Assessment of climatic drivers for winter wildfire burned area prediction in northern Italy

The Mediterranean region is a focal point for wildfires. Climate change is projected to affect the Mediterranean hydrological cycle, resulting in intensified drought conditions and increased fire hazard. Even though northern Italy is rich in water resources, wildfires have become increasingly prevalent in recent decades, occurring not only during the summer but also in the winter season.

This study explores for the first time the climatic drivers influencing the monthly burned area (BA) during winter fire season in northern Italy from 2008 to 2022. To this end, we build multi-regression data-driven models that highlighted the main burned area drivers for the overall area. The GPS-based BA perimeters analysed here are provided by the monitoring campaigns performed by the Carabinieri Command of Units for Forestry, Environmental, and Agri-food protection. For winter (November - April) fire season, the monthly percentage of burned area at 0.11 degrees of resolution for the 2008-2022 period was obtained. A total of 150 daily precipitation and maximum and minimum ground station series were collected, converted at monthly scale, reconstructed, homogenised and spatialised at 0.11° resolution by mean of Universal Kriging with auxiliary variables. Subsequently, several climatic indices were computed for precipitation (Precipitation, Consecutive Dry and Wet Days (CDD and CWD)), temperature (Maximum and Minimum Temperature and Evapotranspiration (ET0)) and drought (SPI, SPEI and Water Balance (WB)). To find the best BA predictors, first we checked the pair correlations of BA with different temporal aggregations of climatic indices. The Pearson’s correlation test between the detrended and standardised monthly time series of BA and of climatic indices was performed for each pixel and only the strongest and significant correlations were retained. Based on the CORINE Land Cover map, the vegetation classes that were most susceptible to wildfires, and their typical elevation ranges, were identified. Then, for each pixel, we performed multilinear regressions models using every possible combination of the best predictors that exhibit the lowest correlations with each other. The selection of the best regression models was based on an out-of sample procedure, and the model performance was tested by comparing the predicted BA with the observed, analysing the explained variance and correlation.

This study shows that in northern Italy, fires are predominantly found in the Alps, Apennines, and pre-Alpine regions. In these areas, the fire return period ranges from 1 to 1.5 years, in contrast to the Po Valley, where it exceeds 7.5 years. Deciduous Broadleaf Forests appear to be the most fire-susceptible vegetation class in these fire-prone regions. Modeling results for the 2008–2022 period indicate that fires in northern Italy are primarily influenced by water stress rather than high temperature rates. In fact, the best predictors of BA were mainly precipitation and water balance recorded between December and March of the current fire year. Moreover even if burned areas are not directly correlated with drought, the study figured out the presence of a brief time window during winter months between the end of a prolonged drought and the onset of precipitation when fire risk is high.