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Global land use for 2015-2100 at 0.05° resolution under diverse socioeconomic and climate scenarios.


ABSTRACT: Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05°?×?0.05°) and spreads under a more comprehensive set of SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. We compared our dataset with the Land Use Harmonization version 2 (LUH2) dataset and found our results are in general spatially consistent with LUH2. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.

SUBMITTER: Chen M 

PROVIDER: S-EPMC7532189 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Global land use for 2015-2100 at 0.05° resolution under diverse socioeconomic and climate scenarios.

Chen Min M   Vernon Chris R CR   Graham Neal T NT   Hejazi Mohamad M   Huang Maoyi M   Cheng Yanyan Y   Calvin Katherine K  

Scientific data 20201002 1


Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented  ...[more]

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