A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

TLDR

Distributed hydrological models can improve streamflow forecasts and simulate spatial dynamics, yet they are difficult to parameterize, leading to significant predictive uncertainty. This study aims to develop an automated, diagnostic parameter‑estimation strategy that incorporates watershed‑specific performance diagnostics. The authors employ hydrologically relevant signature measures—rather than mean squared error—to assess model performance across water balance, vertical redistribution, and temporal redistribution functions. Applying the approach to the National Weather Service’s Hydrology Laboratory Distributed Hydrologic Model demonstrates that diagnostic evaluation can yield a powerful, intuitive basis for deriving consistent watershed‑model parameter estimates.

Abstract

Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictive uncertainty in the model results. Although a priori parameter estimates derived from observable watershed characteristics can help to minimize obstacles to model implementation, there exists a need for powerful automated parameter estimation strategies that incorporate diagnostic information regarding the causes of poor model performance. This paper investigates a diagnostic approach to model evaluation that exploits hydrological context and theory to aid in the detection and resolution of watershed model inadequacies, through consideration of three of the four major behavioral functions of any watershed system; overall water balance, vertical redistribution, and temporal redistribution (spatial redistribution was not addressed). Instead of using classical statistical measures (such as mean squared error), we use multiple hydrologically relevant “signature measures” to quantify the performance of the model at the watershed outlet in ways that correspond to the functions mentioned above and therefore help to guide model improvements in a meaningful way. We apply the approach to the Hydrology Laboratory Distributed Hydrologic Model (HL‐DHM) of the National Weather Service and show that diagnostic evaluation has the potential to provide a powerful and intuitive basis for deriving consistent estimates of the parameters of watershed models.