Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data

Human development at the foot of the mountains faces sediment-laden flood
hazards characterized by high-velocity, erosive flows carrying mud and
debris, and when flood control infrastructure that protects communities
fills with sediment, it loses capacity. The estimation and management of
sediment-laden floods have proven challenging because cycles of wildfire,
precipitation, and infrastructure sedimentation are still poorly
understood. Efforts to model compound hazards such as post-fire floods are
relatively new, and existing models do not consider the role of flood
control infrastructure, such as debris retention basins and flood
channels, in the development of post-fire floods. Here we present data
sources and calibration methods to estimate sediment-laden flood hazards
downstream of infrastructure 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. 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 whether existing
infrastructure is undersized to contain present-day and future overtopping
hazards based on current design standards.