Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-865-2022
https://doi.org/10.5194/essd-14-865-2022
Data description paper
 | 
24 Feb 2022
Data description paper |  | 24 Feb 2022

New high-resolution estimates of the permafrost thermal state and hydrothermal conditions over the Northern Hemisphere

Youhua Ran, Xin Li, Guodong Cheng, Jingxin Che, Juha Aalto, Olli Karjalainen, Jan Hjort, Miska Luoto, Huijun Jin, Jaroslav Obu, Masahiro Hori, Qihao Yu, and Xiaoli Chang

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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, 2018. 
Abu-Hamdeh, N. H.: Thermal properties of soils as affected by density and water content, Biosyst Eng., 86, 97–102, 2003. 
Ali, S. N., Quamar, M. F., Phartiyal, B., and Sharma, A.: Need for permafrost researches in Indian Himalaya, J. Clim. Chang., 4, 33–36, 2018. 
Allard, M., Sarrazin, D., and L'Hérault, E.: Borehole and near-surface ground temperatures in northeastern Canada, Version 1.3 (1988–2014), Nordicana D [data set], https://doi.org/10.5885/45291SL-34F28A9491014AFD, 2015. 
Awad, M. and Khanna, R.: Support Vector Regression, in: Efficient Learning Machines, Apress, Berkeley, CA, https://doi.org/10.1007/978-1-4302-5990-9_4, 2015. 
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Short summary
Datasets including ground temperature, active layer thickness, the probability of permafrost occurrence, and the zonation of hydrothermal condition with a 1 km resolution were released by integrating unprecedentedly large amounts of field data and multisource remote sensing data using multi-statistical\machine-learning models. It updates the understanding of the current thermal state and distribution for permafrost in the Northern Hemisphere.
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