UC Irvine

Fire occurrence and spread are influenced by various factors including climate, vegetation, land use, and human activities. Understanding the underlying mechanisms of wildfires and quantifying their impacts on the Earth system is crucial for improving our resilience and preparedness for future wildfires. However, the complexity of interactions among different drivers makes it challenging to comprehend the underlying causes, effects, and feedback. Wildfires alter the land cover of hydrologic basins and significantly increase overland flow and debris movement, reducing the time of concentration. Additionally, wildfires can have adverse effects on the soil properties in burned areas, including reduced moisture content and soil stability, increased risk of post-wildfire landslides, mud and debris flows, erosion, and excessive runoff. This is especially concerning when extreme precipitation falls over burned areas, leading to floods and debris flows, such as the 2018 debris flow in Montecito, California. Such compound events, where two consecutive/concurrent events result in severe societal impacts, pose a significant threat. In this study, one objective is to collect post-fire data to investigate changes in soil characteristics in burned areas and their applications to hydrology, debris flow modeling, and land surface evolution. In this dissertation, I employed statistical and machine learning techniques in conjunction with large-scale meteorology, vegetation, topography, and social datasets to investigate fire-climate-land interactions. This dissertation includes original field data collected for analysis of small-scale processes, assessment of large-scale satellite observations, and development of a machine learning model for improving fire prediction.