| Literature DB >> 32162439 |
Jinxun Liu1, Benjamin M Sleeter1, Zhiliang Zhu1, Thomas R Loveland1, Terry Sohl1, Stephen M Howard1, Carl H Key1, Todd Hawbaker1, Shuguang Liu2, Bradley Reed1, Mark A Cochrane3, Linda S Heath4, Hong Jiang5, David T Price6, Jing M Chen7, Decheng Zhou8, Norman B Bliss1, Tamara Wilson1, Jason Sherba1, Qiuan Zhu9, Yiqi Luo10, Benjamin Poulter11.
Abstract
Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.Entities:
Keywords: DGVM; carbon sequestration; ecosystem model; ecosystem productivity; land use and land cover change; wildfire
Year: 2020 PMID: 32162439 DOI: 10.1111/gcb.15079
Source DB: PubMed Journal: Glob Chang Biol ISSN: 1354-1013 Impact factor: 10.863