Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

Abstract

Many satellite soil moisture products are today globally available in near real-time. These observations are of paramount importance for enhancing the understanding of the hydrological cycle and particularly useful for flood forecasting purposes. In recent decades, several studies assimilated satellite soil moisture observations into rainfall-runoff models to improve their flood forecasting skills. The rationale is that a better representation of the catchment states leads to a better stream flow estimation. By exploiting the strong physical connection between the soil moisture dynamic and rainfall, some recent studies demonstrated that satellite soil moisture observations can be also used for enhancing the quality of rainfall observations. Given that the quality of the rainfall is one of the main drivers of the hydrological model uncertainty, this begs the question-to what extent updating soil moisture states leads to better flood forecasting skills than correcting rainfall forcing? In this study, we try to answer this question by using rainfall-runoff observations from 10 catchments throughout the Mediterranean area and a continuous rainfall-runoff model-MISDc-forced with reanalysis-and satellite-based rainfall observations. Satellite soil moisture retrievals from the Advanced SCATterometer (ASCAT) are either assimilated into MISDc model via the Ensemble Kalman filter to update model states or, alternatively, used to correct rainfall observations derived from a reanalysis and a satellite-based product through the integration with soil moisture-based rainfall estimates. 4-9 years (depending on the catchment) of stream flow observations are organized into calibration and validation periods to test the two different schemes. Results show that the rainfall correction is favourable if the target is the predictions of high flows while for low flows there is a small advantage of the state correction scheme with respect to the rainfall correction. The improvements for high flows are particularly large when the quality of the rainfall is relatively poor with important implications for large-scale flood forecasting in the Mediterranean area.

A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products

“…Other techniques that exploited SM observations relied upon data assimilation approaches based on sequential filtering techniques, like Kalman filter-based methods (SMART, Crow et al (2011)) and particle filters (Pellarin et al, 2013;Zhan et al, 2015;Román-Cascón et al, 2017). All of them demonstrated a real benefit for flood forecasting applications (Alvarez-Garreton et al, 2016;Chen et al, 2014;Massari et al, 2018).…”

An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

“…However, SPPs observations are subjected to uncertainties resulting from retrieval algorithms. To reduce these uncertainties and to obtain high-quality precipitation estimates, researchers have focused on merging individual SPPs [16,22,26,70].…”

ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

“…A large number of studies have been implemented to assimilate the RS SM in the land surface model for the purpose of obtaining a more accurate and reliable profile SM data set on a regional or global scale [20][21][22][23][24][25][26]. Nevertheless, the assimilation of coarse-scale RS SM in the hydrological model targeted at improving the rainfall-runoff process is implemented in relatively few studies [10,[27][28][29][30][31]. Currently, there is still no consensus on the improvement of streamflow modeling through satellite soil moisture assimilation [4,7].…”