Soil water and heat transfer is especially complicated during the freezing and thawing processes over the high‐altitude cold regions. In this study, four sensitivity tests of soil water and heat transfer parameterizations including replacing soil property data (SP1), soil resistance scheme modification (SP2), soil thermal conductivity scheme (SP3) and virtual temperature scheme (SP4), and four combination experiments (SP1+SP2+SP3/SP5, SP1+SP2+SP4/SP6, SP1+SP3+SP4/SP7, and SP1+SP2+SP3+SP4/SP8) were done using Community Land Model (CLM5.0) to examine its performances for soil water and heat transfer modeling on the Tibetan Plateau (TP) both in single‐point and regional simulations. The observed data from five eddy covariance sites, four soil moisture and temperature networks and 60 sites of soil temperature observations on the TP were used to evaluate the results. Single‐point simulations show that SP2 experiment reduced the wet biases of soil moisture in semiarid area, but enhanced the error of soil temperature. SP6 shows the best performances in simulating soil moisture, and SP3 in soil temperature. Regional simulations show that the SP7 experiment had the best performances for soil water and heat transfer simulation on the TP, and improved the simulation of soil freezing‐thawing processes. Compared to CLM5.0 default simulation, SP7 shows the best performances. For soil moisture, it reduced average Bias by 23%, Root Mean Square Error (RMSE) by 18%, and increased the Correlation Coefficient (Corr) by2%. For soil temperature, it reduced the Bias by 9%, 10%, 23%, and 13% at four soil depths on the TP, respectively.

Soil water and heat transfer is one of the most important parts of water and energy partition between atmosphere and land surface, and it is more complicated over the cold regions. In this study, the observed soil moisture and temperature are selected from four sites over the Tibetan Plateau (TP) to evaluate the performances two versions of Community Land Model (CLM), that is, CLM4.5 and CLM5.0. In addition, soil temperature observations from 67 sites and soil moisture observations from Maqu and Naqu monitoring network over the TP were used to evaluate the performances of regional simulations. The results indicated that the simulated soil temperature generally coincided with that of the observed, while CLM5.0 outputs are closer to the observed soil temperature in the arid and semiarid regions compared to CLM4.5. Generally, CLM5.0 tended to overestimate soil moisture at most sites at four soil depths (5, 10, 20, and 40 cm) but got some improvements at Maqu site. The overestimation of soil moisture was mainly caused by the introduction of a dry surface layer‐based (DSL) soil evaporation resistance parameterization in CLM5.0, which improves the soil evaporation simulation over the TP, especially in the semiarid region. Moreover, we tried to distinguish the factors that affect the soil water and heat transfer in the models. The results showed that soil property data play a main role in soil water and heat transfer modeling.

Lakes are sensitive indicators of climate change. There are thousands of lakes on the Tibetan Plateau (TP), and more than 1200 of them have an area larger than 1 km²; they respond quickly to climate change, but few observation data of lakes are available. Therefore, the thermal condition of the plateau lakes under the background of climate warming remains poorly understood. In this study, the China regional surface meteorological feature dataset developed by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS), MODIS lake surface temperature (LST) data and buoy observation data were used to evaluate the performance of lake model FLake, extended by simple parameterizations of the salinity effect, for brackish lake and to reveal the response of thermal conditions, radiation and heat balance of Qinghai Lake to the recent climate change. The results demonstrated that the FLake has good ability in capturing the seasonal variations in the lake surface temperature and the internal thermal structure of Qinghai Lake. The simulated lake surface temperature showed an increasing trend from 1979 to 2012, positively correlated with the air temperature and the downward longwave radiation while negatively correlated with the wind speed and downward shortwave radiation. The simulated internal thermodynamic structure revealed that Qinghai Lake is a dimictic lake with two overturn periods occurring in late spring and late autumn. The surface and mean water temperatures of the lake significantly increased from 1979 to 2012, while the bottom temperatures showed no significant trend, even decreasing slightly from 1989 to 2012. The warming was the strongest in winter for both the lake surface and air temperature. With the warming of the climate, the later ice-on and earlier ice-off trend was simulated in the lake, significantly influencing the interannual and seasonal variability in radiation and heat flux. The annual average net shortwave radiation and latent heat flux (LH) both increase obviously while the net longwave radiation and sensible heat flux (SH) decrease slightly. Earlier ice-off leads to more energy absorption mainly in the form of shortwave radiation during the thawing period, and later ice-on leads to more energy release in the form of longwave radiation, SH and LH during the ice formation period. Meanwhile, the lake–air temperature difference increased in both periods due to shortening ice duration.

Evapotranspiration (ET) is composed of soil evaporation (ET_s), canopy transpiration (ET_c), and canopy intercepted water evaporation (ET_w), and it plays an indispensable role in the water and energy cycles of land surface processes. More accurate estimations of variations in ET are essential for natural hazard monitoring and water resource management. An improved algorithm for ET_s with soil moisture and texture (SMT) and an optimized ET_c based on the MOD16 model (MOD16‐STM) are proposed for the cold, arid, and semiarid regions of the Tibetan Plateau (TP). The nonlinear relationships between the soil surface resistance (r_s^s) and soil surface hydration state in different soil textures were redefined by using five flux tower measurements over the TP. The value of the mean potential stomatal conductance per unit leaf area (C_L) used for ET_c calculation in grasslands was optimized at 0.0038 (m s⁻¹). The MOD16‐STM was compared with the MOD16 and a soil water index‐based Priestley‐Taylor algorithm (SWI‐PT) at five independent sites. The results showed that the half‐hourly, daily, and monthly estimations from the MOD16‐STM were closer to observations than those from the other two models. The results indicated that the MOD16‐STM increased R² by 0.2/0.05 and the index of agreement by 0.37/0.23 and deceased RMSE by 43.89/42.77 W m⁻² and the absolute mean bias (|MB|) by 46.23/45.89 W m⁻², when compared to the results of the MOD16 at daily/monthly scales. The results suggested that the MOD16‐STM will produce more accurate ET estimates over the flux sites and has great potential for ET estimation and land surface model improvement for the TP region.

Thousands of lakes on the Tibetan Plateau (TP) play a critical role in the regional water cycle, weather, and climate. In recent years, the areas of TP lakes underwent drastic changes and have become a research hotspot. However, the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear, which inhibits model development and the accurate simulation of lake climate effects. The source region of the Yellow River (SRYR) has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP. Since 2011, three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces. This study presents an eight-year (2012–19), half-hourly, observation-based dataset related to lake–atmosphere interactions composed of three sites. The three sites represent the lake surface, the lakeside, and the land. The observations contain the basic meteorological elements, surface radiation, eddy covariance system, soil temperature, and moisture (for land). Information related to the sites and instruments, the continuity and completeness of data, and the differences among the observational results at different sites are described in this study. These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model. The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.

The Tibetan Plateau (TP) lakes are sensitive to climate change due to ice-albedo feedback, but almost no study has paid attention to the ice albedo of TP lakes and its potential impacts. Here we present a recent field experiment for observing the lake ice albedo in the TP, and evaluate the applicability of the Moderate Resolution Imaging Spectroradiometer (MODIS) products as well as ice-albedo parameterizations. Most of the observed lake ice albedos on TP are <0.12, and the clear blue ice albedo is only 0.075, much lower than reported in the previous studies. Even that of ice covered with snow patches is only 0.212. MOD10A1 albedo product has the best agreement with observations, followed by those of MYD10A1. MCD43A3 product is consistently higher than the observations. Due to an error of snow flag and inconsistent time windows in MCD43A2 and MCD43A3, at certain times, the albedo of the ice without snow is even higher than that covered with snow. When the solar zenith angle is not considered, there is no significant correlation between the albedo and the ice surface temperature. None of the existing ice-albedo parameterizations can reproduce well the observed relationship of the albedo and surface temperature.