
),(1998,11257.4),(1999,12492.6),(2000,11452.8),(2001,11913.7),(2002,12651.2
4
),(2003,11725.7),(2004,11161.5),(2005,10848.1),(2006,9612.83),(2007,9288.35
),(2008,9074.48),(2009,11061.9),(2010,11061.9),(2011,11061.9),(2012,11061.9
),(2013,11061.9),(2014,11061.9),(2015,11061.9),(2016,11061.9),(2017,11061.9
),(2018,11061.9),(2019,11061.9),(2020,11061.9),(2021,11061.9),(2022,11061.9 ),(2023,11061.9),(2024,11061.9),(2025,11061.9),(2026,11061.9),(2027,11061.9
),(2028,11061.9),(2029,11061.9),(2030,11061.9),(2031,11061.9),(2032,11061.9
),(2033,11061.9),(2034,11061.9),(2035,11061.9),(2036,11061.9),(2037,11061.9 ),(2038,11061.9),(2039,11061.9),(2040,11061.9),(2041,11061.9),(2042,11061.9
),(2043,11061.9),(2044,11061.9),(2045,11061.9),(2046,11061.9),(2047,11061.9
),(2048,11061.9),(2049,11061.9),(2050,11061.9),(2051,11061.9))) Units:€/ha
(034) CF=DELAYFIXED(ChoiceTourism,1,0)
Units:**undefined**
(035) CF2=DELAYFIXED(ChoiceHouseholds,1,0) Units:**undefined**
(036) CF4=DELAYFIXED(ChoiceFarmers,1,0)
Units:**undefined** (037) CF5=DELAYFIXED(PolicyConsciousConsumptionDomestic,1,0)
Units:**undefined**
(038) CF6=DELAYFIXED(PolicyConsciousConsumptionTourism,1,0) Units:**undefined**
(039) CF7=DELAYFIXED(PolicyConsciousConsumptionAgriculture,1,0)
Units:**undefined**
(040) CF8=DELAYFIXED(ChoiceDomesticGreyWater,1,0)
Units:**undefined**
(041) CF9=DELAYFIXED(ChoiceTourismGreyWater,0.25,0) Units:**undefined**
(042) ChoiceDomesticGreyWater=WITHLOOKUP(Years,
([(1900,0)-(2050,100)],(1975,0),(2000,1),(2008.49,2),(2049.77,5)))
Units:**undefined** (043) ChoiceFarmers=WITHLOOKUP(Years,([(1975,0)-
(2050,100)],(1975,60),(2000,80),(2050,85)))
Units:**undefined** (044) ChoiceHouseholds=WITHLOOKUP(Years,([(1975,60)-
(2050,100)],(1975.23,60),(2000,69.9),(2011.7,70.8772),(2038.53,71.4035),(2080.12,72),(2108
.79,72))) Units:**undefined**
(045) ChoiceTourism=WITHLOOKUP(Years,
([(1975,0)-(2050,100)],(1975,50),(2000,68.4),(2050,75)))
Units:**undefined** (046) ChoiceTourismGreyWater=WITHLOOKUP(Years,
([(1975,0)-(2055,60)],(1975,0),(2001.15,1.92982),(2005.96,2.63158),(2010
,3),(2014.63,4.21053),(2017.81,5.52632),(2022.22,6.57895),(2029.31,7.89474 ),(2034.94,8.42105),(2041.06,9.21053),(2050,10)))
Units:**undefined**
(047) Cleaningact=CleaningStandard-(CleaningStandard-CleaningOptimumtec)*TechnologicalEfficiency/100-(CleaningStandard
-CleaningOptimumbeh)*BehavioralEfficiencyDomestic/100
Units:l/hh
(048) CleaningOptimumbeh=32.8 Units:l/hh
(049) CleaningOptimumtec=44.4
Units:l/hh (050) CleaningStandard=47.3
Units:l/hh
5
(051) CompensationGWforSW=MAX(IFTHENELSE(WeightDamsWithdrawal<=0.5:AND:
WeightGroundwaterPumping>=WeightDamsWithdrawal+0.2,1+(1-
WeightDamsWithdrawal)*Adjustment,1),0)
Units:**undefined** (052) CompensationSWforGW=MAX(IFTHENELSE(WeightGroundwaterPumping<=0.5:AND:
WeightDamsWithdrawal-WeightGroundwaterPumping>=0.2,1+(1-
WeightGroundwaterPumping) *Adjustment,1),0)
Units:**undefined**
(053) Desalination=DesalinationCapacity/12 Units:Mm³/Month
(054) DesalinationCapacity=WITHLOOKUP(Years,
([(1975,0)-(2100,150)],(1995.57,0),(1997,7.3),(1999,14.6),(1999.85,14.9123
),(2001,33.58),(2008.1,34.2105),(2009,44.53),(2010.55,44.53),(2050.92,44.53))) Units:Mm³/Year
(055) DevelopmentEffectDomestic=WITHLOOKUP(EconomicDevelopment/10493.2,
([(-10,-10)-(10,10)],(-10,-10),(0,0),(10,10))) Units:**undefined**
(056) DishWasheract=
DishWasherStandard-(DishWasherStandard-DishWasherOptimumtec)*TechnologicalEfficiency
/100-(DishWasherStandard-DishWasherOptimumbeh)*BehavioralEfficiencyDomestic/100
Units:l/hh
(057) DishWasherOptimumbeh=54.6 Units:l/hh
(058) DishWasherOptimumtec=20.3
Units:l/hh (059) DishWasherStandard=68.3
Units:l/hh
(060) "DomesticinfluenceonLandscaping&AmenitiesWaterDemand"=WITHLOOKUP
(EconomicDevelopment/11318,([(0,0)-(20,20)],(0,0),(0.5,0.5),(0.8,0.8),(1,1),(2,2),(4,4),(10,10),(20
,20)))
Units:**undefined** (061) DomesticWaterDemand=PerHouseholdDailyWaterDemand*Households*30.44/1e+009
Units:Mm³/Month
(062) EconomicDevelopment=AgricultureSector+OtherSectors+TourismSector Units:m€
(063)EffectofBehavioralEfficiencyonDevelopmentEffect=WITHLOOKUP(BehavioralEfficien
cyDomestic/100,([(-2,-2)-(2,2)],(-2,-2),(0,0),(1,1),(2,2)))
Units:**undefined** (064)
EffectofBehavioralEfficiencyonGDPEffect=WITHLOOKUP(BehavioralEfficiencyTo
urism/100, ([(-10,-10)-(100,100)],(0,0),(10,10)))
Units:**undefined**
(065 )EffectofGDPTourismonperCapitaDemand=WITHLOOKUP(TourismSector/1000, ([(0,0)-(10,10)],(0,0),(1,1),(6.26911,6.44737),(10,10)))
Units:**undefined**
(066) "EffectofTourismonLandscaping&AmenitiesWaterDemand"=WITHLOOKUP
(TourismSector/873.2,([(0,0)-(20,20)],(0,0),(0.5,0.5),(0.8,0.8),(1,1),(2,2),(4,4),(10,10),(20 ,20)))
Units:**undefined**
(067 )EffectiveArea= AgricultureSector*1e+006/BIPperArea
Units:ha
6
(068)EffluenttoAquifer=
MAX(RecyclingRateAquifer/100*(PotableWaterUse+IndustryWaterUse)*RecyclingRatereal
,0)
Units:Mm³/Month (069) EFGW=
EnvironmentalFlowGW/12*MIN((1-EnvironmentalFlowGW/Aquifer),1)
Units:Mm³/Month (070) EFSW=
MAX(EnvironmentalFlowSW*MIN((1-EnvironmentalFlowSW/SurfaceWaterStorage),1),0)
Units:Mm³/Month (071) EnvironmentalFlow=WITHLOOKUP(Years,
([(1975,0)-(2050,40)],(1975,10),(2000,12.5),(2005,14),(2010,16),(2020,20),(2050,25)))
Units:**undefined**
(072) EnvironmentalFlowGW=0.58*EnvironmentalFlow/12 Units:**undefined**
(073) EnvironmentalFlowSW=0.42*EnvironmentalFlow/12
Units:**undefined** (074) FINALTIME=913Units:Month
Thefinaltimeforthesimulation.
(075) GardenIrrigationact=GardenIrrigationStandard-(GardenIrrigationStandard-GardenIrrigationoptimumtec)*TechnologicalEfficiency/100-(GardenIrrigationStandard-
GardenIrrigationoptimumbeh)*BehavioralEfficiencyDomestic/100*(1-
GreywaterRecyclingDomestic/100)
Units:**undefined** (076) GardenIrrigationoptimumbeh=39.7
Units:l/hh
(077) GardenIrrigationoptimumtec=58.8 Units:l/hh
(078) GardenIrrigationStandard=73.5
Units:l/hh
(079) GreywaterRecyclingDomestic=INTEG(Investmentingreywaterrecyclingdomestic, 0)
Units:**undefined**
(080) GreywaterTreatment=INTEG(Investmentingreywaterrecyclingtourism,0) Units:**undefined**
(081) GroundwaterLayer1=INTEG(PercolationI-Baseflow-PercolationII,400)
Units:Mm³ (082 )GroundwaterLayer1StorageCapacity=1000
Units:**undefined**
(083) GroundwatertoSea=MAX(PercolationII*0.35+SaturationEffectGW*PercolationII
*0.65+0.02*Aquifer,0) Units:Mm³/Month
(084 )Households=
Population/AverageNumberofPersonsperHousehold Units:hh
(085) IndustryWaterDemand=WITHLOOKUP(
Years,([(1975,0)-(2050,10)],(1975.23,1.84211),(1981.65,1.92982),(1990.83,2.19298 ),(1996.56,2.80702),(2000,3.5),(2005,5),(2010,6),(2020,7),(2050,10)))
Units:**undefined**
(086) IndustryWaterUse=IndustryWaterDemand/12
Units:Mm³/Month (087) Infiltration=MAX(MIN(PotententialInfiltrationRate,SurfaceWater),0)
Units:Mm³/Month
(088) INITIALTIME=1 Units:Month
Theinitialtimeforthesimulation.
7
(089) InvestmentinBehavioralEfficiencyAgriculture=PolicyConsciousConsumptionAgriculture-CF7
Units:**undefined**
(090) InvestmentinBehavioralEfficiencyDomestic=PolicyConsciousConsumptionDomestic-CF5
Units:**undefined** (091) InvestmentinBehavioralEfficiencyTourism=PolicyConsciousConsumptionTourism-CF6
Units:**undefined**
(092) Investmentingreywaterrecyclingdomestic=ChoiceDomesticGreyWater-CF8 Units:**undefined**
(093) Investmentingreywaterrecyclingtourism=ChoiceTourismGreyWater-CF9
Units:**undefined** (094) Investmentinwatersavingtechnologyagriculture=ChoiceFarmers-CF4
Units:**undefined**
(095) Investmentinwatersavingtechnologydomestic=(ChoiceHouseholds-CF2)
Units:**undefined** (096) Investmentinwatersavingtechnologytourism=(ChoiceTourism-CF)
Units:**undefined**
(097) IrrigationWaterDemand=AgricultureWaterDemand+"Landscaping&AmenitiesWaterDemand" Units:Mm³/Year
(098) IrrigationWaterUse=MIN(IrrigationWaterDemand,"Non-PotableWaterSupply")
Units:Mm³/Month (099) "Landscaping&AmenitiesWaterDemand"=("DomesticinfluenceonLandscaping&
AmenitiesWaterDemand"*0.4+"EffectofTourismonLandscaping&AmenitiesWaterDemand"*0
.6)/12*8.5
Units:Mm³/Year (100) "Landscaping&Amenities"="Landscaping&AmenitiesWaterDemand"*0
Units:Mm³/Month
(101) LenghtofStay=WITHLOOKUP(Years, ([(1975,0)-(2050,30)],(1975,14.5),(1980,14),(1993,12.2),(1994,12),(1995,
11.5),(1996,11),(1997,11.5),(1998,11.3),(1999,11.3),(2000,11.3),(2010,11),
(2020,11),(2050,11)))
Units:**undefined** (102) MaximumAquiferPercolationRate=400
Units:**undefined**
(103) MaximumInfiltrationRate=600 Units:mm
(104) MaximumSoilPercolationRate=150
Units:mm (105) Monthly=
RAMP(1,0,912)-(STEP(12,13)+STEP(12,25)+STEP(12,37)+STEP(12,49)+STEP(12,61)+
STEP(12,73)+STEP(12,85)+STEP(12,97)+STEP(12,109)+STEP(12,121)+STEP(12,133)+
STEP(12,145)+STEP(12,157)+STEP(12,169)+STEP(12,181)+STEP(12,193)+STEP(12,205)+ STEP(12,217)+STEP(12,229)+STEP(12,241)+STEP(12,253)+STEP(12,265)+STEP(12,277)+
STEP(12,289)+STEP(12,301)+STEP(12,313)+STEP(12,325)+STEP(12,337)+STEP(12,349)+
STEP(12,361)+STEP(12,373)+STEP(12,385)+STEP(12,397)+STEP(12,409)+STEP(12,421)+ STEP(12,433)+STEP(12,445)+STEP(12,457)+STEP(12,469)+STEP(12,481)+STEP(12,493)+
STEP(12,505)+STEP(12,517)+STEP(12,529)+STEP(12,541)+STEP(12,553)+STEP(12,565)+
STEP(12,577)+STEP(12,589)+STEP(12,601)+STEP(12,613)+STEP(12,625)+STEP(12,637)+ STEP(12,649)+STEP(12,661)+STEP(12,673)+STEP(12,685)+STEP(12,697)+STEP(12,709)+
STEP(12,721)+STEP(12,733)+STEP(12,745)+STEP(12,757)+STEP(12,769)+STEP(12,781)+
STEP(12,793)+STEP(12,805)+STEP(12,817)+STEP(12,829)+STEP(12,841)+STEP(12,853)+
STEP(12,865)+STEP(12,877)+STEP(12,889)+STEP(12,901)) Units:**undefined**
(106) MonthlyPrecipitation=AnnualDistributionofRainfall*AnnualPrecipitationData
Units:mm/Year (107) NaturalStorageCapacity=80
Units:mcm
8
(108) "Non-PotableWaterDemand"=IrrigationWaterDemand
Units:Mm³/Year
(109) "Non-PotableWaterSupply"=INTEG("PumpingforNon-
PotableWaterSupply"+ReuseforIrrigation+"WithdrawalforNon-PotableWaterSupply" -IndustryWaterUse-IrrigationWaterUse-"Landscaping&Amenities",2)
Units:Mm³
(110) OptimalBehaviroralEfficiencyAgriculture=5413 Units:l/ha
(111) OptimalTechnicalEfficiencyAgriculture=4758.1
Units:l/ha (112) OtherSectors=WITHLOOKUP(Years,
([(1975,0)-(2050,30000)],(1975,8450.21),(1978,8379.96),(1980,7861.09),(1985
,7315.39),(1990,8304.23),(1993,8215.36),(1995,8239.29),(1996,8429.96),(1997
,8551.55),(1998,9020.48),(1999,9493.33),(2000,9962.55),(2001,10482.3),(2002 ,10655.6),(2003,11095.7),(2004,11776.7),(2005,12269.8),(2006,12904.8),(2007
,13687.9),(2008,14289.2),(2009,14503.5),(2010,14721.1),(2011,14941.9),(2012
,15166),(2013,15393.5),(2014,15624.4),(2015,15858.8),(2016,16096.7),(2017, 16338.1),(2018,16583.2),(2019,16831.9),(2020,17084.4),(2021,17340.7),(2022
,17600.8),(2023,17864.8),(2024,18132.8),(2025,18404.8),(2026,18680.8),(2027
,18961.1),(2028,19245.5),(2029,19534.2),(2030,19827.2),(2031,20124.6),(2032 ,20426.4),(2033,20732.8),(2034,21043.8),(2035,21359.5),(2036,21679.9),(2037
,22005.1),(2038,22335.2),(2039,22670.2),(2040,23010.2),(2041,23355.4),(2042
,23705.7),(2043,24061.3),(2044,24422.2),(2045,24788.6),(2046,25160.4),(2047
,25537.8),(2048,25920.9),(2049,26309.7),(2050,26704.3))) Units:m€
(113) PerCapitaDemandTourism=
(ReferenceTourismperCapitaDemand2000-(ReferenceTourismperCapitaDemand2000 -TourismDemandOptimumtec)*TechnologicalEfficiencyTourism/100-
(ReferenceTourismperCapitaDemand2000
-TourismDemandOptimumbeh)*BehavioralEfficiencyTourism/100-
0.15*ReferenceTourismperCapitaDemand2000*GreywaterTreatment/100)*(1+(EffectofGDPTourismonperCapitaDemand-1)*(1-EffectofBehavioralEfficiencyonGDPEffect))
Units:l/cap
(114) PerhaWaterDemandAgriculture= (ReferenceWaterDemandAgriculture2000-(ReferenceWaterDemandAgriculture2000
-OptimalBehaviroralEfficiencyAgriculture)*BehavioralEfficiencyAgriculture/100-
(ReferenceWaterDemandAgriculture2000-OptimalTechnicalEfficiencyAgriculture )*TechnologicalEfficiencyAgriculture/100)*(1+(PlantingofProfitableCrops-1)*(1-
PlantingofAdaptedCrops)*0.5)
Units:l/ha
(115) PerHouseholdDailyWaterDemand= (Bathact+Cleaningact+DishWasheract+Showeract+Tapsact+Toiletactual+
WashingMashineact)*DevelopmentEffectDomestic
*(1-EffectofBehavioralEfficiencyonDevelopmentEffect) Units:l/hh
(116) PercolationI=
MAX(MIN(SoilWater-0.3*SoilStorageCapacity,PotentialSoilPercolationRate),0) Units:Mm³/Month
(117) PercolationII=MIN(0.98*GroundwaterLayer1,PotentialAquiferPercolationRate)
Units:Mm³/Month
(118) PercolationtoGW=MAX(Agriculture*0.7,0) Units:Mm³/Month
(119) PlantingofAdaptedCrops=WITHLOOKUP(BehavioralEfficiencyAgriculture/100,
([(0,0)-(10,10)],(0,0),(10,10))) Units:**undefined**
(120) PlantingofProfitableCrops=WITHLOOKUP(AgricultureSector/310.6,
9
([(0,0)-(20,20)],(0,0),(1,1),(10,10),(20,20)))
Units:**undefined**
(121) PolicyConsciousConsumptionAgriculture=WITHLOOKUP(Years,
([(1900,80)-(2100,100)],(1975,85),(2000,91),(2050,93))) Units:**undefined**
(122) PolicyConsciousConsumptionDomestic=WITHLOOKUP(Years,
([(1975,0)-(2050,150)],(1975,75.78),(2000,77.9),(2010.09,80),(2049.77,80))) Units:**undefined**
(123) PolicyConsciousConsumptionTourism=WITHLOOKUP(Years,
([(1970,0)-(2100,100)],(1970.98,78.9474),(1975,80),(2000,86.8),(2050,88))) Units:**undefined**
(124) Population=PopulationTimeSerie
Units:**undefined**
(125) PopulationTimeSerie=WITHLOOKUP(Years, ([(1975,0)-(2050,1e+006)],(1975,498300),(1976,497600),(1977,498000),(1978
,501300),(1979,505800),(1980,512300),(1981,518200),(1982,524600),(1983,531500
),(1984,538400),(1985,544600),(1986,550900),(1987,556600),(1988,562700),(1989 ,572700),(1990,587100),(1991,603100),(1992,619200),(1993,632900),(1994,645400
),(1995,656300),(1996,666300),(1997,675200),(1998,682900),(1999,690500),(2000
,697500),(2001,705500),(2002,705539),(2007,748217),(2012,784762),(2017,813407 ),(2022,832061),(2027,845466),(2032,851810),(2037,851754),(2042,845776),(2047
,835747),(2052,822069)))
Units:**undefined**
(126) PotableWaterDemand= TourismWaterDemand+DomesticWaterDemand
Units:Mm³/Month
(127) PotableWaterSupply=INTEG(Desalination+PumpingforDomesticSector+ WithdrawalforDomesticUse-PotableWaterUse,2)
Units:Mm³
(128) PotableWaterUse=
MIN(PotableWaterDemand,PotableWaterSupply) Units:Mm³/Month
(129) PotententialInfiltrationRate=
MaximumInfiltrationRate-SoilWater/(SoilStorageCapacity)*MaximumInfiltrationRate Units: Mm³/Month
(130) PotentialAquiferPercolationRate=
MaximumAquiferPercolationRate*GroundwaterLayer1/GroundwaterLayer1StorageCapacity *(1-(Aquifer/AquiferCapacity^6))
Units:**undefined**
(131) PotentialEvapotranspiration=AnnualDistributionofEvapotranspiration/100*1750*AreaCyprus
/1000 Units:**undefined**
(132) PotentialSoilPercolationRate=MaximumSoilPercolationRate*0.7*
SoilWater/SoilStorageCapacity*(1-GroundwaterLayer1/GroundwaterLayer1StorageCapacity) Units:**undefined**
(133) PrecipitationFlow=MonthlyPrecipitation*AreaCyprus/1000
Units:Mm³/Month (134) ProductivityPotatoes=35
Units:**undefined**
(135) PumpingforDomesticSector=MAX(MIN("RatioGW/WaterNeedDomestic"
*(PotableWaterDemand-Desalination)*CompensationGWforSW, WeightGroundwaterPumping*"RatioGW/WaterNeedDomestic"*(PotableWaterDemand
-Desalination)*CompensationGWforSW),0)
Units:Mm³/Month (136) "PumpingforNon-PotableWaterSupply"=
MAX(MIN("RatioGW/WaterNeedIrrigation"*(IrrigationWaterDemand
10
-ReuseforIrrigation+"Landscaping&Amenities"+IndustryWaterUse)*CompensationGWforSW
,WeightGroundwaterPumping*"RatioGW/WaterNeedIrrigation"*(IrrigationWaterDemand
-ReuseforIrrigation+"Landscaping&Amenities"+IndustryWaterUse)
*CompensationGWforSW),0) Units:Mm³/Month
(137) "RatioGW/WaterNeedDomestic"=1-"RatioSW/WaterNeedDomestic"
Units:**undefined** (138) "RatioGW/WaterNeedIrrigation"=1-"RatioSW/WaterNeedIrrigation"
Units:**undefined**
(139) "RatioSW/WaterNeedDomestic"=0.47 Units:**undefined**
(140) "RatioSW/WaterNeedIrrigation"=0.43
Units:**undefined**
(141) RationGDPTourismperCapita=WITHLOOKUP(Years, ([(1900,200)-(2100,500)],(1975,3814.35),(1976,1937.01),(1977,1432.52),(1978
,1134.17),(1979,985.158),(1980,861.207),(1981,726.738),(1982,603.415),(1983
,562.628),(1984,515.441),(1985,488.975),(1986,498.819),(1987,466.043),(1988 ,430.687),(1989,375.287),(1990,355.207),(1991,402.032),(1992,306.934),(1993
,334.166),(1994,314.647),(1995,327.238),(1996,341.538),(1997,337.452),(1998
,334.862),(1999,332.171),(2000,325.068),(2001,328.88),(2002,338.676),(2003 ,337.523),(2004,324.605),(2005,313.595),(2006,334.621),(2007,338.896),(2008
,329.111),(2009,329.124),(2010,329.106),(2011,329.096),(2012,329.093),(2013
,329.096),(2014,329.102),(2015,329.111),(2016,329.122),(2017,329.098),(2018
,329.109),(2019,329.119),(2020,329.127),(2021,329.097),(2022,329.097),(2023 ,329.125),(2024,329.113),(2025,329.126),(2026,329.099),(2027,329.126),(2028
,329.112),(2029,329.117),(2030,329.11),(2031,329.12),(2032,329.117),(2033,
329.098),(2034,329.122),(2035,329.101),(2036,329.118),(2037,329.117),(2038 ,329.123),(2039,329.11),(2040,329.104),(2041,329.101),(2042,329.103),(2043
,329.106),(2044,329.111),(2045,329.115),(2046,329.118),(2047,329.12),(2048
,329.118),(2049,329.112),(2050,329.101),(2051,329.106)))
Units:€/cap (142) Recyclingrate=AnnualCapacityforWastewaterTreatment/12/(PotableWaterUse
+IndustryWaterUse)
Units:**undefined** (143) RecyclingRateAgriculture=WITHLOOKUP(Years,
([(1000,0)-(2100,100)],(1950,0),(1973.55,0),(1993.12,0),(2004,71),(2006,
71),(2007,74),(2009.02,75),(2015,90),(2050,90))) Units:**undefined**
(144) RecyclingRateAquifer=WITHLOOKUP(Years,
([(1975,0)-(2050,100)],(1975,10.2632),(1986.24,10.7895),(1992.66,11.0526
),(1997.94,11.2281),(2000.69,11.4035),(2004,12),(2005,15),(2005.96,16.2281 ),(2007.34,15.7895),(2008.72,16.2281),(2013.76,15.3509),(2023.85,13.1579),
(2038.3,10.5263),(2043.58,9.21053),(2050.23,7.89474)))
Units:**undefined** (145) RecyclingRatereal=MIN(Recyclingrate,0.8)
Units:**undefined**
(146) RecyclingRatetotheSea=100-RecyclingRateAgriculture-RecyclingRateAquifer Units:**undefined**
(147) ReductionFactorforTensionZone=WITHLOOKUP(SoilWater/(0.3*SoilStorageCapacity),
([(0,0)-(5,1)],(0,0),(0.5,0.5),(0.6,1),(1,1),(20,1)))
Units:**undefined** (148) ReferenceBehavorialEfficiencyAgriculture2000=
OptimalBehaviroralEfficiencyAgriculture/ReferenceWaterDemandAgriculture2000*100
Units:**undefined** (149) ReferenceBehavorialEfficiencyDomestic2000=(Bathoptimumbeh+
11
CleaningOptimumbeh+DishWasherOptimumbeh+GardenIrrigationoptimumbeh+Showeroptim
umbeh+Tapsoptimumbeh+Toiletoptimumbeh+WashingMashineOptimumbeh)/(BathStandard
+CleaningStandard+DishWasherStandard+GardenIrrigationStandard+ShowerStandard+TapsS
tandard+ToiletStandard+WashingMashineStandard)*100 Units:**undefined**
(150) ReferenceBehavorialEfficiencyTourism2000=TourismDemandOptimumbeh/
ReferenceTourismperCapitaDemand2000*100 Units:**undefined**
(151) ReferenceTechnologicalEfficiencyAgriculture2000=OptimalTechnicalEfficiencyAgriculture/
ReferenceWaterDemandAgriculture2000*100 Units:**undefined**
(152) ReferenceTechnologicalEfficiencyDomestic2000=(Bathoptimumtec+CleaningOptimumtec
+DishWasherOptimumtec+GardenIrrigationoptimumtec+Showeroptimumtec+Tapsoptimumte
c+Toiletoptimumtec+WashingMashineOptimumtec)/(BathStandard+CleaningStandard+DishWasherStandard+GardenIrrigationStandard+ShowerStandard+TapsStandard+ToiletStandard+
WashingMashineStandard)*100
Units:**undefined** (153) ReferenceTechnologicalEfficiencyTourism2000=TourismDemandOptimumtec/
ReferenceTourismperCapitaDemand2000*100
Units:**undefined** (154) ReferenceTourismperCapitaDemand2000=465
Units:l/cap
(155) ReferenceWaterDemandAgriculture2000=5948
Units:l/ha (156) ReuseforIrrigation=(PotableWaterUse+IndustryWaterUse)*(RecyclingRateAgriculture/100)
*RecyclingRatereal
Units:Mm³/Month (157) Runoff=MAX(SMOOTH(MAX(SurfaceWater-Infiltration,0)*0.284,2),0)
Units:Mm³/Month
(158) Runoffplusbaseflow=Baseflow+Runoff
Units:**undefined** (159) SaturationDam=MAX((SurfaceWaterStorage/(NaturalStorageCapacity+StorageCapacity))^11
,0)
Units:**undefined** (160) SaturationEffectGW=MAX((Aquifer/AquiferCapacity)^15,0)
Units:**undefined**
(161) SAVEPER= TIMESTEP
Units:Month
Thefrequencywithwhichoutputisstored.
(162) Showeract= ShowerStandard-(ShowerStandard-Showeroptimumtec)*TechnologicalEfficiency
/100-(ShowerStandard-Showeroptimumbeh)*BehavioralEfficiencyDomestic/100
Units:l/hh (163) Showeroptimumbeh=57.9
Units:l/hh
(164) Showeroptimumtec=57.9 Units:l/hh
(165) ShowerStandard=60.6
Units:l/hh
(166) SoilStorageCapacity=1000 Units:**undefined**
167) SoilWater=INTEG(Infiltration+PercolationtoGW-ActualEvapotranspiration2-PercolationI,
210) Units:Mm³
168) StorageCapacity=WITHLOOKUP(Years,
12
([(1975,0)-(2050,500)],(1975,63.7),(1976,63.7),(1977,63.9),(1978,63.9),(
1979,63.9),(1980,64.3),(1981,64.7),(1982,118.8),(1983,119.3),(1984,119.9),
(1985,152.5),(1986,176.5),(1987,183.4),(1988,298.4),(1993,298.4),(1994,300.1
),(1995,300.1),(1996,300.4),(1997,300.4),(1998,304.6),(1999,304.6),(2000,304.7 ),(2001,304.7),(2050,304.7)))
Units:mcm
169) SurfaceWater=INTEG(PrecipitationFlow-ActualEvapotranspirationI-Infiltration-Runoff, 170) SurfaceWaterStorage=INTEG(Baseflow+Runoff-EFSW-SurfaceWatertoOcean-
WithdrawalforDomesticUse-"WithdrawalforNon-PotableWaterSupply"-EFSW,100)
Units:Mm³ 171) SurfaceWatertoOcean=MAX((Baseflow+Runoff)*0.05+SaturationDam*(Baseflow+Runoff)
*0.95+0.13*SurfaceWaterStorage,0)
Units:Mm³/Month
172) Tapsact= TapsStandard-((TapsStandard-Tapsoptimumtec)*(TechnologicalEfficiency
/100))-((TapsStandard-Tapsoptimumbeh)*(BehavioralEfficiencyDomestic/100))
Units:l/hh 173) Tapsoptimumbeh=32.1
Units:l/hh
(174) Tapsoptimumtec=21 Units:l/hh
175) TapsStandard=42
Units:l/hh (176) TechnologicalEfficiency=(TechnologyEfficiencyDomesticStock-
ReferenceTechnologicalEfficiencyDomestic2000)*100/(100-
ReferenceTechnologicalEfficiencyDomestic2000) Units:**undefined**
177) TechnologicalEfficiencyAgriculture=
(WaterSavingIrrigationTechniques-ReferenceTechnologicalEfficiencyAgriculture2000
)*100/(100-ReferenceTechnologicalEfficiencyAgriculture2000) Units:**undefined**
178) TechnologicalEfficiencyTourism=
(TechnologyEfficiencyTourismStock-ReferenceTechnologicalEfficiencyTourism2000 )*100/(100-ReferenceTechnologicalEfficiencyTourism2000)
Units:**undefined**
(179) TechnologyEfficiencyDomesticStock=INTEG(Investmentinwatersavingtechnologydomestic, 1)
Units:**undefined**
180) TechnologyEfficiencyTourismStock=INTEG(Investmentinwatersavingtechnologytourism,
0) Units:**undefined**
(181) TIMESTEP=0.125
Units:Month Thetimestepforthesimulation.
(182) Toiletactual=
ToiletStandard-(ToiletStandard-Toiletoptimumtec)*TechnologicalEfficiency /100-(ToiletStandard-Toiletoptimumbeh)*BehavioralEfficiencyDomestic/100
*(1-GreywaterRecyclingDomestic/100)
Units:l/hh
(183) Toiletoptimumbeh=108.9 Units:l/hh
(184) Toiletoptimumtec=88.2
Units:l/hh (185) ToiletStandard=147
Units:l/hh
13
(186) TourismDemandOptimumbeh=402
Units:l/cap
(187) TourismDemandOptimumtec=318
Units:l/cap (188) TourismSector=WITHLOOKUP(Years,
([(1975,0)-(2050,3000)],(1975,179.6),(1976,209.86),(1977,242.96),(1978,261.83
),(1979,287.78),(1980,304.33),(1981,312),(1982,330.78),(1983,349.24),(1984 ,379.87),(1985,397.83),(1986,412.99),(1987,442.07),(1988,478.85),(1989,517.01
),(1990,554.65),(1991,556.87),(1992,611.11),(1993,615.2),(1994,651),(1995,
687.2),(1996,666),(1997,704.6),(1998,744.3),(1999,808.6),(2000,873.2),(2001 ,886.9),(2002,819),(2003,777.4),(2004,762.5),(2005,774.6),(2006,803.4),(2007
,818.8),(2008,791.1),(2009,803),(2010,815),(2011,827.2),(2012,839.6),(2013
,852.2),(2014,865),(2015,878),(2016,891.2),(2017,904.5),(2018,918.1),(2019
,931.9),(2020,945.9),(2021,960),(2022,974.4),(2023,989.1),(2024,1003.9),(2025 ,1019),(2026,1034.2),(2027,1049.8),(2028,1065.5),(2029,1081.5),(2030,1097.7
),(2031,1114.2),(2032,1130.9),(2033,1147.8),(2034,1165.1),(2035,1182.5),(2036
,1200.3),(2037,1218.3),(2038,1236.6),(2039,1255.1),(2040,1273.9),(2041,1293 ),(2042,1312.4),(2043,1332.1),(2044,1352.1),(2045,1372.4),(2046,1393),(2047
,1413.9),(2048,1435.1),(2049,1456.6),(2050,1478.4),(2051,1500.6)))
Units:m€ (189) TourismWaterDemand=PerCapitaDemandTourism*VariablePopulation
*LenghtofStay/1000/1e+006
Units:Mm³/Month
(190) UnusedDischarge= (1-RecyclingRatereal)*(PotableWaterUse+IndustryWaterUse)+RecyclingRatetotheSea
/100*(PotableWaterUse+IndustryWaterUse)*RecyclingRatereal
Units:Mm³/Month (191) ValidationRunoff=
AnnualDistributionofRunoff/100*AnnualRunoff
Units:**undefined**
(192) VariablePopulation= TourismSector/RationGDPTourismperCapita*YearlyVariationofTourists*1e+006
Units:cap
(193) VirtualWater=Agriculture*0.3 Units:Mm³/Month
(194) WashingMashineact=
WashingMashineStandard-(WashingMashineStandard-WashingMashineOptimumtec )*TechnologicalEfficiency/100-(WashingMashineStandard-WashingMashineOptimumbeh
)*BehavioralEfficiencyDomestic/100
Units:l/hh
(195) WashingMashineOptimumbeh=33.1 Units:l/hh
(196) WashingMashineOptimumtec=26.5
Units:l/hh (197) WashingMashineStandard=36.8
Units:l/hh
(198) Wastewater=INTEG(IndustryWaterUse+PotableWaterUse-EffluenttoAquifer-ReuseforIrrigation-UnusedDischarge,
0)
Units:Mm³
(199) WastewaterCapacity=AnnualCapacityforWastewaterTreatment Units:Mm³/Year
(200) WaterSavingIrrigationTechniques=INTEG(Investmentinwatersavingtechnologyagriculture,
0) Units:**undefined**
14
(201) WaterScarcityAgriculture=ACTIVEINITIAL(SMOOTH3((IrrigationWaterDemand-
IrrigationWaterUse)/IrrigationWaterDemand*1.2,8),0)
Units:**undefined**
(202) "WaterScarcityDomestic+Tourism"=SMOOTH3((1-PotableWaterUse/(PotableWaterDemand)),8)
Units:**undefined**
(203) WaterScarcityTotal=WaterScarcityAgriculture+"WaterScarcityDomestic+Tourism" Units:**undefined**
(204) WaterShortageDams=WITHLOOKUP(Years,
([(1975,0)-(2050,10)],(1987,0.41),(1988,0.21),(1989,0.25),(1990,0.66),(1991 ,0.47),(1992,0.39),(1993,0.34),(1994,0.26),(1995,0.32),(1996,0.6),(1997,0.75
),(1998,0.83),(2000,0.79)))
Units:**undefined**
(205) WaterShortageDamsMonthly=WaterShortageDams/12 Units:**undefined**
(206) WeightDamsWithdrawal=
MAX(MIN(0.9*SurfaceWaterStorage/(("RatioSW/WaterNeedDomestic"*(PotableWaterDemand-Desalination)+"RatioSW/WaterNeedIrrigation"*(IrrigationWaterDemand-
ReuseforIrrigation))),1),0)
Units:**undefined** (207) WeightGroundwaterPumping=
MAX(MIN(0.8*Aquifer/("RatioGW/WaterNeedDomestic"*(PotableWaterDemand
-Desalination)+"RatioGW/WaterNeedIrrigation"
*(IrrigationWaterDemand-ReuseforIrrigation)),1),0) Units:**undefined**
(208) WithdrawalforDomesticUse=
MAX(MIN(WeightDamsWithdrawal*"RatioSW/WaterNeedDomestic"*(PotableWaterDemand-Desalination)*CompensationSWforGW,"RatioSW/WaterNeedDomestic"
*(PotableWaterDemand-Desalination)*CompensationSWforGW),0)
Units:Mm³/Month
(209) "WithdrawalforNon-PotableWaterSupply"= MAX(MIN("RatioSW/WaterNeedIrrigation"*(IrrigationWaterDemand-
ReuseforIrrigation+"Landscaping&Amenities"+IndustryWaterUse)*CompensationSWforGW
,WeightDamsWithdrawal*"RatioSW/WaterNeedIrrigation"*(IrrigationWaterDemand -ReuseforIrrigation+"Landscaping&Amenities"+IndustryWaterUse)
*CompensationSWforGW),0)
Units:Mm³/Month (210) YearlyVariationofTourists=WITHLOOKUP(Monthly,
([(1,0)-(13,0.5)],(1.5,0.025),(2.5,0.031),(3.5,0.052),(4.5,0.083),(5.5,0.105
),(6.5,0.11),(7.5,0.139),(8.5,0.136),(9.5,0.121),(10.5,0.108),(11.5,0.05),
(12.5,0.041))) Units:**undefined**
(211) Years=RAMP(1,1,912)/12+1975
Units:Year
1
Appendix H: Examples for the compensation mechanism
The following examples help to clarify the compensation mechanism. Tables 1a+b show five different
initial conditions with water shortages in the groundwater storage. The first allocation rule tests if the
water shortage exceeds 50% of the demand. The second rule compares the withdrawal rates and
releases the final share at which the shortages are compensated.
Table 1a: Examples of compensation mechanisms, part a.
Ex. GW
Demand Available GW CGW
SW Demand Available CSW
[Mm³] [Mm³] [Mm³] [Mm³] [Mm³]
1 15 7 8 0.47 15 30 2
2 15 7 8 0.47 15 18 1.2
3 15 9 6 0.6 15 20 1.33
4 15 4 11 0.27 15 12 0.8
5 15 6 9 0.4 15 10,5 0.7
Table 1b: Examples of compensation mechanisms, part b.
Ex. C C SW Demand new Available SW
new 𝐶𝐺𝑊𝑒𝑓𝑓
[%] [Mm³] [Mm³] [%] [Mm³]
1 75 6 21 1.43 6
2 75 6 21 0,86 5.14
3 0 0 15 1.33 0
4 75 8.25 23,25 0.52 4.26
5 20 1.8 16.8 0.63 1.13
GW ≡ Groundwater
SW ≡ Surface Water
C ≡ Compensation flow for time step t
𝐶𝐺𝑊𝑒𝑓𝑓
≡ effective volume of compensation flow for time step t
R ≡ Factor from figure 39
In example 1, the shortage in groundwater supply amounts to 8 Mm³ and only 47% of the demand can
be satisfied. On the contrary, there is an overcapacity in the surface water storage so that 75% (0.75 x
8 Mm³=6 Mm³) of the shortage is taken over by the surface resource. In Example 2, the values are
similar except that the available surface water storage can not carry the additional demand from the
compensation mechanism. The emerging shortage in the surface store is multiplied with the initial and
compensation demand. Consequently, not 75% but 64% (0.75x0.86) of the missing water is
counterbalanced (5.14 Mm³). Example 3 also shows abundant water in the surface storage, but more
than 50% of the groundwater demand can be satisfied. Consequently, the water stress is not considered
to be severe enough to justify the effort to convey additional amounts from other sources. The
situation in example 4 comprises water shortages in both storages. Nevertheless, the difference
between the capacities exceeds 20% that justifies a compensation practice. Thus, 75% of the water
shortage in the groundwater storage is added to the water demand on surface waters. As the surface
resource also suffers from water stress, the additional demand of 8.25 can not be met so that,
eventually, only 39% (0.75x0.52) of the shortage will be satisfied (4.26 Mm³). In the last example, the
2
difference of the capacities from surface- and groundwater constitutes 0.3 which induces a
compensation-ratio of 20% and the balancing-flow of 1.13 Mm³ (0.2x0.63x9 Mm³).
Appendix I: Water balance of the Republic of Cyprus (WDD 2009).
Appendix J: Example for a decision rule of water rationing
The following figures display simple decision rules for water rationing in the domestic and tourism
sectors (Figure 1), and the agriculture sector (Figure 2). Both table functions have the respective water
scarcity indicators as inputs which represent real water scarcity. In order to avoid depleting water
storages the policy setter rations the water at rates which are above the real water scarcity rate.
If the real water scarcity indicator is zero, no rationing is applied. In case of 100% water scarcity, the
decision-maker is forced to ration 100% of the water accordingly. Between these extreme points, the
policy setter can vary the rates, as depicted in the figures.
The domestic sector has the limit of 15% rationing for most of the values (Figure 1). On the contrary,
the agriculture sector rationing is unproportional with a value of about 70% in the majority of cases
(Figure 2).
Figure 1: Water Ratining in the domestic sector
Figure 2: Water Rationing in the agriculture sector
Year 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
mm 563 471 549 439 582 574 425 437 448 498 438 520 625 481 363
Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
mm 282 637 509 417 493 383 399 388 473 363 468 604 561 545 412
Year 2008 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
mm 272 494 414 482 386 511 504 373 384 393 437 385 457 549 422
Year 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038
mm 319 248 559 447 366 433 336 350 341 415 319 411 530 493 479
Year 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 mm 362 340 455 439 386 264 320 505 303 242 318 425
Data Sources:
1970-2004: Meteorological Service 2005
2008: Meteorological Service 2009
2010-2039: Estimation
2040-2050: PRECIS (Providing REgional Climates for Impact Studies) Regional Climate Model
Appendix K: Yearly Cyprus-wide Precipitation Rates
Appendix L: Reference Modes of Behavior
L1: Reference Modes of Behavior - Scenario 1a
L2: Reference Modes of Behavior - Scenario 1b
L3: Reference Modes of Behavior - Scenario 2a
L4: Reference Modes of Behavior - Scenario 2b
L5: Reference Model of Behavior – Scenario 2c
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run2
"Water Scarcity Domestic + Tourism" : run2
Annual Precipitation Data : run2 mm/Year
Natural Water Supplies
4,000 Mm³
400 Mm³
3,000 Mm³
300 Mm³
2,000 Mm³
200 Mm³
1,000 Mm³
100 Mm³
0 Mm³
0 Mm³
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Aquifer : run2 Mm³
Surface Water Storage : run2 Mm³
L1 Reference Modes of Behavior - Scenario 1a
Figure 1: Annual precipitation levels as well as agriculture, and domestic + tourism water scarcity indicators (Scenario
1a)
Figure 2: Storage levels of natural water supply: surface waters and aquifers (Scenario 1a)
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
Supply Management
150 Mm³/Year
150 Mm³/Year
1
112.5 Mm³/Year
112.5 Mm³/Year
0.75
75 Mm³/Year
75 Mm³/Year
0.5
37.5 Mm³/Year
37.5 Mm³/Year
0.25
0 Mm³/Year
0 Mm³/Year
0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Desalination Capacity Mm³/Year
Wastwater Capacity Mm³/Year
Recycling Rate real : run2
Figure 3: Supply management – Supply management – Annual capacities of desalination and wastewater recycling and
the real recycling rate (share of sewage that is recycled) (Scenario 1a)
Figure 4: Monthly water demands of the domestic, tourism, and agriculture sector (Scenario 1a)
Appendix L2: Reference Model of Behavior – Scenario 1b
Figure 1: Annual precipitation levels as well as agriculture, and domestic + tourism water scarcity indicators (Scenario
1b)
Figure 2: Storage levels of natural water supply: surface waters and aquifers (Scenario 1b)
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run1
"Water Scarcity Domestic + Tourism" : run1
Annual Precipitation Data : run1 mm/Year
Natural Water Supplies
4,000 Mm³
400 Mm³
3,000 Mm³
300 Mm³
2,000 Mm³
200 Mm³
1,000 Mm³
100 Mm³
0 Mm³
0 Mm³
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Aquifer : run1 Mm³
Surface Water Storage : run1 Mm³
Figure 3: Supply management – Annual capacities of desalination and wastewater recycling and the real recycling rate
(share of sewage that is recycled) (Scenario 1b)
Figure 4: Monthly water demands of the domestic, tourism, and agriculture sector (Scenario 1b)
Supply Management
150 Mm³/Year
150 Mm³/Year
1
112.5 Mm³/Year
112.5 Mm³/Year
0.75
75 Mm³/Year
75 Mm³/Year
0.5
37.5 Mm³/Year
37.5 Mm³/Year
0.25
0 Mm³/Year
0 Mm³/Year
0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Desalination Capacity Mm³/Year
Wastwater Capacity Mm³/Year
Recycling Rate real : run1
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
Appendix L3: Reference Model of Behavior – Scenario 2a
Natural Water Supplies
4,000 Mm³
400 Mm³
3,000 Mm³
300 Mm³
2,000 Mm³
200 Mm³
1,000 Mm³
100 Mm³
0 Mm³
0 Mm³
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Aquifer : run2 Mm³
Surface Water Storage : run2 Mm³
Figure 1: Annual precipitation levels as well as agriculture, and domestic + tourism water scarcity indicators (Scenario
2a)
Figure 2: Storage levels of natural water supply: surface waters and aquifers (Scenario 2a)
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run2
"Water Scarcity Domestic + Tourism" : run2
Annual Precipitation Data : run2 mm/Year
Figure 3: Supply management – Annual capacities of desalination and wastewater recycling and the real recycling rate
(share of sewage that is recycled) (Scenario 2a)
Figure 4: Monthly water demands of the domestic, tourism, and agriculture sector (Scenario 2a)
Supply Management
150 Mm³/Year
150
1
112.5 Mm³/Year
112.5
0.75
75 Mm³/Year
75
0.5
37.5 Mm³/Year
37.5
0.25
0 Mm³/Year
0
0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Desalination Capacity Mm³/Year
Wastwater Capacity
Recycling Rate real : run2
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run4
"Water Scarcity Domestic + Tourism" : run4
Annual Precipitation Data : run4 mm/Year
Appendix L4: Reference Model of Behavior – Scenario 2b
Figure 1: Annual precipitation levels as well as agriculture, and domestic + tourism water scarcity indicators (Scenario
2b)
Figure 2: Storage levels of natural water supply: surface waters and aquifers (Scenario 2b)
Natural Water Supplies
4,000 Mm³
400 Mm³
3,000 Mm³
300 Mm³
2,000 Mm³
200 Mm³
1,000 Mm³
100 Mm³
0 Mm³
0 Mm³
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Aquifer : run2 Mm³
Surface Water Storage : run2 Mm³
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
Figure 3: Supply management – Annual capacities of desalination and wastewater recycling and the real recycling rate
(share of sewage that is recycled) (Scenario 2b)
Figure 4: Monthly water demands of the domestic, tourism, and agriculture sector (Scenario 2b)
Supply Management
150 Mm³/Year
150
1
112.5 Mm³/Year
112.5
0.75
75 Mm³/Year
75
0.5
37.5 Mm³/Year
37.5
0.25
0 Mm³/Year
0
0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Desalination Capacity Mm³/Year
Wastwater Capacity
Recycling Rate real : run2
Natural Water Supplies
4,000 Mm³
400 Mm³
3,000 Mm³
300 Mm³
2,000 Mm³
200 Mm³
1,000 Mm³
100 Mm³
0 Mm³
0 Mm³
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Aquifer : run2 Mm³
Surface Water Storage : run2 Mm³
Appendix L5: Reference Model of Behavior – Scenario 2c
Water Scarcity
1
1
800 mm/Year
0.75
0.75
600 mm/Year
0.5
0.5
400 mm/Year
0.25
0.25
200 mm/Year
0
0
0 mm/Year
1978 1996 2015 2033 2051
Years
Water Scarcity Agriculture : run2
"Water Scarcity Domestic + Tourism" : run2
Annual Precipitation Data : run2 mm/Year
Figure 1: Annual precipitation levels as well as agriculture, and domestic + tourism water scarcity indicators (Scenario
2c)
Figure 2: Storage levels of natural water supply: surface waters and aquifers (Scenario 2c)
Sectoral Water Demands
20 Mm³/Month
6 Mm³/Month
40 Mm³/Month
15 Mm³/Month
4.5 Mm³/Month
30 Mm³/Month
10 Mm³/Month
3 Mm³/Month
20 Mm³/Month
5 Mm³/Month
1.5 Mm³/Month
10 Mm³/Month
0 Mm³/Month
0 Mm³/Month
0 Mm³/Month
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Domestic Water Demand : run4 Mm³/Month
Tourism Water Demand : run4 Mm³/Month
Agriculture Water Demand : run4 Mm³/Month
Figure 4: Monthly water demands of the domestic, tourism, and agriculture sector (Scenario 2c)
Supply Management
150 Mm³/Year
150
1
112.5 Mm³/Year
112.5
0.75
75 Mm³/Year
75
0.5
37.5 Mm³/Year
37.5
0.25
0 Mm³/Year
0
0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051
Years
Desalination Capacity Mm³/Year
Wastwater Capacity
Recycling Rate real : run2
Figure 3: Supply management – Annual capacities of desalination and wastewater recycling and the real recycling rate
(share of sewage that is recycled) (Scenario 2c)
Appendix M: Example for a policy interface (from Stave 2004)
The model simulates polices for urban air quality improvement. Measures can easily tested by moving the policy leverage on the left side of the figure. Hence, different set of policies can be entered and the
outcomes assessed by the reference modes of behavior on the right side of the screen.
Figure 1: Policy interface of a system dynamics ‘Management Flight Simulator’
Erklärung
Hiermit versichere ich, dass die Arbeit selbstständig angefertigt wurde und keine anderen als
die angegebenen und bei Zitaten kenntlich gemachten Quellen und Hilfsmittel benutzt
wurden.
Siegen, 13. Mai 2009
Johannes Halbe