Yu Sheng

Seasonal freezing depth change is important in many environmental science and engineering applications. However, such changes are rare at region scales, especially over China, in the long time series. In this study, we evaluated the annual changes in seasonal maximum freezing depth (MFD) over China from 1971 to 2020 using an ensemble-modeling method based on support vector machine regression (SVMR) with 600 repetitions. Remote sensing data and climate data were input variables used as predictors. The models were trained using a large amount of annual measurement data from 600 meteorological stations. The main reason for using SVMR here was because it has been shown to perform better than random forests (RF), k-nearest neighbors (KNN), and generalized linear regression (GLR) in these cases. The prediction results were generally consistent with the observed MFD values. Cross validation showed that the model performed well on training data and had a better spatial generalization ability. The results show that the freezing depth of seasonally frozen ground in China decreased year by year. The average MFD was reduced by 3.64 cm, 7.59 cm, 5.54 cm, and 5.58 cm, in the 1980s, 1990s, 2000s, and 2010s, respectively, compared with the decade before. In the last 50 years, the area occupied by the freezing depth ranges of 0–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, and 120–140 cm increased by 99,300 square kilometers, 146,200 square kilometers, 130,300 square kilometers, 115,600 square kilometers, and 83,800 square kilometers, respectively. In addition to the slight decrease in freezing depth range of 100–120 cm, the reduced area was 29,500 square kilometers. Freezing depth ranges greater than 140 cm showed a decreasing trend. The freezing depth range of 140–160 cm, which was the lowest range, decreased by 89,700 square kilometers. The 160–180 cm range decreased by 120,500 square kilometers, and the 180–200 cm range decreased by 161,500 square kilometers. The freezing depth range greater than 200 cm, which was the highest reduction range, decreased by 174,000 square kilometers. Considering the lack of data on the change in MFD of seasonally frozen ground in China in recent decades, machine learning provides an effective method for studying meteorological data and reanalysis data in order to predict the changes in MFD.

With permafrost degeneration caused by climate change, water accumulation has increased in permafrost regions during recent decades. Water accumulation will deteriorate the existing status of engineering in cold regions. Water accumulation can have a thermal effect on permafrost during its formation, even resulting in failure of the subgrade. Moreover, the thermal effect is related to water temperature. However, temperature variation of water accumulation is complex, and its influencing factors include air temperature, environment, scope of water accumulation and so on. In order to conduct analysis of the damage mechanism of water accumulation on permafrost, it is necessary to explore the internal temperature change of water accumulation. This paper proposes a review of temperature calculation method for water accumulation in cold environment. The thermal calculation method for the space between the air and the water boundary of water accumulation is summarized. Water temperature change of water accumulation of various types is analyzed. The thermal calculation considering phase transformation in water accumulation is discussed, and heat transfer from the bottom of the water accumulation to the underlying soil is further studied. Finally, the key factors that are advantageous for conducting research about the thermal effect of water accumulation in permafrost are proposed to optimize the calculation method.

With global warming and increasingly frequent human activities in permafrost regions, it is of great significance to accurately and scientifically evaluate the probability and scope of thermal hazards in permafrost regions. Based on remote sensing image interpretation and field survey, the weight of evidence method (WoEM) was used to comprehensively evaluate the risk of thermal hazards in the source area of the Datong River. There were 10 factors, such as ground ice, mean annual ground temperature, mean annual air temperature, and ground soil type etc., selected in the WoEM. The results showed that the thermal hazard occurrences were closely influenced by ground ice, mean annual ground temperature, ground soil type, etc. The thermal hazards mainly occurred in the unstable permafrost with MAGT of –0.5 to –1.5 °C, accounting for 54.72% of the thermal hazards. The distribution area of thermal hazards in ground ice Level I and II accounts for 66.42%. Thermal hazards mainly occur in the soil types of bog soil and sapropel bog soil, accounting for 41.24% and 29.62% of the total thermal hazards area, respectively. Based on the influence factors and WoEM of thermal hazards occurrence, the probability map of thermal hazards occurrence in the source area was obtained. Additionally, the characteristics of the region with a high probability of thermal hazards occurrence and their causes were also comprehensively analyzed.

With permafrost degenerating caused by climate change, water accumulation increases in permafrost regions during last decades. Water accumulation will deteriorate existing status of engineering in cold regions. Water accumulation can make a thermal effect on permafrost under construction, even result in failure of the subgrade. Moreover, the thermal effect is related to water temperature. However, temperature variation of water accumulation is complex, and its influence factors include air temperature, environment, scope of water accumulation and so on. In order to make analysis of the damage mechanism of water accumulation on permafrost, it is necessary to explore the internal temperature change of water accumulation. This paper proposes a review about temperature calculation method of water accumulation in cold environment. The thermal calculation method between air and water boundary of water accumulation is summarized. Water temperature change of water accumulation with various type is analyzed. The thermal calculation considering phase transformation in water accumulation is discussed, and heat transfer from the bottom of water accumulation to the underlying soil is further studied. Finally, the key factors which are advantageous to make research about the thermal effect of water accumulation in permafrost is proposed to optimize calculation method.

The mechanism of pipe–soil interaction under frost heaving is complicated due to many factors affecting the pipe–soil system. In order to analyze the sensitivity of various pipe–soil interaction influencing factors and highlight the relationship between the factors and the pipe’s mechanical characteristics during frost heaving, a pipe–soil interaction model based on a semi-infinite elastic frozen soil foundation is developed. Besides, the mechanical indices characterizing the influence factors and their change law are emphatically explored. The results show that the pipe stress changes most obviously at the transition region between the frost-heaving and non-frost-heaving regions. The equivalent stress increases nonlinearly with the increase of foundation coefficient, linearly with the increase of frost heave and elastic modulus of pipe, and decreases nonlinearly with the increase of transition length and pipe wall thickness. The peak stress of the pipe increases linearly with the increase of temperature difference. Moreover, the maximum allowable frost heave deformation decreases nonlinearly with the increase of oil pressure. This study helps provide theoretical reference for the adjustment, control, and prediction of stress and deformation in the design of buried pipelines under frost heaving.

Permafrost distribution is of great significance for the study of climate, ecology, hydrology, and infrastructure construction in high-cold mountain regions with complex topography. Therefore, updated high-resolution permafrost distribution mapping is necessary and highly demanded in related fields. This case study conducted in a small catchment in the northeast of the Qinghai Tibet Plateau proposes a new method of using ground-penetrating radar (GPR) to detect the stratigraphic structure, interpret the characteristics of frozen ground, and extract the boundaries of permafrost patches in mountain areas. Thus, an empirical–statistical model of mountain frozen ground zonation, along with aspect (ASP) adjustment, is established based on the results of the GPR data interpretation. The spatial mapping of the frozen ground based on this model is compared with a field survey dataset and two existing permafrost distribution maps, and their consistencies are all higher than 80. In addition, the new map provides more details on the distribution of frozen ground. In this case, the influence of ASP on the distribution of permafrost in mountain areas is revealed: the adjustment of ASP on the lower limit of continuous and discontinuous permafrost is 180–200 m, the difference in the annual mean ground temperature between sunny and shady slopes is up to 1.4–1.6 °C, and the altitude-related temperature variation and uneven distribution of solar radiation in different ASPs comprehensively affect the zonation of mountain frozen ground. This work supplements the traditional theory of mountain permafrost zonation, the results of which are of value to relevant scientific studies and instructive to engineering construction in this region.

Spatial information of particle size fractions (PSFs) is primary for understanding the thermal state of permafrost in the Qinghai-Tibet Plateau (QTP) in response to climate change. However, the limitation of field observations and the tremendous spatial heterogeneity hamper the digital mapping of PSF. This study integrated log-ratio transformation approaches, variable searching methods, and machine learning techniques to map the surficial soil PSF distribution of two typical permafrost regions. Results showed that the Boruta technique identified different covariates but retained those covariates of vegetation and land surface temperature in both regions. Variable selection techniques effectively decreased the data redundancy and improved model performance. In addition, the spatial distribution of soil PSFs generated by four log-ratio models presented similar patterns. Isometric log-ratio random forest (ILR-RF) outperformed the other models in both regions (i.e., R² ranged between 0.36 to 0.56, RMSE ranged between 0.02 and 0.10). Compared with three legacy datasets, our prediction better captured the spatial pattern of PSFs with higher accuracy. Although this study largely improved the accuracy of spatial distribution of soil PSFs, further endeavors should also be made to improve model accuracy and interpretability for a better understanding of the interaction and processes between environmental predictors and soil PSFs at permafrost regions.

This study investigated the differential response to rainfall of soil moisture infiltration in the permafrost area of the downhill slope and in the seasonally frozen ground area of the uphill slope in Kangqiong small basin on the Qinghai-Tibet Plateau. The results show that: (1) influenced by the barrier of the permafrost layer, the suprapermafrost water level in the permafrost area rises significantly, while in the seasonally frozen ground area, the soil moisture infiltration is dominated by lateral flow and downward infiltration. (2) With the thawing of soil, the soil water content in the permafrost area gradually increases to reach a saturated state. Influenced by terrain, soil texture, evapotranspiration and infiltration capacity, the variation in the topsoil moisture content is large in the seasonally frozen ground area. (3) The presence of regional talik groundwater leads to a slight increase in the water level on the uphill slope. In addition to rainfall infiltration, the change in water level on the downhill slope is also affected by talik groundwater, which causes the rapid rise of water level on the down slope.

Degradation of permafrost with a thin overlying active layer can greatly affect vegetation via changes in the soil water and nutrient regimes within the active layer, while little is known about the presence or absence of such effects in areas with a deep active layer. Here, we selected the northeastern Qinghai-Tibet Plateau as the study area. We examined the vegetation communities and biomass along an active layer thickness (ALT) gradient from 0.6 to 3.5 m. Our results showed that plant cover, below-ground biomass, species richness, and relative sedge cover declined with the deepening active layer, while the evenness, and relative forb cover showed a contrary trend. The vegetation indices and the dissimilarity of vegetation composition exhibited significant changes when the ALT was greater than 2.0 m. The vegetation indices (plant cover, below-ground biomass, evenness index, relative forb cover and relative sedge cover) were closely associated with soil water content, soil pH, texture and nutrient content. Soil water content played a key role in the ALT–vegetation relationship, especially at depths of 30–40 cm. Our results suggest that when the ALT is greater than 2.0 m, the presence of underlying permafrost still benefits vegetation growth via maintaining adequate soil water contents at 30–40 cm depth. Furthermore, the degradation of permafrost may lead to declines of vegetation cover and below-ground biomass with a shift in vegetation species.