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33 high-resolution scenarios of land use and vegetation change in the Great Plains LCC region

Metadata Updated: June 15, 2024

A new version of USGS’s FORE-SCE model was used to produce unprecedented landscape projections for four ecoregions in the Great Plains (corresponding to the area represented by the Great Plains Landscape Conservation Cooperative). The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (29 land use and land cover classes), 3) broad spatial extent (covering much of the Great Plains), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change. A variety of scenarios were modeled from 2014 to 2100, with decadal timesteps (i.e., 2014, 2020, 2030, etc.). Modeled land use and natural vegetation classes were responsive to projected future changes in environmental conditions, including changes in groundwater and water access. Eleven primary land-use scenarios were modeled, from four different scenario families. The land-use scenarios focused on socioeconomic impacts on anthropogenic land use (demographics, energy use, agricultural economics, and other socioeconomic considerations). The following provides a brief summary of the 11 major land-use scenarios. 1) Business-as-usual - Based on an extrapolation of recent land-cover trends as derived from remote-sensing data. Overall trends were provided by 2001 to 2011 change in the National Land Cover Database, while change in crop types were extrapolated from 2008 to 2014 change in the Cropland Data Layer. Overall the scenario is marked by expansion of high-value traditional crops (corn, soybeans, cotton), with a concurrent decline in dryland wheat and some other lower-value crops. 2) Billion Ton Update scenario ($40 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update (BTU). The $40 scenario represents likely agricultural conditions under an assumed farmgate price of $40 per dry ton of biomass (for the production of biofuel). This is the least aggressive BTU scenario for placing "perennial grass" (for biofuel feedstock) on the landscape. 3) Billion Ton Update scenario ($60 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update. The $60 scenario represents likely agricultural conditions under an assumed farmgate price of $60 per dry ton of biomass (for the production of biofuel). At the higher farmgate price, the perennial grass class expands dramatically. 4) Billion Ton Update scenario ($80 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update. The $80 scenario represents likely agricultural conditions under an assumed farmgate price of $80 per dry ton of biomass (for the production of biofuel). With the high farmgate price, this scenario shows the highest expansion of perennial grass among the 11 modeled scenarios. 5) GCAM Reference scenario - Based on global-scale scenarios from the GCAM model, the "reference" scenario provides a likely landscape under a world without specific carbon or climate mitigation efforts. As such, it's another form of a "business-as-usual" scenario. 6) GCAM 4.5 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 4.5 model represents a mid-level mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of ~4.5 W/m2 by 2100. Agriculture becomes even more concentrated in the Great Plains and Midwestern US, resulting in substantial increases in cropland (including perennial grass used as feedstock for cellulosic biofuel production). 7) GCAM 2.6 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 2.6 model represents a very aggressive mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of only ~2.6 W/m2 by 2100. Agriculture becomes even more concentrated in the Great Plains and Midwestern US, resulting in substantial increases in cropland (including perennial grass used as feedstock for cellulosic biofuel production). 8) SRES A1B scenario - A scenario consistent with the Intergovernmental Panel on Climate Change (IPCC's) Special Report on Emissions Scenarios (SRES) A1B storyline. In the A1B scenario, economic activity is prioritized over environmental conservation. Agriculture expands substantially, including use of perennial grasses for biofuel production. 9) SRES A2 scenario - A scenario consistent with the IPCC's SRES A2 storyline. In the A2 scenario, global population levels reach 15 billion by 2100. Economic activity is prioritized over environmental conservation. This scenario has the highest overall expansion of traditional cropland, given the very high demand for foodstuffs and other agricultural commodities. 10) SRES B1 scenario - A scenario consistent with the IPCC's SRES B1 storyline. In the B1 scenario, environmental conservation is valued, as is regional cooperation. Much less agricultural expansion occurs as compared to the A1B or A2 scenarios. 11) SRES B2 scenario - A scenario consistent with the IPCC's SRES B2 storyline. In the B2 scenario, environmental conservation is highly valued. Of the eleven modeled scenarios, the B2 scenarios has the smallest overall agricultural footprint (traditional cropland, hay/pasture, perennial grasses). For each of the eleven land-use scenarios, three alternative climate / vegetation scenarios were modeled, resulting in 33 unique scenario combinations. The alternative vegetation scenarios represent the potential changes in quantity and distribution of the major vegetation classes that were modeled (grassland, shrubland, deciduous forest, mixed forest, and evergreen forest), as a response to potential future climate conditions. The three alternative vegetation scenarios correspond to climate conditions consistent with 1) The Intergovernmental Panel on Climate Change (IPCC's) Representative Concentration Pathway (RCP) 8.5 scenario (a scenario of high climate change), 2) the RCP 4.5 scenario (a mid-level climate change scenario), and 3) a mid-point climate that averages RCP4.5 and RCP8.5 conditions Data are provided here for each of the 33 possible scenario combinations. Each scenario file is provided as a zip file containing 1) starting 2014 land cover for the region, and 2) decadal timesteps of modeled land-cover from 2020 through 2100. The "attributes" section of the metadata provides a key for identifying file names associated with each of the 33 scenario combinations.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date May 31, 2023
Metadata Updated Date June 15, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date May 31, 2023
Metadata Updated Date June 15, 2024
Publisher Climate Adaptation Science Centers
Maintainer
@Id http://datainventory.doi.gov/id/dataset/a4ed46cbbbd2a4b00750b5045602d0cb
Identifier 7abf66ba-6785-4fd9-91dc-61273981ab15
Data Last Modified 2020-08-18
Category geospatial
Public Access Level public
Bureau Code 010:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id ce49530b-a2d5-43d7-bb35-dba39c960265
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -106.31,30.473888,-96.6094444,43.3286111
Source Datajson Identifier True
Source Hash c8fcd0c255fe5cf4fcc5a1473ea9645a8f2d6473f2ee0f0ab2bf50d326348cf7
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -106.31, 30.473888, -106.31, 43.3286111, -96.6094444, 43.3286111, -96.6094444, 30.473888, -106.31, 30.473888}

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