Articles | Volume 16, issue 3
https://doi.org/10.5194/tc-16-825-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-825-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Mar 2022
Research article |
| 11 Mar 2022
| 11 Mar 2022
Convective heat transfer of spring meltwater accelerates active layer phase change in Tibet permafrost areas
Yi Zhao
Key Laboratory of Ministry of Education on Virtual Geographic
Environment, Nanjing Normal University, Nanjing, 210023, China
Key Laboratory of Ministry of Education on Virtual Geographic
Environment, Nanjing Normal University, Nanjing, 210023, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing, 210023, China
Hailong Ji
Key Laboratory of Ministry of Education on Virtual Geographic
Environment, Nanjing Normal University, Nanjing, 210023, China
School of Geographical Sciences, Nanjing University of Information
Science & Technology, Nanjing 210044, China
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Yuhong Chen, Zhuotong Nan, Wenbiao Tian, Yi Zhao, Shuping Zhao, Dongkai Yang, Guifei Jing, and Fujun Niu
The Cryosphere, 20, 5697–5723, https://doi.org/10.5194/tc-20-5697-2026, https://doi.org/10.5194/tc-20-5697-2026, 2026
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This study presents a 1-km permafrost map of the Qinghai-Tibet Plateau for 2020, generated via a frost number model and space-for-time substitution. Permafrost covers 1.038×10⁶ km² (39.35 %), a 1.82 % decline since 2010, while seasonally frozen ground covers 1.466×10⁶ km² (55.57 %). Validated against boreholes, the map achieves 0.84 accuracy (Kappa= 0.58). This map offers a critical, up-to-date baseline for cryospheric research.
Pengfei Li, Zhuotong Nan, Jiwei Xu, and Fujun Niu
EGUsphere, https://doi.org/10.5194/egusphere-2026-5035, https://doi.org/10.5194/egusphere-2026-5035, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Most susceptibility models reduce multi-year inventories to binary occurrence labels and make little use of when failures occurred. We use the temporal ordering of pre-initiation states as weak supervision for susceptibility. Using annual retrogressive thaw slump inventories, the framework captured 89.45 % of 2021–2022 initiations within the top 10 % susceptibility area. The principle may apply to landslides and other mass-movement processes with multi-temporal initiation inventories.
Erji Du, Tonghua Wu, Liyun Dai, Youhua Ran, Yufang Min, Lin Zhao, Lingxiao Wang, Jimin Yao, Zhibin Li, Keting Feng, Yao Xiao, Guojie Hu, Defu Zou, Guangyue Liu, Zanpin Xing, Xiaofan Zhu, and Ji Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-330, https://doi.org/10.5194/essd-2026-330, 2026
Preprint under review for ESSD
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We present the first 90 m resolution dataset of active layer moisture covering the entire permafrost region of the Qinghai–Tibet Plateau. It was produced from 342 field observations (2009–2024) using four machine learning models combined through bias-aware fusion, and is accompanied by an area-of-applicability mask and per-pixel uncertainty estimates. The dataset is freely available at https://doi.org/10.12072/ncdc.permafrost.db7312.2026.
Yao Xiao, Nan Zhou, Guojie Hu, Ren Li, Guangyue Liu, Tonghua Wu, Xiaodong Wu, Defu Zou, Erji Du, Yonghua Zhao, and Lin Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2026-3134, https://doi.org/10.5194/egusphere-2026-3134, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Snow on the Tibetan Plateau is often widespread in winter but usually shallow. We developed a benchmark that evaluates snow-covered area and snow depth together on each model’s native grid. Most CMIP6 models overestimate both variables in winter, especially over the western and high-elevation Plateau. The benchmark helps users choose snow products for cryospheric, hydrological and climate studies.
Xiangfei Li, Jinzhi Tian, Chengzhi Wu, Shuo Wang, and Lin Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2026-4290, https://doi.org/10.5194/egusphere-2026-4290, 2026
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Snow droughts can threaten water supplies, ecosystems, and communities, but studies often use different methods and data, making results hard to compare. We developed an open-source Python tool that combines snow drought detection methods, data download, mapping, and standardized output in one workflow. Tests with global snow data show that the tool identifies annual, monthly, and daily events consistently. This makes snow drought studies easier to repeat, compare, and use in planning.
Lingxiao Wang, Wei Wan, Lin Zhao, Wei Chen, Chong Wang, Shibo Liu, Guangyue Liu, Yuanwei Wang, Junhao Qu, Defu Zou, Erji Du, Guojie Hu, Yao Xiao, Yonghua Zhao, and Minxuan Xiao
EGUsphere, https://doi.org/10.5194/egusphere-2026-2170, https://doi.org/10.5194/egusphere-2026-2170, 2026
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This study reveals permafrost ground deformation from sub-daily to multi-year timescales at a central Tibetan Plateau supersite using satellite, contact, and non-contact field observations. A new automated system captured five-minute deformation changes, revealing distinct deformation processes during different freeze–thaw stages. The results evaluate different monitoring methods and provide a benchmark for understanding and interpreting permafrost change.
Zanpin Xing, Xiaojun Li, Frédéric Frappart, Gabrielle De Lannoy, Thomas Jagdhuber, Jian Peng, Lei Fan, Hongliang Ma, Lanka Karthikeyan, Xiangzhuo Liu, Mengjia Wang, Lin Zhao, Yongqin Liu, and Jean-Pierre Wigneron
Earth Syst. Sci. Data, 18, 4047–4073, https://doi.org/10.5194/essd-18-4047-2026, https://doi.org/10.5194/essd-18-4047-2026, 2026
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Microwave satellite observations of Earth’s land surface are important for tracking global soil moisture and vegetation water content. We use data from the Soil Moisture and Ocean Salinity satellite to first reduce noise and contamination in microwave signals, then produce more reliable long-term records of these variables. Tests against ground stations and other satellites show that the new record performs better than existing products and supports drought, freeze-thaw, and carbon monitoring.
Lihua Chen, Xingyu Ding, Shuping Zhao, Fujun Niu, Keting Feng, and Zhuotong Nan
Earth Syst. Sci. Data, 18, 3779–3801, https://doi.org/10.5194/essd-18-3779-2026, https://doi.org/10.5194/essd-18-3779-2026, 2026
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This study presents a high-resolution (1 km) physically-based solum thickness dataset for the Qinghai–Tibet Plateau. Grounded in a mechanistic mass-balance model that couples climate weathering with multi-process erosion, the data resolve geomorphological patterns and offer superior accuracy over conventional statistical products. This dataset provides essential model parameter for hydrological, ecological, and cryosphere models, facilitating more reliable assessments under a changing climate.
Jiwei Xu, Shuping Zhao, Zhuotong Nan, Fujun Niu, and Yaonan Zhang
Geosci. Model Dev., 19, 2919–2943, https://doi.org/10.5194/gmd-19-2919-2026, https://doi.org/10.5194/gmd-19-2919-2026, 2026
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Permafrost is warming, causing more ground collapses known as retrogressive thaw slumps that damage ecosystems and infrastructure. We created a new computer model to predict how these slumps grow and spread over time. By combining satellite data, statistics, and rules that mimic natural erosion, the model can reproduce changes with high accuracy. This helps scientists and planners better forecast future permafrost hazards.
Wenbiao Tian, Hu Yu, Shuping Zhao, Yuhe Cao, Wenjun Yi, Jiwei Xu, and Zhuotong Nan
Geosci. Model Dev., 19, 57–72, https://doi.org/10.5194/gmd-19-57-2026, https://doi.org/10.5194/gmd-19-57-2026, 2026
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Accurately predicting how permafrost will thaw with land surface models is a grand challenge in Earth science. We created a new computer model by rebuilding a traditional physics model to work with artificial intelligence. Our results show this new approach is much faster and more reliable for tuning model parameters with data. This provides a better tool to build the next generation of climate models and improve predictions of permafrost's future.
Jianting Zhao, Lin Zhao, Zhe Sun, Guojie Hu, Defu Zou, Minxuan Xiao, Guangyue Liu, Qiangqiang Pang, Erji Du, Zhibin Li, Xiaodong Wu, Yao Xiao, Lingxiao Wang, and Wenxin Zhang
The Cryosphere, 19, 4211–4236, https://doi.org/10.5194/tc-19-4211-2025, https://doi.org/10.5194/tc-19-4211-2025, 2025
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We used the Moving-Grid Permafrost Model (MVPM) to simulate the permafrost thermal regime in West Kunlun (55,669 km², NW Qinghai–Tibet Plateau), driven by remote-sensing-based land surface temperature (LST; 1980–2022). The model showed high accuracy and stability. Despite ongoing warming (+0.40 °C per decade), permafrost extent remained stable, reflecting delayed deep responses. The permafrost thermal regime reveals altitude- and soil-dependent responses to climate change and offers valuable insights into thermal states in data-scarce regions.
Defu Zou, Lin Zhao, Guojie Hu, Erji Du, Guangyue Liu, Chong Wang, and Wangping Li
Earth Syst. Sci. Data, 17, 1731–1742, https://doi.org/10.5194/essd-17-1731-2025, https://doi.org/10.5194/essd-17-1731-2025, 2025
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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.
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
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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.
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
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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
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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.
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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.
Convective heat transfer (CHT) is important in affecting thermal regimes in permafrost regions....
