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Wildfire causes in mainland Portugal: Municipal-scale hotspots and non-stationary spatial drivers
A N Nunes1, L Lourenço1, A C Castro Meira2
1Department of Geography and Tourism, CEGOT - Centre of Studies in Geography and Spatial Planning, University of Coimbra, 3004-530, Coimbra, Portugal.
Abstract:
Understanding the spatiotemporal dynamics of wildfire ignitions in mainland Portugal is crucial for effective fire management and risk reduction. This study examines ignition patterns at the municipal scale, where governance structures play a central role in fire prevention and suppression. Three objectives guided the analysis: (i) mapping ignition hotspots, coldspots, and causes (negligence, intentional, and reactivation); (ii) identifying biophysical and human drivers using Multiscale Geographically Weighted Regression (MGWR); and (iii) assessing how these relationships vary across municipalities. Using national wildfire records from 1996 to 2023, we applied Moran's I to evaluate spatial autocorrelation, Getis-Ord Gi∗ statistics to detect hotspots, and MGWR with Golden Section Search bandwidth optimization to model spatial non stationarity. Results indicate that wildfire ignitions are strongly clustered, with persistent hotspots in the northwest and Lisbon regions and coldspots in southern Portugal. Negligence accounted for 47% of ignitions, followed by intentional causes (27%) and reactivations (more than 25%). MGWR revealed spatially varying relationships between ignition patterns and their drivers. Ignition density (adjusted R2 = 0.774) was positively associated with population density, precipitation, and shrubland cover. Negligent ignitions (R2 = 0.654) were primarily linked to agroforestry, pasture, and agricultural land uses, as well as terrain ruggedness. Intentional ignitions (R2 = 0.543) showed negative associations with rugged terrain and positive associations with illiteracy rates in specific municipalities. Reactivations (R2 = 0.530) were positively associated with firefighter density, reflecting operational allocation patterns rather than causal effects, and were influenced by shrubland cover. These findings highlight the importance of spatial scale and non-stationary modelling for understanding wildfire ignition processes. Municipal scale spatial analysis provides an operationally relevant framework to support targeted fire prevention strategies and evidence-based policies aimed at reducing human caused ignitions and mitigating wildfire risk under increasingly severe fire weather conditions.
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