Space-time data-driven modeling of wildfire initiation in the mountainous region of Trentino-Alto Adige, Italy

Wildfires are frequently occurring hazards worldwide which are moving higher in elevation and threatening mountain regions. Each year, they result in substantial economic losses, fatalities, and carbon emissions. In addition, the interplay of climate change, land use changes, and socioeconomic factors is expected to increase the frequency and intensity of wildfires. In this context, developing reliable tools and early warning systems is critical to mitigate and reduce future impacts. At regional scales, data-driven analyses are commonly used to evaluate wildfire susceptibility based on static environmental conditions. However, the integration of the spatial and temporal domains remains challenging. Currently, there is evidence of an increasing trend in wildfires in the region of Trentino-Alto Adige, located in the northeastern part of the Italian Alps. Although this area has experienced limited impacts from wildfires in the past, new tools and applications are needed to prepare for worsening conditions.

This work aims to predict the occurrence of wildfires in space and time (i.e., the ‘where’ and the ‘when’) in Trentino-Alto Adige (13,600 km²). The analyses built upon a generalized additive model (GAM), multitemporal wildfire data from 2000 to 2020, and static and dynamic environmental controls (e.g., topography, land cover, daily precipitation, and temperature). The methodical framework involves filtering the wildfire inventory (wildfire presence data), sampling wildfire absences in space and time, extracting the environmental predictors, and removing trivial terrain and periods. The resulting predictions change dynamically as a function of static factors, seasonality, dynamic precipitation and temperature and are transferred into space under varying precipitation and temperature conditions to hindcast wildfire events. The model output is linked to known performance measures in order to estimate wildfire susceptibility thresholds that can be interpreted in analogy to commonly used empirical landslide rainfall thresholds. The validation routines confirm the high generalizability and predictive power of the model while providing insights into the interplay of environmental factors for wildfire occurrence in Trentino-Alto Adige. Application possibilities are presented.

The research that led to these results is related to the EO4MULTIHA project, which received funding from the European Space Agency (ESA).