On 10-11 September 2023, Storm Daniel, an exceptionally deep, early-season Mediterranean cyclone triggered an extreme flood in the city of Derna, Libya. The flood destroyed two dams that were built to protect the city, resulting in more than 5,000 casualties, making it one of the deadliest hydrometeorological disasters on record. Here, we aim to unravel (a) whether the catastrophic impact was inevitable, caused by an extremely rare storm, or was it foredoomed because of inadequate dam design, (b) how pivotal were the dam bursts in the tragic outcome, and (c) what insights this event provides for risk management and mitigation strategies elsewhere. Based on atmospheric reanalysis, satellite-based precipitation data, and rainfall-runoff modelling, we estimate the return period of both rainfall and flood to be only a few decades, meaning that the event was not as extreme as could have been implied by its disastrous consequences. Hydraulic simulations of the flood's stream power reveal that the damage caused by the collapse of the dams was approximately 20 times higher than would have been if dams were not built in the Wadi. Our methodological framework, which is based on globally available data, is applicable to risk assessment in other regions 1 as well. It underscores the importance of uncertainty-aware frequency analysis and suggests that distributed flood prevention and warning techniques should be favourable in mitigating flood risks.

Water volume estimates of shallow desert lakes are the basis for water balance calculations, important both for water resource management and paleohydrology/climatology. Water volumes are typically inferred from bathymetry mapping; however, being shallow, ephemeral and remote, bathymetric surveys are scarce in such lakes. We propose a new, remote-sensing based, method to derive the bathymetry of such lakes using the relation between water occurrence, during >30-yr of optical satellite data, and accurate elevation measurements from the new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). We demonstrate our method at three locations where we map bathymetries with ~0.3 m error. This method complements other remotely sensed, bathymetry-mapping methods as it can be applied to: (a) complex lake systems with sub-basins, (b) remote lakes with no in-situ records, and (c) flooded lakes. The proposed method can be easily implemented in other shallow lakes as it builds on publically accessible global data sets.