A Sustainable Decision Support Framework for Urban ... - Springer Link

Water Resour Manage (2010) 24:363–376 DOI 10.1007/s11269-009-9450-1

A Sustainable Decision Support Framework for Urban Water Management Leonie J. Pearson · Anthea Coggan · Wendy Proctor · Timothy F. Smith

Received: 17 December 2007 / Accepted: 13 April 2009 / Published online: 12 May 2009 © Springer Science+Business Media B.V. 2009

Abstract This paper develops a decision support framework that assists managers in the urban water industry to analyse a mix of water service options, at the whole-ofcity scale. The decision support framework moves decision-making in urban water systems from traditional command and control approaches that tend to focus on an outcome at a point in time to a more sustainable, inclusive and dynamic decisionmaking process driven by social learning and engagement. While available models and evaluation techniques provide valuable input to decision-making, the complex nature of urban water systems requires more than just social and economic criteria to be considered as part of decision support frameworks. The authors believe that current decision support frameworks need to be presented in a way that incorporates adaptive management and integrated urban water management strengths at the strategic and operational level. The inclusion of social learning and engagement is necessary to achieving this end. Keywords Social learning · Sustainable decision-making · Urban water

L. J. Pearson (B) CSIRO Sustainable Ecosystems, P. O. Box 56 Highett, Victoria, Australia 3190 e-mail: [email protected] A. Coggan CSIRO Sustainable Ecosystems, St Lucia QLD, Australia, 4067 W. Proctor CSIRO Sustainable Ecosystems, Gungahlin ACT, Australia, 2061 T. F. Smith University of the Sunshine Coast, Maroochydore QLD, Australia, 4558

364

L.J. Pearson et al.

1 Introduction Urban water planning is entering an era of the sustainable allocation of a scarce resource (Islam et al. 2007). Urban areas are demanding more resources (e.g. Lindah and Gilbrich 1996; Yoo 2007) and current supply management practices have: ‘mined’ resources at unsustainable rates (Dwyer 2007); not accounted for uncertainty, e.g. droughts, flash floods (e.g. Moorehouse 2000); and not dealt with the complexity of social–ecological systems (Syme and Hatfield-Dodds 2007). These driving factors have resulted in urban water being a scarce resource requiring sustainable planning and decision-making in a complex and uncertain environment (Fang et al. 2007; Jury and Vaux 2005; Pahl-Wostl 2007). Traditionally, urban water planning was dominated by top down technical solutions to supply management issues (see Hall 2007; Harremoes 2002). These top down approaches dealt with issues of water quality and water supply (Hellstrom et al. 2004; Erbe et al. 2002). They did not deal with demand management issues and/or public engagement (Syme 1996). This has changed in recent times with water becoming a scarce resource that requires sustainable management of both supply and demand issues (Rauch et al. 2005). Recently, decision support frameworks for water managers have emerged and many are grappling to deal with the issue of internalising social engagement and learning to achieve sustainable decision-making in the urban water context (see Hall and Lobina 2007; Hellstrom et al. 2000; Lai et al. 2009; Lundie et al. 2005). Sustainable water systems are defined as “water systems that are managed to satisfy changing demands placed on them (both human and environmental) now and into the future, whilst maintaining ecological and environmental integrity of water systems” (ASCE/UNESCO 1998). To operationalise sustainable water systems, which is regarded as the current challenge, recent work states that sustainability is “not a state to be arrived at but a broad evaluative framework for understanding and justifying social practice” (Lundie et al. 2005:1). As such, sustainable water management is not about achieving an end point (or even measuring for this end point) but rather it is the process of influencing what people believe (the cognitive process) and what they do (the behavioural process) (Lundie et al. 2005; Syme and Hatfield-Dodds 2007). With people’s values, attitudes and behaviour influenced by trust in institutions, equity, fairness and risk (Leviston et al. 2006), engagement of the public in decision-making and enabling the public to learn is critical and the biggest challenge to sustainable water management (Marks and Zadoroznyj 2005 and Harremoes 1997:19). This engagement with the public and the resulting interactions that enable the public to learn from the process are two critical aspects of sustainable water management (see Pahl-Wostl et al. 2007a, 2008; and Tàbara and Pahl-Wostl 2007). The challenge for urban water managers is that little work has been done to apply the general learning of sustainable water management at the catchment scale to the urban decision-making process. This paper proposes a framework to support sustainable decision-making that incorporates the process known as ‘social learning’ through effective stakeholder engagement. The intended audience of this framework are urban water managers. Social learning is defined as a dynamic process, which enables individuals to engage in new ways of thinking together to address difficult decision problems such as the unsustainable use of water. Social learning is a process that incorporates learning

Sustainable Decision Support Framework

365

through inclusion, interaction and engagement with stakeholders. In this paper, we show how incorporation of an effective social learning process in decisions about urban water management will assist practitioners in developing more sustainable management practices. As well, we propose that the decision-making framework that should be adopted by urban water managers is broadened with the incorporation of both an adaptive management and integrated urban water management approach at both the strategic and operational level of decision-making. The decision support framework (DSF) is developed and described in the following sections. Section 2 presents the case for including social learning in a combined adaptive management and integrated urban water management decision-making framework. Section 3 is the development of the DSF starting with a summary of engagement and learning in processes of decision-making followed by a presentation of the authors’ decision framework which incorporates knowledge transfer, monitoring evaluation and stakeholder engagement. A discussion follows which establishes a niche for the ‘social learning based’ framework amongst peer frameworks and applications, followed by suggestions for further research and application.

2 Challenges for Current Practice in Urban Water Decision-making From a social science perspective, the authors perceive that there are two important challenges facing sustainable urban water decision-making; incorporation of adaptive management; and integrated urban water management (IUWM). Adaptive management (AM) has been heralded as the decision-making process in urban water management for including the dynamic environmental and social system feedbacks found in urban systems (Pahl-Wostl 2007). The principle of adaptive management is the incorporation of evolving knowledge and understanding of what, how and why decisions are made, (as included in a social learning process (see Holling 1978; Allan and Curtis 2003; Lawrence and Bennett 2002)). AM represents an iterative cycle of critical processes beginning with data and information collection, analysis, plan development, implementation and then monitoring and evaluation. The information gained from the monitoring can then be used to adjust the plan as needed. There are two central tenants of AM, first is social learning which requires the involvement of stakeholder engagement to ensure acceptance, ownership and uptake and therefore success of the planning process (Hatfield-Dodds et al. 2007). Second is engagement, which has had extensive research in the water resource management area (see special issue on Methods for Participatory Water Resources Management, (Pahl-Wostl and Borowski 2007)) and is a necessary but not sufficient criteria for managing complex social-environmental systems like urban water. Allan and Curtis (2003) stress that in order to maximise on-going benefits from implementing AM, active or participatory engagement must be combined with learning processes. Additionally AM has been cited as crucial for working towards sustainable outcomes (Lee 1999). To date, attention has been placed on the elements of the AM process and the participatory processes involved rather than on the social learning mechanism that drives it (Smith and Lazarow 2004). AM has been identified as critical to the strategic level or macro scale of water decision-making. Two water examples include the European Water Framework Directive (EC 2000) and the New South Wales Metropolitan Water Plan

366

L.J. Pearson et al.

(NSW 2006). Both use AM very generally in the sense of ‘a need to revisit decisionmaking and learn from the experience’. However, as stated by Pahl-Wostl (2007), application of AM at the operational or micro scale is still required. Concurrent to this interest in AM has been the development of Integrated Urban Water Management (IUWM) (Rauch et al. 2002; Brown 2005). IUWM is a process that promotes water utilities to plan and manage water supply, wastewater and stormwater systems in a coordinated manner to minimise their impact on the natural environment, maximise their contribution to economic vitality and to engender overall community wellbeing and improvement (Maheepala and Blackmore 2007). IUWM has been focused around the development of a tool or model which provides adequate understanding and analysis of the managed water systems in urban areas (Malqvist and Palmquist 2005). These models have adopted a measurement approach and have borrowed heavily from some well known indicator/measurement approaches to sustainable development including triple or quadruple bottom line reporting (e.g. GRI 2002), life cycle analysis (e.g. ISO 14040 1999) and ecological footprints (e.g. Rees 1992). These models have traditionally dealt with water quantity and quality (eg Hellstrom et al. 2004; Erbe et al. 2002), but an ‘emerging’ area is the incorporation of public or social issues to address contemporary sustainability issues (Lundqvist et al. 2005). Therefore, AM is developing as the strategic approach to urban water management, whilst IUWM is the operational approach—both requiring social learning and engagement for success. Even though social learning and engagement are becoming important to water management (Global Water Partnerships 2000; Rauch et al. 2005; Pahl-Wostl et al. 2007b), a number of barriers to their incorporation have been identified, including: 1. Methods of engaging stakeholders are largely unsuitable for target communities and outcomes, e.g. a top-down style of engagement is used (see Rauch et al. 2005); 2. Professional norms and practices of UWM are based on engineering methods and rationale and generally not appropriate for consultation and community engagement activities (see McManus and Brown 2002); 3. Organisational structures and norms provide barriers to implementation of outcomes and ownership of consultation processes including timing (see McManus and Brown 2002); 4. Capacities of communities to participate are not catered for or understood (see McManus and Brown 2002); and 5. Willingness and/or desire of communities to participate may not be apparent. The first three of these could be considered barriers due to the knowledge and decision-making processes undertaken by urban water managers. These barriers emerge as a result of a lack of understanding about why to engage with the public, how to engage with the public and how to “learn” or enact the results from public engagement (Hatfield-Dodds et al. 2007). The last two barriers are related to the capacity and willingness of the community to engage and learn from the process, these are both critical to the success of any urban water decision but we do not deal with these in this paper. In summary, the inclusion of social learning and engagement in urban water decision-making is critical for the macro and micro level urban water management.


367

Importantly the two driving challenges in urban water decision-making; incorporation of AM and IUWM are struggling to successfully incorporate social learning and engagement in sustainable decision frameworks. A decision support framework based on AM and IUWM but driven by social learning and engagement is presented in the next section as a contribution to address this challenge.

3 Developing a Framework for Sustainable Decisions in Urban Water Management 3.1 Engagement and Learning in Processes of Decision-making Emerging discourses on environmental management recognise the need for participatory approaches to environmental management in order to enhance adoption and practice change (e.g. Ewing et al. 2000; Wondolleck and Yaffee 2000; Korfmacher 2001). Participatory approaches are even more fundamental to facilitate adaptive management for on-going adoption and practice change. The question challenging many decision-makers is what degree of engagement is needed and how to learn from the engagement and from the participants? To answer this question the authors discuss the concept of ‘engagement’ and how it relates to the enhancement of longterm practice change through an emphasis on learning. Community engagement refers to the process of involving communities in the decisions that affect them. While engagement hierarchies have evolved since being introduced by Arnstein in 1969 (e.g. Pretty 1995; Ross et al. 2002), engagement can generally be viewed on a continuum from tokenistic engagement to full citizen control and may be described in terms of either being instrumental (i.e. achieving a particular end) or transformational (i.e. resulting in problem ownership) (Buchy and Race 2001). The authors argue that greater emphasis on the latter which focuses on learning and adaptation is required to achieve long-term and sustained outcomes. Learning hierarchies have been used in fields such as higher education (e.g. Biggs 1999) but there has been no connection with engagement hierarchies to determine effective strategies for long-term sustainable decision-making. However, Smith and Smith (2006) have developed a “contextual learning framework” which describes how learning processes can achieve systemic improvements in coastal management. This learning framework is equally applicable in urban water decision-making (Table 1). The contextual learning framework presented by Smith and Smith (2006) highlights that the sustainability of the decision-making outcome is critically influenced by the learning process engaged. The optimal learning process for improved decisionmaking outcomes is one which is transformative—all stakeholders are engaged and all knowledge is shared and used to improve the outcome. Smith and Smith’s learning framework also demonstrates the difference in an improvement in systems knowledge and an improved system outcome. Critically, using systems thinking to understand the system does not equate to enacting a transformative knowledge process of learning. Decision making that seeks long term sustainable outcomes such as that used for urban water management needs systems thinking that includes transformative knowledge and transformative learning. The nature of engagement in learning processes may vary considerably depending on the emphasis of various principles or motivations e.g. equity and fairness (e.g. Renn et al. 1995).

368

L.J. Pearson et al.

Table 1 Contextual learning framework to enhance system outcomes (Smith and Smith 2006) Learning type

Learning process

Learner outcomes

System outcomes

Prestructural Unistructural

Undefined One-way information transfer Multi-way information transfer

Misses point Identifies Follows procedure Enumerates Describes Combines Compares and contrasts Explains causes Analyses Relates Applies Theorises Hypothesises Reflects

No outcome Information distribution

Multistructural

Systems thinking

Transformative information

Extended systems thinking

Transformative knowledge

Describes system

Improves knowledge of system

Improves system

Fien (1993) highlights that effective environmental learning is needed to create change. This occurs through a process of building an active civil society that can become informed agents of change. Correspondingly, in the urban water context, greater public engagement that shares ownership and achieves ‘transformation’ qualities, such as changing social expectations, practices and behaviours through learning and awareness has an increasing role in the sustainable management of these systems (see Burkhard et al. 2000). 3.2 A Framework that Addresses Sustainable Decisions in Urban Water Systems There are two focal scales to a sustainable decision in urban water systems (1) strategic or macro level (what is your future ‘sustainable’ scenario path or goal?); and (2) operational or micro level (what is the process enacted so every decision can be sustainable and help achieve the strategic aim?) (Pahl-Wostl et al. 2007a, b). Urban water managers implementing IUWM continually make decisions over time, based on strategic goals. These strategic goals will influence the way future decisions are made (i.e. a strategy team has a future scenario in mind when deciding priorities for action and investment). Figure 1 is a graphical representation of two different strategic goals; incorporating social learning over time (Scenario A), or not including social learning (Scenario B). The time dimension is to indicate that the achievement of each scenario is a cumulative succession of individual decision points, rather than implying speed of decision-making. Each decision point (i.e. D1, D2..Dn) is considered an adaptive decision, which institutionalises social learning (or the lack thereof depending on the scenario) into the operational (micro) process of decisionmaking. To this end, the initial decision is made (D1) to either include social learning and progress along a trajectory (D1..D4). This trajectory of enhanced social learning achieves understanding of the whole system and improvements in decision-making and outcomes (see learning hierarchy Table 1). Alternatively, a strategic goal to not include social learning from the initial decision point (D1) i.e. no return arrows from the decision-making points (D5, D6,

369

SOCIAL LEARNING

Extended systems thinking


D4

Scenario A

D3

D2

Prestrucutral

D6 Scenario B D1

Dn

D5 TIME

Future

Fig. 1 The strategic decision-making environment of urban water managers focusing on social learning over time

Dn) achieves a pre-structural (see Table 1) or no specific learning outcome over time (Scenario B). The decision support framework (DSF) developed in this paper is integrating AM and IUWM with a focus on social learning (and the appropriate engagement process). In essence, the DSF provides the detailed steps required to generate each decision point in Fig. 1 (i.e. D1, D2,... Dn). The DSF is presented in Fig. 2, it is composed of ten steps used to achieve a general IUWM decision process (see Lai et al. 2009 for further discussion) and three core components of social learning which are necessary for achieving an AM decision: 1. knowledge transfer for transformation; 2. monitoring and evaluation of the decision process; and 3. stakeholder engagement. 3.3 Knowledge Transfer for Transformation The critical element of the adaptive management cycle which ensures it achieves a transformative and sustainable outcome is the ‘learning’ from the decision undertaken through the multitude of information, engagement and knowledge generated in making that decision. We internalise this learning in the DSF as knowledge transfer. The authors see that both participatory and reflective knowledge transfer needs to occur with each of these knowledge transfers requiring appropriate resources and capacity to achieve the envisaged sustainable scenario. Participatory knowledge transfer is focused on ‘learning’ from the engagement processes enacted in the decision process. Reflective transfer of knowledge is a

370

L.J. Pearson et al.

Knowledge Transfer Monitor and Evaluate Decision Process 10. Evaluate outcome: did we achieve what we wanted?

9. Implement decision: e.g.

Stakeholder Engagement 1. Problem identification: what is the big issue & what do we want to solve?

2. Context analysis: context of the problem

build more pipes

8. Enacting decision: use a decision tool, e.g. MultiCriteria Analysis

are they and how do we engage

7. Criteria identification & evaluation: identify the criteria to be used to evaluate the options

3. Social actor analysis: who

6. Option generation: what are the feasible set of decision options

4. Social actor implementation: what is the process for engagement

5. Problem definition: problem defined for evaluation

Fig. 2 Social learning focused adaptive decision support framework for urban water managers

process of understanding the whole decision process and ‘reflecting’ on this understanding. It should incorporate participatory knowledge transfer, but as engagement is a focus of this DSF it has been explicitly highlighted in Fig. 2. 3.4 Monitoring and Evaluation of the Decision Process Traditionally, monitoring and evaluation of a decision and its outcome has only occurred at the end of the decision-making process (e.g. see Fig. 2 steps 8 and 10). However, it is recognised that between and within each step of the DSF, decisions occur and a structured rationale and process needs to be established early as to the critical elements of monitoring and evaluation. This component will be guided by the information required in the knowledge transfer component. Mitchell (2006) clearly states the role and need for monitoring and evaluation to be undertaken in UWM, specifically in Australia. Additionally, it is noted that efforts should be invested in performance monitoring and post implementation assessments of projects and that information should be disseminated and shared from all experiences. 3.5 Stakeholder Engagement Stakeholder engagement describes the process by which effective decisions can be made by the people that are most critical in the process. Stakeholders are considered to be any relevant individual or group that is affected by the decision. As such it covers, political entities, industry groups, community groups, specific individuals, e.g. homeowners or tourists. By engaging with the stakeholders it addresses the essential


371

Table 2 A simplified example of practical processes for including social learning into the urban water decision-making framework Traditional decision making processes Knowledge transfer

Decision making focusing on social learning

Clarify what decision was made and how to define this in the changing and complex environment of water management.

Determine the system outcome required from the decision (see Table 1) and then determine the learning type required and what type of information is required to implement this, including: • scoping relevant information on the engagement process (participatory) • understanding how and why the decision was made and what were their controlling factors (reflective) Monitoring and Quantitatively driven focused on Clarify the learner and system outcomes evaluation analysis techniques such as: (see Table 1) and then monitoring and Least cost analysis evaluate to determine if these have been Cost benefit analysis met, could include: Cost-effectiveness analysis • Clear scope: what is relevant for the small Multi-criteria assessment ‘operational’ decision and what is relevant for the whole project; • Have we gathered the appropriate: data, perspectives and people, • Is it an assumption or delimitation (e.g. something we have to live with) or are we consciously choosing the next step. Stakeholder Engage with the wider community Determine the learning type and process engagement and inform them of process and required to achieve the desired system necessary outcomes outcomes (see Table 1) then identify the appropriate engagement strategies and roles for stakeholders to become involved.

issue of who should be making decisions about UWM. The most significant benefit of engagement from start to finish includes: 1. 2. 3. 4.

legitimate ownership/ empowerment of the decision; learning from the decision; inclusion of local knowledge and understanding of the problem; and conflict/ trade-offs investigated and potentially resolved through the decisionmaking process.

Table 2 provides a comparison summary of when to operationalise the three components of social learning and how they are different to traditional decision making processes.

4 Discussion on Decision Support Frameworks in Urban Water This section has two components—the first is a review of selected current decisionmaking frameworks in urban water management and identification of the niche that this paper fills. The second is a discussion of the future areas of work that need to

372

L.J. Pearson et al.

be undertaken for urban water managers to deal with the increasing complexity and uncertainty that they face in decision-making.

4.1 Where Does the Decision Support Framework Sit in Current Research? This paper has proposed that for urban water systems to achieve ‘sustainable’ decisions, two critical elements are necessary—social learning and engagement. To this end, we have proposed a decision support framework that centres on both of these elements (Fig. 2). A brief review of readily available decision support frameworks available for application in urban water management (including; Hall and Lobina 2007; Hellstrom et al. 2000; Lundie et al. 2005; Taylor et al. 2006; Makropoulos et al. 2008) show that there are 3 factors which ensure this framework is both unique and contributes to current work in urban water management. •

•

•

All the frameworks assessed incorporated some level of adaptive management concepts and clearly discussed operationalisation through models and tools as advocated in IUWM. However, no frameworks explicitly focused on the three components of social learning (i.e. knowledge transfer, monitoring and evaluation and stakeholder engagement). The frameworks also varied considerably in their operationalisation of sustainability, although all had a clear (though different) perception of how to achieve sustainability. For example Lundie et al.’s framework is very participatory, whilst Hellstrom et al.’s was more quantitative and modelling based. None of the frameworks identified for urban water application explicitly addressed the issue of both strategic and operational inclusion of social learning.

Our framework is also not perfect. Limitations of our framework include: •

•

We have not provided a stand-alone framework. It requires understanding of engagement processes and rationales and alignment of operational and strategic goals. Therefore, it requires considerable prior knowledge of urban water issues and social learning and engagement practices; and We have not fully discussed the need to ensure that there is capacity and willingness of various communities of place, interest or association to engage and hence learn through the decision process. This is a necessary issue for any DSF based on social learning. This often depends on an effective communication strategy prior to the engagement process in terms of getting the message of what is being done out to stakeholders who may take part and to try and increase their interest in the process. This may also be achieved by specifically ensuring that the input being received from stakeholders will be effectively used and is simply not just a ‘token’ step.

Based on this review we recognise that although sustainability is addressed to some extent in all frameworks, it is done so in very different ways. There is a need for a consistent framework which addresses all aspects of sustainable urban water decision-making. It should be noted that many of these decision-making frameworks


373

can be implemented simultaneously or combined where strengths of each could provide a more robust and transformative decision-making process. For example, the social learning strengths of the DSF presented in this paper could be combined with the quantitative measurement strengths presented by Hellstrom et al. (2000). However, to achieve a more universal sustainable urban water system, a consistent application of a decision support framework is recommended. The framework used needs to include social learning, in order for urban water management programs to be successful in dealing with the current complexity of demand and supply management decisions. 4.2 Areas of Further Work This paper has dealt with the issue of broadening current decision frameworks used in urban water management to focus on social learning and engagement to drive improved decision-making capacity in applying adaptive management and IUWM. The paper has been conceptualised in a very narrow construction of urban water issues. There are however at least three broader issues that the framework could deal with but have not yet been fully explored in the urban water literature: •

•

•

Integrated urban water management has to-date focused primarily on the supply management side of urban water (for example, water supply, urban drainage and wastewater treatment, and rainwater harvesting) (Larsen and Gujer 1997; Mitchell 2006). It has so far failed to deal with issues such as evapo-transpiration management, surface and ground water systems and management of environmental flows that occur within a region. The recent inclusion of potable recycled water and desalination plants into the ‘traditional’ water management system of South East Queensland, Australia highlight the growing need for decision processes to be flexible enough to deal with Total Water Cycle Management issues. The delineation of urban and rural water decisions is a division that is finding less traction and use in resolving issues in many urban water areas. Many rural and urban water systems are linked, therefore with water becoming more scarce there needs to be an understanding of the impact of water decisions on these two spatially converging groups. This work has been started under integrated modelling (see for example Mannina et al. 2006 and Butler and Schütze 2005), however further work is required to fully integrate the water systems and social systems to adequately deal with increasing issues of water scarcity. Identifying and focusing on water in an urban context is limiting the achievement of sustainable decisions at the city-scale. In many instances urban communities make trade-offs between resources (substitutable resource use). Additionally many resources used in a city are linked (complimentary resource use), for example if less water is demanded at the household level then there is a decrease in energy consumption for water heating. Additionally if more water is demanded at the city level then extra power is required to produce potable water from divergent sources (e.g. desalination and recycled water). Work has been started to explore this issue by others (see for exmaple Butler and Makropoulos 2006) however, again further work is required that links the decision process to the social processes.

374

L.J. Pearson et al.

5 Conclusion This paper presents a sustainable decision support framework incorporating social learning and engagement. This generic framework will assist practitioners in developing more sustainable management practices by broadening the current decision frameworks used in urban water management by incorporating elements of adaptive management and integrated urban water management at both the strategic and operational level of decision-making. Specifically, the generic framework provides insight at two scales: strategically, sustainability is not an ‘end point’ but a process where scenarios of a ‘future’ are deemed sustainable and each decision contributes to achieving that; operationally, a sustainable decision can only be achieved when social learning is incorporated. Operationally our decision support framework emphasised three core components of social learning: 1. knowledge transfer for transformation; 2. monitoring and evaluation of the decision process; and 3. stakeholder engagement. The work is novel in that it introduces to the literature of urban water management the role of learning hierarchies. This is essential to achieving social learning and engagement and sustainable management decisions. This paper is a start in exploring the issues of how, when and why to include social learning and engagement in urban water management. Acknowledgements This research was funded by CSIRO Water for a Healthy Country. We thank Dr Mark O’Donohue and Dr Shiroma Maheepala for their comments and suggestions on earlier drafts. All remaining errors and omissions are the responsibility of the authors.

References Allan C, Curtis A (2003) Learning to implement adaptive management. Nat Resour Manage 6:23–28 Arnstein S (1969) A ladder of citizen participation. J Am Inst Plann 35(4):216–224. doi:10.1080/ 01944366908977225 ASCE/ UNESCO (1998) Sustainability criteria for water resource systems. Prepared by the Task Committee on Sustainability Criteria Water Resources Planning and Management Division, ASCE, Reston VA Biggs J (1999) Teaching for quality learning at university. Society for Research into Higher Education and Open University Press, Buckingham, UK Brown R (2005) Impediments to integrated urban stormwater management: the need for institutional reform. Environ Manag 36(3):455–468 Buchy M, Race D (2001) The twists and turns of community participation in natural resource management in Australia: what is missing? J Environ Plan Manag 44(3):293–308 Burkhard R, Deletic A, Craig A (2000) Techniques for water and wastewater management: a review of techniques and their integration in planning. Urban Water 2:197–221 Butler D, Makropoulos C (2006) Water related infrastructure for sustainable communities. Technological options and scenarios for infrastructure systems. Science report SC05002501. Environment Agency. ISBN: 184432611X 125 pp. Available online: http://publications. environment-agency.gov.uk Butler D, Schütze M (2005) Integrating simulation models with a view to optimal control of urban wastewater systems. Environ Model Softw 20(4):415–426 Dwyer T (2007) Urban water policy: in need of economics. Agenda 13(1):3–16


375

Erbe V, Frehmann T, Geiger W et al (2002) Integrated modelling as an analysing and optimisation tool for urban watershed management. Water Sci Technol 46:141–150 European Commission (EC) (2000) Water framework directive 2000/60/EC. European Commission, Brussels Ewing S, Grayson R, Argent R (2000) Science, citizens, and catchments: decision support for catchment planning in Australia. Soc Nat Resour 13:443–459 Fang C-L, Bao C, Huang J-C (2007) Management implications to water resources constraint force on socio-economic system in rapid urbanization: a case study of Hexi Corridor, NW China. Water Resour Manag 21:1613–1633 Fien J (1993) Education for the environment: critical curriculum theorising and environmental education. Deakin University Press, Geelong Global Water Partnership (2000) Introducing effective water governance. Global Water Partnership, Stockholm GRI (2002) Sustainability reporting guidelines, G3. Available via http://www.globalreporting.org/ NR/rdonlyres/ED9E9B36-AB54–4DE1-BFF2–5F735235CA44/0/G3_GuidelinesENU.pdf. Cited 17 July 2007 Hall D (2007) The watertime project. Util Policy 15(2):61–160 Hall D, Lobina E (2007) Watertime: good practice recommendations. Available from http:// watertime.net/. Citied 19 July 2007 Harremoes P (1997) Integrated water and waste management. Water Sci Technol 35(9):11–20 Harremoes P (2002) Water ethics—a substitute for over-regulation. Water Sci Technol 45(8):113–124 Hatfield-Dodds S, Syme G, Leitch A (2007) Improving Australian water management: the contribution of social values research. Reform 89:44–48 Hellstrom D, Jeppsson U, Karrman E (2000) A framework for systems analysis of sustainable urban water management. Environ Impact Assess Rev 20(3):311–321 Hellstrom D, Hjerpe M, van Moeffaert D (2004) Indicators to assess ecological sustainability in the Urban Water sector. Report 2004:3, The Mistra Programme, Urban Water Holling C (ed) (1978) Adaptive environmental management and assessment. Wiley, Chichester Islam M, Oki T, Kanae S et al (2007) A grid-based assessment of global water scarcity including virtual water trading. Water Resour Manag 21(1):19–33 ISO 14040 (1999) ISO 14041: Environmental management—life cycle assessment—goal and scope definition and inventory analysis Jury W, Vaux H Jr (2005) The role of Science in solving the world’s emerging water problems. Science 102(44):15715–15720 Korfmacher K (2001) The politics of participation in watershed modelling. Environ Manag 27:161–176 Lai E, Lundie S, Ashbold NJ (2009) Review of multi-criteria decision aid for integrated sustainability assessment of urban water system. Urban Water J 5(4):315–327 Larsen T and Gujer W (1997) The concept of sustainable urban water management. Water Sci Technol 35(9):3–10 Lawrence P, Bennett J (2002) Improved planning and management in coastal environments using an adaptive management framework. J Australian Water Assoc 29(6):18–27 Lee KN (1999) Appraising adaptive management. Conservation Ecology 3:3–16 Leviston Z, Porter N, Nancarrow B (2006) Interpreting householder preferences to evaluate water supply systems. Stage 3. Report to CSIRO Water for a Healthy Country. CSIRO Land and Water, Perth Lindah G, Gilbrich W (1996) Water in an urbanizing world. Nat Resour 32(2):31–34 Lundie S, Ashbolt N, Livingston D et al (2005) Methodology for evaluating the overall sustainability of urban water systems. The University of New South Wales, Centre for Water and Waste Technology Lundqvist J, Tortajada C, Varis O, Biswas A (2005) Water management in megacities. Ambio 34(3):267–268 Maheepala S, Blackmore J (2007) Integrated urban water management for cities. In: Newton P (ed) Transition. CSIRO publishing Makropoulos CK, Natsis K, Liu S, Mittas K, Butler D (2008) Decision support for sustainable option selection in integrated urban water management. Environ Model Softw 23(12):1448–1460 Malqvist P-A, Palmquist H (2005) Decision support tools for urban water and wastewater systems— focusing on hazardous flows assessment. Water Sci Technol 51(8):41–49 Mannina G, Freni G, Viviani G, Sægrov S, Hafskjold LS (2006) Integrated urban water modelling with uncertainty analysis. Water Sci Technol 54(6–7):379–386

376

L.J. Pearson et al.

Marks J, Zadoroznyj M (2005) Managing sustainable urban water reuse: structural context and cultures of trust. Soc Nat Resour 18:557–572 McManus R, Brown R (2002) The increasing organisational uptake of source control approaches for sustainable stormwater management. In: Proceedings of the 9th international conference on urban storm drainage—CDROM, 30 August–3 September 2002, Portland, OR, USA Mitchell V (2006) Applying integrated urban water management concepts: a review of Australian experience. Environ Manag 37(5):589–605 Moorehouse B (2000) Climate impacts on urban water resources in the Southwest; the importance of context. J Am Water Resour Assoc 36(2):265–277 NSW (2006) 2006 Metropolitan water plan: water for life. State Government of New South Wales Pahl-Wostl C (2007) Transitions towards adaptive management of water facing climate and global change. Water Resour Manag 21(1):49–62 Pahl-Wostl C, Borowski I (2007) Methods for participatory water resources management. Water Resour Manag 21(7):1047–1261 Pahl-Wostl C, Craps M, Dewulf A et al (2007a) Social learning and water resources management. Ecol Soc 12(2):5. http://www.ecologyandsociety.org/vol12/iss2/art5/ Pahl-Wostl C, Craps M, Dewulf A, Mostert E, Tabara D, Taillieu T (2007b) Social learning and water resources management. Ecol Soc 12(2):5. http://www.ecologyandsociety.org/vol12/ iss2/art5/ Pahl-Wostl C, Mostert E, Tàbara D (2008) The growing importance of social learning in water resources management and sustainability science. Ecol Soc 13(1):24. http://www. ecologyandsociety.org/vol13/iss1/art24/ Pretty J (1995) Participatory learning for sustainable agriculture. World Dev 23(8):1247–1263 Rauch W, Bertrand-Krajewski J, Krebs P et al (2002) Mathematical modelling of integrated urban drainage systems. Water Sci Technol 45:81–94 Rauch W, Seggelke K, Brown R, Krebs P (2005) Integrated approaches in urban storm drainage: where do we stand? Environ Manag 35(4):396–409 Rees W (1992) Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environ Urban 4(2):121–130 Renn O, Webler T, Wiedeman P (eds) (1995) Fairness and competence in citizen participation; evaluating models for environmental discourse. Kluwer Academic, London Ross H, Buchy M, Proctor W (2002) Laying down the ladder: a typology of public participation in Australian natural resource management. Aust J Environ Manag 9:205–217 Smith T, Lazarow N (2004) Social learning and the adaptive management framework. J Coast Res SI 39:952–954, ISSN 0749-0208 Smith T, Smith D (2006) Institutionalising adaptive learning for coastal management. In: Lazarow N, Souter R, Fearon R, Dovers S (eds) Coastal management in Australia: key institutional and governance issues for coastal natural resource management and planning. CRC for Coastal Zone, Estuary and Waterway Management, Brisbane, pp 115–120 Syme G (1996) The future of public involvement. Water J 23(1):3 Syme G, Hatfield-Dodds S (2007) The role of communication and attitude research in the evolution of effective resource management arrangements. In: Hussey K, Dovers S (eds) Managing water for Australia: the social and institutional challenges. CSIRO Publishing Tàbara JD, Pahl-Wostl C (2007) Sustainability learning in natural resource use and management. Ecol Soc 12(2):3. http://www.ecologyandsociety.org/vol12/iss2/art3/ Taylor A, Fletcher T, Peljo L (2006) Triple-bottom-line assessment of stormwater quality projects: advances in practicality, flexibility and rigour. Urban Water J 3(2):79–90(12) Wondolleck J, Yaffee S (2000) Making collaboration work: lessons from innovation in natural resource management. Island Press, Washington DC Yoo S-H (2007) Urban water consumption and regional economic growth: the case of Taejeon, Korea. Water Resour Manag 21(8):1353–1361