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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 R2value 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. 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. 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 Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrains Machine 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

The eco-environment in many underdeveloped areas is vulnerable. Elucidating the changes and relationships in ecosystem services (ESs) is a fundamental basis for achieving sustainable development in these regions. Utilizing multiple data sources and models, we quantified water provision (WP), habitat quality (HQ), carbon fixation (CF), soil conservation (SC), and wind erosion prevention (WEP) on the Mongolian plateau during 1992-2018. We then analyzed the spatial-temporal changes, trade-offs/synergies, and bundles relationship of these ESs. The results indicated there was high spatial variability in five ESs across the plateau, with overall higher ESs in forested areas, and grassland and cropland areas had higher WEP values. The annual mean values of WP, HQ, CF, SC, and WEP in the plateau during 1992-2018 were 27.15 mm, 0.57, 176.17 gC·m2, 48.83 t·hm2, and 58.31 t·hm2, respectively. Due to fluctuations in precipitation, vegetation cover and land use, the five ESs decreased from 1992 to 2000s, but increased from 2000s to 2018, Notably, the HQ changed most significantly (p<0.01), while the WP changed insignificantly (p>0.05). The different ESs on the plateau overall exhibited synergistic effects, trade-offs between several pairs of ESs related to HQ, were observed in cropland areas. We further identified five distinct categories of ES bundles and proposed targeted recommendations for spatial planning and management of these ESs, tailored to their characteristics. The findings offer a valuable reference for high-quality environmental management, as well as the restoration and enhancement of ESs in arid inland areas in a warming climate.

The source region of the Yangtze River (SRYR, 1.4 × 105km2above 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. The Yangtze River is the longest river in China; its source region (1.4 × 105km2, above Zhimenda station) has 78% permafrost coverage and a 935 km3ground 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. 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 Quantitative analysis of the effects of permafrost thawing on the water balance in the permafrost‐affected watershed Subsidence rates and seasonal deformation were used combined to evaluate the amount of ground ice melting release water Runoff 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

Isolating seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) time‐series is critical to quantitative understanding the freeze‐thaw processes in permafrost regions. Physics‐ or statistics‐based approaches have been developed to extract seasonal deformation, yet both constraining their evolution in time domain, and thus impeded the quantification of their amplitude variability especially over large scales. By applying Independent Component Analysis (ICA) on Sentinel‐1 InSAR measurements during 2015–2019 on the central Qinghai‐Tibet Plateau, we reveal that the averaged seasonal deformation is increasing with a linear trend of around 0.17 cm/year. The growing seasonal amplitude is attributed to an 8 cm increase of the Equivalent Water Thickness in the active layer. The results demonstrate the capability of ICA‐based decomposition on isolating freeze‐thaw‐related deformation from other components. The large‐scale spatial distribution of varied seasonal deformation can provide new insight into quantifying the water mass balance in vast permafrost regions. Permafrost is ground that remains at or below 0°C for at least 2 years. The variations of water content in the seasonally thawed layer underlain by permafrost have strong impacts on the water storage balance, the ecosystems and the infrastructure stability in cold regions. Their inter‐annual variations can be reflected by ground deformation, but are hard to quantify over large scales. Here, we apply a satellite remote sensing technique to measure ground deformation during 2015–2019 on the central Qinghai‐Tibet Plateau. Using a multivariate statistical method, called independent component analysis, we successfully extract the ground deformation related to the inter‐annual soil water variations. We infer the total water content increase in our study area and corroborate it with independent remote sensing products and in‐situ measurements. This study demonstrates the potential of remote sensing measurements in characterizing the subsurface soil water variations in cold regions with permafrost. Interferometric Synthetic Aperture Radar and independent component Analysis capture varied seasonal deformation in a large permafrost regionIncreasing amplitude of seasonal deformation is observed on the central Qinghai‐Tibet Plateau between 2015 and 2019Increase of equivalent water thickness in active layer may correspond to intensified hydrologic cycle Interferometric Synthetic Aperture Radar and independent component Analysis capture varied seasonal deformation in a large permafrost region Increasing amplitude of seasonal deformation is observed on the central Qinghai‐Tibet Plateau between 2015 and 2019 Increase of equivalent water thickness in active layer may correspond to intensified hydrologic cycle

Thaw settlement caused by permafrost degradation has great impacts on engineering infrastructure, hydrological cycles and ecosystems in recent decades under climate warming. We combined the moving‐grid method (Lagrangian method) with the heat conduction equation to develop a new moving‐grid permafrost model. Our model takes into account not only the cryostratigraphy of permafrost, but also the thaw settlement. We used the model to simulate the thaw settlement and the permafrost changes at the three sites along the Qinghai‐Tibet Engineering Corridor from 1966 to 2018. The monitoring of the ground temperature and the geodetic leveling in situwere used for model validation. The results show that our model can accurately reproduce the observed interannual thaw settlement and ground temperature fields. The risk of thawing settlement in ice‐rich permafrost regions in the zero‐temperature‐gradient stage is high. Under climate warming, the thawing of permafrost in Arctic and alpine mountain has induced ground surface settlement (thaw settlement). Thaw settlement may lead to geologic hazards damaging engineering foundation and changing local hydrological cycles and ecosystems. It is essential to accurately assess permafrost changes and thaw settlement by models. Few current permafrost simulations, however, take thaw settlement into account. We developed a new permafrost model based on the moving‐grid method. Our model can quantitatively assess not only permafrost changes but also associated thaw settlement. We used the new model to simulate the permafrost changes and the thaw settlement at three sites along the Qinghai‐Tibet Engineering Corridor (Qinghai‐Tibet Engineering Corridor) over a span of 50 recent years. The monitoring of the ground surface settlement and the ground temperatures in nearly a decade from the three sites were used to validate the simulated results. The simulated results were well consistent with the observations, which indicated that our model could capture thaw settlement processes and permafrost changes in a warming climate. The simulation results show that those permafrost regions with slowly rising ground temperature but quickly ground ice thawing may have a great risk of thawing settlement. We develop a new moving‐grid permafrost model taking thaw settlement into account based on the Lagrangian moving‐grid methodThaw settlement and permafrost changes at the three sites along the Qinghai‐Tibetan Engineering Corridor from 1966 to 2018 are modeledHigh risk of thawing settlement in ice‐rich permafrost regions occurs in the zero‐temperature‐gradient stage We develop a new moving‐grid permafrost model taking thaw settlement into account based on the Lagrangian moving‐grid method Thaw settlement and permafrost changes at the three sites along the Qinghai‐Tibetan Engineering Corridor from 1966 to 2018 are modeled High risk of thawing settlement in ice‐rich permafrost regions occurs in the zero‐temperature‐gradient stage

The permafrost degradation can cause long‐term ground surface subsidence, and the surface undergoes frost heave and thaw settlement due to the ice‐water phase change in the active layer. The multi‐year surveys by leveling observations and Interferometric Synthetic Aperture Radar monitoring (InSAR) are helpful to understand the characteristics of seasonal deformation and to model the permafrost terrain surface deformation. In this paper, we studied the characteristics of seasonal deformation over permafrost terrain in Xidatan, obtained by leveling measurements from 2010 to 2018, Sentinel‐1 data from 2014 to 2020, and hydrothermal‐data‐based simulation. The results consistently showed a four‐stage pattern of seasonal deformation characteristics: Summer thaw subsiding, warm‐season stable‐standing, winter freeze heaving and stable‐standing. The leveling measurements proved that spatial heterogeneity also exists on a small spatial scale (400 m2). The deformation amplitude obtained by leveling data is the largest, and the InSAR data is the smallest. The active layer plays a key role in land surface processes in permafrost areas. Due to the volume change associated with the ice‐water phase transition, this causes periodic deformation of the ground surface. Surface deformation in permafrost areas has a great negative impact on cold‐region infrastructures, and it is also an indicator of permafrost changes in a warming climate. Since 2010, we have been conducting a large number of observations along the Qinghai‐Tibet Highway. The meteorological observation field provides data for simulation and the analysis of deformation. And Interferometric Synthetic Aperture Radar (InSAR) has significant advantages in providing high time‐resolved deformation results. Here, the characteristics of permafrost surface intra‐annual deformation are firstly investigated in detail by using a multi‐data source. The applicability of InSAR and simulation results in the permafrost region was evaluated using leveling measured data, and the reasons for the difference were analyzed. Seasonal deformation was studied in detail by leveling measurement, InSAR monitoring and hydrothermal‐data‐based simulationThe leveling observations of surface deformation in the permafrost site of Xidatan from 2010 to 2018 were first presentedA four‐stage intra‐annual deformation pattern: summer thaw subsiding, warm‐season stable‐standing, winter freeze heaving and stable‐standing Seasonal deformation was studied in detail by leveling measurement, InSAR monitoring and hydrothermal‐data‐based simulation The leveling observations of surface deformation in the permafrost site of Xidatan from 2010 to 2018 were first presented A four‐stage intra‐annual deformation pattern: summer thaw subsiding, warm‐season stable‐standing, winter freeze heaving and stable‐standing

Despite rising interest in the impacts of long‐term gradual climate warming on permafrost changes, little is known about the changes in active‐layer thermal conditions after transient and abrupt extreme warming events. Here, we investigated the Tanggula permafrost region of the Qinghai‐Tibet Plateau using in situ meteorological and active‐layer hydrothermal data. The results showed that the magnitude of variation in the thermal condition of the active layer was positively correlated with the duration and intensity of extreme warming events. The average contribution rate of extreme warming events to seasonally thawing depth was 13.5% ± 4.9% from April to October, and higher rates occurred in the initial and final stages of the thawing process. The thawing process of the active layer was the main factor controlling the thermal responses of this layer to extreme warming events. Moreover, the interannual differences in active‐layer thermal conditions during the thawing period were likely related to the interannual differences in the frequency and intensity of extreme warming events. Under the influences of frequent extreme warming events with short duration and high intensity, how much is the impact on seasonally thawing process of active layer? Until now, it remains unclear, and some models related to permafrost exist the knowledge gap in describing the physical mechanism scheme for the impacts. Here, we selected the Tanggula permafrost region of Qinghai‐Tibet Plateau, and used in situ meteorological and active layer hydrothermal data to investigate it. Our results showed that the average contribution rates of extreme warming events to thawing depth was 13.5% ± 4.9% from April to October. Higher contribution rates occurred in the initial and final stages of thawing process. In addition, the active layer thawing process mainly controlled its thermal responses to extreme warming events. Moreover, the inter‐annual differences of active layer thermal conditions during thawing period was likely related to the inter‐annual differences of extreme warming events in frequency and intensity. These results will contribute to understand the influence mechanism of extreme warming events on the active layer thawing process, which can provide useful references for further improving models from the perspective of extreme events.

The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai‐Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root‐mean‐square error of 0.53°C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (2000–2015) permafrost area on the QTP is 1.04 × 106km2(0.80–1.28 × 106km2), and the average MAGT and ALT are −1.35 ± 0.42°C and 2.3 ± 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (2061–2080), the near‐surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region‐specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP. The combined statistical method with machine learning is efficient to obtain the thermal regime of permafrost on the Qinghai‐Tibet Plateau (QTP)The present permafrost area on the QTP is ∼1.04 × 106km2, and the average mean annual ground temperature and active layer thickness are −1.35 ± 0.42°C and 2.3 ± 0.60 m, respectivelyThe future changes of permafrost are projected to be pronounced due to climate change, but region‐specific The combined statistical method with machine learning is efficient to obtain the thermal regime of permafrost on the Qinghai‐Tibet Plateau (QTP) The present permafrost area on the QTP is ∼1.04 × 106km2, and the average mean annual ground temperature and active layer thickness are −1.35 ± 0.42°C and 2.3 ± 0.60 m, respectively The future changes of permafrost are projected to be pronounced due to climate change, but region‐specific