UCLA

This work has investigated the policy of wildfires, modeling techniques for post-fire assessment, and the influence of controlling variables on post-fire recovery. Post-fire mitigation and management require reliable predictions of immediate hydrologic consequences and long-term recovery to pre-fire conditions. This research shows that models used by agencies are not adaptable to all geographical and climatological conditions. Results show inconsistencies between model predictions for peak discharge events across the sites and less confidence associated with larger return periods (25- and 50-year peak flow events). Remote sensing techniques improve spatial and temporal resolution of data streams for model parameters and post-fire recovery predictions. This research shows that recovery is dependent on many variables, including burn severity, slope aspect, and vegetation biomass. The lack of vegetation recovery across watersheds results in significant changes in annual and seasonal discharge throughout the study period. Understanding these key controlling variables will improve post-fire hydrological predictions. Previously established remote sensing algorithms can be applied and adapted to burned areas to improve hydrologic and recovery predictions. This work encourages new tools that can be incorporated into policies that minimize development at the WUI, improve homeowner preparation in fire-prone areas, and improve post-fire recovery predictions. This work improves post-fire modeling and predictions primarily with remote sensing applications to guide accurate, efficient, and cost-effective management decisions.