Marcela Fabiana Brugnach

Toward a Relational Concept of Uncertainty: about Knowing Too Little, Knowing Too Differently, and Accepting Not to Know

Uncertainty of late has become an increasingly important and controversial topic in water resource management, and natural resources management in general. Diverse managing goals, changing environmental conditions, conflicting interests, and lack of predictability are some of the characteristics that decision makers have to face. This has resulted in the application and development of strategies such as adaptive management, which proposes flexibility and capability to adapt to unknown conditions as a way of dealing with uncertainties. However, this shift in ideas about managing has not always been accompanied by a general shift in the way uncertainties are understood and handled. To improve this situation, we believe it is necessary to recontextualize uncertainty in a broader way?relative to its role, meaning, and relationship with participants in decision making?because it is from this understanding that problems and solutions emerge. Under this view, solutions do not exclusively consist of eliminating or reducing uncertainty, but of reframing the problems as such so that they convey a different meaning. To this end, we propose a relational approach to uncertainty analysis. Here, we elaborate on this new conceptualization of uncertainty, and indicate some implications of this view for strategies for dealing with uncertainty in water management. We present an example as an illustration of these concepts. Key words: adaptive management; ambiguity; frames; framing; knowledge relationship; multiple knowledge frames; natural resource management; negotiation; participation; social learning; uncertainty; water management

Time scale and intensity dependency in multiplicative cascades for temporal rainfall disaggregation

[1] Multiplicative random cascades (MRCs) can parsimoniously generate highly intermittent patterns similar to those in rainfall. The elemental MRC model parameter is the cascade weight, which determines how rainfall at one scale is partitioned at the next smallest scale in the cascade. While it is known that the probability density of these weights may vary with both time scale and rainfall intensity, nearly all previous studies have considered either time scale or intensity separately. We examined the simultaneous dependency of the weights on both factors and assessed the impacts of explicitly including these dependencies in the MRC model. On the basis of the observed relationships between cascade weights and time scale and intensity, four progressively more “dependent” models were constructed to disaggregate a long time series of daily rainfall to hourly intervals. We found that inclusion of the intensity dependency on the model parameters that generate dry intervals greatly improved performance. For the relatively small range of time scales over which the rainfall was disaggregated, varying model parameters with time scale resulted in minor improvement.

More is not always better: coping with ambiguity in natural resources management

Coping with ambiguities in natural resources management has become unavoidable. Ambiguity is a distinct type of uncertainty that results from the simultaneous presence of multiple valid, and sometimes conflicting, ways of framing a problem. As such, it reflects discrepancies in meanings and interpretations. Under the presence of ambiguity it is not clear what problem is to be solved, who should be involved in the decision processes or what is an appropriate course of action. Despite the extensive literature about methodologies and tools to deal with uncertainty, not much has been said about how to handle ambiguities. In this paper, we discuss the notions of framing and ambiguity, and we identify five broad strategies to handle it: rational problem solving, persuasion, dialogical learning, negotiation and opposition. We compare these approaches in terms of their assumptions, mechanisms and outcomes and illustrate each approach with a number of concrete methods.

Daily reference evapotranspiration for California using satellite imagery and weather station measurement interpolation

Important water resources in Californias agricultural and urban landscapes are at risk without more efficient management strategies. Improved monitoring can increase the efficiency of water use and mitigate these potential risks. The California Irrigation Management Information System (CIMIS) programme helps farmers, turf managers, and other resource managers develop water budgets that improve irrigation scheduling and monitor water stress. The CIMIS system is a repository of meteorological data collected at over 130 computerised weather stations. These are located at key agricultural and municipal sites throughout California and provide comprehensive, timely, weather data collected hourly and daily. In this article, the CIMIS sensor system is combined with hourly NOAA Geostationary Operational Environmental Satellite (GOES) visible satellite data to develop a methodology to extend reference evapotranspiration (ET0) station estimations to spatial daily ET0 maps of California. The maps are calculated on a (2 km)2 grid, a high spatial resolution compared with the density of CIMIS stations. The hourly GOES satellite images are used to estimate cloud cover, which are used in turn to modify clear sky radiation estimates. These are combined with interpolated CIMIS weather station meteorological data to satisfy the Penman–Monteith ET0 equation.

A broadened view on the role for models in natural resource management: Implications for model development

Models play a central tool in the development and implementation of management strategies. In this paper we identify four major modeling purposes that are important for understanding and managing complex socioenvironmental 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. We 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). Here, we specifically investigate the implications different purposes have in dealing with uncertainties. We present a set of strategies modelers can use to guide their developments. 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. We present an example from the adaptive management field.

Determining the significance of threshold values uncertainty in rule-based classification models

Ecological models not only represent a tool for studying ecosystems, but also function as a key source for informing environmental policy. As such, these models are expected to present and convey information and uncertainty as accurately as possible. As the complexity of these models increases, the resultant output also becomes more complex and difficult to interpret. However, understanding model output is not limited to interpreting complex dynamics; scientists must also contend with the possibility of model error and uncertainty. It is the uncertainty associated with the output that provides information about the reliability of model predictions, and therefore plays a pivotal role in model evaluation and interpretation. Despite its importance, uncertainty frequently is ignored. We present a methodology to determine the significance of uncertainty in rule-based classification models. This is a stepwise methodology that, using a genetic algorithm, determines the effects of uncertainty in model output and identifies the most likely alternative results. It also computes a measure of confidence to help the modeler evaluate how the predictions are affected by uncertainties. We present a case study applying the methodology to the global vegetation model Mapped Atmosphere-Plant-Soil System (MAPSS) where the measure of confidence contains information about the dissimilarity between alternative outcome classes, and their spatial configuration on the landscape. Fuzzy sets are used to portray most likely alternative results as integral part of model output in a spatially explicit format.

Uncovering the origin of ambiguity in nature-inclusive flood infrastructure projects.

We aimed to uncover the origin of ambiguity in flood infrastructure projects using Building with Nature (BwN) design principles. BwN is a new approach in flood management that simultaneously integrates societal goals, such as flood safety and recreation development, with nature development goals by actively using natural dynamics and materials in the project’s design. Because BwN projects affect multiple stakeholders and several societal functions, participatory project development is of key importance to successfully implement these projects. In such a multiactor decision-making process, a diversity of actors are involved, all of whom have their own view of the project based on their interests, values, beliefs, backgrounds, and past experiences. As a consequence, BwN projects are susceptible to being hampered by the presence of ambiguity, a kind of uncertainty that results from the simultaneous presence of multiple frames. For two BwN case study projects, we identified where the ambiguities potentially affecting project development resided, derived the different actor frames, and addressed the attributes underlying these frames. Our main finding was that ambiguity in BwN projects seems to originate from a contradiction between the beliefs held by different actors. Furthermore, our results suggest that in the current practice of BwN projects the scientific knowledge of experts is perceived as more legitimate than the local knowledge and experiences of lay actors, which implies that experts have a more powerful position in multiactor decision making. Thus, our research underlines the difficulty of bringing local knowledge and past experiences of lay actors into collective decision making

The Space in Between: Where Multiple Ways of Knowing in Water Management Meet

Aligning what we know with what we do is one of the major challenges of contemporary water governance. Solving current water problems transcends the decision-making power and resources of any single actor and requires coordinated actions among a diversity of actors from different organizational levels and sectors. In these decision contexts of water issues and the networks of actors concerned with them, there are many ways of knowing (WoKs). In her scholarly work, Helen Ingram has often stated that effective problem solving in a democracy requires the integration of different ways of knowing. This implies enlarging and connecting existing WoKs in such a way that they reflect common collective goals and mutually acceptable solutions. But, she also recognizes that doing so is challenging, since problem definitions and solutions are often ambiguous and enmeshed in different networks of actors and institutional arrangements, where power differentials, conflicting interests, and different access to resources exist. She notes that all too often, this diversity in ways of knowing is arbitrated by appealing to a single perspective (where a technical one is preferred), resulting in solutions to water problems that do not fit the contexts in which they are implemented. Here, I have examined what it means to embrace ambiguity from the perspective of WoKs theory, paying particular attention to the relational aspects underlying the generation of knowledge for action, and proposed knowledge co-production processes that support collaboration and connection among multiple WoKs. I illustrate these ideas with examples of groundwater management from two case studies in Spain and Italy, in which I have been involved.

Application of an Interactive Water Simulation Model in urban water management: a case study in Amsterdam

Water simulation models are available to support decision-makers in urban water management. To use current water simulation models, special expertise is required. Therefore, model information is prepared prior to work sessions, in which decision-makers weigh different solutions. However, this model information quickly becomes outdated when new suggestions for solutions arise and are therefore limited in use. We suggest that new model techniques, i.e. fast and flexible computation algorithms and realistic visualizations, allow this problem to be solved by using simulation models during work sessions. A new Interactive Water Simulation Model was applied for two case study areas in Amsterdam and was used in two workshops. In these workshops, the Interactive Water Simulation Model was positively received. It included non-specialist participants in the process of suggesting and selecting possible solutions and made them part of the accompanying discussions and negotiations. It also provided the opportunity to evaluate and enhance possible solutions more often within the time horizon of a decision-making process. Several preconditions proved to be important for successfully applying the Interactive Water Simulation Model, such as the willingness of the stakeholders to participate and the preparation of different general main solutions that can be used for further iterations during a work session.

Coping with Uncertainty in River Management: Challenges and Ways Forward

Coping with uncertainties is inherent to river management planning and policymaking. Yet, policymakers often perceive uncertainty as a complicating factor. We examine the challenges faced by policymakers when coping with uncertainties and provide an action perspective on how to best cope with these challenges to inform the policy debate. Integrating social and natural scientist’s perspectives on uncertainties and learning theories, we present a holistic, management perspective for coping with uncertainty. Based on a literature review about uncertainty concepts, strategies and learning, we identify three challenges in current river management: balancing social and technical uncertainties, being conservative and avoiding to end up a lock-in situation. We then provide a step-wise strategy and concrete actions for policymakers, which are illustrated with several examples. We conclude that coping with uncertainty may require paradigm shifts that can only be achieved through organisational learning. This, we claim, requires reflection, learning and flexibility of policymakers and their organisation.