HR: 15:20h
AN: H53I-07 INVITED    [Abstracts]
TI: How can models better aid the decision processes for the management of water resources?
AU: * Brugnach, M
EM: mbrugnac@usf.uni-osnabrueck.de
AF: University of Osnabrueck, Barbarastr. 12, Osnabrueck, 49076, Germany
AU: Pahl-Wostl, C
EM: pahl@usf.uni-osnabrueck.de
AF: University of Osnabrueck, Barbarastr. 12, Osnabrueck, 49076, Germany
AB: Despite the long history of development and use of models in policy making, there is still a poor integration between modeling and the decision process. This is influenced by several factors, such as a lack of understanding of models from policy makers, limitations in the applicability and use of models, modeller's behaviour, and a lack of stakeholder involvement in the whole modelling process. But, most relevant of all, is the fact that models are perceived as non reliable tools for policy making, due to the uncertainty associated with them and the lack of confidence this uncertainty generates. Commonly, in fields like natural resource management, and in particular water management, models are build to predict, in space or time, the state of the system to be managed (e.g. real time flood forecasting). These models are then used by policy makers as a surrogate of a real system to inform their decisions. In this view, the efficacy of a model depends on how well it can approximate reality and how much confidence policy makers can have on model's results. However, even though predictive models can be used to convey scientific argumentation that can aid decision making, these models fall short in supporting the processes of negotiation, learning, and communication, which constitute the basis for policy making. In this presentation we explore and discuss the relationship between models used in water resource management and policy making: how models can be used and how uncertainty should be treated depending on the purpose models are supposed to serve. We identify four major modeling purposes that are important for understanding and managing complex environmental systems: prediction, exploratory analysis, communication and learning, and investigate the implications of the different purposes in dealing with uncertainty. In predictive models, the presence of uncertainty is understood as a critical constraint for decision making, and as such it ought to be eliminated or reduced as much as possible. On the other hand, in models for learning the presence of uncertainty become useful to identify the commonalities and differences in views, and can highlight point of conflicts, opening room for discussion and space for negotiation among different interest parties. Using these concepts, we present a set of strategies that can guide the development and use of models in support of the policy making process.
DE: 1847 Modeling
DE: 1880 Water management (6334)
SC: Hydrology [H]
MN: 2007 Fall Meeting