Differentiating Fire Regimes and Their Biophysical Drivers in Central Portugal

Abstract

We characterize fire regimes in central Portugal and investigate the degree to which the differences between regimes are influenced by a set of biophysical drivers. Using civil parishes as units of analysis, we employ three complementary parameters to describe the fire regime over a reference period of 44 years (1975–2018), namely cumulative percentage of parish area burned, Gini concentration index of burned area over time, and area-weighted total number of wildfires. Cluster analysis is used to aggregate parishes into groups with similar fire regimes based on these parameters. A classification tree model is then used to assess the capacity of a set of potential biophysical drivers to discriminate between the different parish groups. The results allowed us to distinguish four types of fire regime and show that these can be significantly differentiated using the biophysical drivers, of which land use/land cover (LULC), slope, and spring rainfall are the most important. Among LULC classes, shrubland and herbaceous vegetation play the foremost role, followed by agriculture. Our results highlight the importance of vegetation type, availability, and rate of regeneration, as well as that of topography, in influencing fire regimes in the study area, while suggesting that these regimes should be subject to specific wildfire prevention and mitigation policies.

Keywords

• fire regime
• biophysical drivers
• machine learning
• classification and regression trees
• central Portugal