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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. [ABSTRACT FROM AUTHOR]

The landscape and landforms in permafrost regions are transforming due to climate change and permafrost thaw. This study uses optical and radar remote sensing, alongside spatial analysis, to examine thermokarst features and their driving factors in the source region of the Yangtze River (SRYR) on the central Tibetan Plateau. We analyse the distribution, interaction, and key environmental factors influencing thermokarst ponds and ground surface deformation, which are the two widespread and noticeable thermokarst features. Since the 1960s, the number of small water bodies has doubled from approximately ~2 × 104 to ~4 × 104 by the 2020s, with the median size of these water bodies decreasing from 2.3 × 104 m2 to 1.4 × 104 m2. The permafrost terrain has an average subsidence rate of 6.8 mm/a. About 50.9% of the SRYR exhibits evident thermokarst features. Surficial geological factors, especially geomorphology and slope, are primary factors in shaping the spatial distributions of thermokarst features. Both seasonal deformation and long‐term subsidence rates are more pronounced in areas with thermokarst ponds. However, once pond coverage exceeds around 5%, the amplifying effect on long‐term subsidence rates and seasonal deformation diminishes. The investigation further reveals that the relationship between seasonal deformation and long‐term subsidence is not strictly linear and that the combined increase in seasonal deformation and long‐term subsidence applies only to areas with seasonal deformation below approximately 20 mm. Beyond this threshold, the long‐term subsidence rate is no longer exacerbated by increased seasonal deformation. [ABSTRACT FROM AUTHOR]

Extreme events with increasing frequency and intensity are significantly affecting the permafrost environment. Analysis using the ERA5-Land reanalysis data revealed that the permafrost region of the central Qinghai-Tibet Plateau (QTP) experienced the summer heat wave in 2022. Four active layer sites experienced maximum active layer thicknesses (ALT) in 2022 (mean: 207.7 cm), which was 20% higher than the mean ALT during 2000–2021 (mean: 175.9 cm). The mean annual ground temperature (MAGT) observed in 2022 was also the highest, exceeding the average of the previous years by 10%. The contribution fraction of heat wave to the seasonal thaw depth of active layer was quantified using Stefan model with ranging from 6.6% to 13.6%, and the maximum contribution fraction occurs in 2022. These findings are helpful to better understand the impact processes of extreme events on the active layer and permafrost. [ABSTRACT FROM AUTHOR]

Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. Plain Language Summary: Seasonal ground deformation, including both subsidence and uplift, is common in areas with a layer of ground that freezes and thaws seasonally, underlain by permafrost‐a type of ground that remains at or below 0°C for at least 2 years. These deformations are crucial indicators of changes in water content and thickness of this layer, offering insights into the freeze‐thaw dynamics of cold environments and their sensitivity to climate change. However, accurately mapping ground deformation over large areas has been challenging. In this study, we developed machine learning (ML) models that use radar remote sensing data, statistical methods, and a set of environmental variables to predict these seasonal ground movements. Our models can accurately forecast seasonal deformation using readily available environmental data. We find that slope of the terrain is the main factor influencing seasonal deformation, with climate and soil conditions also playing significant roles. This research offers new ways to measure and understand ground deformation in remote permafrost regions and demonstrates how ML can be used to predict such deformations on a continental or even global scale large. Our findings provide valuable insights for environmental scientists and could help inform strategies for managing these regions under changing climatic conditions. Key Points: Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrainsMachine learning models predict full‐coverage seasonal deformation with high accuracy (R2 = 0.91, Root Mean Squared Error [RMSE] = 0.5 cm)Seasonal deformation is primarily determined by terrain slope and regulated by climate and soil conditions [ABSTRACT FROM AUTHOR]

The ground temperature at a fixed depth is a crucial boundary condition for understanding the properties of deep permafrost. However, the commonly used mean annual ground temperature at the depth of the zero annual amplitude (MAGTdzaa) has application limitations due to large spatial heterogeneity in observed depths. In this study, we utilized 231 borehole records of mean annual ground temperature at a depth of 15 meters (MAGT15m) from 2010 to 2019 and employed support vector regression (SVR) to predict gridded MAGT15m data at a spatial resolution of nearly 1 km across the Qinghai-Tibet Plateau (QTP). SVR predictions demonstrated a R2 value of 0.48 with a negligible negative overestimation (-0.01 °C). The average MAGT15m of the QTP permafrost was -1.85 °C (±1.58 °C), with 90% of values ranging from -5.1 °C to -0.1 °C and 51.2% exceeding -1.5 °C. The freezing degree days (FDD) was the most significant predictor (p<0.001) of MAGT15m, followed by thawing degree days (TDD), mean annual precipitation (MAP), and soil bulk density (BD) (p<0.01). Overall, the MAGT15m increased from northwest to southeast and decreased with elevation. Lower MAGT15m values are prevail in high mountainous areas with steep slopes. The MAGT15m was the lowest in the basins of the Amu Darya, Indus, and Tarim rivers (-2.7 to -2.9 °C) and the highest in the Yangtze and Yellow River basins (-0.8 to -0.9 °C). The baseline dataset of MAGT15m during 2010–2019 for the QTP permafrost will facilitates simulations of deep permafrost characteristics and provides fundamental data for permafrost model validation and improvement. [ABSTRACT FROM AUTHOR]

Permafrost ground ice melting could alter hydrological processes in cold regions by releasing water. Currently, there is a lack of gridded data of ground ice from the Qinghai‐Tibet Plateau (QTP). Using 664 borehole sample records, we applied a random forest (RF) method to predict the ground ice content of permafrost between 2 and 10 m depth in three layers (2–3, 3–5, and 5–10 m) at a spatial resolution of 1 km. The RF predictions demonstrated an R2 value exceeding 0.80 for all three layers with a negligible positive overestimation (0.98%–1.85%). The ground ice content of the first layer (2–3 m) can be predicted primarily using climate variables, but the contribution of terrain and soil variables increases as the depth increases. The total water storage of ground ice across the QTP permafrost (2–10 m depth) is approximately 3330.0 km3, with 403.5 km3 in the 2–3 m layer, 857.2 km3 in the 3–5 m layer, and 2069.3 km3 in the 5–10 m layer. This study generated for the first time a gridded dataset of the shallow permafrost ground ice content across the entire QTP which can be used to improve simulations of hydrological processes in the permafrost regions. [ABSTRACT FROM AUTHOR]

The Qinghai‐Tibet Plateau (QTP) is the largest permafrost region in the world at low and middle latitudes and high elevation. Permafrost is being degraded on the QTP due to global warming, which has a significant effect on regional climate, hydrological, and ecological processes. This paper provides a summary of recent progress in methods used in permafrost research, the permafrost distribution, and basic data relevant to permafrost research on the QTP. The area of permafrost was 1.32 × 106 km2 over the QTP, which accounts for approximately 46% of the QTP. Moreover, simulation studies of the hydrothermal process and permafrost change were reviewed and evaluated the effect of permafrost degradation on hydrological and ecological processes. The results revealed that the effects of permafrost on runoff were closely related to soil temperature, and the effect of permafrost degradation on the carbon cycle requires further study. Finally, current challenges in simulation of permafrost change processes on the QTP were discussed, emphasizing that permafrost degradation under climate change is a slow and non‐linear process. This review will aid future studies examining the mechanism underlying the interaction between permafrost and climate change, and environmental protection in permafrost regions on the QTP. [ABSTRACT FROM AUTHOR]

In this paper, we used data from 42 soil temperature observation sites in permafrost regions throughout the Northern Hemisphere to analyze the characteristics and variability in soil temperature. The observation data were used to evaluate soil temperature simulations at different depths from 10 CMIP6 models in the permafrost region of the Northern Hemisphere. The results showed that the annual average soil temperature in the permafrost regions in the Northern Hemisphere gradually decreased with increasing latitude, and the soil temperature gradually decreased with depth. The average soil temperatures at different depths were mainly concentrated around 0 °C. The 10 CMIP6 models performed well in simulating soil temperature, but most models tended to underestimate temperatures compared to the measured values. Overall, the CESM2 model yielded the best simulation results, whereas the CNRM-CM6-1 model performed the worst. The change trends in annual average soil temperature across the 42 sites ranged from −0.17 °C/10a to 0.41 °C/10a from 1900 to 2014, the closer to the Arctic, the faster the soil warming rate. The rate of soil temperature change also varied at different depths between 1900–2014 and 1980–2014. The rate of soil temperature change from 1980 to 2014 was approximately three times greater than that from 1900 to 2014. [ABSTRACT FROM AUTHOR]

Ground observation of precipitation is fundamental in preparing and evaluating various precipitation datasets, which are essential for running hydrologic and climatologic models. The undercatch of traditional precipitation gauges leads to significant uncertainty in ground observation, which is particularly severe in the central Qinghai–Tibet Plateau (QTP). To evaluate the undercatch biases, intercomparison experiments between Geonor T‐200B, the Chinese standard precipitation gauge (CSPG), and OTT Parsivel2 disdrometer were conducted at Tanggula (TGL) and Wudaoliang (WDL) sites. The adjustment schemes for 30‐min and daily precipitation were proposed and applied to 30‐min precipitation at TGL over 10 years and the monthly precipitation of the national meteorological stations on the QTP over 37 years. The results indicated that after adjustment the annual precipitation increased by 49.09% at TGL and 16.41% over the QTP. Snow took up 9.91% of the annual precipitation over the QTP, while it took up over 60% at TGL. Because of the high snow proportion, the central QTP had lower catch ratio and higher adjustment ratio than the QTP average. From 1981 to 2017, the central QTP presented a wetter trend, and the annual precipitation increased by 3.15 mm/yr, higher than the QTP average. Moreover, in the central QTP, precipitation increased more in wet than dry seasons, which were 2.45 and 0.83 mm/yr, respectively. While on the entire QTP, the dry season precipitation increased slightly more than the wet season, which were 0.67 and 0.50 mm/yr, respectively. On the QTP, adjustment amount presented an increasing trend from the northwest to the southeast, whereas annual adjustment ratio was the opposite. This study could enhance the knowledge of precipitation characteristics over the QTP, especially in the central region. More effort will be given to the calibration and uncertainty evaluation of the proposed schemes in future work. [ABSTRACT FROM AUTHOR]

We develop a moving-mesh permafrost model that contains a ground subsidence computation module to estimate the effects of ground subsidence on permafrost simulation under different warming scenarios. Including the ground subsidence process in the permafrost simulation produces only a relatively small improvement in the simulation performance of the ground temperature field, as validated by observations from two sites on the Qinghai–Tibetan Plateau (QTP). It is shown that ignoring ground subsidence tends to achieve a larger active layer thickness (ALT) but a smaller original thickness of permafrost that has thawed when simulating permafrost changes in a warming climate. The heat consumed by permafrost changes will be underestimated in simulations that do not consider ground subsidence. The effects that ground subsidence exerts within the permafrost simulation are clearly demonstrated under a strong warming scenario, which will influence the global energy budget. Projections indicate that the permafrost in the continuous permafrost area of the QTP may be close to the phase transition temperature to become zero thermal gradients in 2030–2040 under the SSP5-8.5 scenario, and there will be a great risk of ground subsidence by that stage. For permafrost regions with rich ground ice, the downward propagating temperature signals caused by ground subsidence are more attenuated. However, the heat calculation error will be larger in a simulation that does not consider ground subsidence there. This study quantifies the effects of ground subsidence, which can provide a better understanding of the permafrost thaw and energy budget of the QTP. [ABSTRACT FROM AUTHOR]

The source region of the Yangtze River (SRYR, 1.4 × 105 km2 above Zhimenda station) on the Tibetan Plateau (TP) has 78% permafrost coverage. The streamflow depth increased at a rate of 2.5 mm/a since 2000. Quantification of the water contribution brought by permafrost thawing is a difficult task. In this study, we used Sentinel‐1 data and the SBAS‐InSAR technique to monitor terrain deformation from September 2016 to December 2021, and then utilized the long‐term deformation rate to assess ground ice meltwater release and the seasonal deformation to evaluate water storage in the active layer. Results reveal that 55.3% of the terrain in the SRYR has subsidence >2.5 mm/a, indicating widespread ground ice melting. The release rate of ground ice meltwater is 4.3 mm/a in the entire SRYR, above 6 mm/a at the Dangqu and Tuotuo River subbasins. The water release rate is relatively small (∼3%) in comparison to the streamflow depth of 151 mm per year during the investigation period of 2017–2021. We did not detect a strong increasing or decreasing trend among the 5‐year seasonal deformation, which reflects that the total soil water content in the active layer did not change significantly during the short investigation period. The results provide a data basis for ground ice richness and loss information in the SRYR and help to understand the impact of permafrost thawing on the regional water cycle in the permafrost environment. Plain Language Summary: The Yangtze River is the longest river in China; its source region (1.4 × 105 km2, above Zhimenda station) has 78% permafrost coverage and a 935 km3 ground ice reserve. The hydrological processes may have been significantly impacted by the permafrost degradation over the past few decades. This study presented a quantitative analysis of the effects of permafrost thawing on the water balance using terrain deformation derived from InSAR monitoring. Subsidence rates and seasonal deformation were first used combined to estimate permafrost thawing release water. The entire source region of the Yangtze River experienced extensive thaw subsidence and ground ice melting, and the rate of ground ice meltwater release was 4.3 mm per year during 2016–2021. The 5‐year seasonal deformation for 2017 to 2021 did not show strong increasing or decreasing trends. It suggests that the water storage in the active layer doesn't have significant changes. It may also suggest that most of the water from melting ground ice is released as surface or subsurface runoff and the supply to the active layer is small during the investigation period. Key Points: Quantitative analysis of the effects of permafrost thawing on the water balance in the permafrost‐affected watershedSubsidence rates and seasonal deformation were used combined to evaluate the amount of ground ice melting release waterRunoff depth of 4.3 mm/a by ground ice melting water and small variation of active layer water storage in the study area during 2016–2021 [ABSTRACT FROM AUTHOR]

The source region of the Yellow River (SRYR) is situated on the permafrost boundary in the northeast of the Qinghai-Tibet Plateau (QTP), which is an area highly sensitive to climate change. As a result of increasing global temperatures, the permafrost in this region has undergone significant degradation. In this study, we utilized Sentinel-1 to obtain ground surface deformation data in the SRYR from June 2017 to January 2022. We then analyzed the differences in terrain deformation under various environmental conditions. Our findings indicated an overall subsidence trend in the SRYR, with a long-term deformation velocity of −4.2 mm/a and seasonal deformation of 8.85 mm. Furthermore, the results showed that terrain deformation varied considerably from region to region, and that the Huanghe' yan sub-basin with the highest permafrost coverage among all sub-basins significantly higher subsidence rates than other regions. Topography strongly influenced ground surface deformation, with flat slopes exhibiting much higher subsidence rates and seasonal deformation. Moreover, the ground temperature and ground ice richness played a certain role in the deformation pattern. This study also analyzed regional deformation details from eight boreholes and one profile line covering different surface conditions, revealing the potential for refining the permafrost boundary. Overall, the results of this study provide valuable insights into the evolution of permafrost in the SRYR region. [ABSTRACT FROM AUTHOR]