Details

Autor(en) / Beteiligte

• Thyer, Mark
• Gupta, Hoshin
• Westra, Seth
• McInerney, David
• Maier, Holger R.
• Kavetski, Dmitri
• Jakeman, Anthony
• Croke, Barry
• Simmons, Craig
• Partington, Daniel
• Shanafield, Margaret
• Tague, Christina

Titel

Virtual Hydrological Laboratories: Developing the Next Generation of Conceptual Models to Support Decision Making Under Change

Ist Teil von

• Water resources research, 2024-04, Vol.60 (4), p.n/a

Ort / Verlag

Washington: John Wiley & Sons, Inc

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• As hydrological systems are pushed outside the envelope of historical experience, the ability of current hydrological models to serve as a basis for credible prediction and decision making is increasingly challenged. Conceptual models are the most common type of surface water hydrological model used for decision support due to reasonable performance in the absence of change, ease of use and computational speed that facilitate scenario, sensitivity and uncertainty analysis. Hence, conceptual models in effect represent the current “shopfront” of hydrological science as seen by practitioners. However, these models have notable limitations in their ability to resolve internal catchment processes and subsequently capture hydrological change. New thinking is needed to confront the challenges faced by the current generation of conceptual models in dealing with a changing environment. We argue the next generation of conceptual models should combine the parsimony of conceptual models with our best available scientific understanding. We propose a strategy to develop such models using multiple hydrological lines of evidence. This strategy includes using appropriately selected physically resolved models as “Virtual Hydrological Laboratories” to test and refine the simpler models' ability to predict future hydrological changes. This approach moves beyond the sole focus on “predictive skill” measured using metrics of historical performance, facilitating the development of the next generation of conceptual models with hydrological fidelity (i.e., models that “get the right answers for the right reasons”). This quest is more than a scientific curiosity; it is expected by policy makers who need to know what to plan for. Key Points New thinking is needed to improve the predictive skill of conceptual hydrological models as they are confronted by multi‐faceted change Next generation conceptual models need to increase their “hydrological fidelity” founded upon multiple hydrological lines of evidence Virtual Hydrological Laboratories accelerate this development through controlled testing for future changes yet to be observed