Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology

TLDR

Field studies have catalogued the heterogeneity and complexity of rainfall‑runoff processes across watersheds, yet generalizing these findings to ungauged regions is difficult and current detailed models poorly represent internal states and dynamics. The authors propose moving beyond explicit characterization of landscape heterogeneity in calibrated models to explore organizing principles that underlie watershed heterogeneity and complexity. They outline new research avenues—comparative analysis, classification, optimality principles, and network theory—to define, understand, and predict watershed function and key functional traits.

Abstract

Field studies in watershed hydrology continue to characterize and catalogue the enormous heterogeneity and complexity of rainfall runoff processes in more and more watersheds, in different hydroclimatic regimes, and at different scales. Nevertheless, the ability to generalize these findings to ungauged regions remains out of reach. In spite of their apparent physical basis and complexity, the current generation of detailed models is process weak. Their representations of the internal states and process dynamics are still at odds with many experimental findings. In order to make continued progress in watershed hydrology and to bring greater coherence to the science, we need to move beyond the status quo of having to explicitly characterize or prescribe landscape heterogeneity in our (highly calibrated) models and in this way reproduce process complexity and instead explore the set of organizing principles that might underlie the heterogeneity and complexity. This commentary addresses a number of related new avenues for research in watershed science, including the use of comparative analysis, classification, optimality principles, and network theory, all with the intent of defining, understanding, and predicting watershed function and enunciating important watershed functional traits.