Can the catchment features influence the performance of the conceptual hydrological and deep learning models? A study using large sample hydrologic data

The prior literature on hydrologic model performance is dispersed, encompassing a small number of catchments, different methodology, and rarely linking the results to specific catchment characteristics. This study addresses these constraints by systematically attributing model performance to catchment variables in 671 US catchments, providing a formal framework for determining the best models for specific conditions. Daily streamflow estimation was performed using eight process-based (PB) models and three deep learning (DL) models, with performance measured using the Nash-Sutcliffe Efficiency (NSE). The PB models were tested with a variety of optimization techniques, and the most effective approach for each model was chosen based on the number of catchments that exceeded a predetermined performance threshold. Four models were selected as the top performers based on three performance metrics. Further analyses, such as Classification and Regression Tree (CART) and SHAPley, were used to correlate model performance with catchment variables across all models.
The results showed that PB models (GR4J, HBV, and SACSMA) performed well in catchments with low to medium aridity and a high Q/P ratio, indicating quick hydrologic responses. In contrast, the LSTM-based DL model performed well in medium to high aridity regions but had limits in catchments with rapid precipitation responses and low sand percentages. These findings provide a thorough understanding of the links between model performance and catchment descriptors.

Keywords: Process-based models, Deep learning model, CART analysis, SHAPley analysis, catchment characteristics.