Daniel Manier
Gordon Reese
Natasha Carr
Lucy Burris
2019
Potential productivity and change for Indiangrass in the Great Plains Landscape Conservation Cooperative area
Raster Digital Data Set
Online
U.S. Geological Survey
https://doi.org/10.5066/P9DGJHEP
Daniel J. Manier
Natasha B. Carr
Gordon C. Reese
Lucy Burris
2019
Using scenarios to evaluate vulnerability of grassland communities to climate change in the Southern Great Plains of the United States
publication
n/a
US Geological Survey
https://doi.org/10.3133/ofr20191046
This data set includes the relative production scenarios for Indiangrass [0.17(Precip) + 0.02(Sand) - 7.4]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario (ACCESS 1-0, RCP8.5, Collier and Uhe, 2012). The results were binned into 7 classes based on breaks in the data and comparison with field observations.Climate change has been identified as a high-priority threat to grasslands by the Great Plains Landscape Conservation Cooperative (GPLCC) and as a priority change agent for grasslands in the Southern Great Plains Rapid Ecoregional Assessment by the Bureau of Land Management. The area of interest includes four level III ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. To address this priority information need for multiple stakeholders, we evaluated the potential vulnerability of four grassland communities (shortgrass, mixed-grass, and tallgrass prairies, and semiarid grasslands) using four climate change scenarios (representing hotter-drier, hotter-wetter, warmer-drier, and warmer-wetter conditions, relative to contemporary conditions). We used relative above-ground productivity models (Epstein et al., 1998) to evaluate the potential for change in productivity for each grassland community using mean annual precipitation and temperature for the contemporary climate (1981-2010) and the four climate scenarios (2016-2045), and the percent of sand, silt, and clay from the dominant soils component from the Natural Resource Conservation Service (Earth System Science Center, 2008). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis) and buffalo grass (Bouteloua dactyloides); mixedgrass prairie: sideoats grama (Bouteloua curtipendula) and little bluestem (Schizachyrium scoparium); tallgrass prairie: big bluestem (Andropogon gerardii) and Indiangrass (Sorghastrum nutans); and semiarid grasslands: black grama (Bouteloua eriopoda) and tobosagrass (Pleuraphis mutica). For each indicator species, we evaluated the potential change in relative productivity for each climate scenario compared to the contemporary climate. We used standard deviations to classify the differences between predicted productivity relative to the contemporary predicted productivity to evaluate whether the distributions of the indicator species were expected to remain stable, decrease, or expand for each scenario.Spatial data representing the estimated relative productivity of grassland species in the Southern Great Plains are provided as a 1-square kilometer gridded surface (raster dataset). This information will help to address priority management questions for grassland conservation in the GPLCC and Southern Great Plains regions and can be used to inform other regional-level land management decisions.Collier, Mark, and Uhe, Peter, 2012, CMIP5 datasets from the ACCESS1.0 and ACCESS1.3 coupled climate models: Centre for Australian Weather and Climate Research Technical Report No. 059, 25 p.Earth System Science Center, 2008, Soil fraction data: College of Earth and Mineral Sciences at The Pennsylvania State University, accessed January 7, 2016, at http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov&fract&datasets&alb.Epstein, H.E., Lauenroth, W.K., Burke, I.C., and Coffin, D.P., 1998, Regional productivities of plant species in the Great Plains of the United States: Plant Ecology, v. 134, p. 173-195.Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., and Nijssen, B., 2002, A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States: Journal of Climate, v. 15, no. 22, p. 3237-3251.Natural Resources Conservation Service [NRCS], Surface Soils Geographic Database [gSSURGO], United States Department of Agriculture Natural Resources Conservation Service, at https://catalog.data.gov/dataset/gridded-soil-survey-geographic-gssurgo-10-database-for-the-conterminous-united-states-10-m.Neale, R.B.; Chen, Chih-Chieh; Gettelman, Andrew; Lauritzen, P.H.; Park, Sungsu; Williamson, D.L.; Conley, A.J.; Garcia, Rolando; Kinnison, Doug; Lamarque, Jean-Francois; Marsh, Dan; Mills, Mike; Smith, A.K.; Tilmes, Simone; Vitt, Francis; Morrison, Hugh; Cameron-Smith, Philip; Collins, W.D.; Iacono, M.J.; Easter, R.C.; Ghan, S.J.; Liu, Xiaohong; Rasch, P.J.; Taylor, M.A., 2010, Description of the NCAR Community Atmosphere Model (CAM 5.0): National Center for Atmospheric Research Technical Note NCAR/TN-486+STR, 274 p.Schmidt, G.A., M. Kelley, L. Nazarenko, R. Ruedy, G.L. Russell, I. Aleinov, M. Bauer, S.E. Bauer, M.K. Bhat, R. Bleck, V. Canuto, Y.-H. Chen, Y. Cheng, T.L. Clune, A. Del Genio, R. de Fainchtein, G. Faluvegi, J.E. Hansen, R.J. Healy, N.Y. Kiang, D. Koch, A.A. Lacis, A.N. LeGrande, J. Lerner, K.K. Lo, E.E. Matthews, S. Menon, R.L. Miller, V. Oinas, A.O. Oloso, J.P. Perlwitz, M.J. Puma, W.M. Putman, D. Rind, A. Romanou, M. Sato, D.T. Shindell, S. Sun, R.A. Syed, N. Tausnev, K. Tsigaridis, N. Unger, A. Voulgarakis, M.-S. Yao, and J. Zhang, 2014: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst., 6, no. 1, 141-184, doi:10.1002/2013MS000265.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M., 2011, MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845-872, https://doi.org/10.5194/gmd-4-845-2011.
To address this priority information need for multiple stakeholders, we evaluated the potential vulnerability of four grassland communities (shortgrass, mixed-grass, and tallgrass prairies, and semiarid grasslands) using four climate change scenarios (representing hotter-drier, hotter-wetter, warmer-drier, and warmer-wetter conditions, relative to contemporary conditions) in the GPLCC region. The climate scenarios are not predictions of the future, but demonstrate potential conditions based on complicated global climate models. As such, the projections of relative production and potential change in relative production should be interpreted similarly, as model results that provide insights to potential future conditions, and not as predictions of the future.
The data used to build the contemporary models (daily summary of Maurer 2002, http://www.hydro.washington.edu/SurfaceWaterGroup/Data/VIC_retrospective/index.html) is no longer being made available online. Please contact eclark@hydro.washington.edu to download, or alternatively, visit http://www.engr.scu.edu/~emaurer/data.shtml for comparable data. See http://www.hydro.washington.edu/SurfaceWaterGroup/Data/VIC_retrospective/README.download for more information.Documentation for the GCMs used to model the future species distributions can be found at http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html#About. This data can be found at ftp://gdo-dcp.ucllnl.org/pub/dcp/archive/cmip5/hydro/BCSD_mon_forc_nc.
1981
2045
publication date
None planned
Great Plains Landscape Conservation Cooperative region
-108.7268
-96.1259
43.9436
30.1454
ISO 19115 Topic Category
environment
biota
BLM-THEME
disturbance
energy
management
wilderness
wildlife
vegetation
None
Great Plains Landscape Conservation Cooperative
Southern Great Plains
Rapid Ecological Assessment
USGS Metadata Identifier
USGS:5cae3154e4b0c3b00654ceef
None
Southern Great Plains
Common geographic areas
Colorado
Kansas
Nebraska
New Mexico
Oklahoma
South Dakota
Texas
Wyoming
None
Sorghastrum nutans
Kingdom
Plantae
plants
Subkingdom
Viridiplantae
green plants
Infrakingdom
Streptophyta
land plants
Superdivision
Embryophyta
Division
Tracheophyta
vascular plants
tracheophytes
Subdivision
Spermatophytina
spermatophytes
seed plants
Class
Magnoliopsida
Superorder
Lilianae
monocots
monocotyledons
Order
Poales
Family
Poaceae
Genus
Sorghastrum
Species
Sorghastrum nutans
yellow indian-grass
TSN: 42102
None. Please see 'Distribution Info' for details.
None. Users are advised to read the data set's metadata thoroughly to understand appropriate use and data limitations.
U.S. Geological Survey Fort Collins Science Center (FORT)
Natasha B Carr
Ecologist
mailing address
2150 Centre Avenue Bldg C
Fort Collins
CO
80526-8118
US
970-226-9446
970-226-9230
carrn@usgs.gov
Data was created by the United States Geological Survey Fort Collins Science Center.
Environment as of Metadata Creation: Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.3.1 (Build 4959) Service Pack N/A (Build N/A)
The attribute table was checked for typos, and to ensure column headers were named consistently.
Data was checked to ensure that all values fell within expected ranges.
Data set is considered complete for the information presented. Users are advised to read the rest of the metadata record carefully for additional details.
This output raster was checked to ensure that there was no spatial deviation from the inputs.
A formal accuracy assessment of the vertical positional information in the data set has not been conducted because it does not apply.
Data originators vary with input used. See processing steps for more information.
Unknown
Source inputs used vary with products. See processing steps for more information.
Maps Data
Digital and/or Hardcopy Resources
Unknown
Unknown
InputData
Data/resources for the described processing and analysis.
The contemporary and future composition of grass-dominated ecological communities in the Southern Great Plains region were estimated using species productivity models of Epstein et al., (1998), which are derived from soil texture (percentages of sand, silt, and clay in the surface layer), mean annual precipitation, and mean annual temperature. The Surface Soils Geographic Database [gSSURGO] contains soil texture components representing the percentages of sand, silt, and clay across soil layers, as summarized by the Earth System Science Center (2008). Following the methods of Epstein et al., (1998), we used percentages from the surface layer, 0-5 cm, in all models, both contemporary and future. For the contemporary models, we averaged mean annual precipitation and temperature 1981-2010 from Maurer (2002, http://www.hydro.washington.edu/SurfaceWaterGroup/Data/VIC_retrospective/README.download). We estimated potential relative production in the future using precipitation and temperature data from each of four GCMs 2016-2045, representing possible divergent climate conditions. We used monthly data and either summed or averaged across months within a given year, for precipitation and temperature data, respectively, and then averaged across the 30-year period. The four selected GCMs represent relatively warmer-wetter (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer-drier (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier conditions (ACCESS 1-0, RCP8.5, Collier and Uhe, 2012). This data can be found at ftp://gdo-dcp.ucllnl.org/pub/dcp/archive/cmip5/hydro/BCSD_mon_forc_nc.We used the Raster Calculator (ESRI, ArcGIS v. 10.3) to apply the productivity models for each indicator species from Epstein et al., 1998 to the soil and mean average temperature and precipitation rasters. The output is a gridded surface (raster dataset) with continuous estimates of relative productivity for each indicator species. For Indiangrass (Sorghastrum nutans) we used Relative production = 0.17(Precip) + 0.02(Sand) - 7.4. To facilitate interpretation, the continuous productivity data were classified using the following information to establish productivity class breakpoints. We first examined natural breaks in the data and the range of productivity values for the associated grassland community. We also compared modeled outputs for the contemporary time frame with field observations from ecological mapping (Elliot, 2010), field measurements of production (Natural Resources Conservation Service, 2015), and a map of the historic distribution of grassland communities (Callan et al., 2016).The differences between contemporary production estimates, and those provided by the four climate scenarios were also calculated for each grid cell using the continuous production estimates. The difference surfaces were classified based on 1 standard deviation increments to demonstrate where model results suggested potential for widespread changes in species’ production as indicated from projected changes in climate variables.To merge the contemporary production estimates with the climate scenarios, we used the Combine (Spatial Analyst) in ESRI ArcGIS.
Unknown
Raster
Grid Cell
1485
1012
1
Albers Conical Equal Area
29.5
45.5
-96.0
23.0
0.0
0.0
row and column
1000.0
1000.0
meters
North_American_Datum_1983
GRS 1980
6378137.0
298.2572221010042
sonu_combined_GPLCC.tif
Raster geospatial data file.
Producer defined
OID
Internal object identifier.
Producer defined
Sequential unique whole numbers that are automatically generated.
Value
Unique numeric values contained in each raster cell.
Producer defined
3
2443
Count
Number of raster cells with this value.
Producer defined
10.0
72919.0
sonu_maur
Current relative production for Indiangrass based on model results with 30-year contemporary climate means, and binned into 7 classes.
Producer defined
0
Index of relative production including model results ≤ 0
Producer defined
4
Index of relative production containing values ranging from 0 to 4
Producer defined
12
Index of relative production containing values ranging from 8 to 12
Producer defined
8
Index of relative production containing values ranging from 4 to 8
Producer defined
16
Index of relative production containing values ranging from 12 to 16
Producer defined
21
Index of relative production containing values ranging from 16 to 21
Producer defined
26
Index of relative production containing values ranging from 21 to 26
Producer defined
sonu_cesrp
Relative production scenario for Indiangrass based on model results with climate inputs from the GCM CESM1-BGC, RCP 4.5, and binned into 7 classes.
Producer defined
0
Index of relative production including model results ≤ 0
Producer defined
8
Index of relative production containing values ranging from 4 to 8
Producer defined
4
Index of relative production containing values ranging from 0 to 4
Producer defined
16
Index of relative production containing values ranging from 12 to 16
Producer defined
12
Index of relative production containing values ranging from 8 to 12
Producer defined
21
Index of relative production containing values ranging from 16 to 21
Producer defined
26
Index of relative production containing values ranging from 21 to 26
Producer defined
sonu_gisrp
Relative production scenario for Indiangrass based on model results with climate inputs from the GCM GISS-E2-R, RCP 4.5, and binned into 7 classes.
Producer defined
0
Index of relative production including model results ≤ 0
Producer defined
8
Index of relative production containing values ranging from 4 to 8
Producer defined
4
Index of relative production containing values ranging from 0 to 4
Producer defined
12
Index of relative production containing values ranging from 8 to 12
Producer defined
16
Index of relative production containing values ranging from 12 to 16
Producer defined
21
Index of relative production containing values ranging from 16 to 21
Producer defined
26
Index of relative production containing values ranging from 21 to 26
Producer defined
sonu_m8rrp
Relative production scenario for Indiangrass based on model results with climate inputs from the GCM Miroc-ESM, RCP 8.5, and binned into 7 classes.
Producer defined
0
Index of relative production including model results ≤ 0
Producer defined
8
Index of relative production containing values ranging from 4 to 8
Producer defined
4
Index of relative production containing values ranging from 0 to 4
Producer defined
16
Index of relative production containing values ranging from 12 to 16
Producer defined
12
Index of relative production containing values ranging from 8 to 12
Producer defined
21
Index of relative production containing values ranging from 16 to 21
Producer defined
26
Index of relative production containing values ranging from 21 to 26
Producer defined
sonu_accrp
Relative production scenario for Indiangrass based on model results with climate inputs from the GCM Access1.0, RCP 8.5, binned into 7 classes.
Producer defined
0
Index of relative production including model results ≤ 0
Producer defined
8
A class containing values ranging from 4 to 8
Producer defined
4
A class containing values ranging from 0 to 4
Producer defined
16
A class containing values ranging from 12 to 16
Producer defined
12
A class containing values ranging from 8 to 12
Producer defined
21
A class containing values ranging from 16 to 21
Producer defined
26
A class containing values ranging from 21 to 26
Producer defined
sonu_cesdf
The difference in models of current relative production and projected relative production in 2016-2045 under the climate scenario CESM1-BGC, RCP 4.5. Classified with break point intervals of 1 Standard Deviation.
Producer defined
0
Within 1 Standard Deviation overlapping zero comparing Maurer 1981 - 2010 and CESM1-BGC, RCP 4.5.
Producer defined
1
Results from CESM1-BGC, RCP 4.5 are 1 Standard Deviation higher than current (Maurer, 1981 - 2010).
Producer defined
3
Results from CESM1-BGC, RCP 4.5 are 3 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
2
Results from CESM1-BGC, RCP 4.5 are 2 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
-1
Results from CESM1-BGC, RCP 4.5 are 1 Standard Deviation lower than current (Maurer, 1981 - 2010).
Producer defined
-2
Results from CESM1-BGC, RCP 4.5 are 2 Standard Deviations lower than current (Maurer, 1981 - 2010).
Producer defined
-3
Results from CESM1-BGC, RCP 4.5 are 3 Standard Deviations lower than current (Maurer, 1981 - 2010).
Producer defined
sonu_gisdf
The difference in models of current relative production and projected relative production in 2016-2045 under the climate scenario GISS-E2-R, RCP 4.5. Classified with break point intervals of 1 Standard Deviation.
Producer defined
1
Results from GISS-E2-R, RCP 4.5 are 1 Standard Deviation higher than current (Maurer, 1981 - 2010).
Producer defined
2
Results from GISS-E2-R, RCP 4.5 are 2 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
0
No Standard Deviations between Maurer 1981 - 2010 and GISS-E2-R, RCP 4.5.
Producer defined
-1
Results from GISS-E2-R, RCP 4.5 are 1 Standard Deviation lower than current (Maurer, 1981 - 2010).
Producer defined
-3
Results from GISS-E2-R, RCP 4.5 are 3 Standard Deviations lower than current (Maurer, 1981 - 2010).
Producer defined
-2
Results from GISS-E2-R, RCP 4.5 are 2 Standard Deviations lower than current (Maurer, 1981 - 2010).
Producer defined
sonu_m8rdf
The difference in models of current relative production and projected relative production in 2016-2045 under the climate scenario Miroc-ESM, RCP 8.5. Classified with break point intervals of 1 Standard Deviation.
Producer defined
0
Within 1 Standard Deviation overlapping zero comparing Maurer 1981 - 2010 and Miroc-ESM, RCP 8.5.
Producer defined
1
Results from Miroc-ESM, RCP 8.5 are 1 Standard Deviation higher than current (Maurer, 1981 - 2010).
Producer defined
2
Results from Miroc-ESM, RCP 8.5 are 2 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
3
Results from Miroc-ESM, RCP 8.5 are 3 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
-1
Results from Miroc-ESM, RCP 8.5 are 1 Standard Deviation lower than current (Maurer, 1981 - 2010).
Producer defined
-2
Results from Miroc-ESM, RCP 8.5 are 2 Standard Deviations lower than current (Maurer, 1981 - 2010).
Producer defined
sonu_accdf
The difference in models results of current relative production and projected relative production in 2016-2045 under the climate scenario Access1.0, RCP 8.5. Classified with break point intervals of 1 Standard Deviation.
Producer defined
1
Results from Access1.0, RCP 8.5 are 1 Standard Deviation higher than current (Maurer, 1981 - 2010).
Producer defined
2
Results from Access1.0, RCP 8.5 are 2 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
4
Results from Access1.0, RCP 8.5 are 4 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
3
Results from Access1.0, RCP 8.5 are 3 Standard Deviations higher than current (Maurer, 1981 - 2010).
Producer defined
0
Within 1 Standard Deviation overlapping zero comparing Maurer 1981 - 2010 and Access1.0, RCP 8.5.
Producer defined
-1
Results from Access1.0, RCP 8.5 are 1 Standard Deviation lower than current (Maurer, 1981 - 2010).
Producer defined
bogr_maur, relative aboveground net primary productivity of blue grama (Bouteloua gracilis), in percent, derived from soil texture data and contemporary climate data from Maurer and others (2002) as averaged over 1981-2010; bogr_cesrp, relative aboveground net primary productivity of blue grama, in percent, derived from soil texture data and a relatively warm-wet climate scenario (CESM1-BGC, RCP 4.5) as averaged over 2016-2045; bogr_gisrp, relative aboveground net primary productivity of blue grama, in percent, derived from soil texture data and a relatively warm-dry climate scenario (GISS-E2-R, RCP 4.5) as averaged over 2016-2045; bogr_m8rrp, relative aboveground net primary productivity of blue grama, in percent, derived from soil texture data and a relatively hot-wet climate scenario (MIROC-ESM, RCP 8.5) as averaged over 2016-2045; bogr_accrp, relative aboveground net primary productivity of blue grama, in percent, derived from soil texture data and a relatively hot-dry climate scenario (ACCESS1-0, RCP 8.5) as averaged over 2016-2045; bogr_cesdf, classified difference between warm-wet climate scenario (CESM1-BGC, RCP 4.5) and contemporary (Maurer), classification breaks determined by 1 standard deviation (SD) increments, range in SD is -3 to 3; bogr_gisdf, classified difference between warm-dry climate scenario (GISS-E2-R, RCP 4.5) and contemporary (Maurer), classification breaks determined by 1 standard deviation (SD) increments, range in SD is -3 to 3; bogr_accdf, classified difference between hot-dry climate scenario (ACCESS1-0, RCP 8.5) and contemporary (Maurer), classification breaks determined by 1 standard deviation (SD) increments, range in SD is -3 to 3; bogr_m8rdf, classified difference between hot-wet climate scenario (MIROC-ESM, RCP 8.5) and contemporary (Maurer), classification breaks determined by 1 standard deviation (SD) increments, range in SD is -3 to 3;
Producer defined
U.S. Geological Survey Fort Collins Science Center (FORT)
Geospatial Services and Support
mailing and physical address
2150 Centre Avenue Bldg C
Fort Collins
Colorado
80526-8118
USA
Please email
fortdatamanagement@usgs.gov
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
Digital Data
https://doi.org/10.5066/P9DGJHEP
None. No fees are applicable for obtaining the data set.
20220418
Metadata Resources Staff / GIS Team
U.S. Geological Survey Fort Collins Science Center (FORT)
Geospatial Services and Support
mailing address
2150 Centre Avenue Bldg C
Fort Collins
CO
80526-8118
US
970-226-9100
970-226-9230
fortdatamanagement@usgs.gov
FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
FGDC-STD-001.1-1999
Record created using USGS Metadata Wizard tool. (https://github.com/usgs/fort-pymdwizard)