In:
Global Change Biology, Wiley, Vol. 24, No. 1 ( 2018-01), p. 184-196
Abstract:
The uncertainties of China's gross primary productivity ( GPP ) estimates by global data‐oriented products and ecosystem models justify a development of high‐resolution data‐oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982–2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year 2 from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend ( p 〈 .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP . The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability ( IAV ) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP . The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold‐wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.
Type of Medium:
Online Resource
ISSN:
1354-1013
,
1365-2486
DOI:
10.1111/gcb.2018.24.issue-1
Language:
English
Publisher:
Wiley
Publication Date:
2018
detail.hit.zdb_id:
2020313-5
SSG:
12
