These data sets and scripts allow for the creation of all figures and supplementary figures and tables from the following manuscript. When using the code, data, or figures and tables please cite the following manuscript.

Norlen, C.A., Goulden, M.L. (2023) "Recent tree mortality dampens semi-arid forest die-off during subsequent drought" AGU Advances, 4 (3), e2022AV000810. https://doi.org/10.1029/2022AV000810

Data Access

The data sets required to create the figures are available in the following DRYAD repository:
Norlen, Carl; Goulden, Michael (2023). Recent tree mortality dampens semi-arid forest die-off during subsequent drought [Dataset]. Dryad. https://doi.org/10.7280/D1FQ2B

Description of the data and file structure

Shape File of USFS Ecological Subsections used to create Manuscript Figure 1a

• S_USA.EcomapSubsections.shp, S_USA.EcomapSubsections.prj, S_USA.EcomapSubsections.dbf, S_USA.EcomapSubsections.shx

Geotiffs used to create Manuscript Figure 1a

• Drought_first_300m_v7, Drought_second_300m_v7 (1 = drought, 0 = no drought)

CSV files used to Data sets for creation of Manuscript Figure 2. The files include dNDMI_mean, dNDMI_stdDev (unitless), PET_mean, PET_stdDev (mm/year), and tpa_mid_sum (# of tpa pixels > 5), tpa_count (total number of TPA pixels), and system:time_start (start date of values) as a time series for the two drought sequences.

• dNDMI_NDMI_PET_Temp_ADS_trajectories_second_drought_full_region_300m_v9.csv (data for the 2nd Drought Only sequence)
• dNDMI_NDMI_PET_Temp_ADS_trajectories_first_drought_full_region_300m_v9.csv (data for the Both Droughts sequence)

CSV file with data set for four-year Evapotranspiration (mm/4yr), four year precipitation (mm/4yr), four-year temperature (C), biomass (Mg/ha), ADS (dead trees/acre), dNDMI (unit less), Precipitation minus ET (mm/yr), elevation (meters), latitude, longitude, forty-eight month standardized precipitation index (unit less), USFS Zone (261 = Sierra Nevada, 262 = Southern California), and four year Precipitation minus ET (mm/4yr) for each grid cell. There are multiple columns for each variable with years at the end of the column name that represent different time periods (i.e, 2002, 2015). The data is used in the following scripts: fig5_correlation_simple.r, fig3_sfigs_grids.r, and sfig4_spatial_autocorrelation.r
to create Figures 1b, 3, 5, S2, S3, S5, S8, S10, S12, S14, S15, S16 to S20, and Tables S1, S2, and S6.

• Regression_all_socal_300m_v23_v3.csv

Geotiffs used to create Manuscript Figures S1, S6, S7, S9, S11, and S13.

• dNDMI_2004_bigger_region_300m_v4.tif, dNDMI_2017_bigger_region_300m_v4.tif (unit less)
• biomass_1999_bigger_region_300m.tif, biomass_2012_bigger_region_300m.tif (Mg/ha)
• PrET_2002_bigger_region_300m_v4.tif, PrET_2015_bigger_region_300m_v4.tif (mm/4yr)
• tmax_2002_bigger_region_300m.tif, tmax_2015_bigger_region_300m.tif (C)
• ADS_2004_bigger_region_300m.tif, ADS_2017_bigger_region_300m.tif (dead trees/acre)
• Drought_second_300m_v7.tif (0 = no 2nd drought, 2 = 2nd drought)
• Drought_first_300m_v7.tif (0 = no 1st drought, 1 = 1st drought)
• hillshade_bigger_region_300m_v2.tif (unit less)

CSV files of annual data from 10 eddy covariance tower locations 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).

• Pinyon_Annual_EClos_Hinojo.csv
• P301_Annual_EClos_Hinojo.csv
• LowDesert_Annual_EClos_Hinojo.csv
• James_Annual_EClos_Hinojo.csv
• Grass_Annual_EClos_Hinojo.csv
• PinyonBurn_Annual_EClos_Hinojo.csv
• Sage_Annual_EClos_Hinojo.csv
• Shorthair_Annual_EClos_Hinojo.csv
• SJER_Annual_EClos_Hinojo.csv
• Soaproot_Annual_EClos_Hinojo.csv

CSV file of annual NDVI (unit less), year, Site (FluxNext Site ID), Pixel # (Number of 9 upwind Landsat pixels from tower) for each of the 10 eddy covariance sites used to create Figure S4.

• UCIupwind_pixels_NDVI_met_30m.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
• 48-month Standardized Precipitation Index (SPI48) data: https://wrcc.dri.edu/wwdt/archive.php
• California state perimeter: https://developers.google.com/earth-engine/datasets/catalog/TIGER_2016_States
• California Fire Perimeters: https://frap.fire.ca.gov/mapping/gis-data/
• LANDFIRE Existing Vegetation Type: https://www.landfire.gov/viewer/
• USFS Ecological Subsections: 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
• USFS Forest Inventory and Analysis data for California (2019 version): https://apps.fs.usda.gov/fia/datamart/
• Precipitation data: https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m
• Temperature data: 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.
• Eddy Covariance Data: https://www.ess.uci.edu/~california/
• Above ground biomass data: http://emapr.ceoas.oregonstate.edu/pages/data/viz/index.html

Code/Software

The code shared with this submission were written in JavaScript for Google Earth Engine and R 4.02 run using RStudio.
The R code requires the tidyverse, sf, RSQlite, rFIA, RSToolbox, patchwork, ggpubr, kableExtra, and gstat packages. GEE code can
be added to the code editor using the following link https://code.earthengine.google.com/?accept_repo=users/cnorlen/subsequent_drought.

R Code

Script used to create Figure 1 of the manuscript.

• fig1_map.r

Script used to create Figure 2 of the manuscript.

• fig2_time_series.r

Script used to create Figure 3 of the manuscript, and Figures S2, S3, S5, S8, S10, S12 and S19.

• fig3_sfigs_grids.r

Script used to create Figure 4 of the manuscript, Figure S6, and Tables S3 to S5.

• fig4_sfig_FIA_analysis.r

Script used to create Figure 5 of the manuscript, Tables S1, S2, and S6, and Figures S14, S15, S18, and S20.

• fig5_correlation_simple.r

Script used to create Figures S1, S6, S7, S9, S11, and S13.

• sfig1_maps.r

Script used to create Figure S4.

• sfig3_ndvi_flux_tower

Script used to create Figures S16, S17.

• sfig4_spatial_autocorrelation

GEE JavaScript Code

Script used to create a Landsat Tasseled Cap Brightness composites and save the GeoTiffs as GEE assets

• Export_Landsat_Brightness_Composite_CA.js

Script used to create an NDMI time series and save the GeoTiffs as GEE assets

• Export_Landsat_NDMI_TimeSeries_CA.js

Script used to create an NDVI time series and save the GeoTiffs as GEE assets

• Export_Landsat_NDVI_TimeSeries_CA.js

Script used to process and export the data used to create manuscript Figure 1

• Fig1_drought_exposure_map.js

Script used to process and export the data used to create manuscript Figure 2

• Fig2_time_series.js

Script used to process and export the data used to create manuscript Figures 3, 4, and 5

• Fig3_5_SFigs_regression_data

Script used to process and export the data used to create manuscript Figures S13

• SFig_13_NDVI_ET_scaling.js

Script used to process and export the hillshade data used to create additional Supplementary manuscript Figures

• SFigs_hillshade_base_map.js

Script used to process and export the data used to create additional Supplementary mansucript Figures assessing spatial autocorrelation

• SFigs_spatial_autocorrelation_data.js

Functions used to create a time series of Aerial Detection Survey (ADS) data and add it to another script

• ads.js

Functions used to create a time series stack of Landsat data and add that to another script

• All_Landsat.js

Functions used to process an biomass time series dataset and add it to another script

• biomass.js

Functions used to process climate time series data and add it to another script

• climate.js

Functions used to process climatology data and add it to another script

• climatology.js

Functions used to create time series composites of Landsat data at various time lenghts and add it to another Script

• composites.js

Functions used to download data as a CSV file for further Analysis

• download.js

Functions used to add wildfire data (FRAP) to another Script

• frap.js

Functions used to join data sets together

• join.js

Functions used to mask raster data sets

• mask.js

Functions used to process Pr-ET time series data and add it to other scripts

• pet.js

Functions used to change the spatial resolution of raster data sets

• resolution.js

Functions used to calculate vegetation indices for Landsat data

• veg_indices.js

Version changes

27-May-2025: Added JavaScript code (Zenodo) 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.