Water resources data, models and decisions: International expert opinion on knowledge management for an uncertain but resilient future

Ward, Sarah; Borden, D. Scott; Kabo-Bah, Amos; Fatawu, Abdul Nasirudeen; Mwinkom, Xavier Francis

Abstract

© 2019 The Authors. Assessing the resilience of water resources systems requires knowledge of properties and performance, which depends on data availability and use within models and decision making. Connections between data, models and decision making are crucial to plan for uncertainty and invest in interventions. To explore international perceptions of these connections, we conducted a threeround Delphi survey with an expert panel (see Supplementary material, available with the online version of this paper). Consensus and divergence existed within and between countries on ability to manage data, modelling and decision making, with the most consensus seen on use of hydrometric databases. There was a wide range of models and tools utilised by participants and a shift occurred between first and second rounds to a preference for trying new modelling. There was consensus between and within all countries that every data type was important. River flow data consistently scored highest. Access to data and models primarily impacted evaluating future capacity, planning under uncertainty, policy implementation and conflict resolution. The panel called for reviewing existing and developing new policy, collaborative research and available funding all focusing on water resources data-model-decision integration. Findings offer a strategic view on knowledge management regarding connections between data, models and decision making through identification of consensus areas for future focus and dissensus areas for reprioritisation.

• Journal Article Type: Article
• Acceptance Date: Aug 14, 2018
• Online Publication Date: Nov 19, 2018
• Publication Date: Jan 1, 2019
• Deposit Date: Nov 21, 2018
• Publicly Available Date: Nov 21, 2018
• Journal: Journal of Hydroinformatics
• Print ISSN: 1464-7141
• Publisher: IWA Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 1
• Pages: 32-44
• DOI: https://doi.org/10.2166/hydro.2018.104
• Keywords: data, decision making, Delphi, management, modelling, water resources
• Public URL: https://uwe-repository.worktribe.com/output/876448
• Publisher URL: http://dx.doi.org/10.2166/hydro.2018.104
• Contract Date: Nov 21, 2018