Impacts of Climate Change and Land Use/Cover Change on the Net Primary Productivity of Vegetation in the Qinghai Lake Basin

Abstract

The Qinghai Lake Basin acts as a natural barrier, preventing the western desert from spreading eastward. This is an important link in preserving the ecological stability of the northeastern region of the Qinghai–Tibet Plateau (QTP). Therefore, quantitative research into the net primary productivity (NPP) of vegetation and its driving force in the Qinghai Lake Basin is required. The effects of land use/cover change (LUCC) and climate change on NPP in the Qinghai Lake Basin were studied using R-contribution ratio and partial correlation analysis methods using MOD17A3H products, Land Use/Land Cover (LULC) data, and meteorological data. (1) The LULC of the Qinghai Lake Basin showed a trend that "the area of grassland, cultivated land, and unused land continued to decrease, while the area of other LULC types increased" from 2000 to 2020, according to the study's findings. Grassland, water bodies, construction land, and unused land dominated the mutual transformation of LULC types. (2) The NPP of the basin showed a growing trend, with a growth rate of 3.93 gC·m–2·a–1 before 2010 and 0.88 gC·m–2·a–1 after 2010. Significant regional heterogeneity was found in NPP, with gradients decreasing from southeast to northwest. (3) The impact of LUCC on overall NPP changes had gradually increased. Climate change has been the primary driver of NPP changes in the Qinghai Lake Basin over the last 20 years.

Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China

“…The results of this study revealed the presence of obvious differences in the average NPP of different land-use types in Yangzhou City from 2000 to 2020. Specifically, the values were ranked in descending order as forest > farmland > unused land > urban > water, indicating that the NPP level was related to land-use type, which is consistent with previous research results [26,39,45]. Vegetation NPP values in Yangzhou City exhibited significant temporal and spatial differentiation.…”

“…Geographical detectors were used to analyze 10 driving factors related to climate, society, and ecology. The main climate factors were temperature (TEM), rainfall, and solar radiation (RAD), which were highly correlated with vegetation growth [26]. The ecological factors were NDVI, WET, NDBSI, etc., which reflect the environmental ecological quality in the remote-sensing ecological index proposed by Xu Hanqiu [33].…”

“…For example, Zhang et al used the R-contribution ratio and partial correlation analysis to study the impact of land use and climate change on the spatiotemporal evolution characteristics of vegetation NPP in the Qinghai Lake Basin from 2000 to 2020. They found that climate change was the main driving factor for NPP change, and at the same time, the impact of land-use type change on NPP gradually increased [26]. Tian et al studied the effects of climate, human activities, and soil factors on the spatial differentiation of vegetation NPP in the Changhe Basin based on the moving window method and Pearson correlation analysis method.…”

“…At present, the main models for analyzing the trend of NPP changes are the combination of the Theil-Sen median trend analysis and the Mann-Kendall test [28]. The prediction of future development is mainly achieved through R/S analysis and the Hurst index method [26,28]. However, current research cannot fully explain the interaction between NPP changes and many driving factors.…”

The outflow characteristics of PV water lifting systems based on multiple tracking strategies

“…With lovely weather, it is cold in winter cold and warm and cool in summer and autumn. The average temperature in January is -17.5℃ and 11℃ in July, and the annual average temperature is -0.6℃ [7]. The annual sunshine hours in Guncha County are 3037 hours, with a 68% sunshine percentage.…”