Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China.
Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China.
复制标题
中国北方半干旱草原净初级生产力时空变化的实证和模型估计
DOI：
10.1371/journal.pone.0187678
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
A Adams M
中科院分区：
文献类型：
--
作者：
Zhang S;Zhang R;Liu T;Song X;A Adams M
Spatiotemporal variations in net primary productivity (NPP) reflect the dynamics of water and carbon in the biosphere, and are often closely related to temperature and precipitation. We used the ecosystem model known as the Carnegie-Ames-Stanford Approach (CASA) to estimate NPP of semiarid grassland in northern China counties between 2001 and 2013. Model estimates were strongly linearly correlated with observed values from different counties (slope = 0.76 (p < 0.001), intercept = 34.7 (p < 0.01), R2 = 0.67, RMSE = 35 g C·m-2·year-1, bias = -0.11 g C·m-2·year-1). We also quantified inter-annual changes in NPP over the 13-year study period. NPP varied between 141 and 313 g C·m-2·year-1, with a mean of 240 g C·m-2·year-1. NPP increased from west to east each year, and mean precipitation in each county was significantly positively correlated with NPP—annually, and in summer and autumn. Mean precipitation was positively related to NPP in spring, but not significantly so. Annual and summer temperatures were mostly negatively correlated with NPP, but temperature was positively correlated with spring and autumn NPP. Spatial correlation and partial correlation analyses at the pixel scale confirmed precipitation is a major driver of NPP. Temperature was negatively correlated with NPP in 99% of the regions at the annual scale, but after removing the effect of precipitation, temperature was positively correlated with the NPP in 77% of the regions. Our data show that temperature effects on production depend heavily on recent precipitation. Results reported here have significant and far-reaching implications for natural resource management, given the enormous size of these grasslands and the numbers of people dependent on them.
登录
查看更多内容
影响因子：
8.7
作者：
Li, Wen Jun;Ali, Saleem H.;Zhang, Qian
通讯作者：
Zhang, Qian
通过CASA模型结合时间序列遥感数据和地面测量估算不同植被的最大光利用效率
影响因子：
5
作者：
Li, Ainong;Bian, Jinhu;Huang, Chengquan
通讯作者：
Huang, Chengquan
MODIS遥感数据与光利用效率模型相结合估算和分析净初级生产力
影响因子：
3.1
作者：
Li, Jia;Cui, Yaoping;Qin, Yaochen
通讯作者：
Qin, Yaochen
影响因子：
11.6
作者：
Luo, Yiqi;Gerten, Dieter;Rustad, Lindsey
通讯作者：
Rustad, Lindsey
影响因子：
1.3
作者：
Akiyama, Tsuyoshi;Kawamura, Kensuke
通讯作者：
Kawamura, Kensuke