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Improving risk assessment in environmental decision making through robust uncertainty estimation (IMPROVE)
Start date: May 1, 2013, End date: Apr 30, 2015 PROJECT  FINISHED 

Close to three quarters of Europeans think that the EU should propose additional measures to address water problems in Europe. Increasing impacts of human-induced changes on our climate and our landscape threaten important freshwater-related ecosystem services and functions, while the pressure to water security for society increases. Uncertainty in environmental model predictions limits the value of model-based information for characterising and managing water resources both under current conditions and for scenarios of potential future changes. Robust hydrological predictions with minimum uncertainty are needed to support the development of water-management strategies that minimise socio-economic risks at local, regional and global scales.The overall purpose of this project is to improve risk assessment in environmental decision making through robust quantification, reduction and communication of uncertainties in model predictions. A novel framework connecting local and regional information will be developed and evaluated for prediction of water-resources availability under current and changing conditions. The approach combines for the first time a suite of local environmental models (tailored to local conditions), regional understanding on catchment functioning (to constrain model behaviour) and local spot measurements (of key variables in key locations and time periods) in an integrated uncertainty framework.Optimal merging of these information sources for different hydro-climatic and data-availability conditions will be evaluated for regions in the UK, Sweden and Italy, and applied to design cost-effective data-collection strategies at ungauged sites. Optimal strategies to communicate the uncertainty in the predictions to decision makers will be investigated in collaboration with experimental psychologists. Successful execution of this research would create a novel framework for environmental predictions applicable across the world.

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