Modeling Future Landslide Propensity in the Colombian Andes: A GAM-based Projection from GCM Multi-Model Extreme Rainfall Indices

Landslides represent a significant geohazard worldwide, whose frequency and impacts are being amplified by climate change materialized through more intense and extreme rainfall. Projecting climate-driven landslide risk in tropical mountains such as the Colombian Andes requires methodologies that integrate climate projections with geomorphological triggers, going beyond traditional static susceptibility maps toward dynamic process-based frameworks. This study presents a novel methodology to assess future landslide propensity, integrating statistically downscaled climate projections with climate-informed probabilistic landslide models. A performance-weighted multi-model ensemble was constructed from 20 global models from the CMIP6 project (GCMs), selected according to their ability to reproduce observed rainfall patterns and trends during a historical baseline period (1981–2014). This ensemble provided future monthly climate data (2024–2100) for three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). These data enabled the calibration of monthly generalized additive models (GAMs) for landslide probability, trained with more than 10,000 events and using 15 extreme rainfall indices as explanatory variables, along with slope gradient as topographic control. To improve interpretability and robustness, the model results, originally at the climate model grid scale, were aggregated into slope units, generating maps of relative landslide propensity in probabilistic terms, a more appropriate spatial representation for future risk assessment than point estimates.

Our analysis revealed strong seasonal control: landslide triggers shift from high-intensity rainfall during the main wet seasons (April-May, October-November) toward antecedent dryness metrics in transition months. Future projections indicate a marked intensification in landslide propensity, especially in the Central and Western mountain ranges. Projected increases in mean rainfall, from approximately 20% in the short term (2024–2040) to more than 50% toward the end of the century (2081–2100) under SSP5-8.5, were correlated with a notable expansion of areas classified with high landslide propensity. Critically, the methodological framework identified not only where, but also when, propensity is highest within the annual rainfall cycle. This work improves landslide risk assessment by providing continuous probabilistic forecasts over time (monthly), which are highly sensitive to climate variability. Our results provide practical, scenario-based information to identify critical time windows and geographical priorities that support adaptive land use planning and early warning systems in a region highly vulnerable to geological hazards. Future work in progress will aim to refine and expand this framework, considering the inclusion of additional predictors, such as soil moisture, temperature, and changes in land cover, in order to address the occurrence of the phenomenon under study in a more holistic manner.