Global Soil Moisture Products for Flood Modeling in a Semi-Arid Area

Devastating floods in the Mediterranean region are caused by heavy rainfall. Flood forecasting systems are essential in Maghreb countries like Morocco to reduce the consequences and impacts of floods. Developing such a system for ungauged areas is challenging. Even though there is a shortage of observed data, remote sensing products offer a promising solution to fill these data gaps. Different soil moisture and precipitation products are evaluated against in situ data for flood modeling applications. Using an event-based hydrological model with an hourly time step, the results show that observed soil moisture is strongly related to the SMOS-IC satellite product and the ERA5 reanalysis. The comparison of soil moisture records allowed us to calculate the initial soil moisture state using the Soil Conservation Service Curve Number (SCS-CN). Daily in situ soil moisture data may not represent basin soil moisture conditions; however, ASCAT, SMOS-IC, and ERA5 products performed similarly in terms of validation for flood modeling. The daily time step may not accurately represent the saturation state just before a flood, as soil moisture in these semi-arid areas is depleted more quickly after rainfall. For the hourly time step, the initial soil moisture conditions of the SCS-CN model were found to be more accurately represented by ERA5 and in situ data. This work highlights the potential of remote sensing products to improve flood forecasting in semi-arid regions, providing valuable information for the development of robust hydrological models where traditional data are scarce.