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 m × 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 which are typically employed in Land Surface Models (LSM) for global scale applications. The method consists in applying a data assimilation approach, the Extended Kalman Filter (EKF) algorithm, to correct the Manning roughness coefficients of the ISBA-TRIP Continental Hydrologic System. Indeed, parameters such as the Manning coefficient, used within such models to describe water basin characteristics, are generally derived from geomorphological relationships, which might have locally significant errors. The current study focuses on the Niger basin, a trans-boundary river, which is the main source of fresh water for all the riparian countries. In addition, geopolitical issues in this region can restrict the exchange of hydrological data, so that SWOT should help improve this situation by making hydrological data freely available. In a previous study, the model was first evaluated against in-situ and satellite derived data sets within the framework of the international African Monsoon Multi-disciplinary Analysis (AMMA) project. 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 algorithm over an 18 month period leads 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 despite potential problems related to equifinality. Discharge is also improved by the assimilation, but to a lesser extent than for the water levels (7%). Moreover, the method allows a better prediction 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 in several continental reservoirs, especially the groundwater and the aquifer reservoirs. Results obtained in this preliminary study demonstrate SWOT potential for global hydrologic modeling, especially to improve model parameters.