Assessing human-caused wildfire ignition likelihood across Europe

Abstract. This study features a cohesive modelling approach of human-caused wildfire ignitions applied to a set of representative regions in terms of fire activity across Europe (pilot sites, PS). Our main goal was to develop a common approach to model human-caused ignition probability at a fine-grained spatial resolution (100 m) and identify the main drivers of their emerge. Specifically, we (i) ascertain which factors influence ignitions in each PS; (ii) deliver a spatial-explicit representation of ignition probability, and (iii) provide a framework for comparison with regional-scale models among PS. To do so, we calibrated Random Forest models from historical fire records compiled by local fire agencies, and geospatial layers of land cover, accessibility, population density and dead fine-fuel moisture content (DFMC). Models were built individually for each PS, comparing them with a full model constructed from all PS. Furthermore, special attention was given to the effect of spatial autocorrelation in model performance. All models achieved sufficient predictive performance (AUCs from 0.70 to 0.89). For all PS models, the yearly anomaly in DFMC was the most influential variable. Among human-related factors, distance to the Wildland Urban Interface emerged as the most relevant variable, followed by proximity to roads, population density, and the fraction of wildland coverage. The performance of the full model achieved an AUC value of 0.81, with mean DFMC and anomaly being the main ignition factors, modulated by distance to roads and population density. The local performance of the full model dropped by 0.10 for AUC in both Southern Sweden and Attica (Greece) regions. The wildfire occurrence models developed in this study are essential for understanding wildfire ignition hazard and may help implement integrated wildfire risk management strategies and mitigation policies in fire-prone EU landscapes.