Alpine meadows are vital ecosystems on the Qinghai-Tibet Plateau, significantly contributing to water conservation and climate regulation. This study examines the energy flux patterns and their driving factors in the alpine meadows of the Qilian Mountains, focusing on how the meteorological variables of net radiation (Rn), air temperature, vapor pressure deficit (VPD), wind speed (U), and soil water content (SWC) influence sensible heat flux (H) and latent heat flux (LE). Using the Bowen ratio energy balance method, we monitored energy changes during the growing and non-growing seasons from 2022 to 2023. The annual average daily Rn was 85.29 W m-2, with H, LE, and G accounting for 0.56, 0.71, and -0.32 of Rn, respectively. Results show that Rn is the main driver of both H and LE, highlighting its crucial role in turbulent flux variations. Additionally, a negative correlation was found between air temperature and H, suggesting that high temperatures may suppress H. A significant positive correlation was observed between soil moisture and LE, further indicating that moist soil conditions enhance LE. In conclusion, this study demonstrates the impact of climate change on energy distribution in alpine meadows and calls for further research on the ecosystem's dynamic responses to changing climate conditions.

Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling. This study emphasized the influence of the initial soil temperature (ST) and soil moisture (SM) conditions on a land surface energy and water simulation in the permafrost region in the Tibetan Plateau (TP) using the Community Land Model version 5.0 (CLM5.0). The results indicate that the default initial schemes for ST and SM in CLM5.0 were simplistic, and inaccurately represented the soil characteristics of permafrost in the TP which led to underestimating ST during the freezing period while overestimating ST and underestimating SLW during the thawing period at the XDT site. Applying the long-term spin-up method to obtain initial soil conditions has only led to limited improvement in simulating soil hydrothermal and surface energy fluxes. The modified initial soil schemes proposed in this study comprehensively incorporate the characteristics of permafrost, which coexists with soil liquid water (SLW), and soil ice (SI) when the ST is below freezing temperature, effectively enhancing the accuracy of the simulated soil hydrothermal and surface energy fluxes. Consequently, the modified initial soil schemes greatly improved upon the results achieved through the long-term spin-up method. Three modified initial soil schemes experiments resulted in a 64%, 88%, and 77% reduction in the average mean bias error (MBE) of ST, and a 13%, 21%, and 19% reduction in the average root-mean-square error (RMSE) of SLW compared to the default simulation results. Also, the average MBE of net radiation was reduced by 7%, 22%, and 21%.

Surface energy budget and soil hydrothermal regime are crucial for understanding the interactions between the atmosphere and land surface. However, large uncertainties in current land surface process models exist, espe-cially for the permafrost regions in the Qinghai-Tibet Plateau. In this study, observed soil temperature, moisture, and surface energy fluxes at four sites in permafrost regions are chosen to evaluate the performance of CLM5.0. Furthermore, the soil property data, different thermal roughness length schemes, and dry surface layer (DSL) scheme are investigated. The results show that the soil property data is important for CLM5.0. The default scheme in CLM5.0 yields large errors for surface energy fluxes. The combination of the thermal roughness length and DSL scheme significantly improved the simulation of surface energy fluxes, especially for latent heat flux. The optimization of DSL scheme significantly improved soil temperature simulation and decreased the RMSE from 1.95 degrees C, 2.07 degrees C, 2.02 degrees C, and 2.95 degrees C to 1.34 degrees C, 1.35 degrees C, 1.35 degrees C and 2.29 degrees C in TGL site, respectively. The combination of the thermal roughness length and DSL scheme performed the best in shallow soil moisture, decreasing the RMSE from 0.136 m3 m- 3 to 0.049 m3 m- 3 in the XDT site but slightly enhancing the errors in middle soil. The interactions between surface energy and soil hydrothermal regime also discussed. However, the thermal roughness length and the DSL schemes are highly dependent on the condition of the underlying surface. Different schemes should be selected for different regions.

To understand the water, energy, and carbon cycles in the Tibetan Plateau (TP), it is essential to estimate seasonal and inter-annual variations in energy fluxes and evapotranspiration (ET) for alpine meadow ecosystems. The multiyear (2014-2019) energy fluxes and ET, for a typical alpine meadow at Arou station (northeastern TP), and their environmental and biophysical controls were evaluated using the eddy covariance method in this study. Latent heat flux (LE) was the dominant component of energy consumption during the growing season, whereas sensible heat flux (H) dominated energy partitioning during the non-growing season. H showed the opposite trend to LE, while the seasonal variation of soil heat flux (G) was small. The daily ET was primarily controlled by the available energy on the seasonal scale. Soil water content (SWC) and normalized difference vegetation index (NDVI) displayed secondary effects on ET during the non-growing and growing seasons, respectively. The inter-annual ET was relatively stable, ranging from 562.6 to 661.9 mm (coefficient of variation; CV = 7.4 %); this was slightly higher than the annual precipitation despite large variations in inter-annual precipitation (CV = 19.9 %) and was most likely due to snow and frozen ground melting. The cumulative ET in the growing season was about 77 % of the annual ET. There was a nonlinear increase in the daily Priestley-Taylor coefficient (alpha = ET/ETeq, where ETeq is the equilibrium evaporation) with an increase in bulk surface conductance (g(c)), which was insensitive to increases in g(c) that exceeded 15 mm s(-1). There was a good relationship between gc and NDVI. This study provides insights into the driving mechanisms of long-term variations in the energy partitioning and biophysical controls on ET in alpine meadow ecosystems.

Accurate simulation of the daily actual evaporation (E) is important for understanding and predicting the hydrological climate and terrestrial water-carbon cycle. However, the inclement environment and sparse observation network in the high-altitude areas of the Tibetan Plateau hinder the reliable estimation of actual evaporation. The Complementary Relationship (CR) of evaporation, which is a simple method for estimating the actual evaporation implemented with only routine meteorological data, can be used to study the complex feedback between the atmosphere and the surface. In this study, the eddy covariance and meteorological data were used to test the existence of the CR in the Fenghuo Mountains in the permafrost regions of the Tibetan Plateau. We further compared the application of the generalized nonlinear CR (B2015) and the latest calibrationfree CR (S2017) in estimating the actual daily evaporation. The results show that a nonlinear CR of evaporation exists in the Tibetan Plateau. The calibration-free nonlinear principle implemented improvements in the boundary condition shows a more robustness advantage than the generalized method. In addition, we also found that, except rainfall, the freezing-thawing process of active layer is a main reason of seasonal variation characteristics in energy fluxes. These findings broaden our understanding of the applicability of the CR theory and provide a simple and promising method for simulating evaporation on the Tibetan Plateau with the minimum data sets.

Understanding how land surfaces respond to climate change requires knowledge of land-surface processes, which control the degree to which interannual variability and mean trends in climatic variables affect the surface energy budget. We use the latest version of the Community Land Model version 3.5 (CLM3.5), which is driven by the latest updated hybrid reanalysis-observation atmospheric forcing dataset constructed by Princeton University, to obtain global distributions of the surface energy budget from 1948 to 2000. We identify climate change hotspots and surface energy flux hotspots from 1948 to 2000. Surface energy flux hotspots, which reflect regions with strong changes in surface energy fluxes, reveal seasonal variations with strong signals in winter, spring, and autumn and weak ones in summer. Locations for surface energy flux hotspots are not, however, fully linked with those for climate change hotspots, suggesting that only in some regions are land surfaces more responsive to climate change in terms of interannual variability and mean trends.