Monthly drought index for the conterminous United States: 6-month and 36-month Standardized Precipitation Evapotranspiration Index (SPEI) for 10 climate scenarios, 1950-2070

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Costanza, Jennifer K.
Originator: Koch, Frank H.
Originator: Reeves, Matthew C.
Publication_Date: 2023
Title:
Monthly drought index for the conterminous United States: 6-month and 36-month Standardized Precipitation Evapotranspiration Index (SPEI) for 10 climate scenarios, 1950-2070
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2022-0075
Description:
Abstract:
Drought is an important stressor affecting forests and rangelands. The Standardized Precipitation Evapotranspiration Index (SPEI) is a meteorological drought index that can be used to investigate trends in drought over time. The SPEI allows for comparisons among locations for historical as well as future conditions, and can be computed over multiple time scales, making it useful for monitoring drought in different ecological contexts. We calculated the 6-month and 36-month SPEI, which assigns values for a given month by comparing the cumulative climatic water balance (precipitation minus potential evapotranspiration, or PET) for the previous 6- or 36-month period to the same cumulative 6- or 36-month water balance for all months in a reference period (defined here as 1950 to 2005). Because of the 6- and 36-month lags in the calculation, the first month in these data sets with data are July 1950 (6-month SPEI) and January 1953 (36-month SPEI).

This data publication consists of 10 sets of geoTIFF (TIF) raster files with monthly values for the SPEI for the period 1950-2070. We calculated the SPEI using temperature and potential evapotranspiration (PET) data from the MACAv2-METDATA for five global climate models for a historical modeled period (1950-2005) and a future period (2006-2070). For the future period, two Representative Concentration Pathways (RCPs), RCP 4.5 and 8.5 were used. The five global climate models used were: MRI-CGCM3, HadGEM2-ES, IPSL-CM5A-MR, CNRM-CM5, NorESM-M. The result was a total of ten future climates. Those are the same ten climates used in the 2020 Resources Planning Act (RPA) Assessment. The spatial extent and resolution of the data match those of the MACAv2-METDATA, covering the conterminous United States at a grid cell size of approximately 4 kilometers (1/24 degree) on a side.
Purpose:
These data were developed to assess the exposure of U.S. forests and rangelands to meteorological drought in the 2020 Resources Planning Act (RPA) Assessment (https://www.fs.usda.gov/research/inventory/rpaa), Chapter on Disturbances to Forests and Rangelands.
Supplemental_Information:
These data were published on 03/06/2023. Metadata updated on 05/18/2023 to include reference to newly published article and again on 10/17/2023 to include reference to published RPA Assessment.

For more information about these data, see Costanza et al. (2023, https://doi.org/10.1002/ecs2.4525).
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 19500101
Ending_Date: 20701231
Currentness_Reference:
Publication date
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent:
The 1/24 degree grids represent the conterminous United States within the provided bounding coordinates.
Bounding_Coordinates:
West_Bounding_Coordinate: -124.77220
East_Bounding_Coordinate: -67.06480
North_Bounding_Coordinate: 49.39600
South_Bounding_Coordinate: 25.06310
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: climatologyMeteorologyAtmosphere
Theme_Keyword: environment
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Climate change
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: drought
Theme_Keyword: Standardized Precipitation Evapotranspiration Index
Theme_Keyword: RPA Assessment
Theme_Keyword: Resources Planning Act Assessment
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: conterminous United States
Place_Keyword: CONUS
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Costanza, Jennifer K.; Koch, Frank H.; Reeves, Matthew C. 2023. Monthly drought index for the conterminous United States: 6-month and 36-month Standardized Precipitation Evapotranspiration Index (SPEI) for 10 climate scenarios, 1950-2070. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2022-0075
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Southern Research Station
Contact_Person: Jennifer Costanza
Contact_Position: Research Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 3041 E. Cornwallis Rd.
City: Research Triangle Park
State_or_Province: NC
Postal_Code: 27709
Country: USA
Contact_Voice_Telephone: 919-549-4055
Contact_Electronic_Mail_Address: jennifer.costanza@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Data_Set_Credit:
Funding for this project was provided by the USDA Forest Service, Resources Planning Act (RPA); USDA Forest Service, Southern Research Station (SRS); and the USDA Forest Service, Rocky Mountain Research Station (RMRS).


Author Information:

Jennifer K. Costanza
USDA Forest Service, Southern Research Station
https://orcid.org/0000-0002-3747-538X

Frank H. Koch
USDA Forest Service, Southern Research Station
https://orcid.org/0000-0002-3750-4507

Matthew C. Reeves
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0000-0002-3948-9574
Cross_Reference:
Citation_Information:
Originator: Abatzoglou, John T.
Originator: Brown, Timothy J.
Publication_Date: 2012
Title:
A comparison of statistical downscaling methods suited for wildfire applications
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: International Journal of Climatology
Issue_Identification: 32(5): 772-780
Online_Linkage: https://doi.org/10.1002/joc.2312
Cross_Reference:
Citation_Information:
Originator: Costanza, Jennifer K.
Originator: Koch, Frank H.
Originator: Reeves, Matthew C.
Publication_Date: 2023
Title:
Future exposure of forest ecosystems to multi-year drought in the United States
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Ecosphere
Issue_Identification: 14(5): e525
Online_Linkage: https://doi.org/10.1002/ecs2.4525
Online_Linkage: https://www.fs.usda.gov/research/treesearch/66126
Cross_Reference:
Citation_Information:
Originator: Costanza, Jennifer K.
Originator: Koch, Frank H.
Originator: Reeves, Matthew C.
Publication_Date: 2023
Title:
Monthly drought index for the conterminous United States: 6-month and 36-month Standardized Precipitation Evapotranspiration Index (SPEI) for observed climate data, 1950-2018
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2022-0086
Cross_Reference:
Citation_Information:
Originator: Costanza, Jennifer K.
Originator: Koch, Frank H.
Originator: Reeves, Matthew C.
Originator: Potter, Kevin M.
Originator: Schleeweis, Karen
Originator: Riitters, Kurt H.
Originator: Anderson, Sarah
Originator: Brooks, Evan B.
Originator: Coulston, John W.
Originator: Joyce, Linda A.
Originator: Nepal, Prakash
Originator: Poulter, Benjamin
Originator: Prestemon, Jeffrey P.
Originator: Varner, J. Morgan
Originator: Walker, David
Publication_Date: 2023
Title:
Chapter 5: Disturbances to forests and rangelands
Geospatial_Data_Presentation_Form: document
Other_Citation_Details:
5-1 - 5-55
Online_Linkage: https://doi.org/10.2737/WO-GTR-102-Chap5
Larger_Work_Citation:
Citation_Information:
Originator: U.S. Department of Agriculture, Forest Service
Publication_Date: 2023
Title:
Future of America’s Forest and Rangelands: Forest Service 2020 Resources Planning Act Assessment
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: General Technical Report
Issue_Identification: WO-102
Publication_Information:
Publication_Place: Washington, DC
Other_Citation_Details:
348 p.
Online_Linkage: https://doi.org/10.2737/WO-GTR-102
Cross_Reference:
Citation_Information:
Originator: Joyce, Linda A.
Originator: Abatzoglou, John T.
Originator: Coulson, David P.
Publication_Date: 2018
Title:
Climate data for RPA 2020 Assessment: MACAv2 (METDATA) historical modeled (1950-2005) and future (2006-2099) projections for the conterminous United States at the 1/24 degree grid scale.
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2018-0014
Analytical_Tool:
Analytical_Tool_Description:
R package: SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index (version 1.7)
Tool_Access_Information:
Online_Linkage: https://CRAN.R-project.org/package=SPEI
Tool_Access_Instructions:
see website for details
Tool_Citation:
Citation_Information:
Originator: Beguería, Santiago
Originator: Vicente-Serrano, Sergio M.
Publication_Date: 2017
Title:
SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index
Geospatial_Data_Presentation_Form: model
Online_Linkage: https://CRAN.R-project.org/package=SPEI
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
The Standardized Precipitation Evapotranspiration Index (SPEI) is a meteorological drought index that summarizes the departure of the climatic water balance from normal conditions, akin to a z-score. Negative SPEI values indicate drier than normal conditions for a given location, while positive values are wetter than normal. For details on the index, see Vicente-Serrano et al. (2010) and Begueria et al. (2014).

Here, we calculated SPEI from MACAv2 METDATA (Joyce et al. 2018). The Multivariate Adapted Constructed Analogs (MACA) method is a statistical method for downscaling Global Climate Models (GCMs) from their native coarse resolution to a higher spatial resolution that captures both the scales relevant for impact modelling while preserving time-scales and patterns of meteorology as simulated by GCMs. This method has been shown to be slightly preferable to direct daily interpolated bias correction in regions of complex terrain due to its use of a historical library of observations and multivariate approach (Abatzoglou and Brown 2012).

Because SPEI values are calibrated based on a historical reference period (defined here as 1950 to 2005) and the index is unbounded, the SPEI function in R can produce values of -Inf or Inf in months when the water balance is substantially outside the historical distribution. Values of Inf and -Inf were recoded to -9999 and 9999, respectively. Prior studies have noted the increased uncertainty associated with extreme values beyond the range [-3, 3] (Stagge et al. 2015, 2016). While we provide the full range of values here (excluding -Inf and Inf), we recommend using caution when applying SPEI values beyond that range. In our own work, we have reclassified SPEI values into categories and retained values beyond the [-3, 3] range as -3 or 3 (Costanza et al. 2023).


Abatzoglou, John T.; Brown, Timothy J. 2012. A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology. 32(5): 772–780. https://doi.org/10.1002/joc.2312

Begueria, Santiago; Vicente-Serrano, Sergio M.; Reig, Fergus; Latorre, Borja. 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology. 34(1): 3001–3023. https://doi.org/10.1002/joc.3887

Costanza, Jennifer K.; Koch, Frank H.; Reeves, Matthew C. 2023. Future exposure of forest ecosystems to multi year drought in the United States. Ecosphere. 14(5): e4525. https://doi.org/10.1002/ecs2.4525 and https://www.fs.usda.gov/research/treesearch/66126

Joyce, Linda A.; Abatzoglou, John T.; Coulson, David P. 2018. Climate data for RPA 2020 Assessment: MACAv2 (METDATA) historical modeled (1950-2005) and future (2006-2099) projections for the conterminous United States at the 1/24 degree grid scale. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2018-0014

Stagge, James H.; Tallaksen, Lena M.; Gudmundsson, Lukas; Van Loon, Anne F.; Stahl, Kerstin. 2015. Candidate Distributions for Climatological Drought Indices (SPI and SPEI). International Journal of Climatology. 35(13): 4027–4040. https://doi.org/10.1002/joc.4267

Stagge, James H.; Tallaksen, Lena M.; Gudmundsson, Lukas; Van Loon, Anne F.; Stahl, Kerstin.2016. Response to comment on “Candidate Distributions for Climatological Drought Indices (SPI and SPEI).” International Journal of Climatology. 36(4): 2132–2138. https://doi.org/10.1002/joc.4564

Vicente-Serrano, Sergio M.; Begueria, Santiago; Lopez-Moreno, Juan I. 2010. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate. 23(7): 1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Logical_Consistency_Report:
A quality check was done on the data to ensure there were no missing values. There are no known errors in the data.
Completeness_Report:
There are no missing data to our knowledge.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
Not applicable
Vertical_Positional_Accuracy:
Vertical_Positional_Accuracy_Report:
Not applicable
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Joyce, Linda A.
Originator: Abatzoglou, John T.
Originator: Coulson, David P.
Publication_Date: 2018
Title:
Climate data for RPA 2020 Assessment: MACAv2 (METDATA) historical modeled (1950-2005) and future (2006-2099) projections for the conterminous United States at the 1/24 degree grid scale
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2018-0014
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1950
Ending_Date: 2099
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Joyce et al. (2018)
Source_Contribution:
Monthly precipitation and potential evapotranspiration
Source_Information:
Source_Citation:
Citation_Information:
Originator: Abatzoglou, John T.
Originator: Brown, Timothy J.
Publication_Date: 2012
Title:
A comparison of statistical downscaling methods suited for wildfire applications
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: International Journal of Climatology
Issue_Identification: 32(5): 772–780
Online_Linkage: https://doi.org/10.1002/joc.2312
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1950
Ending_Date: 2099
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Abatzoglou and Brown (2012)
Source_Contribution:
Describes the downscaling method for Multivariate Adapted Constructed Analogs (MACA)v2 METDATA.
Process_Step:
Process_Description:
1. MACAv2 METDATA were downloaded from https://doi.org/10.2737/RDS-2018-0014 (Joyce et al. 2018) for the ten climates. Variables downloaded were monthly precipitation (P) and potential evapotranspiration (PET).
Source_Used_Citation_Abbreviation:
Joyce et al. (2018); Abatzoglou and Brown (2012)
Process_Date: 2022
Process_Step:
Process_Description:
2. For each of the climate scenarios, the 6-month and 36-month SPEI were calculated using the spei() function in the SPEI R package, version 1.7 (Beguería & Vicente-Serrano 2017). All default parameters were used in the function, with the exception of reference period, which we set to January 1950-December 2005.


For additional details, see Costanza et al. (2023).

Costanza, Jennifer K.; Koch, Frank H.; Reeves, Matthew C. 2023. Future exposure of forest ecosystems to multi year drought in the United States. Ecosphere. 14(5): e4525. https://doi.org/10.1002/ecs2.4525 and https://www.fs.usda.gov/research/treesearch/66126
Source_Used_Citation_Abbreviation:
Beguería, Santiago; Vicente-Serrano, Sergio M. 2017. SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index. R package version 1.7. https://CRAN.R-project.org/package=SPEI.
Process_Date: 2022
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Geographic:
Latitude_Resolution: 0.04166667
Longitude_Resolution: 0.04166667
Geographic_Coordinate_Units: Decimal Degrees
Geodetic_Model:
Horizontal_Datum_Name: World Geodetic System of 1984
Ellipsoid_Name: World Geodetic System of 1984
Semi-major_Axis: 6378137.0000
Denominator_of_Flattening_Ratio: 298.25722210
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Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
Below you will find a list and detailed description of the files included in this data publication.

DATA FILES

This data publication includes 10 sets of geoTIFF (TIF) files with monthly values for the Standardized Precipitation-Evapotranspiration Index (SPEI) for the period 1950-2070. A historical model (hist) and two future scenarios (RCP 4.5 and RCP 8.5) were used in each of five climate models (CNRM-CM5, HadGEM2-ES365, IPSL-CM5A-MR, MRI-CGCM3, and NorESM1-M). For each of these 5 climate scenarios, the 6-month and 36-month SPEI were calculated. The 6-month SPEI files begin in July 1950 and the 36-month SPEI files begin in January 1953. This resulted in 2,226 files for each of the climate models for the 6-month SPEI and 2,196 files for each of the climate models for the 36-month SPEI.

The file name indicates:
1) whether the data are 6-month ("spei_06") or 36-month SPEI ("spei_36"),
2) the climate model (e.g., "cnrm_cm5", "hadgem2_es365"),
3) historical ("hist" = historical scenario) or future scenario ("rcp45" = Representative Concentration Pathway (RCP) 4.5, "rcp85" = Representative Concentration Pathway (RCP) 8.5),
4) the year [YYYY], and
5) the month [mm].

File attributes:
Value = 6-month or 36-month SPEI. SPEI can produce values of -Inf or Inf for extreme values. Those were recoded to -9999 and 9999, respectively.


6-month SPEI data sets:
1. \Data\spei_06_cnrm_cm5\spei_06_cnrm_cm5_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 6-month SPEI for the period between 1950-2070 for the CNRM-CM5 climate model. There are a total of 2,226 files in this folder.

2. \Data\spei_06_hadgem2_es365\spei_06_hadgem2_es365_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 6-month SPEI for the period between 1950-2070 for the HadGEM2-ES365 climate model. There are a total of 2,226 files in this folder.

3. \Data\spei_06_ipsl_cm5a_mr\spei_06_ipsl_cm5a_mr_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 6-month SPEI for the period between 1950-2070 for the IPSL-CM5A-MR climate model. There are a total of 2,226 files in this folder.

4. \Data\spei_06_mri_cgcm3\spei_06_mri_cgcm3_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 6-month SPEI for the period between 1950-2070 for the MRI-CGCM3 climate model. There are a total of 2,226 files in this folder.

5. \Data\spei_06_noresm1_m\spei_06_noresm1_m_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 6-month SPEI for the period between 1950-2070 for the NorESM1-M climate model. There are a total of 2,226 files in this folder.


36-month SPEI data sets:
6. \Data\spei_36_cnrm_cm5\spei_36_cnrm_cm5_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 36-month SPEI for the period between 1950-2070 for the CNRM-CM5 climate model. There are a total of 2,196 files in this folder.

7. \Data\spei_36_hadgem2_es365\spei_36_hadgem2_es365_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 36-month SPEI for the period between 1950-2070 for the HadGEM2-ES365 climate model. There are a total of 2,196 files in this folder.

8. \Data\spei_36_ipsl_cm5a_mr\spei_36_ipsl_cm5a_mr_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 36-month SPEI for the period between 1950-2070 for the IPSL-CM5A-MR climate model. There are a total of 2,196 files in this folder.

9. \Data\spei_36_mri_cgcm3\spei_36_mri_cgcm3_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 36-month SPEI for the period between 1950-2070 for the MRI-CGCM3 climate model. There are a total of 2,196 files in this folder.

10. \Data\spei_36_noresm1_m\spei_36_noresm1_m_[SCENARIO]_[YYYY]_[MM].tif: TIF raster file containing monthly values for the 36-month SPEI for the period between 1950-2070 for the NorESM1-M climate model. There are a total of 2,196 files in this folder.


SUPPLEMENTAL FILES

1. \Supplements\calculateSPEI_MACA_for_archive.R: R code used to 1) convert MACAv2 modeled climate data for 10 GCP x RCP combinations to .tif files, 2) calculate monthly precipitation minus PET, and 3) calculate 36-month and 6-month SPEI.
Entity_and_Attribute_Detail_Citation:
Costanza, Jennifer K.; Koch, Frank H.; Reeves, Matthew C. 2023. Future exposure of forest ecosystems to multi year drought in the United States. Ecosphere. 14(5): e4525. https://doi.org/10.1002/ecs2.4525 and https://www.fs.usda.gov/research/treesearch/66126

Costanza, Jennifer K.; Koch, Frank H.; Reeves, Matt; Potter, Kevin M.; Schleeweis, Karen; Riitters, Kurt; Anderson, Sarah M.; Brooks, Evan B.; Coulston, John W.; Joyce, Linda A.; Nepal, Prakash; Poulter, Benjamin; Prestemon, Jeffrey P.; Varner, J. Morgan; Walker, David M. 2023. Disturbances to Forests and Rangelands. In: U.S. Department of Agriculture, Forest Service. 2023. Future of America s Forest and Rangelands: Forest Service 2020 Resources Planning Act Assessment. Gen. Tech. Rep. WO-102. Washington, DC: 5-1 - 5-55. Chapter 5. https://doi.org/10.2737/WO-GTR-102-Chap5
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of October 2023. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2022-0075
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: TIFF
Format_Version_Number: see Format Specification
Format_Specification:
Georeferenced (GeoTIFF) raster file
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2022-0086
Digital_Form:
Digital_Transfer_Information:
Format_Name: TEXT
Format_Version_Number: see Format Specification
Format_Specification:
Text file (*.R) containing R code
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2022-0086
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20231017
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Jennifer Costanza
Contact_Organization: USDA Forest Service, Southern Research Station
Contact_Position: Research Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 3041 E. Cornwallis Rd.
City: Research Triangle Park
State_or_Province: NC
Postal_Code: 27709
Country: USA
Contact_Voice_Telephone: 919-549-4055
Contact_Electronic_Mail_Address: jennifer.costanza@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
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