sanitas& sustainable&and&integrated&urban

Sustainable and Integrated Urban Water System Management

Deliverable Nr : 3.1 Deliverable Title : Knowledge bases and performance

criteria developed and utilised in the design and management of UWS

SANITAS

SUSTAINABLE AND INTEGRATED URBAN WATER SYSTEM MANAGEMENT Marie Curie Network for Initial Training

Seventh Framework Programme Grant Agreement Nr. 289193

Knowledge bases and performance criteria developed and utilised in the design and management of UWS

Deliverable reference 3.1 Partner in charge UNIVERSITAT DE GIRONA Authors A. Hadjimichael, Ll. Corominas, J. Comas Target dissemination PU Coordinator institution UNIVERSITAT DE GIRONA Date of delivery 30/08/2014

The research leading to these results has received funding from the People Program (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-‐2013, under REA

agreement 289193.

This publication reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein.

Sustainable and Integrated Urban Water System Management Deliverable Nr : 3.1

Deliverable Title : Knowledge bases and performance criteria developed and utilised in the design and

management of UWS

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Index

Abstract 2

Introduction and state of the art 3

1. Methodology for UWS management 7

2. Conclusions 11

3. References 12



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Abstract In view of ever-‐changing socio-‐economic, environmental and political conditions, Urban Wastewater System (UWS) managers face constant decision-‐making challenges threatening the well functioning of their systems creating the need for a prospective view of the system. Furthermore, technological advances provide UWS management with more possibilities for improvement and tackling challenges than ever before. In order to evaluate how well the system meets specific objectives influenced by current and emerging challenges, relevant social, economic and environmental indicators need to be employed. Given their often conflicting nature however, there is a need for methodologies standardising the application and combination of tools and indicators. The objective of this paper is to describe knowledge bases and developed performance criteria implemented into a methodology to assess environmental and socio-‐economic impacts of UWS management options under the various present and future challenges they face.

Keywords: decision support, methodology, social benefits, environmental indicators, economic analysis



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Introduction and state of the art

Finding themselves in ever-‐changing socio-‐economic, environmental and political conditions, Urban Wastewater System (UWS) managers face constant decision-‐making challenges threatening the well functioning of their systems. At the same time, technological advances provide UWS management with more possibilities for improvement and tackling challenges than ever before. The European Water Framework Directive (WFD), aiming for a more sustainable and integrated approach in UWS management, promotes the development and use of decision-‐support tools to aid UWS decision makers (DMs).

Future challenges, such as the impacts of climate change on river systems, as well as on the sewer system, have been widely studied for many years. But as Langeveld et al. (2013) point out, not much attention has been given on the effects of the possible combinations of climate change that might occur on the system, such as the intensification of rainfall, temperature increase and others. Furthermore, as climate change can impact all the components of the UWS (catchment, sewer system, wastewater treatment plant (WWTP) and receiving water body), considering the whole system when assessing these effects is therefore of great importance. Extremely relevant are also the issues of wet-‐weather flow management and drought, as the intensification of rainfall is sure to cause significant disturbances to the system hindering the efficient operation of the system or increasing the treatment needs. Factors besides climate change, such as population growth and urbanisation can have a great impact on the future of the quality and quantity of water in urbanised catchments (Fu et al., 2009; He et al., 2008). However, the combined or relative effects of future changes, such as climate change, urbanisation and population growth, on the UWS have not been given extensive research focus thus far (Astaraie-‐Imani et al., 2012; Yang et al., 2012). In addition, the evolution of important economic factors – principally, water and energy prices – can potentially disturb the operational equilibrium applied by UWS managers. Therefore, the future growth of relevant environmental, economic and social conditions must not be neglected especially when decisions on new investments in UWSs are being made.

Public acceptance of the sanitation services is also of great importance, since it can undermine the success of a decision-‐making process and the resulting applied measure (Nancarrow et al., 2009). This is especially the case in situations where great alterations are taking place in the system or new technology is about to be implemented (Bdour et al., 2009). However, since social perception is often very hard to quantify, studies often do not usually address this aspect. In order to comply with sustainability standards, the methodology presented in this study will take into account environmental, economic but also social objectives.



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To assess the performance of the system of interest in terms of the specified objectives, the methodology will make use of existing indicators but also new quantitative indicators associated with each defined objective. These indicators will be metrics of the “implications” in sustainability terms (economic/social/ecological) of various planning or operational measures considered by DMs. The methodology will assess the measures by means of economic analyses (Cost-‐Benefit Analysis and Financial Analysis) and uncertainty analyses as recommended by the “Guide to Cost-‐Benefit Analysis of investment projects”(European Commission, 2008).

Cost-‐Benefit analysis (CBA) is a rational and systematic approach used in public or private decision-‐making to evaluate whether the long-‐term benefits of an action outweigh the costs in monetary terms. When applying a CBA to environmental issues, the idea of an externality -‐ a third party detrimental (or beneficial) effect for which no price is exacted -‐ becomes central (OECD, 2006; Pearce, 1983). In the context of UWSs, economic externalities can consist of positive externalities (for example, groundwater recharge from irrigation or water reuse) and negative externalities (for example, the release of pollutants in a receiving water body) (OECD, 2010). Based on the principles of CBA, a project should be supported only if the benefits for the gainers are sufficiently greater than the costs for the losers, so they could -‐ in principle -‐ compensate the losers and still be better off (OECD, 2006). In reality, very few CBA analyses have taken into account environmental externalities that are difficult to quantify, qualify and assign tangible monetary values to (Fan et al., 2013). A promising approach to this issue is through the use of proxy or hypothetical values, so-‐called shadow prices. Shadow prices are constructed prices for externalities for which real market prices do not exist, such as emissions, pollution, environmental impacts and environmental quality (de Bruyn et al., 2010). Molinos-‐Senante et al. (2010) used the valuation methodology of distance function to estimate the shadow prices of the pollutants released into the receiving medium and therefore estimate the avoided cost provided by their removal.

A Financial Analysis (FA) is similar to a CBA, however it is performed from the point of view of the agency responsible for financing and activating a decision (Belli, 2001). It is not therefore sensible to take into account in a FA negative or positive environmental externalities (costs and benefits) that cannot -‐or will not-‐ be realised by the financing body. Nevertheless, given the nature of an integrated urban wastewater system, it would be short-‐sighted to ignore the possible tangible benefits arising from the use of services provided by the local ecosystem – typically a surface water body. Ecosystem services are defined as the “benefits people obtain from ecosystems” (Millennium Ecosystem Assessment, 2005). In the case of UWSs ecosystem services can provide significant benefits by: making use of the dilution and purification of discharged pollutants in the river -‐ termed



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as “immission-‐based management” by literature -‐ (Corominas et al., 2013); provide thermal water regulation to increase water treatment efficiency (Honey-‐Rosés et al., 2013); and regulating water flow (Price, 2011). Attainable costs and benefits emerging from the use of such services should therefore be considered and accounted for in economic and financial analyses aiming to find the most profitable out of an array of options.

With regards to uncertainty, numerous and various definitions of the concepts of robustness, reliability, resilience, flexibility, functionality, stability, sensitivity and vulnerability can be found in literature. These definitions are not always in agreement, especially in the literature between different disciplines, and are often used interchangeably. However, the general notion captured by most of them is the idea of satisficing (or not) over the many plausible states a system might be found in (Hall et al., 2012). Satisficing or not is hindered by uncertainty. Herder and Verwater-‐Lukszo (2006) defined two types of uncertainty: context and valuation uncertainty. Context uncertainty is related to the internal or external to the system factors that define the context of the system, whereas valuation uncertainty is related to the choices and methods we apply in order to describe and assess the system.

External context factors are the relevant socio-‐economic, environmental and technical situations, such as market prices, social perceptions, climate and legislation, that affect UWSs and ultimately shape their efficacy (Zhang and Babovic, 2011). These factors are also subject to both (future) long-‐term and short-‐term variation and whether or not this variation is taken into account usually depends on the time window of the evaluation. An obvious example of external variation in the context of UWSs, are the climatic conditions affecting the system. In this case, climatic long-‐term variability is driven by climate change affecting river flows, catchment runoff, mean annual temperature, etc., whereas climatic short-‐term variability are individual storm events causing increased system stress and overflows.

Internal context factors lay within our operational space and are the functioning settings that the system operator is able to manage their system with (for example, control set-‐points or use of tanks) and the different values the parameters characterising the system might take. In UWSs, this system variability is inherent and inevitable given the fact that they describe physical and bio-‐chemical processes. On the other hand, this variability is also facilitating the ability for operational adaptation to ensure the most desirable outcome.

Finally, valuation uncertainty emerges when we attempt to describe and assess the system. For example, if the system is to be modelled, the modelling process itself involves a relative inaccuracy in the predictions. This variance is intrinsic to the process of modelling, i.e. when interpreting and attempting to represent natural processes using mathematical models. This type of uncertainty, though inherent, is often neglected in evaluations and



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decision-‐support tools based on modelling (Belia et al., 2009). Other valuation uncertainties arise with choices regarding assessment, for example which criteria are considered the most appropriate by the decision makers and how they influence the evaluation outcome (Dominguez et al., 2011) or values of factors such as the discount rate in a CBA (European Commission, 2008).

Analyses aiming to thus evaluate the causes and the effects of these current and future uncertainties are very important in order to: understand the system’s operational and economical sensitivity to the aforementioned internal and external factors (Flores-‐Alsina et al., 2008; Taleb, 2010); minimise possible unexpected risks of applied measures, for example, exceeding a legal effluent standard (Rousseau et al., 2001); understand the economic evolution of an investment project in the future (European Commission, 2008); evaluate the transferability of measures and control strategies to different plants or operating conditions, especially for benchmarking purposes (Vanrolleghem and Gillot, 2002); take robustness and uncertainty of measure into account when providing decision support, as part of good decision-‐making practice (Benedetti et al., 2012; Gervásio and Simões da Silva, 2012); and engage stakeholders with different expectations of future possibilities into the decision-‐support process(National Research Council, 2009). Finally, understanding the uncertainty of a system better allows for flexibility in decision-‐making for design, which can ultimately improve the life-‐cycle performance of a system (Deng et al., 2013).

Adaptive management is widely considered to be the best available approach for managing biological systems in the presence of uncertainty, based on the premise that our ability to predict key drivers affecting ecosystems is inherently limited (Pahl-‐Wostl, 2007; Westgate et al., 2013). The idea of adaptive management has already been discussed in the field of ecosystem management for quite some time now (for example, in Holling (1978) and Walters (1986)). Accordingly, seeing the innate dependence of water and wastewater systems on natural systems, literature has been suggesting a shift in management to a more adaptive and flexible approach to ensure operation under fast changing socio-‐economic and environmental conditions (Meire et al., 2008; Pahl-‐Wostl, 2007).

1. Aim of study

The objective of this paper is to describe a methodology to assess the environmental and socio-‐economic impacts of UWS retrofitting practices under different scenarios. With a specific objective defined by the user, the proposed methodology assesses and compares the various measures that can be taken towards that goal.



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The ultimate purpose of this methodology is to be implemented into an Environmental Decision Support System (EDSS) to aid in the planning of retrofitting measures to be applied in UWSs. The EDSS will aim to support the assessment of upgrading measures that current and future scenarios demand, as well as to serve as an intermediary tool between the available and developing technology, models and indicators and the potential end-‐users, which might include concerned researchers, wastewater managers and policy-‐makers. EDSSs are intelligent information systems, integrating mathematical models and automatic control with knowledge-‐based systems, that can support the decision making process in an environmental domain by reducing the time in which decisions are made and improving the consistency and quality of those decisions (Poch et al., 2004).

2. Methodology for UWS management

Problem Statement and proposed measures

Periodic Assessment – Adaptive Management

Long-term Analysis (e.g. Cost-Benefit Analysis)

Robustness Analysis (External context)

General Assessment – Strategic Planning decisions

Optimisation OR Pareto front

Uncertainty Analysis (Valuation)

Optimisation – Technical decisions

Short-term Analysis (e.g.

Financial Analysis) Reliability Analysis (Internal context)

Specific Assessment – Conceptual Design decisions



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Problem Statement and proposed measures

Assessment objective

The purpose of the assessment is to assess and compare the possible measures or courses of action that the DM might follow to reach a predefined objective. The DM should thus first and foremost clearly define the desired objective. With that in mind, the DM will then need to decide which criteria/indicators will be used to encapsulate the economic, environmental and social implications of each measure.

Proposed measures under assessment

Measures to be applied or managerial strategies to be followed with the DM’s objective as the ultimate goal: application of automatic control, control strategies, WWTP upgrades and extensions (e.g. reactor extensions, addition of tanks, addition of tertiary treatment, wetlands etc.), upgrades in sewer system (e.g. storage tanks, bypasses), measures in the receiving water body (e.g. rehabilitation).

General Assessment – Strategic Planning decisions

The General Assessment step is an assessment of the long-‐term performance of the system in terms of an economic indicator of choice (CBA) and of a Robustness Analysis (ROA). The purpose of this assessment stage is twofold:

By means of CBA: Explore the overall long-‐term value of an investment project by contrasting the total capital, operation and maintenance costs of a measure with the total benefits resulting from the investment, directly and in the form of externalities.

By means of ROA: Explore the long-‐term robustness of the investment project under different external context conditions: precipitation, temperature, population, energy and water prices, urbanisation, industrial activity and legislation.

During the CBA the direct and indirect (externalities) costs and benefits resulting from the application of each measure are estimated. The main externality in the context of UWSs, is the detrimental effects resulting from the release of pollutants in a receiving water body (OECD, 2010). Valuing environmental externalities means expressing their value to society in monetary terms. Because in many cases the value of environmental aspects cannot be obtained directly (for example, via a market price), it must be estimated through calculation (De Bruyn et al., 2010). The valuation methodology of distance function is therefore employed in this framework to estimate the shadow prices of the pollutants released into the receiving medium (Molinos-‐Senante et al., 2010). Shadow prices are constructed prices for goods or production factors that are not traded in actual markets and for which real market prices do



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not exist. These prices can therefore provide an indication of the positive or negative value of an externality – in this case the discharged pollutants – to society (De Bruyn et al., 2010). The overall benefit of the project can then be demonstrated by the use of CBA indicators such as Net Present Value, Benefit-‐Cost Ratio and Pay-‐back Period.

For the ROA scenarios about the future are employed to investigate how the system might respond to social, economic and environmental changes. Parson et al. (2007) have defined scenarios as “descriptions of potential future conditions developed to inform decision-‐making under uncertainty”. DMs often face a big variety of plausible futures, but they often have limited cognitive bandwidth so they need a concise summary of the futures they might face (Lempert, 2013). Scenarios are thus very useful in that respect as they use a small number of plausible values for key planning variables (population and precipitation, for example) to create storylines for future conditions in a system (Kasprzyk et al., 2013). To estimate the robustness of a proposed measure, said variables are applied on the system either by means of modelling and simulation or simple feasibility estimations based on literature and expert knowledge.

The objective of this stage is to assess the robustness of each proposed measure against expected changes in the long-‐term distant future. Specific perturbation events (such as storms) are practically impossible to predict at scales of 20-‐30 years ahead deducing dynamic modelling of the system possibly unnecessary at this stage. In addition, the main planning and design information required at this step can be directly derived either from pre-‐existing data, literature or expert advice. This information can be for example, average cost per year of operation, average performance of technology and conformity with legislation, ability to serve estimated habitant equivalents and volume of inflow, etc.

Specific Assessment – Conceptual Design decisions

The Specific Assessment step is an assessment of the short-‐term performance of the system in terms of a financial indicator of choice (FA) and of a Reliability Analysis (REA). The purpose of this assessment stage is twofold:

By means of FA: Estimate the short-‐term benefit of a measure by contrasting the total capital, operation and maintenance costs of a measure with the total tangible benefits resulting directly from the investment.

By means of REA: Explore the short-‐term reliability of a measure under different internal context conditions: storm events, control set-‐points, sensor location, flow regulation thresholds, use of storm tanks and others.



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For the purposes of FA, tangible costs and benefits arising by the application of a measure should be calculated and accrued. Most commonly used costs and benefits in this type of analysis are summarised in Table 1.

Table 1. Summary of commonly used tangible costs and benefits

In the case of UWSs ecosystem services can also provide significant benefits to the operation mainly through supporting process efficiency and thus reducing costs, and by resource provision (water). Possible attainable costs and benefits emerging from the use of such services should therefore be considered and accounted for in a FA. Based on Millennium Ecosystem Assessment (2005), The Economics of Ecosystems and Biodiversity (2010) proposed a framework encompassing a typology of 22 ecosystem services categorised in provisioning, regulating, habitat and cultural & amenity services. Studies have then subsequently presented a list of ecosystem services provided by a water body to various stakeholders, including the water supply and sanitation sectors as well as social stakeholders. However, from the point of view of an UWS manager and a financing body, the only relevant ecosystem services are those directly benefitting them. As such the ecosystem services taken into account in a FA are more easily quantifiable through operational cost savings and additional resource provision.

The REA at this stage is performed to explore the short-‐term reliability of each proposed measure given variations within its internal context. Expected perturbations to the desirable operation (storm events and seasonal variations, for example) should be investigated taking into consideration all the possible operational space of the system. The operational space of a system refers to all the feasible combinations of operational decisions that can be made in a system (control set-‐points, sensor location, flow regulation thresholds, use of storm tanks). Each proposed measure will consequently bring about a reformed operational space, allowing for new operational combinations or restricting old ones.

Costs Benefits Aeration energy Methane production Pumping energy Energy production Mixing energy Chemical recovery Heating energy Reuse water production Sludge treatment/Sludge production Chemical addition Maintenance (buildings & installations) Labour Fine payments Investment Land use



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At this stage, the aim is to assess the reliability of each proposed measure against all expected perturbations in the short-‐term near future. For this reason, dynamic modelling of the system is an indispensable process in order to be able to simulate the effects of individual perturbation events (storm events, for example) more realistically. In addition, software facilitating efficient simulation of numerous different operational combinations (such as the Monte Carlo procedure) might also be necessary at this stage.

Optimisation Assessment – Technical decisions

The Optimisation stage is a procedure aimed at establishing the combination(s) of parameters generating the most desirable outcome or a set of equally good optimal solutions (commonly known as a Pareto front).

At the Optimisation stage, the Uncertainty Analysis explores uncertainties stemming from modelling and valuation assumptions – the third type of uncertainty as previously elaborated. These include variation in modelling assumptions made during the procedure of modelling, for example selected biokinetic model parameters or unexpected sensor-‐actuator settings. In addition, the uncertainty in the selection of valuation parameters should be explored, for example the discount rate chosen for the CBA and the FA and values assumed for the estimation of shadow prices among others.

The purpose of the Uncertainty analysis in this stage is to assess the uncertainty of achieving the outcome deemed as optimal or the Pareto front.

Periodic Assessment – Adaptive Management

The Periodic Assessment stage is to be repeated periodically for each of the previous stages (General Assessment, Specific Assessment, Optimisation Assessment). This is to ensure coherence with the principles of adaptive management and guarantee effective operation and continuous improvement under the ever-‐changing conditions of the system (Pahl-‐Wostl, 2007). This stage therefore consists of two procedures: i) examining whether the (internal and external) context and valuation conditions occurring at the time when the assessments were performed still hold; and if not ii) re-‐perform the assessment stage to adapt the decision taken accordingly. Considering that these assessment steps might be time-‐consuming, it is recommended that the Periodic Assessment stage is repeated at least at the timeframe resolution chosen at each step.

3. Conclusions

There is a justified need for decision-‐support tools taking into account multiple criteria and challenges to aid managers of UWS. This report thus aimed to present a methodology to support decision making in UWS by considering issues of future changes, uncertainty and



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socio-‐economic and environmental impacts. The knowledge bases and performance criteria and indicators developed to support the application of this methodology have also been described.

To demonstrate the usefulness and applicability of the presented methodology, its application on a real UWS case study is to follow. The studied UWS in northeast Spain had to make some important retrofitting decisions. The methodology is thus going to be applied by the means of modelling and simulation to investigate whether the decision taken was indeed the most viable out of the possible options.

The methodology is ultimately aimed to be applied in an EDSS to support decision making in integrated urban wastewater systems.

4. References

Astaraie-‐Imani, M., Kapelan, Z., Fu, G., Butler, D., 2012. Assessing the combined effects of urbanisation and climate change on the river water quality in an integrated urban wastewater system in the UK. J. Environ. Manage. 112, 1–9. doi:10.1016/j.jenvman.2012.06.039

Bdour, A.N., Hamdi, M.R., Tarawneh, Z., 2009. Perspectives on sustainable wastewater treatment technologies and reuse options in the urban areas of the Mediterranean region. Desalination 237, 162–174. doi:10.1016/j.desal.2007.12.030

Belia, E., Amerlinck, Y., Benedetti, L., Johnson, B., Sin, G., Vanrolleghem, P.A., Gernaey, K.V., Gillot, S., Neumann, M.B., Rieger, L., Shaw, A., Villez, K., 2009. Wastewater treatment modelling: dealing with uncertainties. Water Sci. Technol. 60, 1929. doi:10.2166/wst.2009.225

Belli, P., 2001. Economic analysis of investment operations: analytical tools and practical applications. World Bank Publications.

Benedetti, L., Batstone, D.J., De Baets, B., Nopens, I., Vanrolleghem, P.A., 2012. Uncertainty analysis of WWTP control strategies made feasible. Water Qual. Res. J. Can. 47, 14–29.

Corominas, L., Acuña, V., Ginebreda, A., Poch, M., 2013. Integration of freshwater environmental policies and wastewater treatment plant management. Sci. Total Environ. 445–446, 185–191. doi:10.1016/j.scitotenv.2012.12.055

De Bruyn, S., Korteland, M., Markowska, A., Davidson, M., de Jong, F., Bles, M., Sevenster, M., 2010. Shadow Prices Handbook: Valuation and weighting of emissions and environmental impacts. Delft, CE Delft, Delft, the Netherlands.

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 Res. doi:10.1016/j.watres.2013.09.064

Dominguez, D., Truffer, B., Gujer, W., 2011. Tackling uncertainties in infrastructure sectors through strategic planning: the contribution of discursive approaches in the urban water sector. Water Policy 13, 299–316.

European Commission, 2008. Guide to cost-‐benefit analysis of investment projects. Struct. Funds Cohes. Fund Instrum. Pre-‐Access. Bruss.



management of UWS

13

Fan, Y., Chen, W., Jiao, W., Chang, A.C., 2013. Cost-‐benefit analysis of reclaimed wastewater reuses in Beijing. Desalination Water Treat. 0, 1–10. doi:10.1080/19443994.2013.859102

Flores-‐Alsina, X., Rodríguez-‐Roda, I., Sin, G., Gernaey, K.V., 2008. Multi-‐criteria evaluation of wastewater treatment plant control strategies under uncertainty. Water Res. 42, 4485–4497. doi:10.1016/j.watres.2008.05.029

Gervásio, H., Simões da Silva, L., 2012. A probabilistic decision-‐making approach for the sustainable assessment of infrastructures. Expert Syst. Appl. 39, 7121–7131. doi:10.1016/j.eswa.2012.01.032

Hall, J.W., Lempert, R.J., Keller, K., Hackbarth, A., Mijere, C., McInerney, D.J., 2012. Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info-‐Gap Methods. Risk Anal. 32, 1657–1672. doi:10.1111/j.1539-‐6924.2012.01802.x

Herder, P.M., Verwater-‐Lukszo, Z., 2006. Towards next generation infrastructures: an introduction to the contributions in this issue. Int. J. Crit. Infrastruct. 2, 113–120.

Holling, C.S. (Ed.), 1978. Adaptive environmental assessment and management., International Series on Applied Systems Analysis. John Wiley & Sons, Chichester-‐New York-‐Brisbane-‐Toronto.

Honey-‐Rosés, J., Acuña, V., Bardina, M., Brozović, N., Marcé, R., Munné, A., Sabater, S., Termes, M., Valero, F., Vega, À., Schneider, D.W., 2013. Examining the Demand for Ecosystem Services: The Value of Stream Restoration for Drinking Water Treatment Managers in the Llobregat River, Spain. Ecol. Econ. 90, 196–205. doi:10.1016/j.ecolecon.2013.03.019

Kasprzyk, J.R., Nataraj, S., Reed, P.M., Lempert, R.J., 2013. Many objective robust decision making for complex environmental systems undergoing change. Environ. Model. Softw. doi:10.1016/j.envsoft.2012.12.007

Langeveld, J.G., Schilperoort, R.P.S., Weijers, S.R., 2013. Climate change and urban wastewater infrastructure: There is more to explore. J. Hydrol. 476, 112–119. doi:10.1016/j.jhydrol.2012.10.021

Lempert, R., 2013. Scenarios that illuminate vulnerabilities and robust responses. Clim. Change 117, 627–646. doi:10.1007/s10584-‐012-‐0574-‐6

Meire, P., Coenen, M., Lombardo, C., Robba, M., Sacile, R. (Eds.), 2008. Integrated Water Management -‐ Practical Experiences and Case Studies, Earth and Environmental Sciences. Springer & NATO Public Diplomacy Division, Dordrecht, The Netherlands.

Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-‐Being: Synthesis. Island Press, Washington, DC.

Molinos-‐Senante, M., Hernández-‐Sancho, F., Sala-‐Garrido, R., 2010. Economic feasibility study for wastewater treatment: A cost–benefit analysis. Sci. Total Environ. 408, 4396–4402. doi:10.1016/j.scitotenv.2010.07.014

Nancarrow, B.E., Leviston, Z., Tucker, D.I., 2009. Measuring the predictors of communities’ behavioural decisions for potable reuse of wastewater. Water Sci. Technol. 60, 3199. doi:10.2166/wst.2009.759

National Research Council, 2009. Informing Decisions in a Changing Climate. Washington, DC: The National Academies Press.

OECD, 2006. Cost-‐Benefit Analysis and the Environment. Organisation for Economic Co-‐operation and Development, Paris.



management of UWS

14

OECD, 2010. Pricing Water Resources and Water and Sanitation Services. Organisation for Economic Co-‐operation and Development, Paris.

Pahl-‐Wostl, C., 2007. Transitions towards adaptive management of water facing climate and global change. Water Resour. Manag. 21, 49–62. doi:10.1007/s11269-‐006-‐9040-‐4

Parson, E., Burkett, V., Fisher-‐Vanden, K., Keith, D., Mearns, L., Pitcher, H., Rosenzweig, C., Webster, M., 2007. Global-‐change scenarios: their development and use. US Dep. Energy Publ. 7.

Pearce, D.W., 1983. Cost Benefit Analysis. Macmillan Press Limited. Poch, M., Comas, J., Rodríguez-‐Roda, I., Sànchez-‐Marrè, M., Cortés, U., 2004. Designing and

building real environmental decision support systems. Environ. Model. Softw. 19, 857–873. doi:10.1016/j.envsoft.2003.03.007

Price, K., 2011. Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: A review. Prog. Phys. Geogr. 35, 465–492. doi:10.1177/0309133311402714

Rousseau, D., Verdonck, F., Moerman, O., Carrette, R., Thoeye, C., Meirlaen, J., Vanrolleghem, P.A., 2001. Development of a risk assessment based technique for design/retrofitting of WWTPs. Water Sci. Technol. 43, 287–294.

Taleb, N.N., 2010. The Black Swan: The Impact of the Highly Improbable Fragility, 2nd ed. Random House LLC.

The Economics of Ecosystems and Biodiversity, 2010. The Economics of Ecosystems and Biodiversity: Ecological and Economic Foundations. UNEP/Earthprint.

Vanrolleghem, P.A., Gillot, S., 2002. Robustness and economic measures as control benchmark performance criteria. Water Sci. Technol. 45, 117–126.

Walters, C.J., 1986. Adaptive management of renewable resources. Macmillan, New York. Westgate, M.J., Likens, G.E., Lindenmayer, D.B., 2013. Adaptive management of biological

systems: A review. Biol. Conserv. 158, 128–139. doi:10.1016/j.biocon.2012.08.016 Yang, J.S., Chung, E.S., Kim, S.U., Kim, T.W., 2012. Prioritization of water management under

climate change and urbanization using multi-‐criteria decision making methods. Hydrol. Earth Syst. Sci. 16, 801–814.

Zhang, S.X., Babovic, V., 2011. An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions. Decis. Support Syst. 51, 119–129. doi:10.1016/j.dss.2010.12.001

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