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Drought-Induced Reduction in Net Primary Productivity across Mainland China from 1982 to 2015

Authors :
Chengguang Lai
Jun Li
Zhaoli Wang
Xiaoqing Wu
Zhaoyang Zeng
Xiaohong Chen
Yanqing Lian
Haijun Yu
Peng Wang
Xiaoyan Bai
Source :
Remote Sensing, Vol 10, Iss 9, p 1433 (2018)
Publication Year :
2018
Publisher :
MDPI AG, 2018.

Abstract

Terrestrial net primary productivity (NPP) plays an essential role in the global carbon cycle as well as for climate change. However, in the past three decades, terrestrial ecosystems across mainland China suffered from frequent drought and, to date, the adverse impacts on NPP remain uncertain. This study explored the spatiotemporal features of NPP and discussed the influences of drought on NPP across mainland China from 1982 to 2015 using the Carnegie Ames Stanford Application (CASA) model and the standardized precipitation evapotranspiration index (SPEI). The obtained results indicate that: (1) The total annual NPP across mainland China showed an non-significantly increasing trend from 1982 to 2015, with annual increase of 0.025 Pg C; the spring NPP exhibited a significant increasing trend (0.031 Pg C year−1, p < 0.05) while the summer NPP showed a higher decreasing trend (0.019 Pg C year−1). (2) Most areas of mainland China were spatially dominated by a positive correlation between annual NPP and SPEI and a significant positive correlation was mainly observed for Northern China; specific to the nine sub-regions, annual NPP and SPEI shared similar temporal patterns with a significant positive relation in Northeastern China, Huang-Huai-Hai, Inner Mongolia, and the Gan-Xin Region. (3) During the five typical drought events, more than 23% areas of mainland China experienced drought ravage; the drought events generally caused about 30% of the NPP reduction in most of the sub-regions while the NPP in the Qinghai-Tibet Plateau Region generally decreased by about 10%.

Details

Language :
English
ISSN :
20724292
Volume :
10
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.0d3e7a817c4442bf8ae84abe44013f5a
Document Type :
article
Full Text :
https://doi.org/10.3390/rs10091433