There are thousands of lakes in the Tibetan Plateau (TP), and most are saline. However, little is known about the responses of TP lakes to climate change, especially saline ones. We investigated the thermal responses of the largest freshwater lake (Ngoring Lake) in the TP and its nearby small saline lake (Hajiang Salt Pond) to climate change using the improved lake scheme in the Community Land model (CLM4-LISSS), in which we primarily developed the salinity parameterizations previously evaluated in the Great Salt Lake in USA and further considered the effect of salinity on the temperature of the maximum density of saline water in the present study. The improved lake model with salinity parameterizations was first applied to a saline lake in the TP, where saline lakes make up the majority of water bodies. The CLM4-LISSS model could effectively simulate lake surface water temperature (LSWT), lake water temperature (LT) and ice thickness in Ngoring Lake. Additionally, the model including our salinity parameterizations significantly improved simulations of LSWT and LT in Hajiang Salt Pond, especially in winter. The LSWT of the two completely opposite lakes were warming in the simulations at a rate above 0.6 °C/decade. Meteorological forces were the main driving factor, with increasing downward longwave radiation, air temperature and air humidity, as well as weakening winds contributing to LSWT increase. Compared to a hypothetical shallow freshwater lake, the greater depth of Ngoring Lake made its surface warm faster, and salinity slightly accelerated the warming of Hajiang Salt Pond. Monthly mean LSWT differences between the two lakes were induced by salinity effects in cold periods and lake depth in the unfrozen period. In response to a warming climate, the LSWT in the ice-free Hajiang Salt Pond rapidly increased from January to April due to the warming climate, whereas the LSWT of Ngoring Lake increased faster in the first and last month of the ice-cover period due to later ice-on and earlier ice-off. This study will provide a useful tool for saline lakes in the TP and help deepen our knowledge about the responses of TP lakes, especially the saline lakes, to climate change, as well as response differences between freshwater and saline lakes and the reasons for these differences.

Wind disasters are responsible for significant physical destruction, injury, loss of life, and economic damage. This study examined the extreme wind triggering mechanism over a typical mountain area with complex terrain, i.e., Dali city in Yunnan Province on the Yunnan-Guizhou Plateau in China. Using the observation data, we first optimized the Weather Research and Forecasting (WRF) model configuration and parametrization schemes for better simulating the wind in this area using a 1-month simulation. Then, the triggering mechanism of extreme wind was investigated by performing a series of sensitive experiments based on a typical extreme wind case. The results indicate that terrain uplift is critical for triggering the local 8–9-scale (the wind velocity between 17.2 and 24.4 m/s) extreme winds over high topography regions. When a large-scale atmospheric circulation is passing, accompanied with regional terrain lifting, the instantaneous wind velocity can reach 9- to 10-scale (the mean wind velocity between 20.8 and 28.4 m/s), causing broken power lines. These results suggest that it is essential to avoid sites where these factors can affect the operation of power transmission lines, or to establish warning systems in the existing systems.

There are numerous lakes in the Tibetan Plateau (TP) that significantly impact regional climate and aquatic ecosystems, which often freeze seasonally owing to the high altitude. However, the special warming mechanisms of lake water under ice during the frozen period are poorly understood, particularly in terms of solar radiation penetration through lake ice. The limited understanding of these processes has posed challenges to advancing lake models and improving the understanding of air–lake energy exchange during the ice-covered period. To address this, a field experiment was conducted at Qinghai Lake, the largest lake in China, in February 2022 to systematically examine thermal conditions and radiation transfer across air–ice–water interfaces. High-resolution remote sensing technologies (ultrasonic instrument and acoustic Doppler devices) were used to observe the lake surface changes, and MODIS imagery was also used to validate differences in lake surface conditions. Results showed that the water temperature under the ice warmed steadily before the ice melted. The observation period was divided into three stages based on surface condition: snow stage, sand stage, and bare ice stage. In the snow and sand stages, the lake water temperature was lower due to reduced solar radiation penetration caused by high surface reflectance (61% for 2 cm of snow) and strong absorption by 8 cm of sand (absorption-to-transmission ratio of 0.96). In contrast, during the bare ice stage, a low reflectance rate (17%) and medium absorption-to-transmission ratio (0.86) allowed 11% of solar radiation to penetrate the ice, reaching 11.70 W·m⁻², which increased the water temperature across the under-ice layer, with an extinction coefficient for lake water of 0.39 (±0.03) m⁻¹. Surface coverings also significantly influenced ice temperature. During the bare ice stage, the ice exhibited the lowest average temperature and the greatest diurnal variations. This was attributed to the highest daytime radiation absorption, as indicated by a light extinction coefficient of 5.36 (±0.17) m⁻¹, combined with the absence of insulation properties at night. This study enhances understanding of the characteristics of water/ice temperature and air–ice–water solar radiation transfer through effects of different ice coverings (snow, sand, and ice) in Qinghai Lake and provides key optical radiation parameters and in situ observations for the refinement of TP lake models, especially in the ice-covered period.

The simulator for hydrological unstructured domains (SHUD) is a cutting-edge, distributed hydrological model based on the finite volume method, representing the next generation of coupled surface–subsurface hydrological simulations. Its applicability in high-altitude, cold regions covered by snow and permafrost, such as the Yellow River source region, necessitates rigorous validation. This study employed the China Meteorological Forcing Dataset (CMFD) to simulate streamflow in the Yellow River source region from 2006 to 2018, comprehensively assessing the suitability of the SHUD model in this area. The SHUD model excels in simulating monthly streamflow in the Yellow River source region, while its performance at the daily scale is comparable to existing models. It demonstrated significantly better performance in the warm season compared to the cold season, particularly in the middle and lower reaches of the region. Distinct seasonal and regional differences were observed in simulation performance across sub-basins. However, the model encounters limitations when simulating the extensively distributed permafrost areas in the upstream region, primarily due to oversimplification of the permafrost thawing and freezing processes, which points the direction for future model improvements. Additionally, the model’s shortcomings in accurately simulating peak streamflow are closely related to uncertainties in calibration strategies and meteorological data inputs. Despite these limitations, the calibrated SHUD model meets the hydrological simulation needs of the Yellow River Source Region across various temporal scales, providing significant scientific reference for hydrological simulation and streamflow prediction in cold regions with snow and permafrost.