Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan |
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| Authors: | N.J. Mount H.R. Maier E. Toth A. Elshorbagy D. Solomatine F.-J. Chang |
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| Affiliation: | 1. School of Geography, University of Nottingham, University Park, Nottingham, UKnick.mount@nottingham.ac.uk;3. School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, Australia;4. Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, Bologna, Italy;5. Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, Canada;6. UNESCO-IHE Institute for Water Education, Delft, The Netherlands;7. Water Resources Section, Delft University of Technology, Delft, The Netherlands;8. Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan |
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| Abstract: | ABSTRACT“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process. EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned |
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| Keywords: | data-driven hydrologic modelling socio-hydrology hypothesis conceptual modelling knowledge extraction |
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