Articles | Volume 18, issue 6
https://doi.org/10.5194/essd-18-3779-2026
© Author(s) 2026. 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-18-3779-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A physically consistent soil thickness map of the Qinghai–Tibet Plateau derived from coupled erosion mechanisms
Lihua Chen
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environment and Geographic Sciences, Shanghai Normal University, Shanghai 200234, China
Key Laboratory of Ministry of Education on Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
Xingyu Ding
Hangzhou No. 14 High School Qingshanhu, Hangzhou 311300, China
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environment and Geographic Sciences, Shanghai Normal University, Shanghai 200234, China
Key Laboratory of Ministry of Education on Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
Fujun Niu
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environment and Geographic Sciences, Shanghai Normal University, Shanghai 200234, China
Keting Feng
Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environment and Geographic Sciences, Shanghai Normal University, Shanghai 200234, 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
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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.
Yuhong Chen, Zhuotong Nan, Wenbiao Tian, Yi Zhao, Shuping Zhao, Dongkai Yang, Guifei Jing, and Fujun Niu
EGUsphere, https://doi.org/10.5194/egusphere-2026-345, https://doi.org/10.5194/egusphere-2026-345, 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.
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.
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.
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
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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.
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Short summary
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.
This study presents a high-resolution (1 km) physically-based solum thickness dataset for the...
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