The Qinghai-Tibet Plateau (QTP) has the largest amount of permafrost in the low and middle latitudes, making it highly susceptible to the effects of global warming. In particular, the degradation of permafrost can be intensified by anomalous amplified warming. To accurately model the hydrothermal dynamics of permafrost and its future trends, the accumulation of high -precision, long-term data for the soil thermal conductivity (STC) in the active layer is crucial. However, no previous research has systematically investigated the spatio-temporal variation in the STC on the QTP over an extended period. Therefore, this study aims to fill this gap using the XGBoost model to analyze the STC in the permafrost on the QTP from 1980 to 2020. The findings of this study provide some preliminary insights. First, areas with high variation in the STC between the freeze-thaw periods over the 40 years gradually migrated from the western region to the central region. Second, since 2015, STC in more than 90 % of the permafrost region in the thawing period has shown positive growth. While, during the freezing period, the STC also exhibited an increase over most regions of the QTP, though the western region and parts of the northeastern region exhibited a decrease. Third, the spatial center of gravity for the STC during the freezing and thawing periods from 1980 to 2020 shifted. The mean STC was larger in the eastern and northeastern regions during the freezing period and larger in the western region during the thawing period. Fourth, both alpine swamp meadow and alpine meadow exhibited a gradual increase in the STC during the freeze-thaw period from 1980 to 2020. The conclusions and data products from this study are expected to support spatiotemporal modeling of the permafrost on the QTP and assist in the prognosis for its future.

Soil thermal conductivity (lambda), which describes the ability of the soil to transfer heat, is critical to understand the thermal regime of ground surfaces. In this study, in situ measurements of lambda were conducted at two field sites in the permafrost region of the central Qinghai-Tibet Plateau (QTP) and the results were used to evaluate 11 schemes of lambda at depths of 10-50 cm during the freeze-thaw cycle period. Our analyses revealed that lambda had a remarkable seasonal variation, due to the significant effects of soil moisture content and ice-water phase changes as temperature changed during the freeze-thaw cycle period. Among the selected schemes, the Johansen scheme, its three derivatives (i.e., the He scheme, Yang scheme and Zhao scheme), and the Campbell scheme were significantly superior to others. Moreover, the Johansen scheme ranked among the top schemes for frozen soil, while the Campbell scheme gave the most accurate values for unfrozen soil. The effects of different estimation methods of quartz content (q), dry lambda and the Kersten number (K-e) on the predicted schemes results were also evaluated. The results showed that, the methods used for the estimation of q and K-e had the greatest influence on the calculation results for the permafrost region. Overall, this research provides insights for the development of a lambda scheme for the permafrost region of the central QTP.

The Qinghai-Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai-Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai-Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai-Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study provide important baseline data for the subsequent analysis and simulation of the permafrost on the Qinghai-Tibet Plateau.

The Arctic amplification (AA) has exacerbated permafrost degradation, posing a serious threat to infrastructure security and other areas. Therefore, it is crucial to accurately assess the current status and future changes of permafrost, and reliable soil thermal conductivity (STC) is an important prerequisite for permafrost prediction. However, few methods and products are available for regional-scale STC simulations in permafrost of the Arctic, which lead to greater uncertainty in the simulation of land surface temperatures. This study conducted a preliminary STC simulation based on the XGBoost method. The results show that the average STC during the freezing period is between 0.71 similar to 0.73 W center dot m-1K-1, and around 0.67 W center dot m-1K-1 during the thawing period; The variation of STC between the thawing and freezing period ranged from -0.34-0.23 W center dot m-1K-1, with an average value of -0.02 W center dot m-1K-1; The areas where STC of the thawing period is smaller than that of the freezing period are mainly concentrated in the marginal areas near the sea on the continental side of North America and in the typical areas of plains, lowlands, and plateaus on the continental side of Eurasia. The areas with large STC during the thawing period are concentrated in mountainous areas.

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation-evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai-Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas.

Soil thermal conductivity (lambda), describing the ability of transferring heat in the soil, plays an important role in soil thermal behavior. The estimation of lambda at dryness (lambda(dry)) is essential for obtaining accurate lambda. This study aims to develop a new model for lambda(dry) across a wide range of the soil dry density (rho(d)) for soils with different textures. The lambda(dry) measurements of 75 soil samples from literature and 19 new soils from Qinghai-Tibet Plateau are used to establish the segmented relationships between lambda(dry) and rho(d) based on clustering algorithms. Our analyses reveal that when rho(d) = 1.4 g cm(-3), other soil properties must be taken into account. So, the performances of 12 widely used models are evaluated in these two different rho(d)& nbsp;ranges. Results show that when rho(d)& nbsp;=& nbsp;1.4 g cm(-3). This further confirms the necessity of segmentation. Finally, with a demarcation point of 1.4 g cm(-3), a new model with different calculation methods is proposed herein for predicting dry. The new model exhibits the highest accuracy in predicting & nbsp;lambda(dry) with the highest correlation coefficient (R), lowest root mean square error (RMSE), and smaller mean bias error values; compared to the previous models, the new model RMSE values are reduced by 16.6% on average for soils with rho(d)& nbsp;=& nbsp;1.4 g cm(-3), respectively. Namely, the new model is highly suitable for studying lambda(dry)& nbsp;for different rho(d)& nbsp;due to its simplicity and applicability.

Soil thermal conductivity (STC) is essential parameter for revealing thermodynamic changes and projecting changes in soil thermal regimes. However, the incorporation of different STC schemes into land surface process models (LSMs) can afford large errors. Thus, to accurately simulate soil thermal regimes in permafrost regions, a suitable STC scheme in LSMs is important. Herein, we selected nine normalized STC schemes and evaluated their performance in simulating STC and soil temperatures with in situ measurements in permafrost regions on the Tibetan Plateau (TP). These schemes were divided into three categories and incorporated into the latest version of the Community Land Model (CLM5.0). The results showed that the category comprising minerals, soil organic matter, and gravel soil afforded better performance at most sites than the other categories. The Balland and Arp (BA2005), Chadburn (C2015), and Bao (B2016) schemes had better performances in their affiliated categories, respectively. The BA2005 scheme ranked the best among the selected schemes with an average root-mean-square error decreased of 56.2% and 15.0% in simulating STC and soil temperatures compared to the default scheme, respectively. Additionally, the different schemes yielded a maximum difference of 2.69 W.m(-1) K-1 and 2.55 degrees C in simulating STC and soil temperature, respectively. Possible causes affecting the results were also investigated. The results indicated that soil moisture is a determinant: slight changes in soil moisture may cause large changes in thermal processes. However, the CLM5.0 yields large uncertainties of soil moisture. In addition, soil properties, atmospheric forcing data, and model structures also yielded errors in the simulated results. Note that no single STC scheme can be applied to all regions with satisfactory results. Therefore, multiple schemes need to be employed depending on their suitability in different regions. And more studies should focus on the accuracy of the hydraulic processes, especially soil hydraulic conductivity, unfrozen water, and snow processes.

The Qinghai-Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai-Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai-Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai-Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study provide important baseline data for the subsequent analysis and simulation of the permafrost on the Qinghai-Tibet Plateau.

Thermal conductivity is a key soil property widely used for agricultural production, land surface processing research, and geothermal resource development, among others. Although the rapid and accurate determination of soil thermal conductivity (A) has been a hot topic in recent years, there is still no unified model for the different soil types of soil. Furthermore, the lack of data on thermal conductivity and soil properties leads to errors in parametric models of thermal conductivity. In order to overcome the data shortage, a comprehensive lambda dataset of 2972 items was established and 10 influential parameters on thermal conductivity were identified in this study. Based on this, an empirical comparison was made between four classical parametric models and nine machinelearning models with and without an intelligent optimization algorithm was carried out. Of all the methods, the ensemble machine-learning methods perform better in lambda simulations. The XGBoost model has better simulation accuracy and generalization capability. Soil moisture properties are the key parameters in performing lambda simulations, while the soil texture-related properties such as bulk density and solid thermal conductivity, along with the sand content, also play an important role. The results of this study can provide basic thermal conductivity data and a parameterization scheme for referencing in land surface processing research.

By altering the physical properties of soil through root activity, plants can act as important agents in affecting soil hydrothermal properties. However, we still know little about how plant roots regulate these properties in certain ecosystems, such as alpine meadows. Thus, we studied the influence of roots on soil hydrothermal properties in the Qinghai-Tibet Plateau (QTP). Root biomass as well as soil physicochemical and hydrothermal properties were examined at a depth of 0-30 cm at three study sites in the QTP. The relationship between root biomass and saturated soil hydraulic conductivity (K-s) was examined, as was the applicability of common soil hydrothermal properties models to the alpine meadow system. Results revealed that approximately 91.10%, 72.52%, and 76.84% of root biomass was located in the top 0-10 cm of soil at Maqu, Arou, and Naqu, respectively. Compared with the bulk soil, the water-holding capacity of rhizosphere soil was enhanced by 20%-50%, while K-s was decreased by at least 2- to 3-fold. The thermal conductivity (lambda) of rhizosphere soils was lower than that of the bulk soil by 0.23-0.82 W m(-1).K-1 on average. Lastly, soil hydrothermal properties models that do not explicitly consider root effects overestimated the Ks and lambda in the rhizosphere soil of these systems. Overall, our results revealed distinctive differences in soil hydrothermal properties between the rhizosphere soil and the bulk soil in the QTP. This research has important implications for future modeling of soil hydrothermal processes of alpine meadow soils.