The planetary boundaries framework defines a safe operating space for humanity based on the intrinsic biophysical processes that regulate the stability of the Earth system. Here, we revise and update the planetary boundary framework, with a focus on the underpinning biophysical science, based on targeted input from expert research communities and on more general scientific advances over the past 5 years. Several of the boundaries now have a two-tier approach, reflecting the importance of cross-scale interactions and the regional-level heterogeneity of the processes that underpin the boundaries. Two core boundaries—climate change and biosphere integrity—have been identified, each of which has the potential on its own to drive the Earth system into a new state should they be substantially and persistently transgressed.

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Planetary boundaries: Guiding human development on a changing planet

Protecting the world’s freshwater resources requires diagnosing threats over a broad range of scales, from global to local. Here we present the first worldwide synthesis to jointly consider human and biodiversity perspectives on water security using a spatial framework that quantifies multiple stressors and accounts for downstream impacts. We find that nearly 80% of the world’s population is exposed to high levels of threat to water security. Massive investment in water technology enables rich nations to offset high stressor levels without remedying their underlying causes, whereas less wealthy nations remain vulnerable. A similar lack of precautionary investment jeopardizes biodiversity, with habitats associated with 65% of continental discharge classified as moderately to highly threatened. The cumulative threat framework offers a tool for prioritizing policy and management responses to this crisis, and underscores the necessity of limiting threats at their source instead of through costly remediation of symptoms in order to assure global water security for both humans and freshwater biodiversity.

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Global threats to human water security and river biodiversity

This paper reviews the concept of adaptation of human communities to global changes, especially climate change, in the context of adaptive capacity and vulnerability. It focuses on scholarship that contributes to practical implementation of adaptations at the community scale. In numerous social science fields, adaptations are considered as responses to risks associated with the interaction of environmental hazards and human vulnerability or adaptive capacity. In the climate change field, adaptation analyses have been undertaken for several distinct purposes. Impact assessments assume adaptations to estimate damages to longer term climate scenarios with and without adjustments. Evaluations of specified adaptation options aim to identify preferred measures. Vulnerability indices seek to provide relative vulnerability scores for countries, regions or communities. The main purpose of participatory vulnerability assessments is to identify adaptation strategies that are feasible and practical in communities. The distinctive features of adaptation analyses with this purpose are outlined, and common elements of this approach are described. Practical adaptation initiatives tend to focus on risks that are already problematic, climate is considered together with other environmental and social stresses, and adaptations are mostly integrated or mainstreamed into other resource management, disaster preparedness and sustainable development programs. r 2006 Elsevier Ltd. All rights reserved.

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Adaptation, adaptive capacity and vulnerability

Over the past few decades, a new form of governance has emerged to replace adversarial and managerial modes of policy making and implementation. Collaborative governance, as it has come to be known, brings public and private stakeholders together in collective forums with public agencies to engage in consensus-oriented decision making. In this article, we conduct a meta-analytical study of the existing literature on collaborative governance with the goal of elaborating a contingency model of collaborative governance. After reviewing 137 cases of collaborative governance across a range of policy sectors, we identify critical variables that will influence whether or not this mode of governance will produce successful collaboration. These variables include the prior history of conflict or cooperation, the incentives for stakeholders to participate, power and resources imbalances, leadership, and institutional design. We also identify a series of factors that are crucial within the collaborative process itself. These factors include face-to-face dialogue, trust building, and the development of commitment and shared understanding. We found that a virtuous cycle of collaboration tends to develop when collaborative forums focus on ‘‘small wins’’ that deepen trust, commitment, and shared understanding. The article concludes with a discussion of the implications of our contingency model for practitioners and for future research on collaborative governance. Over the last two decades, a new strategy of governing called ‘‘collaborative governance’’ has developed. This mode of governance brings multiple stakeholders together in common forums with public agencies to engage in consensus-oriented decision making. In this article, we conduct a meta-analytical study of the existing literature on collaborative governance with the goal of elaborating a general model of collaborative governance. The ultimate goal is to develop a contingency approach to collaboration that can highlight conditions under which collaborative governance will be more or less effective as an

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Collaborative governance in theory and practice

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

management under uncertainty). This special feature showcases some of the key work efforts conducted under the umbrella of the NeWater project. One major goal of NeWater has been the development of a conceptual and methodological framework for the transition of prevailing watermanagement regimes to adaptive ones. Based on this framework, specific approaches and tools have been further developed for practical applications in a number of case studies in Europe (Elbe, Guadiana, Rhine, and Tisza), in Central Asia (Amudarya), and in Africa (Nile and Orange). The emphasis of the work reported in this special feature is on the development of conceptual foundations and methodological innovations.

Guest Editorial, part of a Special Feature on New Methods for Adaptive Water Management Resources Management in Transition

description of a complex system is not easy. The uncertainty and indeterminacy present in complex systems, drive modelers to make subjective decision about the behavior of the system and its most relevant features. This implies embedding a series of nested, and sometimes iterative and evolving assumptions in the model. This in turn, incorporates uncertainty into the model at various levels; affecting the model, the way in which it is developed and the value of its inference. But what does it mean for modelers and the way we do modeling? First, complexity affects the way in which models are implemented. Traditionally, models are thought to be developed as a sequential of three main activities: conceptualization, implementation and evaluation. However, modeling complex systems requires an iterative process of model formulation, more like a trial and error approach, where modules at different levels of detail are considered in conjunction with different assumptions and hypotheses about how the real system works. As a result, the simple sequence of steps of conceptualization-implementation-evaluation, in reality becomes a series of cycles of conceptualization, implementation, re-conceptualization, code modification, implementation, etc. This iteration typically occurs over the course of time as new knowledge and ideas are generated and subsequently used to modify existing models. Second, from a conceptual standpoint, the presence of complexity shifts the goals of modeling from the creation of an exact replication of a system in which uncertainties ought to be eliminated, to being a creative process in which the different sources of uncertainties can be embraced. It enlarges the role of models as an “external” representation of reality. Models, and in particular the whole process of model development and application, may be perceived as part of a learning process to make transparent different perspectives and frames. Current model applications expand beyond prediction, to include exploratory analyses, communication and learning. This in turn, changes the type of knowledge needed and how this knowledge is elicited. But most important of all, it changes the inferences making possibilities. Even though these are not novel ideas, and despite the increasing awareness about these issues, practical applications commonly fail in addressing the complexity and uncertainty of current problems. Generally, complexity gets diluted in simplifications done during model conceptualization and uncertainties are avoided as much as possible as being something undesirable. Commonly, once a model is developed, the assumptions and subjective decisions embedded into model representation are forgotten and only single causes of uncertainties are, sometimes addressed. For many, the presence of uncertainty completely invalidates the use of models to drive inference about a real problem. But, by avoiding complexity when modeling, aren’t we throwing the baby out with the bath water? What we believe is necessary are approaches that bring symmetry to our inference capacity. Complexity has brought a different way of viewing and understanding systems, in parallel, the modeling arena has seen the emergence of several new methods that are able to embrace these new concepts. However, despite these great advances, the goal of looking at a single best, simple and objective explanation still permeates the modeling exercise. We suggest that a more comprehensive way of dealing and handling complexity and uncertainties in modeling is still needed. Brugnach and Pahl Wostl (in prep) in their paper identified four major modeling purposes that are important for understanding and managing complex human environmental systems: prediction, exploratory analysis, communication and learning. Each of these purposes highlights different system characteristics, role of uncertainty, the properties of the model and its validation. They argue that uncertainty has no meaning in isolation, but only relative to a particular modeling activity and the purpose for which a model is developed (e.g., when a model is developed for predictive purposes uncertainty needs to be eliminated as much as possible, while when a model is developed for exploration uncertainty can be considered a source of creative thoughts). In light of these concepts, the modeling activity is re-contextualized, from being a process that aims at representing objectively an external reality, to one that can only be defined according to the characteristics of the problem at hand: its level of complexity, the knowledge available, the purpose of the model and the modeling tools. The purpose of this workshop is to bring together these concepts at an operational level and to develop as final product a joint paper. The idea is to illustrate through study cases how the theoretical concepts presented in the paper above mentioned, can be put into practice. We consider this is important to improve our understanding of models and the whole modeling process, allowing us to better design and implement effective modeling strategies. The study cases presented correspond to the contributions made by the workshop participants, showing applications from a range of different fields. The first case, “Modelling in transition research: the role of unpredictable innovations”, discusses the role of models in transition research, focusing on uncertainties that result from the creation, selection and adaptation to innovations. Here models constitute an exploratory device that can be used to better understand when and why regimes break up and a transition sets off. Genetic algorithm is presented as a modelling tool able to capture the evolutionary dynamics of a changing regime. The second case, Identification of major sources of uncertainty in IWRM and current approaches for including them in IWRM, discusses the role of models in integrated water resource management where uncertainties are of key importance. In this example, models serve for many different purposes and different modeling strategies appear relevant during the different phases of the managing cycle. The third case, “Questions and methods to model emergence of land use patterns in coastal zone area”, discusses the role of models to understand costal land use pattern generation as emerging from individual stakeholder interactions. Here, models constitute mainly an exploratory device that allows considering stakeholder behavior and the associated uncertainties at multiple levels of aggregation. Agent based models is the modeling strategy proposed and the treatment of uncertainty suggests various participatory methods, such as scenario development and role playing games. The forth case, Issues of uncertainty, complexity, scale and transferability in river water quality modeling with a focus on the Saale River, Germany, discusses issues of model complexity and uncertainty in river water quality models, focusing in uncertainties associated with parameters, data input and model structure, as well as scaling and transferability problems. The fifth case, Exploring regionalization of hydrological behavior within a model averaging framework, discusses the topic of complexity and uncertainty in a case study of Australian catchments where a model averaging framework is utilised for ungauged streamflow prediction.

Workshop 8: Complexity and uncertainty and a new role for models

Netzwerktheorie – Analyse von Stoff‐ und Energietransfers

In this paper we compare 10 established frameworks for analyzing social-ecological systems. We limited ourselves to frameworks that were explicitly designed to be used by a wider community of researchers and practitioners. Although all these frameworks seem to have emerged from the need for concepts that permit structured, interdisciplinary reasoning about complex problems in social-ecological systems, they differ significantly with respect to contextual and structural criteria, such as conceptualization of the ecological and social systems and their interrelation. It appears that three main criteria suffice to produce a classification of frameworks that may be used as a decision tree when choosing a framework for analysis. These criteria are (i) whether a framework conceptualizes the relationship between the social and ecological systems as being uni- or bidirectional; (ii) whether it takes an anthropocentric or an ecocentric perspective on the ecological system; and (iii) whether it is an action-oriented or an analysis-oriented framework.

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Research, part of a Special Feature on A Framework for Analyzing, Comparing, and Diagnosing Social-Ecological Systems Comparison of Frameworks for Analyzing Social-ecological Systems

Claudia Pahl-Wostl

Papers

Guest Editorial, part of a Special Feature on New Methods for Adaptive Water Management Resources Management in Transition

Workshop 8: Complexity and uncertainty and a new role for models

Netzwerktheorie – Analyse von Stoff‐ und Energietransfers

Research, part of a Special Feature on A Framework for Analyzing, Comparing, and Diagnosing Social-Ecological Systems Comparison of Frameworks for Analyzing Social-ecological Systems

Zukunftsfähige Agrarpolitik - Natur erhalten, Umwelt sichern. Ergebnisbericht des Projekts ZA-NExUS