Socio-hydrology modelling for an uncertain future, with examples from the USA and Canada
Published:January 01, 2017
Patricia Gober, Dave D. White, Ray Quay, David A. Sampson, Craig W. Kirkwood, 2017. "Socio-hydrology modelling for an uncertain future, with examples from the USA and Canada", Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges, A. T. Riddick, H. Kessler, J. R. A. Giles
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Socio-hydrology brings an interest in human values, markets, social organizations and public policy to the traditional emphasis of water science on climate, hydrology, toxicology and ecology. It also conveys a decision focus in the form of decision support tools, stakeholder engagement and new knowledge about the science–policy interface. This paper demonstrates how policy decisions and human behaviour can be better integrated into climate and hydrological models to improve their usefulness for decision support. Examples from SW USA and western Canada highlight uncertainties, vulnerabilities and critical tradeoffs facing water decision makers in the face of rapidly changing environmental and societal conditions. Irreducible uncertainties in downscaled climate and hydrological models limit the usefulness of climate-driven, predict-and-plan methods of water resource planning and management. Thus, it is argued that such methods should be replaced by approaches that use exploratory modelling, scenario planning and risk assessment in which the emphasis is on managing uncertainty rather than on reducing it. Model fusion supports all of these processes in integrating human and biophysical aspects of water systems, allowing policy impacts to be quantified and clarified, and fostering public engagement with water resource modelling.
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Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges
The discipline of Integrated Environmental Modelling (IEM) has developed in order to solve complex environmental problems, for example understanding the impacts of climate change on the physical environment. IEM provides methods to fuse or link models together, this in turn requires facilities to make models discoverable and also to make the outputs of modelling easily visualized.
The vision and challenges for IEM going forward are summarized by leading proponents. Several case studies describe the application of model fusion to a range of real-world problems including integrating groundwater and recharge models within the UK Environment Agency, and the development of ‘catastrophe’ models to predict better the impact of natural hazards. Communicating modelling results to end users who are often not specialist modellers is also an emerging area of research addressed within the volume. Also included are papers that highlight current developments of the technology platforms underpinning model fusion.