Search Results (113)

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

Interferometric synthetic aperture radar (InSAR) plays a significant role in monitoring permafrost deformation. However, owing to environmental constraints in permafrost regions, some regions exhibit temporal incoherence, which results in deformation with fewer measurement points and difficulties with deformation automatic detection. In this study, a full-coverage deformation rate map of the 10 km buffer of the Qinghai–Tibet Engineering Corridor (QTEC) was generated by combining nine driving factors and the deformation rate of the 5 km buffer along the QTEC based on three machine learning methods. The importance of the factors contributing to ground deformation was explored. The experimental results show that support vector regression (SVR) yielded the best performance (R2 = 0.98, RMSE = 0.76 mm/year, MAE = 0.74 mm/year). The 10 km buffer of deformation data obtained not only preserved the original deformation data well, but it also filled the blank areas in the deformation map. Subsequently, we trained the Faster R-CNN model on the deformation rate map simulated by SVR and used it for the automatic detection of permafrost thaw settlement areas. The results showed that the Faster R-CNN could identify the permafrost thawing slump quickly and accurately. More than 300 deformation areas along the QTEC were detected through our proposed method, with some of these areas located near thaw slump and thermokarst lake regions. This study confirms the significant potential of combining InSAR and deep learning techniques for permafrost degradation monitoring applications. Full article

Accurate permafrost mapping in mountainous regions is hindered by sparse in situ observations and heterogeneous terrain. This study develops a GPR-augmented machine learning framework to map mountain permafrost in the northeastern Qinghai–Tibet Plateau. A total of 1037 presence–absence samples were compiled from boreholes, soil pits, 128 GPR transects collected in 2009, and 22 additional empirical points above 4700 m, covering diverse topographic and thermal conditions. Thirteen classification algorithms were evaluated using 5-fold cross-validation repeated 40 times, with LightGBM, CatBoost, XGBoost, and RF achieving top performance (F1 > 0.98). Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. Aspect-controlled permafrost boundaries were captured, with modeled lower elevation limits varying by >200 m across slope directions. SHAP analysis showed that climate and soil variables contributed nearly 80% to model outputs, with LST, FDD, BD, and TDD being dominant. Several predictors exhibited threshold or nonlinear responses, reinforcing their physical relevance. Additional experiments confirmed that integration of GPR and high-elevation constraint samples significantly improved model generalization, especially in underrepresented terrain zones. This study demonstrates that a GPR-augmented machine learning framework can support cost-effective, physically informed mapping of frozen ground in complex alpine environments. Full article

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R²: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost. Full article

The sunny–shady slope effect (SSSE) disrupts the thermal balance of permafrost subgrades, resulting in asymmetric thaw plates that lead to structural deformations such as longitudinal cracking and slope instability along the Qinghai–Tibet Highway (QTH). This study proposes three morphological indicators—road shoulder thawing depth difference (RSTDD), offset distance (OD), and active layer thickness difference (ALTD)—to quantitatively characterize the asymmetry of thaw plates. Through integrating remote sensing data and large-scale geological survey results with an earth–atmosphere coupled numerical model and a random forest (RF) prediction framework, we assessed the spatial distribution of thaw asymmetry along the permafrost section of the QTH. The results indicate the following: (1) The ALTD values are overall very small and almost unaffected by the SSSE. The RSTDD increases with mean annual ground temperature (MAGT) before stabilizing, while the OD shows no significant response to the MAGT. The RSTDD and OD ranges are 0–3.38 m and 0–8.65 m, respectively, and they are greatly affected by the SSSE. (2) The RSTDD and OD show obvious spatial differences in different geographical regions of the QTH. An RSTDD greater than 2 m is concentrated in the Xidatan Faulted Basin and Chumar River High Plain. An OD greater than 3 m is mainly distributed from the Chumar River High Plain to the Tongtian River Basin. (4) The RSTDD and OD are most affected by subgrade orientation with importance values of 49.84% and 51.80%, respectively. The importance of the effect of mean average ground temperature (MAGT) on the active layer thickness is 80.58%. Full article

Influenced by a warm and humid climate, the permafrost on the Qinghai–Tibet Plateau is undergoing significant degradation, leading to the occurrence of extensive thermokarst landforms. Among the most typical landforms in permafrost areas is thaw slump. This study, based on three periods of data from keyhole images of 1968–1970, the fractional images of 2006–2009 and the Gaofen (GF) images of 2018–2019, combined with field surveys for validation, investigates the distribution characteristics and spatiotemporal variation trends of thaw slumps in the Hoh Xil area and evaluates the susceptibility to thaw slumping in this area. The results from 1968 to 2019 indicate a threefold increase in the number and a twofold increase in total area of thaw slumps. Approximately 70% of the thaw slumps had areas less than 2 × 10⁴ m². When divided into a grid of 3 km × 3 km, about 1.3% (128 grids) of the Hoh Xil region experienced thaw slumping from 1968 to 1970, while 4.4% (420 grids) showed such occurrences from 2018 to 2019. According to the simulation results obtained using the informativeness method, the area classified as very highly susceptible to thaw slumping covers approximately 26% of the Hoh Xil area, while the highly susceptible area covers about 36%. In the Hoh Xil, 61% of the thaw slump areas had an annual warming rate ranging from 0.18 to 0.25 °C/10a, with 70% of the thaw slump areas experiencing a precipitation increase rate exceeding 12 mm/10a. Future assessments of thaw slump development suggest a possible minimum of 41 and a maximum of 405 thaw slumps occurrences annually in the Hoh Xil region. Under rapidly changing climatic conditions, apart from environmental risks, there also exist substantial potential risks associated with thaw slumping, such as the triggering of large-scale landslides and debris flows. Therefore, it is imperative to conduct simulated assessments of thaw slumping throughout the entire plateau to address regional risks in the future. Full article

Lake surface water area (LSWA) and lake surface water temperature (LSWT) are critical indicators of climate change, responding rapidly to global warming. However, studies on the synergistic variations of LSWA and LSWT are scarce, and the coupling relationships among lakes with different environmental characteristics remain unclear. In this study, the relative growth rate of LSWA (RK_LSWA); the absolute growth rates of annual maximum, mean, and minimum LSWTs (i.e., K_{LSWT_max}, K_{LSWT_mean}, K_{LSWT_min}); and the absolute growth rates of the difference between maximum and minimum LSWT (LSWT_mmd) (K_{LSWT_mmd}) were investigated across more than 4000 lakes in China using long-term Landsat data, and their coupling relationships among different lake types (i.e., permafrost and non-permafrost recharge, endorheic or exorheic lakes, and natural and artificial lakes) were comprehensively analyzed. Results indicate significant differences in the trends of LSWA and LSWT, as well as their interrelationships across various regions and lake types. In the Qinghai–Tibet Plateau (QTP), 57.8% of lakes showed an increasing trend in LSWA, with 2.4% of the lakes showing moderate expansion (RK_LSWA values of 0.1–0.2), while over 27.5% of lakes in the South China (SC) region displayed shrinkage in LSWA (RK_LSWA values were between −0.1~0%/year). Regarding LSWTs, 49.8% of lakes in the QTP exhibited a K_{LSWT_max} greater than 0, and 47.9% of lakes showed a K_{LSWT_mean} greater than 0. In contrast, 48.1% of lakes in the Middle and Lower Yangtze River Plain (MLYP) had a K_{LSWT_max} less than 0, and 48.5% of lakes had a K_{LSWT_mean} less than 0. Additionally, lakes supplied by permanent permafrost demonstrated more significant growth in both LSWA and LSWT than those supplied by non-permanent permafrost. Further analysis revealed that approximately 20.2% of the lakes experienced a concurrent increase in both mean LSWT and LSWA, whereas around 18.9% of the lakes exhibited a simultaneous rise in both LSWT_mmd and LSWA. This suggests that the expansion of lakes in China is correlated with both rising temperatures and greater temperature differences. This study provides deeper insights into the response of Chinese lakes to climate change and offers important references for lake resource management and ecological conservation. Full article

Thermokarst lakes are widely distributed on the Qinghai-Tibet Plateau (QTP). However, owing to the lack of high-precision remote sensing imagery and the difficulty of in situ monitoring of permafrost regions, quantifying the changes in the distribution of thermokarst lakes is challenging. In this study, we used four machine learning methods—random forest (RF), gradient boosting decision tree (GBDT), classification and regression tree (CART), and support vector machine (SVM)—and combined various environmental factors to assess the distribution of thermokarst lakes from 2015 to 2020 via the Google Earth Engine (GEE). The results indicated that the RF model performed optimally in the extraction of thermokarst lakes, followed by GBDT, CART, and SVM. From 2015 to 2020, the number of thermokarst lakes increased by 52%, and the area expanded by 1.6 times. A large proportion of STK lakes (with areas less than or equal to 1000 m²) gradually developed into MTK lakes (with areas between 1000 and 10,000 m²) in the central part of the QTP. Additionally, thermokarst lakes are located primarily at elevations between 4000 and 5000 m, with slopes ranging from 0 to 5°, and the sand content is approximately 65%. The normalized difference water index (NDWI) and enhanced vegetation index (EVI) were the most favourable factors for thermokarst lake extraction. The results provide a scientific reference for the assessment and prediction of dynamic changes in thermokarst lakes on the QTP in the future, which will have important scientific significance for the studies of carbon and water processes in alpine ecosystems. Full article

As the climate continues to warm, the thawing of ice-rich permafrost leads to changes in the polygonal patterned ground (PPG) landscape, exhibiting an array of spatial heterogeneity in trough patterns, governing permafrost stability and hydrological and ecosystem dynamics. Developing accurate methods for detecting trough areas will allow us to better understand where the degradation of PPG occurs. The Geomorphon approach is proven to be a computationally efficient method that utilizes digital elevation models (DEMs) for terrain classification across multiple scales. In this study, we firstly evaluate the appliance of the Geomorphon algorithm in trough mapping in Prudhoe Bay (PB) in Alaska and the Wudaoliang region (WDL) on the central Qinghai–Tibet Plateau. We used the optimized DEM resolution, flatness threshold (t), and search radius (L) as input parameters for Geomorphon. The accuracy of trough recognition was evaluated against that of hand-digitized troughs and field measurements, using the mean intersection over union (mIOU) and the F1 Score. By setting a classification threshold, the troughs were detected where the Geomorphon values were larger than 6. The results show that (i) the lowest t value (0°) captured the microtopograhy of the troughs, while the larger L values paired with a DEM resolution of 50 cm diminished the impact of minor noise, improving the accuracy of trough detection; (ii) the optimized Geomorphon model produced trough maps with a high accuracy, achieving mIOU and F1 Scores of 0.89 and 0.90 in PB and 0.84 and 0.87 in WDL, respectively; and (iii) compared with the polygonal boundaries, the trough maps can derive the heterogeneous features to quantify the degradation of PPG. By comparing with the traditional terrain indices for trough classification, Geomorphon provides a direct classification of troughs, thus advancing the scientific reproducibility of comparisons in PB and WDL. This work provides a valuable method that may propel future pan-Arctic studies of trough mapping. Full article

Understanding the dynamics of soil respiration (Rs) in response to freeze–thaw cycles is crucial due to permafrost degradation on the Qinghai–Tibet Plateau (QTP). We conducted continuous in situ observations of Rs using an Li-8150 automated soil CO₂ flux system, categorizing the freeze–thaw cycle into four stages: completely thawed (CT), autumn freeze–thaw (AFT), completely frozen (CF), and spring freeze–thaw (SFT). Our results revealed distinct differences in Rs magnitudes, diurnal patterns, and controlling factors across these stages, attributed to varying thermal regimes. The mean Rs values were as follows: 2.51 (1.10) μmol·m⁻²·s⁻¹ (CT), 0.37 (0.04) μmol·m⁻²·s⁻¹ (AFT), 0.19 (0.06) μmol·m⁻²·s⁻¹ (CF), and 0.68 (0.19) μmol·m⁻²·s⁻¹ (SFT). Cumulatively, the Rs contributions to annual totals were 89.32% (CT), 0.79% (AFT), 5.01% (CF), and 4.88% (SFT). Notably, the temperature sensitivity (Q10) value during SFT was 2.79 times greater than that in CT (4.63), underscoring the significance of CO₂ emissions during spring warming. Soil temperature was the primary driver of Rs in the CT stage, while soil moisture at 5 cm depth and solar radiation significantly influenced Rs during SFT. Our findings suggest that global warming will alter seasonal Rs patterns as freeze–thaw phases evolve, emphasizing the need to monitor CO₂ emissions from alpine meadow ecosystems during spring. Full article

In the context of global warming, landscapes with ice-rich permafrost, such as the Qinghai–Tibet Plateau (QTP), are highly vulnerable. The expansion of thermokarst lakes erodes the surrounding land, leading to collapses of various scales and posing a threat to nearby infrastructure and the environment. Assessing the susceptibility of thermokarst lakes in remote, data-scarce areas remains a challenging task. In this study, Landsat imagery and human–computer interaction were employed to improve the accuracy of thermokarst lake classification. The study also identified the key factors influencing the occurrence of thermokarst lakes, including the lake density, soil moisture (SM), slope, vegetation, snow cover, ground temperature, precipitation, and permafrost stability (PS). The results indicate that the most susceptible areas cover 19.02% of the QTP’s permafrost region, primarily located in southwestern Qinghai, northeastern Tibet, and the Hoh Xil region. This study provides a framework for mapping the spatial distribution of thermokarst lakes and contributes to understanding the impact of climate change on the QTP. Full article

Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We aimed to analyze seasonal variations in the surface water temperature, clarify historical trends in the phenological characteristics of lake ice, and predict future temperature changes in surface water of the thermokarst lakes using the air2water model. The results indicated that in comparison with air temperature, the thermokarst lake’s surface water temperature showed a certain lag and significantly higher values in the warm season. The warming rate of the thermokarst lake’s average surface water temperature based on historical data from 1957 to 2022 was 0.21 °C per decade, with a notably higher rate in August (0.42 °C per decade) than in other months. Furthermore, the ice-covered period steadily decreased by 2.12 d per decade. Based on the Coupled Model Intercomparison Project 6 projections, by 2100, the surface water temperatures of thermokarst lakes during the warm season are projected to increase by 0.38, 0.46, and 2.82 °C (under scenarios SSP126, SSP245, and SSP585), respectively. Compared with typical tectonic lakes on the Qinghai–Tibet Plateau, thermokarst lakes have higher average surface water temperatures during ice-free periods, and they exhibit a higher warming rate (0.21 °C per decade). These results elucidate the response mechanisms of thermokarst lakes’ surface water temperature and the phenological characteristics of lake ice in response to climate change. Full article

The Qilian Mountains, located on the northeastern edge of the Qinghai–Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai–Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze–thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze–thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east–west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai–Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety. Full article

Soil particle distribution is one of the basic parameters for many Earth system models, while the soil texture data are largely not available. This is especially true for complex terrains due to the difficulties in data acquisition. Here, we selected an area, Wenquan area, with rolling mountains and valleys, in the eastern Qinghai–Tibet Plateau (QTP) as the study area. Using the random forest model, we established quantitative models of silt, clay, and sand content, and environmental variables, including elevation, slope, aspect, plane curvature, slope curvature, topographic wetness index, NDVI, EVI, MAT, and MAP at different depths based on the survey data of 58 soil sample points. The results showed that sand content was the highest, accounting for more than 75% of the soil particles. Overall, the average values of clay and silt gradually decreased with increasing soil profile depth, while sand showed the opposite pattern. In terms of spatial distribution, clay and silt are higher in the southeast and lower in the northwest in each standard layer, while sand is just the opposite. The random forest regression model showed that vegetation condition was a controlling factor of soil particle size. These results showed that random forest applies to predicting the spatial distribution of soil particle sizes for areas with complex terrains. Full article

Exploring the complex relationship between the freeze–thaw cycle and the surface energy budget (SEB) is crucial for deepening our comprehension of climate change. Drawing upon extensive field monitoring data of the Qinghai-Tibet Plateau, this study examines how surface energy accumulation influences the thawing depth. Combined with Community Land Model 5.0 (CLM5.0), a sensitivity test was designed to explore the interplay between the freeze–thaw cycle and the SEB. It is found that the freeze–thaw cycle process significantly alters the distribution of surface energy fluxes, intensifying energy exchange between the surface and atmosphere during phase transitions. In particular, an increase of 65.6% is observed in the ground heat flux during the freezing phase, which subsequently influences the sensible and latent heat fluxes. However, it should be noted that CLM5.0 has limitations in capturing the minor changes in soil moisture content and thermal conductivity during localized freezing events, resulting in an imprecise representation of the complex freeze–thaw dynamics in cold regions. Nevertheless, these results offer valuable insights and suggestions for improving the parameterization schemes of land surface models, enhancing the accuracy and applicability of remote sensing applications and climate research. Full article