Dryad
Published April 30, 2024 | Version v1
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Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data

Creators

  • 1. University of California, Irvine

Description

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.

Notes

Funding provided by: National Science Foundation
ROR ID: https://ror.org/021nxhr62
Award Number: HDBE-2031535

Methods

This repository contains the model scripts and data used to parameterize, calibrate, and validate the Post-Fire Flood Hazard Model (PF2HazMo).  PF2HazMo is a stochastic modeling framework that utilizes continuous simulation to resolve interactions between cycles of wildfire, rainfall, and the filling of protective flood control infrastructure with sediment.  This repository also contains model scripts and data used to conduct a sensitivity analysis and error propagation analysis of the model, as well as to produce post-fire flood hazard projections after model calibration.

The model was developed using and is designed to run in MATLAB.

A detailed description of the methods used to retrieve data and parameterize, calibrate, and validate PF2HazMo is provided in the publication associated with this dataset.  Below are links to the publicly available databases from which data was retrieved to parameterize and calibrate the model:

The topographic data used to estimate watershed morphology are publicly available via https://www.usgs.gov/the-national-map-data-delivery.  The land cover data used to estimate the curve number are publicly available at the Multi-Resolution Land Characteristics (MRLC) consortium via https://www.mrlc.gov/data.  The soil properties data used to estimate the curve number are publicly available at the Web Soil Survey via https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx.  The spatially distributed precipitation frequency estimates used to estimate the runoff response parameter and post-fire bulking factors are publicly available at the Precipitation Frequency Data Server via https://hdsc.nws.noaa.gov/pfds/pfds_gis.html.  The watershed-scale discharge frequency estimates used to estimate the runoff response parameter and post-fire bulking factors are publicly available at the StreamStats website via https://streamstats.usgs.gov/ss/.  The vegetation indices used to estimate the recovery timescale are publicly available at Google Earth Engine via https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13Q1.  The precipitation record used to develop the stochastic rainfall model are publicly available from the Climate Data Online database via https://www.ncei.noaa.gov/cdo-web/search?datasetid=GHCND.  The rain gauge and debris basin excavation data were provided by Riverside County Flood Control and Water Conservation District with written permission to share with the public.  The data repository also includes supplemental information:  a memorandum with discharge estimates from two storms following the 2018 Holy Fire that are referenced in the Discussion section of the publication associated with this data repository; this memorandum was provided by the California Geological Survey with written permission to share with the public as part of this data repository.

Files

CGS_Holy_Fire_Discharge_Estimates_11-29-2018_and_12-06-2018_storms_4-22-2024_FINAL.pdf

Files (749.3 kB)

Additional details

Related works

Is derived from
10.5061/dryad.8w9ghx3t2 (DOI)