Weather pattern and wildfire interplay in Tsavo Conservation Area, Kenya

Abstract Background In recent years, wildfires have caused devastating destruction in many protected areas globally. The Tsavo Conservation Area in Kenya is one such place where wildfires have become more frequent and severe. The incidence of wildfires has been associated to climate change. However, the relationship between weather patterns and wildfires in this area is not well understood. To aid in wildfire management, prediction, and decision-making in this protected area, we aimed to establish the relationship between weather patterns and wildfires. Methods We utilized fire data from the MODIS satellite between 2002 and 2022, along with climatic factors, to establish a relationship between wildfire hotspots and weather patterns. Weather data for the same period was obtained from different remotely sensed images. The weather factors included temperature, rainfall, wind speed, and relative humidity. We used a generalized additive model with Poisson distribution and Spearman correlation to analyze the relationship between wildfire hotspot densities and weather variables. We developed a matrix model that can identify the months likely to experience increased fire hotspots to inform wildfire forecasting for management preparedness. Results We identified fire season in Tsavo Conservation Area as between July to October. The highest wildfire hotspot densities were recorded in 2020, particularly in Chyulu Hill and Tsavo East National Parks. In the same national parks, the highest average wildfire hotspot densities were observed in September. In Tsavo West, the highest density occurred in July. We established that no individual weather parameter was consistently and significantly correlated with wildfire density across most of national parks. However, multiple drought propagation factors were strongly and positively correlated with wildfire density in Chyulu Hill and Tsavo West National Parks. Additionally, our study found that wildfire hotspot densities are influenced by the amount of rainfall in the preceding year. Conclusion We established a matrix model to identify the months of the year most likely to experience high wildfires. Our findings suggest that this model can help inform wildfire likelihoods for management preparedness. The study also underscores the importance of analyzing a combination of various weather parameters for accurate wildfire diagnostics in the Tsavo Conservation Area.