DOI: 10.7280/d16q59
title: Data from: Compound post-fire flood hazards considering infrastructure sedimentation

description: Flood and debris hazards are heightened following wildfires but are
 challenging to quantify due to interdependence between fire frequency and
 severity, peak flows during precipitation events, sediment loads, and
 sedimentation within infrastructure that reduces flow capacity. Herein we
 present a stochastic simulation framework to estimate compound flood and
 debris hazards from sequences of wildfires and rainstorms and the
 accumulation of sediment within flood infrastructure. Application of the
 framework to a hypothetical watershed representative of southern
 California shows that the present-day compound hazard may be up to six
 times greater than the marginal hazard posed by peak flows in the absence
 of wildfire, and that future compound hazards could be up to eleven times
 greater than the marginal hazard based on future increases in wildfire
 frequency. Numerous sensitivities are investigated, including
 infrastructure design and maintenance, which are shown to be crucial for
 moderating future increases in post-fire flood hazards.
description: # Data from: Compound post-fire flood hazards considering infrastructure
 sedimentation
 [https://doi.org/10.7280/D16Q59](https://doi.org/10.7280/D16Q59) ##
 Description of file structure ### Folder List: 1. Compound_Hazard_Model
 (Zenodo - Software) 2. MCMC_Rainfall_Simulator (Zenodo - Software) 3.
 Rainfall_Data (Dryad) ### Supplemental Information: 1.
 GHCND_documentation.pdf ## Detailed description of folder contents ### 1.
 Compound\_Hazard\_Model (Zenodo) #### File List: 1. DayToMonth.mat 2.
 fires_100y_RIrng_1p25to2p86.mat 3. genprcp.m 4. MCMCv2_100yrsrlzn_EVA.mat
 5. modelparams_v2.mat 6. P_EVA.m 7. pfireocc.m 8. returnlvls_v2.mat 9.
 WULF_Variable_Area.m 10. WULF_Variable_BurnSev.m 11.
 WULF_Variable_FireInt.m #### Description of folder contents: *
 WULF_Variable_Area.m, WULF_Variable_BurnSev.m, and WULF_Variable_FireInt.m
 are scripts that contain the compound hazard model and are identical
 except for the formatting of their output.  The scripts are designed to
 format the model output based on varying watershed area, burn severity,
 and fire interval, respectively. * genprcp.m is a function script that
 generates a daily precipitation time series by running the Monte Carlo
 Markov Chain (MCMC) Rainfall Simulator.   * P_EVA.m is a script used to
 determine the design capacity of flood infrastructure based on the return
 levels of the 50-year and 100-year storms from an extreme value analysis
 of a synthetic 100-year daily precipitation time series generated by the
 MCMC Rainfall Simulator. * pfireocc.m is a function script that outputs
 the probability that a fire will occur during a given day of the fire
 season based on the user-specified fire return interval in years. ####
 Relationship between files, if important: *
 WULF_Variable_Area, WULF_Variable_BurnSev, and WULF_Variable_FireInt
 require the following codes and/or data files to run (i.e., these are
 dependencies of the scripts): * DayToMonth * fires_100y_RIrng_1p25to2p86 *
 genprcp * MCMCv2_100yrsrlzn_EVA * modelparams_v2 * pfireocc *
 returnlvls_v2 - If "options_loadprecip" is set equal to 1, then
 genprcp.m will require modelparams_v2.mat as a dependency. - P_EVA.m
 requires MCMCv2_100yrsrlzn_EVA.mat as a dependency. ### 2.
 MCMC\_Rainfall\_Simulator (Zenodo) #### File List: 1.
 assess_synthetic_series.m 2. dry_year.m 3. estimate_params_v2.m 4.
 modelparams_v2.mat 5. precipBTD.mat 6. process_precip_record.m 7.
 R_85yrs100rlzns.mat 8. run_MC_Generator.m 9. wet_year.m #### Description
 of folder contents: * process_precip_record.m is a script that cleans and
 formats the long-term daily precipitation dataset in preparation for
 estimating model parameters for the MCMC Rainfall Simulator.  It requires
 as dependencies the fiveComma Separated Values (CSV) files of rainfall
 data contained in the Rainfall_Data folder. * estimate_params_v2.m is a
 script that estimates the transition probabilities and probability
 distribution function parameters needed to run the MCMC Rainfall
 Simulator. * run_MC_Generator.m is a script that runs the MCMC Rainfall
 Simulator. * assess_synthetic_series.m is a script that compares the
 statistics of the observed and synthetic rainfall time series to assess
 the performance of the MCMC Rainfall Simulator. * dry_year.m and
 wet_year.m are function scripts that calculate the daily precipitation
 values in a dry year or wet year, respectively. #### Relationship between
 files, if important: * process_precip_record.m requires as dependencies
 the five CSV files of rainfall data contained in the Rainfall_Data folder.
 * estimate_params_v2.m requires as a dependency precipBTD.mat. *
 run_MC_Generator.m requires as
 dependencies modelparams_v2.mat, dry_year.m, and wet_year.m. *
 assess_synthetic_series.m requires as dependencies precipBTD.mat and
 R_85yrs100rlzns.mat. ### 3. Rainfall\_Data (Dryad) #### File List: 1.
 BigTujungaDam_19320101_19501231.csv 2. BigTujungaDam_19510101_19701231.csv
 3. BigTujungaDam_19710101_19901231.csv 4.
 BigTujungaDam_19910101_20101231.csv 5. BigTujungaDam_20110101_20201231.csv
 #### Description of folder contents: * The five CSV files contain daily
 precipitation data from 1932-2020 collected at the Big Tujunga Dam station
 in Los Angeles, California (ID: USC00040798) operated by the National
 Oceanic and Atmospheric Administration (NOAA). * The file name convention
 of the CSV files is as follows:  `[Station Name]_[Start Date of Record in
 YYYYMMDD Format]_[End Date of Record in YYYYMMDD Format].csv` * For
 example, "BigTujungaDam_19320101_19501231.csv" indicates the
 station name is "Big Tujunga Dam," the start date of the
 precipitation record is 1 January 1932 and the end date of the
 precipitation record is 31 December 1950. #### Relationship between files,
 if important: N/A ## Data-specific information The following metadata
 applies to all CSV files included in the "Rainfall_Data" folder:
 * Number of variables: 8 * Variable List: 1. STATION: station
 identification code 2. NAME: name of the station (usually city/airport
 name) 3. LATITUDE: latitude (decimated degrees w/northern hemisphere
 values &gt; 0, southern hemisphere values &lt; 0) 4. LONGITUDE: longitude
 (decimated degrees w/western hemisphere values &lt; 0, eastern hemisphere
 values &gt; 0) 5. ELEVATION: elevation above mean sea level (meters) 6.
 DATE: date of record 7. PRCP: precipitation (inches to hundredths place)
 8. PRCP_ATTRIBUTES: flag; see documentation file included as Supplemental
 Information (titled "GHCND_documentation.pdf") for more
 information * Missing data codes: None (no empty cells are included) *
 Specialized formats or other abbreviations used: None ## Variable list for
 model scripts This section contains variable lists pertaining to each
 model script in the repository. Each variable list in this section defines
 variables not covered by the preceding variable list (to prevent
 repetition of variables used across all scripts). ### genprcp.m * P_DW:
 probability of transitioning from dry year to wet year * P_WW: probability
 of transitioning from wet year to wet year * P01_dry_sm: 365 x 1 array of
 daily probabilities of transitioning from dry day to wet day during a dry
 year * P01_wet_sm: 365 x 1 array of daily probabilities of transitioning
 from dry day to wet day during a wet year * P11_dry_sm: 365 x 1 array of
 daily probabilities of transitioning from wet day to wet day during a dry
 year * P11_wet_sm: 365 x 1 array of daily probabilities of transitioning
 from wet day to wet day during a wet year * parmhat_dry: array of model
 parameters for probability distribution of precipitation magnitudes during
 a dry year * parmhat_wet: array of model parameters for probability
 distribution of precipitation magnitudes during a wet year * n: number of
 years to simulate * m: number of daily time series realizations to
 simulate * R: 365 x n x m 3D array of multi-year daily precipitation time
 series (units of mm) ### P\_EVA.m * P:  precipitation time series (daily
 interval) generated by MCMC Rainfall Simulator; used as model forcing (mm)
 * annmax:  time series of annual maximum precipitation (mm) * returnT: 
 return periods for extreme precipitation events (years) * returnP: 
 exceedance probabilities corresponding to the return periods in
 "returnT" * PRLs_50_100:  50-year and 100-year return levels
 used to determine flood infrastructure capacity in the compound hazard
 model (years) ### pfireocc.m * T: return period for wildfires (years) *
 fslen: length of fire season (days) * p_ann: annual probabilty a wildfire
 *does not* occur * p: probability of fire occuring on a given day during
 the fire season ### WULF\_Variable\_Area.m, WULF\_Variable\_BurnSev.m, and
 WULF\_Variable\_FireInt.m * alpha: fraction by which debris basin
 attenuates flood peak, with a value of 1 representing no flood peak
 attenuation (unitless) * Crational: runoff coefficient used by the
 Rational Method to calculate peak runoff; represents ratio of runoff to
 rainfall (unitless) * clean_rate: daily excavation rate for debris basin
 during wet season (m^3/day) * clean_thresh: fraction of flood control
 infrastructure volume filled with sediment that triggers cleaning
 (unitless) * daily_exc_basin: sediment removed from debris basin daily
 (m^3) * daily_exc_chan: sediment removed from flood channel daily (m^3) *
 J_c: sediment flux into flood channel (m^3) * k: bulking factor; ratio of
 sum of water and sediment flows to water flow alone (unitless) * k_design:
 design bulking factor used to size flood control infrastructure (unitless)
 * k0: baseline bulking factor when watershed is unaffected by fire
 (unitless) * k1: post-fire bulking factor; bulking factor value
 immediately after fire occurs (unitless) * k1rng: two-element array of
 post-fire bulking factors representing range of possible bulking factors
 produced by a given wildfire; when wildfire occurs, post-fire bulking
 factor is uniformly randomly sampled from this range (unitless) * nsims:
 number of simulations per run * nyears: number of years in simulation; if
 simulation lasts less than a year, set to 1 (years) * P_channel_design:
 magnitude of design storm used to size flood control infrastructure (mm) *
 Q_c: volumetric water discharge into flood channel (m^3/s) * Q_c_max:
 effective channel capacity (as a volumetric discharge) (m^3/s) *
 Q_c_max_clean: capacity of clean flood channel (as a volumetric discharge)
 (m^3/s) * Q_hazard: magnitude of overbank flows (i.e., flows in excess of
 flood channel capacity); includes both clear-water and bulked flows
 (m^3/s) * Q_w: clear-water (or "water only") volumetric
 discharge from watershed outlet (m^3/s) * RIfire: wildfire return interval
 (years) * t: time (days) * T_recovery: recovery timescale; time scale for
 watershed vegetation recovery after fire (days) * T_w: time to peak;
 duration from beginning of storm to time of peak runoff, i.e., rising limb
 of triangular hydrograph (seconds) * Vol_b: volume of sediment in debris
 basin (m^3) * Vol_b_max: capacity of clean debris basin (m^3) * Vol_c:
 volume of sediment in flood channel (m^3) * Vol_c_max: capacity of clean
 flood channel (as a volume) (m^3) * Vol_s: volume of sediment from
 watershed outlet (m^3) * Vol_s_remov_basin: time series of volumes of
 sediment removed from debris basin due to excavation (m^3) *
 Vol_s_remov_chan: time series of volumes of sediment removed from flood
 channel due to excavation (m^3) * Vol_w: volume of water from watershed
 outlet (m^3) * w: wet season cleaning waiting period; number of days with
 no rain needed before debris basin cleaning can commence (days) *
 WatershedArea: contributing drainage area (km^2) ###
 assess\_synthetic\_series.m * btd_noNaN: observed 85-year (1932-2020)
 daily precipitation time series from Big Tujunga Dam in Los Angeles, CA
 with missing values removed (mm) ### dry\_year.m * See variable list for
 file "genprcp.m," above ### estimate\_params\_v2.m * DD: number
 of times dry year follows dry year * WW: number of times wet year follows
 wet year * DW: number of times wet year follows dry year * WD: number of
 times dry year follows wet year * P_DD: empirical probability that dry
 year follows dry year * P_WW: empirical probability that wet year follows
 wet year * P_DW: empirical probability that wet year follows dry year *
 P_WD: empirical probability that dry year follows wet year * DD_wet:
 number of times dry day follows dry day during wet year * WW_wet: number
 of times wet day follows wet day during wet year * DW_wet: number of times
 wet day follows dry day during wet year * WD_wet: number of times dry day
 follows wet day during wet year * DD_dry: number of times dry day follows
 dry day during dry year * WW_dry: number of times wet day follows wet day
 during dry year * DW_dry: number of times wet day follows dry day during
 dry year * WD_dry: number of times dry day follows wet day during dry year
 * P01_wet: empirical probability that wet day follows dry day during a wet
 year; "P01_wet_sm" results from smoothing this variable using a
 moving mean * P11_wet: empirical probability that wet day follows wet day
 during a wet year; "P11_wet_sm" results from smoothing this
 variable using a moving mean * P01_dry: empirical probability that wet day
 follows dry day during a dry year; "P01_dry_sm" results from
 smoothing this variable using a moving mean * P11_dry: empirical
 probability that wet day follows wet day during a dry year;
 "P11_dry_sm" results from smoothing this variable using a moving
 mean ### process\_precip\_record.m, run\_MC\_Generator.m * No additional
 variables to define ### wet\_year.m * See variable list for file
 "genprcp.m," above ## Sharing/Access information *
 Licenses/restrictions placed on the
 data:  [CC0](https://creativecommons.org/public-domain/cc0/) *
 Licenses/restrictions placed on the software:  GNU General Public License
 v3.0 or later * Licenses/restrictions placed on the supplemental
 information: Creative Commons Attribution 4.0 International (CC BY 4.0)
 license * Publications that cite or use the data:  Jong‐Levinger, A.,
 Banerjee, T., Houston, D., & Sanders, B. F. (2022). Compound post‐fire
 flood hazards considering infrastructure sedimentation. *Earth's
 Future*, *10*(8), e2022EF002670. * The daily precipitation data were
 downloaded from the Climate Data Online database operated by the National
 Oceanic and Atmospheric Administration. Daily precipitation values from
 the Big Tujunga Dam rain gauge station (ID: USC00040798) from 1 January
 1932 to 31 December 2020 were used to develop the precipitation model.
 Direct link to data used to develop the precipitation model:
 [https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00040798/detail](https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00040798/detail) ## Code/Software MATLAB Version 9.10.0.1602886 (r2021a) was used to write and run the model scripts.