Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework

TLDR

Despite advances in computational power and distributed hydrologic modeling, uncertainty in hydrologic predictions remains a critical challenge that must be addressed to realize the full practical potential of environmental decision‑making, yet no well‑accepted integrated guidelines exist. This paper reviews common data‑assimilation methods used for state, parameter, and system identification in hydrologic modeling and proposes an integrated hierarchical framework to progressively reduce uncertainty. The authors discuss data‑assimilation concepts, methods, and issues from a systems perspective and outline an integrated hierarchical framework to guide the application of these techniques for uncertainty reduction.

Abstract

Despite significant recent developments in computational power and distributed hydrologic modeling, the issue of how to adequately address the uncertainty associated with hydrological predictions remains a critical and challenging one. This issue needs to be properly addressed for hydrological modeling to realize its maximum practical potential in environmental decision‐making processes. Arguably, the key to properly addressing hydrologic uncertainty is to understand, quantify, and reduce uncertainty involved in hydrologic modeling in a cohesive, systematic manner. Although general principles and techniques on addressing hydrologic uncertainty are emerging in the literature, there exist no well‐accepted guidelines about how to actually implement these principles and techniques in various hydrologic settings in an integrated manner. This paper reviews, in relevant detail, the common data assimilation methods that have been used in hydrologic modeling to address problems of state estimation, parameter estimation, and system identification. In particular, the paper discusses concepts, methods, and issues involved in hydrologic data assimilation from a systems perspective. An integrated hierarchical framework is proposed for pursuing hydrologic data assimilation in several progressive steps to maximally reduce uncertainty in hydrologic predictions.