Africa is severely affected by floods, with an increasing vulnerability to these events in the most recent decades. Our improved preparation against and response to this hazard would benefit from an enhanced understanding of the physical processes at play. Here, a database of 399 African stream gauges is used to analyze the seasonality of observed annual maximum flood, precipitation and soil moisture between 1981 and 2018. The database includes a total of 11,302 flood events, covering most African regions. The analysis is based on directional statistics to compare the annual maximum river flood with annual maximum rainfall and soil moisture. The results show that the annual maximum flood in most areas is more strongly linked to the annual peak of soil moisture than of annual maximum precipitation. In addition, the interannual variability of flood magnitudes is better explained by the variability of annual maximum soil moisture than by the variability in the annual maximum precipitation. These results have important implications for flood forecasting and the analysis of the long‐term evolution of these hydrological hazards in relation with their drivers.

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A new precipitation dataset is provided since 2014 by the Global Precipitation Measurement (GPM) satellite constellation measurements combined in the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm. This recent GPM-IMERG dataset provides potentially useful precipitation data for regions with a low density of rain gauges. The main objective of this study is to evaluate the accuracy of the near real-time product (IMERG-E) compared to observed rainfall and its suitability for hydrological modeling over a mountainous watershed in Morocco, the Ghdat located upstream the city of Marrakech. Several statistical indices have been computed and a hydrological model has been driven with IMERG-E rainfall to estimate its suitability to simulate floods during the period from 2011 to 2018. The following results were obtained: (1) Compared to the rain gauge data, satellite precipitation data overestimates rainfall amounts with a relative bias of +35.61% (2) In terms of the precipitation detection capability, the IMERG-E performs better at reproducing the different precipitation statistics at the catchment scale, rather than at the pixel scale (3) The flood events can be simulated with the hydrological model using both the observed and the IMERG-E satellite precipitation data with a Nash–Sutcliffe efficiency coefficient of 0.58 and 0.71, respectively. The results of this study indicate that the GPM-IMERG-E precipitation estimates can be used for flood modeling in semi-arid regions such as Morocco and provide a valuable alternative to ground-based precipitation measurements.

Abstract. The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking.

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