Reservoir-Induced Seismicity (RIS) poses a significant geological risk for large-scale hydropower projects. However, its nationwide characteristics remain poorly understood in China, the country with the world's largest hydropower capacity. This study integrates a high-resolution national reservoir dataset (CRD) with a unified earthquake catalog since 1970 to establish a screening-eligible sample of 1435 large/medium-sized reservoirs (impounded between 1971 and 2015) in China. Based on spatiotemporal clustering criteria, we systematically identified 88 induced-type reservoirs – that is, reservoirs exhibiting spatiotemporally clustered seismicity significantly associated with impoundment. Analysis of this dataset reveals fundamental characteristics of RIS in China: Spatially, induced-type reservoirs are selectively concentrated in tectonically active regions like the eastern margin of the Tibetan Plateau, showing significant coupling with active faults and historical seismicity. In the near field, 95 % of RIS events occur within 24.8 km of the reservoir shoreline. Temporally, the time lag between impoundment and peak seismicity, together with the distance-dependent occurrence rate, follows patterns governed primarily by pore pressure diffusion, leading to the development of a W-score model for probabilistic RIS identification. In terms of sequence characteristics, RIS exhibits statistically distinct parameters of their frequency-magnitude distribution (e.g., lower α-value, higher b-value) compared to natural earthquakes, revealing a unique physical process of activating near-critical faults via pore pressure diffusion. The spatial evolution of the b-value further confirms an effective permeability boundary of pore water penetration at approximately 30–40 km, far exceeding the current Chinese national standard (10 km). Correlation analysis shows that static reservoir parameters (e.g., reservoir capacity, dam height, and reservoir major axis length) are statistically intercorrelated; thus, only one parameter should be selected for hazard analysis. Reservoir capacity greater than 3.2 × 108 m3 or reservoir major axis length exceeding 15.9 km could serve as quantitative thresholds for preliminary risk screening during the planning stage. However, correlation matrix analysis reveals only weak correlations between all static/dynamic parameters and the maximum magnitude (Mmax), highlighting the urgent need for physics-based mechanistic models. This study provides the first national-scale systematic characterization of RIS in China, a quantitative identification tool (W-score), and key engineering thresholds for RIS risk assessment in China, pointing towards future directions beyond traditional empirical models.
Flood early warning in the White Volta Basin of northern Ghana is complicated by unmonitored dam releases from Burkina Faso's Bagre Reservoir, which existing globally calibrated systems do not represent. We present an end-to-end AI-driven flood early warning system that integrates observed gauge discharge, CHIRPS satellite rainfall, ERA5-Land reanalysis forcing, and a novel JRC-derived Bagre reservoir storage proxy. An ensemble of Random Forest, XGBoost, and LSTM models trained on these inputs achieved Kling–Gupta Efficiency scores of 0.984, 0.974, and 0.957 at 1, 3, and 5 d lead times on an independent test period dominated by moderate flows (maximum 1206 m3 s−1, below the Orange alert threshold); higher-tier alert skill rests on leave-one-year-out cross-validation rather than held-out extreme events. Benchmarking against simple baselines shows the ensemble's advantage over a persistence forecast is marginal at 1 d lead but grows with lead time and concentrates on rising-limb and high-flow conditions, indicating that recent observed discharge carries most of the short-range skill while the ensemble's value lies at the longer horizons relevant to warning. For direct comparison, GloFAS v4 reanalysis discharge at Nawuni achieves KGE = −0.526 on the same test period, performing worse than a climatological mean predictor, primarily because LISFLOOD does not represent Bagre Dam release operations. A four-tier alert system calibrated to 30-year flood return periods achieved a cross-validated Red-tier probability of detection of 0.902 (false alarm ratio 0.134) at 1 d lead, declining to 0.762 at 5 d. Sentinel-1 SAR mapping provided spatial grounding for the discharge-based threshold framework, with Red-tier exceedance periods associated with floodplain inundation extents of 50 to 149 km2 across three validation seasons. The system integrates into Ghana's existing myDEWETRA-VOLTALARM platform without requiring new institutional infrastructure. The Bagre reservoir proxy, derived from freely available JRC Global Surface Water imagery and validated against independent satellite altimetry (r = 0.94), provides a measure of reservoir state. Because the reservoir fills to capacity in most wet seasons, this signal functions as an explicit model input and as seasonal situational awareness that the reservoir is capable of spilling, rather than as a standalone flood trigger.
The study presents a novel framework for integrating hydrological uncertainties and the related sensitivity of the hydrodynamic modelling results into flood hazard mapping. Uncertainties in flood frequency analysis (FFA), including anticipated impact of climate change and sensitivity to channel and floodplain roughness, were examined through selected scenario ensembles. The study area is the lower Vipava river, a transboundary catchment shared between Slovenia and Italy with variable floodplain topography. Sensitivity analysis revealed that uncertainty in FFA and river channel roughness significantly influenced the inundation spatial extent and inundation depth, the impact of floodplain roughness appears to be limited. The analysis shows that natural successional changes in river channel roughness substantially impact the results of a 10 year return period (RP) event, increasing flood extent by 45 %. The increase in inundated areas is less pronounced for 100 and 500 year RP floods, with increases of 15 % and 11 %, respectively. The weighted exceedance probability of inundation (WEPI) based on scenario ensembles provided an informative identification of areas most susceptible to potential changes in flood hazard. Our results highlight the value of targeted scenario ensembles that incorporate FFA uncertainty and hydraulic roughness sensitivity, thereby producing more informative flood hazard mapping that can complement deterministic mapping approaches and support more adaptive future land use planning.
In Germany, severe widespread winter floods are often generated by rain-on-snow (ROS) phenomena. Under suitable conditions, when rain falls on the snow cover, the snow can melt and produce extreme amounts of runoff. In a warming climate, the frequency of ROS events is expected to change locally depending on elevation and regionally based on the general climate conditions. Consequently, the characteristics of ROS-driven winter floods are also anticipated to change. To investigate these changes, streamflow for multiple gauge stations in Germany was simulated using an ensemble of Long Short-Term Memory models based on an ensemble of downscaled climate projections. Germany, as a representative mid-latitude country with a considerable portion of historical floods generated by ROS, offers extensive spatial and temporal coverage of hydrological observations spanning long temporal scales, and hence warranting efficient training of the deep learning model. We used an explainable artificial intelligence method, integrated gradients, to examine flood-generating processes, focusing primarily on ROS, for every simulated flood peak. Changes in frequency, feature importance, and a ranking based severity of ROS flood events were assessed for individual streamflow gauges and for widespread floods affecting multiple catchments across four major river basins in Germany. We found that with regard to the ensemble median, the frequency of ROS floods will mostly decrease at the scale of individual gauges by up to 60 %, as well as at the widespread scale for the Rhine, Elbe, and Weser River basins but increase at gauges located in the Danube River basin for the corresponding widespread floods. For all regions, the snowmelt component during ROS floods becomes less relevant, whereas the contribution of rainfall to these events increases. Interestingly, the severity of the mean and the most extreme widespread ROS floods is projected to increase compared to the historical period in all major river basins in Germany. Only for individual gauges the severity may decrease in the future. Despite the overall agreement in the trends of the input features across climate models, the resulting trends in ROS floods are considerably disparate. This discrepancy is primarily attributed to complex model chains in the climate models leading to variability in rainfall and snowmelt, and to different melting seasons.
The Warwan sub-basin in Jammu and Kashmir is a remote glaciated region containing several glacial lakes and has recently experienced population growth and infrastructure development. Due to inaccessibility and geomorphic masking, multiple mass movement events, including avalanches and glacial lake outburst floods (GLOFs), have remained largely unreported. This study analyzes three major avalanche events from the past two decades. The September 2005 and September 2020 avalanches originated from glaciers GL-B and GL-A within the same glacier complex, while the March 2020 rock–ice avalanche initiated from the headwall of glacier GL-F in an adjacent valley and terminated before reaching its ablation zone. Runout mapping shows that the September 2020 avalanche descended from the headwall of GL-A and impacted its proglacial lake, triggering a GLOF. In contrast, the September 2005 event terminated before reaching the glacial lake that began forming in 1999 at the terminus of GL-B. Geomorphic analysis indicates persistent sediment influx from meltwater streams of GL-D and GL-E into the lake associated with GL-A, progressively infilling the lake. Continued glacier retreat caused meltwater to accumulate behind the sediment infill, dividing the lake into two disconnected lakes. Impact from the September 2020 avalanche led to complete drainage of stored water and sediment, generating a downstream debris flow. Pre- and post-GLOF imagery reveal breaching and widening of the outflow channel and deposition of a debris fan downstream. The repeated history of mass movements and continued growth of glacial lakes raise serious hazard concerns in the Warwan sub-basin. GLOF modeling identifies Lake-B as a potentially hazardous lake in its present condition, posing significant downstream risk. Settlements such as Youdu and Qaderna, along with bridges, roads, and residential structures, lie within potential GLOF inundation zones. High sediment availability from past mobilization and deposition along the valley increases the potential of debris-flow cascades, posing elevated risks to downstream infrastructure and hydropower, underscoring the need for preparedness and mitigation planning. GLOF modeling suggests that an early warning system closer to the source will be able to provide a good lead time in case of potential GLOFs in the valley.
