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Global warming is exacerbating flood hazards, making the robustness of flood risk management a critical issue. Without considering future scenarios, flood risk analysis built only on historical knowledge may not adequately address the coming challenges posed by climate change. A comprehensive risk analysis framework based on both historical inundations and future projections to tackle uncertainty is still lacking. In this view, a scenario-based, data-driven risk analysis framework that for the first time integrates recent historical floods and future risk trends is here presented, consisting of flood inundation-prone and high-risk zones. Considering the Poyang Lake Eco-Economic Zone (PLEEZ) in China as the study area, we reproduced historical inundation scenarios of major flood events by using Sentinel-1 imagery from 2015 to 2021, and used them to build the risk baseline model. The results show that 11.7% of the PLEEZ is currently exposed to the high-risk zone. In the SSP2-RCP4.5 scenario, the risk would gradually decrease after peaking around 2040 (with a 19.3% increase in high-risk areas), while under the traditional fossil fuel-dominated development pathway (SSP5-RCP8.5), the risk peak would occur with a higher intensity about a decade earlier. The attribution analysis results reveal that the intensification of heavy rainfall is the dominant driver of future risk increase and that the exploitation of unused land such as wetlands induces a significant increase in risk. Finally, a hierarchical panel of recommended management measures was developed. We hope that our risk analysis framework inspires newfound risk awareness and provides the basis for more effective flood risk management in river basins.

Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area.