Articles | Volume 20, issue 7
https://doi.org/10.5194/tc-20-3933-2026
© 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/tc-20-3933-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2026
Research article |
| 17 Jul 2026
| 17 Jul 2026
Long-term InSAR and streamflow recession analysis reveal accelerated permafrost degradation in the mining area of the Qilian Mountains
Tian Chang
Center for Spatial Information Science and Sustainable Development Applications, Tongji University, Shanghai, 200092, China
College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China
Graduate School of Science, Hokkaido University, Sapporo, 060-0810, Japan
Yonghong Yi
CORRESPONDING AUTHOR
Center for Spatial Information Science and Sustainable Development Applications, Tongji University, Shanghai, 200092, China
College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China
Masato Furuya
Department of Earth and Planetary Sciences, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan
Arctic Research Center, Hokkaido University, Sapporo, 001-0021, Japan
Huiru Jiang
Center for Spatial Information Science and Sustainable Development Applications, Tongji University, Shanghai, 200092, China
College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China
Heihe Remote Sensing Experimental Research Station, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
Youhua Ran
Heihe Remote Sensing Experimental Research Station, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
Rongxing Li
Center for Spatial Information Science and Sustainable Development Applications, Tongji University, Shanghai, 200092, China
College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China
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Lin Li, Aiguo Zhao, Tiantian Feng, Xiangbin Cui, Lu An, Ben Xu, Shinan Lang, Liwen Jing, Tong Hao, Jingxue Guo, Bo Sun, and Rongxing Li
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Xiaohua Hao, Guanghui Huang, Tao Che, Wenzheng Ji, Xingliang Sun, Qin Zhao, Hongyu Zhao, Jian Wang, Hongyi Li, and Qian Yang
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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
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Guoqing Zhang, Youhua Ran, Wei Wan, Wei Luo, Wenfeng Chen, Fenglin Xu, and Xin Li
Earth Syst. Sci. Data, 13, 3951–3966, https://doi.org/10.5194/essd-13-3951-2021, https://doi.org/10.5194/essd-13-3951-2021, 2021
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Cited articles
Abe, T., Iwahana, G., Tadono, T., and Iijima, Y.: Ground Surface Displacement After a Forest Fire Near Mayya, Eastern Siberia, Using InSAR: Observation and Implication for Geophysical Modeling, Earth and Space Science, 9, e2022EA002476, https://doi.org/10.1029/2022EA002476, 2022.
Bazaluk, O., Kuchyn, O., Saik, P., Soltabayeva, S., Brui, H., Lozynskyi, V., and Cherniaiev, O.: Impact of ground surface subsidence caused by underground coal mining on natural gas pipeline, Sci. Rep., 13, 19327, https://doi.org/10.1038/s41598-023-46814-5, 2023.
Bense, V. F., Kooi, H., Ferguson, G., and Read, T.: Permafrost degradation as a control on hydrogeological regime shifts in a warming climate, J. Geophys. Res.-Earth, 117, https://doi.org/10.1029/2011JF002143, 2012.
Biggs, J., Wright, T., Lu, Z., and Parsons, B.: Multi-interferogram method for measuring interseismic deformation: Denali Fault, Alaska, Geophys. J. Int., 170, 1165–1179, https://doi.org/10.1111/j.1365-246X.2007.03415.x, 2007.
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Kröger, T., Lambiel, C., Lanckman, J.-P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost is warming at a global scale, Nat. Commun., 10, 264, https://doi.org/10.1038/s41467-018-08240-4, 2019.
Cao, W., Sheng, Y., Wu, J., Li, J., Li, J., and Chou, Y.: Simulation analysis of the impact of excavation backfill on permafrost recovery in an opencast coal-mining pit, Environ. Earth Sci., 75, 837, https://doi.org/10.1007/s12665-016-5659-5, 2016.
Cao, Z. and Furuya, M.: Decades-Long Evolution of Post-Fire Permafrost Deformation Detected by InSAR: Insights From Chronosequence in North Yukon, AGU Advances, 6, e2025AV001849, https://doi.org/10.1029/2025AV001849, 2025.
Chang, T., Yi, Y., Jiang, H., Li, R., Lu, P., Liu, L., Wang, L., Zhao, L., Zwieback, S., and Zhao, J.: Unraveling the non-linear relationship between seasonal deformation and permafrost active layer thickness, npj Clim. Atmos. Sci., 7, 308, https://doi.org/10.1038/s41612-024-00866-0, 2024.
Chen, J., Wu, T., Zou, D., Liu, L., Wu, X., Gong, W., Zhu, X., Li, R., Hao, J., Hu, G., Pang, Q., Zhang, J., and Yang, S.: Magnitudes and patterns of large-scale permafrost ground deformation revealed by Sentinel-1 InSAR on the central Qinghai-Tibet Plateau, Remote Sens. Environ., 268, 112778, https://doi.org/10.1016/j.rse.2021.112778, 2022.
Chen, R. H., Michaelides, R. J., Zhao, Y., Huang, L., Wig, E., Sullivan, T. D., Parsekian, A. D., Zebker, H. A., Moghaddam, M., and Schaefer, K. M.: Permafrost Dynamics Observatory (PDO): 2. Joint Retrieval of Permafrost Active Layer Thickness and Soil Moisture From L-Band InSAR and P-Band PolSAR, Earth and Space Science, 10, e2022EA002453, https://doi.org/10.1029/2022EA002453, 2023.
Cooper, M. G., Zhou, T., Bennett, K. E., Bolton, W. R., Coon, E. T., Fleming, S. W., Rowland, J. C., and Schwenk, J.: Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession, Water Resour. Res., 59, e2022WR033154, https://doi.org/10.1029/2022WR033154, 2023.
Copernicus Climate Change Service (C3S): ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Costantini, M.: A novel phase unwrapping method based on network programming, IEEE T. Geosci. Remote, 36, 813–821, https://doi.org/10.1109/36.673674, 1998.
Daout, S., Doin, M.-P., Peltzer, G., Socquet, A., and Lasserre, C.: Large-scale InSAR monitoring of permafrost freeze–thaw cycles on the Tibetan Plateau, Geophys. Res. Lett., 44, 901–909, https://doi.org/10.1002/2016GL070781, 2017.
Daout, S., Dini, B., Haeberli, W., Doin, M.-P., and Parsons, B.: Ice loss in the Northeastern Tibetan Plateau permafrost as seen by 16 yr of ESA SAR missions, Earth Planet. Sc. Lett., 545, 116404, https://doi.org/10.1016/j.epsl.2020.116404, 2020.
Doin, M.-P., Lodge, F., Guillaso, S., Jolivet, R., Lasserre, C., Ducret, G., Grandin, R., Pathier, E., and Pinel, V.: Presentation of the Small Baseline NSBAS Processing Chain on a Case Example: The Etna Deformation Monitoring from 2003 to 2010 Using ENVISAT Data, in: Proceedings of the Fringe 2011 Workshop, Frascati, Italy, 19–23 September 2011, ESA SP-697, European Space Agency, ISBN 978-92-9092-261-2, https://www.geologie.ens.fr/~jolivet/Publications_files/PDFs/Proceedings_of_the_Fringe_symposium_2011_Doin.pdf (last access: 10 July 2026), 2012.
European Space Agency: Copernicus Global Digital Elevation Model, OpenTopography [data set], https://doi.org/10.5069/G9028PQB, 2021.
Evans, S. G., Yokeley, B., Stephens, C., and Brewer, B.: Potential mechanistic causes of increased baseflow across northern Eurasia catchments underlain by permafrost, Hydrol. Process., 34, 2676–2690, https://doi.org/10.1002/hyp.13759, 2020.
Fan, C., Liu, L., Zhao, Z., and Mu, C.: Pronounced Underestimation of Surface Deformation Due To Unwrapping Errors Over Tibetan Plateau Permafrost by Sentinel-1 InSAR: Identification and Correction, J. Geophys. Res.-Earth, 130, e2024JF007854, https://doi.org/10.1029/2024JF007854, 2025a.
Fan, C., Mu, C., Liu, L., Zhang, T., Jia, S., Wang, S., Sun, W., and Zhao, Z.: Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement, ISPRS J. Photogramm., 222, 167–185, https://doi.org/10.1016/j.isprsjprs.2025.02.019, 2025b.
Fan, L., Ji, F., Kuang, X., Guo, Z., Zhang, R., and Zheng, C.: Impacts of permafrost degradation on streamflow in the northern Himalayas, Sci. China Earth Sci., 67, 1990–2000, https://doi.org/10.1007/s11430-023-1297-4, 2024.
Feng, X., Duan, L., Kurylyk, B. L., and Cai, T.: Impacts of permafrost thaw on streamflow recession in a discontinuous permafrost watershed of northeastern China, Sci. Total Environ., 847, 157624, https://doi.org/10.1016/j.scitotenv.2022.157624, 2022.
Gao, H., Han, C., Chen, R., Feng, Z., Wang, K., Fenicia, F., and Savenije, H.: Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai–Tibet Plateau, Hydrol. Earth Syst. Sci., 26, 4187–4208, https://doi.org/10.5194/hess-26-4187-2022, 2022.
Goldstein, R. M. and Werner, C. L.: Radar interferogram filtering for geophysical applications, Geophys. Res. Lett., 25, 4035–4038, https://doi.org/10.1029/1998GL900033, 1998.
Guan, S., Wang, C., Tang, Y., Zou, L., Yu, P., Li, T., and Zhang, H.: North American Circum-Arctic Permafrost Degradation Observation Using Sentinel-1 InSAR Data, Remote Sens-Basel, 16, 2809, https://doi.org/10.3390/rs16152809, 2024.
Guo, J., Qi, Y., Wang, H., Zhang, J., Yang, R., and Cao, Y.: Variation of land surface temperature and its influencing factors in Muli Coalfield, Qinghai Province, Journal of Glaciology and Geocryology, 45, 980–992, https://doi.org/10.7522/j.issn.1000-0240.2023.0074, 2023.
Hinzman, A. M., Sjöberg, Y., Lyon, S., Schaap, P., and van der Velde, Y.: Using a mechanistic model to explain the rising non-linearity in storage discharge relationships as the extent of permafrost decreases in Arctic catchments, J. Hydrol., 612, 128162, https://doi.org/10.1016/j.jhydrol.2022.128162, 2022.
Hjort, J., Streletskiy, D., Doré, G., Wu, Q., Bjella, K., and Luoto, M.: Impacts of permafrost degradation on infrastructure, Nat. Rev. Earth Environ., 3, 24–38, https://doi.org/10.1038/s43017-021-00247-8, 2022.
Hu, L., Tang, X., Tomás, R., Li, T., Zhang, X., Li, Z., Yao, J., and Lu, J.: Monitoring surface deformation dynamics in the mining subsidence area using LT-1 InSAR interferometry: A case study of Datong, China, Int. J. Appl. Earth Obs., 131, 103936, https://doi.org/10.1016/j.jag.2024.103936, 2024.
Jiang, H., Yi, Y., Yang, K., Zhao, L., Chen, D., Kimball, J. S., and Lu, F.: Soil freeze/thaw dynamics strongly influences runoff regime in a Tibetan permafrost watershed: Insights from a process-based model, CATENA, 243, 108182, https://doi.org/10.1016/j.catena.2024.108182, 2024.
Jin, X., Jin, H., Iwahana, G., Marchenko, S. S., Luo, D., Li, X., and Liang, S.: Impacts of climate-induced permafrost degradation on vegetation: A review, Advances in Climate Change Research, 12, 29–47, https://doi.org/10.1016/j.accre.2020.07.002, 2021.
Jones, B. M., Grosse, G., Arp, C. D., Jones, M. C., Walter Anthony, K. M., and Romanovsky, V. E.: Modern thermokarst lake dynamics in the continuous permafrost zone, northern Seward Peninsula, Alaska, J Geophys. Res.-Biogeo., 116, https://doi.org/10.1029/2011JG001666, 2011.
Jorgenson, K. L., Douglas, T. A., Jorgenson, M. T., Pastick, N. J., and Harms, T. K.: Permafrost and Rain Influence Summer Hydrologic Flowpaths in Boreal Catchments, Water Resour. Res., 61, e2024WR039024, https://doi.org/10.1029/2024WR039024, 2025.
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment characterization, rainfall–runoff modeling, and doing hydrology backward, Water Resour. Res., 45, https://doi.org/10.1029/2008WR006912, 2009.
Li, C., Zhao, L., Wang, L., Liu, S., Zhou, H., Li, Z., Liu, G., Du, E., Zou, D., and Hou, Y.: Ground Deformation and Permafrost Degradation in the Source Region of the Yellow River, in the Northeast of the Qinghai-Tibet Plateau, Remote Sens.-Basel, 15, 3153, https://doi.org/10.3390/rs15123153, 2023.
Li, J., Sheng, Y., Chen, J., Zhang, X., Zhang, B., Wu, J., and Cao, Y.: Modeling permafrost distribution using remote sensing-derived vegetation data in the source region of the Datong River in the northwestern China, in: 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, British Columbia, Canada, 24–29 July 2011, 2225–2228, https://doi.org/10.1109/IGARSS.2011.6049611, 2011.
Li, S., Xu, W., and Li, Z.: Review of the SBAS InSAR Time-series algorithms, applications, and challenges, Geodesy and Geodynamics, 13, 114–126, https://doi.org/10.1016/j.geog.2021.09.007, 2022a.
Li, Y., Yang, K., Zhang, J., Hou, Z., Wang, S., and Ding, X.: Research on time series InSAR monitoring method for multiple types of surface deformation in mining area, Nat. Hazards, 114, 2479–2508, https://doi.org/10.1007/s11069-022-05476-8, 2022b.
Lin, L., Gao, M., Liu, J., Wang, J., Wang, S., Chen, X., and Liu, H.: Understanding the effects of climate warming on streamflow and active groundwater storage in an alpine catchment: the upper Lhasa River, Hydrol. Earth Syst. Sci., 24, 1145–1157, https://doi.org/10.5194/hess-24-1145-2020, 2020.
Lin, X., Wu, T., Chen, J., Chen, J., Li, R., Zhu, X., and Lou, P.: Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning, Ecol. Indic., 178, 113977, https://doi.org/10.1016/j.ecolind.2025.113977, 2025.
Liu, L., Zhang, T., and Wahr, J.: InSAR measurements of surface deformation over permafrost on the North Slope of Alaska, J. Geophys. Res.-Earth, 115, https://doi.org/10.1029/2009JF001547, 2010.
Liu, L., Schaefer, K., Zhang, T., and Wahr, J.: Estimating 1992–2000 average active layer thickness on the Alaskan North Slope from remotely sensed surface subsidence, J. Geophys. Res.-Earth, 117, https://doi.org/10.1029/2011JF002041, 2012.
Liu, L., Yi, Y., Jiang, H., Ran, Y., and Chen, D.: ERA5-Land overestimates runoff coefficient but underestimates runoff recession rate in the central Tibetan permafrost region, Journal of Hydrology: Regional Studies, 53, 101792, https://doi.org/10.1016/j.ejrh.2024.101792, 2024.
Liu, S., Li, X., Xu, Z., Che, T., Xiao, Q., Ma, M., Liu, Q., Jin, R., Guo, J., Wang, L., Wang, W., Qi, Y., Li, H., Xu, T., Ran, Y., Hu, X., Shi, S., Zhu, Z., Tan, J., Zhang, Y., and Ren, Z.: The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China, Vadose Zone J., 17, 1–21, https://doi.org/10.2136/vzj2018.04.0072, 2018.
Liu, S., Che, T., Xu, Z., Zhang, Y., Tan, J., Ren, Z., and Li, X.: Dataset of Heihe integrated observatory network: automatic weather station of Yakou station, 2022, National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.300652, 2023.
Liu, S., Zhao, L., Wang, L., Liu, L., Zou, D., Hu, G., Sun, Z., Zhang, Y., Chen, W., Wang, X., Wang, M., Zhou, H., and Qiao, Y.: Ground surface deformation in permafrost region on the Qinghai-Tibet Plateau: A review, Earth-Sci. Rev., 265, 105109, https://doi.org/10.1016/j.earscirev.2025.105109, 2025.
Lu, P., Han, J., Yi, Y., Hao, T., Zhou, F., Meng, X., Zhang, Y., and Li, R.: MT-InSAR Unveils Dynamic Permafrost Disturbances in Hoh Xil (Kekexili) on the Tibetan Plateau Hinterland, IEEE T. Geosci. Remote, 61, 1–16, https://doi.org/10.1109/TGRS.2023.3253937, 2023.
Meng, F., Su, F., Li, Y., and Tong, K.: Changes in Terrestrial Water Storage During 2003–2014 and Possible Causes in Tibetan Plateau, J. Geophys. Res.-Atmos., 124, 2909–2931, https://doi.org/10.1029/2018JD029552, 2019.
Morishita, Y., Lazecky, M., Wright, T. J., Weiss, J. R., Elliott, J. R., and Hooper, A.: LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor, Remote Sens.-Basel, 12, 424, https://doi.org/10.3390/rs12030424, 2020.
Mu, C., Wu, X., Zhao, Q., Smoak, J. M., Yang, Y., Hu, L., Zhong, W., Liu, G., Xu, H., and Zhang, T.: Relict Mountain Permafrost Area (Loess Plateau, China) Exhibits High Ecosystem Respiration Rates and Accelerating Rates in Response to Warming, J. Geophys. Res.-Biogeo., 122, 2580–2592, https://doi.org/10.1002/2017JG004060, 2017.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Murton, J. B.: Permafrost and climate change, Chapt. 14, in: Climate Change, 3rd edn., edited by: Letcher, T. M., Elsevier, 281–326, https://doi.org/10.1016/B978-0-12-821575-3.00014-1, 2021.
Nitzbon, J., Westermann, S., Langer, M., Martin, L. C. P., Strauss, J., Laboor, S., and Boike, J.: Fast response of cold ice-rich permafrost in northeast Siberia to a warming climate, Nat. Commun., 11, 2201, https://doi.org/10.1038/s41467-020-15725-8, 2020.
Oliver-Cabrera, T., Jones, C. E., Yunjun, Z., and Simard, M.: InSAR Phase Unwrapping Error Correction for Rapid Repeat Measurements of Water Level Change in Wetlands, IEEE T. Geosci. Remote, 60, 1–15, https://doi.org/10.1109/TGRS.2021.3108751, 2022.
Peng, S., Peng, X., Frauenfeld, O. W., Yang, G., Tian, W., Tian, J., and Ma, J.: Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai-Tibet Plateau, J. Geophys. Res.-Earth, 128, e2022JF006782, https://doi.org/10.1029/2022JF006782, 2023.
Qin, J., Ding, Y., Shi, F., Cui, J., Chang, Y., Han, T., and Zhao, Q.: Links between seasonal suprapermafrost groundwater, the hydrothermal change of the active layer, and river runoff in alpine permafrost watersheds, Hydrol. Earth Syst. Sci., 28, 973–987, https://doi.org/10.5194/hess-28-973-2024, 2024.
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.
Ran, Y., Cheng, G., Dong, Y., Hjort, J., Lovecraft, A. L., Kang, S., Tan, M., and Li, X.: Permafrost degradation increases risk and large future costs of infrastructure on the Third Pole, Commun. Earth Environ., 3, 1–10, https://doi.org/10.1038/s43247-022-00568-6, 2022.
Rogger, M., Chirico, G. B., Hausmann, H., Krainer, K., Brückl, E., Stadler, P., and Blöschl, G.: Impact of mountain permafrost on flow path and runoff response in a high alpine catchment, Water Resour. Res., 53, 1288–1308, https://doi.org/10.1002/2016WR019341, 2017.
Runge, A., Nitze, I., and Grosse, G.: Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr, Remote Sens. Environ., 268, 112752, https://doi.org/10.1016/j.rse.2021.112752, 2022.
Schaaf, C. and Wang, Z.: MODIS/Terra + Aqua BRDF/Albedo Daily L3 Global – 500 m V061, NASA Land Processes Distributed Active Archive Center, https://doi.org/10.5067/MODIS/MCD43A3.061, 2025.
Sheng, Y.: Distribution data of underground ice in permafrost regions of Qilian Mountains (2013–2015), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Geocry.tpdc.270933, 2020.
St. Jacques, J.-M. and Sauchyn, D. J.: Increasing winter baseflow and mean annual streamflow from possible permafrost thawing in the Northwest Territories, Canada, Geophys. Res. Lett., 36, https://doi.org/10.1029/2008GL035822, 2009.
Tao, R., Lau, A., Mossefin, M. E., Kong, G., Nordal, S., and Pan, Y.: Monitoring of ground displacement-induced railway anomalies using PS-InSAR techniques, Measurement, 248, 116863, https://doi.org/10.1016/j.measurement.2025.116863, 2025.
Vermote, E. and Wolfe, R.: MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid V006, NASA Land Processes Distributed Active Archive Center, https://doi.org/10.5067/MODIS/MOD09GA.006, 2025.
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic Impacts of Thawing Permafrost – A Review, Vadose Zone J., 15, vzj2016.01.0010, https://doi.org/10.2136/vzj2016.01.0010, 2016.
Wan, Z., Hook, S., and Hulley, G.: MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V061, NASA Land Processes Distributed Active Archive Center, https://doi.org/10.5067/MODIS/MYD11A1.061, 2025.
Wang, H., Qi, Y., Zhang, J., Zhang, J., Yang, R., Guo, J., Luo, D., Wu, J., and Zhou, S.: Influence of Open-Pit Coal Mining on Ground Surface Deformation of Permafrost in the Muli Region in the Qinghai-Tibet Plateau, China, Remote Sens.-Basel, 14, 2352, https://doi.org/10.3390/rs14102352, 2022.
Wang, J., Zhang, T., and Fu, B.: A measure of spatial stratified heterogeneity, Ecol. Indic., 67, 250–256, https://doi.org/10.1016/j.ecolind.2016.02.052, 2016.
Wang, L., Qu, J. J., Hao, X., and Zhu, Q.: Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices, Int. J. Remote Sens., 29, 7065–7075, https://doi.org/10.1080/01431160802226034, 2008.
Wang, L., Marzahn, P., Bernier, M., Jacome, A., Poulin, J., and Ludwig, R.: Comparison of TerraSAR-X and ALOS PALSAR Differential Interferometry With Multisource DEMs for Monitoring Ground Displacement in a Discontinuous Permafrost Region, IEEE J.-STARS, 10, 4074–4093, https://doi.org/10.1109/JSTARS.2017.2707337, 2017.
Wang, S., Sheng, Y., Wu, J., Li, J., and Huang, L.: Based on geomorphic classification to estimate the permafrost ground ice reserves in the source area of the Datong River, Qilian Mountains, Journal of Glaciology and Geocryology, 42, 1186–1194, https://doi.org/10.7522/j.issn.1000-0240.2018.0305, 2020.
Wang, T., Yang, H., Yang, D., Qin, Y., and Wang, Y.: Quantifying the streamflow response to frozen ground degradation in the source region of the Yellow River within the Budyko framework, J. Hydrol., 558, 301–313, https://doi.org/10.1016/j.jhydrol.2018.01.050, 2018.
Werner, C., Wegmüller, U., Strozzi, T., and Wiesmann, A.: GAMMA SAR and interferometric processing software, in: Proceedings of the ERS–ENVISAT Symposium, Gothenburg, Sweden, 16–20 October 2000, Gamma Remote Sensing AG, https://www.gamma-rs.ch/uploads/media/2000-1_GAMMA_Software.pdf (last access: 10 July 2026), 2000.
Xiao, W., Guo, J., He, T., Lei, K., and Deng, X.: Assessing the ecological impacts of opencast coal mining in Qinghai-Tibet Plateau – a case study in Muli coal field, China, Ecol. Indic., 153, 110454, https://doi.org/10.1016/j.ecolind.2023.110454, 2023.
Yanagiya, K. and Furuya, M.: Post-Wildfire Surface Deformation Near Batagay, Eastern Siberia, Detected by L-Band and C-Band InSAR, J. Geophys. Res.-Earth, 125, e2019JF005473, https://doi.org/10.1029/2019JF005473, 2020.
Yang, K.: A high-resolution near-surface meteorological forcing dataset for the Third Pole region (TPMFD, 1979–2022), https://doi.org/10.11888/Atmos.tpdc.300398, 2023.
Yi, Y., Wu, T., Wu, M., Jiang, H., Yang, Y., and Rogers, B. M.: Abrupt thaw and its effects on permafrost carbon emissions in the Tibetan Plateau: A remote sensing and modeling perspective, Earth-Sci. Rev., 261, 105020, https://doi.org/10.1016/j.earscirev.2024.105020, 2025.
Yu, C., Li, Z., Penna, N. T., and Crippa, P.: Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations, J. Geophys. Res.-Sol. Ea., 123, 9202–9222, https://doi.org/10.1029/2017JB015305, 2018.
Zhang, J., Liu, L., Su, L., and Che, T.: Three in one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau, The Cryosphere, 15, 3021–3033, https://doi.org/10.5194/tc-15-3021-2021, 2021.
Zhang, Z., Lin, H., Wang, M., Liu, X., Chen, Q., Wang, C., and Zhang, H.: A Review of Satellite Synthetic Aperture Radar Interferometry Applications in Permafrost Regions: Current status, challenges, and trends, IEEE Geoscience and Remote Sensing Magazine, 10, 93–114, https://doi.org/10.1109/MGRS.2022.3170350, 2022.
Zou, D., Zhao, L., Sheng, Y., Chen, J., Hu, G., Wu, T., Wu, J., Xie, C., Wu, X., Pang, Q., Wang, W., Du, E., Li, W., Liu, G., Li, J., Qin, Y., Qiao, Y., Wang, Z., Shi, J., and Cheng, G.: A new map of permafrost distribution on the Tibetan Plateau, The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, 2017.
Zwieback, S., Liu, L., Rouyet, L., Short, N., and Strozzi, T.: Advances in InSAR Analysis of Permafrost Terrain, Permafrost Periglac., 35, 544–556, https://doi.org/10.1002/ppp.2248, 2024.
Short summary
We combined a long-term surface deformation dataset derived from multi-frequency Interferometric Synthetic Aperture Radar with streamflow recession analysis to assess potential destruction effects of historic mining on permafrost in the Qilian Mountains. Results show that post-mining surface deformation intensifies alongside marked recession slowdown, signaling permafrost thaw. These findings highlight long-lasting effects of human disturbance on permafrost degradation under regional warming.
We combined a long-term surface deformation dataset derived from multi-frequency Interferometric...
