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The recent droughts in the American Southwest have led to increasing risks of wildfires, which pose multiple threats to the regional and national economy and security. Wildfires cause serious air quality issues during dry seasons and can increase the number of mud and landslides in any subsequent rainy seasons. However, while wildfires are often correlated with warm and dry climates, this relationship is not linear, implying that there may be other factors influencing these fires. The objective of this study was to detect and classify any nonlinear patterns in weather data by applying Topological Data Analysis (TDA) to various weather variables, such as temperature, relative humidity, and precipitation, and the five most and least intense summer fire seasons as determined by the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products. In addition to TDA, persistence diagrams and frequency plots were also used to compare fire seasons and regions in the American Southwest. Active fire seasons were more likely to have a significant correlation between the weather variables and wildfires, the Fire Weather Index (FWI) alone was not an accurate predictor for wildfires in California and Nevada, and fire weather is highly dependent upon the region and season.

Deciphering Active Wildfires in the Southwestern USA Using Topological Data Analysis