The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service (CEMS) addresses the challenges and impacts that are caused by flooding. The GFM system provides global, near-real time flood extent masks for each newly acquired Sentinel-1 Interferometric Wide Swath Synthetic Aperture Radar (SAR) image, as well as flood information from the whole Sentinel-1 archive from 2015 on. The GFM flood extent is an ensemble product based on a combination of three independently developed flood mapping algorithms that individually derive the flood information from Sentinel-1 data. Each flood algorithm also provides classification uncertainty information that is aggregated into the GFM ensemble likelihood product as the mean of the individual classification likelihoods. As the flood detection algorithms derive uncertainty information with different methods, the value range of the three input likelihoods must be harmonized to a range from low [0] to high [100] flood likelihood. The ensemble likelihood is evaluated on two test sites in Myanmar and Somalia, showcasing the performance during an actual flood event and an area with challenging conditions for SAR-based flood detection. The Myanmar use case demonstrates the robustness if flood detections in the ensemble step disagree and how that information is communicated to the end-user. The Somalia use case demonstrates a setting where misclassifications are likely, how the ensemble process mitigates false detections and how the flood likelihoods can be interpreted to use such results with adequate caution.

Data Assimilation (DA) is a powerful tool to optimally combine uncertain model simulations and observations. Among DA techniques, the Particle Filter (PF) has gained attention for its capacity to deal with non-linear systems and for its relaxation of the Gaussian assumption. However, the PF may suffer from degeneracy and sample impoverishment. In this study, we propose an innovative approach, based on a Tempered Particle Filter (TPF), aiming at mitigating PFs issues, thus extending over time the assimilation benefits. Flood probabilistic maps derived from Synthetic Aperture Radar data are assimilated into a flood forecasting model through an iterative process including a particle mutation in order to keep diversity within the ensemble. Results show an improvement of the model forecasts accuracy, with respect to the Open Loop (OL): on average the RMSE of water levels decrease by 80% at the assimilation time and by 60% two days after the assimilation. A comparison with the Sequential Importance Sampling (SIS), is carried out showing that although SIS performances are generally comparable to the TPF ones at the assimilation time, they tend to decrease more quickly. For instance, on average TPF-based RMSE are by 20% lower compared to the SIS-based ones two days after the assimilation. The application of the TPF determines higher CSI values compared to the SIS. On average the increase in performances lasts for almost 3 days after the assimilation. Our study provides evidence that the application of the variant of the TPF enables more persistent benefits compared to the SIS.

Climate change will likely lead to more regular and more severe drought events in the near future, with large impacts on agriculture, especially during long-lasting precipitation deficits or heat waves. This study focuses on agricultural droughts, which are generally defined as soil moisture deficits so severe, that vegetation is negatively impacted. However, during short soil moisture drought events, vegetation is not always negatively affected, and sometimes even thrives under increased solar input. Because of this duality in agricultural drought impacts, the use of the term agricultural droughts is a potential issue. Here we show that, in major European droughts over the past two decades, clear asynchronies did occur between soil moisture and vegetation anomalies. A wrong use of the term agricultural droughts could lead to misclassification of drought events and false drought alarms, and for that reason, a distinction is necessary between soil moisture and vegetation droughts.