Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets

Data assimilation (DA) techniques that integrate remotely sensed observations and in-situ measurements into hydrological models have been extensively implemented, but few studies have applied multisource actual evapotranspiration (ETa) products to evaluate its impacts on the DA performances through a large number of basins with different catchment conditions. This study performs a DA experiment to assess the influence of remotely sensed ETa products on streamflow simulations over 149 Catchment Attributes and Meteorology for Large-sample Studies basins across the contiguous United States, and the relationships between the DA results and multiple catchment characteristics are further analyzed. In this experiment, three satellite ETa products from the GLEAM, REA-ET, and GLDAS are assimilated into a conceptual rainfall-runoff model (i.e., HYMOD) using the ensemble Kalman filter. The results show that assimilating the three ETa products provides relatively small improvements compared to the open loop (OL) scenario in approximately 48%-58% of the study basins, and around 14%-19% of these basins exhibit an increase in Nash-Sutcliffe Efficiency over 0.01. Specifically, the GLEAM ETa shows the greatest improvements in runoff, followed by the GLDAS and REA-ET products. The OL performance plays a significant role in runoff improvements, greater improvements are achieved for basins with poor OL runoff simulations. The assimilation efficiency is revealed to be relevant to the accuracy of the satellite ETa products. While the catchment conditions such as the aridity index, mean slope, the fraction of forest, and catchment area are found to have a limited effect on the spatial pattern of ET assimilation performances.