| description abstract | The recent surge in the frequency, severity, and extent of wildfires, along with the increased risk of wildfire-induced flooding, highlights the need to quantify the potential impacts of wildfires on peak flood flows. However, supporting wildfire impact assessments with imprecise models can be challenging due to the detailed information typically required about the severity and extent of wildfires, degree of dynamic forest recovery, and a lack of postburn flow data. Moreover, making reasonable assumptions about wildfire impacts becomes difficult. To address this challenge, we propose a novel methodology for screening wildfire impact scenarios on peak flood flows in regions with limited data before a wildfire has occurred. This methodology includes prefire process-based hydrological modeling, sequentially screening short wildfire impacts, and flood frequency analysis. As a proof of concept, the current strategy has been applied to four fire-prone watersheds in Canada. Unburned and worst-burn scenarios were generated and compared to quantify changes in peak flood flows and flood frequency curves. The results indicated that annual peak flows and flood frequency curves experienced an increase in the short-term worst-burn scenario across all four watersheds. The proposed screening methodology estimates the upper limits of postfire peak flood flows, offering insights into which watersheds may be disproportionately impacted by a wildfire regime. This model outputs can be seamlessly integrated into a risk management framework to inform wildfire management decisions aimed at hazard prevention and risk reduction. This study introduces a groundbreaking methodology for screening the potential impact of wildfires on peak flood flows, even in regions with limited data and before a fire occurs. By using prefire hydrological models, simulating short-term wildfire effects, and analyzing flood frequency, this approach allows for early identification of watersheds that are highly vulnerable to postfire flooding. Specifically, it distinguishes between watersheds that are strongly or weakly affected by wildfire in the worst-case scenario, where little information is available about the extent or severity of the burn. This methodology may eventually be enhanced with additional data on burn severity for specific forest types, yet it currently provides a critical tool for categorizing watershed vulnerability to wildfire-related flooding. | |
