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

Abstract

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).In recent years, SM estimates from remote sensing measurements have experienced growth in their availability and accuracy (Karthikeyan et al., 2017;McCabe et al., 2017). Multiple operational and research satellite SM data sets from active to passive microwave sensors are currently available (e.g., Mecklenburg et al., 2016;Wagner et al., 2013) with coarse-scale surface retrievals and approximately daily coverage. Data assimilation (DA) is a theoretically powerful way to integrate remotely sensed SM observations into hydrological models ; however, its actual benefit is still controversial regarding streamflow prediction, with several studies showing neither systematic nor significant improvements on streamflow prediction, due to a number of technical, mathematical and hydrological factors (e.g.

Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

“…Many land DA systems have used microwave observations to estimate surface and deeper soil moisture (de Rosnay et al, 2014;Reichle et al, 2019), and related variables such as discharge (Lievens et al, 2015;De Santis et al, 2021), turbulent fluxes (Lu et al, 2020), and even groundwater in peatlands (Bechtold et al, 2020). With the activation of dynamic vegetation models, the assimilation of optical vegetation indices (e.g., leaf area index) and microwave vegetation optical depth (Fairbairn et al, 2017;Kumar et al, 2020;Mucia et al, 2022) has gained interest, including to improve evapotranspiration (ET) and runoff.…”

Toward improved regional hydrological model performance using a novel soil data-informed calibration method

“…To improve simulation fidelity, a number of different techniques have been employed including data assimilation and manual or automated calibration. So far, data assimilation has been the primary technique to improve soil moisture simulations in hydrological models and it has shown promising results by incorporating remotely-sensed soil moisture data (Crow and Van den Berg 2010;De Santis et al 2021;Loizu et al 2018). It is also found that assimilating observational soil moisture can improve the accuracy of both soil moisture and streamflow predictions in various types of models (Aubert et al 2003;Lee et al 2011).…”

Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters

“…To improve simulation fidelity, a number of different techniques have been employed including data assimilation and manual or automated calibration. So far, data assimilation has shown promising results by incorporating remotely sensed soil moisture data (Crow & Van den Berg, 2010; De Santis et al., 2021; Loizu et al., 2018). It is also found that assimilating observational soil moisture can improve the accuracy of both soil moisture and streamflow predictions in various types of models (Aubert et al., 2003; Lee et al., 2011).…”