Revisiting the Hydrological Evaluation of Precipitation Products: Don’t Forget to Check Their Correlation with Streamflow

Abstract

The hydrological performance of precipitation products -widely used as forcing inputs in hydrological models- has been extensively evaluated in recent years. While factors such as similarity to observed precipitation, rain gauge data incorporation, spatial resolution, geographic location, climate classes, and catchment characteristics are commonly cited elements to explain differences in model outcomes, the correlation between precipitation products and observed streamflow (CC(P-product,Q)) has received little attention. This study investigates the role of this often-overlooked element in influencing the hydrological performance of precipitation datasets. Through four complementary experiments -using six precipitation inputs, eight structurally different hydrological models, and a large-sample hydrology approach- we demonstrate that CC(P-product,Q) is an important explanatory factor of model performance differences. Specifically, our results show that (i) CC(P-product,Q) is significantly related to model performance, (ii) it can be used as a proxy to identify hydrologically best-performing products, and (iii) these best-performing products are not necessarily the ones with the best physically realistic representation of precipitation over a given study area. Our findings highlight the need to investigate CC(P-product,Q) alongside the use of traditional evaluation metrics (e.g., KGE) to gain a more comprehensive evaluation of precipitation products in hydrological modeling. This study also emphasizes the importance of not overlooking the evaluation of precipitation products against ground precipitation observations, whenever such data are available, to avoid achieving high model performance at the cost of accurate precipitation representation.