Machine Learning and Remote Sensing Projections for Peruvian Glaciers (2016–2100)

The Peruvian Andes contain over 70% of the world's tropical glaciers, which are vital for regional water security and are rapidly destabilising due to climate change. Current large-scale projections often lack the spatial resolution required for localised glacial melt modelling or rely on climate reanalysis products that are too coarse in rugged terrain. This study introduces a unified framework that combines high-resolution remote sensing (Sentinel-2, Landsat-8) with machine learning to characterise, monitor, and forecast glacial evolution across Peru from 2016 to 2100.

We propose a machine-learning modelling approach that addresses both the where and when of glacial retreat. We developed a spatial Random Forest classifier to generate country-wide melt vulnerability maps. Ensemble analysis of driving parameters reveals that "distance-to-edge" and topographic factors (elevation, slope) are significantly stronger predictors of melt spatiality than available coarse-resolution temperature and precipitation datasets. Our spatial model achieves a 74.9% overlap accuracy between simulated and observed melt (2016–2023), nearly doubling the performance of benchmark Multi-Criteria Decision Analysis methods (39.3%).

Complementing this spatial analysis, we developed a temporal, area-based melt model from annual inventories of over 2,000 individual glacier polygons. Using a Huber regression to fit negative power laws to ablation rates, we identified a clear acceleration in retreat for smaller ice bodies, consistent with albedo-ice feedback mechanisms. Between 2016 and 2023, we observed a relative area loss of 15 ± 4% (180 ± 70 km²).

Integrating these models to forecast future scenarios, we project that only ~30% (26–43%) of the 2020 glacial surface area will remain by 2100, with several cordilleras facing near-total extinction. This workflow establishes a new standard for observation-based, scalable glacial modelling, providing the high-resolution spatial and temporal insights necessary for effective water resource management and adaptation strategies in the tropical Andes.