Water stock monitoring is a major issue for society on a local and global scale. Sentinel-1&2 satellites provide frequent acquisitions to track water surface dynamics, proxy variables to enable water surface volume monitoring. How do we combine such observations along time for each sensor? What advantages and disadvantages of single-date, monthly or time-windowed estimations? In this context, we analysed the impact of merging information through different types and lengths of time-windows. Satellite observations were processed separately on optical (Sentinel-2) and radar (Sentinel-1) water detectors at 10 m resolution. The analysis has been applied at two scales. First, validating with 26 large scenes (110 × 110 km) in different climatic zones in France, time-windows yielded an improvement on radar detection (F1-score improved from 0.72 to 0.8 for 30 days on average logic) while optical performances remained stable (F1-score 0.89). Second, validating reservoir area estimations with 29 instrumented reservoirs (20–1250 ha), time-windows presented in all cases an improvement on both optical and radar error for any window length (5–30 days). The mean relative absolute error in optical area detection improved from 16.9% on single measurements to 12.9% using 15 days time-windows, and from 22.15% to 15.1% in radar detection). Regarding reservoir filling rates, we identified an increased negative bias for both sensors when the reservoir is nearly full. This work helped to compare accuracies of separate optical and radar capabilities, where optical statistically outperforms radar at both local and large scale to the detriment of less frequent measurements. Furthermore, we propose a geomorphological indicator of reservoirs to predict the quality of radar area monitoring (R² = 0.58). In conclusion, we suggest the use of time-windows on operational water mapping or reservoir monitoring systems, using 10–20 days time-windows with average logic, providing more frequent and faster information to water managers in periods of crisis (e.g., water shortage) compared to monthly estimations.

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.

Flood simulation and forecast capability have been greatly improved thanks to advances in data assimilation (DA) strategies incorporating various types of observations; many are derived from Earth Observations from space. This paper focuses on the assimilation of 2D flood observations derived from Synthetic Aperture Radar (SAR) images acquired during a flood event with a dual state-parameter Ensemble Kalman Filter (EnKF). Resulting binary wet/dry maps are here expressed in terms of wet surface ratios (WSR) over a number of subdomains of the floodplain. This ratio is assimilated jointly with in-situ water-level observations to improve the flow dynamics within the floodplain. However, the non-Gaussianity of the observation errors associated with these SAR-derived measurements break a major hypothesis for the application of the EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of this paper lies in the treatment of the non-Gaussianity of the SAR-derived WSR observations with a Gaussian anamorphosis (GA) process. This DA strategy was validated and applied over the Garonne Marmandaise catchment (South-west of France) represented with a TELEMAC-2D hydrodynamic model, first in a twin experiment and then for a major flood event that occurred in January-February 2021. It was shown that assimilating SAR-derived WSR observations, in complement to the in-situ water-level observations significantly improves the representation of the flood dynamics. The GA process brings further improvement to the DA analysis, while has also been demonstrated to be a non-essential element. This study heralds a reliable solution for flood forecasting over poorly gauged catchments thanks to available remote-sensing datasets.

Knowledge of river bathymetry is crucial for accurately simulating river flows and floodplain inundation. However, field data are scarce, and the depth and shape of the river channels cannot be systematically observed via remote sensing. Therefore, an efficient methodology is necessary to define effective river bathymetry. This research reconstructs the bathymetry from existing global digital elevation models (DEMs) and water surface elevation observations with minimum human intervention. The methodology can be considered a 1D geometric inverse problem, and it can potentially be used in gauged or ungauged basins worldwide. Nine global DEMs and two sources of water surface elevation (in situ and remotely sensed) were analyzed across two study areas. Results highlighted the importance of preprocessing cross-sections to align with water surface elevations, significantly improving discharge estimates. Among the techniques tested, one that combines the slope-break concept with the principles of mass conservation consistently provided robust discharge estimates for the different DEMs, achieving good performance in both study areas. Copernicus and FABDEM emerged as the most reliable DEMs for accurately representing river geometry. Overall, the proposed methodology offers a scalable and efficient solution for cross-section reconstruction, supporting global hydraulic modeling in data-scarce regions.