Soil thermal conductivity (STC) plays a crucial role in regulating the energy distribution of both the surface and underground soil layers. It is widely applied in various fields, including engineering design, geothermal resource development and climate change research. A rapid and accurate estimation of STC remains a key focus in the study of soil thermodynamic parameters. However, the methods for estimating STC and their distinct characteristics have yet to be systematically reviewed. In this study, we used bibliometrics to comprehensively and systematically review the literature on STC, focusing on knowledge graph characteristics to analyze the development trend of calculation schemes. The main conclusions drawn from the study are as follows: (1) In recent years, most studies have been focused on soil thermal characteristics and their main contributing factors, the soil hydrothermal process in the Qinghai-Tibet Plateau, geothermal equipment and numerical simulations, and the exploration of geothermal resources. (2) A systematic review of various schemes indicates that no single scheme is universally applicable to all soil types. Moreover, a single parameterization scheme fails to meet the practical requirements of land surface process models. We evaluated the advantages and disadvantages of the traditional heat conduction schemes, parameterization schemes, and machine learning-based schemes and the findings suggest that a comprehensive scheme that integrates these three different schemes for STC simulations should be urgently developed.

Dynamical downscaling generally performs poorly on the Tibetan Plateau (TP), due to the region's complex topography and several aspects of model physics, especially convection and land surface processes. This study investigated the effects of the cumulus parameterization scheme (CPS) and land-surface hydrology scheme (LSHS) on TP climate simulation during the wet season using the RegCM4 regional climate model. To address these issues and seek an optimal simulation, we conducted four experiments at a 20 km resolution using various combinations of two CPSs (Grell and MIT-Emanuel), two LSHSs (the default TOPMODEL [TOP], and Variable Infiltration Capacity [VIC]). The simulations in terms of 2-m air temperature, precipitation (including large-scale precipitation [LSP] and convective precipitation [CP]), surface energy-water balance, as well as atmospheric moisture flux transport and vertical motion were compared with surface and satellite-based observations as well as the ERA5 reanalysis dataset for the period 2006-2016. The results revealed that the model using the Grell and TOP schemes better reproduced air temperature but with a warm bias, part of which could be significantly decreased by the MIT scheme. All schemes simulated a reasonable spatial distribution of precipitation, with the best performance in the experiment using the MIT and VIC schemes. Excessive precipitation was produced by the Grell scheme, mainly due to overestimated LSP, while the MIT scheme largely reduced the overestimation, and the simulated contribution of CP to total precipitation was in close agreement with the ERA5 data. The RegCM4 model satisfactorily captured diurnal cycles of precipitation amount and frequency, although there remained some differences in phase and magnitude, which were mainly caused by the CPSs. Relative to the Grell scheme, the MIT scheme yielded a weaker surface heating by reducing net radiation fluxes and the Bowen ratio. Consequently, anomalous moisture flux transport was substantially reduced over the southeastern TP, leading to a decrease in precipitation. The VIC scheme could also help decrease the wet bias by reducing surface heating. Further analysis indicated that the high CP in the MIT simulations could be attributed to destabilization in the low and mid-troposphere, while the VIC scheme tended to inhibit shallow convection, thereby decreasing CP. This study's results also suggest that CPS interacts with LSHS to affect the simulated climate over the TP.