Estimating Compound Post-Fire Flood Hazards using a Stochastic Simulation Framework

Post-fire flood risks are growing due to urban expansion and the increasing frequency of wildfires and intense precipitation. A stochastic simulation framework was recently developed to simulate the (compound) risk of flood channel overtopping accounting for sequences of wildfires and rainfall that produce large volumes of mud and debris and impair the ability of flood control infrastructure to contain storm peaks (Jong-Levinger et al., 2022). This work showed that wildfires can increase channel overtopping risks by roughly an order of magnitude, and that the level of hazard amplification is especially sensitive to wildfire severity, the timescale for vegetation recovery, and infrastructure maintenance approaches used by flood control agencies. Here we describe methodologies to parameterize the model using commonly available geospatial data including precipitation time series, burn severity information, and Normalized Difference Vegetation Index imagery. Furthermore, we document uncertainties in post-fire flood hazard amplification based on parametric uncertainties. Parameterization of the model for a set of catchments in Riverside County, California documents spatial variability in post-fire hazard amplification, and supports insights that can help to prioritize flood hazard reduction measures based on site-specific factors.