Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in Over 700 Catchments Throughout Europe

Abstract

Abstract In this study, we perform a data assimilation (DA) experiment on a very large number (>700) of small‐ and medium‐scale (150–10,000 km 2 ) European catchments to assess the impact of satellite soil moisture (SM) DA on streamflow simulations for different climatic and hydrologic conditions. In the experiment, Climate Change Initiative SM active, passive and combined products are assimilated over a time period 2003–2016 via an Ensemble Kalman Filter (EnKF). The results show that, on average, the assimilation of the three products provides relatively small improvements as compared to analogous open loop (OL) results (i.e., with an increase on median Kling‐Gupta Efficiency equal to 0.0048, 0.0033, and 0.0022 [−] for the active, the passive, and the combined products, respectively). OL performance itself is found to be a significant driver of the assimilation results: greater improvements are observed in catchments with poor OL streamflow predictions and inaccurate precipitation estimates. The remotely sensed product accuracy also emerges as relevant for assimilation efficiency, while factors affecting SM retrievals such as vegetation density, topographic complexity and basin area are found to have only a limited impact on the spatial pattern of performance. Small and detrimental effects of SM assimilation are observed over fully humid catchments and at high latitudes where pre‐storm soil moisture has reduced control on runoff generation as well as in basins where the hydrological model contains structural limitations.