4.7 Article Data Paper

1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020-2100

Journal

SCIENTIFIC DATA
Volume 9, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41597-022-01204-w

Keywords

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Funding

  1. Key National Natural Science Foundation of China [42130107]
  2. National Key R&D Program of China [2017YFA0604402]
  3. Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University [KLGIS2020A02]

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In the past decades, China has experienced significant land use/land cover changes, with expectations for continued impact on the environment in the future. Efforts have been made to project future changes in LULC following specific pathways for sustainability. A new high-resolution LULC dataset has been developed for China under 24 scenarios, providing a valuable alternative for research in earth system modeling, ecosystem services, and carbon neutrality.
In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China's future LULC following the Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs). To supplements existing datasets with a high spatial resolution, comprehensive pathway coverage, and delicate account for urban land change, here we present a 1-km gridded LULC dataset for China under 24 comprehensive SSP-RCP scenarios covering 2020-2100 at 10-year intervals. Our approach is to integrate the Global Change Analysis Model (GCAM) and Future Land Use Simulation (FLUS) model. This dataset shows good performance compared to remotely sensed CCI-LC data and is generally spatio-temporally consistent with the Land Use Harmonization version-2 dataset. This new dataset (available at ) provides a valuable alternative for multi-scenario-based research with high spatial resolution, such as earth system modeling, ecosystem services, and carbon neutrality.

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