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

D De Santis,W T Crow,S Modanesi,C Massari,L Brocca,S Camici,D Biondi

doi:10.1029/2021wr029643

Abstract

AbstractIn this study, we perform a data assimilation (DA) experiment on a very large number (>700) of small‐ and medium‐scale (150–10,000 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 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.

Highlights

Soil moisture (SM) pre-storm conditions are a key factor in runoff production and can explain much of the observed hydrological response of a basin (e.g., Berthet et al, 2009; Penna et al, 2011)
We expand upon the geographic coverage of past studies that were mainly confined within national boundaries (e.g., Draper et al, 2011; Fairbairn et al, 2017; Ridler et al, 2014); the set of basins, we explore is representative of a wide range of climates, as well as vegetation and orography conditions for which the generalization of research findings and conclusions in different contexts is not obvious and cannot be automatically extended
A negative assimilation impact is found for low-flow conditions, with a worsening of the KGEinv index in more than half of the study catchments

Summary

Introduction

Soil moisture (SM) pre-storm conditions are a key factor in runoff production and can explain much of the observed hydrological response of a basin (e.g., Berthet et al, 2009; Penna et al, 2011). For this reason, the integration of SM observations into hydrological models are considered a valuable practice to improve streamflow predictions (e.g., Coustau et al, 2012; Tramblay et al, 2010). It appears to be a logical step to update modeled SM via DA—as a more accurate knowledge of the state of the system should lead to a better streamflow forecast—there are many underlying factors responsible for the relative success or the failure of the assimilation task, such as: The actual connection between SM and runoff and its model representation, the accuracy of assimilated remote sensing retrievals, and the procedural choices within the DA system configuration—including the fulfillment of DA method theoretical assumptions

Methods

Results

Conclusion