| Abstract: | Ground temperature is an important factor influencing ground source heat pumps, ground energy storage systems, land-atmosphere processes, and ecosystem dynamics. This paper presents an accurate development model (DM) based on a segment function: it can derive ground temperatures in permafrost regions of the Qinghai-Tibetan Plateau (QTP) from air temperature in case of shallow soil depths and without using air temperature data in case of deep soil depths. Here, we applied this model to simulate the active layer and permafrost ground temperature at the Tanggula observation station. The DM results were compared with those from the artificial neural network (ANN), support vector machine (SVM), and multiple linear regressions (MLR) models, which were based on climatic variables from prior models and on ground temperatures derived from observations at different depths. The results revealed that the effect of air temperature on simulated ground temperatures decreased with increasing depth; moreover, ground temperatures fluctuated greatly within the shallow layers but remained rather stable with deeper layers. The results also indicated that the DM has the best performance for the estimation of soil temperature compared to the MLR, SVM, and ANN models. Finally, we obtained the three average statistics indexes, i.e., mean absolute error (MAE), root mean square error (RMSE), and the normalized standard error (NSEE) at TGL site: they were equal to 0.51 °C, 0.63 °C, and 0.15 °C for the ground temperature of active layer and to 0.08 °C, 0.09 °C, and 0.07 °C for the permafrost temperature. |
