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12nd International Conference on Urban Drainage, Porto Alegre/Brazil, 11-16 September

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Accounting for climate change in urban drainage and flooding: contrasting alternative approaches to devising adaptive strategies

B. Gersonius1*, R. Ashley1, A. Jeuken2, A. Pathirana1 and C. Zevenbergen1

1 UNESCO-IHE Institute for Water Education, Westvest 7, Delft, 2611 AX, The Netherlands. 2 Deltares, Daltonlaan 400, Utrecht, 3584 BK, The Netherlands.

*Corresponding author, e-mail [email protected]

ABSTRACT Two frameworks are presented that can be used to account for climate change uncertainty in investment decision making related to urban drainage and flooding systems, either cause-based or effect-based. In the former, Real-In-Options (RIO) is proposed as an approach to identify the optimal set of adaptive strategies in response to advances in knowledge about future climate change. Other approaches align with the effect-based framework. A relatively simple approach to implementing this framework is to use Adaptation Tipping Points (ATPs), which have been defined as the points where the magnitude of climate change is such that the current strategy can no longer meet the pre-set objectives. Responses for this approach aim to extend the location and timing of these ATPs to some acceptable future point. This paper compares the RIO approach with the ATP approach. The emphasis is on the procedural steps, benefits and limitations. The paper concludes with a summary of the key characteristics to assist in choosing the most appropriate approach. KEYWORDS Adaptation Tipping Points; climate change; flood risk management; Real-In-Options; urban drainage 1. INTRODUCTION Decision making for investments in urban drainage and flooding systems needs to take account of climate change uncertainty. This is because of two features associated with such systems. First, the consequences of investment decisions for these systems have to be lived with for a long time, which means that the associated uncertainties can grow larger. Second, potential irreversibilities in choices can lead to a need for larger construction initially, particularly in 'hard' structural measures; which allows for headroom for later adjustment. New approaches to devising adaptive strategies are needed to address these uncertainties (Kundzewicz et al., 2008). Otherwise, such strategies can be maladaptive, resulting in unnecessary costs of potentially irreversible measures (Barnett and O'Neill, 2010). Two frameworks are presented that can be used to account for climate change uncertainty in investment decision making related to urban drainage and flooding systems (Jones and Preston, 2010), framed around a Driver-Pressure-State-Impact-Response (DPSIR) continuum. The cause-based framework begins by considering the climate system (drivers) and moves through the pressures, state process to predict the impacts. Responses are then formulated to cope with these in a way that maintains expected performance levels. The alternative, effect-based framework starts with quantifying the outcome in the form of impact thresholds that define possible system states, and then the likelihood of attaining or exceeding this outcome is assessed.


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The cause-based framework is most widely used in practice. For instance, the conventional Net Present Value (NPV) approach uses climate scenarios to predict the impacts and to develop adaptive strategies based on these impacts. The limitation of the conventional NPV approach is the reliance on best estimate climate scenarios, which are expected to be precise forecasts of future climate change. However, such precise forecasts cannot be produced by climate modelling (Dessai et al., 2009). Where irreducible uncertainties exist, adaptive strategies can still be developed in the face of these uncertainties. Real-In-Options (RIO) is a relatively new approach that can be used for managing future flood risk in the cause-based framework, which explicitly acknowledges and allows for the lack of certainty about climate change by building in flexibility into infrastructure systems (Gersonius et al., submitted). Other approaches align with the effect-based framework, and they can be carried out almost independently of climate scenarios. For example, Kwadijk et al. (in press) have developed a relatively simple approach based on the concept of Adaptation Tipping Points (ATPs) to assess whether, and for how long, the current strategy or alternative, adaptive strategies will continue to be effective under different climate conditions. This paper compares the RIO approach with the ATP approach, based on literature and our implementation experience for two case studies. The emphasis is on the procedural steps, benefits and limitations. The paper concludes with a summary of the key characteristics to assist in choosing the most appropriate approach. 2. REAL-IN-OPTIONS APPROACH Real Options (RO) is a recognised procedure to handle uncertainties in infrastructure investments through valuing flexibility. Originally developed as a means to predict the value of financial options, RO analysis integrates expected changes in future levels of uncertainty into economic analysis. It therefore offers a major development on the conventional Net Present Value (NPV) approach. RO can be categorised as those that are either ‘on’ or ‘in’ systems (De Neufville, 2003). RO ‘on’ systems are options applied to the investment decision making process related to infrastructure systems, treating the infrastructure design as a black box. RO ‘in’ systems (or: RIO) however, are created by changing the infrastructure design as uncertainty is resolved. In the RIO concept, the option is thus based on a technical characteristic of the infrastructure system that is not apparent if the system is treated as a black box. For RIO analysis, a number of characteristics are required that traditional RO approaches do not deal with, such as technical details and interdependency/path-dependency among options. Wang and de Neufville (2004) have proposed an optimisation-based RIO analysis procedure that is able to manage such interdependency/path-dependency features. Their procedure is based on the scenarios established by a binomial (path-dependent) tree. Framework used RIO is a relatively new concept to account for uncertainty in the cause-based framework. This approach uses climate scenarios, as climate is the main driver of the impacts, to devise an optimal set of adaptive strategies. The development of RIO analysis provides a procedure to find out which flexibilities, that permit the infrastructure design to be adjusted over time, are worth their cost. The value of flexibility is determined based on a probability distribution for possible system states in future epochs. This approach assumes that the uncertainty cannot be completely resolved over time, but rather that due to advances in knowledge the probability distribution will be adjusted in the future. The adjustment of probability distributions for climate change plays a significant role in informing the size and timing of investments in adaptation. Such distributions can either be based on climate modelling data or subjectively formulated by experts.


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Procedural steps The procedure for RIO analysis has been developed by Wang and De Neufville (2004), and later modified by Gersonius et al. (2010) for the context of adapting urban drainage and flooding systems to climate change. It comprises the following steps: 1. Define system and objectives 2. Quantify drivers and possible system states 3. Identify potential options for adapting 4. Simulation and options analysis 5. Optimisation by GAs Gersonius et al. (submitted) provide an example of the application of RIO analysis to the modification an urban drainage system in West Garforth, England. The system consists of 85 sewer conduits, 9 possible storage facilities and 15 sub catchments. Associated with these components are the design variables, which define possible system configurations. The design variables can be changed over time, reflecting an incremental process of adaptation to climate change with a flexible design. There are different ways to build in the flexibility into the infrastructure design. In the example used, flexibility arises from the possibility of replacing sewer conduits, upsizing storage facilities, and disconnecting back roofs in the sub catchments. Due to the large number of design variables and their interactions, coupled with the considerations of technical and real option constraints, cost-effective system design is complex. In this regard, optimisation by GAs has been applied to identify the optimal set of adaptive strategies (consisting of the first-epoch configuration and subsequent configurations) in response to advances in knowledge about future climate change. The optimisation model has been implemented in a computer program written in C++ which has then been used for the identification and analysis of possible strategies. The objective function was to minimise the expected life cycle cost, subject to the condition that there is sufficient hydraulic capacity in place in each epoch and each climate state to always meet the protection standard in that state. In West Garforth, the protection standard is: no surface flooding for a design storm with a 1 in 30 year recurrence interval. However, due to climate change, there is uncertainty about the intensity of the design storm for future epochs. To represent this uncertainty it is assumed that the change in intensity follows a geometric Brownian motion (GBM). This assumption has the property that the variance of the uncertain parameter increases over time. GBM is one of the most important basic notions of stochastic processes, and in particular, is the basis of options theory. The input parameters for the GBM model used were: a drift rate μ of 0.018% per year and a volatility σ of 1.573% per year for the change in intensity of the design storm. This was obtained from the climate modelling data available from the UKCP09 probabilistic projections (Murphy et al., 2009). The outputs from the RIO analysis model give the optimal set of adaptive strategies over the three defined epochs (1990s-2020s, 2020s-2050s, and 2050s-2080s) (Table 1). The resulting strategy is to build configuration A1 in the first epoch, to build configuration A2 if the intensity goes up by 13%, and to build configuration A3 only if the intensity increases by 28%.


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Table 1. Optimal set of adaptive strategies Start of epoch 1 (1990s-2020s) Start of epoch 2 (2020s-2050s) Start of epoch 3 (2050s-2080s) Climate change factor = 1.28

Configuration A3 build Climate change factor = 1.13

Configuration A2 build Climate change factor = 1.13 No build

Climate change factor = 1.00 No build

Climate change factor = 1.13 Configuration A2 build

Climate change factor = 1.00 Configuration A1 build

Climate change factor = 1.00No build




Climate change factor = 0.89No build



Benefits and limitations RIO analysis has considerable potential to support investment decision making for urban drainage and flooding systems, particularly if the required adaptation is particularly sensitive to the magnitude of climate change. The benefit of the approach is that it identifies the optimal set of adaptive strategies in reaction to changes in knowledge about future climate change. This will allow the size and timing of investments to be informed by new information regarding climate change, and so improve the economic efficiency of such investments. The total life cycle costs will therefore likely be lower, as demonstrated by some recent applications (Gersonius et al., submitted; Woodward et al., 2010). Using RIO also reduces initial capital costs and spreads the costs over the lifetime of the infrastructure system which is important, especially in a time of economic stringency. The main limitation of the application of RIO is that it requires accurate probability distributions for climate change. The accuracy of climate projections is, however, limited by fundamental, irreducible uncertainties (Dessai et al., 2008). The uncertainties associated with climate projections arise from model errors, internal variability, and emissions scenario uncertainty (Cox and Stephenson, 2007). Whilst some of these uncertainties can potentially be reduced by more research (e.g., model errors), other uncertainties simply cannot (e.g., emissions scenario uncertainty). This means that there will always be some level of irreducible uncertainty associated with predictions of climate change. Other limitations of the RIO approach are that it is complicated and time-intensive to develop and run (Water Utility Climate Alliance, 2010). This is due to the potentially large number of design variables, epochs and boundary conditions. In addition, there is no procedure for monitoring information in order to adjust or update/reassess the resulting adaptive strategy over time (Rahman, Walker and Marchau, 2008). 3. TIPPING POINT APPROACH The ATP approach is aimed at assessing whether, and for how long, the current strategy or alternative, adaptive strategies will continue to be effective under future climate conditions. It uses the concept of ATPs, reached if the magnitude of climate change is such that the current strategy can no longer meet the pre-set objectives (Kwadijk et al., in press).


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Framework used The ATP approach is effect-based. In this, an impact threshold is defined according to the imposed standards or decided upon by the stakeholders and the likelihood of attaining or exceeding this threshold is assessed. The approach starts the analysis with the policy objectives, which determine the maximum allowable impact. The current strategy to achieve these objectives is also defined. This is followed by a sensitivity analysis of the strategy in dealing with future climate conditions to determine the system boundary conditions (state) where the impact thresholds are exceeded. Up to this point the analysis is independent of climate scenarios. Next, the state of the system is related to pressures in terms of the climate scenarios. The climate scenarios are used to define the moment in time when an alternative, adaptive strategy will be needed. Analysing the potential options for adapting and the ATPs will result in the definition of a number of adaptive strategies. Once a realistic and acceptable adaptive strategy has been selected, this strategy will then need to be continually monitored and reviewed. Procedural steps The procedure for the ATP approach, following Kwadijk et al. (in press), is: 1. Define system and objectives 2. Quantify the maximum allowable Impacts 3. Assess under which boundary conditions the impact thresholds are exceeded 4. Identify potential options for adapting 5. Repeat step 2-3 for the identified set of options An example of the ATP approach is provided by Gersonius et al. (in preparation), who have applied it to a minor drainage system in the neighbourhood Wielwijk in Dordrecht, the Netherlands, comprised of a combined sewer network. The relevant objective here was to provide adequate flood protection. According to the protection standard, the minor drainage system is expected to be large enough to convey the full flow for a 1 in 2 year recurrence interval event without surface flooding. Hydrological and hydraulic simulation of the sewer network was used to assess the system boundary conditions, where the system fails to meet the protection standard. The location of the ATP for the current strategy is shown in Figure 1 (indicated by the end of the horizontal bar). It can be seen from the location of the ATP that, in the current situation, the minor drainage system fails to meet the protection standard determined by the municipality.

KNMI G scenarioKNMI W scenario

Rainfall change (%)

Current situation

Redevelopment without disconnection measures

Redevelopment with disconnection measures

205020 % 40 % 60 % 80 % 100 %

2050

Figure 1. ATP analysis for the minor drainage system The next step has been to devise an incremental process of adaptation, starting with those options that achieve quick gains for minimal cost. Therefore, the options identification aimed to make use of opportunities for bringing together flood risk management with urban regeneration and renewal. Taking the case where opportunities occur within the building and development programme, Table 2 shows the planned adaptation options for the short and medium-term horizon. These options involve disconnecting 1.16 ha of closed paved area, 10.24 ha of open paved area and 4.78 ha of roofed area, and diverting the runoff water to the open water system. This has been proposed in a series of workshops attended by urban


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planners, sewerage managers, water managers (including the waterboard), project managers, scientists, and inhabitants. Table 2. Planned adaptation options for the short to medium-term horizon Roof

disconnection Disconnection of open paved areas

Disconnection of closed paved areas

[ha] [ha] [ha] Reddersbuurt/Westervoeg Tromptuinen Wielwijkpark Total 4.78 10.24 1.16 Assessing the ATPs for the adaptive strategy showed that the conveyance capacity of the minor drainage system will remain effective up to a change in intensity of rainfall of 30% after the planned adaptation options have been implemented. The KMNI’06 G and W climate scenarios (Hurk, 2007) were used to assess how long it takes until this ATP is reached. According to the W climate scenario, this will occur around 2070. This ATP would lead to a reconsideration of the adaptive strategy. Benefits and limitations The benefit of the ATP approach is that it is almost independent of climate scenarios, and in particular of climate change probabilities. Rather, it requires a range of plausible climate scenarios that can be used to assess the durability of the current strategy and alternative, adaptive strategies. The ATP approach examines the effects of different possible magnitudes of climate change on the protection standard, without considering probability distributions for climate change. Climate change becomes relevant for investment decision making only if it would lead to the crossing of an impact threshold. In this sense, the approach is more dependent on stakeholder engagement to define the impact thresholds and the adaptive strategy that is realistic and acceptable. Kwadijk et al. (in press) point out that the application of ATPs answers the basic question of decision makers and other stakeholders: How much climate change can the current strategy cope with? They found that expressing uncertainty in terms of the period that the current strategy is effective (i.e. when an impact threshold will be reached), appears more understandable for decision makers, than defining the likelihood of a specific outcome in a specific epoch. Based on these findings, they conclude that the ATP approach is useful to reduce the complexity of effect-based approaches. Another benefit identified by Kwadijk et al. (in press) is that the ATP approach allows easier updating, when new climate scenarios become available. In addition, Gersonius et al. (in preparation) recognise that the ATP approach allows easier integration with adaptive capacity approaches. The integration with adaptive capacity approaches includes the recognition of the wider contexts (physical, social, political and economicl) in which adaptation has to take place. A focus on connecting adaptation with urban dynamics could help to reduce climate adaptation costs and to make use of synergistic benefits. Although the outputs from the ATP approach are easier to understand for decision makers, it can still be difficult to make a decision based on the outputs. This is because the approach forces the decision makers to explicitly decide, through their choice of strategy, those future climate conditions under which the system is likely to lack resilience (Lempert et al., 2004). In addition, expressing uncertainties in time with bar charts leads to a pseudo-certainty effect. The bar charts depict a definite limit, which is a simplification of the actual system reaction. The approach does not represent uncertainties associated with, for example, model simplification and the choice of the standard. The treatment of uncertainty is therefore


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incomplete, and there is an argument here to use other uncertainty approaches, such as Monte-Carlo simulation, to quantify the uncertainty about the system reaction. Finally, the sensitivity analysis over the range of future climate conditions, as needed to determine the system boundary conditions where the impact thresholds are exceeded, can be a time-intensive process due to the potentially lengthy run times of hydrological and hydraulic models. 4. CHOOSING AN APPROACH Table 3 summarizes each approach, in terms of the following the key characteristics. 1. Framework: identifies the direction in which the cause and effect chain (e.g. from pressure

to state to impact) is followed in the reasoning. 2. Aim: gives the main aim of using the approach. 3. Focus: determines whether scenarios and/or stakeholders are driving the approach. 4. Scenario requirements: considers the type of scenarios needed to apply the approach. 5. Ease of development: considers the capacities and capabilities needed to develop the

approach. 6. Ease of running: considers the level of effort needed to run the approach. 7. Ease of output use: defines the ease in interpreting and making decisions based on the

results obtained. 8. Ease of updating: defines the ease in updating/reassessing the strategy, when new climate

scenarios become available. 9. Ease of integration with adaptation: defines the ease in integrating the approach with

adaptive capacity approaches. Table 3. Key characteristics of each approach RIO approach ATP approach Framework Cause Effect Effect Cause Aim Optimised strategy Socially acceptable strategy Focus Scenario-driven Scenario-/stakeholder-driven Scenario requirements Probabilistic scenarios Plausible scenarios Ease of development Low High Ease of running Low Medium Ease of output use High Medium Ease of updating Low High Ease of integration with adaptation Medium High The characteristics summarized in Table 3 can be used as a starting point for identifying which approach to use under what circumstances. The selection of an approach will depend on a number of factors, including: (1) the knowledge about the probabilities of climate change; (2) the knowledge about the likely success of options; and (3) the capacities and capabilities available. If the probabilities of the drivers from climate change are available and there is agreement on the potential options for adapting, then the RIO approach may be most appropriate as an attempt to identify the optimal set of adaptive strategies. This will however, be dependent on the capacities and capabilities available to effectively use the probabilistic climate data in optimisation procedures. If probabilities of climate change are not available and/or there is not yet agreement on the potential options for adapting, then the ATP approach may be most appropriate in an attempt to identify a realistic and acceptable adaptive strategy. In the same way, this will be dependent on the capacities and capabilities available to meaningful engage all the relevant stakeholders in the decision making process. Finally, the type of adaptation is also important. For mainstreamed adaptation, the ATP approach is recommended, because it allows easier integration with adaptive capacity approaches. For


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stand-alone adaptation, however, integration with adaptive capacity approaches is typically less important and either approach may be used. 5. CONCLUSIONS This paper has compared the RIO approach with the ATP approach. These approaches differ in fundamental ways, as shown in the discussions above. It is concluded that each approach has merits under particular circumstances. In addition, there are some cases where both approaches could be used in conjunction. For example, at the early stages of the adaptation planning process the ATP approach might be adopted in an attempt to short-list one or more acceptable strategies. At a later stage, these strategies may then be analysed with the help of RIO in order to optimise the size and timing of the investments. Lastly (where required), the economic efficiency of the alternative, adaptive strategies can be compared in order to aid the final decision making process. ACKNOWLEDGEMENT This work has been supported by the EU's Interreg IVB project MARE. REFERENCES Barnett J. and O’Neill S. (2010). Maladaptation. Global Environmental Change, 20, 211-213. Cox P. and Stephenson D. (2007). A changing climate for prediction. Science, 317, 207. Dessai S., Hulme M., Lempert R. and Pielke Jr R. (2008). Climate prediction: a limit to adaptation. Living with

climate change: are there limits to adaptation, 49-57. Gersonius B., Ashley R., Pathirana A. and Zevenbergen C. (2010) Managing the flooding system's resiliency to

climate change. Proceedings of the ICE-Engineering Sustainability, 163, 15-23. Gersonius B., Ashley R., Pathirana A. and Zevenbergen C. (submitted) Climate change, real or not; building

flexibility into water infrastructures. Submitted to Climatic Change. Gersonius B., Ashley R., Nasruddin F., Pathirana A. and Zevenbergen C. (in preparation). A hybrid approach to

devising climate adaptation: application to a stormwater system in Dordrecht, the Netherlands. Hurk van den B. (2007). New climate change scenarios for the Netherlands. Water science and technology, 56,

27-33. Jones R.N. and Preston B.L. (2010) Adaptation and risk management. Climate Change Working Paper No. 15.

Centre for Strategic Economic Studies, Victoria University, Melbourne, Australia. Kundzewicz Z., Mata L., Arnell N., Doll P., Jimenez B., Miller K., Oki T., Sen Z. and Shiklomanov I. (2008).

The implications of projected climate change for freshwater resources and their management. Hydrological Sciences Journal/Journal des Sciences Hydrologiques, 53, 3-10.

Kwadijk J., Haasnoot M., Mulder J., Hoogvliet M., Jeuken A., Krogt R., Oostrom N., Schelfhout H., Velzen E., Waveren H. and Wit M. (in press) Adapting to sea-level rise in the Netherlands. Submitted to Wiley Interdisciplinary Reviews: Climate Change.

Lempert R., Nakicenovic N., Sarewitz D. and Schlesinger M. (2004). Characterizing Climate-Change Uncertainties for Decision-Makers. An Editorial Essay. Climatic Change, 65, 1-9.

Murphy J., Sexton D., Jenkins G., Booth B., Brown C., Clark R., Collins M., Harris G., Kendon E. and Betts R. (2009). UK Climate Projections Science Report: Climate Change Projections. Met Office Hadley Centre, Exeter, UK.

Neufville R. (2003). Real Options: Dealing With Uncertainty in Systems Planning and Design. Integrated Assessment, 4, 26-34.

Rahman S., Walker W. and Marchau V. (2008). Coping with Uncertainties about Climate Change in Infrastructure Planing – An Adaptive Policymaking Approach. Final Report.

Water Utility Climate Alliance. (2010). Decision Support Planning Methods: Incorporating Climate Change Uncertainties into Water Planning.

Wang T. and Neufville de R. (2004). Building Real Options into Physical Systems with Stochastic Mixed-Integer Programming, pp. 17–19.

Woodward M., Gouldby B., Kapelan Z., Khu S.T. and Townend I. (2010). The use of real options in optimum flood risk management decision making.

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