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501 © IWA Publishing 2015 Journal of Water and Climate Change | 06.3 | 2015

Toward an innovative interdisciplinary method for

vulnerability assessments: the case of Taiwan

Chia-Chi Lee, Ching-Pin Tung, Tzu-Ming Liu, Jung-Hsuan Tsao,

Gin-Rong Liu, Yi-Chang Chiang and Kuo-Ching Huang

ABSTRACT

Humans live in complicated social-ecological systems within which we interact with our

surrounding environment. This interaction is of concern to various disciplines, which focus on

various system elements (factors), many of which are mutually interacting. Assessments of

vulnerability to climate change assist us in realizing the magnitude of the impact of various

climate change factors, allowing us to determine and adopt appropriate adaptation measures.

Nevertheless, previous impact-driven vulnerability assessments are either disciplinary or

multidisciplinary and cannot easily account for the interaction between different disciplines. This

paper proposes an interdisciplinary vulnerability assessment method (IVAM) to develop a

framework by which interdisciplinary vulnerabilities can be understood. In addition, IVAM

processes can promote the emergence of an interdisciplinary system, which could be used to

identify the scope of interdisciplinary influence of a particular policy, along with the critical

elements (factors) and government stakeholders of such policies. This research seeks to further

the policy goals of the national government of Taiwan vis-à-vis climate change, covering the joint

cooperation of experts from fields including environmental disaster management, public health,

food security, ecology, and water resource management. The specific advantage of IVAM,

however, is that this universal model is not limited to any of these specific disciplines.

doi: 10.2166/wcc.2014.256

Chia-Chi Lee (corresponding author)Center for Environmental Studies,National Central University, Taoyuan County,Chinese TaiwanE-mail: [email protected]

Ching-Pin TungTzu-Ming LiuJung-Hsuan TsaoDepartment of Bioenvironmental Systems

Engineering,National Taiwan University, Taipei City,Chinese Taiwan

Gin-Rong LiuCenter for Space and Remote Sensing Research,National Central University, Taoyuan County,Chinese Taiwan

Yi-Chang ChiangDepartment of Architecture and Urban Design,Chinese Culture University, Taipei City,Chinese Taiwan

Kuo-Ching HuangGraduate Institute of Urban Planning,National Taipei University, New Taipei City,Chinese Taiwan

Key words | adaptation, interdisciplinary, Taiwan, vulnerability

INTRODUCTION

Adaptation has received increased attention in recent years,

gradually replacing mitigation as the focus of human

response to climate change as a general consensus has

emerged on mitigation-related issues through the use of

international conventions to reduce the emission of green-

house gases. However, humans are still exploring ways to

adapt to climate variation. Whereas previous discussions

in the climate change research community focused exclu-

sively on mitigation, nowadays both mitigation and

adaptation should be emphasized (UNFCCC ; Linham

& Nicholls ; Christiansen et al. ; Clements et al.

; Elliot et al. ).

Practicing adaptation measures depends on the

implementation of adaptation technology. ‘Hard’

technology (hardware) refers to material measures such as

dikes and ditches, while ‘soft’ technology (software) refers

to non-material measures such as information, policies,

and institutional arrangements. Lately, ‘organizational’ tech-

nology (orgware) was proposed by Clements et al. () and

Trærup et al. () as a form of soft technology. Organiz-

ational technology refers to ‘the institutional set-up and

coordination mechanisms required to support the

implementation of hardware and software’ (Vincent et al.

, p. 69). Regardless of the kind(s) of adaptation technol-

ogies adopted, adaptation generally uses and integrates

numerous existing technologies, as opposed to mitigation

which largely depends on the development of recent tech-

nologies (UNFCCC ; Trærup et al. ). Thus, it is

mailto:[email protected]

502 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015

not surprising that many approaches to adaptation have

emerged, usually involving considerable numbers of stake-

holders, such as various sectors, government organizations

(UNFCCC ; Linham & Nicholls ; Christiansen

et al. ; Clements et al. ; Elliot et al. ), and even

NGOs and other social actors (Thomas & Twyman ).

These approaches require a tool which effectively supports

government policy-making in response to climate change.

Such a tool could identify points of vulnerability within

the complexity of real-world situations, allowing us to

determine correct adaptive action, pre-assess the outcome

of potential policies at the planning stage and help identify

government stakeholders to facilitate cooperation and

accountability.

Organized by Taiwan’s Ministry of Science and Tech-

nology (www.most.gov.tw/mp.aspx), the Taiwan integrated

research program on climate change adaptation technology

(TaiCCAT, http://taiccat.ncu.edu.tw) developed the interdis-

ciplinary vulnerability assessment method (IVAM) as an

innovative tool to support adaptation decision-making.

Based on a framework referred to as driving force-state-

response (DSR), IVAM started with the development of sub-

system mind maps from different disciplines. The system

dynamics model for each subsystem was completed by

ensuring causality between factors in each subsystem. The

subsystems were then connected to each other by shared

factors as well as causality between the factors of the var-

ious subsystems. The software for this interdisciplinary

system features a ‘uses tree’ function which allows the

user to pre-assess the extent of influence of a specific

policy on climate change and to identify that policy’s gov-

ernment stakeholders. Meanwhile, major state factors such

as ‘supplies of services’ and ‘demands for services’ could

be used to identify and estimate the interdisciplinary vulner-

ability index for each subsystem, including the effects of

factors from other subsystems. This allows the interdisci-

plinary system to determine points of vulnerability and

corresponding adaptive measures to be taken.

This article begins with a discussion of interdisciplinary/

transdisciplinary research and vulnerability assessment.

IVAM is then described, and a simple case study is pre-

sented to demonstrate IVAMs applicability. Finally, the

features and inadequacies of IVAM are analyzed.

LITERATURE REVIEW

Interdisciplinary/transdisciplinary research

Some researchers consider ‘interdisciplinary’ and ‘transdis-

ciplinary’ to be synonymous, but more and more

researchers have attempted to distinguish the terms. Tress

et al. (, ) suggested the common features of ‘inter-

disciplinary’ and ‘transdisciplinary’ included a crossing of

disciplinary boundaries, common goal setting among disci-

plines, integration of disciplines and development of

integrated knowledge and theory. However, only ‘trans-

disciplinary’ invited the perspectives of non-academic

participants trying to cross scientific/academic boundaries

to promote cooperation between science and society. The

IVAM approach described here was designed to serve the

academic community and only one category of non-

academic stakeholders (government agencies), so IVAM is

seen as being ‘interdisciplinary’ rather than ‘transdisciplin-

ary’. Still, ‘transdisciplinary’ approaches remain the

ultimate aim of TaiCCAT. The significance of transdisciplin-

ary research is discussed below.

In the recently publishedHandbook of Transdisciplinary

Research, Hirsch Hadorn et al. () argue that transdisci-

plinarity was developed as a form of research to solve

problems stemming from the conventional separation of

scientific knowledge from practical knowledge in the real

world. ‘Scientific knowledge is universal, explanatory,

demonstrated to be true by a standard method, teachable

and learnable’ (Hirsch Hadorn et al. , p. 20). Neverthe-

less, based on scientific knowledge, existing scientific

research methods are unable to address issues characterized

by high-knowledge uncertainty, intrinsic controversy, or

multiple stakeholders, such as issues related to climate

change. In this context, transdisciplinary research is

oriented toward problem solving. ‘It can grasp the complex-

ity of problems, take into account the diversity of life-world

and scientific perceptions of problems, link abstract and

case-specific knowledge, and develop knowledge and prac-

tices that promote what is perceived to be the common

good’ (Pohl & Hirsch Hadorn , pp. 431–432). Not sur-

prisingly, transdisciplinary research is also applicable in

the field of climate change research.

http://www.most.gov.tw/mp.aspx

http://taiccat.ncu.edu.tw

http://taiccat.ncu.edu.tw


The epistemic and methodological foundations of trans-

disciplinary research are pragmatic (Häberli et al. ;

Zierhofer & Burger ; Hinkel ; Hirsch Hadorn et al.

). Hirsch Hadorn et al. () indicated that transdisci-

plinary research is necessarily associated with three types of

mutually affected knowledge: ‘systems knowledge’, ‘target

knowledge’, and ‘transformation knowledge’. Systems knowl-

edge addresses ‘questions about the genesis and possible

further development of a problem, and about interpretations

of the problem in the life-world’ (Pohl & Hirsch Hadorn

, p. 431). Target knowledge deals with ‘questions related

to determining and explaining the need for change, desired

goals, and better practices’ (Pohl & Hirsch Hadorn ,

p. 431). Transformation knowledge is concerned with ‘techni-

cal, social, legal, cultural, and other possible means of acting

that aim to transform existing practices and introduce desired

ones’ (Pohl & Hirsch Hadorn , p. 432). The actual pro-

cedures of transdisciplinary research iteratively rotate

among ‘problem identification and problem structuring’ (‘tak

[ing] into account the state of knowledge that exists in the rel-

evant disciplines and among actors in society to define the

problem, identify important aspects, and determine the

research questions and who should be involved’, Hirsch

Hadorn et al. , p. 35), ‘problem analysis’ (‘determin[ing]

what forms of thematic collaboration and organization are

adequate to take into account different interests and circum-

stances’, Hirsch Hadorn , p. 35), and ‘bringing the

results to fruition’ (‘embed[ding] the project into the social

and scientific contexts [and] test[ing] the expected impact’,

Hirsch Hadorn , pp. 35–37).

Vulnerability assessments

Over the last decade, vulnerability has emerged as one of

the most frequently applied and discussed concepts, yet it

also remains rather poorly defined (Adger ; Ionescu

et al. ; Hufschmidt ). In the field of climate

change, vulnerability assessments have become more feas-

ible due to the IPCC’s () definition of vulnerability,

comprising exposure, sensitivity, and adaptive capacity. A

considerable number of researchers have assessed vulner-

ability following the three above-mentioned components

(Hahn et al. ; Center for Environmental Systems

Research ; Commonwealth of Australia ).

Füssel & Klein () distinguished four different stages

of climate change vulnerability assessments: impact

assessments, first- and second-generation vulnerability

assessments, and adaptation policy assessments. There is a

tendency among the four stages toward ‘adaptation’ (from

mitigation), ‘normative approaches’ (from positive

approaches), ‘full consideration of climate variability, non-

climatic factors and adaptation, uncertainty, high inte-

gration of natural and social sciences’, and ‘a high degree

of stakeholder involvement’, where non-climatic factors

imply a wide range of environmental, economic, social,

demographic, technological, and political factors. In Füssel

and Klein’s view, the four assessment stages are not

mutually exclusive or necessarily sequential, and three

former stages need not to be flatly abandoned. The appropri-

ate assessment stage depends on the purpose of the

assessment. Beyond the three former impact-driven stages,

adaptation policy assessments aim to provide specific adap-

tation measures beneficial to policy-making. These measures

are formed by stakeholder involvement, emphasize the vul-

nerability of people living under climate variability along

with the formulation and evaluation of corresponding pol-

icies, and integrate existing adaptation measures. The

proposed IVAM approach is directed toward adaptation

policy assessment and is introduced in the next section.

IVAM

Previous vulnerability assessment methods approached the

problem from the perspective of individual disciplines. How-

ever, the impacts we confront in the life-world, e.g. climate

change, are typically cross-sectoral. Climate change touches

on many different mutually interacting disciplines, and all dis-

ciplines belong to a complicated human/social ecology

(Warner et al. ; Hilhorst ;Walker et al. ; Schröter

et al. ; Cash et al. ; Eakin & Luers ; Miller et al.

; Gotham & Campanella ) at a certain spatial and

societal scale (Adger ). A discipline-specific approach to

vulnerability assessment may neglect/ignore subtle or critical

effects of other disciplines, diminishing the reliability or val-

idity of the assessment. In addition, competing values from

various disciplines frequently result in trade-offs and spillover

effects when taking practical adaptation action. For example,

construction of dikes to withstand flooding increases the


vulnerability of the ecological environment (Junker et al.

), while increasing food production reduces available

water resources, and expanding aquifer use for drinking

water during drought may increase the risk of waterborne dis-

eases. To plan appropriate adaptation strategies, government

agencies need integrated vulnerability assessment tools.

IVAM was constructed in TaiCCAT to take an interdis-

ciplinary approach to vulnerability assessment and consists

of six steps: (1) constructing the mind maps of disciplinary

subsystems using the DSR framework, (2) constructing the

system dynamics models of disciplinary subsystems, (3) con-

necting subsystems via shared factors, (4) confirming the

causality between factors of different subsystems via

relationship matrices, (5) using the concept of information

flow to achieve information integration, and (6) conducting

interdisciplinary vulnerability assessments. All six steps

rotate as iterative communication processes (Figure 1).

The various constituent units of IVAM are described.

Constructing the mind maps of disciplinary subsystems

using the DSR framework

The disciplinary section is used to develop the subsystems of

distinct disciplines (i.e. disciplinary subsystems), including

the precise issue each subsystem would examine (i.e. the

subsystem’s purpose), all factors of each subsystem (i.e. sub-

system scope), and causal relationship between them (i.e.

subsystem constitution). A disciplinary subsystem begins

by determining the major state factors, supplies of services

and demands for services. A subsystem is sustainable only

Figure 1 | Interdisciplinary vulnerability assessment method (IVAM).

if the supply of a given service within a subsystem is greater

than or equal to the demand for that service. The factors

influencing service supply and demand are then determined

using the DSR framework.

TheDSR frameworkwas developed by theUnitedNations

commission for sustainable development in 1996 and is widely

used to promote sustainable development. ‘The term ‘driving

force’ represents human activities, processes, and patterns

that impact on sustainable development either positively or

negatively. State indicators provide a reading on the condition

of sustainable development, while response indicators rep-

resent societal actions aimed at moving toward sustainable

development’ (UN DESA/DSD ). The main advantage of

this framework is that it uses the interaction between the driv-

ing force (D), state (S), and response (R) indicators to clarify the

causal relationship of various factors within each subsystem.

How do the various influence factors impact the major state

factors, and service supply and demand? And how do the var-

ious influence factors impact each other? Such questions assist

us in completing the scope and constitution of a subsystem

assuring the subsystem’s completeness and interpretability.

Moreover, the DSR framework can facilitate the development

of sustainable strategies as response to avoid negative driving

forces while enhancing positive ones by evaluating sustainable

states, and eventually achieving subsystem sustainability.

The mind maps of disciplinary subsystems are con-

structed using the DSR framework. Mind maps are

imaging tools used to assist cognition. On the map a seman-

tic network, which represents semantic relations among

concepts related to the central topic, is gradually grown by

brainstorming, enriching our cognition, and knowledge of

the central topic. Mind maps are also experience based

and can be used to explore and solve problems; thus, similar

to the DSR framework, they are heuristic in that they encou-

rage the user to discover and learn things for him or herself.

These characteristic features of mind maps are also useful

for understanding the causality of factors, thus the mind

map and DSR framework are mutually complementary.

Constructing the system dynamics models of

disciplinary subsystems

Though, the mind map can flexibly assist in the identifi-

cation and solving of problems, advanced qualitative (i.e.

Table 1 | A sample relationship matrix. D1 and D2 are different disciplines. Factors f11, f12,

and f13 pertain to discipline D1 while factors f21, f22, and f23 pertain to discipline D2.

Blue and red arrows, respectively, represent positive and negative causal relation-

ships, and the direction of an arrow indicates the direction of causality. For

instance, in this table, f11 positively affects f21, and f22 negatively affects f12.

The rest may be deduced by analogy. N/A indicates that no explicitly causal

relationship was found between f13, and f22. Please refer to the online version

of this paper to see this table in colour: http://www.iwaponline.com/jwc/toc.htm.

Discipline D1

Factor f11 f12 f13

D2 f21

f22 N/A

f23


causal) analysis of disciplinary subsystems is accomplished

through system dynamics models. Pioneered by Jay Forres-

ter in the 1950s, system dynamics features stocks, flows,

and their compound feedback loops which could be used

to explain the complex and non-linear dynamics of systems.

System dynamics has been shown to be one of the most

effective ways to resolve system complexity and problems.

In the field of public policy, for instance, Schwaninger

et al. () have argued that system dynamics can be

applied to complex transdisciplinary issues.

The system dynamics software Vensim (www.vensim.com)

was used to construct the system dynamicsmodels for the dis-

ciplinary subsystems according to their corresponding mind

maps. Based on the input of experts in various disciplines

or academic research literature, positively or negatively

causal relationships between two factors were identified

for each subsystem, facilitating comprehension of their

qualitative relationship.

Connecting subsystems via shared factors

After the system dynamics models are built, subsystems are

integrated into a single interdisciplinary system. Connecting

subsystems via shared factors is an intuitive and simple

approach. However, semantic ambiguity may arise in terms

of a given factor is viewed in various disciplines, and this poten-

tial ambiguity must be clarified by semantic ascent (Hinkel

) (i.e. semantic clarification). In this study, factors from

different subsystems which share names and meanings are

regarded as identical, and subsystems are then connected by

linking identical factors. If factors from different subsystems

have different names but the same meaning, their names are

unified, and then connected via common factors in their corre-

sponding subsystems. If factors share a namebut have different

meanings, their names are altered to create distinction.

Confirming the causality between factors of different

subsystems via relationship matrices

Except where two or more factors share a common meaning,

for a connection to be established, causalitymust exist between

the various factors of the different subsystems. Causality was

confirmed by relationship matrices completed by experts.

Compared to the causality between factors in a single

subsystem, the causality in different subsystems is usually

beyond the scope of current academic research and requires

examination by experts from distinct disciplines. Assuming

that factors f11 and f21, respectively, belong to disciplines D1

and D2 (cf. Table 1), once the relationship determinations of

the experts in the two distinct disciplines converge, the causal-

ity between factors in different subsystems is concluded.

By connecting factors across different subsystems and

confirming the causality between the factors of different sub-

systems, all disciplinary subsystems were linked together as

an interdisciplinary system. This system not only allows for

the identification of factor causality, but for information

integration as described.

Using the concept of information flow to achieve

information integration

One of the key components of interdisciplinary vulnerability

assessments is information transfer between different disci-

plines. The concept of information flow has been

popularized in information management, business manage-

ment, logistics and organizational management to describe

and explain the processes of information transmission.

This article defines information flow as ‘the processes of

information transmission when assessing interdisciplinary

vulnerability’. Information flows facilitate understanding of

information transmission, ensuring the correctness and

accuracy of calculating factors by numerical models in var-

ious disciplines ‘beyond’ the single interdisciplinary system

set-up by system dynamics. In other words, for the sake of

convenience, factor calculations are obtained by numerical

http://www.vensim.com

http://www.iwaponline.com/jwc/toc.htm


models in various disciplines (disciplinary numerical

models) ‘rather than’ a single interdisciplinary system,

even if they can be accomplished by a system dynamics

model. It is very time-consuming and difficult to build an

interdisciplinary system dynamics model with all the func-

tions of numerical models in various disciplines. To grasp

the information flow between different factors, one should

note the factor-related information including input infor-

mation from other factor(s) (e.g. units, spatial, and

temporal scales) in calculating factor values as well as

output information from this factor (e.g. units, spatial, and

temporal scales), which is then used to calculate other

factor(s) (Figure 2). Downscaling or upscaling will be

necessary if two factors with a causal relationship have

different spatial or temporal scales. The degree to which

one factor affects the other in the same discipline will be

quantitatively estimated based on the existing disciplinary

numerical models. Furthermore, to calculate the quantitat-

ive results of one factor (fA) influenced by another factor

(fB) in a different discipline, researchers can adjust the

input value(s) in a specific disciplinary numerical model,

according to the influence of fB (e.g. via empirical formulas,

artificial neural networks, etc.). For example, if one would

like to know the impact of river discharge (water resources

discipline) on paddy rice productivity (food security disci-

pline), the input value(s) in the decision support system for

agrotechnology transfer model, which is used to simulate

agricultural crop growth, can be adjusted by the influence

of river discharge to obtain the paddy rice productivity.

Conducting interdisciplinary vulnerability assessments

To use the interdisciplinary system to assess interdisciplin-

ary vulnerability, the first step is to set up the vulnerability

Figure 2 | A sample information flow between different factors (f1, f2,..., f8). Arrows

represent positive or negative causal relationships. Estimating f1 requires the

quantitative degree to which f2, f3, and f4 affect f1 (i.e., input information for f1).

We also focus on the quantitative degree to which f1 affects f5 and f6 (output

information for f1) because this is the input information used for estimating f5and f6 as well.

index of each subsystem. As mentioned before, the values

of the two types of major state factors (i.e. supplies of ser-

vices and demands for services), respectively, represent the

amounts of a subsystem’s supply (i.e. carrying capacity)

and demand (i.e. loading). The vulnerability index value

expresses the demand divided by the supply for a given sub-

system. As demand exceeds supply, the subsystem becomes

increasingly vulnerable. If a subsystem’s vulnerability index

is greater than or equal to one, the subsystem is unsustain-

able. Assessing a subsystem’s vulnerability, index is

interdisciplinary in that it considers the interdisciplinary

influence of how a subsystem’s major state factors are

affected by factors inside and outside the subsystem.

Iterative communication processes

The UNFCCC () proposed ‘iterative steps in planned

adaptation to climate change’, consisting of four steps ‘infor-

mation awareness’, ‘planning design’, ‘implementation’, and

‘monitoring and evaluation’. Necessary information is col-

lected in the information awareness step. Technical

feasibility, national development goals, and policy criteria

(e.g. cost–benefit analysis, sustainability, cultural, and

social compatibility, etc.) are then considered in planning

responses to climate change. Following the planning

design step, systemic methods actively favored by formal/

informal institutions are chosen for implementation. Ulti-

mately, monitoring and evaluation reveal that adaptation

technology can be adjustable, amendable, and innovative.

The entire process is iterative.

Climate change research by the UNFCCC () and

transdisciplinary research by Hirsch Hadorn et al. ()

mention iterative modification. On the other hand, Després

et al. () have noted that communication is another key

element of transdisciplinary research to reach ‘communica-

tive rationality’ as defined by Jürgen Habermas (, ),

so IVAM stresses iterative communication processes to

enable rolling modification rather than instrumental ration-

ality. Given the highly experimental/exploratory nature of

IVAM, the execution of TaiCCAT entailed many uncertain-

ties such as objectives, operational approaches, and

expected results, requiring constant communication and

confirmation among participants. Second, team members

originating from different disciplines are ‘not’ equally


familiar with IVAM-related concepts (e.g. vulnerability,

interdisciplinarity/transdisciplinarity, supplies of services,

demands for services, etc.) or research tools (DSR frame-

work, mind maps, and system dynamics model). This lack

of mutual understanding can only be remedied through con-

tinuing dialog. In particular, every step in the IVAM process

demands the participation of team members from different

disciplines to ensure that all research outcomes can be inte-

grated into IVAM to assess interdisciplinary vulnerability.

CASE STUDY: TAICCAT EXPERIENCE

The number and total funding of research projects on cli-

mate change in Taiwan has been increasing recently. The

Ministry of Science and Technology, which is responsible

for academic research and development in Taiwan, pro-

vided financial support for 72 research projects on climate

change in 2011, a four-fold increase from 2007 (extracted

and estimated from https://nscnt12.nsc.gov.tw/was2/

award/AsAwardMultiQuery.aspx). Despite these efforts,

adaption research is still insufficient and characterized by

a deficiency in understanding the functions and methods

of adaption, a lack of interdisciplinary/transdisciplinary

and integrated adaption research, and most importantly,

adaptation policy assessments. TaiCCAT was launched in

2009 to build an integrated information platform, long-

term sustainability indicators and data validation mechan-

isms to promote technologies for adaptation to climate

change and support policy decision-making in Taiwan.

The ongoing research program takes an interdisciplinary/

transdisciplinary approach to environmental system analysis,

vulnerability assessment and adaptation governance, and

focuses on formulating policies and plans to help urban,

rural, alpine, coastal, river-basin, and offshore-island areas

cope with and respond to climate change. This essay pre-

sents a partial outcome of TaiCCAT, specifically IVAM,

whose objective is consistent with adaptation policy assess-

ments by Füssel & Klein (), offering the government an

innovative and supportive decision-making tool. According

to IVAM, we would first construct the interdisciplinary

system, and then, based on ‘adaptation strategy to climate

change in Taiwan’ (ASCCT), define the scope of adaptation

policies/issues and assess interdisciplinary vulnerability.

Constructing the interdisciplinary system

There are five disciplines and corresponding subsystem(s)

discussed in the interdisciplinary vulnerability assessment

in TaiCCAT: environmental disaster management (with

the flood protection and landslide prevention subsystems),

public health (with the public health subsystem), food secur-

ity (with the aquatic food safety and crop safety subsystems),

ecology (with the ecology subsystem), and water resource

management (with the water supply subsystem). Figure 3

shows the mind map of the water supply subsystem (see

above under Constructing the mind maps of disciplinary

subsystems using the DSR framework). The purpose of con-

structing Figure 3 is to discuss whether the water supply is

sufficient, issue-related factors, and the relationships

between those factors. After confirming the positive/nega-

tive relationships between factors, Figure 4 displays the

system dynamics model of the water supply subsystem (see

above under Constructing the system dynamics models of

disciplinary subsystems). All relationships between the var-

ious factors are shown in Table 2.

Through linking common factors in different subsystems

(see above under Connecting subsystems via shared factors)

and determining the causality between factors of different

subsystems via relationship matrices (see above under Con-

firming the causality between factors of different subsystems

via relationship matrices), all seven supply subsystems (i.e.

flood protection, landslide prevention, public health, aquatic

food safety, crop safety, ecology, and water supply) could be

integrated in an interdisciplinary system as shown in Figure 5.

Defining the scope of adaptation policies/issues

In the interdisciplinary system, the factor(s) which affect(s)

some specific factor and the factor(s) some specific factor

affects can be, respectively, determined by the causes tree

and uses tree functions of the Vensim software. The tree

function can be used to help identify the scope and stake-

holders of a given adaptation policy on climate change.

Analyzing the impact of a particular adaptation policy on

climate change involves three steps: (1) An adaptation

policy is added to the interdisciplinary system as a new

factor, (2) the new factor is linked to other factor(s),

https://nscnt12.nsc.gov.tw/was2/award/AsAwardMultiQuery.aspx



Figure 4 | The water supply subsystem constructed using Vensim. Blue and red arrows, respectively, represent positive and negative causal relationships. Black text indicates ordinary

factors. Green text represents service supply, while the orange text represents service demand – both of which belong to major state factors. Please refer to the online version

of this paper to see this figure in colour: http://www.iwaponline.com/jwc/toc.htm.

Figure 3 | The mind map of the water supply subsystem. Black text indicates ordinary factors. Green text represents service supply, while the orange text represents service demand –

both of which belong to major state factors. Black lines represent positive or negative causal relationships between factors. Please refer to the online version of this paper to

see this figure in colour: http://www.iwaponline.com/jwc/toc.htm.




Table 2 | Factors in the water supply subsystem and their relationships

Ordinary factor Ra Ordinary factor Ra Major state factorb (variable) Ra Subsystem

Active storage capacity Daily amount of public water supply insurface water (SW1)

Water supplysubsystem

Diversion capacity

Domestic water quality

Land use

Rainfall

Revenue water ratio

River discharge

Water storage facilities

Leakage rate

Active storage capacity Daily amount of agricultural water supplyin surface water (SW2)Revenue water ratio Diversion capacity

Leakage rate

Domestic water quality

Land use

Rainfall

River discharge

Soil type

Active storage capacity Water storage facilities

Channel length

Channel water conveyancelosses

Flow release

Leakage rate

Land use Daily amount of public water supply ingroundwater (SW3)Pumping facilities

Rainfall

Soil type

Forest coverage

Water purification capacity Amount of alternative water resourcessupply (SW4)

Forest coverage Daily amount of agricultural water supplyin groundwater (SW5)Pumping facilities

Rainfall

Soil type

Desalination (SW6)

Air temperature Domestic water demand (DW1)

Daily domestic waterconsumption per person

Gross domestic product (GDP)

Population

(continued)


Table 2 | continued

Ordinary factor Ra Ordinary factor Ra Major state factorb (variable) Ra Subsystem

Discount water supply

Non-tap water withdrawal

Industrial wastewaterrecycling

Water purification capacity

Urban sewagerecycling

Air temperature Industrial water demand (DW2)

Area of the industrial zone

Economic output value

Gross domestic product (GDP)



Water purification capacity

Air temperature Agricultural water demand (DW3)

Aquaculture water

Crop output value

Crop species

Irrigated area

Livestock water



Rainfall

Return flow

aCausal relationship: positive ( ) or negative ( ).b‘Service supply’ (green text) or ‘service demand’ (orange text). Please refer to the online version of this paper to see this table in colour: http://www.iwaponline.com/jwc/toc.htm.


and (3) the uses tree is used to analyze the scope and stake-

holders of the new factor (adaptation policy).

The ASCCT was approved by the Executive Yuan

(Council for Economic Planning & Development ) in

June 2012, and serves as the future prime directive of Tai-

wan’s national government on climate change. For

example, the policy includes ‘activation of existing water

storage capacity’ as an adaptation policy for water

resource management. To determine the influence of this

action, ‘activation of existing water storage capacity’ is

added as a new factor to the interdisciplinary system

which finds that it has an apparently positive affect (i.e.

a positive causal relationship) on the factor ‘water storage

facilities’, thus a link is drawn to denote their relationship.

The uses tree of ‘water storage facilities’ clearly shows that

the adaptation policy ‘activation of existing water storage

capacity’ will affect ‘water storage facilities’ which, in

turn, will affect ‘daily amount of public water supply in

surface water’ as well as ‘daily amount of agricultural

water supply in surface water’. Many other factors and

causal relationships related to ‘activation of existing

water storage capacity’ are displayed by arrows in Figure 6,

which also depicts the policy coverage of ‘activation of

existing water storage capacity’. Before adopting this

policy, the positive or negative effect it may have on the

factor(s) could be jointly considered to determine whether

this policy should be adopted.

On the other hand, it is noteworthy that ‘daily amount of

agricultural water supply in surface water’ (belonging to the

water supply subsystem in the water resources discipline)

influences the ‘amount of agricultural water supply’ (belong-

ing to the crop safety subsystem in the food security


Figure 5 | The interdisciplinary system constructed using Vensim consists of seven subsystems: flood protection, landslide prevention, public health, aquatic food safety, crop safety,

ecology, and water supply. Though the arrowheads are too small to see, the blue and red arrows, respectively, represent positive and negative causal relationships. This

complicated system includes 232 factors (i.e. nodes) and their causality. Please refer to the online version of this paper to see this figure in colour: http://www.iwaponline.com/

jwc/toc.htm.


discipline), while the ‘daily amount of public water supply in

surfacewater’ (belonging to thewater supply subsystem in the

water resources discipline) influences the ‘daily amount of

surface water supply’ (belonging to the aquatic food safety

subsystem in the food security discipline), illustrating the

uptake of interdisciplinary effects. The cross-sectoral compe-

tent authorities (i.e. the government stakeholders) of the

‘activation of existing water storage capacity’ policy, specifi-

cally, the Water Resources Agency (for water resources,

www.wra.gov.tw) and the Council of Agriculture (for food

security, www.coa.gov.tw/show_index.php) are determined

to promote cross-sectoral cooperation and accountability.

The interdisciplinary system provides government with the

capacity to consider cross-sectoral effects in formulating

policy, which is necessary given the complexities of modern

society.

Assessing interdisciplinary vulnerability

All quantitative results in this article are obtained through the

information flow concept (see above under Using the concept

of information flow to achieve information integration).

http://www.wra.gov.tw

http://www.coa.gov.tw/show_index.php



Figure 6 | The uses tree of the ‘activation of existing water storage capacity’ adaptation policy in water resources. Triangles refer to the specific policy; ellipses indicate factors in the

interdisciplinary system (Figure 5); rectangles represent the subsystems. Blue shapes belong to the water supply subsystem while brown shapes belong to the crop safety

subsystem, and gold shapes belong to the aquatic food safety subsystem. Please refer to the online version of this paper to see this figure in colour: http://www.iwaponline.com/

jwc/toc.htm.


Vulnerability index was established by two types of major

state factors, i.e. service supplies and demands (see above

under Conducting interdisciplinary vulnerability assess-

ments). Equation (1) is the vulnerability index of the water

supply subsystem. The factors within the water supply subsys-

tem can be used to estimate the so-called ‘interdisciplinarity’

in IVAM results from DW1, DW2, DW3 and SW1, SW2,…, SW6

(see Table 2) alongwith the interdisciplinary effects belonging

to the factors of other disciplines (subsystems) (see Figure 5).

Equation (2) is the vulnerability index in thewater supply sub-

system under the adaptation policy ‘activation of existing

water storage capacity’ in ASCCT (cf. Figure 6). These indices

are used to determine whether ‘activation of existing water

storage capacity’ would be comprehensively beneficial to

the water supply subsystem. The magnitude (positive or nega-

tive) of the impact of every factor could also be calculated.

These kinds of information are provided as a reference for

decision makers to determine whether the policy should be

implemented.

VW ¼ DW1 þDW2 þDW3

SW1 þ SW2 þ SW3 þ SW4 þ SW5 þ SW6(1)

V 0W ¼ DW1 þDW2 þDW3 þ ΔW2 þ ΔW3

SW1 þ SW2 þ SW3 þ SW4 þ SW5 þ SW6(2)




Equation (3) is the vulnerability index in the crop safety

subsystem (m and n, respectively, depend on the numbers of

major state factors, service supply and service demand), and

Equation (4) is that in the aquatic food safety subsystem (p

and q, respectively, depend on the numbers of major state fac-

tors, service supply and service demand). Equations (5) and

(6) take the influence of ‘activation of existing water storage

capacity’ into consideration (Figure 6).

VC ¼ DC1 þDC2 þ . . .þDCm

SC1 þ SC2 þ . . .þ SCn(3)

VA ¼ DA1 þDA2 þ . . .þDAp

SA1 þ SA2 þ . . .þ SAq(4)

V 0C ¼ DC1 þDC2 þ . . .þDCm

SC1 þ SC2 þ . . .þ SCn þ ΔC2 þ ΔC3 þ ΔC4 þ ΔC5(5)

V 0A ¼ DA1 þDA2 þ . . .þDAp

SA1 þ SA2 þ . . .þ SAq þ ΔA3(6)

Moreover, in Figure 6, ‘activation of existingwater storage

capacity’, through ‘water storage facilities’, ‘daily amount of

agricultural water supply in surface water’ and ‘daily

amount of public water supply in surface water’, positively

impacts the water supply subsystem by increasing its adaptive

capacity (Vw0 <Vw). Similarly, adaptive capacity of the

aquatic food safety subsystemwill be enhanced through inter-

mediate factors ‘water storage facilities’, ‘daily amount of

public water supply in surfacewater’, ‘daily amount of surface

water supply’, ‘amount of aquaculture water supply’, and

‘amount of aquatic food supply in aquaculture’ (VA0 <VA).

This is a spillover effect.

DISCUSSION

Methodological advantages of IVAM

IVAM provides three major methodological advantages as

follows:

1. Interdisciplinary approach: past vulnerability assessments

have adopted either disciplinary or multidisciplinary

approaches. In the latter, every discipline shares a

common goal but lacks enough integration and interaction

with other disciplines so that all disciplines are

intrinsically independent of the others (Tress et al. ,

; Repko ). Examples include computing the Live-

lihood Vulnerability index by means of the composite

index approach by Hahn et al. (), and assessing vul-

nerability from the IPCC’s () prevalent definition by

combining exposure, sensitivity, and adaptive capacity

(Hahn et al. ; Center for Environmental Systems

Research ; Commonwealth of Australia ). These

kinds of vulnerability assessments pertain to multidiscipli-

narity, not interdisciplinarity. Almost all vulnerability

assessment approaches lack interdisciplinarity because it

is very difficult, challenging and resource-intensive to inte-

grate various disciplines as well as to develop integrated

knowledge and theory. Different from the forward-looking

and cross-sectoral CLIMSAVE IA Platform (http://86.120.

199.106/IAP/#/Introduction), which can be considered

an integrated assessment tool (Harrison et al. ),

IVAM provides an interdisciplinary vulnerability assess-

ment by calculating service supply and demand, which

includes the effects of other factors from the same and

different disciplines. Meanwhile, IVAM is suitable to inte-

grate most disciplines and is not limited to the five

disciplines examined herein.

2. Problem-solving orientation: interdisciplinary research

is characterized by its problem-solving orientation

(Häberli et al. ; Zierhofer & Burger ; Hinkel

; Hirsch Hadorn et al. ). Supported by the Min-

istry of Science and Technology, TaiCCAT has focused

on practicable necessities in the context of Taiwan,

mainly in terms of adaptation technologies. IVAM is

the practical outcome of TaiCCAT and will be submitted

to the Ministry of Science and Technology along with

the National Development Council (www.ndc.gov.tw),

the government agency responsible for promoting

national economic development and planning future

national development directions. Several principal inves-

tigators on TaiCCAT directly participated in formulating

the ASCCT. Their dual roles guarantee that the research

results of TaiCCAT, and thus IVAM, take a highly

realistic approach toward satisfying the government’s

demands.

3. Academic interdisciplinarity inspiration: one of the

approaches to connecting subsystems draws on the

relationshipmatrices determined by experts fromdifferent

http://86.120.199.106/IAP/#/Introduction



http://www.ndc.gov.tw


disciplines. The causality between the factors of different

subsystems is still subject to considerable uncertainty.

For example, various experts may differ in their assess-

ment regarding the positivity or negativity of certain

causalities between factors of different subsystems, or the

degree of causality due to a lack of effective methods to

prove causality. These kinds of causality, confirmed by dis-

ciplinary experts, are not based on existing research

evidence, and thus offer a valuable interdisciplinary

research direction/gap for climate change adaptation.

Extension research of IVAM

This study only proposes the qualitative impact of ASCCT.

We are now trying to calculate the quantitative interdisci-

plinary vulnerability of the public health subsystem under

the influence of the flood protection subsystem, which

means the estimation of infection rate of some waterborne

Table 3 | IVAM performance according to Hirsch Hadorn et al. (2008)

Three necessary types of knowledge in transdisciplinary research(Hirsch Hadorn et al. )

I

Systems knowledge addresses ‘questions about the genesis andpossible further development of a problem, and aboutinterpretations of the problem in the life-world’ (Pohl & HirschHadorn , p. 431)

C

Target knowledge addresses ‘questions related to determining andexplaining the need for change, desired goals and better practices’(Pohl & Hirsch Hadorn : 431)

E

Transformation knowledge addresses ‘technical, social, legal,cultural, and other possible means of acting that aim to transformexisting practices and introduce desired ones’ (Pohl & HirschHadorn , p. 432)

U

Three phases of research in a transdisplinary research process(Hirsch Hadorn et al. )

I

Problem identification and problem structuring: ‘tak[ing] intoaccount the state of knowledge that exists in the relevantdisciplines and among actors in society to define the problem,identify important aspects, and determine the research questionsand who should be involved’ (Hirsch Hadorn et al. , p. 35)

O

Problem analysis: ‘determin[ing] what forms of thematic collaborationand organization are adequate to take into account differentinterests and circumstances’ (Hirsch Hadorn et al. , p. 35)

N

Bringing the results to fruition: ‘embed[ding] the project into thesocial and scientific contexts [and] test[ing] the expected impact’(Hirsch Hadorn et al. , p. 35)

N

disease due to different inundation depths (Tung et al.

). Aside from the quantitative results to completely

verify the application of IVAM, future research on IVAM

is needed as follows.

Filling gaps between IVAM and transdisciplinary research

Table 3 examines IVAM under the definition proposed by

Hirsch Hadorn et al. () for transdisciplinary research.

As for types of knowledge, IVAM covers systems knowledge,

target knowledge, and transformation knowledge. In terms

of research phases, IVAM provides partial ‘problem identifi-

cation and problem structuring’ but lacks ‘problem analysis’

and ‘bringing the results to fruition’. IVAM must be

improved to include more stakeholders; determine the

forms of thematic collaboration and organization; and

embed the transdisciplinary research project in social and

scientific contexts and test its expected impact. Of these

VAM performance

onstructing mind maps of disciplinary subsystems using the DSRframework, and then establishing the interdisciplinary system byconforming relationships between factors. Analyzing key issues ofsome specific policy through the interdisciplinary system

stimating the vulnerability index and the number of subsystemfactors to determine whether we should implement a particularpolicy

sing the outcomes of IVAM to replace those of disciplinaryvulnerability assessments

VAM performance

nly the academic stakeholders (TaiCCAT members) andgovernment stakeholders (e.g. the Water Resources Agency andthe Council of Agriculture in the case study) can participate in theproblem identification and problem structuring process

/A

/A


requirements, including more stakeholders is the most

important task because it is a prerequisite for the others. Fur-

thermore, the practical application of the results of IVAM in

the real world is of vital importance. IVAM will be sub-

mitted to the government of Taiwan as a useful assessment

tool, and embedding the outcomes of IVAM into Taiwan

society will verify the ability of IVAM to achieve ‘problem

analysis’ and ‘bringing the results to fruition’ leading to

real-social change.

Increasing stakeholder participation

A greater absolute number of factors in the interdisciplinary

system (e.g. 232 factors in Figure 5) will increase the degree

of difficulty in estimating a subsystem’s interdisciplinary vul-

nerability. Considerable causality between factors should be

qualitatively and quantitatively ascertained, requiring signifi-

cant research resources. In addition, the information flow,

which is related to the dynamics of the factor-related infor-

mation, has to be precisely accounted for to ensure the

reliability and validity of assessing a subsystem’s interdisci-

plinary vulnerability. These denote the disadvantages of

IVAM in some way, but we still have to pay to deal with

such complicated interdisciplinary systems to understand

the social-ecological ones we live in.

Not all researchers are confident of their ability to harness

these complicated systems. Warner et al. () claimed that

the disaster paradigm has been shifted from the technocratic,

behavioral, and vulnerable to complexity paradigms, which

emerged out of ‘a growing understanding of the complex inter-

relationships of ecology and society’ (Warner et al. ,

p. 10). Climate change, overloaded ecosystems, and

exhausted natural resources cause people to reflect on the

interactive and causal relationships between humans and

the environment. Humans play the dual role of victims and

producers of disasters. In Hilhorst’s () view of the com-

plexity paradigm, research on three different social

domains, including ‘the domain of science and disaster man-

agement, the domain of disaster governance and the domain

of local responses’ (Hilhorst , p. 57), is a type of therapy

for ‘system thinking’, which refers to a concept in which

elements of a system and the system itself are functionally

and predictably relevant and will damage human agency

and diversity. ‘The study of social domains allows us to

focus upon the everyday practices and movements of actors

who negotiate the conditions and effects of vulnerability

and disaster’ (Hilhorst , p. 52). In the complexity para-

digm, humans seek neither a hegemonic explanation nor a

perfect solution to complexity. Rather, we have to concentrate

on the contradictory and inconsistent nature of the different

disaster discourses of social actors (stakeholders), and how

the final disaster discourse is established. In a way, Warner

et al. () and Hilhorst () coincide with the well-

known concept of post-normal science suggested by Funto-

wicz & Ravetz (, ). For Funtowicz & Ravetz (,

), the extended peer community (i.e. enhanced stake-

holder participation) is indispensable to post-normal

science: given the high-decision stakes and system uncer-

tainty, such a community allows us to effectively critique the

rationality of many decision alternatives. Transdisciplinary

research responds in a similar way as Hirsch Hadorn et al.

() argue: ‘It has been argued that transdisciplinary

research is necessary when knowledge about a societally rel-

evant problem field is uncertain, when the concrete nature of

problems is disputed, and when there is a great deal at stake

for those concerned with the problems and involved in inves-

tigating them’ (Hirsch Hadorn et al. , p. 37). The

foregoing discussion acknowledges the uncertainties govern-

ing such complicated systems by present scientific methods,

and emphasizes the need to involve more stakeholders in

decision-making processes, thus increasing the satisfaction

of community members.

This research explores the complexity of interdisciplin-

ary systems which inevitably raises various types of

uncertainty which cannot be completely resolved. In

addition to the academic stakeholders (TaiCCAT members)

and government stakeholders (e.g. the Water Resources

Agency and the Council of Agriculture in the case study),

other stakeholders such as NGOs, private sectors and com-

munity members should be included when using IVAM

(Füssel & Klein ). Unlike interdisciplinarity, non-

academic stakeholder participation is the pivotal feature of

transdisciplinarity (Tress et al. , ; Repko ;

Hirsch Hadorn et al. ). With broad non-academic stake-

holder participation, IVAM will transcend the outdated

technocratic, expert control logic (Hilhorst ; Funtowicz

& Ravetz , ; Beck , ), as will the future

outcomes of TaiCCAT.


ACKNOWLEDGEMENTS

This study is a partial outcome of the TaiCCAT (Technology

Development Project for Establishing Cross-Sectoral

Vulnerability Assessment and Resilience) project. The

authors gratefully acknowledge financial support from the

National Science Council (NSC 100-2621-M-002-036), the

predecessor of the Ministry of Science and Technology.

This study benefitted from the active collaboration of

many team members from various TaiCCAT projects

(disciplines) including Prof. Kwan-Tun Lee, Prof. Huey-Jen

Su, Prof. Huu-Sheng Lur, Prof. Ming-Hsu Li, Prof. Hsueh-

Jung Lu, Dr Ming-Huei Yao, Prof. Shih-Liang Chan, Prof.

Hsing-Juh Lin, Prof. Pei-Fen Lee, Associate Prof. Hwa-

Lung Yu, Dr Wen-Dar Guo, Dr Yi-Ying Chen, Mr Jung-

Feng Shih, Mr De-Jen Peng, Ms Fane-Ching Liao, Ms Mu-

Jean Chen, Mr Shang-Chen Ku, Mr Kuo-Chan Hung and

Mr Chin-Chang Lu.

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