Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods

2026-08-12

AI Summary

This study addresses two critical questions in glacial lake outburst floods, landslides, and glacier-related flooding in the Himalayas: where hazards are prone to occur and when they are triggered. For the first time, it decouples deformation and meteorological signals into distinct spatial susceptibility and temporal triggering prediction tasks, leveraging only freely available satellite data (e.g., InSAR and weather observations) and topographic features to build a self-contained predictive model that does not rely on neighboring-region information. Rigorous spatiotemporal cross-validation mitigates geographic overfitting, revealing that simpler models—such as gradient-boosted trees—outperform complex deep learning approaches. Meteorological indicators achieve AUC scores of 0.73–0.83 for trigger timing prediction, whereas terrain-based features show limited discriminative power for susceptibility (AUC 0.54–0.76). These findings underpin a prioritized hazard risk inventory for Nepal.

Abstract

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.

Problem

• glacial lake outburst
• landslide prediction
• ice flood
• satellite remote sensing
• hazard susceptibility

Innovation

• free satellite data
• glacial lake outburst flood
• spatiotemporal cross-validation
• hazard susceptibility and timing
• interpretable machine learning