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Xianglan Li ^a,b, Shunlin Liang^a,b,c,* , Guirui Yu^d, Wenping Yuan^a,b, Xiao Cheng^a,b, Jiangzhou Xia^a,b, Tianbao Zhao^e, Jinming Feng^b,e, Zhuguo Ma^e, Mingguo Ma^f, Shaomin Liu^g, Jiquan Chen^h,i, Changliang Shao^j, Shenggong Li^k, Xudong Zhang^l, Zhiqiang Zhang^m, Shiping Chen^j, Takeshi Ohtaⁿ, Andrej Varlagin^o, Akira Miyata^p, Kentaro Takagi^q, Nobuko Saiqusa^r, Tomomichi Kato^s

 

^a State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing 100875, China;

^b College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;

^c Department of Geography, University of Maryland, College Park, MD 20742, USA;

^d Key Laboratory of Ecosystem Network Observation and Modeling, Synthesis Research Center of Chinese Ecosystem Research Network, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

^e Key Laboratory of Regional Climate-Environment Research for the Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;

^f Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;

^g Sate Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China;

^h International Center for Ecology, Meteorology and Environment (IceMe), Nanjing University of Information Science and Technology, Nanjing 210044, China;

ⁱ Landscape Ecology and Ecosystem Science, Department of Environmental Sciences, University of Toledo, Toledo, OH 43606, USA;

^j State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;

^k Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

^l Institute of Forestry Research, Chinese Academy of Forestry, Beijing 100091, China;

^m College of Soil & Water Conservation, Beijing Forestry University, Beijing 100083, China;

ⁿ Graduate School of Bioagricultural Sciences, Nagoya University, Faro-Cho, Chikusa-Ku, Nagoya 464-8601, Japan;

^o A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Lenisky pr., 33, Moscow 119071, Russia;

^p National Institute for Agro-Environmental Sciences, Tsukuba 305-8604, Japan;

^q Teshio Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Toikanbetsu, Horonobe, Teshio 098-2943, Japan;

^r Center for Global Environmental Research, National Institute for Environmental Studies, Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan;

^s National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8569, Japan.

 

ABSTRACT

Gross primary production (GPP) is of significant importance for the terrestrial carbon budget and climate change, but large uncertainties in the regional estimation of GPP still remain over the terrestrial ecosystems in China. Eddy covariance (EC) flux towers measure continuous ecosystem-level exchange of carbon dioxide (CO2) and provide a promising way to estimate GPP. We used the measurements from 32 EC sites to examine the performance of a light use efficiency model (i.e., EC-LUE) at various ecosystem types, including 23 sites in China and 9 sites in adjacent areas with the similar climate environments. No significant systematic error was found in the EC-LUE model predictions, which explained 79% and 62% of the GPP variation at the validation sites with C3 and C4 vegetation, respectively. Regional patterns of GPP at a spatial resolution of 10 km × 10 km from 2000 to 2009 were determined using the MERRA (Modern Era Retrospective-analysis for Research and Applications) reanalysis dataset and MODIS (MODerate resolution Imaging Spectroradiometer). China's terrestrial GPP decreased from southeast toward the northwest, with the highest values occurring over tropical forests areas, and the lowest values in dry regions. The annual GPP of land in China varied between 5.63 Pg C and 6.39 Pg C, with a mean value of 6.04 Pg C, which accounted for 4.90–6.29% of the world's total terrestrial GPP. The GPP densities of most vegetation types in China such as evergreen needleleaf forests, deciduous needleleaf forests, mixed forests, woody savannas, and permanent wetlands were much higher than the respective global GPP densities. However, a high proportion of sparsely vegetated area in China resulted in the overall low GPP. The inter-annual variability in GPP was significantly influenced by air temperature (R2 = 0.66, P < 0.05), precipitation (R2 = 0.71, P < 0.05), and normalized difference vegetation index (NDVI) (R2 = 0.83, P < 0.05), respectively.

 

KEY WORDS: EC-LUE model; Gross primary production; Eddy covariance; MODIS; MERRA

 

PUBLISHED IN: ECOLOGICAL MODELLING, 2013, 261-262: 80-92.

 

SOURCE: http://www.sciencedirect.com/science/article/pii/S0304380013001890