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Pahl-Wostl, C., J. Sendzimir, P. Jeffrey, J. Aerts, G. Berkamp, and K. Cross. 2007. Managing change toward adaptive water management through social learning. Ecology and Society 12(2): 30. [online] URL: http://www.ecologyandsociety.org/vol12/iss2/art30/
Research, part of Special Feature on New Methods for Adaptive Water Management Managing Change toward Adaptive Water Management through Social Learning
1University of Osnabrueck, 2International Institute for Applied Systems Analysis, 3Cranfield University, 4Vrije Universiteit Amsterdam, 5IUCN - The World Conservation Union
The management of water resources is currently undergoing a paradigm shift toward a more integrated and participatory management style. This paper highlights the need to fully take into account the complexity of the systems to be managed and to give more attention to uncertainties. Achieving this requires adaptive management approaches that can more generally be defined as systematic strategies for improving management policies and practices by learning from the outcomes of previous management actions. This paper describes how the principles of adaptive water management might improve the conceptual and methodological base for sustainable and integrated water management in an uncertain and complex world. Critical debate is structured around four questions: (1) What types of uncertainty need to be taken into account in water management? (2) How does adaptive management account for uncertainty? (3) What are the characteristics of adaptive management regimes? (4) What is the role of social learning in managing change? Major transformation processes are needed because, in many cases, the structural requirements, e.g., adaptive institutions and a flexible technical infrastructure, for adaptive management are not available. In conclusion, we itemize a number of research needs and summarize practical recommendations based on the current state of knowledge.
Key words: adaptive management; integrated water resources management; social learning; adaptive governance; change management; uncertainty
In the past, water resources management focused on well-defined problems that grew increasingly urgent during the 19th and 20th centuries as urban populations became more concentrated and industrial and agricultural productivity intensified. Public health problems within cities and the seemingly insatiable demand for more water drove major efforts in urban water management. Eutrophication problems in lakes and coastal seas triggered more involved research and legislation. Rivers were controlled to protect cities and dryland agriculture from flooding. In the short run, technological fixes proved to be very efficient in solving a number of these urgent environmental problems, e.g., the increasing sophistication of wastewater treatment plants addressing problems related to hygiene and pollution. However, these problems were generally dealt with in isolation, and potentially undesirable long-term consequences were not taken into consideration. The system paradigm on which traditional water management has been based has often been characterized as a “command-and-control” approach. System design was typically targeted at high predictability and controllability.
For the past two decades, new and more integrated approaches to water management have been developed and are being implemented to address perceived shortcomings in earlier approaches. During the last decade, the principle of integrated water resources management (IWRM) has, for example, been used as a framework for the implementation of such integrated approaches to water management (GWP-TEC 2000). “Integrated” clearly indicates a desire to functionally engage a range of perspectives by formally considering a wide range of potential trade-offs at different scales in space and time. Such an approach attempts to overcome the shortcomings of technical end-of-pipe solutions that deal with individual problems in isolation and run the risk of causing unexpected consequences (Pahl-Wostl 2007a). However, the implementation of an IWRM approach that fully accounts for the complexity and interdependencies of human-technology-environment (HTE) systems has yet to be realized. The increasing awareness of the complexity of environmental problems and of HTE systems has encouraged the development of new management approaches based on the insight that the systems to be managed are, in broad terms, complex, unpredictable, and characterized by unexpected responses to intervention (Committee on Grand Canyon Monitoring and Research 1999, Pahl-Wostl 2002, Prato 2003, Pahl-Wostl 2007b; S. Light and K. Blann, unpublished manuscript). Such complex adaptive systems are characterized as hierarchies of components interacting within and across scales, with emergent properties that cannot be predicted by knowing the components alone (Lansing 2003). Control is distributed rather than central (Allen and McGlade 1985, Pahl-Wostl 1995). Rather than trying to change the structure of complex, adaptive systems to make them controllable by external intervention, innovative management approaches aim to make use of the self-organizing properties of the systems to be managed.
Increasing awareness of complexity is a necessary but not a sufficient condition for changing water management practices. Recent attempts (e.g., Pahl-Wostl 2002, Galaz 2005, Jeffrey and Gearey 2006, W. Medema, B. McIntosh, and P. Jeffrey, unpublished manuscript) to manage water systems have revealed that major knowledge gaps in the following areas may impede the successful implementation of new management approaches.
The management paradigm
Current approaches to realizing integrated water management build on the heritage of a command-and-control paradigm that has been dominating the water management community for decades. Such a paradigm requires that system behavior be highly predictable. The failure to implement integrated approaches may due less to the principle of integration itself than to the mental models that frame the process of its implementation.
The conceptual foundation
An understanding of the concepts involved in system change, especially when this occurs in surprising and nonlinear ways, is needed to make researchers ask questions and think about ways to manage change. Basic concepts of this type capture insights into what blocks or foments change over what area and for how long, all of which is key information for management decisions. Concepts such as resilience, vulnerability, and adaptive capacity can inspire new management approaches and help us to better understand and express the results of exploratory analyses of and experiments involving complex adaptive systems. These concepts can also help us understand how management influences system change. Concepts of this type can help to characterize water management regimes, by accounting for both their components and their interdependencies, and to analyze their performance under current and future drivers in terms of, e.g., reaching management objectives and the ability of the management regime to adapt to change. This conceptual foundation would be greatly improved by better integrating the complexity and unpredictability of policy and social learning processes. The implementation of water management policies in a given river basin must take into account its political, economic, and social realities and thus requires a transparent and open discourse between scientists and policy makers
Current water management regimes have evolved over decades, and changing them will take some time. How can we directly study and analyze transition processes when the scale of change approaches or exceeds the time horizon of academic projects or careers? A better understanding of the transition processes and the barriers and facilitators for change is essential to catalyze change and foster the implementation of a transition process toward integrated and adaptive resource and water management regimes. Management failures, despite superior technology and well-financed central control, give rise to a key question: How can we improve understanding and trust through a social process of learning and negotiated change?
We maintain that, in environmental, economic, and social terms, sustainable water management can be successfully implemented only if more attention is given to understanding and closing these knowledge gaps, including the need to deal with uncertainties. Numerous technical and quantitative approaches already exist to account for uncertainties in policy analysis and formulation (Morgan and Henrion 1990). Qualitative uncertainty can be tackled via a variety of participatory approaches targeted at achieving social learning processes and negotiations to reach consensus despite different perspectives (Gunderson et al. 1995, Newig et al. 2005; C. Pahl-Wostl, M. Craps, A. Dewulf, E. Mostert, D. Tabara, and T. Taillieu, unpublished manuscript).
However, a change in the overall management paradigm is needed to account for all the uncertainties in a more comprehensive fashion. This paper describes how the principles of adaptive water management might improve the conceptual and methodological basis for achieving sustainable and integrated water management in an uncertain and complex world. Sections 2 through 5 address the following questions:
Because of a tradition rooted in the hydrological and engineering sciences, water managers have a vast array of experiences, methods, and tools with which to address environmental and quantifiable uncertainties. However, the knowledge and methods needed to address uncertainties in learning and decision-making processes are largely lacking. Rogers and Hall (2003) make plain the need for governance systems to be more flexible and to take uncertainty into account. Rees (2003) addresses the need for new strategies to manage risks in integrated water resource management and emphasizes the need to consider economic, social, and political uncertainties, which are often of more importance than environmental uncertainties. By embracing a wider perspective, it is possible to distinguish the different types of uncertainty that need to be taken into account when addressing a management problem (Funtowicz and Ravetz 1990, Pahl-Wostl et al. 1998, Oberkampf et al. 2001, Walker et al. 2003).
Types of uncertainty
The best-known type of uncertainty is a lack of knowledge because of the limited availability and the variability of data. Quite a few technical approaches exist to include such uncertainties in simulation models. Uncertainties may be captured by including uncertainty bounds in results from model simulations or other types of quantitative assessments.
An equally important but often less recognized type of uncertainty is uncertainty in our understanding of the system itself, not only its historical trends but also the system elements and interactions, including nonlinearities, feedback loops, and delays, that generate those trends. This applies in particular to socioeconomic systems and human behavior in those cases in which there is more than one possible interpretation of the same phenomenon. Also, our understanding of ecosystems is not as well developed as that of hydrological systems, the home turf of many scientists and practitioners working in water management. Furthermore, integrated water management requires an understanding of the full complexity of coupled human-environment-technology systems.
Another source of uncertainty inherent in system behavior rather than in the knowledge about it is the unpredictability of certain factors. In loosely coupled systems, the trajectory a system follows depends greatly on the original conditions around which it self-organizes. One prominent example is climate change and corresponding changes in nature and the likelihood of extreme events.
Uncertainty may also arise from the diversity of the rules and underlying mental models that determine and possibly constrain stakeholder perceptions and actions. Stakeholders may have different ideas about the causes of problems and appropriate and legitimate solutions, and use these ideas to construct quite different concepts of what is at stake, the goals to be achieved, the likelihood of the success of a particular measure, etc. The simultaneous presence of multiple frames of reference when seeking to understand a phenomenon is also called “ambiguity” (Dewulf et al. 2005).
What does such a broad understanding of uncertainty imply for the different steps in the management cycle, particularly in terms of the ways in which different kinds of uncertainties can be assessed as part of the whole sequence of steps, from defining the problem to monitoring the performance of the management strategies implemented?
Ambiguity, i.e., the possibility of more than one interpretation, is often encountered when defining the nature of the problem such as this: Is nitrate pollution of groundwater by agriculture caused by a profit-maximizing agricultural industry or by the refusal of consumers to pay higher prices for agricultural products? Even when the strategic management goal is prescribed by law, e.g., the European Water Framework Directive, the setting of operational targets in areas such as the desirable ecological state of a river and the acceptable economic impacts of management measures is open to interpretation. Given uncertainties in the data, more than one legitimate interpretation may be compatible with the available body of knowledge. Different perceptions and conflicts of interest thus require participatory problem definition and goal setting, although not by experts alone, plus a clear recognition of the uncertainties in this process.
Choice of measures
The outcomes of management measures are uncertain because of the complexity of the system to be managed and uncertainties in the environmental and socioeconomic developments that affect the performance of the chosen management strategies. As a result, robust strategies that perform well under a wide range of uncertain but possible future developments might be chosen over strategies that perform best under certain conditions but fail if those conditions are not met (J. C. J. H. Aerts, W. Botzen, A. Van der Veen, J. Krykrow, and S. Werners, unpublished manuscript).
Implementation of measures and monitoring of performance
New knowledge about system behavior or changes in environmental and/or socioeconomic conditions may demand changes in management strategies. When actors behave differently than expected, management measures sometimes lead to undesirable effects. The relevance and meaning of indicators for success or failure may be judged differently by different groups, and thus lead to different assessments of the performance of management strategies.
In the face of these challenges, ignorance and/or negligence is not an appropriate way to deal with uncertainty in water management. Management first needs to broaden the public debate and understanding of such uncertainties and the consequences of failing to address them or learning to live with them. Adaptive management explicitly accounts for such uncertainties and also requires a basic rethinking of the role of management in an uncertain and changing world.
Adaptive management can more generally be defined as a systematic process for improving management policies and practices by learning from the outcomes of management strategies that have already been implemented. Adaptive water management aims to increase the adaptive capacity of the water system by putting in place both learning processes and the conditions needed for learning processes to take place. As pointed out by Bormann et al. (1993), “Adaptive management is learning to manage by managing to learn.” In this case, learning encompasses a wide range of processes that span the ecological, economic, and socio-political domains in the testing of hard and soft approaches (Pahl-Wostl 2002, Gleick 2003). In this respect, adaptive management emphasizes the importance of the management process rather than focusing on goals, but without claiming that the process is an end in itself. It explicitly recognizes that management strategies and even goals may have to be adapted during the process as new information becomes available, and that the quality of the process, e.g., who is involved and which kind of information is taken into account, is essential for the outcomes finally achieved.
Among environmental scientists, adaptive management is generally best known because of its application in the field of ecosystem management. The idea of adaptive management has been discussed in ecosystem management for quite some time (Holling 1978, Walters 1986, Pahl-Wostl 1995, Lee 1999). Adaptive management acknowledges that our ability to predict the future key drivers of any given ecosystem, as well as system behavior and responses, is inherently limited. As a result, management must include the ability to change management practices by incorporating the insights gained from past experience. A most influential publication on this topic was the seminal book of Walters (1986), who claimed that scientific understanding will come from the experience of management as an ongoing, adaptive, and experimental process, rather than through basic research or the development of ecological theory. What has been perceived as the most effective form of adaptive management, known as active adaptive management, uses management programs that are designed to experimentally compare selected policies or practices by evaluating alternative hypotheses about the system being managed (e.g., Gunderson et al. 1995, Kiker et al. 2001, Richter et al. 2003). This implies that hypotheses can be generated and tested via the outcomes of experiments. However, when extending management to social-ecological systems, we must recognize that testing hypotheses about human behavior is not the same as testing hypotheses about the dynamics of ecosystems. Actors may change the rules under which they operate when they are exposed to a model of themselves and confronted with the possible consequences of their behavior. Here, experiments and the generation of hypotheses may support processes of social learning that develop the capacity of the actors to deal with uncertainties and to learn from experience. Adaptive management supports reflexive governance, and the actors within the system adopt the strategy of rethinking and renegotiating their assumptions.
Adaptive management includes at its core an assessment and learning cycle. Close inspection of an AEAM (Adaptive Environmental Assessment and Management) learning cycle (Fig. 1) shows that it can be portrayed as a recasting of the scientific method of hypothesis testing, coupled interactively with modeling, such that it is fully integrated with the formulation of policy and the implementation or management of action plans based on policy. Hypotheses refer to working assumptions that make it possible to structure arguments and the policy debate. Modeling permits careful elaboration of the system structure and quantitative relations implied in hypotheses such that participants in the process can explore the dynamic implications of each hypothesis under different scenarios. Policy within the scientific context is seen more as an expression of how to test the hypotheses or questions generated during assessment. This experimental framework relieves decision makers of the responsibility to deliver policies as “solutions,” but it challenges them to join the experiment and test, rather than defend, assumptions. Getting stuck with a policy, even one that started as a good policy, strangles innovation and understanding in a changing world. Therefore, striving to answer good questions, i.e., inquiry, is as important as implementing good policy, i.e., management. The latter should be part of the former, and vice versa.
To take into account the different kinds of uncertainties (see Section 2) and to implement and sustain the capacity for change, the whole process of policy development and implementation requires a number of steps that are part of an iterative cycle as represented in Fig. 2. All of these steps should be participatory. In the definition of the problem (0), different perspectives need to be taken into account. The design of policies (1) should include scenario analyses to identify key uncertainties and find strategies that perform well under different possible, but initially uncertain, future developments; this is preferable to searching for the best strategy for very specific conditions, e.g., climate, because that strategy may not perform well if those conditions are not met. Policies must be understood as semi-open experiments that require a careful evaluation of potential positive or negative feedback mechanisms by planning and implementing other related policies (1, 2). Decisions should be evaluated in part by how much it would cost to reverse them. Large-scale infrastructure or rigid regulatory frameworks increase the costs of change, but costs may also be related to a loss of trust and credibility if uncertainties and the possible need for changes are not addressed by the competent authority during policy development (3). The design of monitoring programs should include processes that can pinpoint undesirable developments at an early stage. This might imply different kinds of knowledge, including community-based monitoring systems (3). The policy cycle must include support for institutional settings in which actors assess the performance of management strategies and implement change if needed (4). Continuous replanning and reprogramming based on the results of monitoring and evaluation should be institutionalized (4).
Moberg and Galaz (2005) introduce “adaptive co-management” as a new term that emphasizes the collaboration between agencies, researchers, and local stewards and contrast this concept with that of adaptive management, which they refer to as embracing a top-down governance approach. We do not consider it necessary to introduce yet another term and concept. The definition of adaptive management is broad enough as it stands and in no way excludes a polycentric governance approach (D. Huitema, W. Egas, S. Möllenkamp, E. Mostert, C. Pahl-Wostl, and R. Yalcin, unpublished manuscript). Its implementation should be based on a participatory assessment and implementation process that takes into account the particular institutional, cultural, and socioeconomic contexts in a given river basin.
Furthermore, research shows the importance of managing uncertainty and unexpected shocks to a system by using a more flexible and diverse management style. For example, Fraser et al. (2005) base their view of the importance of portfolio management and diversifaction in reducing vulnerability in agro-environmental systems on panarchy theory (Gunderson and Holling 2002). A number of researchers (J. C. J. H. Aerts, W. Botzen, A. Van der Veen, J. Krykrow, and S. Werners, unpublished manuscript; J. C. J. H. Aerts and S. Werners, unpublished manuscript) are working on concepts applicable to portfolio management and diversification in water management. Extensive literature on diversification in management as a means of becoming more adaptive is found in financial research (e.g., Markovitz 1952), and more recently in biodiversity research (Figge 2004).
Finally, integrating learning with changes in policy is possible only if the policies already implemented can be changed. The transition to adaptive management relies on increasing the adaptive capacity of the (water) system by meshing management and policy with learning. Adaptive management aims to design integrated systems based on an understanding of the interdependence between technologies, economic factors, and formal and informal institutions. The problem to be tackled is to increase the ability of the whole system to learn about and change the context within which it responds to change, rather than reacting to undesirable impacts of change. Institutionalizing this learning capability over the long term will secure the adaptive foundation of management.
Adaptiveness suggests a diversity of means available to address a challenge. Confining one’s approach, even in the attempt to become adaptive, to a single method could constrain adaptiveness over the longer term. There is no ideal path to adaptiveness. Choosing the most appropriate management approach may depend on how well it satisfies certain criteria at this stage of development (see van Eeten and Roe 2002 for other alternatives). For example, one may ask if current water management regimes that have evolved under a very different management paradigm possess the structural requirements needed to implement adaptive management approaches.
A management regime is here referred to as the whole complex of technologies, institutions, environmental factors, and paradigms that are highly interconnected and essential to the functioning of the management system that is targeted to fulfill a societal function such as water supply or flood protection. It is noted that “institution” is used to refer to the formal, e.g., laws, and informal, e.g., norms, rules that determine the behavior of actors, but not to the physical structures that are referred to as organizations. Because of their high interconnectedness and internal logic, it is assumed that the individual elements of the regime cannot be exchanged arbitrarily. A transition from centralized water treatment technologies to decentralized technologies at household scale requires, for example, major changes in the roles of actors, legal regulations, consumer habits, etc. (Panebianco and Pahl-Wostl 2006). How a regime’s performance is influenced by these factors and their relationships can be one basis of analysis.
Another approach is to assess a regime’s capacity to be adaptive based on how well its performance meets certain requirements that can be summarized as follows:
The transition from a prediction-and-control to an integrated adaptive regime is difficult. One reason for this is the obstacles that impede the practical implementation of adaptive management, such as the high costs of information gathering and monitoring, resistance from managers who may fear increased transparency and loss of control, political risks because of the uncertainty of future benefits, the lack of stable funding, and fear of failure (Lee 1993). In an analysis of the implementation of the adaptive management framework in the Florida Everglades, Gunderson (1999) identified three major barriers to the successful implementation of adaptive management: inflexibile social systems, ecological systems that lack resilience, and the technical challenges associated with designing experiments.
Institutional challenges can also restrict the usefulness of adaptive management approaches. For example, Lee (1993) identifies complicated social dynamics and institutional rigidities as possible barriers to the successful application of an adaptive management approach. Institutional needs for information are an associated feature here. Learning is information-intensive and requires active stakeholder participation (Margoluis and Salafsky 1998). The level of cooperation that is required to gather the information needed for adaptive management indicates that many different stakeholders need to maintain a commitment to the learning process.
To initiate the transition to adaptive management, there must be widespread dissatisfaction with the current or anticipated performance of existing management strategies, those involved must have the ability to detect and monitor potential gaps between the achieved and desired goals of the current management system, and those involved must be willing to change. In this type of process, the initial phase is particularly crucial, and the development of informal actor networks can contribute to success (Olsson et al. 2006, Pahl-Wostl et al. 2006). An alternative perspective on policy and change is necessary in situations in which social learning processes are precursors to more adaptive management regimes.
The role of social learning in understanding and managing change
Why is social learning needed in the transition to adaptive water management?
As pointed out in the previous section, there is often a mutual dependence among regime elements such as technical infrastructure, e.g., large technical infrastructure for flood protection; citizen behavior, e.g., expectations regarding safety in floodplains, risk perception; and engineering rules of good practice. In many cases they have co-evolved over a long period of time. Mutual relations of this type can develop into pathological path dependence or so-called “lock-in” situations that block changes toward new resource management schemes and require collective learning and decision-making processes (Pahl-Wostl 2002). To escape lock-in, actors need to learn to recognize how their own frames of reference influence and constrain their thinking and that other legitimate frames of reference exist. Collective action and the resolution of conflicts require that people recognize their interdependence and their differences and learn to deal with them constructively. Mutual recognition of shared paradigms can open the door to compromise. The different groups need to learn and increase their awareness of their biophysical environment and the complexity of social interactions. This does not imply that a consensus must be achieved, but what is required is the development of a minimum level of trust as a basis for transparent and efficient communication. Social learning in river basin management is needed to develop and sustain the capacity of different authorities, experts, interest groups, and the public to manage their river basins in a sustainable way and balance multiple and competing interests for the benefit of the social-ecological system as a whole.
Social learning for adaptive management
A concept for social learning in river basin management has been developed in the context of the European project HarmoniCOP (Harmonizing COllaborative Planning). The main objectives of HarmoniCOP (www.harmonicop.info) were to increase the understanding of participatory river basin management in Europe; generate practical, useful information about and improve the scientific base of social learning and the role of information and communication technology tools in river basin management; and support the implementation of the European Water Framework Directive The approach adopted by the HarmoniCOP project is characterized by a broad understanding of social learning that is rooted in the more interpretative strands of the social sciences. Figure 3 represents the framework for social learning developed to account for learning processes in actor networks in water resources management (Pahl-Wostl 2002, Craps et al. 2003, Bouwen and Taillieu 2004). The framework is structured into context, process, and outcomes, plus a feedback loop to account for change in a cyclic and iterative learning process. The context refers to the governance structure and the natural environment in a river basin. To improve the state of the environment implies in practice most often a change in the governance structure. The process refers to multiparty interactions in formal or informal negotiations; processes of this type are at the heart of iterative policy cycles (see Fig. 2). Social learning is assumed to occur at two levels: (1) at short to medium time scales, it occurs at the level of processes between actors, and (2) at medium to long time scales, at the level at which structural context shifts the governance structure.
One distinct feature is the emphasis on relational processes, which takes into account the fact that dealing with factual information about a problem is always embedded in a context of negotiated meaning.
The process concept that refers to multiparty interactions in actor networks has two pillars (Fig. 3). They relate to the processing of factual information about a problem, i.e., content management, and engaging in processes of social exchange, i.e., social involvement. Social involvement refers to essential elements of social processes, such as the framing of the problem, the management of the boundaries between different stakeholder groups, the type of ground rules and negotiation strategies chosen, or the role of leadership in the process. As an example, the role of framing is explained in more detail.
During the initial stages of dealing with a problem, the framing and reframing of a problem domain determine the direction of the overall process. Frames may be derived from the culture, social roles, scientific disciplines, etc. Actors have frames that determine how they make sense of and interpret information and their physical and social environment. Differences in how an issue is framed are among the key reasons for problems in communication and entrenched conflicts among actors. The framing of an issue includes, for example, what is at stake, who should be included, and which roles the different actors should play. Processes of framing and reframing are essential elements of group social dynamics during the negotiation of the meaning of key issues such as the goals to be achieved or how to measure management success. Experienced negotiators know that powerful actors often impose their frames or interpretations of an issue onto a process. A relational practice may be a moderated role-playing game or policy exercise in which actors are willing to reflect and discuss their own perspectives as well as listen to others. This type of social learning does not necessarily lead to consensus, but develops the ability to deal with differences constructively when transparency makes clear everyone's intentions.
A central hypothesis of the concept of social learning is that social involvement and the management of content are strongly interdependent and cannot be separated. The overall process aims to improve both technical qualities, such as the improvement of the state of the environment, and relational qualities, such as an increase in the capacity of a stakeholder group to manage a problem and/or institutional change. This leads as well to a different interpretation of the role of information and of information and communication tools and the ability of an actor network to use new information in social learning processes and to determine collective action. Knowledge relevant for decision making cannot be reduced to objective facts devoid of context and subjective interpretation. The development of joint interpretations and the implementation of collective action need to integrate tacit knowledge, which is not externalized and codified and can thus only be shared through joint activities that require physical proximity (Nonaka 1991). Participatory methods such as group model building and role-playing games are based on relational practices and can thus support social learning in actor groups (Pahl-Wostl and Hare 2004, Maurel et al. 2007). Such learning environments are perceived to be crucial for the adaptive governance of social-ecological systems (Folke et al. 2005, Pahl-Wostl 2005). Hence, an entirely new element of monitoring refers to the quality of the communication process in actor networks and the appropriateness of the chosen institutional setting.
Such multi-actor processes are embedded in a structural governance context that has a strong influence and may constrain or facilitate learning processes (Tippett et al. 2005).
Toward guidance for managing change
The discussion in the previous section on social learning in actor networks developed the image of dynamic and multilevel policy arenas and highlighted the fact that there may be barriers to change, given the importance of context and path dependence. How can we guide and manage change to overcome such barriers?
As highlighted in Fig. 3, decisions and the management of water resources do not take place in isolation, but are rather complex political processes that take shape at different semi-autonomous political levels. To link this understanding to concrete political actions it may be useful to distinguish the following levels explicitly (Pahl-Wostl et al. 2006):
These ideas form the basis of a coherent framework for analyzing the political context within which an adaptive capacity needs to be developed for river basin management. Table 2 summarizes 12 political actions that actors need to consider if they wish to develop adaptive capacity for the management of a river basin. These actions require active social learning that involves perceptions, tools, actors, and institutions at the context, network, and game levels. In the following subsections the different actions are described in more detail.
The context level
The context level refers to the wider context within which river basin management takes shape. It refers to societal views, national constitutions and laws, the approaches and tools used for management, and the existing landscape of actors and institutions that is formed over long periods of time. It typically affects the management of several river basins because it generally constrains and determines practices at larger spatial scales, such as countries or socioeconomic regions. The two main activities that take place at the context level are:
The network level refers primarily to the provincial context of river basin management, even though basins span continents. It pertains to the relationships established between interdependent institutions and how they do or do not cooperate. The context level determines how the network level will be formed and function, and in turn the network level determines how organizations will play the game, i.e., their approach to decision making and their attitudes to new tools for river basin management. The network level is formed over years and usually applies to the management of a regional river basin. At the network level, the actors focus on:
Networks, which are the patterns of relationships between actors, are the context in which games take place. At the same time, games change and influence the shape of networks. Actors within networks choose strategies, i.e., policy-making processes, that seem rational according to the network they interact with, their individual goals, and the overall context of the policy-making process. Furthermore, the actors driving river basin management at the game level are influenced by other forms of management, e.g., agricultural management, and the relationships developed in the network through present and past interactions. A characteristic feature of a game is that the result derives from the interactions between the strategies of all the actors involved. The rules of the game interactions put constraints on actors but are at the same time the product of their interactions (Kickert et al. 1999). The game level includes individuals and organizations that make decisions over periods of several months. Their essential activities include:
A range of strong arguments supports the claim for a paradigm shift in water management. The emphasis on reducing complexity, externalizing the human dimension, and designing technical systems that can be controlled has resulted in quite rigid and inflexible management systems that do not perform well in times of uncertainty and change. To implement integrated management approaches that take into account the complexity of the systems to be managed requires a change in the understanding of what management implies, how and which kinds of uncertainties are addressed, and how risks are managed. Complex social-ecological systems cannot be predicted and controlled, and a more adaptive approach is required.
However, integrated and adaptive water management approaches cannot be implemented without profound structural changes. In industrialized countries with a strong prediction-and-control tradition of water management, a complete system redesign comprising technologies, organizational structure, regulations, and thus a major transition is needed. In many developing countries, the base for management has yet to be developed. The changes needed are no less profound, but these countries face entirely different problems. Many countries suffer from a lack of political stability and the absence of any reliable administration. The knowledge base and monitoring capacity for implementing water management are often missing. Building the capacity to manage water has to be seen in a larger context of socioeconomic development. Given these uncertainties, an adaptive and flexible management approach seems to be mandatory. These countries need to develop and implement management strategies tailored to their needs and the political and environmental context rather than trying to adopt blueprints for institutions or technologies that may be entirely unsuitable for their situation.
We argue that change and the design and implementation of integrated and adaptive, and thus sustainable, water management regimes cannot be brought about by top-down implementation but require a process of learning and change. This can be explored by recognizing how decisions evolve at the context, network, and game levels. Dynamic and flexible actor platforms are needed that allow different perspectives and the interpretation and negotiation of the different dimensions of sustainability. As numerous analyses have shown, learning often occurs in shadow networks outside the formal water management context. We argue that learning cycles should be implemented as a recognized and important element of the established management regime. Social learning is of major importance to initiate change in, to build, and to sustain the adaptive capacity of water management systems. A range of knowledge gaps still exists. Nevertheless, it is possible to make these recommendations for policy makers to develop, implement, and sustain adaptive management practices to support sustainable water management in times of increased uncertainty because of global change:
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ACKNOWLEDGMENTSWe would like to thank our partners in the NeWater project for numerous inspiring discussions on the themes elaborated in this paper, and Georg Holtz and Art Dewulf for their critical review of an earlier draft of the current paper. The work presented in this paper was financially supported by the European Commission (Contract No. 511179 - NEWATER).
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