Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean

Abstract

Rising wildfire incidents in South America, potentially exacerbated by climate change, require an exploration of sustainable approaches for fire risk reduction. This study investigates wildfire-prone meteorological conditions and assesses the susceptibility in Colombia’s megadiverse northern region. Utilizing this knowledge, we apply a machine learning model and the Monte Carlo approach to evaluate sustainability strategies for mitigating fire risk. The findings indicate that a substantial number of fires occur in the southern region, especially in the first two seasons of the year, and in the northeast in the last two seasons. Both are characterized by high temperatures, minimal precipitation, strong winds, and dry conditions. The developed model demonstrates significant predictive accuracy with the HIT, FAR, and POC of 87.9%, 28.3%, and 95.7%, respectively, providing insights into the probabilistic aspects of fire development. Various scenarios showed that a decrease in soil temperature reduces the risk mostly in lower altitudes and leaf skin reservoir content in the highest altitudes, as well as in the north region. Sustainability strategies, such as tree belts, agroforestry mosaics, and forest corridors emerge as crucial measures. The results underscore the importance of proactive measures in mitigating wildfire impact, offering actionable insights for crafting effective sustainability strategies amid escalating fire risks.