Grassland cover is strongly influenced by climate change. The response of grassland cover to climate change becomes complex with background climate. There have been some advances in research on the sensitivity of grassland vegetation to climate change around the world, but the differences in climate sensitivity among grassland types are still unclear in alpine grassland. Therefore, we applied MODIS NDVI data and trend analysis methods to quantify the spatial and temporal variation of grassland vegetation cover on the Qinghai-Tibet Plateau. Then, we used multiple regression models to analyze the sensitivity of fractional vegetation cover (FVC) to climatic factors (Temperature, Precipitation, Solar radiation, Palmer drought severity index) and summarized the potential mechanisms of vegetation sensitivity to different climatic gradients. Our results showed (1) a significant increasing trend in alpine desert FVC from 2000–2018 (1.12 × 10⁻³/a, R² = 0.56, p < 0.001) but no significant trend in other grassland types. (2) FVC sensitivity to climatic factors varied among grassland types, especially in the alpine desert, which had over 60% of the area with positive sensitivity to temperature, precipitation and PDSI. (3) The sensitivity of grassland FVC to heat factors decreases with rising ambient temperature while the sensitivity to moisture increases. Similarly, the sensitivity to moisture decreases while the sensitivity to thermal factors increases along the moisture gradient. Furthermore, the results suggest that future climate warming will promote grassland in cold and wet areas of the Qinghai-Tibet Plateau and may suppress vegetation in warmer areas. In contrast, the response of the alpine desert to future climate is more stable. Studying the impact of climate variation at a regional scale could enhance the adaptability of vegetation in future global climates.

Methane (CH₄) is one of the key greenhouse gases (GHGs) in the atmosphere with current concentration of 1859 ppb in 2017 due to climate change and anthropogenic activities. Rivers are of increasing concern due to sources of atmospheric CH₄. However, knowledge and data limitations exist for field studies of subtropical agricultural river catchments, particularly in southern China. The headspace balance method and the diffusion model method were employed to assess spatiotemporal variations of CH₄ diffusive fluxes from April 2015 to January 2016 in four order reaches (S1, S2, S3, and S4) of the Tuojia River, Hunan, China. Results indicated that both the dissolved concentrations and diffusive fluxes of CH₄ showed obvious spatiotemporal variations. The observed mean concentration and diffusive flux of CH₄ were 0.40 ± 0.02 μmol L⁻¹ and 41.19 ± 2.50 µg m⁻² h⁻¹, respectively, showing the river to be a strong source of atmospheric CH₄. The CH₄ diffusive fluxes during the rice-growing seasons were significantly greater than the winter fallow season (an increase of 80.26%). The spatial distribution of CH₄ diffusive fluxes increased gradually from (17.58 ± 1.42) to (55.56 ± 4.32) µg m⁻² h⁻¹ due to the organic and nutrient loading into the river waterbodies, with the maximum value at location S2 and the minimum value at location S1. Correlation analysis showed that the CH₄ diffusive fluxes exhibited a positive relationship with the dissolved organic carbon (DOC), salinity, and water temperature (WT), while a negative correlation occurred between CH₄ diffusive fluxes and the dissolved oxygen (DO) concentration, as well as the pH value. Our findings highlighted that a good understanding of exogenous nutrient loading in agricultural catchments will clarify the influence of human activities on river water quality and then constrain the global CH₄ budget.