A dynamic landslide model for early warnings in Colombia's roads

Landslides pose a critical threat to Colombia’s Andean region, where steep topography and intense rainfall events frequently disrupt road infrastructure. Although data-driven models are widely used for landslide susceptibility, they often focus on static conditioning factors without fully capturing the temporal dimension essential for early warning. Integrating space and time into a single model remains challenging due to data heterogeneity, incomplete inventories, and the complexity of rainfall triggers. In this study, we address these gaps by developing a space-time data-driven landslide model tailored for an Early Warning System (EWS) that targets roadblocks.

We address this challenge by combining multiple landslide inventories, satellite rainfall estimates (CHIRPS), and 15-day ensemble rainfall forecasts (CHIRPS-GEFS), the project aims to provide forecasted landslide probabilities. The workflow is structured into three phases. First, a landslide inventory is compiled by harmonizing multiple datasets—each varying in quality, completeness, and spatial-temporal granularity. We address inconsistencies across institutional, academic, and regional inventories to derive a consolidated database of over 17,000 rainfall-induced landslides. Second, with this inventory, we extract data on static and dynamic predictors such as slope steepness, geology, land cover, and rainfall. Using generalized additive models (GAMs), we estimate daily landslide probabilities at a spatial resolution suitable for critical road segments. We compare short-term (1–3 days) to medium-term (up to 15 days) forecasting accuracy to assess model performance. Third, results are translated into spatial dynamic probability thresholds. These thresholds are designed to alert authorities about imminent or escalating risks of landslide-induced roadblocks.

Preliminary tests indicate that this type of space-time model is particularly suitable for integrating forecast-based rainfall data and testing multi-day lead times. The final outcome is a prototype EWS component where probabilistic landslide alerts are updated daily, contributing to risk-informed decision-making for road infrastructure management in Colombia. This contribution discusses the methods, preliminary results, and future steps.