Articles | Volume 13, issue 7
https://doi.org/10.5194/essd-13-3453-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-13-3453-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Jul 2021
Data description paper |
| 16 Jul 2021
| 16 Jul 2021
A 1 km resolution soil organic carbon dataset for frozen ground in the Third Pole
Dong Wang
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
Southern Marine Science and Engineering Guangdong Laboratory,
Guangzhou 511458, China
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
School of Geographical Sciences, Nanjing University of Information
Science & Technology, Nanjing 210000, China
Cuicui Mu
Key Laboratory of Western China's Environmental Systems (Ministry
of Education), College of Earth and Environmental Sciences, Lanzhou
University, Lanzhou 730000, China
Ren Li
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
Xianhua Wei
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
College of Geography and Environmental Science, Northwest Normal
University, Lanzhou 730070, China
Guojie Hu
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
Xiaofan Zhu
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
Junmin Hao
School of Civil Engineering, Lanzhou University of Technology,
Lanzhou 730050, China
Jie Ni
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Xiangfei Li
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Wensi Ma
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Amin Wen
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Chengpeng Shang
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yune La
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Xin Ma
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Xiaodong Wu
Cryosphere Research Station on the Qinghai–Tibetan Plateau, State
Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu
730000, China
Related authors
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.
Gang Wei, Xiaoqing Peng, Oliver W. Frauenfeld, Lajia Weisai, Chen Yang, Guanqun Chen, Panpan Wang, Gubu Qiumo, Hengxing Luo, Guangshang Yang, Xuanjia Li, and Cuicui Mu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2726, https://doi.org/10.5194/egusphere-2025-2726, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Climate warming is causing more landslides in thawing permafrost, endangering ecosystems and infrastructure. We created a new satellite-based method to detect hidden small landslides in China's Qilian Mountains with 93 % accuracy. Fast-moving areas (>10 mm/year) with seasonal changes proved most vulnerable. This approach helps safeguard infrastructure and enhance warnings in cold regions globally.
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
Short summary
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.
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.
Yuqi Zhu, Chao Liu, Rui Liu, Hanxi Wang, Xiangwen Wu, Zihao Zhang, Shuying Zang, and Xiaodong Wu
EGUsphere, https://doi.org/10.5194/egusphere-2025-126, https://doi.org/10.5194/egusphere-2025-126, 2025
Short summary
Short summary
Water-soluble organic carbon (WSOC) is crucial in boreal forests, but its behavior across soil depths is poorly understood. Our study found that shallow soils contain complex WSOC molecules with high biodegradability, while deeper soils have simpler molecules that are also highly biodegradable. These results reveal the significant role of WSOC in carbon cycling across soil layers, improving our understanding of carbon dynamics in boreal ecosystems impacted by climate change.
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.
Francisco José Cuesta-Valero, Hugo Beltrami, Almudena García-García, Gerhard Krinner, Moritz Langer, Andrew H. MacDougall, Jan Nitzbon, Jian Peng, Karina von Schuckmann, Sonia I. Seneviratne, Wim Thiery, Inne Vanderkelen, and Tonghua Wu
Earth Syst. Dynam., 14, 609–627, https://doi.org/10.5194/esd-14-609-2023, https://doi.org/10.5194/esd-14-609-2023, 2023
Short summary
Short summary
Climate change is caused by the accumulated heat in the Earth system, with the land storing the second largest amount of this extra heat. Here, new estimates of continental heat storage are obtained, including changes in inland-water heat storage and permafrost heat storage in addition to changes in ground heat storage. We also argue that heat gains in all three components should be monitored independently of their magnitude due to heat-dependent processes affecting society and ecosystems.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
Short summary
Short summary
Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
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.
Cuicui Mu, Xiaoqing Peng, Ran Du, Hebin Liu, Haodong Jin, Benben Liang, Mei Mu, Wen Sun, Chenyan Fan, Xiaodong Wu, Oliver W. Frauenfeld, and Tingjun Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-347, https://doi.org/10.5194/essd-2022-347, 2022
Revised manuscript not accepted
Short summary
Short summary
Permafrost warming lead to greenhouse gases release to the atmosphere, resulting in a positive feedback to climate change. But, there are some uncertainties for lacks of observations. Here, we summarized a long-term observations on the meteorological, permafrost, and carbon to publish. This datasets include 5 meteorological stations, 21 boreholes 12 active layer sites, and 10 soil organic carbon contents. These are important to study the response of frozen ground to climate change.
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.
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.
Xu Chen, Cuicui Mu, Lin Jia, Zhilong Li, Chengyan Fan, Mei Mu, Xiaoqing Peng, and Xiaodong Wu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-378, https://doi.org/10.5194/essd-2020-378, 2021
Revised manuscript not accepted
Short summary
Short summary
Thermokarst lakes have attracted significant attention because of their ability to regulate carbon cycle. Now, the distribution of thermokarst lakes on QTP remains largely unknown, hindering our understanding of the response of permafrost's carbon feedback to climate change. Here, based on the GEE platform, we examined the modern distribution (2018) of thermokarst lakes on the QTP using Sentinel-2A data. Results show that the total thermokarst lake area on the QTP is 1730.34 m2 km2.
Cited articles
Amundson, R.: The Carbon Budget in Soils, Ann. Rev. Earth
Planet. Sci., 29, 535–562,
https://doi.org/10.1146/annurev.earth.29.1.535, 2001.
Batjes, N. H.: Harmonized soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61–68, https://doi.org/10.1016/j.geoderma.2016.01.034, 2016.
Cheng, G. and Wu, T.: Responses of permafrost to climate change and their
environmental significance, Qinghai-Tibet Plateau, J. Geophys.
Res.-Ea. Surf., 112, F02S03, https://doi.org/10.1029/2006JF000631,
2007.
Cheng, G., Zhao, L., Li, R., Wu, X., Sheng, Y., Hu, G., Zou, D., Jin,, H., Li, X.,
and Wu, Q.: Characteristic, changes and impacts of permafrost on
Qinghai-Tibet Plateau, Chin. Sci. Bull., 64, 2783–2795,
https://doi.org/10.1360/TB-2019-0191, 2019 (in Chinese).
Ding, J., Li, F., Yang, G., Chen, L., Zhang, B., Liu, L., Fang, K., Qin, S.,
Chen, Y., Peng, Y., Ji, C., He, H., Smith, P., and Yang, Y.: The permafrost
carbon inventory on the Tibetan Plateau: a new evaluation using deep
sediment cores, Glob. Change Biol., 22, 2688–2701,
https://doi.org/10.1111/gcb.13257, 2016.
Ding, J., Wang, T., Piao, S., Smith, P., and Zhao, L.: The paleoclimatic
footprint in the soil carbon stock of the Tibetan permafrost region, Nat.
Commun., 10, 4195, https://doi.org/10.1038/s41467-019-12214-5, 2019.
Ding, Y., Mu, C., Wu, T., Hu, G., Zou, D., Wang, D., Li, W., and Wu, X.:
Increasing cryospheric hazards in a warming climate, Earth-Sci. Rev.,
213, 103500, https://doi.org/10.1016/j.earscirev.2020.103500, 2021.
Drake, J. M. and Guisan, R. A.: Modelling Ecological Niches with Support
Vector Machines, J. Appl. Ecol., 43, 424–432,
https://doi.org/10.1111/j.1365-2664.2006.01141.x, 2006.
Elith, J., Leathwick, J. R., and Hastie, T.: A working guide to boosted
regression trees, J. Anim. Ecol., 77, 802–813,
https://doi.org/10.1111/j.1365-2656.2008.01390.x, 2008.
Fan, J., Cao, Y., Yan, Y., Lu, X., Wang, X., Fan, J., Cao, Y., Yan, Y., Lu,
X., and Wang, X.: Freezing-thawing cycles effect on the water soluble
organic carbon, nitrogen and microbial biomass of alpine grassland soil in
Northern Tibet, Afr. J. Microbiol. Res., 6, 562–567,
https://doi.org/10.5897/AJMR11.1218, 2012.
Hao, Y., Luo, X., Zhong, B., and Yang, A.: Methods of the National
Vegetation Classification based on Vegetation Partition, Remote Sens.
Technol. Appl., 32, 315–323,
https://doi.org/10.2991/mmme-16.2016.60, 2017.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M.,
Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, Niels H.; Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based
on machine learning, PLoS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., and Kuhry, P.: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps, Biogeosciences, 11, 6573–6593, https://doi.org/10.5194/bg-11-6573-2014, 2014.
Jiang, L., Chen, H., Zhu, Q., Yang, Y., Li, M., Peng, C., Zhu, D., and He,
Y.: Assessment of frozen ground organic carbon pool on the Qinghai-Tibet
Plateau, J. Soil. Sediment., 19, 128–139,
https://doi.org/10.1007/s11368-018-2006-3, 2019.
Jobbagy, E. G. and Jackson, R. B.: The vertical distribution of soil organic
carbon and its relation to climate and vegetation, Ecol. Appl.,
10, 423–436, https://doi.org/10.2307/2641104, 2000.
Koven, C. D., Ringeval, B., Friedlingstein, P., Ciais, P., Cadule, P.,
Khvorostyanov, D., Krinner, G., and Tarnocai, C.: Permafrost carbon-climate
feedbacks accelerate global warming, P. Natl. Acad.
Sci. USA, 108, 14769–14774, https://doi.org/10.1073/pnas.1103910108, 2011.
Li, F., Zang, S., Liu, Y., Li, L., and Ni, H.: Effect of Freezing–Thawing
Cycle on Soil Active Organic Carbon Fractions and Enzyme Activities in the
Wetland of Sanjiang Plain, Northeast China, Wetlands, 40, 167–177,
https://doi.org/10.1007/s13157-019-01164-9, 2020.
Liang, S., Zhao, X., Liu, S., Yuan, W., Cheng, X., Xiao, Z., Zhang, X., Liu,
Q., Cheng, J., Tang, H., Qu, Y., Bo, Y., Qu, Y., Ren, H., Yu, K., and
Townshend, J.: A long-term Global Land Surface Satellite (GLASS) dataset for
environmental studies, Int. J. Digit. Earth, 6, 5–33,
https://doi.org/10.1080/17538947.2013.805262, 2013.
Liu, F., Zhang, G., Song, X., Li, D., Zhao, Y., Yang, J., Wu, H., and Yang, F.: High-resolution and three-dimensional mapping of soil texture of China, Geoderma, 361, 114061, https://doi.org/10.1016/j.geoderma.2019.114061, 2020.
Lombardozzi, D. L., Bonan, G. B., Smith, N. G., Dukes, J. S., and Fisher, R.
A.: Temperature acclimation of photosynthesis and respiration: A key
uncertainty in the carbon cycle–climate feedback, Geophys. Res.
Lett., 42, 8624–8631, https://doi.org/10.1002/2015GL065934, 2016.
McGuire, A. D., Lawrence, D. M., Koven, C., Clein, J. S., Burke, E., Chen,
G., Jafarov, E., Macdougall, A. H., Marchenko, S., Nicolsky, D., Peng, S.,
Rinke, A., Ciais, P., Gouttevin, I., Hayes, D. J., Ji, D., Krinner, G.,
Moore, J. C., Romanovsky, V., Schädel, C., Schaefer, K., Schuur, E. A.
G., and Zhuang, Q.: Dependence of the evolution of carbon dynamics in the
northern permafrost region on the trajectory of climate change, P. Natl. Acad. Sci. USA, 115, 3882–3887,
https://doi.org/10.1073/pnas.1719903115, 2018.
Mishra, U., Jastrow, J. D., Matamala, R., Hugelius, G., Koven, C. D.,
Harden, J. W., Ping, C. L., Michaelson, G. J., Fan, Z., and Miller, R. M.:
Empirical estimates to reduce modeling uncertainties of soil organic carbon
in permafrost regions: a review of recent progress and remaining challenges,
Environ. Res. Lett., 8, 1402–1416, https://doi.org/10.1088/1748-9326/8/3/035020, 2013.
Mu, C., Zhang, T., Wu, Q., Peng, X., Cao, B., Zhang, X., Cao, B., and Cheng, G.: Editorial: Organic carbon pools in permafrost regions on the Qinghai–Xizang (Tibetan) Plateau, The Cryosphere, 9, 479–486, https://doi.org/10.5194/tc-9-479-2015, 2015.
Mu, C., Abbott, B. W., Norris, A. J., Mu, M., Fan, C. Y., Chen, X., Jia,
L., Yang, R. M., Zhang, T. J., Wang, K., Peng, X. Q., Wu, Q. B.,
Guggenberger, G., and Wu, X. D.: The status and stability of permafrost
carbon on the Tibetan Plateau, Earth-Sci. Rev., 211, 103433,
https://doi.org/10.1016/j.earscirev.2020.103433, 2020a.
Mu, C., Shang, J., Zhang, T., Fan, C., Wang, S., Peng, X., Zhong, W., Zhang,
F., Mu, M., and Jia, L.: Acceleration of thaw slump during 1997–2017 in the
Qilian Mountains of the northern Qinghai-Tibetan plateau, Landslides, 17,
1051–1062, https://doi.org/10.1007/s10346-020-01344-3, 2020b.
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H.,
Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov,
A., Khomutov, A., Kääb, A., Leibman, M. O., Lewkowicz, A. G., Panda,
S. K., Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin,
J., and Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling
for 2000–2016 at 1 km2 scale, Earth-Sci. Rev., 193, 299–316,
https://doi.org/10.1016/j.earscirev.2019.04.023, 2019.
Ping, C. L., Jastrow, J. D., Jorgenson, M. T., Michaelson, G. J., and Shur, Y. L.: Permafrost soils and carbon cycling, SOIL, 1, 147–171, https://doi.org/10.5194/soil-1-147-2015, 2015.
Ran, Y., Li, X., and Cheng, G.: Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau, The Cryosphere, 12, 595–608, https://doi.org/10.5194/tc-12-595-2018, 2018.
Schuur, E. A. G., McGuire, A. D., Schädel, C., Grosse, G., Harden, J.
W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M.,
Natali, S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M.
R., Treat, C. C., and Vonk, J. E.: Climate change and the permafrost carbon
feedback, Nature, 520, 171–179, https://doi.org/10.1038/nature14338, 2015.
Shi, J. and Song, G.: Native database in China–based on the second national soil soil survey data sets, V1, China
Scientific Data, https://doi.org/10.11922/sciencedb.180.88,
2016 (in Chinese).
Song, X. D., Brus, D. J., Liu, F., Li, D.-C., Zhao, Y. G., Yang, J. L., and
Zhang, G. L.: Mapping soil organic carbon content by geographically weighted
regression: A case study in the Heihe River Basin, China, Geoderma, 261,
11–22, https://doi.org/10.1016/j.geoderma.2015.06.024, 2016.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, B., and Midgley, B. M.: Climate Change 2013: The Physical Science Basis,
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK,
New York, NY, USA, 95–123, 2013.
Stow, D. A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F.,
Houston, S., Racine, C., Sturm, M., Tape, K., Hinzman, L., Yoshikawa, K.,
Tweedie, C., Noyle, B., Silapaswan, C., Douglas, D., Griffith, B., Jia, G.,
Epstein, H., Walker, D., Daeschner, S., Petersen, A., Zhou, L., and Myneni,
R.: Remote sensing of vegetation and land-cover change in Arctic Tundra
Ecosystems, Remote Sens. Environ., 89, 281–308,
https://doi.org/10.1016/j.rse.2003.10.018, 2004.
Tian, Y., Ouyang, H., Xu, X., Song, M., and Zhou, C.: Distribution
characteristics of soil organic carbon storage and density on the
Qinghai-Tibet Plateau, Acta Pedologica Sinica, 45, 933–942, 2008.
Tin Kam, H.: Random subspace method for constructing decision forests, IEEE
T. Pattern Anal., 20, 832–844,
https://doi.org/10.1109/34.709601, 1998.
Turetsky, M. R., Abbott, B. W., Jones, M. C., Walter Anthony, K., Olefeldt,
D., Schuur, E. A. G., Koven, C., McGuire, A. D., Grosse, G., Kuhry, P.,
Hugelius, G., Lawrence, D. M., Gibson, C., and Sannel, A. B. K.: Permafrost
collapse is accelerating carbon release, Nature, 569, 32–34,
https://doi.org/10.1038/d41586-019-01313-4, 2019.
Vitharana, U., Mishra, U., and Mapa, R. B.: National soil organic carbon
estimates can improve global estimates, Geoderma, 337, 55–64,
https://doi.org/10.1016/j.geoderma.2018.09.005, 2019.
Wang, G., Qian, J., Cheng, G., and Lai, Y.: Soil organic carbon pool of
grassland soils on the Qinghai-Tibetan Plateau and its global implication,
Sci. Total Environ., 291, 207–217,
https://doi.org/10.1016/S0048-9697(01)01100-7, 2002.
Wang, T. H., Yang, D. W., Yang, Y. T., Piao, S. L., Li, X., Cheng, G. D.,
and Fu, B. J.: 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, Q., Zhang, T., and Liu, Y.: Thermal state of the active layer and permafrost along the Qinghai-Xizang (Tibet) Railway from 2006 to 2010, The Cryosphere, 6, 607–612, https://doi.org/10.5194/tc-6-607-2012, 2012.
Wu, X., Zhao, L., Fang, H., Zhao, Y., Smoak, J. M., Pang, Q., and Ding, Y.:
Environmental controls on soil organic carbon and nitrogen stocks in the
high-altitude arid western Qinghai-Tibetan Plateau permafrost region,
J. Geophys. Res.-Biogeosci., 121, 176–187,
https://doi.org/10.1002/2015JG003138, 2016.
Wu, Y., Liu, G., Fu, B., and Guo, Y.: Study on the vertical distribution of
soil organic carbon density in the Tibetan Plateau, Acta Sci.
Circum., 28, 362–367, https://doi.org/10.3724/SP.J.1148.2008.00259,
2008.
Xu, L., Yu, G., and He, N.: Increased soil organic carbon storage in Chinese
terrestrial ecosystems from the 1980s to the 2010s, J. Geogr.
Sci., 29, 49–66, https://doi.org/10.1007/s11442-019-1583-4, 2019.
Yang, Y., Fang, J., Tang, Y., Ji, C., Zheng, C., He, J., and Zhu, B.:
Storage, patterns and controls of soil organic carbon in the Tibetan
grasslands, Glob. Change Biol., 14, 1592–1599,
https://doi.org/10.1111/j.1365-2486.2008.01591.x, 2008.
Yang, Y., Fang, J., Ma, W., Smith, P., Mohammat, A., Wang, S., and Wang, W.:
Soil carbon stock and its changes in northern China's grasslands from 1980s
to 2000s, Glob. Change Biol., 16, 3036–3047,
https://doi.org/10.1111/j.1365-2486.2009.02123.x, 2010.
Yao, T., Thompson, L. G., Mosbrugger, V., Zhang, F., Ma, Y., Luo, T., Xu,
B., Yang, X., Joswiak, D. R., Wang, W., Joswiak, M. E., Devkota, L. P.,
Tayal, S., Jilani, R., and Fayziev, R.: Third Pole Environment (TPE),
Environ. Develop., 3, 52–64,
https://doi.org/10.1016/j.envdev.2012.04.002, 2012.
Zeng, Y., Feng, Z., Cao, G., and Xu, L.: The Soil Organic Carbon Storage and
Its Spatial Distribution of Alpine Grassland in the Source Region of the
Yellow River, Acta Geogr. Sin., 59, 497–504,
https://doi.org/10.1007/BF02873091, 2004.
Zhou, G., Zhou, X., He, Y., Shao, J., Hu, Z., Liu, R., Zhou, H., and
Hosseinibai, S.: Grazing intensity significantly affects belowground carbon
and nitrogen cycling in grassland ecosystems: a meta-analysis, Glob. Change
Biol., 23, 1167–1179, https://doi.org/10.1111/gcb.13431, 2017.
Zhao, L., Wu, X., Wang, Z., Sheng, Y., Fang, H., Zhao, Y., Hu, G., Li, W.,
Pang, Q., Shi, J., Mo, B., Wang, Q., Ruan, X., Li, X., and Ding, Y.: Soil
organic carbon and total nitrogen pools in permafrost zones of the
Qinghai-Tibetan Plateau, Sci. Rep., 8, 3656,
https://doi.org/10.1038/s41598-018-22024-2, 2018.
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.
The Third Pole regions are important components in the global permafrost, and the detailed...
Special issue
Altmetrics
Final-revised paper
Preprint