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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #381202

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Assimilation of satellite soil moisture products for river flow prediction: An extensive experiment in over 700 catchments throughout Europe

item DE SANTIS, D. - University Of Calabria
item BLONDI, D. - University Of Calabria
item Crow, Wade
item CAMICI, S. - National Research Council - Italy
item MONDANESI, S. - National Research Council - Italy
item BROCCA, L. - National Research Council - Italy
item MASSARI, C. - National Research Council - Italy

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2021
Publication Date: 5/5/2021
Citation: De Santis, D., Blondi, D., Crow, W.T., Camici, S., Mondanesi, S., Brocca, L., Massari, C. 2021. Assimilation of satellite soil moisture products for river flow prediction: An extensive experiment in over 700 catchments throughout Europe. Water Resources Research. 57.e2021WR029643.

Interpretive Summary: Estimating pre-storm levels of soil moisture in agricultural basins is a critical component of forecasting streamflow and generating reliable flood predictions. New advances in our ability to directly measure soil moisture from satellite-based sensors can potentially be leveraged to improve such predictions. However, past efforts to incorporate satellite-based soil moisture estimates to enhance flood prediction have yielded inconsistent results. That is, improvement is shown in some agricultural basins but not in others. In order to better understand this variability, this study examines a very large number of European basins and measures the value of remotely sensed soil moisture retrievals in each. In this way, we isolate specific characteristics (with regards to, e.g., climate, land cover, topography and soil) that make hydrological forecasts within a particular basin more (or less) sensitive to improvements in the quality of pre-storm soil moisture estimates. These results will eventually be used by hydrological forecasters to better leverage on-going improvements in soil moisture remote sensing and improve the predictability of floods.

Technical Abstract: In this study, we perform a data assimilation (DA) experiment on a very large number (> 700) of small- and medium-scale (150 to 10000 km2) 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 (CCI) 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 provide relatively small improvements as compared to analogous open loop (OL) results (i.e., with an increase on median KGE 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 performance patterns. 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 shows structural limitations