Integrating Meteorological and Geological Data for Landslide Early Warning in the Pyrenees: The SPIRAL Project

Rainfall-induced landslides are a major hazard in mountainous regions such as the Pyrenees, where intense or prolonged precipitation frequently triggers slope failures. The SPIRAL project (EFA039/01,POCTEFA 2021–2027) aims to improve preparedness and response capacity by developing an operational Landslide Early Warning System (LEWS) that integrates meteorological and geological data streams into a unified workflow for civil protection agencies in Spain, France, and Andorra.

The system combines dynamic rainfall information—observed and forecast—with static susceptibility maps to estimate hazard levels at two scales: territorial (1 km²) and regional (30 m). Data sources include rain gauge networks (AEMET, SMC, Meteo-France, CHE), radar observations, and numerical weather prediction models (ECMWF-IFS, Harmonie). Observed precipitation is processed hourly, generating accumulations over 1 h, 6 h, 12 h, and 24 h. For real-time analysis, rainfall fields are derived using inverse distance weighting (territorial domain) and Conditional Merging of radar and gauge data (regional domain), ensuring spatial continuity and quantitative accuracy. Forecast horizons up to 72 h are incorporated using ECMWF outputs blended with radar-based nowcasting to maintain temporal consistency.

Hazard estimation relies on decision matrices that cross rainfall thresholds with susceptibility values for landslides and rockfalls. Products are generated in raster and slope-unit formats at both scales. Each hour, the system updates hazard maps and computes maximum risk levels across all accumulation intervals. Alerts are classified into four qualitative levels (Very Low, Low, Medium, High) and visualized through the Argos platform—a cloud-based multi-hazard early warning system enabling real-time monitoring, intuitive map visualization, and automated notifications to civil protection agencies.

SPIRAL demonstrates the feasibility of integrating heterogeneous data streams into a unified operational workflow. Key innovations include: (i) dynamic blending of observed and forecast precipitation for seamless short-term prediction; (ii) multi-scale hazard modeling combining susceptibility and triggering factors; and (iii) full interoperability with existing risk management platforms. Preliminary tests using historical rainfall episodes confirm the system’s ability to capture spatial and temporal variability of hazard conditions, supporting timely decision-making for emergency response.

Future developments will focus on refining rainfall thresholds, incorporating real-time in-situ monitoring (e.g., piezometers, crackmeters), and validating performance under operational conditions. This work contributes to advancing LEWS design by coupling meteorological forecasting with geospatial susceptibility analysis in a transboundary mountain environment.