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

Abstract

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.

A Sandwich With Water: Bayesian/Frequentist Uncertainty Quantification under Model Misspecification

“…A default assumption in almost all this work is that the likelihood function (aka model) is correctly specified. However, hydrologic models have notable limitations in their ability to resolve internal catchment processes and capture hydrological change (Thyer et al, 2024). Thus, the likelihood function is almost always violated, thereby corrupting model training and statistical inference.…”