Abstract ID: 3.10172 | Accepted as Poster | Talk/Oral | TBA | TBA

This study explores, for the first time, the climatic drivers influencing the monthly burned area (BA) during the winter fire season in northern Italy from 2008 to 2022, with a particular focus on the mountainous regions. The GPS-based BA perimeters were converted into the monthly percentage of burned area for the winter fire season (November to April) at a spatial resolution of 0.11 degrees. The results of the analysis indicated that in northern Italy, wildfires predominantly occur in mountainous regions, including the Alps, Apennines, and pre-Alpine areas. These fire-prone regions exhibit a winter fire regime, with high fire return period, ranging from 1 to 1.5 years, whereas in the Po Valley, it exceeds 7.5 years. Based on the CORINE Land Cover map, the vegetation classes most susceptible to wildfires and their typical elevation ranges were detected and Deciduous Broadleaf Forests were found to be the most susceptible vegetation class. Together, a total of 150 daily ground series of precipitation and of maximum and minimum temperature were collected, aggregated at a monthly scale, reconstructed, homogenised, and spatialised (0.11° spatial resolution ) by mean of Universal Kriging with auxiliary variables. Several climatic indices were computed for precipitation for temperature and for drought. To detect the best BA predictors, we computed the Pearson’s correlation test between BA and different temporal aggregations of climatic indices. Only the strongest and statistically significant correlations were retained. For each pixel, we constructed multilinear regression models using all possible combinations of the significant divers. The best model regressions were selected by mean of an out-of-sample procedure, and the model performance was tested by comparing predicted BA with the observed data, analysing explained variance and correlation. The modelling results for the 2008-2022 winter fire season in northern Italy has revealed that fire activity in is primarily driven by water stress rather than high temperatures. The most influential predictors of were precipitation and water balance recorded between December and March of the current fire year.