Towards supporting prescribed fire management decisions in the Cabreira Mountain, Portugal

The importance of implementing preventive fuel reduction strategies to build wildfire resilient landscapes has been increasingly present in the Portuguese politicians’ agenda. Science-based information is crucial to guide decision-makers, to better allocate resources, to decrease the projected increasing impacts of large wildfires following climate change, and to ensure the sustainability of environmental resources. Currently, fuel management is implemented without prior evaluation of wildfire exposure or optimization of strategic location of landscape treatments units, impairing a greater reduction in wildfire hazard and losses.

Prescribed burning can be used to create spatial fuel discontinuities in the landscape thus, to mitigate wildfire impacts. This study proposes to evaluate wildfire exposure in a large and diverse fire-prone area (~193 000ha) containing the Cabreira Mountain, located in Northwestern Portugal.  The main goal is to locate vegetation patches where fuel management can decrease landscape connectivity, fire spread (ROS) and fireline intensity (FLI), simultaneously reducing wildfire burn probability (BP). To address this, we run simulations using the FlamMap MTT fire spread model and quantify landscape connectivity using indexes from the graph theory, under different weather scenarios. Input data on fuels and topography were assembled in a binary landscape file at 100m spatial resolution.

Fire regime analysis was done for burned areas larger than 100 ha, from 2001 to 2019. Using the national fire ignition database and satellite data, the dates of active fire progression and fire durations are calculated. Daily weather variables (temperature, relative humidity, wind speed and direction) corresponding to those dates are compiled. To calibrate the fire model, we compare the observed and the estimated distributions of fire sizes, and the observed fire frequency with the estimated BP. A hierarchical clustering analysis identified three historical weather scenarios. Besides these a 95^th percentile extreme weather scenario is also defined.

Results show a strong relationship between wind speed and landscape connectivity. The contribution of old, burned Pine stands and shrubland areas, mainly located at the east part of the Cabreira Mountain, is high for the overall landscape connectivity. For the extreme weather scenario, assessment of the impact of different fuel treatment extents (Treatment Optimization Model), from 5 to 30%, on the landscape connectivity and on the decrease of the FLI values showed that with a 20% of fuel treatment area (~39 000ha): 1) landscape connectivity decreases 85%; 2) the proportion of the two most extreme FLI classes decreases to ~10% within the study area.

Based on the results, we discuss the best strategies to reduce wildfire hazard for multi criteria based on the studied fire regime and under different weather scenarios, providing information to support a fire management plan. This study explores the potential of fire spread models and graph theory to assess wildfire landscape connectivity and to identify the landcover patches that mostly contribute to that, to determine optimal landscape treatment proportion and to evaluate the impact of treatment locations on the decrease of wildfire properties, ultimately leading to a more comprehensive and effective wildfire management strategy.