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Volume 20, issue 7 | Copyright
Hydrol. Earth Syst. Sci., 20, 2827-2840, 2016
https://doi.org/10.5194/hess-20-2827-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 14 Jul 2016

Research article | 14 Jul 2016

Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin

Delphine J. Leroux1,2, Thierry Pellarin3,4, Théo Vischel3, Jean-Martial Cohard3, Tania Gascon3, François Gibon3, Arnaud Mialon2, Sylvie Galle3,5, Christophe Peugeot6, and Luc Seguis6 Delphine J. Leroux et al.
  • 1CNES, LTHE, Laboratoire d'Étude des Transferts en Hydrologie et Environnement, Grenoble, France
  • 2CNRS, CESBIO, Centre d'Etudes Spatiales de la Biosphère, Toulouse, France
  • 3University Grenoble Alpes, LTHE, Grenoble, France
  • 4CNRS, LTHE, Grenoble, France
  • 5IRD, LTHE, Grenoble, France
  • 6IRD, HydroSciences, Montpellier, France

Abstract. Precipitation forcing is usually the main source of uncertainty in hydrology. It is of crucial importance to use accurate forcing in order to obtain a good distribution of the water throughout the basin. For real-time applications, satellite observations allow quasi-real-time precipitation monitoring like the products PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, TRMM (Tropical Rainfall Measuring Mission) or CMORPH (CPC (Climate Prediction Center) MORPHing). However, especially in West Africa, these precipitation satellite products are highly inaccurate and the water amount can vary by a factor of 2. A post-adjusted version of these products exists but is available with a 2 to 3 month delay, which is not suitable for real-time hydrologic applications. The purpose of this work is to show the possible synergy between quasi-real-time satellite precipitation and soil moisture by assimilating the latter into a hydrological model. Soil Moisture Ocean Salinity (SMOS) soil moisture is assimilated into the Distributed Hydrology Soil Vegetation Model (DHSVM) model. By adjusting the soil water content, water table depth and streamflow simulations are much improved compared to real-time precipitation without assimilation: soil moisture bias is decreased even at deeper soil layers, correlation of the water table depth is improved from 0.09–0.70 to 0.82–0.87, and the Nash coefficients of the streamflow go from negative to positive. Overall, the statistics tend to get closer to those from the reanalyzed precipitation. Soil moisture assimilation represents a fair alternative to reanalyzed rainfall products, which can take several months before being available, which could lead to a better management of available water resources and extreme events.

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Water is one of the most valuable resources and has an undeniable influence on every aspect of life. Being a very good indicator of the water cycle, the soil water content can be monitored by satellites from space. The region studied here is located in Benin, West Africa, where people have to face major water-related risks every year during the monsoon season. By adjusting the model simulations with satellite observations, river discharge and water table levels have greatly been improved.
Water is one of the most valuable resources and has an undeniable influence on every aspect of...
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