Estimation of surface energy fluxes in the permafrost region of the Tibetan Plateau based on in situ measurements and the surface energy balance system model

Abstract

The surface energy balance is a key issue in land surface process research and important for studies of climate and hydrology. In this paper, the surface energy fluxes (net radiation, ground heat flux, sensible heat flux and latent heat flux) at the Tanggula (TGL) and Xidatan (XDT) sites were measured and the distributions of the regional surface energy fluxes on the Tibetan Plateau were obtained using a revised surface energy balance system (SEBS) model. The results show that the surface energy fluxes have obvious seasonal variations. At both sites, the sensible heat flux is highest in spring and lowest in summer, and the latent heat flux is highest in summer and lowest in winter. The high elevation, snow cover, freeze–thaw process, precipitation, vegetation and soil texture are important influencing factors for land surface energy fluxes. The time‐phase difference between the net radiation and ground heat flux for bare soils is estimated to be 2–3 hr. The ratio of ground heat flux and net radiation ranged from approximately 0.18 to 0.33, and a parameterization scheme for the remote sensing of ground heat flux over the Tibetan Plateau bare soil in summer is developed. The simulation results of the regional surface energy fluxes show that the distributions of surface parameters, such as vegetation, soil texture and soil moisture content, are important for understanding regional changes in the surface energy fluxes.

The surface energy balance in a cold and arid permafrost environment, Ladakh, Himalayas, India

“…The coefficients β Y P and α Y P (Eqs. 8 and 9) are calculated according to the parameterization of Ye and Pielke (1993) which considers evaporation as the sum of the proper evaporation from the surface and diffusion of water vapour in soil J. M. Wani et al: The surface energy balance in a cold and arid permafrost environment pores at greater depths:…”

Permafrost modelling with OpenFOAM®: New advancements of the permaFoam solver

“…As said above the coupling with surface processes models such as for instance Surface Energy Balance models (e.g. : [64,52,66]) may be necessary in some cases for building top boundary conditions for permaFoam. One can refer to Endrizzi et al [9] or to Painter et al [45] for examples of permafrost modelling tool with integrated coupling of surface and subsurface processes.…”

Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning

“…Beside the factors analyzed in this study, other factors are also reported to have an important role in influencing surface energy fluxes changes. For example, Yao et al [33] found that snow cover, soil content and soil texture have a large influence on the surface energy balance in high-elevation areas of the Tibet plateau. Therefore, more localized and in-depth analyses are required in the future.…”

“…However, the performance of machine learning methods depend highly on the selection of predictor variables, especially for their applications in surface energy fluxes, which are strongly influenced by human activities and natural environmental conditions [31,32]. For example, concerning the LE, the dominant role of vegetation changes in controlling global LE variation over the past several decades has been widely reported, while other factors, such as CO 2 concentration, precipitation and downward radiation continue to play crucial role in influencing local LE changes [33][34][35]. Therefore, quantifying the importance of predictor variables is necessary to improve our understanding of how the surface energy fluxes in response to ambient conditions change.…”