Articles | Volume 19, issue 1
https://doi.org/10.5194/gmd-19-57-2026
© 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/gmd-19-57-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
06 Jan 2026
Model description paper |
| 06 Jan 2026
| 06 Jan 2026
NoahPy: a differentiable Noah land surface model for simulating permafrost thermo-hydrology
Wenbiao Tian
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Ministry of Education on Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
Hu Yu
North Information Control Research Academy Group Co., Ltd., Nanjing 211153, China
Key Laboratory of Ministry of Education on Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
Yuhe Cao
College of Forestry, Northeast Forestry University, Harbin 150040, China
Wenjun Yi
Key Laboratory of Ministry of Education on Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
Jiwei Xu
Key Laboratory of Ministry of Education on Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
State Key Laboratory of Climate System Prediction and Risk Management, 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
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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.
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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.
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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.
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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.
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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
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.
Accurately predicting how permafrost will thaw with land surface models is a grand challenge in...
