Assessing the effects of climate variability and vegetation conversion on variations of net primary productivity in the mountainous area of North China

Abstract

Net primary productivity (NPP) is an important indicator of the terrestrial carbon cycle. Climate variability and land use changes are the two main factors contributing to spatial–temporal variations of NPP, and accurate estimations of these factors are crucial for understanding carbon cycling. In this study, the spatial and temporal patterns of NPP with climate variability and vegetation conversion in the mountainous area of North China were investigated over 2000–2018 by utilizing the Carnegie-Ames-Stanford Approach (CASA) model and remote sensing data, which provides a better understanding of how NPP varied after the implementation of the Grain to Green Program. The results indicate that the annual NPP follows a rising trend at a rate of 7.18 gC/m2·yr and with a mean value of 395.80 gC/m2·yr. Spatially, significant regional heterogeneity was detected in NPP with gradients decreasing from the southeast to the northwest, while steep increases were found in northern Hebei and southern Shanxi. Regarding different vegetation types, the mean annual NPP decreased following the order of broadleaf forest > mixed forest > needleleaf forest > shrubland > grassland > cropland. Furthermore, all vegetation types showed an increasing trend during the study period. Over the conversion from cropland (with low NPP) to forest (with high NPP), the NPP of cropland increased by 9.27 gC/m2·yr, suggesting that in relatively water-scarce regions, forest could fully utilize limited water resources for its growth. Among various meteorological factors, precipitation and DSI had a higher correlation with NPP in the mountainous area of North China. This shows that moisture indexes rather than temperature and solar radiation are the main driving factors of regional NPP. Finally, the combined effects of meteorological factors on NPP were quantified, and the cumulative contribution rate of precipitation, temperature, solar radiation, and DSI to NPP variation is 68%. These results will aid future water resources management and fragile ecosystem optimization to guarantee the sustainable utilization of water resources.