Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan

Mount, Nick J.; Maier, Holger R.; Toth, Elena; Elshorbagy, Amin; Solomatine, Dimitri; Chang, Fi-John; Abrahart, R. J.

Abstract

© 2016 IAHS. “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.

Citation

Mount, N. J., Maier, H. R., Toth, E., Elshorbagy, A., Solomatine, D., Chang, F., & Abrahart, R. J. (2016). Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan. Hydrological Sciences Journal, 61(7), 1192-1208. https://doi.org/10.1080/02626667.2016.1159683

• Journal Article Type: Article
• Acceptance Date: Feb 23, 2016
• Online Publication Date: Mar 3, 2016
• Publication Date: Apr 12, 2016
• Deposit Date: Feb 25, 2016
• Journal: Hydrological Sciences Journal
• Print ISSN: 0262-6667
• Electronic ISSN: 2150-3435
• Publisher: Taylor & Francis Open
• Peer Reviewed: Peer Reviewed
• Volume: 61
• Issue: 7
• Pages: 1192-1208
• DOI: https://doi.org/10.1080/02626667.2016.1159683
• Keywords: Data-driven; Hydrologic modelling; Socio-hydrology; Hypothesis; Conceptual modelling; Knowledge extraction
• Public URL: https://nottingham-repository.worktribe.com/output/980282
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/02626667.2016.1159683
• Additional Information: This is an Author's Original Manuscript of an article published by Taylor & Francis in Hydrological Sciences Journal on 12/04/2016 available online at https://www.tandfonline.com/doi/full/10.1080/02626667.2016.1159683.