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Accompanying Data to Hampton, Lin, and Basu (2022) "Forest fire effects on stream water quality at continental scales: a meta-analysis"

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Forested watersheds supply over two thirds of the world's drinking water. The last decade has seen an increase in the frequency and intensity of wildfires that is threatening these source watersheds, and necessitating more expensive water treatment to address degrading water quality. Given increasing wildfire frequency in a changing climate, it is important to understand the magnitude of water quality impacts following fire. Here, we conducted a meta-analysis to explore post-fire changes in the concentrations of nitrogen (N) and phosphorus (P) species, dissolved organic carbon, and total suspended sediments in 121 sites around the world. Changes were documented over each study's respective duration, which for 90% of sites was five years or fewer. We find concurrent increases in C, N and P species, highlighting a tight coupling between biogeochemical cycles in post-fire landscapes. We find that fire alters N and P speciation, with median increases of 40%–60% in the proportion of soluble inorganic N and P relative to total N and P. We also found that fire decreases C:N and C:P ratios, with median decreases ranging from 60% to 70%. Finally we observe a 'hockey stick'-like response in changes to the concentration distribution, where increases in the highest concentration ranges are much greater than increases at lower concentrations. Our study documents strong heterogeneity in responses of water quality to wildfire that have been unreported so far in the literature.

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Tyler Hampton 2022-04-27


This R Markdown README file accompanies Hampton et al. (2022): Forest fire effects on stream water quality at continental scales: a meta-analysis

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

This document details the scripts and data accompanying this Rproject

Data Package and R Project

From Hydroshare, download the ZIP file to your local computer. Using your local file zipping software (I used WinRAR), unzip the folder using the “unzip here” feature. The zip file has a nested folder called “FireMetaWQ_Rproj”. Navigate to that folder.

To perform the calculations in this project, I used R version 4.1.2 (2021-11-01), and R Studio version 1.4.1717. You will need the R packages: ggplot2, tidyr, dplyr, gridExtra, grid, RColorBrewer, maps, sf, data.table, spData, and magick.

In the folder “FireMetaWQ_Rproj”, open the R Project file “FireMetaWQ_Rproj.Rproj”. This will set your directory to the named folder, allowing shortened file paths while running scrips.


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

## [1] "Data_Analysis_Code.R"   "FireMetaWQ_Rproj.Rproj" "README.Rmd"


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.

Input Files

r list.files(path="inputs")

## [1] "Aridity_Data.csv"           "Parameters"                
## [3] "RustData.csv"               "SiteData"                  
## [5] "StudiesData_TableSI_1.xlsx" "UnitConversion.csv"


This folder contains csv input files for each study in the meta-analysis. Each study file has information on:

  • Years of data

  • Catchments in the study “Sub-area”

  • Catchment Grouping “Group ID”, sorts catchments into control-impact pairs, if applicable

  • Disturbance Dates “Year of last disturbance”

  • Disturbance reference period “Period”

  • Disturbance information:

    • “Activity”, F for fire, C for control
    • “Burn type”, N for natural, P for prescribed
    • “Intensity”, Low Medium or High
    • “% Affected”, Percent catchment area burned
  • Catchment Attributes:

    • Latitude
    • Longitude
    • Annual Precipitation (cm)
    • Snowfall present
    • Annual Runoff (cm)
    • Slope (%)
    • Area (ha)
  • Data Metadata:

    • Sampling Location
    • Frequency, qualitative description of water quality data collection
    • Data source, description of source for study data
    • Data Type, qualitative description of published format of water quality data


This folder contains input files for water quality data used in this meta-analysis. Data is sorted into folders by water quality parameter, and then sub-folders by the study where the data came from. In each sub-folder for a parameter and study, the data is described using the following files:

  • JPG Files: Screenshots of tables or images with data

  • JSON Files: JSON files exported from WebPlotDigitizer that was used to extract plot data into CSV files

  • CSV Files, format “X_Extracted-R.csv”: clean data in “tall” format that are read in by “Data_Analysis_Code.R” to process and produce results. Contains the following columns:

    • Site
    • SampleDate
    • Conc
    • Units
  • other CSV files: data either transcribed from tables, or the export of WebPlotDigitizer. These files are NOT read in by code, and are only intermediaries.


This CSV file contains information used to normalize units across all parameters. This file is read by “Data_Analysis_Code.R” to convert, for example: microgram to milligram, mol to mg, mg NO3 to mg NO3-N.


This is the master workbook that was used to collect the results of the initial meta-analysis, narrow down the results to the final list of studies, and record their meta-data for future analysis. All the contained information is qualitative, and includes the following attributes:

  • Study information, name, authors, DOI, weblink
  • Inclusion or Exclusion
    • Including groupings of exclusion criteria
  • Study focus and duration
  • Catchment ecosystem type, vegetation description, location
  • Disturbance information, fire, any harvesting to avoid in data processing
  • Water quality information: which parameters
  • Other study notes

Output Files

r list.files(path="outdata")

##  [1] "deciledata.csv"                    "Fig1_site_metadata.csv"           
##  [3] "Fig3metadata.csv"                  "Fig5data.csv"                     
##  [5] "meandata.csv"                      "TableSI_1_go_to_inputs_folder.txt"
##  [7] "TableSI_2_SiteMetaData.csv"        "TableSI_3_datacount.csv"          
##  [9] "TableSI_4_Fig4data.csv"            "TableSI_5_Fig5lms.csv"            
## [11] "yearmeandata.csv"

This folder contains all data tables generated using “Data_Analysis_Code.R” and shown in the manuscript figures or displayed in the supporting information.


r list.files(path="figures")

##  [1] "Fig1_map.pdf"                     "Fig1_map.png"                    
##  [3] "Fig2_Example.pdf"                 "Fig2_Example.png"                
##  [5] "Fig3_sitemeta.pdf"                "Fig3_sitemeta.png"               
##  [7] "Fig4_RatioParams.pdf"             "Fig4_RatioParams.png"            
##  [9] "Fig5_matrixplot.pdf"              "Fig5_matrixplot.png"             
## [11] "Fig6_RatioParams.pdf"             "Fig6_RatioParams.png"            
## [13] "Fig7_decilechanges.pdf"           "Fig7_decilechanges.png"          
## [15] "FigSI_1_YR15toMean.png"           "FigSI_2_CRMxCRCV.png"            
## [17] "FigSI_3_decilechanges.png"        "FigSI_4_decilechangeratio.png"   
## [19] "FigSI_5_firearea-area.png"        "FigSI_6_matrixcause.png"         
## [21] "FigSI_X_decileabsolutechange.png"

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

How to Cite

Hampton, T. B., N. B. Basu, S. G. Lin (2022). Accompanying Data to Hampton, Lin, and Basu (2022) "Forest fire effects on stream water quality at continental scales: a meta-analysis", HydroShare,

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


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