Assimilation of satellite data to optimize large-scale hydrological model parameters: a case study for the SWOT mission
Abstract. During the last few decades, satellite measurements have been widely used to study the continental water cycle, especially in regions where in situ measurements are not readily available. The future Surface Water and Ocean Topography (SWOT) satellite mission will deliver maps of water surface elevation (WSE) with an unprecedented resolution and provide observation of rivers wider than 100 m and water surface areas greater than approximately 250 x 250 m over continental surfaces between 78° S and 78° N. This study aims to investigate the potential of SWOT data for parameter optimization for large-scale river routing models. The method consists in applying a data assimilation approach, the extended Kalman filter (EKF) algorithm, to correct the Manning roughness coefficients of the ISBA (Interactions between Soil, Biosphere, and Atmosphere)-TRIP (Total Runoff Integrating Pathways) continental hydrologic system. Parameters such as the Manning coefficient, used within such models to describe water basin characteristics, are generally derived from geomorphological relationships, which leads to significant errors at reach and large scales. The current study focuses on the Niger Basin, a transboundary river. Since the SWOT observations are not available yet and also to assess the proposed assimilation method, the study is carried out under the framework of an observing system simulation experiment (OSSE). It is assumed that modeling errors are only due to uncertainties in the Manning coefficient. The true Manning coefficients are then supposed to be known and are used to generate synthetic SWOT observations over the period 2002–2003. The impact of the assimilation system on the Niger Basin hydrological cycle is then quantified. The optimization of the Manning coefficient using the EKF (extended Kalman filter) algorithm over an 18-month period led to a significant improvement of the river water levels. The relative bias of the water level is globally improved (a 30% reduction). The relative bias of the Manning coefficient is also reduced (40% reduction) and it converges towards an optimal value. Discharge is also improved by the assimilation, but to a lesser extent than for the water levels (7%). Moreover, the method allows for a better simulation of the occurrence and intensity of flood events in the inner delta and shows skill in simulating the maxima and minima of water storage anomalies, especially in the groundwater and the aquifer reservoirs. The application of the assimilation method in the framework of an observing system simulation experiment allows evaluating the skill of the EKF algorithm to improve hydrological model parameters and to demonstrate SWOT's promising potential for global hydrology issues. However, further studies (e.g., considering multiple error sources and the difference between synthetic and real observations) are needed to achieve the evaluation of the method.