Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity

Abstract. Streamflow reanalysis is vital for water resources management and climate impact assessment; however, the extent to which it is affected by precipitation forcing errors remains poorly understood. Focusing on the reanalysis dataset of Global Flood Awareness System driven by the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (GloFAS-ERA5), this paper details how streamflow reanalysis errors respond to precipitation errors. Specifically, the root mean square errors (RMSEs) are calculated by hydrological year for reanalysis products across 671 catchments in the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset; and by combining catchment-specific linear regression with global panel regression, the effects of precipitation errors on streamflow errors are quantified. The results demonstrate an improved performance from GloFAS-ERA5 v2.1 to v4.0, with the median RMSE decreasing from 2.16 mm to 1.81 mm. For GloFAS-ERA5 v4.0, the panel regression indicates that for every 1 mm increase in precipitation RMSE, the corresponding streamflow RMSE increases by an average of 0.51 mm–reflecting the buffering capacity of catchment storage. In the meantime, the corresponding catchment-specific increase of streamflow RMSE reaches up to 2.5 mm in humid catchments but remains below 0.7 mm in arid catchments. These divergent responses reflect that the saturation-excess mechanism makes the precipitation errors immediately affect the streamflow error while soil moisture deficits dampen their effects. Furthermore, incorporating interaction terms into panel regression increases the coefficient of determination (R²) from 0.16 to 0.36, indicating that error responses are modulated by catchment heterogeneity. This modulation is further confirmed by targeted case studies, indicating that the temperature controls the storage and release of snow water, thereby dampening and delaying the responses of streamflow errors to precipitation errors in snow-dominated catchments. These findings provide a valuable diagnostic method and practical guidance for applications of global streamflow reanalysis to complex, heterogeneous catchments.