Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress. A companion feasibility study found that deformation indicates which lake is destabilizing and weather indicates when it is at risk, but proposed no predictive model. To address this gap, we propose and evaluate models that predict which site is susceptible and when a trigger arrives. We test three related hazards on free data alone: large moraine- and ice-dammed bursts, rainfall-triggered landslides, and smaller floods from ponds on and around a glacier. Each hazard gets two questions, never blended. Using 589 dated outbursts from HMAGLOFDB and several thousand catalogued landslides, we match each event against similar but unfailed sites, and hold every model to a strong simple baseline under spatial cross-validation that withholds whole map tiles, so no model succeeds by recognising a trained-on neighbourhood. Antecedent weather times the trigger at ROC 0.73 for big bursts, 0.83 for landslides, and 0.82 for small floods. Terrain ranks susceptibility only in part: scored naively it appears near 0.9, largely because catalogued failures cluster in wetter ranges; matched against comparable nearby sites the honest figures are 0.76, 0.71, and 0.54 (no better than chance). The burst signal holds within single regions, reaching 0.89 in Nepal alone. Five deep-learning models do not decisively beat a simple gradient-boosted baseline. Three score marginally higher on landslides, a hint too small to confirm. For the lake hazards the baseline wins outright, reproduced by a three-rule decision tree on ruggedness and monsoon rainfall. We close with a ranked Nepal watchlist, a prioritisation aid, not a prediction, and note where free data reaches its limits.
This study reviews the unusual lifecycle and reach of Cyclone Asna (August–September 2024) over the North Arabian Sea with special focus on its inland re-intensification after landfall, a behavior not typically seen in that region. Remote sensing satellite platforms combined with geographic information systems (GIS) allow a multi-parameter assessment of Asna's track, rain patterns, flood extents, and resulting damage across Pakistan's coastal belt and deeper inland areas. Sentinel's 2 NDWI images map surface water changes before and after the storm, while integrated IMERG rainfall estimates and time-stamped damage reports identify high-risk areas and illuminate the cyclone's social and economic costs. Karachi and adjacent coastal towns recorded isolated totals above 266 mm, sparking extensive urban floods, power outages, and collapse of key transport links, all driven by Asna's slow forward motion and prolonged onshore presence. Asna's inland intensification was caused by a rare overlap of monsoon moisture feeding in from both the Arabian Sea and Bay of Bengal, upper-level divergence, saturated soils, high surface heat flux, and persistent mid- to upper-level cyclonic vorticity factors that together sustained the storm's vertical structure far beyond normal landfall limits. Broadly speaking, both the warmer Arabian Sea and its greater moisture-holding capacity are driving a recent increase in hybrid cyclone systems across the region. This study confirms that trend and points to satellite monitoring as an invaluable tool for bettering disaster preparedness and adaptive planning. As the case of cyclone Asna shows, predictive models and urban resilience plans must now be revised to reflect unusual cyclone behavior strengthened by evolving climate dynamics.
Muzammil Bilal· Natural and Applied Sciences...· 0 citations
Glacial lakes in the steep, high-altitude Himalaya are both hazardous and ecologically vital. They can trigger sudden glacial lake outburst floods (GLOFs) that endanger downstream communities, while also storing crucial freshwater, supporting cold-adapted biodiversity, and serving as indicators of climate change. The in-situ lake bathymetry data are extremely scarce in the Himalaya. The Chandra-Bhaga valley in the Western Himalaya hosts several high-risk lakes that remain largely data-deficient. This limits the ability to model GLOF hazards, assess risks to downstream populations, and evaluate ecosystem vulnerability in this semi-arid, high-elevation landscape, which includes Ramsar-designated wetlands. Using an unmanned surface vehicle (USV), we acquired high-resolution bathymetry for three priority lakes: two very high-risk proglacial lakes (Gepang Gath and Kadu Nala) and the high-altitude Ramsar wetland Chandra Tal, enabling detailed mapping of lake-bed topography and robust estimation of water storage. Our findings show that commonly used empirical formulas for estimating glacial lake volume are substantially biased in this region, prompting more in-situ surveys and data sharing. This unique dataset can be valuable to glacio-hydrologists, geomorphologists, climate scientists, GLOF hazard modelers, conservation biologists, and disaster risk planners.
Wind is a fundamental driver of wildfire behavior, yet wind–fire relationships remain poorly characterized in the mountainous regions of South Asia, where ground-based observations are scarce. This study examines the wildfire in the Suleiman Range of western Pakistan for May 2022, integrating Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection, Landsat-derived burn severity, ECMWF Reanalysis v5 (ERA5) meteorological data, and Shuttle Radar Topography Mission (SRTM) topography data. Twenty-nine wildfire-classified detections (Fire Radiative Power, FRP range 6.0–52.1 megawatts (MW)) were analyzed across the Sherani, Musakhel, and Dera Ismail Khan (D.I. Khan) districts between 18 and 29 May 2022. The ERA5 wind speed at the fire points was moderately positively correlated with the FRP, although strong collinearity with temperature prevented the separation of the effects of wind and temperature. The wind direction was highly consistent throughout the event. Spread events were defined as consecutive detection pairs; among pairs separated by more than 2 km, four were aligned with the ERA5 downwind direction. These findings are consistent with synoptic winds broadly contributing to eastward fire progression, whereas local-scale spread was likely modulated by the terrain-channeled winds that ERA5 cannot resolve at its ~27 km grid scale. Elevation was strongly negatively correlated with the FRP. The burn severity analysis indicated that approximately 86 km2 of burn occurred, predominantly at low-to-moderate severity. This integrated workflow offers a transferable template for characterizing wildfire behavior in data-sparse mountainous regions.
Rainfall-induced landslides are among the main geodynamic processes reshaping the Earth’s surface and causing human and material losses worldwide. In Chile, their spatial analysis remains constrained by the scarcity of systematic inventories, particularly in the Andean Cordillera. In this context, this study presents a novel inventory of rainfall-induced landslides in the Biobío Region during 2023 and 2024, based on the analysis of changes in SAR backscatter from Sentinel-1 imagery processed in Google Earth Engine (GEE), complemented by visual interpretation of Sentinel-2 optical images, GIS-based mapping, and selective field validation. Four intense rainfall events were identified; however, only two of them (June and August 2023) triggered detectable landslides, totaling 55 processes dominated by debris flows and debris avalanches. In contrast, no landslides were detected in association with the 2024 event, which was characterized by lower rainfall intensity in the study area. The strong contrast in landslide occurrence between events provides clear evidence of differences in rainfall magnitude and impact, highlighting the role of rainfall intensity and temporal structure in controlling slope instability. In this sense, landslide occurrence can be interpreted as a direct geomorphic response to rainfall forcing, reflecting the effectiveness of precipitation in triggering slope failure. The results demonstrate the high capability of a multitemporal SAR-based approach to detect rainfall-induced landslides in mountainous environments characterized by dense vegetation cover and complex climatic conditions, validating its applicability in Andean territories. The resulting inventory provides a fundamental baseline for calibrating precipitation thresholds, improving susceptibility, hazard, and risk models, and strengthening monitoring systems under scenarios of intensifying hydroclimatic extremes associated with climate change. It also offers a valuable dataset for the development and training of machine learning models aimed at automated landslide detection and regional-scale hazard assessment.
Luis Alberto Gajardo Pino, Edilia Jaque-Castillo, M. Lillo‐Saavedra· Progress in Physical Geograp...· 0 citations
This study analyzes the cloudburst-induced flood in Buner, Pakistan, in August 2025 through remote sensing and GIS-based multi-parametric criteria. The aim of this work is to define flood-prone areas by investigating the topography and validating the satellite-derived flood hazard areas for spatial modeling. Moreover, the CHIRPS daily precipitation, AW3D30 DEM, and Normalized Difference Water Index (NDWI) are studied from satellite images for the pre-and post-event. The rainfall analysis estimated the precipitation to be high, with a value of 66 mm/day on 15 August 2025, to induce a cloudburst event. The terrain survey revealed that the flood affected the low-elevation (357 to 900 m elevation) region with a slope of less than 6° to incur surface runoff. According to the weighted overlay model, three paramount parameters have been added (rainfall, 50%, slope 30%, and elevation 20%) in the Flood Hazard Index to subdivide the region of Buner into four zones of the hazard, i.e., Low, Moderate, High, and Very High. The results of testing the actual magnitude of the flood indicated that there was a good spatial compatibility of 88% of the flooding area, which was enclosed by the Very High Hazard area, thus providing evidence of the model. This study demonstrated the efficiency of the integration of remote sensing indices, rainfall data, and topographic parameters into a GIS environment to perform a rapid evaluation of the risks of floods.
Abdullah Khan· Natural and Applied Sciences...· 0 citations
