Floods pose significant risks to human lives, infrastructure, and the environment. Timely and accurate flood forecasting plays a pivotal role in mitigating these risks. This study proposes a Digital Twin proof-of-concept framework aimed at improving flood forecasting and validated its effectiveness through a pilot study of the 2021 flood event in Luxembourg. The baseline forecasting method combines GloFAS ensemble streamflow forecasts with a high-resolution flood hazard datacube generated using a LISFLOOD-FP hydrodynamic model and then averaging among the member forecasts. To dynamically update the flood forecasts and improve their accuracy, the framework integrates satellite-based Earth observations (EOs)—specifically Sentinel-1-derived flood probability maps from the Global Flood Monitoring service—via a particle filter-based data assimilation (DA) process. As such, the simulations with more coherence with the observed Sentinel-1-derived flood probability maps are prioritized. This results in a Digital Twin capable of delivering daily flood depth forecasts, at detailed spatial resolution, up to 30 days ahead, with reduced prediction uncertainty. Using the 2021 flood event, we evaluate the performance of the Digital Twin in assimilating EO data to refine hydraulic model simulations and issue accurate flood forecasts. Although certain challenges persist—particularly the difficulty in quantifying the error structure of GloFAS discharge forecasts—the proposed approach demonstrates clear improvements in forecast accuracy compared to open-loop simulations. As a result, the approach reduces water level prediction errors by an average of 15–33% and increases the Nash–Sutcliffe Efficiency of discharge predictions by approximately 15–36%. Future work will aim to refine the flood hazard datacube and advance the characterization and modeling of uncertainties associated with both GloFAS streamflow forecasts and Sentinel-1-derived flood maps, thereby further enhancing the system’s predictive capability.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio-temporal resolution (~70 m, 1-5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio-temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature retrievals, we generated ECOSTRESS ET products over Europe and Africa using three structurally contrasting models, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non-parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites over 6 different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ~70 W m ^-2 ). However, the relatively complex TSEB model produced a higher RMSE of ~90 W m ^-2 . Comparison between STIC ET estimate and the operational ECOSTRESS ET product from NASA PT-JPL model showed a difference in RMSE between the two ET products around 50 W m ^-2 . Substantial overestimation (>80 W m ^-2 ) was noted in PT-JPL ET estimates over shrublands and savannas presumably due to the weak constraint of LST in the model. Overall, the EEH is promising to serve as a support to the Land Surface Temperature Monitoring (LSTM) mission.

In order to analyze the descriptions of various active start-ups, we have developed a web application to retrieve and to analyze available textual data about them. The tool aims at extracting the frequent topics and applying semantic similarity analysis to the start-up descriptions.