The Erhai Lake Basin is a crucial water resource of the Dali prefecture. This research used the soil and water assessment tool (SWAT) and the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) to estimate blue and green water flows. Then the spatial and temporal change of blue and green water flows was investigated. With the hypothetical climate change scenarios, the sensitivity of blue and green water flows to precipitation and temperature has also been analyzed. The results showed that: (1) The CMADS reanalysis dataset can capture the observed probability density functions for daily precipitation and temperature. Furthermore, the CMADS performed well in monthly variables simulation with relative bias and absolute bias less than 7% and 0.5 °C for precipitation and temperature, respectively; (2) blue water flow has increased while green water flow has decreased during 2009 to 2016. The spatial distribution of blue water flow was uneven in the Erhai Lake Basin with the blue water flow increased from low altitudes to mountain areas. While the spatial distribution of green water flow was more homogeneous; (3) a 10% increase in precipitation can bring a 20.8% increase in blue water flow with only a 2.5% increase in green water flow at basin scale. When temperature increases by a 1.0 °C, the blue water flow and green water flow changes by −3% and 1.7%, respectively. Blue and green water flows were more sensitive to precipitation in low altitude regions. In contrast, the water flows were more sensitive to temperature in the mountainous area.

In the context of global warming, snow cover changes have an extremely important impact on the hydrological cycle and the redistribution of water resources in arid and semi-arid regions. In this paper, based on the daily cloud-free snow area remote sensing product data in the source region of Yangtze River (SRYR) from 2000 to 2019, the snow phenology variables such as the snow cover day (SCD), snow onset date (SOD), snow end date (SED), and snow duration day (SDD) were extracted separately for each hydrological year, and the vertical distribution of snow cover area (SCA) in the SRYR was analyzed by combining with the digital elevation model (DEM). In addition, we also combined climate factors and land cover types to further explore the spatiotemporal variation characteristics of snow phenology in response to different influencing factors, in order to reveal the spatiotemporal variation patterns of snow cover in the SRYR. The results showed that: (1) The SCA in the SRYR has a more obvious vertical distribution, with the maximum SCA reaching 61.58% at high elevation, while at low elevation, the SCA is mostly below 20%. The distribution of SCD in the study area showed a significant exponential correlation with DEM (R² = 0.87). (2) The area of SOD in the SRYR showed an advanced trend of about 63.37%, while the area of SED showed a delayed trend of about 69.59%, and the area which showed significant trends is 4.29% and 4.36%, respectively. Therefore, the trends of both SOD and SED showed insignificant changes. (3) Temperature change is the main factor affecting the change of snow cover in the SRYR. Among them, 90.9% of the regions showed a significant positive correlation between temperature and SCD, while precipitation showed a significant negative correlation with SCD in about 75.3% of the total area of SRYR. Under the stable snow area (SCD > 60), the land cover type is glacial or permanent snow about 1.5 × 10³ km², which covers almost the entire glacial or permanent snow of the SRYR.

The Qinghai–Tibet Plateau (QTP), known as the Earth’s third pole, is highly sensitive to climate change. Various environmental degradation has occurred due to the effects of climate warming such as the degradation of permafrost and the thickening of active layers. Evapotranspiration, as a key element of hydrothermal coupling, has become a key factor of the plateau environment for deciphering deterioration, and the FAO P-M model has a good physical foundation and simple model data requirements as a primary tool to study the plateau evapotranspiration. There has been a large research base, but the estimation of evapotranspiration in alpine regions is still subject to many uncertainties. This is reflected in the fact that the classification of underlying surface types has not been sufficiently detailed and the evapotranspiration characteristics of some special underlying surface types are still unclear. Therefore, in this work, we modified the FAO P-M coefficients based on the characteristics of actual evapotranspiration measured by the Eddy covariance system and the key influencing factors to better simulate the actual evapotranspiration in alpine swamp meadow. The results were as follows: (1) Both ET_a measured by the Eddy covariance system and ET₀ calculated by FAO P-M showed the same trend at the daily and annual scales and hysteresis was confirmed to exist, so the error caused by hysteresis should be considered in further research. (2) The annual ET_a was 566.97 mm and annual ET_a/P was 0.76, and about 11.19% of ET_a occurred during the night. The ET_a was 2.15 during the non-growing seasons, implying that a large amount of soil water was released into the air by evapotranspiration. (3) The evapotranspiration characteristics of alpine swamp meadow are formed under the following conditions: control of net radiation (R_n) affected by VPD during the growing season and affected by soil temperature and humidity during the non-growing season. Precipitation and soil water content are no longer the main controlling factors of evapotranspiration during the growing season at the alpine swamp meadow as the volume soil water content tends to saturate. (4) The basic corrected K_c was 1.14 during the initial and mid-growing season, 1.05 during the subsequent growing season, and 0–0.25 during the non-growing season, and the correction factor process can also provide ideas for correcting the K_c of other vegetation.

Quantifying the soil moisture (SM) response to precipitation is pivotal for predicting hydrologic resilience and ecosystem stability in fragile cold regions. This is true in alpine permafrost environments characterized by variable precipitation phases and strong seasonal freeze–thaw dynamics. However, critical knowledge gaps persist due to the scarcity of high-resolution, multi-layer in situ observations in these remote areas. Using hourly data from three sites in the Tanggula Mountains (2020–2024), this study employs an event-based analytical framework combining logistic regression and linear regression to quantify multi-layer (10–100 cm) SM responses to rain, snow, and mixed-phase precipitation across seasons. Core findings indicate the following: (1) Precipitation thresholds with 80% probability of triggering SM responses rise sharply with depth during the cold period (10 cm: 1–11 mm; 50–100 cm: often >15 mm or unreachable) but increase gradually in the warm period (10 cm: 0.4–5 mm; 50 cm: <15 mm). Mixed-phase precipitation refers to the lowest amount of precipitation (0.4–2.5 mm at 10 cm), followed by rain (1–11 mm) and snow (2–5 mm). (2) Warm-period regression slopes are consistently steeper than cold-period slopes (at 10 cm, 0.0024 vs. 0.0010 for rainfall). Mixed-phase precipitation yields the steepest slopes, approximately 50% higher than rainfall at 10 cm in the warm period (0.0037 vs. 0.0024), due to its longer duration and dual-supply mode. For lag time, cold-period values are more widely dispersed due to multiple interacting factors, while warm-period values are concentrated; only warm-period rainfall exhibits a clear monotonic increase in lag time with depth, consistent with unsaturated flow theory. (3) The quantified regression slopes, threshold values, and phase-specific efficiencies provide transferable metrics for calibrating infiltration models and evaluating frozen-ground hydrology schemes. The finding that mixed-phase events are the primary driver of deep-layer recharge, despite accounting for a smaller fraction of the total event count, has direct implications for water resource assessment in high-altitude catchments where precipitation phase composition is often oversimplified. Overall, this study moves beyond qualitative descriptions by providing quantifiable, transferable metrics that advance the mechanistic understanding of precipitation–SM coupling in alpine permafrost regions.