Alpine grassland in the Qinghai-Tibet Plateau is known to be sensitive to climate change. To quantify the impacts of climate change on alpine ecosystems at small scale, Wulan County in Qinghai Province was taken as the research object, and the relationships between vegetation dynamics and climate changes and the direct and indirect effects of climate factors on vegetation dynamics were analyzed using the methods of ordinary least squares, Pearson correlation analysis and path analysis, on the basis of MOD13A3 data and meteorological data from 2001 to 2020. The results showed that the Normalized Difference Vegetation Index (NDVI) in the growing season of the county and 5 vegetation types showed similar fluctuation processes and relationships with climate factors during the study period. The growing season NDVI (GSN) of shrubland and desert steppe respectively were the highest and lowest. The yearly mean values of GSN over the county ranged from 0.151 to 0.264, and increased extremely significantly with years at a rate of 0.0035 yr⁻¹. Spatially, GSN gradually decreased from northeast to southwest, and 97.2% of the county area showed an increasing trend in GSN. With years, growing season evaporation (GSE) decreased extremely significantly at a rate of 29.6 mm yr⁻¹, while growing season average relative humidity (GSARH) showed a significant increasing trend at a rate of 0.16% yr⁻¹. The correlations and effects of GSE, GSARH, and growing season precipitation (GSP) on vegetation dynamics were weakened in turn. GSE was the main direct effect factor, and the latter two were the indirect effect factors through GSE. The total contribution rates of GSE, GSARH and GSP to vegetation dynamics was about 78.0%. However, growing season average temperature (GSAT) had little effect on vegetation dynamics. This study provides information for understanding the characteristics of vegetation dynamics of alpine grassland in the Qinghai-Tibet Plateau at a small scale.

The calculation of theoretical carrying capacity (TCC) is one of the most fundamental tasks for the evaluation of the forage–livestock balance on grassland pastures. At present, the main methods for calculating TCC are the traditional theory (TT) and key pasture theory (KPT), but they both have obvious limitations in practical applications for the seasonal grazing regions in the alpine desert grasslands of China. In this study, the pastures in Wulan County (PWC) were selected as the research area. The unique features of the research area as well as the faulty applications of TT and KPT were fully analyzed, and then a new method named dynamic key pasture theory (DKPT) was established for calculating TCC by improving KPT with the introduction of the two dynamic factors of the livestock slaughter rate ($\alpha$) and coefficient of grassland productivity ($\beta$). TT, KPT and DKPT were respectively used to calculate the TCC of the PWC under different precipitation scenarios. The forage–livestock balance in the PWC determined using DKPT was assessed by the forage–livestock balance index (FLBI). The results showed that the natural processes of grassland supply and livestock demand were significantly imbalanced in time and space and formed a dynamic cycle with four subprocesses, which was the supporting basis of DKPT; DKPT effectively improved the rationality of TCC and offered greater guidance for the evaluation of the forage–livestock balance in the seasonal grazing regions of China’s alpine desert grasslands. In the PWC, the TCCs of different pastures calculated by DKPT were clearly different from those calculated by TT and KPT; the areas of the pastures divided were extremely imbalanced, with a huge surplus of more than 50% in cool-season pastures; in the representative year of 2016, the pastures in the Xisai Basin were underloaded (FLBI = −35.19%) on the whole, while the pastures in the Chaka Basin were overloaded (FLBI = 24.34%).

The responses of grassland net primary productivity (NPP) to climate change (CC) and human activities (HA) have received much attention and are inconsistent on different spatial scales. The accurate and quantitative evaluation of the impacts of CC and HA on grassland NPP at a county scale is very important to reveal the external driving factors on grassland NPP and guide the protection of the grassland ecosystem in the arid sandy area of China. In this study, the improved CASA model was adopted to quantify the grassland NPP in Otog Banner, China from 2001 to 2020. The spatiotemporal dynamics of grassland NPP and the relationships between grassland NPP and climate factors in space were analyzed using the methods of simple linear regression and relative sensitivity coefficient. Furthermore, the relative contributions to grassland NPP dynamics caused by CC and HA were explored using the quantitative method based on partial derivative. The results revealed that the mean value of grassland NPP was 175.17 g C m⁻² yr⁻¹, and exhibited a significant decrease trend periodically at a rate of 2.14 g C m⁻² yr⁻¹ from 2001 to 2020. The spatial distribution of grassland NPP increased from west to east gradually and ranged in 17.48–498.09 g C m⁻² yr⁻¹. Grassland NPP exhibited significant linear patterns along the gradients of climate factors, and was the most sensitive to sunshine duration (SSD). Approximately 86.83% of the grassland showed a degradation trend and 39.71% showed a serious degradation trend. The CC contribution to grassland NPP dynamics was 0.593 g C m⁻² yr⁻¹, and precipitation was the key driving climate factor, while the contribution of HA was −2.733 g C m⁻² yr⁻¹, which was the primary factor leading to large-scale degradation of grassland in Otog banner. This study indicates that the status of the grassland ecosystem in Otog Banner is not optimistic, and measures for grassland ecosystem restoration and improvement need to be further strengthened.

While climate change and vegetation dynamics have a strong relationship, few studies have specifically measured the effects of these factors on runoff and sediment development in the gully zone of the Loess Plateau. This study investigates the monthly impacts of climatic change and vegetation dynamics on water flow and sediment movement in the gully zone of the Loess Plateau between 2000 and 2016. In this study, the standard gully watershed of the Loess Plateau is investigated using partial least squares structural equation modeling (PLS-SEM). The state of vegetation in the watershed is characterized by utilizing the vegetation index obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS), along with monthly hydro-meteorological and vegetation data. The collective impacts of vegetation dynamics, climate change, and runoff contribute to 74.3% of the monthly fluctuations in sediment levels. The data indicate that 31.6% of the monthly runoff variability can be ascribed to the combined influence of climate change and vegetation dynamics. Climate change significantly influences flow and sediment via direct and indirect mechanisms, primarily by altering the growth and development of vegetation, which subsequently impacts both runoff and sediment. The impact of vegetation on sediment (−0.246) is more pronounced compared to its impact on runoff (−0.239). Furthermore, the impact of vegetation on sediment (−0.038) was significantly less significant compared to the impact on runoff (−0.208). Hence, the vegetation in the watershed primarily mitigates sediment deposition and suspended sediment transit in the water body by regulating runoff, thereby reducing the sediment load. This study examines the intricate correlation between climate change and vegetation dynamics on water flow and sediment deposition in the gully region of the Loess Plateau. It can serve as a helpful resource for managing water resources, allocating agricultural water, and planning soil conservation in the region.

There are nonequilibrium characteristics of grassland ecosystems driven by water, and constraints on the development scale of artificially irrigated grassland caused by the lack of water resources in arid pastoral areas. Based on the interaction of water, forage, and livestock, this study built a model of livestock-carrying capacity within the dynamic water–forage–livestock balance, to analyze the livestock carrying capacity of arid pastoral areas. The results showed that compared with the fixed livestock carrying capacity of 1.0898 million sheep units with a dynamic forage–livestock balance, the livestock carrying capacity based on the dynamic water–forage–livestock balance of OtogBanner were in a multi-equilibrium state due to the fluctuation of rangeland productivity caused by a change in precipitation conditions and the adjustment of the tame grassland irrigation scale caused by the change in water demand of other water users in the pastoral area. Under the conditions of the wet, normal, and dry years, the livestock carrying capacity was 1.632 million standard sheep units under the 26.5 thousand hm² tame grassland developing areas, 1.3037 million standard sheep units under the 25.9 thousand hm² tame grassland developing areas, and 0.9155 million standard sheep units respectively under 22.4 thousand hm² tame grassland developing areas. This fluctuation change was more prominent in the pastoral areas with rangeland as the key field. Besides this, the model could effectively identify the predicament of water and forage resources. At present, the overload of forage resources and water resources coexisted in the pastoral area of OtogBanner, and an important reason for this was that the distribution of water and forage resources was poorly matched with the mode of animal husbandry production. The value of 1.3037 million sheep units was recommended to the livestock-carrying capacity of OtogBanner according to the model. This study could provide a new method for the calculation of livestock carrying capacity, and offered a scientific basis for the protection of the grassland ecological environment and the sustainable development of animal husbandry in the arid pastoral area of OtogBanner.