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Communities near the wildland urban interface (WUI) are exposed to a mix of three interconnected hazards (wildfire, flood, and mudslide), and understanding multi-hazard perceptions is critically important for emergency preparation and hazard mitigation—particularly given the WUI’s rapid expansion and intensifying environmental hazards. Based on a survey of residents living near recent burn scars in Southern California, we document cross-over effects in hazard perceptions, where resident experience with one hazard was associated with greater hazard rankings for other hazards. Additionally, for all three hazards analyzed we document perceptions of increasing hazard levels with increasing spatial scales (home, near-home, neighborhood, and community), providing evidence of spatial optimism, or the tendency to discount proximate hazards. This study stresses the importance of using a multi-hazard and multi-scale approach for understanding and responding to local level environmental hazards. 

Abstract Cycles of wildfire and rainfall produce sediment‐laden floods that pose a hazard to development and may clog or overtop protective infrastructure, including debris basins and flood channels. The compound, post‐fire flood hazards associated with infrastructure overtopping and clogging are challenging to estimate due to the need to account for interactions between sequences of wildfire and storm events and their impact on flood control infrastructure over time. Here we present data sources and calibration methods to estimate infrastructure clogging and channel overtopping hazards on a catchment‐by‐catchment basis using the Post‐Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous simulation to resolve the effects of antecedent conditions and system memory. Publicly available data sources provide parameter ranges needed for stochastic modeling, and several performance measures are considered for model calibration. With application to three catchments in southern California, we show that PF2HazMo predicts the median of the simulated distribution of peak bulked flows within the 95% confidence interval of observed flows, with an order of magnitude range in bulked flow estimates depending on the performance measure used for calibration. Using infrastructure overtopping data from a post‐fire wet season, we show that PF2HazMo accurately predicts the number of flood channel exceedances. Model applications to individual watersheds reveal where infrastructure is undersized to contain present‐day and future overtopping hazards based on current design standards. Model limitations and sources of uncertainty are also discussed.