Abstract. Spatiotemporal variations in net primary productivity (NPP) of vegetation offer insights to surface water and carbon dynamics, and are closely related to temperature and precipitation. We employed the Carnegie-Ames-Stanford Approach ecosystem model to estimate NPP of semiarid grassland in northern China between 2001 and 2013. Model estimates were strongly linearly correlated with observed values (R2 = 0.67, RMSE = 35 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 in annually, summer and autumn. Mean precipitation was also positively correlated with NPP in spring, but the correlation was not significant. 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 as 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.
Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China