Grassland Dynamics and the Driving Factors Based on Net Primary Productivity in Qinghai Province, China

Abstract

Qinghai province is an important part of the Tibetan Plateau, and is characterized by extremely fragile ecosystems. In the last few decades, grasslands in this province have been influenced profoundly by climate change, as well as human activities. Here, we use the Carnegie-Ames-Stanford Approach (CASA) model to assess the dynamics of temperate steppe, alpine steppe, temperate meadow, alpine meadow, sparse grassland and herbaceous wetland via actual net primary productivity (NPPa). Our findings showed that: (1) From 2001 to 2016, the average NPPa in Qinghai province showed a fluctuation presented a generally increasing trend. The mean value of NPPa was 114.27 g C m−2 year−1, and the increase rate was 0.47 g C cm−2 year−1. (2) There were NPPa increase rate discrepancies among the six typical grassland biomes. Herbaceous wetland had the highest change rate, closely followed by alpine steppe, temperate steppe, alpine meadow, temperate meadow and sparse grassland. (3) The largest area of restoration mainly impacted by climate change reached 47.08% of the total grassland area, with human activities accounting for 21.74%. By contrast, the deteriorated area induced by human activities accounted for 9.78% of the total grassland. (4) Temperature may have been a greater factor than precipitation in driving grassland change during the study period. Decreasing grazing intensity and implementing effective protection measures were favorable to grassland restoration.

Spatiotemporal Dynamics of the Carbon Budget and the Response to Grazing in Qinghai Grasslands

“…This was consistent with the widespread unrestricted grazing activities in Qinghai that took place during these years (Dong et al, 2015;Abdalla et al, 2018;Dong S. K. et al, 2020). In general, the GPP under the grazed scenario was slightly lower than that under the ungrazed scenario, and the difference in GPP between these two scenarios tended to increase slowly over the study period, indicating that the extent of excessive grazing was increasing, which is inconsistent with the suggestions of previous studies (Sun et al, 2016;Xu et al, 2016;Wei et al, 2019). These previous studies have shown that ecological restoration driven by the local government coupled with the implementation of effective management measures has greatly improved the ecological environment of Qinghai grasslands.…”

Quantitative Effects of Climate Change on Vegetation Dynamics in Alpine Grassland of Qinghai-Tibet Plateau in a County

“…The area of Wulan County accounts for only 0.52% and 1.85% of the Qinghai-Tibet Plateau and Qinghai province, respectively [53,54]. The climate in Wulan County was relatively uniform and unique and was inconsistent with those reported by several previous studies at larger spatial scales [30,53,[55][56][57]. In particular, the change of temperature is obviously in disagreement with the overall rising trend of temperature in the Qinghai-Tibet Plateau [58,59].…”

“…The area of Wulan County accounts for only 0.52% and 1.85% of the Qinghai-Tibet Plateau and Qinghai province, respectively [53,54]. The climate in Wulan County was relatively uniform and unique and was inconsistent with those reported by several previous studies at larger spatial scales [30,53,[55][56][57].…”

Change in Alpine Grassland NPP in Response to Climate Variation and Human Activities in the Yellow River Source Zone from 2000 to 2020

“…The residual trend method was used to estimate the contribution of each factor to the NPP change for each pixel by constructing a function of grassland NPP change with different climatic factors and human activities [13]. Whereas the model-based anthropogenic NPP allocation method used climate-driven models to simulate grassland NPP p , and used remote sensing models to simulate the NPP a , with an artificially generated NPP defined as the difference between the NPP p and NPP a [14]. However, the residual trend method to quantitatively evaluate the impact of climate change and human activities on grassland productivity is affected by data sources, noise and accuracy, but it still has obvious shortcomings [15].…”

“…However, the residual trend method to quantitatively evaluate the impact of climate change and human activities on grassland productivity is affected by data sources, noise and accuracy, but it still has obvious shortcomings [15]. For example, Luo et al used the residual trend method to analyze the correlation between net primary productivity and climate change and human activities in Qinghai-Tibet Plateau from 2001 to 2015 [13]; Wei et al simulated climate and NPP a with the Zhou Guangsheng model and CAS A model, respectively, and discussed grassland dynamics and its driving factors in Qinghai Province from 2001 to 2016 [14]. Based on the water use efficiency of vegetation determined by the ratio of carbon dioxide flux equation (equivalent to NPP) to water vapor flux equation (equivalent to evapotranspiration) on the vegetation surface, Zhou Guangsheng and Zhang Xinshi deduced a regional evapotranspiration model linking energy balance equations and water balance equations according to two well-recognized water balance equations and heat balance equations.…”