Articles | Volume 17, issue 4
https://doi.org/10.5194/essd-17-1731-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-1731-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Permafrost temperature baseline at 15 m depth on the Qinghai–Tibetan Plateau (2010–2019)
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Guojie Hu
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
Erji Du
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
Guangyue Liu
Cryosphere Research Station on the Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
Chong Wang
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Wangping Li
School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
Related authors
Jianting Zhao, Lin Zhao, Ze Sun, Guojie Hu, Defu Zou, Minxuan Xiao, Guangyue Liu, Qiangqiang Pang, Erji Du, Zhibin Li, Xiaodong Wu, Yao Xiao, Lingxiao Wang, and Wenxin Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3956, https://doi.org/10.5194/egusphere-2024-3956, 2025
Short summary
Short summary
The thermal regime is a key indicator of permafrost evolution. We quantitatively analyzed the spatiotemporal dynamics of the permafrost status in western Tibet since the 1980s, based on numerical simulations using the enhanced, model-forcing-driven Moving-Grid Permafrost Model. Our simulated results indicated that slow and lagged response of permafrost to climate warming, which closely linked to historical thermal conditions.
Jianting Zhao, Lin Zhao, Zhe Sun, Fujun Niu, Guojie Hu, Defu Zou, Guangyue Liu, Erji Du, Chong Wang, Lingxiao Wang, Yongping Qiao, Jianzong Shi, Yuxin Zhang, Junqiang Gao, Yuanwei Wang, Yan Li, Wenjun Yu, Huayun Zhou, Zanpin Xing, Minxuan Xiao, Luhui Yin, and Shengfeng Wang
The Cryosphere, 16, 4823–4846, https://doi.org/10.5194/tc-16-4823-2022, https://doi.org/10.5194/tc-16-4823-2022, 2022
Short summary
Short summary
Permafrost has been warming and thawing globally; this is especially true in boundary regions. We focus on the changes and variability in permafrost distribution and thermal dynamics in the northern limit of permafrost on the Qinghai–Tibet Plateau (QTP) by applying a new permafrost model. Unlike previous papers on this topic, our findings highlight a slow, decaying process in the response of permafrost in the QTP to a warming climate, especially regarding areal extent.
Lingxiao Wang, Lin Zhao, Huayun Zhou, Shibo Liu, Erji Du, Defu Zou, Guangyue Liu, Yao Xiao, Guojie Hu, Chong Wang, Zhe Sun, Zhibin Li, Yongping Qiao, Tonghua Wu, Chengye Li, and Xubing Li
The Cryosphere, 16, 2745–2767, https://doi.org/10.5194/tc-16-2745-2022, https://doi.org/10.5194/tc-16-2745-2022, 2022
Short summary
Short summary
Selin Co has exhibited the greatest increase in water storage among all the lakes on the Tibetan Plateau in the past decades. This study presents the first attempt to quantify the water contribution of ground ice melting to the expansion of Selin Co by evaluating the ground surface deformation since terrain surface settlement provides a
windowto detect the subsurface ground ice melting. Results reveal that ground ice meltwater contributed ~ 12 % of the lake volume increase during 2017–2020.
Lin Zhao, Defu Zou, Guojie Hu, Tonghua Wu, Erji Du, Guangyue Liu, Yao Xiao, Ren Li, Qiangqiang Pang, Yongping Qiao, Xiaodong Wu, Zhe Sun, Zanpin Xing, Yu Sheng, Yonghua Zhao, Jianzong Shi, Changwei Xie, Lingxiao Wang, Chong Wang, and Guodong Cheng
Earth Syst. Sci. Data, 13, 4207–4218, https://doi.org/10.5194/essd-13-4207-2021, https://doi.org/10.5194/essd-13-4207-2021, 2021
Short summary
Short summary
Lack of a synthesis dataset of the permafrost state has greatly limited our understanding of permafrost-related research as well as the calibration and validation of RS retrievals and model simulation. We compiled this dataset, including ground temperature, active layer hydrothermal regimes, and meteorological indexes based on our observational network, and we summarized the basic changes in permafrost and its climatic conditions. It is the first comprehensive dataset on permafrost for the QXP.
Dong Wang, Tonghua Wu, Lin Zhao, Cuicui Mu, Ren Li, Xianhua Wei, Guojie Hu, Defu Zou, Xiaofan Zhu, Jie Chen, Junmin Hao, Jie Ni, Xiangfei Li, Wensi Ma, Amin Wen, Chengpeng Shang, Yune La, Xin Ma, and Xiaodong Wu
Earth Syst. Sci. Data, 13, 3453–3465, https://doi.org/10.5194/essd-13-3453-2021, https://doi.org/10.5194/essd-13-3453-2021, 2021
Short summary
Short summary
The Third Pole regions are important components in the global permafrost, and the detailed spatial soil organic carbon data are the scientific basis for environmental protection as well as the development of Earth system models. Based on multiple environmental variables and soil profile data, this study use machine-learning approaches to evaluate the SOC storage and spatial distribution at a depth interval of 0–3 m in the frozen ground area of the Third Pole region.
Jianting Zhao, Lin Zhao, Ze Sun, Guojie Hu, Defu Zou, Minxuan Xiao, Guangyue Liu, Qiangqiang Pang, Erji Du, Zhibin Li, Xiaodong Wu, Yao Xiao, Lingxiao Wang, and Wenxin Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3956, https://doi.org/10.5194/egusphere-2024-3956, 2025
Short summary
Short summary
The thermal regime is a key indicator of permafrost evolution. We quantitatively analyzed the spatiotemporal dynamics of the permafrost status in western Tibet since the 1980s, based on numerical simulations using the enhanced, model-forcing-driven Moving-Grid Permafrost Model. Our simulated results indicated that slow and lagged response of permafrost to climate warming, which closely linked to historical thermal conditions.
Jianting Zhao, Lin Zhao, Zhe Sun, Fujun Niu, Guojie Hu, Defu Zou, Guangyue Liu, Erji Du, Chong Wang, Lingxiao Wang, Yongping Qiao, Jianzong Shi, Yuxin Zhang, Junqiang Gao, Yuanwei Wang, Yan Li, Wenjun Yu, Huayun Zhou, Zanpin Xing, Minxuan Xiao, Luhui Yin, and Shengfeng Wang
The Cryosphere, 16, 4823–4846, https://doi.org/10.5194/tc-16-4823-2022, https://doi.org/10.5194/tc-16-4823-2022, 2022
Short summary
Short summary
Permafrost has been warming and thawing globally; this is especially true in boundary regions. We focus on the changes and variability in permafrost distribution and thermal dynamics in the northern limit of permafrost on the Qinghai–Tibet Plateau (QTP) by applying a new permafrost model. Unlike previous papers on this topic, our findings highlight a slow, decaying process in the response of permafrost in the QTP to a warming climate, especially regarding areal extent.
Lingxiao Wang, Lin Zhao, Huayun Zhou, Shibo Liu, Erji Du, Defu Zou, Guangyue Liu, Yao Xiao, Guojie Hu, Chong Wang, Zhe Sun, Zhibin Li, Yongping Qiao, Tonghua Wu, Chengye Li, and Xubing Li
The Cryosphere, 16, 2745–2767, https://doi.org/10.5194/tc-16-2745-2022, https://doi.org/10.5194/tc-16-2745-2022, 2022
Short summary
Short summary
Selin Co has exhibited the greatest increase in water storage among all the lakes on the Tibetan Plateau in the past decades. This study presents the first attempt to quantify the water contribution of ground ice melting to the expansion of Selin Co by evaluating the ground surface deformation since terrain surface settlement provides a
windowto detect the subsurface ground ice melting. Results reveal that ground ice meltwater contributed ~ 12 % of the lake volume increase during 2017–2020.
Tonghua Wu, Changwei Xie, Xiaofan Zhu, Jie Chen, Wu Wang, Ren Li, Amin Wen, Dong Wang, Peiqing Lou, Chengpeng Shang, Yune La, Xianhua Wei, Xin Ma, Yongping Qiao, Xiaodong Wu, Qiangqiang Pang, and Guojie Hu
Earth Syst. Sci. Data, 14, 1257–1269, https://doi.org/10.5194/essd-14-1257-2022, https://doi.org/10.5194/essd-14-1257-2022, 2022
Short summary
Short summary
We presented an 11-year time series of meteorological, active layer, and permafrost data at the Mahan Mountain relict permafrost site in northeastern Qinghai-Tibet Plateau. From 2010 to 2020, the increasing rate of active layer thickness was 1.8 cm-year and the permafrost temperature showed slight changes. The release of those data would be helpful to understand the impacts of climate change on permafrost in relict permafrost regions and to validate the permafrost models and land surface models.
Yi Zhao, Zhuotong Nan, Hailong Ji, and Lin Zhao
The Cryosphere, 16, 825–849, https://doi.org/10.5194/tc-16-825-2022, https://doi.org/10.5194/tc-16-825-2022, 2022
Short summary
Short summary
Convective heat transfer (CHT) is important in affecting thermal regimes in permafrost regions. We quantified its thermal impacts by contrasting the simulation results from three scenarios in which the Simultaneous Heat and Water model includes full, partial, and no consideration of CHT. The results show the CHT commonly happens in shallow and middle soil depths during thawing periods and has greater impacts in spring than summer. The CHT has both heating and cooling effects on the active layer.
Xiaowen Wang, Lin Liu, Yan Hu, Tonghua Wu, Lin Zhao, Qiao Liu, Rui Zhang, Bo Zhang, and Guoxiang Liu
Nat. Hazards Earth Syst. Sci., 21, 2791–2810, https://doi.org/10.5194/nhess-21-2791-2021, https://doi.org/10.5194/nhess-21-2791-2021, 2021
Short summary
Short summary
We characterized the multi-decadal geomorphic changes of a low-angle valley glacier in the East Kunlun Mountains and assessed the detachment hazard influence. The observations reveal a slow surge-like dynamic pattern of the glacier tongue. The maximum runout distances of two endmember avalanche scenarios were presented. This study provides a reference to evaluate the runout hazards of low-angle mountain glaciers prone to detachment.
Lin Zhao, Defu Zou, Guojie Hu, Tonghua Wu, Erji Du, Guangyue Liu, Yao Xiao, Ren Li, Qiangqiang Pang, Yongping Qiao, Xiaodong Wu, Zhe Sun, Zanpin Xing, Yu Sheng, Yonghua Zhao, Jianzong Shi, Changwei Xie, Lingxiao Wang, Chong Wang, and Guodong Cheng
Earth Syst. Sci. Data, 13, 4207–4218, https://doi.org/10.5194/essd-13-4207-2021, https://doi.org/10.5194/essd-13-4207-2021, 2021
Short summary
Short summary
Lack of a synthesis dataset of the permafrost state has greatly limited our understanding of permafrost-related research as well as the calibration and validation of RS retrievals and model simulation. We compiled this dataset, including ground temperature, active layer hydrothermal regimes, and meteorological indexes based on our observational network, and we summarized the basic changes in permafrost and its climatic conditions. It is the first comprehensive dataset on permafrost for the QXP.
Lihui Luo, Yanli Zhuang, Mingyi Zhang, Zhongqiong Zhang, Wei Ma, Wenzhi Zhao, Lin Zhao, Li Wang, Yanmei Shi, Ze Zhang, Quntao Duan, Deyu Tian, and Qingguo Zhou
Earth Syst. Sci. Data, 13, 4035–4052, https://doi.org/10.5194/essd-13-4035-2021, https://doi.org/10.5194/essd-13-4035-2021, 2021
Short summary
Short summary
We implement a variety of sensors to monitor the hydrological and thermal deformation between permafrost slopes and engineering projects in the hinterland of the Qinghai–Tibet Plateau. We present the integrated observation dataset from the 1950s to 2020, explaining the instrumentation, processing, data visualisation, and quality control.
Dong Wang, Tonghua Wu, Lin Zhao, Cuicui Mu, Ren Li, Xianhua Wei, Guojie Hu, Defu Zou, Xiaofan Zhu, Jie Chen, Junmin Hao, Jie Ni, Xiangfei Li, Wensi Ma, Amin Wen, Chengpeng Shang, Yune La, Xin Ma, and Xiaodong Wu
Earth Syst. Sci. Data, 13, 3453–3465, https://doi.org/10.5194/essd-13-3453-2021, https://doi.org/10.5194/essd-13-3453-2021, 2021
Short summary
Short summary
The Third Pole regions are important components in the global permafrost, and the detailed spatial soil organic carbon data are the scientific basis for environmental protection as well as the development of Earth system models. Based on multiple environmental variables and soil profile data, this study use machine-learning approaches to evaluate the SOC storage and spatial distribution at a depth interval of 0–3 m in the frozen ground area of the Third Pole region.
Xiangfei Li, Tonghua Wu, Xiaodong Wu, Jie Chen, Xiaofan Zhu, Guojie Hu, Ren Li, Yongping Qiao, Cheng Yang, Junming Hao, Jie Ni, and Wensi Ma
Geosci. Model Dev., 14, 1753–1771, https://doi.org/10.5194/gmd-14-1753-2021, https://doi.org/10.5194/gmd-14-1753-2021, 2021
Short summary
Short summary
In this study, an ensemble simulation of 55296 scheme combinations for at a typical permafrost site on the Qinghai–Tibet Plateau (QTP) was conducted. The general performance of the Noah-MP model for snow cover events (SCEs), soil temperature (ST) and soil liquid water content (SLW) was assessed, and the sensitivities of parameterization schemes at different depths were investigated. We show that Noah-MP tends to overestimate SCEs and underestimate ST and topsoil SLW on the QTP.
Defu Zou, Lin Zhao, Yu Sheng, Ji Chen, Guojie Hu, Tonghua Wu, Jichun Wu, Changwei Xie, Xiaodong Wu, Qiangqiang Pang, Wu Wang, Erji Du, Wangping Li, Guangyue Liu, Jing Li, Yanhui Qin, Yongping Qiao, Zhiwei Wang, Jianzong Shi, and Guodong Cheng
The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, https://doi.org/10.5194/tc-11-2527-2017, 2017
Short summary
Short summary
The area and distribution of permafrost on the Tibetan Plateau are unclear and controversial. This paper generated a benchmark map based on the modified remote sensing products and validated it using ground-based data sets. Compared with two existing maps, the new map performed better and showed that permafrost covered areas of 1.06 × 106 km2. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.
Related subject area
Domain: ESSD – Ice | Subject: Permafrost
Very high resolution aerial image orthomosaics, point clouds, and elevation datasets of select permafrost landscapes in Alaska and northwestern Canada
TPRoGI: a comprehensive rock glacier inventory for the Tibetan Plateau using deep learning
Multisource Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS)
The first hillslope thermokarst inventory for the permafrost region of the Qilian Mountains
An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau
Modern air, englacial and permafrost temperatures at high altitude on Mt Ortles (3905 m a.s.l.), in the eastern European Alps
A new 2010 permafrost distribution map over the Qinghai–Tibet Plateau based on subregion survey maps: a benchmark for regional permafrost modeling
Tabea Rettelbach, Ingmar Nitze, Inge Grünberg, Jennika Hammar, Simon Schäffler, Daniel Hein, Matthias Gessner, Tilman Bucher, Jörg Brauchle, Jörg Hartmann, Torsten Sachs, Julia Boike, and Guido Grosse
Earth Syst. Sci. Data, 16, 5767–5798, https://doi.org/10.5194/essd-16-5767-2024, https://doi.org/10.5194/essd-16-5767-2024, 2024
Short summary
Short summary
Permafrost landscapes in the Arctic are rapidly changing due to climate warming. Here, we publish aerial images and elevation models with very high spatial detail that help study these landscapes in northwestern Canada and Alaska. The images were collected using the Modular Aerial Camera System (MACS). This dataset has significant implications for understanding permafrost landscape dynamics in response to climate change. It is publicly available for further research.
Zhangyu Sun, Yan Hu, Adina Racoviteanu, Lin Liu, Stephan Harrison, Xiaowen Wang, Jiaxin Cai, Xin Guo, Yujun He, and Hailun Yuan
Earth Syst. Sci. Data, 16, 5703–5721, https://doi.org/10.5194/essd-16-5703-2024, https://doi.org/10.5194/essd-16-5703-2024, 2024
Short summary
Short summary
We propose a new dataset, TPRoGI (v1.0), encompassing rock glaciers in the entire Tibetan Plateau. We used a neural network, DeepLabv3+, and images from Planet Basemaps. The inventory identified 44 273 rock glaciers, covering 6 000 km2, mainly at elevations of 4000 to 5500 m a.s.l. The dataset, with details on distribution and characteristics, aids in understanding permafrost distribution, mountain hydrology, and climate impacts in High Mountain Asia, filling a knowledge gap.
Soraya Kaiser, Julia Boike, Guido Grosse, and Moritz Langer
Earth Syst. Sci. Data, 16, 3719–3753, https://doi.org/10.5194/essd-16-3719-2024, https://doi.org/10.5194/essd-16-3719-2024, 2024
Short summary
Short summary
Arctic warming, leading to permafrost degradation, poses primary threats to infrastructure and secondary ecological hazards from possible infrastructure failure. Our study created a comprehensive Alaska inventory combining various data sources with which we improved infrastructure classification and data on contaminated sites. This resource is presented as a GeoPackage allowing planning of infrastructure damage and possible implications for Arctic communities facing permafrost challenges.
Xiaoqing Peng, Guangshang Yang, Oliver W. Frauenfeld, Xuanjia Li, Weiwei Tian, Guanqun Chen, Yuan Huang, Gang Wei, Jing Luo, Cuicui Mu, and Fujun Niu
Earth Syst. Sci. Data, 16, 2033–2045, https://doi.org/10.5194/essd-16-2033-2024, https://doi.org/10.5194/essd-16-2033-2024, 2024
Short summary
Short summary
It is important to know about the distribution of thermokarst landscapes. However, most work has been done in the permafrost regions of the Qinghai–Tibetan Plateau, except for the Qilian Mountains in the northeast. Here we used satellite images and field work to investigate and analyze its potential driving factors. We found a total of 1064 hillslope thermokarst (HT) features in this area, and 82 % were initiated in the last 10 years. These findings will be significant for the next predictions.
Raul-David Şerban, Huijun Jin, Mihaela Şerban, Giacomo Bertoldi, Dongliang Luo, Qingfeng Wang, Qiang Ma, Ruixia He, Xiaoying Jin, Xinze Li, Jianjun Tang, and Hongwei Wang
Earth Syst. Sci. Data, 16, 1425–1446, https://doi.org/10.5194/essd-16-1425-2024, https://doi.org/10.5194/essd-16-1425-2024, 2024
Short summary
Short summary
A particular observational network for ground surface temperature (GST) has been established on the northeastern Qinghai–Tibet Plateau, covering various environmental conditions and scales. This analysis revealed the substantial influences of the land cover on the spatial variability in GST over short distances (<16 m). Improving the monitoring of GST is important for the biophysical processes at the land–atmosphere boundary and for understanding the climate change impacts on cold environments.
Luca Carturan, Fabrizio De Blasi, Roberto Dinale, Gianfranco Dragà, Paolo Gabrielli, Volkmar Mair, Roberto Seppi, David Tonidandel, Thomas Zanoner, Tiziana Lazzarina Zendrini, and Giancarlo Dalla Fontana
Earth Syst. Sci. Data, 15, 4661–4688, https://doi.org/10.5194/essd-15-4661-2023, https://doi.org/10.5194/essd-15-4661-2023, 2023
Short summary
Short summary
This paper presents a new dataset of air, englacial, soil surface and rock wall temperatures collected between 2010 and 2016 on Mt Ortles, which is the highest summit of South Tyrol, Italy. Details are provided on instrument type and characteristics, field methods, and data quality control and assessment. The obtained data series are available through an open data repository. This is a rare dataset from a summit area lacking observations on permafrost and glaciers and their climatic response.
Zetao Cao, Zhuotong Nan, Jianan Hu, Yuhong Chen, and Yaonan Zhang
Earth Syst. Sci. Data, 15, 3905–3930, https://doi.org/10.5194/essd-15-3905-2023, https://doi.org/10.5194/essd-15-3905-2023, 2023
Short summary
Short summary
This study provides a new 2010 permafrost distribution map of the Qinghai–Tibet Plateau (QTP), using an effective mapping approach based entirely on satellite temperature data, well constrained by survey-based subregion maps, and considering the effects of local factors. The map shows that permafrost underlies about 41 % of the total QTP. We evaluated it with borehole observations and other maps, and all evidence indicates that this map has excellent accuracy.
Cited articles
Aalto, J., Karjalainen, O., Hjort, J., and Luoto, M.: Statistical forecasting of current and future circum-Arctic ground temperatures and active layer thickness, Geophys. Res. Lett., 45, 4889–4898, https://doi.org/10.1029/2018GL078007, 2018.
Amatulli, G., Domisch, S., Tuanmu, M. N., Parmentier, B., Ranipeta, A., Malczyk, J., and Jetz, W.: A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, Sci. Data, 5, 180040, https://doi.org/10.1038/sdata.2018.40, 2018.
Basak, D., Pal, S., and Patranabis, D. C.: Support Vector Regression, Neural Information Processing – Letters and Reviews, 11, 203–224, 2007.
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Kröger, T., Lambiel, C., Lanckman, J. P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost is warming at a global scale, Nat. Commun., 10, 1–11, https://doi.org/10.1038/s41467-018-08240-4, 2019.
Cao, B., Zhang, T., Peng, X., Mu, C., Wang, Q., Zheng, L., Wang, K., and Zhong, X.: Thermal characteristics and recent changes of permafrost in the upper reaches of the Heihe River Basin, western China, J. Geophys. Res., 123, 7935–7949, https://doi.org/10.1029/2018JD028442, 2018.
Cao, Z., Nan, Z., Hu, J., Chen, Y., and Zhang, Y.: A new 2010 permafrost distribution map over the Qinghai–Tibet Plateau based on subregion survey maps: a benchmark for regional permafrost modeling, Earth Syst. Sci. Data, 15, 3905–3930, https://doi.org/10.5194/essd-15-3905-2023, 2023.
Dobinski, W.: Permafrost, Earth-Sci. Rev., 108, 158–169, https://doi.org/10.1016/j.earscirev.2011.06.007, 2011.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Jin, H., Zhao, L., Wang, S., and Jin, R.: Thermal regimes and degradation modes of permafrost along the Qinghai-Tibet Highway, Sci. China Ser. D, 49, 1170–1183, doi.org/10.1007/s11430-006-2003-z, 2006.
Jin, X., Jin, H., Iwahana, G., Marchenko, S. S., Luo, D., Li, X., and Liang, S.: Impacts of climate-induced permafrost degradation on vegetation: A review, Adv. Clim. Chang. Res., 12, 29–47, https://doi.org/10.1016/j.accre.2020.07.002, 2021.
Li, J., Sheng, Y., Wu, J., Wang, J., Zhang, B., Ye, B., Zhang, X., and Qin, X.: Modeling regional and local-scale permafrost distribution in Qinghai-Tibet Plateau using equivalent-elevation method, Chinese Geogr. Sci., 22, 278–287, https://doi.org/10.1007/s11769-012-0520-6, 2012.
Li, J., Sheng, Y., Chen, J., Wu, J., and Wang, S.: Variations in permafrost temperature and stability of alpine meadows in the source area of the Datong River, northeastern Qinghai-Tibet Plateau, China, Permafrost Periglac., 25, 307–319, https://doi.org/10.1002/ppp.1822, 2014.
Li, J., Sheng, Y., Wu, J., Feng, Z., Ning, Z., Hu, X., and Zhang, X.: Landform-related permafrost characteristics in the source area of the Yellow River, eastern Qinghai-Tibet Plateau, Geomorphology, 269, 104–111, https://doi.org/10.1016/j.geomorph.2016.06.024, 2016.
Liu, G., Xie, C., Zhao, L., Xiao, Y., Wu, T., Wang, W., and Liu, W.: Permafrost warming near the northern limit of permafrost on the Qinghai-Tibetan Plateau during the period from 2005 to 2017: A case study in the Xidatan area, Permafrost Periglac., 32, 323–334, https://doi.org/10.1002/ppp.2089, 2021.
Liu, J. and Shi, R.: Characteristics of permafrost development in Jingyangling Pass of National Highway 227, Qinghai Province, Highway, 64, 75–80, 2019 (in Chinese).
Luo, D., Jin, H., Lin, L., He, R., Yang, S., and Chang, X.: New progress on permafrost temperature and thickness in the source area of the Huanghe River, Sci. Geogr. Sin., 32, 898–904, 2012 (in Chinese).
Luo, D., Jin, H., Lin, L., You, Y., Yang, S., and Wang, Y.: Distributive features and controlling factors of permafrost and the active layer thickness in the Bayan Har Mountains along the Qinghai-Kangding Highway on northeastern Qinghai-Tibet Plateau, Sci. Geogr. Sin., 33, 635–640, 2013 (in Chinese).
Luo, D., Jin, H., Jin, X., He, R., Li, X., Muskett, R. R., Marchenko, S. S., and Romanovsky, V. E.: Elevation-dependent thermal regime and dynamics of frozen ground in the Bayan Har Mountains, northeastern Qinghai-Tibet Plateau, southwest China, Permafrost Periglac., 29, 257–270, https://doi.org/10.1002/ppp.1988, 2018a.
Luo, D., Jin, H., Marchenko, S. S., and Romanovsky, V. E.: Difference between near-surface air, land surface and ground surface temperatures and their influences on the frozen ground on the Qinghai-Tibet Plateau, Geoderma, 312, 74–85, doi.org/10.1016/j.geoderma.2017.09.037, 2018b.
Luo, J., Niu, F., Lin, Z., Liu, M., Yin, G., and Gao, Z.: Inventory and frequency of retrogressive thaw slumps in permafrost region of the Qinghai–Tibet Plateau, Geophys. Res. Lett., 49, e2022GL099829, https://doi.org/10.1029/2022GL099829, 2022.
Ma, W., Mu, Y., Wu, Q., Sun, Z., and Liu, Y.: Characteristics and mechanisms of embankment deformation along the Qinghai-Tibet Railway in permafrost regions, Cold Reg. Sci. Technol., 67, 178–186, https://doi.org/10.1016/j.coldregions.2011.02.010, 2011.
Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost ground temperature for the Northern Hemisphere (v3.0), CEDA Archive [data set], https://doi.org/10.5285/b25d4a6174de4ac78000d034f500a268, 2021.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Qiu, Y., Guo, H., Chu, D., Zhang, H., Shi, J., Shi, L., Zheng, Z., and Laba, Z.: MODIS daily cloud-free snow cover product over the Tibetan Plateau (V3), Science Data Bank [data set], https://datapid.cn/31253.11.sciencedb.55 (last access: 21 December 2023), 2021.
Ran, Y., Li, X., Cheng, G., Nan, Z., Che, J., Sheng, Y., Wu, Q., Jin, H., Luo, D., Tang, Z., and Wu, X.: Mapping the permafrost stability on the Tibetan Plateau for 2005–2015, Sci. China Earth Sci., 64, 62–79, https://doi.org/10.1007/s11430-020-9685-3, 2021.
Ran, Y., Li, X., Cheng, G., Che, J., Aalto, J., Karjalainen, O., Hjort, J., Luoto, M., Jin, H., Obu, J., Hori, M., Yu, Q., and Chang, X.: New high-resolution estimates of the permafrost thermal state and hydrothermal conditions over the Northern Hemisphere, Earth Syst. Sci. Data, 14, 865–884, https://doi.org/10.5194/essd-14-865-2022, 2022.
Romanovsky, V. E., Smith, S. L., and Christiansen, H. H.: Permafrost thermal state in the polar Northern Hemisphere during the international polar year 2007–2009: a synthesis, Permafrost Periglac., 21, 106–116, https://doi.org/10.1002/ppp.689, 2010.
Smith, S. L., Romanovsky, V. E., Lewkowicz, A. G., Burn, C. R., Allard, M., Clow, G. D., Yoshikawa, K., and Throop, J.: Thermal state of permafrost in North America: a contribution to the international polar year, Permafrost Periglac., 21, 117–135, https://doi.org/10.1002/ppp.690, 2010.
Smith, S. L., O'Neill, H. B., Isaksen, K., Noetzli, J., and Romanovsky, V. E.: The changing thermal state of permafrost, Nat. Rev. Earth Environ., 3, 10–23, https://doi.org/10.1038/s43017-021-00240-1, 2022.
Soil Science Division Staff: Soil survey manual, edited by: Ditzler, C., Scheffe, K., and Monger, H. C., USDA Handbook 18, Government Printing Office, Washington, D.C., 2017.
Sun, Z., Ma, W., Mu, Y., Liu, Y., Zhang, S., and Wang, H.: Permafrost change under natural sites along the Qinghai-Tibet railway during the years of 2006–2015, Adv. Earth Sci., 33, 248–256, 2018 (in Chinese).
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic impacts of thawing permafrost – A review, Vadose Zone J., 15, vzj2016.01.0010, https://doi.org/10.2136/vzj2016.01.0010, 2016.
Wang, D., Wu, T., Zhao, L., Mu, C., Li, R., Wei, X., Hu, G., Zou, D., Zhu, X., Chen, J., Hao, J., Ni, J., Li, X., Ma, W., Wen, A., Shang, C., La, Y., Ma, X., and Wu, X.: A 1 km resolution soil organic carbon dataset for frozen ground in the Third Pole, Earth Syst. Sci. Data, 13, 3453–3465, https://doi.org/10.5194/essd-13-3453-2021, 2021.
Wang, H., Zhan, J., Wang, C., Liu, W., Yang, Z., Liu, H., and Bai, C.: Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021, Front. Plant Sci., 13, 1045290, https://doi.org/10.3389/fpls.2022.1045290, 2022.
Wang, T., Yang, D., Yang, Y., Piao, S., Li, X., Cheng, G., and Fu, B.: Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau, Sci. Adv., 6, eaaz3513, https://doi.org/10.1126/sciadv.aaz3513, 2020.
Wu, J., Sheng, Y., Wu, Q., and Wen, Z.: Processes and modes of permafrost degradation on the Qinghai-Tibet Plateau, Sci. China Ser. D, 53, 150–158, doi.org/10.1007/s11430-009-0198-5, 2010.
Wu, Q., Zhang, T., and Liu, Y.: Permafrost temperatures and thickness on the Qinghai-Tibet Plateau, Global Planet. Change, 72, 32–38, https://doi.org/10.1016/j.gloplacha.2010.03.001, 2010.
Zhang, G., Luo, W., Chen, W., and Zheng, G.: A robust but variable lake expansion on the Tibetan Plateau, Sci. Bull., 64, 1306–1309, https://doi.org/10.1016/j.scib.2019.07.018, 2019.
Zhao, L., Wu, Q., Marchenko, S. S., and Sharkhuu, N.: Thermal state of permafrost and active layer in Central Asia during the international polar year, Permafrost Periglac., 21, 198–207, https://doi.org/10.1002/ppp.688, 2010.
Zhao, L., Zou, D., Hu, G., Wu, T., Du, E., Liu, G., Xiao, Y., Li, R., Pang, Q., Qiao, Y., Wu, X., Sun, Z., Xing, Z., Sheng, Y., Zhao, Y., Shi, J., Xie, C., Wang, L., Wang, C., and Cheng, G.: A synthesis dataset of permafrost thermal state for the Qinghai–Tibet (Xizang) Plateau, China, Earth Syst. Sci. Data, 13, 4207–4218, https://doi.org/10.5194/essd-13-4207-2021, 2021.
Zhao, L., Hu, G., Liu, G., Zou, D., Wang, Y., Xiao, Y., Du, E., Wang, C., Xing, Z., Sun, Z., Zhao, Y., Liu, S., Zhang, Y., Wang, L., Zhou, H., and Zhao, J.: Investigation, monitoring, and simulation of permafrost on the Qinghai-Tibet Plateau: A review, Permafrost Periglac., 35, 412–422, https://doi.org/10.1002/ppp.2227, 2024.
Zou, D., Zhao, L., Sheng, Y., Chen, J., Hu, G., Wu, T., Wu, J., Xie, C., Wu, X., Pang, Q., Wang, W., Du, E., Li, W., Liu, G., Li, J., Qin, Y., Qiao, Y., Wang, Z., Shi, J., and Cheng, G.: A new map of permafrost distribution on the Tibetan Plateau, The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, 2017.
Zou, D., Pang, Q., Zhao, L., Wang, L., Hu, G., Du, E., Liu, G., Liu, S., and Liu, Y.: Estimation of permafrost ground ice to 10 m depth on the Qinghai-Tibet Plateau, Permafrost Periglac., 35, 423–434, https://doi.org/10.1002/ppp.2226, 2024a.
Zou, D., Zhao, L., Hu, G., Du, E., Liu, G., Wang, C., and Li, W.: Permafrost temperature baseline at 15 meters depth in the Qinghai–Tibet Plateau (2010–2019), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Cryos.tpdc.301165, 2024b.
Short summary
This study provides baseline data of permafrost temperature at 15 m depth on the Qinghai–Tibetan Plateau (QTP) over the period 2010–2019 at a spatial resolution of nearly 1 km, using 231 borehole records and a machine learning method. The average MAGT15 m of the QTP permafrost was −1.85 °C, with 90 % of the values ranging from −5.1 to −0.1 °C and 51.2 % exceeding −1.5 °C. The data can serve as a crucial boundary condition for deeper permafrost assessments and a reference for model simulations.
This study provides baseline data of permafrost temperature at 15 m depth on the Qinghai–Tibetan...
Altmetrics
Final-revised paper
Preprint