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.

The present study describes the assimilation of river water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of a one-dimensional hydraulic model which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was applied using a four-day sliding window over the flood event. The background error covariances for water level and discharge were represented with anisotropic correlation functions where the correlation length upstream of the observation points is larger than the correlation length downstream of the observation points. This approach was motivated by the implementation of a Kalman Filter algorithm on top of a diffusive flood wave propagation model. The study was carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state during the flood event leads to a significant improvement in the water level and discharge in both analysis and forecast modes.

The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981–2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.

Flood simulation and forecast capability have been greatly improved, thanks to the advances in data assimilation (DA). Such an approach combines in situ gauge measurements with numerical hydrodynamic models to correct the hydraulic states and reduce the uncertainties in model parameters. However, these methods depend strongly on the availability and quality of observations, thus necessitating other data sources to improve the flood simulation and forecast performances. Using Sentinel-1 images, a flood extent mapping method was carried out by applying a Random Forest algorithm trained on past flood events using manually delineated flood maps. The study area concerns a 50-km reach of the Garonne Marmandaise catchment. Two recent flood events are simulated in analysis and forecast modes, with a +24-h lead time. This study demonstrates the merits of using synthetic aperture radar (SAR)-derived flood extent maps to validate and improve the forecast results based on hydrodynamic numerical models with Telemac2D-ensemble Kalman filter (EnKF). Quantitative 1-D and 2-D metrics were computed to assess water-level time-series and flood extents between the simulations and observations. It was shown that the free run experiment without DA underestimates flooding. On the other hand, the validation of DA results with respect to independent SAR-derived flood extent allows to diagnose a model–observation bias that leads to over-flooding. Once this bias is taken into account, DA provides a sequential correction of area-based friction coefficients and inflow discharge, yielding a better flood extent representation. This study paves the way toward a reliable solution for flood forecasting over poorly gauged catchments, thanks to the available remote sensing datasets.

Flooding is one of the most devastating natural hazards to which our society worldwide must adapt, especially as its severity and occurrence tend to increase with climate changes. This research work focuses on the assimilation of two‐dimensional (2D) flood observations derived from remote‐sensing images acquired during overflowing events. To do so, the resulting binary wet/dry maps are expressed in terms of wet surface ratios (WSR) over a number of floodplain subdomains. This ratio is assimilated jointly with in‐situ water‐level gauge observations to improve the flow dynamics within the floodplain. An Ensemble Kalman Filter (EnKF) with a dual state‐parameter analysis approach is implemented on top of a TELEMAC‐2D hydrodynamic model. The EnKF control vector is composed of spatially‐distributed friction coefficients and a corrective parameter of the inflow discharge. It is extended with the hydraulic states within the floodplain subdomains. This data assimilation strategy was validated and evaluated over a reach of the Garonne river. The observation operator associated with the WSR observations, as well as the dual state‐parameter sequential correction, was first validated in the context of Observing System Simulation Experiments. It was then applied to two real flood events that occurred in 2019 and 2021. The merits of assimilating synthetic aperture radar‐derived WSR observations, in complement to the in‐situ water‐level observations, are shown in the parameter and observation spaces with assessment metrics computed over the entire flood events. It is also shown that the hydraulic state correction within the dual state‐parameter analysis approach significantly improves the flood dynamics, especially during the flood recess.