The global carbon sink potential of terrestrial vegetation can be increased substantially by optimal land management

DOI

It is well agreed that massive emissions of greenhouse gases (GHGs), particularly that from carbon dioxide (CO2), have been driving and will continue to drive global climate changes, one of which is global warming. Traditional measures by cutting carbon emissions are not enough; we need to find ways to sink more carbon from the atmosphere. Land management practices (LMPs) have massive effect on carbon sequestration from vegetation. Optimal land management practices (OLMPs) refer to LMPs that are capable of a higher, if not the highest, target carbon sequestration level given the current climatic and non-climatic conditions. Carbon sequestration potential, which is termed as carbon gap, is the difference in carbon sequestration with- and without- OLMPs.This dataset presents the carbon gap computed on the basis of implementing the OLMPs identified within a 20km local neighborhood under the same conditions in terms of landforms, vegetation biomes and soil profiles. We show that globally an extra of 13.73 PgC per year could be sequestered if OLMPs are implemented.This datasets include an image file Carbongap_2km.tif, the averaged global carbon gap flux for the years 2001-2018, and a number of statistical sheets related to the carbon gap.

Identifier
DOI https://doi.org/10.1594/PANGAEA.926334
Related Identifier https://doi.org/10.1038/s43247-021-00333-1
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.926334
Provenance
Creator Sha, Zongyao ORCID logo; Bai, Yongfei; Li, Ruren; Lan, Hai; Zhang, Xueliang; Li, Jonathon; Liu, Xuefeng; Xie, Yichun ORCID logo
Publisher PANGAEA
Publication Year 2021
Rights Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 22 data points
Discipline Earth System Research