Search

The dynamics of permafrost (including the permafrost thermal state and active layer thicknesses (ALT)) across the Qinghai‐Tibetan Plateau (QTP) have not been well understood on a large scale. Here we simulate the ALT and permafrost thermal state using the Geophysical Institute Permafrost Lab version 2 (GIPL2) model across the QTP. Based on the single‐point simulations, the model is upscaled to the entire QTP. The upscaled model is validated with five investigated regions (IRs), including Wenquan (WQIR), Gaize (GZIR), Aerjin (AEJIR), Xikunlun (XKLIR), and Qinghai‐Tibetan Highway (G109IR). The results show that the modified GIPL2 model improves the accuracy of the permafrost thermal state simulations. Due to our simulated results on the QTP, the average ALT is of 2.30 m (2.21–2.40 m). The ALT decreases with an increase in the altitude and decreases from the southeast to the northwest. The ALT is thin in the central QTP, but it is thick in the high‐elevation mountain areas and some areas surrounding glaciers and lakes. The largest ALT is found in the border areas between permafrost and seasonally frozen ground regions. The simulated results of the MAGT (the mean annual ground temperature) indicate that most of the permafrost is substable, which is sensitive to climate warming. The simulated results would be of great significance on assessing the impacts of permafrost dynamics on local hydrology, ecology, and engineering construction. The permafrost dynamics have been simulated by inputting long‐term in situ observation data to the GIPL2 model on the Qinghai‐Tibetan PlateauThe soil thermal parameters and ground thermal properties are upscaled using a vegetation‐type map and a lot of boreholesThe modeling results are validated with using observed active layer thicknesses and permafrost temperatures (including the no‐man's land)

Soil temperature is a key variable in the control of underground hydro-thermal processes. To estimate soil temperature more accurately, this study proposed a solution method of the heat conduction equation of soil temperature (improved heat conduction model) by applying boundary conditions that incorporate the annual and diurnal variations of soil surface temperature and the temporal variation of daily temperature amplitude, as well as the temperature difference between two soil layers in the Tanggula observation site of the Qinghai-Tibet Plateau of China. We employed both the improved heat conduction model and the classical heat conduction model to fit soil temperature by using the 5 cm soil layer as the upper boundary for soil depth. The results indicated that the daily soil temperature amplitude can be better described by the sinusoidal function in the improved model, which then yielded more accurate soil temperature simulating effect at the depth of 5 cm. The simulated soil temperature values generated by the improved model and classical heat conduction model were then compared to the observed soil temperature values at different soil depths. Statistical analyses of the root mean square error (RMSE), the normalized standard error (NSEE) and the bias demonstrated that the improved model showed higher accuracy, and the average values of RMSE, bias and NSEE at the soil depth of 10–105 cm were 1.41°C, 1.15°C and 22.40%, respectively. These results indicated that the improved heat conduction model can better estimate soil temperature profiles compared to the traditional model.

Water and heat dynamics in the active layer at a monitoring site in the Tanggula Mountains, located in the permafrost region of the Qinghai-Xizang (Tibet) Plateau (QXP), were studied using the physical-process-based COUPMODEL model, including the interaction between soil temperature and moisture under freeze-thaw cycles. Meteorological, ground temperature and moisture data from different depths within the active layer were used to calibrate and validate the model. The results indicate that the calibrated model satisfactorily simulates the soil temperatures from the top to the bottom of the soil layers as well as the moisture content of the active layer in permafrost regions. The simulated soil heat flux at depths of 0 to 20 cm was consistent with the monitoring data, and the simulations of the radiation balance components were reasonable. Energy consumed for phase change was estimated from the simulated ice content during the freeze/thaw processes from 2007 to 2008. Using this model, the active layer thickness and the energy consumed for phase change were predicted for future climate warming scenarios. The model predicts an increase of the active layer thickness from the current 330 cm to approximately 350–390 cm as a result of a 1–2°C warming. However, the effect active layer thickness of more precipitation is limited when the precipitation is increased by 20%–50%. The COUPMODEL provides a useful tool for predicting and understanding the fate of permafrost in the QXP under a warming climate.

AbstractThe climatological conditions for the presence of palsas in the Hudson Bay Lowlands (HBL) in Ontario, Canada, are examined using data from four climate stations: Big Trout Lake, Lansdowne House, Peawanuck, and Fort Severn. These stations sandwich the existing region where palsas occur. The criteria for the formation and occurrence of palsas that were taken from the literature on Fennoscandian and neighboring Québec palsas were applied to the HBL. Thermal thresholds set at -2 °C and 0 °C mean annual air temperature, and number of days below -10 °C per year were met for the two more northerly locations; the two southerly locations were on the edge of the thresholds. Climate projections from two models under two emission scenarios for the 2020s, 2050s, and 2080s indicated that by the 2080s all four locations would fail the -2 °C threshold for palsa formation but at three locations the 0 °C threshold for palsa presence was met for some projection scenarios. Over the next century, it is likely that the climate conditions will continue to be capable of supporting existing palsas; however, by the end of the century the threshold criteria for new palsa formation will not be met for most of the HBL.

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

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

The characteristics of long‐term variation for extreme precipitation events were analyzed at the Tanggula site in the continuous permafrost regions of the Qinghai‐Tibetan Plateau (QTP). In addition, the impacts of extreme precipitation events in summer on soil thermal‐moisture dynamics were also investigated. The results showed that local extreme precipitation indices fluctuated significantly and that the trend magnitudes of local very wet days (R95p), annual total wet‐day precipitation (PRCPTOT), number of heavy precipitation days (R10mm), maximum length of dry spell (CDD), and simple daily intensity index (SDII) were larger than those of the western QTP, other regions of China, and even the global average. The freeze‐thaw cycling in the local active layer occurred from October to the next September during 2006 to 2014. The influence of extreme precipitation event in summer on local soil hydrothermal conditions could reach soil depths up to 105 cm or so, and these were more pronounced than with light or moderate precipitation events. Soil temperature reacted more promptly to local extreme precipitation events than did soil moisture. The rate at which local soil temperature fell after an extreme precipitation event was greater than the rate of increasing temperature on nonprecipitation days. Moreover, the amount of precipitation received during extreme precipitation events had a greater effect on local soil moisture and temperature than duration time for these events. Consecutive extreme precipitation events with a longer duration time did not necessarily to have a greater effect than a single precipitation event with a shorter duration. Finally, the thawing process of active layer and local water migration modes could also affect the response of soil hydrothermal conditions to an extreme precipitation event to a large extent. Local extreme precipitation events have greater influences on soil hydrothermal conditions than light or moderate precipitation events in summerThe precipitation amount of extreme precipitation events has greater influence on the hydrothermal regimes than the duration time of these eventsThe thawing process of active layer can affect the response of soil hydrothermal conditions to summer precipitation events