This paper addresses the challenges of an early flood warning caused by complex convective systems (CSs), by using Low-Earth Orbit and Geostationary satellite data. We focus on a sequence of extreme events that took place in central Vietnam during October 2020, with a specific emphasis on the events leading up to the floods. In this critical phase, several hydrometeorological indicators could be identified thanks to Earth Observation satellites, which enable the characterization and monitoring of a CS, in terms of low-temperature clouds and heavy rainfall. Himawari-8 (H8) images, both individually and in time-series, allow identifying and tracking convective clouds. This is complemented by the observation of heavy/violent rainfall through GPM IMERG data, and the detection of strong winds using radiometers/scatterometers. Collectively, these datasets, along with the estimated intensity and duration of the event from each source, form a comprehensive dataset detailing the intricate behaviors of CSs. All of these factors are significant contributors to the magnitude of flooding and the short-term dynamics anticipated in the studied region.

In spite of astonishing advances and developments in remote sensing technologies, meeting the spatio-temporal requirements for flood hydrodynamic modeling remains a great challenge for Earth Observation. The assimilation of multi-source remote sensing data in 2D hydrodynamic models participates to overcome such a challenge. The recently launched Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite provides a global coverage of water surface elevation at a high resolution. SWOT provides complementary observation to radar and optical images, increasing the opportunity to observe and monitor flood events. This research work focuses on the assimilation of 2D flood extent maps derived from Sentinel-1 C-SAR imagery data, and water surface elevation from SWOT as well as in-situ water level measurements. An Ensemble Kalman Filter (EnKF) with a joint state-parameter analysis is implemented on top of a 2D hydrodynamic TELEMAC-2D model to account for errors in roughness, input forcing and water depth in floodplain subdomains. The proposed strategy is carried out in an Observing System Simulation Experiment based on the 2021 flood event over the Garonne Marmandaise catchment. This work makes the most of the large volume of heterogeneous data from space for flood prediction in hindcast mode paves the way for nowcasting.

As a result of climate change, extreme hydrometeorological events are becoming increasingly frequent. Over the past 20 years, more than 2 billion people have been exposed to consequences of fluvial floods. Flood detection, rapid mapping and risk assessment products play an important role in flood emergency response and management. Within this context, FloodDAM-Digital Twin is a pre-operational prototype which provides an automated service to reliably detect, monitor, assess and predict floods at local and global scale within digital twin Franco-American collaboration. At the end of the project, a proof of concept demonstration will be realized over French and USA selected catchments. This prototype could be commercialized for both public and private entities in the field of water management and risk prevention. The work presented in this paper relies on scientific improvements for each product and services as well as on the digital Twin architecture that allows interoperability with other systems.

Climate change is increasing the frequency and severity of hydrological extremes in many parts of the world. In Europe as well as in Luxembourg, droughts have worsened in intensity and duration in recent years, causing significant loss to several sectors, such as agriculture and forestry. There is a pressing need for developing and setting up advanced drought monitoring and prediction systems. In this context, this research work aims to improve drought prediction accuracy by jointly assimilating, into a hydrological model, various EO-based datasets, namely soil moisture (SM) and total water storage (TWS) derived from S-1 and GRACE & GRACE-FO satellite missions respectively. The assimilation of satellite-observed water content enables an integrated assessment and modeling of water resources through the monitoring and modeling of SM and groundwater availability in Luxembourg and the Greater Region, between 2012 and 2022.

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 article focuses on the assimilation of 2-D 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 (WSRs) 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 breaks a major hypothesis for the application of the EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of this article 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 (southwest 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 and 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 also demonstrating to be a nonessential element. This study heralds a reliable solution for flood forecasting over poorly gauged catchments thanks to available remote sensing datasets.

The challenges in operational flood forecasting lie in producing reliable forecasts given constrained computational resources and within processing times that are compatible with near-real-time forecasting. Flood hydrodynamic models exploit observed data from gauge networks, e.g. water surface elevation (WSE) and/or discharge that describe the forcing time-series at the upstream and lateral boundary conditions of the model. A chained hydrologic-hydraulic model is thus interesting to allow extended lead time forecasts and overcome the limits of forecast when using only observed gauge measurements. This research work focuses on comprehensively reducing the uncertainties in the model parameters, hydraulic state and especially the forcing data in order to improve the overall flood reanalysis and forecast performance. It aims at assimilating two main complementary EO data sources, namely in-situ WSE and SAR-derived flood extent observations.

Owing to advances in data assimilation, notably Ensemble Kalman Filter (EnKF), flood simulation and forecast capabilities have greatly improved in recent years. The motivation of the research work is to reduce comprehensively the uncertainties in the model parameters, forcing and hydraulic state, and consequently improve the overall flood reanalysis and forecast capability, especially in the floodplain. It aims at assimilating SAR-derived (typically from Sentinel-1 mission) flood extent observations, expressed in terms of wet surface ratio. The non-Gaussianity of the observation errors associated with the SAR flood observations violates a major hypothesis regarding the EnKF and jeopardizes the optimality of the filter analysis. Therefore, a special treatment of such non-Gaussianity with a Gaussian anamorphosis process is thus proposed. This strategy was validated and applied over the Garonne Marmandaise catchment (South-west of France) represented with the TELEMAC-2D hydrodynamic model, focusing on a major flood event that occurred in December 2019. The assimilation of the SAR-derived wet surface ratio observations, in complement to the in-situ water surface elevations, is illustrated to consequentially improve the flood representation.

Floods are the most common natural disasters all over the world and they are increasing in frequency and intensity due to climate changes. The Space for Climate Observatory (SCO)-FloodDAM-DT project with a joint collaboration effort between CNES, NASA’s partners and JPL is devoted to developing a federated Earth System Digital Twin (ESDT) for water-cycle applications focused on flood events. In particular, SCO-FloodDAM-DT project aims to provide an automated pre-operational service to reliably detect, monitor and assess floods at global scale within digital twin collaboration with NASA/JPL. The main objective is to connect data and existing models from both agencies in order to combine multi-scale simulations taking into account multiple phases of an entire flood event, from early alerts to post-event impact assessments. At the end, a proof-of-concept demonstration, planned after 18 months, will be presented with its multi-scale aspect over French and USA selected catchments.

Flood simulation and forecast capability have been greatly improved, thanks to the advances in data assimilation (DA). Such an approach combinesin situgauge 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.

Floods are the most common natural disasters all over the world. The space climate observatory (SCO)-FloodDAM project aims at utilizing the capabilities of new observing strategies in order to better alert, detect and map flood events globally. Leveraging from both aerial- and satellite-based platforms (Sentinel, TerraSar-X, SWOT) as well as in-situ based sensors, the main objective of the project is to develop an automatic system to better prevent flood events and assess their consequences. In this paper, we will demonstrate the strategy deployed for the selected test-sites in France.

The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration, the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion.