In particular water infrastructure systems are under stress and the design and ... spectrum of uncertainties that water infrastructure systems face, including socio- ...
DESIGNING ADAPTIVE SYSTEMS FOR ENHANCEMENT OF URBAN WATER RESILIENCE JOOST BUURMAN Institute of Water Policy, Lee Kuan Yew School of Public Policy, National University of Singapore, 469C Bukit Timah Road, Singapore, 259772 VLADAN BABOVIC Department of Civil and Environmental Engineering, Faculty of Engineering, National University of Singapore, 1 Engineering Drive 2, E1A 07-03, Singapore 117576 ABSTRACT: Rapid urbanization and climate change pose challenges to policymakers in the 21st century. In particular water infrastructure systems are under stress and the design and planning approaches from the past may no longer be sufficient for the future. Several new approaches have been developed to support planners, designers and decision-makers in moving towards better infrastructure systems. The challenge is to find practical approaches that address the broad spectrum of uncertainties that water infrastructure systems face, including socio-political uncertainties. This paper proposes that practical approaches can be assembled from a toolbox consisting of different techniques, methodologies and procedures framed by a step-wise approach consisting of the Adaptation Pathways, Adaptive Policymaking and Real Options Analysis approaches. However, rather than seeing future uncertainties as a threat, this approach takes as key premise that uncertainty provides opportunities and flexibility has an important value. This is demonstrated by analysing the historic development of Singapore’s water infrastructure. INTRODUCTION Rapid urbanization and climate change are two large challenges for policymakers in the 21st century. The rate of urbanization, particularly in Asia, is unprecedented: in China alone 265 million people are expected to move to the cities in the next 25 years; Manila, which has currently 12.8 million residents, is growing at a rate of more than 225,000 people per year and Kuala Lumpur is expected to add 2.8 million people to its current population of 6.6 million by 2030 (United Nations, 2014). All these new citizens will require basic services, including clean water, sanitation and flood protection. At the same time, climate change is putting pressure on water resources that are in many places already overexploited. The Intergovernmental Panel on Climate Change concludes in its latest report that warming of the climate is unequivocal: “The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen” (IPCC, 2014 p.2). It is very likely that anthropogenic influences have affected the global water cycle, with impacts such as changes in temperature, precipitation and extreme weather events. Cities can be highly exposed and vulnerable to climate and weather extremes, as shown for instance by flooding of parts of New York due to hurricane Sandy in 2012 and the ongoing droughts affecting cities such as Sao Paulo in Brazil and San Diego in the United States. Climate change will exacerbate existing flood and drought risks and create new risks for cities. However, the exact changes are uncertain: the projected range of
temperature rise is between 0.3°C and 4.8°C by the end of the century as compared to the beginning of last century and there are still many uncertainties in the assessment of temperature rise on frequency and magnitude of floods and droughts (IPCC, 2014), which makes planning for future water infrastructure requirements difficult. Growth and climate change put urban infrastructure systems under stress and create uncertainties. Supplying water to millions of people and keeping them free from flooding under variable conditions requires complex water supply, drainage and flood protection systems. It is increasingly recognized that traditional planning and design approaches in the water sector are no longer sufficient for the future (Deng et al., 2013; Haasnoot et al., 2013; Park et al., 2014). Alternative approaches are proposed for the development of ‘flexible’, ‘adaptive’, ‘resilient’, ‘robust’, ‘sustainable’, ‘antifragile’ or ‘smart’ infrastructure systems: terms used to describe the properties of new systems that can handle the challenges of the future. However, with long time horizons, complex interactions between technical, social, economic, political and environmental systems, and many uncertainties present in these systems, decision-making for urban water infrastructure investments and policies is no easy task and decision-making approaches for new systems are still in development (Walker et al., 2010). The challenge is to find practical approaches that address the broad spectrum of uncertainties that water infrastructure systems face, including socio-political uncertainties and other uncertainties in other systems that interact with water infrastructure systems. In this paper we propose a toolbox approach framed by three complementary approaches: Adaptation Pathways, Adaptive Policymaking and Real Options Analysis. However, rather than seeing uncertainty as a threat, the approach takes as premise that uncertainty provides opportunities and has a value. The following sections will subsequently discuss the types and sources of uncertainty faced by urban water infrastructure systems, different ways of looking at urban water resilience, the research toolbox, and an illustration using the historic development of Singapore’s water infrastructure. INFRASTRUCTURE INVESTMENTS UNDER UNCERTAINTY Sources and Types of Uncertainty Cities have always faced risks and uncertainties. Many cities that have existed for centuries have demonstrated their resilience in the face of resource shortages, natural hazards and conflicts. In the 21st century, global pressures such as climate change, economic fluctuations, disease pandemics and terrorism pose new challenges. Gheorghe and Mili (2004) put forward three reasons to justify new policies for risk management in critical infrastructures: increase in frequency and intensity of extreme climate events, the propensity of operating infrastructures closer to their capacity or stability limits due to new technology and regulatory environments, and the occurrence of cascading disasters, which can start local and become regional or global. An example of the latter is the 2011 flooding of Thailand, which disrupted global supply chains. Risk management in critical infrastructures, and in particular urban water infrastructure requires assessing many uncertainties from a variety of sources. Taking a systems perspective can be helpful in identifying sources of uncertainties. In such a perspective the system of interest is clearly identified and interfaces with other systems that affect the system of interest are described. Figure 1 gives an example of such a systems perspective for urban water infrastructure. The water
infrastructure system is the object of study and consists of the flood protection, water supply and sewage sub-systems. In designing, planning and decision-making for the water infrastructure system sources of uncertainty could be inaccuracies, simplifications and errors in for instance hydrological models, limited rainfall observations requiring extrapolation to determine the magnitude of extreme events, and uncertainties in the operation of the water infrastructure system. The water infrastructure system has interactions with several other systems, which have their own sources of uncertainty. An example is uncertainties in the climate system with respect to future temperatures and the impact on precipitation, which has significant impact on decisions in the water infrastructure system. Another example is the social system: uncertainty in citizens’ willingness to accept occasional flooding and the uncertainty in the public opinion on the allocation of flood damage over different income groups and affected versus not affected people impacts water infrastructure planning and design. In addition to direct interaction between the water infrastructure system and other systems, other systems also interact with each other, which can indirectly impact the water infrastructure system. One of many examples is that uncertainties in the future economic growth affect the available budget for future infrastructure investments and maintenance, which in its turn affects current design choices.
Figure 1. Systems view of uncertainties in planning and designing water infrastructure systems In addition to identifying sources of uncertainties, it is also helpful to make a distinction between different types of uncertainties in order to assess their impacts on decisions. Although many
different terminologies exist, uncertainty can usually be classified into two types: aleatory and epistemic uncertainty (Helton & Burmaster, 1996). Aleatory uncertainty refers to random variability in the system and is a property of the system, while epistemic uncertainty arises from a lack of knowledge of the system (Helton & Burmaster, 1996; Kiureghian & Ditlevsen, 2009). Aleatory uncertainty can be addressed by collecting more data about system variables and performing statistical and probability analysis. For epistemic uncertainty this is not possible as the data does not exist or is not available and analytical models are unknown. With respect to decision and policy-making in a systems perspective, Walker et al. (2010) provide a useful classification of uncertainty that will be adopted in this paper. Uncertainty is the space between determinism, in which everything is known, and ignorance, in which everything is unknown, see Figure 2. In complex infrastructure systems a decision contains elements with different types of uncertainty. For instance, implementing water sensitive urban design in a catchment to address flooding will need to deal with uncertainty in climate projections, where probabilities and models exist (statistical uncertainty), but for future emissions affecting the projections there is only a range of outcomes (deep uncertainty), and while effectiveness of new water sensitive infrastructure could be modelled (statistical uncertainty) there is no data available yet on its actual performance (deep uncertainty).
Figure 2. Different types of uncertainty (based on (Walker et al., 2010)). Resilience and Adaptive Systems According to the Rockefeller Foundation (2015), urban resilience represents the capacity of individuals, communities, institutions, businesses and systems within a city to thrive and survive, adapt and grow no matter what kinds of chronic stresses and acute shocks they experience. Resilience is a term that emerged from the field of ecology to describe the capacity of a system to maintain or recover functionality in the event of disruption or disturbance. The notion of resilience is applicable to cities because they are complex systems which constantly adapt to changing circumstances.
Resilience in the context of climate adaptation is a widely debated subject (Jabareen, 2013; Leichenko, 2011; Linkov et al., 2014; Tyler & Moench, 2012) and alternative or complementary concepts are proposed, such as robustness (Mens et al., 2011), which is the ability of a system to remain functioning under a range of disturbances, or antifragility (Taleb, 2012), which is used to describe systems that improve when exposed to uncertainty and disturbances. In order for a system to be resilient, robust or antifragile, it needs to be flexible and adaptive to deal with anticipated and unanticipated changes. Traditionally systems are designed for an optimal or most likely range of parameters and requirements that are unrealistically fixed (Haasnoot et al., 2013; Zhang & Babovic, 2012), while at the same time evaluation of performance is limited to technological considerations and does not take into account socio-technical elements (Cardin et al., 2013) and interacting systems such as described in Figure 1 above. To deal with uncertainties in complex systems, they should not be designed on a single scenario, but should incorporate flexibility to adapt to changing circumstances (Buurman et al., 2009; Deng et al., 2013; Reeder & Ranger, 2011). Adaptive capacity could be achieved by building flexibility in the engineered system, e.g. by making allowance for future expansion, or by creating flexibility on the system, which means that projects could be delayed, abandoned, or alternative projects could be pursued as part of an adaptive plan (Geltner & de Neufville, 2012). The concept of antifragility goes a step further resilience: antifragile systems ‘learn from mistakes’ and improve under stress. This is not unlike the concept of evolution in biology, where the best performing organisms survive. In this respect, flexibility in and on systems has a value: it allows capturing future opportunities and provides an insurance against negative outcomes. For example, a flexible, modular drainage system using permeable pavements and green roofs could be adjusted step-wise to the pace of climate change and provides opportunities to increase water yields of the local catchment (Deng et al., 2013). However, adaptive systems are usually more expensive to build. Real Options Analysis, discussed in the next section, can be used to determine if the economic benefits of flexibility are higher than the costs of creating this flexibility (Buurman et al., 2009; Deng et al., 2013; Zhang & Babovic, 2012). An outcome of real options analysis is that, in general, the higher the uncertainty, the higher the value of flexibility. Decision-making for Adaptive Systems: the Research Toolbox Decision-making under uncertainty is a broad field that is researched by many disciplines. However, most techniques, methodologies and procedures are designed for situations of statistical uncertainty, while deep uncertainty is only addressed by a few approaches (Walker et al., 2010). Focusing on planning, designing and decision-making for water infrastructure systems, Dynamic Adaptive Policy Pathways was specifically designed to address deep uncertainty and was applied to water management (Haasnoot et al., 2013). Dynamic Adaptive Policy Pathways combines two complementary approaches: Adaptive Policymaking, which is a step-wise planning process to develop adaptive plans, and Adaptation Pathways, which is an analytical approach for exploring and sequencing actions using adaptation tipping points and scenarios with alternative external developments over time. A key element of Adaptation Pathways is that it does not take climate scenarios as a starting point, as they have many uncertainties, but instead looks at infrastructure and policies and determines when they no longer fulfil the stated objectives and a new strategy needs to be initiated – an adaptation tipping point.
Figure 3 shows a step-wise approach for developing adaptive plans based on Dynamic Adaptive Policy Pathways. In the framing step the system is defined, objectives determined and the uncertainties and risks are analysed. Figure 1 above demonstrated an approach to analyse uncertainties. In step 2 possible future situations, transient scenarios towards these situations, and opportunities and vulnerabilities are analysed. This step makes use of climate and other scenarios. The third step is an iterative design process to identify actions, optimize actions combine actions in adaptation pathways. For instance, assuming step 2 identified that the water supply system may fail due to longer droughts, many possible remedial actions can be designed: building desalination or water recycling plants, increasing catchment retention time, building reservoirs, using artificial aquifers, etc. Optimization will screen for infeasible or very expensive solutions, while pathways sequence combinations of possible actions in time. In the selection step a preferred pathway is selected, which is implemented in the execution step. Monitoring will identify when tipping points occur and alternative actions need to be initiated, or when conditions require a change to a different pathway.
Figure 3. Step-wise approach to designing adaptive plans In the design and selection steps of the step-wise approach Real Options Analysis is used. Real Options Analysis takes into account the value of flexibility in the evaluation of costs and benefits of actions. In the optimization step actions with costs higher than benefits can be eliminated, and in the selection step the total net benefits of a pathway computed by Real Options Analysis is an important selection criteria. In addition to being a methodology to calculate a monetary value for flexibility, Real Options Analysis provides a design philosophy for systems that emphasizes incorporation of flexibility: it helps planners and designers think about different options that can make a system more flexible and adaptive. The step-wise approach can handle situations of deep uncertainty, though many steps depend on quantification of uncertainty. Real Options Analysis requires uncertainty to be quantified, and in the literature several computational methods can be found to carry out a Real Options Analysis (see for instance (Buurman et al., 2009; Deng et al., 2013; Geltner & de Neufville, 2012; Park et al., 2014). Decision trees are often used to value real options in infrastructure systems. Decisions on infrastructure and climate adaptation are typically path-dependent: a future option can only be exercised if an earlier option was exercised, e.g. widening a diversion canal in the future is only
possible if the canal was built in the first place. This can be represented by a non-recombining tree, as well as by the Adaptation Pathways, which provides the link between the two approaches. Although quantification of uncertainty is required in the step-wise approach and Real Options Analysis, the approach would still perform much better in under conditions of deep uncertainty than traditional approaches, as uncertainty is anticipated and used to improve the system. The step-wise approach consisting of Adaptive Policymaking, Adaptation Pathways and Real Options Analysis is a high-level framework for developing adaptive plans. To make it operational, analysis in each of the steps requires techniques, methodologies and procedures suitable for the problem at hand. A toolbox approach can be used to assemble tools consisting of technical, financial and policy instruments. A first version of the toolbox focusing on urban water infrastructure contains the following instruments: Technical instruments • Climate projections and downscaling techniques (step 2) o Predictions of rain, temperature and other environmental parameters • Water quality and quantity models (steps 2 & 3) o Impact of changing environmental conditions on water availability and safety o Flood modelling • Visualization and communication tools for flood hazards (steps 3 & 5) o Stakeholder engagement Financial instruments • Risk characterization and modelling tools (steps 1 & 3) • Real Options Analysis (steps 3 & 4) o Cost Benefit Analysis o Decision Trees o Monte Carlo Simulation Policy / Decision-making instruments • Adaptation Pathways (steps 3 & 4) • Adaptation Tipping Points (steps 3, 4 & 5) In addition to the instruments mentioned above, lower-level (more detailed) instruments and tools will be used, such as models for performance of specific infrastructure (networks of pipes, dams, etc.) and models to predict water consumption. URBAN WATER RESILIENCE IN SINGAPORE Singapore is island state and with a population of about 5.5 million people on about 700 square kilometres has one of the highest population densities in the world. Being located in the tropics it receives abundant rainfall throughout the year, though limited possibilities for reservoirs and absence of natural aquifers or groundwater makes it dependent on neighbouring Malaysia for its water supplies. Since independence in 1965 reasons of sovereignty have impelled Singapore to pursue self-sufficiency in water supplies (Tortajada et al., 2013). Singapore provides a unique opportunity to evaluate the step-wise approach for designing adaptive plans as it has clearly identified decision makers with unambiguous goals. Using the development of Singapore’s water
system over the past 50 years (Tortajada et al., 2013 chapter 1), we illustrate how the step-wise approach could be used. At independence in 1965 water in Singapore was supplied by three reservoirs in Singapore and two reservoirs and two rivers in Johor. Before independence water planning revolved around projections of water consumption (focusing on domestic consumption) and selection of alternative sources and schemes, mostly in Johor. This was typically based on a single scenario (doubling of population between 1950 and 1970, increase in consumption from 140 l/capita/day to 225 l/capita/day) with main uncertainty being occurrence of droughts. In 1972 the first Master Plan after independence was developed in a different context (step 1). The existing system consisted of reservoirs and river abstractions, which were partly located outside Singapore. Rapid growth required expansion of the system. The main uncertainties affecting the water infrastructure system were found in the economic system (how economic growth and associated water demand would evolve), the socio-economic system (how water use per capita would evolve), the climate system (probability of droughts) and the political system (uncertainties in the relations with Johor). In addition, uncertainties surrounding technological developments (desalination, water recycling) were considered. These uncertainties are a mix of deep uncertainties and statistical uncertainties: some aspects can be modelled (population growth, drought probability) but assumptions need to be made. The future situation (step 2) would be a reliable water supply providing sufficient water; using the uncertainties several transient scenarios could be developed and gaps identified e.g. additional supply to be realized). The Master Plan identified several options to increase the water supply (step 3a): developing surface water sources by damming estuaries, first in protected catchments and later in unprotected catchments, recycling of sewage water, and desalination. Water recycling and desalination were very expensive due to high costs of energy and membranes (step 3b). These options were, however not discarded but kept for later as improved technology could reduce the costs in the future. The different options, including development of different surface water schemes, which each have different implementation alternatives, resulted in “a complicated matrix that considered aspects such as quantity, quality, costs, reliability and security, to mention only some of them.” (Tortajada et al., 2013 p.20). Adaptation Pathways (step 3c) and Real Options Analysis provide a methodology to analyze and optimize the different options and sequence them in time. Real Options Analysis could, for instance, provide a rationale for investments in research to develop new recycling and desalination technologies, as well as analyze the value of additional supply options in view of the political situation (see Zhang & Babovic, 2012). The Master Plan and its subsequent implementation, including the chosen order of projects, was in essence one pathway that was decided upon (steps 4 and 5). Although the water supply schemes were implemented in the most cost-effective manner, they were extremely expensive compared to water supplied from Johor. With current knowledge the benefits of having a more independent water supply system could be valued and included in a cost benefit analysis. In addition, opportunities, such as the development of a water industry in Singapore could have taken into account. Decreasing costs for water recycling and desalination meant that tipping points for these technologies were reached in 2003 and 2005 respectively. Implementation of recycled water was not only contingent upon technological development, but also had to take into account issues
regarding public acceptance (social system), which posed a risk to the project. Currently Singapore has a water supply system with four so-called ‘taps’: local catchment water, desalination, recycled water and imported water. This diversified portfolio provides flexibility to deal with uncertainties in precipitation (the longest drought on record in 2014 posed no problems for the water supply), and in case of a problem with one ‘tap’, other ‘taps’ could take over. Looking at the future climate change, increasing urbanization, energy prices and changing expectations by the general public pose the main uncertainties (Tortajada et al., 2013 p.229). Application of improved and new technologies, such as next-generation infrastructure (permeable pavements, green roofs, etc.) and variable desalination could provide water resilience. CONCLUSIONS Water management is increasingly challenged by climate-associated changes such as sea level rise and increased spatio-temporal variability of precipitation, as well as by pressures of population growth and rapid urbanization. Furthermore, high investment costs and the long term-nature of water-related infrastructure projects require a long-term planning perspective, sometimes extending over many decades. Adaptation to such changes is not only determined by what is known or anticipated at present, but also by what will be experienced and learned as the future unfolds, as well as by policy responses to social and water events. Water infrastructure investments are affected by a range of uncertainties originating in the water infrastructure system itself and in other systems. The approach presented in this paper to design adaptive systems for urban water resilience comprises of several elements. It explicitly incorporates uncertainty about the future conditions that will ultimately determine the value of today’s adaptation investments. The approach recognizes that many investments in adaptation to climate change are not ‘now-or-never’ investments, but rather that the flexibility often exists to expand, contract, or otherwise modify such investments. In addition, the approach recognizes that adaptation investments are rarely ‘all-or-nothing’ investments, but instead are choices along continua of costs, risks, and benefits. A key insight is that water infrastructure development is intrinsically associated with uncertainties. Uncertainties cannot be avoided, but could be re-cast to present valuable opportunities. Thus uncertainties and the flexibility offered by innovative solutions to the design of the overall water resource systems need to be studied integrally. REFERENCES Buurman, J., Zhang, S., & Babovic, V. (2009). Reducing Risk Through Real Options in Systems Design: The Case of Architecting a Maritime Domain Protection System. Risk Analysis, 29(3), 366-379. Cardin, M.-A., Kolfschoten, G., Frey, D., de Neufville, R., de Weck, O., & Geltner, D. (2013). Empirical evaluation of procedures to generate flexibility in engineering systems and improve lifecycle performance. Research in Engineering Design, 24(3), 277-295. Deng, Y., Cardin, M.-A., Babovic, V., Santhanakrishnan, D., Schmitter, P., & Meshgi, A. (2013). Valuing flexibilities in the design of urban water management systems. Water Research, 47(20), 7162-7174.
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