Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai-Tibet Plateau of China from 2000 to 2015

Abstract

The vegetation ecosystem of the Qinghai-Tibet Plateau in China, considered to be the ′′natural laboratory′′ of climate change in the world, has undergone profound changes under the stress of global change. Herein, we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity (NPP) in the Qinghai-Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models. Subsequently, we quantitatively distinguished the relative effects of climate change (such as precipitation, temperature and evapotranspiration) and human activities (such as grazing and ecological construction) on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data. The average annual NPP in the Qinghai-Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000-2015. With respect to the inter-annual changes, the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015, with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015. In the Qinghai-Tibet Plateau, the regions with the increase in NPP (change rate higher than 10%) were mainly concentrated in the Three-River Source Region, the northern Hengduan Mountains, the middle and lower reaches of the Yarlung Zangbo River, and the eastern parts of the North Tibet Plateau, whereas the regions with the decrease in NPP (change rate lower than-10%) were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau. The gravity center of NPP in the Qinghai-Tibet Plateau has moved southwestward during 2000-2015, indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part. Further, a significant correlation was observed between NPP and climate factors in the Qinghai-Tibet Plateau. The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai-Tibet Plateau, and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai-Tibet Plateau. Furthermore, the relative effects of climate change and human activities on the NPP changes in the Qinghai-Tibet Plateau exhibited significant spatial differences in three types of zones, i.e., the climate change-dominant zone, the human activity-dominant zone, and the climate change and human activity 2

Variation in Reference Evapotranspiration over the Tibetan Plateau during 1961–2017: Spatiotemporal Variations, Future Trends and Links to Other Climatic Factors

“…The Tibetan Plateau (TP) is not only the main source of rivers in China and Asia, but also an important ecological barrier that is highly sensitive to global climate change [29][30][31]. The TP is a unique alpine mountain ecosystem that has had an intense response to climate change, especially in agriculture and animal husbandry [32][33][34]. More importantly, the water budget and energy exchange in the region have a considerable impact on the intensity of the Asian monsoon and high pressure [35,36].…”

Temporal and spatial evolution patterns and prediction of drought in China in recent 500 years

“…The gravity centre of a region is i z defined as the attribute value of the i-th plane space element (Guo et al, 2020c). Given its Cartesian coordinates as ( i x , i y ), then the spatial mean value of the region composed of N plane space elements is defined as a Cartesian coordinate point ( x , y ), and the calculation formula is as follows:…”

“…The fifth assessment report of the Intergovernmental Panel on climate change (IPCC) pointed out that under the stress of global warming, the frequency and intensity of extreme weather and climate events, such as drought and extreme high temperature, are increasing in the global scope (Bai et al, 2020;Guo et al,2020c). In recent 50 years, the annual average surface temperature warming in China is about 1.1℃, and the warming rate is close to 0.22℃/(10 a), which is significantly higher than the global or hemispherical average warming rate in the same period (Ren et al, 2005;Shi,2011;Guo et al, 2020d).…”

Spatial–temporal change patterns of vegetation coverage in China and its driving mechanisms over the past 20 years based on the concept of geographic division

“…These products are widely used in vegetation dynamic change monitoring, land cover change detection, macro vegetation cover classification and net primary productivity estimation and so on (Jepsen et al 2009;Sandra et al 2015;Chakraborty et al 2018). The meteorological data, including daily precipitation, daily maximum temperature, daily minimum temperature, and daily mean temperature, were obtained from the China Meteorological Data Network (available at http://data.cma.cn/) and then gridded data set for meteorological factor were interpolated with the method of Krigering based on 832 meteorological stations and 419 interpolated stations that derived on China Meteorological Forcing Dataset (http://westdc.westgis.ac.cn), especially for the western China with a resolution of 1 km (Guo et al 2014;2020a). The overall interpolated accuracy is 89%, which can satisfy the research requirements.…”

“…However, few studies were applied to analyse the imbalance and deviation of natural phenomenon (such as vegetation) with the gravity canter model (Guo et al 2020a). In order to investigate the differentiation patterns of the vegetation in different sub-regions and its response to the climate change (including the time-lag effect), this paper has introduced the gravity centre model to quantitatively explore the spatial-temporal change patterns of the vegetation coverage from a new perspective and introduced the concept of geographic division to analyse the difference of relationships between vegetation NDVI and climate factors based on MODIS NDVI and SPOT VEGETATION NDVI time series data and meteorological data from 1999 to 2018.…”