Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...

Accompanying Data to Hampton & Basu (2022) "A novel Budyko-based approach to quantify post-forest-fire streamflow response and recovery timescales"

Owners: This resource does not have an owner who is an active HydroShare user. Contact CUAHSI ( for information on this resource.
Type: Resource
Storage: The size of this resource is 41.2 MB
Created: Aug 08, 2019 at 4:14 p.m.
Last updated: Mar 19, 2022 at 5:56 p.m.
DOI: 10.4211/hs.43280a7de6ef48b4b800ab5c12ae58cb
Citation: See how to cite this resource
Sharing Status: Published
Views: 868
Downloads: 13
+1 Votes: Be the first one to 
Comments: No comments (yet)


Recent increases in the incidences of wildfires have necessitated the development of methodologies to quantify the effect of these fires on streamflows. Climate variability has been cited as a major challenge in revealing the true contribution of disturbance to streamflow changes. To address this, we developed an annual Budyko “decomposition” method for (1) statistical change detection of hydrologic signatures post-fire, (2) separating climate-driven and fire-driven changes in streamflow, and (3) estimating hydrologic recovery timescales after fire. We demonstrate the use of this methodology for 17 watersheds in Southern California with high interannual variability in precipitation. We show that while traditional metrics like changes in flow or runoff ratio might not detect a disturbance effect due to confounding climate signals, the Budyko framework can be used successfully for statistical change detection. The Budyko approach was also found to be robust in detecting changes in 5 highly burned catchments (>40% burned area ratio), while changes in less burned (2) and unburned catchments (10) were insignificant. We further used the Budyko approach to quantify the contribution of fire-driven versus climate driven changes in streamflow and found that fire contributed to an average increase in streamflow on the order of 80 mm yr-1, though the effect varied greatly between years. Finally, we estimated hydrologic recovery timescales that varied between 5 to 45 years for four burned catchments. We found a significant linear relationship between recovery time and burned area at medium and high severity for our study catchments, with about 4 years of recovery time per 10% of the watershed burned.

Subject Keywords



Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Los Angeles
North Latitude
East Longitude
South Latitude
West Longitude


Start Date:
End Date:



Tyler Hampton 2020-11-11


This R Markdown README file accompanies Hampton et al. (2020): A novel Budyko-based approach to quantify post-forest-fire streamflow response and recovery timescales

Corresponding Author Information: Tyler B. Hampton Email:; ORCID:

This document details the scripts and data accompanying this Rproject


r list.files(pattern=".R")

## [1] "Budyko1_Rproj.Rproj" "code_0.1.R"          "code_0.2.R"         
## [4] "code_1.0.R"          "README.Rmd"

Note, these scripts rely on custom R packages developed by me, and posted on my github page (tylerbhampton).

r devtools::install_github("tylerbhampton/USGSreadR") devtools::install_github("tylerbhampton/budykoR")


This code outlines the first step of data retrieval used in this analysis. It outlines the use of climate data from PRISM (for citation see Supporting Information (SI) Section 1.2) including calculated gridded values for potential evapotranspiration (PET, See SI Section 1.2) to calculate average values per water-year for each catchment. This code is called from “code_0.2.R”


This code outlines the second step of data retrieval in this analysis. All inputs to this script are local to this R project except for shapefiles of USGS gages (from the GAGES dataset, see Table SI-3). This script applies the filters for catchment selection described in SI Section 1.1.


This is the primary script accompanying this publication. Running it in its entirety will process all the raw data, create figures in the figures folder and write all datasets to the outdata folder.

Note, the section in “code_1.0.R” for Supporting Information Section 2.1 (Figures SI 2) is turned off, as it does require external datasets from the CAMELS dataset which are too large to include with this Rproj. See filepaths in that code section for replication.

Input Files

r list.files(path="inputs")

## [1] "CAMELS"          "GAGES"           "MTBS"            "PRISM"          
## [5] "RiceEmanuel2019" "USGS_Qdata"


This folder contains raw input files for streamflow data for gages in Southern California in the “WholeQseries” folder, and water-year summed values joined with climate data in the “DischargeAnnualSummaries” folder. These files are retrieved and generated by the dataRetrieval package which is called by my custom package USGSread. All files were retrieved in “code_0.2.R”. See “PRISM” folder for climate data and source. Files:

  • “NWISdataX_00060.csv”: Daily Q Records

  • “DischargeSummary_X.csv”: Annual Q Summary File


This folder contains input files from the GAGES dataset (Falcone, 2011), which include watershed characteristics (climate, topology, land cover, etc.) for 9067 USGS stream gage watersheds in the conterminous United States. Files:

  • “conterm_basinid.txt”

  • “conterm_climate.txt”

  • “conterm_lc06_basin.txt”

  • “conterm_topo.txt”

This folder also contains a merged shapefile containing a subset of National Hydrography Dataset watersheds for Southern California used in this analysis (see origin in “code_0.2.R”). Files

  • “southernCA_gages_data”


This folder contains shapefiles and raster files for wildfires in Southern California from the MTBS dataset (Eidenshink et al., 2007). Files:

  • “CA_firedata_all_bndy”: Merged shapefile of California wildfires (1984-2019).

  • “X_dnbr6.dif”: Differenced Normalized Burn Ratio raster files for individual wildfires, showing burn severity.


This folder contains csv files (one for each USGS gage) containing water-year-summed precipitation and potential evapotranspiration for WY 1982-2019. These files are generated according to “code_0.1.R” and called from “code_0.2.R” Files:

  • “PRISMppet_bySite_X.csv”


This folder contains supporting data from Rice & Emanuel (2019), who analyzed interannual variability in storage carryover in the water budgets of watersheds in the U.S. This data is called for Figure SI-1 in “code_1.0.R”


This folder contains inputs from the CAMELS dataset (Addor et al., 2017; See SI Section 2.1). We used gridded climate estimates from CAMELS discritized to watersheds to estimate how different potential evapotranspiration (PET) equations affected total annual PET values.

Output Files

r list.files(path="outdata")

##  [1] "Data_sCABudyko.csv"            "Figure2e_allburndata.csv"     
##  [3] "Figure2e_data.csv"             "Figure3_BudykoFitdata.csv"    
##  [5] "Figure4_MeansPValues.csv"      "Figure5_FlowDecomposition.csv"
##  [7] "Figure6_NLSbudykodata.csv"     "Figure6_Recoverydata.csv"     
##  [9] "Figure6_TimeSeriesData.csv"    "FigureSI2_camelPETcompare.csv"
## [11] "TableSI-1.csv"                 "TableSI-2.csv"

“Data_sCABudyko.csv” This file is generated in “code_0.2.R” and is a product of joining together GAGES catchment characteristics, all-time averages of flow and climate data, and estimates of Fu-type Budyko parameters for pre-fire (\<2004WY) water-year timeseries (using the budyko package).

“TableSI-1.csv” Data generated for Table SI-1 in the supporting information Generated in “PAPER TABLES” code section of “code_1.0.R”

“Table SI2.csv” Data generated for Table SI-2 in the supporting information. Generated in “PAPER TABLES” code section of “code_1.0.R”

“FigureX_xxx.csv” Data generated for Figures in the manuscript. All generated in the “PLOT DATA TABLES” code section of “code_1.0.R”. Numbered according to the Figure.


r list.files(path="figures")

##  [1] "Figure1_BC.pdf"            "Figure1_BC.png"           
##  [3] "Figure2_map.pdf"           "Figure2_map.png"          
##  [5] "Figure2d_LandCover.png"    "Figure2e_BurnPct.png"     
##  [7] "Figure3_Budyko.pdf"        "Figure3_Budyko.png"       
##  [9] "Figure4_bars.pdf"          "Figure4_bars.png"         
## [11] "Figure5_QChangeMerge.pdf"  "Figure5_QChangeMerge.png" 
## [13] "Figure6_Recovery.pdf"      "Figure6_Recovery.png"     
## [15] "FigureSI1_RiceEmanuel.png" "FigureSI2.png"            
## [17] "FigureSI3.png"             "FigureSI4_bars.png"       
## [19] "FigureSI5_flowcomp.png"    "FigureSI6.png"            
## [21] "FigureSI7_Pbars.png"       "Merge_Figures1-6.pdf"

This folder contains all figures generated using “code_1.0.R” and shown in the manuscript and supporting information.


This folder contains the QGIS project file and extra geospatial data used to construct Figure 2 (site map).

  • The file “Old_fire.jpg” is licensed under the Creative Commons Attribution-Share Alike 1.0 Generic license: Schumaker, D. (2003). Old Fire burning in the San Bernardino Mountains. Looking west from Strawberry Peak. Retrieved from

  • The file “California.A2003298.2105.250m.jpg” is from: NASA. (2003, October 25). Grand Prix Fire and Piru Fire near Los Angeles. Greenbelt, MD, USA: NASA Goddard Space Flight Center, EOS Project Science Office, Visible Earth Catalog. Retrieved from

  • The shapefile “CA_shp” is extracted from the spData package in R. Roger Bivand, Jakub Nowosad and Robin Lovelace (2020). spData: Datasets for Spatial Analysis. R package version 0.3.3.

  • The shapefile “2019Budyko_LA_cities_subset” is extracted from the Natural Earth world cities dataset: Made with Natural Earth. Free vector and raster map data @

How to Cite

Hampton, T. B., N. B. Basu (2022). Accompanying Data to Hampton & Basu (2022) "A novel Budyko-based approach to quantify post-forest-fire streamflow response and recovery timescales", HydroShare,

This resource is shared under the Creative Commons Attribution-ShareAlike CC BY-SA.


There are currently no comments

New Comment