Supraglacial Lake Fate Is Knowable Long Before the Season Ends

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.

ERA5 seasonal climate variables contain predictive information about future glacier retreat beyond what satellite imagery alone provides, yet existing deep learning methods focus on mapping current boundaries rather than forecasting future ones. This paper presents GlacierCastAI, which reframes glacier boundary prediction as a multi-modal spatiotemporal forecasting problem, fusing multi-temporal Landsat imagery with ERA5 reanalysis climate variables and Copernicus DEM terrain features to forecast glacier boundaries across five glaciers spanning four climate regimes. The architecture couples a ResNet50 spatial encoder with a ConvLSTM temporal model and a cross-attention climate fusion module. Because forecasting is inherently more uncertain than mapping current boundaries, the reported IoU values (0.320-0.337) are not directly comparable to state-of-the-art mapping models. Comparisons are against traditional baselines and experimental conditions. Through a pre-registered ablation study, adding ERA5 climate signals improves image-only IoU from 0.326 to 0.337 (+3.4%), suggesting that atmospheric forcing carries predictive information beyond imagery alone. All deep learning models substantially outperform persistence and linear trend baselines (IoU 0.160 and 0.169 respectively), with improvements of 89-99% relative IoU. A lightweight climate-only MLP baseline (661K parameters) achieves an IoU of 0.320 (98% of image-only performance) using 85x fewer parameters, suggesting that ERA5 variables encode substantial predictive signal independently of satellite imagery. SHAP attribution analysis suggests that spring solar radiation (MAM) is the dominant climate driver, consistent with the known role of spring insolation in setting melt season trajectories.

Internal ice layers imaged by radar provide key evidence of snow accumulation and ice dynamics, but radar-derived layer boundary observations are often incomplete, with discontinuous traces and sometimes entirely missing layers, due to limited resolution, sensor noise, and signal loss. Existing graph-based models for ice stratigraphy generally assume sufficiently complete layer profiles and focus on predicting deeper-layer thickness from reliably traced shallow layers. In this work, we address the layer-completion problem itself by synthesizing complete ice-layer thickness annotations from incomplete radar-derived layer traces by conditioning on colocated physical features synchronized from physical climate models. The proposed network combines geometric learning to aggregate within-layer spatial context with a transformer-based temporal module that propagates information across layers to encourage coherent stratigraphy and consistent thickness evolution. To learn from incomplete supervision, we optimize a mask-aware robust regression objective that evaluates errors only at observed thickness values and normalizes by the number of valid entries, enabling stable training under varying sparsity without imputation and steering completions toward physically plausible values. The model preserves observed thickness where available and infers only missing regions, recovering fragmented segments and even fully absent layers while remaining consistent with measured traces. As an additional benefit, the synthesized thickness stacks provide effective pretraining supervision for a downstream deep-layer predictor, improving fine-tuned accuracy over training from scratch on the same fully traced data.