DOI: 10.5061/dryad.s4mw6m9f2
title: Recent fire history enhances semi-arid conifer forest drought resistance

description: Interaction effects from past climate change-amplified disturbances in
 fire-prone forests can produce ecological feedbacks that amplify or dampen
 subsequent disturbances. Climate change is increasing wildfire burned
 area, wildfire severity, and incidents of drought-induced forest dieback
 (widespread tree mortality). These climate change amplified disturbances
 threaten forest’s ability to regulate climate, provide water, and store
 carbon. Greater burned area makes interaction effects due to past fires
 more likely, which highlights the importance of understanding whether
 interaction effects produce negative or positive feedback on subsequent
 disturbances such as forest dieback. We constructed a forest
 chrono-sequence by combining a geospatial database of historical fire with
 a time series of Landsat satellite observations for forests in the Sierra
 Nevada of California to assess the impact of fire history on vegetation
 recovery, water use (evapotranspiration), and as feedback on subsequent
 forest dieback. We used these data sets to assess: 1.) How does a history
 of prescribed fire versus wildfire change forest cover and water use? 2.)
 How sensitive are forest structure and water use to fires of varying
 severity? 3.) How do these fire-induced changes to forest structure and
 water use impact forest dieback intensity? Forests with recent fire
 history had reduced tree cover, increased shrub cover, and decreased water
 use, with the greatest changes due to high-severity wildfires. Through 20
 years post-fire water use neared pre-fire conditions across all fire types
 and severities, while decreased tree cover and increased shrub cover
 persisted following high severity wildfires and gradually returned to
 pre-fire conditions for moderate and low severity fire. Fire history
 decreased forest dieback intensity compared to unburned controls, which
 suggests a dampening (negative) feedback. The reductions in forest dieback
 intensity were equal to or greater than reductions in tree cover and
 greater than reductions in water use, which suggests that post-fire
 reductions in tree cover combined with reductions in water use enhanced
 forest drought resistance more than either reduction individually.
  In fire-prone conifer forests, interaction effects from past
 fires will dampen subsequent drought-induced forest dieback, introducing
 novel ecological feedback with implications for forest management and
 climate mitigation.
description: # Recent fire history enhances semi-arid conifer forest drought resistance
 --- These data sets and scripts allow for the creation of all figures and
 supplementary figures, and tables in the following manuscript. When using
 these data and script, please cite the following manuscript. Norlen, C.A.;
 Hemes, K.S.; Wang, J.A.; Randerson, J.T.; Battles, J.J.; Tubbesing, C.L.;
 Goulden, M.L. (2024) "Recent fire history enhances semi-arid conifer
 forest drought resistance" *Forest Ecology and Management*. 573
 (2024): 122331.
 [https://doi.org/10.1016/j.foreco.2024.122331](https://doi.org/10.1016/j.foreco.2024.122331) ## Data Access The data sets required to create the figures are available in the following DRYAD repository: Norlen, C.A.; Hemes, K.S.; Wang, J.A.; Randerson, J.T.; Battles, J.J.; Tubbesing, C.L.; Goulden, M.L. (2024). "Recent fire history enhances semi-arid conifer forest drought resistance" [Dataset]. Dryad. [https://doi.org/10.5061/dryad.s4mw6m9f2](https://doi.org/10.5061/dryad.s4mw6m9f2) ## Description of the data and file structure Shape File of USFS Ecological Subsections used to create Manuscript Figures 1a, S1 * subsection_shp.zip Shape Files of CALFIRE fire perimeters used to create Manuscript Figures 1a,c, S1a,b * fire21_1_shp.zip Shape Files of USFS fire perimeters with added fire severity information used to create Manuscript Figures 1c, S1c * VebBurnSeverity18_1_shp.zip CSV files with samples from CALFIRE wildfire and prescribed fire perimeters (burned) and 2-km buffers (controls). Each time series sample for the data set is in a row of the each CSV file The data includes Evapotranspiration (AET, mm/yr), annual precipitation (ppt, mm/yr), annual temperature (tmax, C), climate normal precipitation (clm_precip_sum, mm/yr), climate normal temperature (clm_temp_mean, C), ADS dieback (tpa, dead trees/acre), NDMI (unit less), Wang et al, 2022 Vegetation Cover: Tree Cover (Tree_Cover, % cover), Shrub Cover (Shrub_Cover, % cover), Herbaceous Cover (Herb_Cover, % cover), Bare-ground Cover (Bare_Cover, % cover), Allred et al, 2021 Vegetation cover: Tree Cover (TRE, % cover), Shrub Cover (SHR, % cover), Annual Forbs and Grasses (AFG, % Cover), Perrennial Forbs and Grasses (PFG, % cover), Bare-ground (BGR, % cover), elevation (meters), latitude, longitude, forty-eight month standardized precipitation index (SPI48, unit less), LANDFIRE Existing Vegetation Type (unitless), most recent fire year in 2010 (fire_year_2010), most recent fire year in 2019 (fire_year_2019), most recent fire year in 2020 (fire_year_2020), most recent fire type in 2010 (fire_type_2010), most recent fire type in 2019 (fire_type_2019), most recent fire type in 2020 (fire_type_2020), number of fires record as of 2010 (fire_count_2010), number of fires record as of 2019 (fire_count_2019), number of fires record as of 2020 (fire_count_2020), Column number for each sample (system.index, unitless), Stratification Layer (stratlayer, unitless) for each grid cell. There are multiple columns for each variable with prefixes from X1\_ (1985) to X35\_ (2019) to indicate years. The data is used in the following script: fig2_fig_4_figs1_table_1_table_s1_frap_rx_fire_recovery_ts_30m.r to create Figures 2, 4, 5, S2, S4, S9, S11, R3, and Tables 1, S1. * fire_south_sierra_FRAP_wildfire_300pt_5_fire_year_10tree_ts4_30m_20231204.csv (wildfire burned samples) * control_south_sierra_FRAP_2km_buffer_300pt_5_fire_year_10tree_ts4_30m_20231204.csv (wildfire control samples) * fire_south_sierra_FRAP_rxfire_300pt_5_fire_year_10tree_ts4_30m_20231204.csv (prescribed fire burned samples) * control_south_sierra_Rx_2km_buffer_300pt_5_fire_year_10tree_ts4_30m_20231204.csv (prescribed fire control samples) CSV files with samples from USFS wildfire perimeters with fire severity information (burned) and 2-km buffers (controls). Each time series sample for the data set is in a row of the each CSV file The data includes Evapotranspiration (AET, mm/yr), annual precipitation (ppt, mm/yr), annual temperature (tmax, C), climate normal precipitation (clm_precip_sum, mm/yr), climate normal temperature (clm_temp_mean, C), ADS dieback (tpa, dead trees/acre), NDMI (unit less), Wang et al, 2022 Vegetation Cover: Tree Cover (Tree_Cover, % cover), Shrub Cover (Shrub_Cover, % cover), Herbaceous Cover (Herb_Cover, % cover), Bare-ground Cover (Bare_Cover, % cover), Allred et al, 2021 Vegetation cover: Tree Cover (TRE, % cover), Shrub Cover (SHR, % cover), Annual Forbs and Grasses (AFG, % Cover), Perennial Forbs and Grasses (PFG, % cover), Bare-ground (BGR, % cover),\ elevation (meters), latitude, longitude, forty-eight month standardized precipitation index (SPI48, unit less), LANDFIRE Existing Vegetation Type (unitless), number of fires record as of 2010 (fire_count_2010), number of fires record as of 2019 (fire_count_2019), number of fires record as of 2020 (fire_count_2020), most recent fire severity in 2010 (fire_sev_2010), most recent fire severity in 2019 (fire_sev_2019), most recent fire severity in 2020 (fire_sev_2020), most recent fire ID in 2010 (fire_ID_2010), most recent fire ID in 2019 (fire_ID_2019), most recent fire ID in 2020 (fire_ID_2020), Column number for each sample (system.index, unitless), Stratification Layer (stratlayer, unitless) for each grid cell. There are multiple columns for each variable with prefixes from X1\_ (1985) to X35\_ (2019) to indicate years. The data is used in the following script: fig3_fig5_fig_s2_table_2_table_s2_fire_sev_recovery_ts_30m.r to create Figures 3, 6, 7, S3, S5, S8, S10, S12, R4, and Tables 1, S2, * fire_south_sierra_USFS_sevfire_600pt_5_fire_year_20tree_ts4_30m_20231204.csv (burned samples) * control_south_sierra_sev_2km_buffer_600pt_5_fire_year_20tree_ts4_30m_20231204.csv (control samples) CSV files of annual data from 10 eddy covariance tower locations were used to create Figure S4. Each file is for one of the 10 eddy covariance sites. Each file contains the following variables: wYEAR (water year), Evapotranspiration (ET, mm/yr), n_days (number of days with data), and ID (site description). CSV file of system index (system:index), annual NDVI (NDVI_mean, unit less), Pixel # year, Pixel # (Number of 9 upwind Landsat pixels from tower), Site (FluxNext Site ID), precipitation (ppt, mm/yr), solar radiation (srad, W/m^2), temperature (tmean, C), year and month (yearmonth), geographic coordinates (.geo) extracted from PRISM, GRDMET, and Landsat for each of the 10 eddy covariance sites used to create Figure S6, S7. * UCIupwind_pixels_NDVI_met_30m_export.csv CSV file of FluxNext Site (Site), date (Mean date), daily ET (Efill.1, mm/day), Predicted ET (Predicted ET, mm/day), Dry Season Drawdown (Bucket 2 DSD, mm), Measured ET divided by Predicted ET (MeasureE/Predicted E, unitless) from combined records of 10 eddy covariance sites. * Monthly_towerdata3_publish.csv ## Sharing/Access information Data was derived from these publicly available sources: * Landsat data on Google Earth Engine (GEE): [https://developers.google.com/earth-engine/datasets/catalog/landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat) * 48-month Standardized Precipitation Index (SPI48) data: [https://wrcc.dri.edu/wwdt/archive.php](https://wrcc.dri.edu/wwdt/archive.php) * California state perimeter: [https://developers.google.com/earth-engine/datasets/catalog/TIGER_2016_States](https://developers.google.com/earth-engine/datasets/catalog/TIGER_2016_States) * California Fire Perimeters: [https://frap.fire.ca.gov/mapping/gis-data/](https://frap.fire.ca.gov/mapping/gis-data/) * LANDFIRE Existing Vegetation Type: [https://www.landfire.gov/viewer/](https://www.landfire.gov/viewer/) * USFS Ecological Subsections: [https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=ecomap+subsection](https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=ecomap+subsection) * USFS Aerial Detection Surveys: [https://www.fs.usda.gov/detail/r5/forest-grasslandhealth/?cid=fsbdev3_046696](https://www.fs.usda.gov/detail/r5/forest-grasslandhealth/?cid=fsbdev3_046696) * PRISM Precipitation data: [https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m](https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m) * PRISM Temperature data: [https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m](https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m) * USGS Digital Elevation Model: [https://developers.google.com/earth-engine/datasets/catalog/USGS_NED](https://developers.google.com/earth-engine/datasets/catalog/USGS_NED). * SRTM Digital Elevation Model: [https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1) * GRIDMET Solar Radiation: [https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET](https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET) * Eddy Covariance Data: [https://www.ess.uci.edu/~california/](https://www.ess.uci.edu/~california/) ## Code/Software The code shared with this submission was written in JavaScript for Google Earth Engine (GEE) and R 4.3.3, run using RStudio. The code requires the tidyverse, sf, RSQlite, rFIA, RSToolbox, patchwork, ggpubr, kableExtra, and gstat packages. GEE code can be added to the GEE Code Editor using the following link: [https://code.earthengine.google.com/?accept_repo=users/cnorlen/fireDieoff](https://code.earthengine.google.com/?accept_repo=users/cnorlen/fireDieoff). ## R Code Script used to create Figure 1, S2 of the manuscript. * fig1_fire_history_maps.r Script used to create Figures 2, 4, 5, and Table 1 of the manuscript and Figures S2, S4, S9, S11, R3, and Table S1. * frap_rx_fire_recovery_dieback_comparison_30m.r Script used to create Figures 3, 6, 7, and Table 1 of the manuscript and Figures S3, S5, S8, S10, S12, R4, and Tables S2, * fire_sev_recovery_dieback_comparison_30m Script used to create Figures S6 and S7. * fig_s9_ndvi_et_scaling ## GEE JavaScript Code Script used to compare Rx perimeters to CCDC disturbance detection * compare_Rx_CCDC_Disturbances.js Script used to export stratified fire data for analysis * Fire_stratified_data_export_for_analysis.js Script used to export the raster version of the wild and prescribed fire data to GEE Assets * Frap_wild_rx_raster_export.js Script used to export monthly NDVI data for the eddy covariance sites for analysis * monthly_NDVI_raster_export.js Script used to export raster versions of the wildfire severity data to GEE Assets * USFS_Fire_severity_raster_export.js Functions that for an Aerial Detection Survey (ADS) time series to be added to scripts * ads.js Functions that allow for rasterized wildfire data to be added to other scripts * frap.js Functions that allow for a time series stack of Landsat data to be added to other scripts * landsat.js ## Version changes Second 28-Oct-2024 version: Added JavaScript code used for processing remote sensing data in Google Earth Engine. Added link to Google Earth Engine code editor repo with publicly available versions of the same code. Third 28-Oct-2024 version: updated this readme with a change log, resulting in a new version