Regionalizing prior parameter ranges for rainfall-runoff models

Rainfall-runoff models are often calibrated by defining feasible parameter ranges and constraining them with streamflow data, and occasionally other hydrological variables. Traditionally, global prior ranges have served as a baseline, containing a wide range of parameter values suitable for various catchment types. However, there might be more information available to reduce a priori parameter uncertainty in a structured way. This study addresses this gap by defining plausible prior parameter ranges based on the distribution of identifiable parameters and their relationship to catchment characteristics. Using a version of the conceptual Probability Distributed Moisture (PDM) model, the study focuses on a large sample of catchments in the United States, covering diverse climatic, land cover, geological, and landscape types. Thus, investigating the effects of these physical and climatic properties on parameter prior ranges. The combination of automatic grouping and catchment attributes resulted in significant reductions in parameter space, with high average predictive accuracies for traditional efficiency measures. Surprisingly, we find distinct and spatially coherent regions within the US where specific prior parameter ranges maintain high levels of performance. More than 75% of the catchments show NSE values above 0.6 and KGE values above 0.7. Our results suggest that regionalizing prior parameter ranges can significantly reduce parameter uncertainty. These findings have significant implications for the prediction of hydrological responses in ungauged catchments.