Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall

• Published in: Advances in water resources 2014-12, Vol.74, p.44-53
• Main Authors: Massari, Christian, Brocca, Luca, Moramarco, Tommaso, Tramblay, Yves, Didon Lescot, Jean-Francois
• Format: Article
• Language: English
• Subjects: Computer simulation; Earth Sciences; Earth, ocean, space; Exact sciences and technology; Floods; Hydrology; Hydrology. Hydrogeology; Modelling; Rainfall; Runoff; Sciences of the Universe; Soil moisture
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2014.08.004

•We demonstrated the high information content retained from soil moisture.•We tested a new algorithm for rainfall estimation for hydrological applications.•We used soil moisture for improving hydrological modelling. Rainfall runoff (RR) models are fundamental tools for reducing flood hazards. Although several studies have highlighted the potential of soil moisture (SM) observations to improve flood modelling, much research has still to be done for fully exploiting the evident connection between SM and runoff. As a way of example, improving the quality of forcing data, i.e. rainfall observations, may have a great benefit in flood simulation. Such data are the main hydrological forcing of classical RR models but may suffer from poor quality and record interruption issues. This study explores the potential of using SM observations to improve rainfall observations and set a reliable initial wetness condition of the catchment for improving the capability in flood modelling. In particular, a RR model, which incorporates SM for its initialization, and an algorithm for rainfall estimation from SM observations are coupled using a simple integration method. The study carried out at the Valescure experimental catchment (France) demonstrates the high information content retained by SM for RR transformation, thus giving new possibilities for improving hydrological applications. Results show that an appropriate configuration of the two models allows obtaining improvement in flood simulation up to 15% in mean and 34% in median Nash Sutcliffe performances as well as a reduction of the median error in volume and on peak discharge of about 30% and 15%, respectively.