The precipitation gradient (PG) is a crucial parameter for watershed hydrological models. Analysis of daily precipitation and elevation data from 30 stations in the southeastern Tibetan Plateau (SETP) during the active phase of the Indian monsoon reveals distinct patterns. Below 3000 m, precipitation generally decreases with increasing altitude. Between 3000 and 4000 m, precipitation patterns are more complex; in western regions, precipitation increases with elevation, whereas in eastern regions, it decreases. Above 4000 m, up to the highest observation point of 4841 m, precipitation continues to decrease with elevation, with a more pronounced decline beyond a critical height. In the SETP, PGs for LYR and NYR are positive, at 11.3 ± 2.7 mm/100 m and 17.3 ± 3.8 mm/100 m, respectively. Conversely, PLZB exhibits a negative PG of −22.3 ± 4.2 mm/100 m. The Yarlung Zangbo River (YLZBR) water vapor channel plays a significant role in these PGs, with the direction and flux of water vapor potentially influencing both the direction and magnitude of the PG. Additional factors such as precipitation intensity, the number of precipitation days, precipitation frequency, and station selection also significantly impact the PG. Notable correlations between elevation and variables such as the number of precipitation days, non-precipitation days, and precipitation intensity. The precipitation intensity gradients (PIGs) are 0.06 ± 0.02 mm/d/100 m, 0.11 ± 0.04 mm/d/100 m, and −0.18 ± 0.04 mm/d/100 m for the three catchments, respectively. Future research should incorporate remote sensing data and expand site networks, particularly in regions above 5000 m, to enhance the accuracy of precipitation–elevation relationship assessments, providing more reliable data for water resource simulation and disaster warning.

The internal response mechanism of vegetation change in fragile high-altitude ecosystems is pivotal for ecological stability. This study focuses on the Lhasa River Basin (LRB) on the Tibetan Plateau (TP), a typical high-altitude fragile ecosystem where vegetation dynamics are highly sensitive to climate change and human activities. Utilizing MODIS surface reflectance data (MOD09Q1), a general regression neural network (GRNN) was applied to create a 250 m resolution fractional vegetation cover (FVC) dataset from 2001 to 2022, whose accuracy was verified with field survey data. Through methods like the Theil–Sen Median trend analysis, Mann–Kendall significance test, Hurst exponent, and geographical detector, the collaborative mechanism of 14 driving factors was systematically explored. Key conclusions are as follows: (1) The FVC in the LRB evolved in stages, first decreasing and then increasing, with 46.71% of the basin area expected to show an improvement trend in the future. (2) Among natural factors, elevation (q = 0.480), annual mean potential evapotranspiration (q = 0.362), and annual mean temperature (q = 0.361) are the main determinants of FVC spatiotemporal variation. (3) In terms of human activities, land use type has the highest explanatory power (q = 0.365) for FVC. (4) The interaction of two factors on FVC is stronger than that of a single factor, with the elevation–land use interaction being the most significant (q = 0.558). These results deepen our understanding of the interactions among vegetation, climate, and humans in fragile high-altitude ecosystems and provide a scientific basis for formulating zoned restoration strategies on the TP.

Climate change has intensified the melting of glaciers and permafrost in high-altitude cold regions, leading to more frequent extreme hydrological events. This has caused significant variations in the spatiotemporal distribution of meltwater runoff from the headwater cryosphere, posing a major challenge to regional water security. In this study, the HBV hydrological model was set up and driven by CMIP6 global climate model outputs to investigate the multi-scale temporal variations of runoff under different climate change scenarios in the Tuotuo River Basin (TRB) within the source region of the Yangtze River (SRYR). The results suggest that the TRB will undergo significant warming and wetting in the future, with increasing precipitation primarily occurring from May to October and a notable rise in annual temperature. Both temperature and precipitation trends intensify under more extreme climate scenarios. Under all climate scenarios, annual runoff generally exhibits an upward trend, except under the SSP1-2.6 scenario, where a slight decline in total runoff is projected for the late 21st century (2061–2090). The increase in total runoff is primarily concentrated between May and October, driven by enhanced rainfall and meltwater contributions, while snowmelt runoff also shows an increase, but accounts for a smaller percentage of the total runoff and has a smaller impact on the total runoff. Precipitation is the primary driver of annual runoff depth changes, with temperature effects varying by scenario and period. Under high emissions, intensified warming and glacier melt amplify runoff, while low emissions show stable warming with precipitation dominating runoff changes.

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai–Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai–Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R²) of 0.76 and a root mean square error (RMSE) of 70 g/m². The average AGB for alpine meadows was determined to be 285 g/m², while for alpine steppes, it was 204 g/m², both surpassing the regional averages in the Qinghai–Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.