Long-term InSAR and streamflow recession analysis reveal accelerated permafrost degradation in the mining area of Qilian Mountain

Chang, Tian; Yi, Yonghong; Furuya, Masato; Jiang, Huiru; Che, Tao; Ran, Youhua; Liu, Lin; Li, Rongxing

doi:10.5194/egusphere-2025-5611

Preprints

https://doi.org/10.5194/egusphere-2025-5611

Preprints

14 Jan 2026

| 14 Jan 2026

Long-term InSAR and streamflow recession analysis reveal accelerated permafrost degradation in the mining area of Qilian Mountain

Tian Chang, Yonghong Yi, Masato Furuya, Huiru Jiang, Tao Che, Youhua Ran, Lin Liu, and Rongxing Li

Abstract. Permafrost underlies about 40 % of the Qinghai-Tibet Plateau (QTP), where climate warming and human activities increasingly threaten fragile alpine ecosystems, necessitating long-term permafrost monitoring. Interferometric Synthetic Aperture Radar (InSAR) enables precise detection of thaw-induced surface deformation, while streamflow recession helps reveal subsurface hydrological changes with permafrost degradation. This study performed a first-time joint analysis of decades-long InSAR surface deformation and streamflow recession to assess the trajectory of permafrost degradation in the source region of the Datong River, an area located in the Qilian Mountains of the northeastern QTP and subject to intensive mining during the 2000s and early 2010s. A data-constrained Small Baseline Subset (SBAS) method was proposed to improve the Sentinel-1 C-band deformation retrievals through integrating a linear–periodic temporal constraint model and using concurrent ALOS-2 retrieved deformation rate as a reference. A consistent long-term (1997–2023) deformation dataset was then generated through combining multi-sensor C- and L-band SAR retrievals. The results reveal minimal surface deformation before the mining, followed by sustained ground subsidence (−15 to −5 mm a⁻¹) and enhanced seasonal deformation (~20–60 mm) during and after mining, indicating accelerated permafrost degradation. This acceleration coincides with a marked slowdown in the post-mining streamflow recession rate derived from daily discharge data of the upper Datong River, likely driven by thaw-induced increases in basin subsurface water storage and flowpath connectivity. This study provides a first comprehensive assessment of permafrost degradation from both surface and subsurface perspectives, offering valuable insights for integrating remote sensing and hydrological observations to assess permafrost vulnerability.

Received: 12 Nov 2025 – Discussion started: 14 Jan 2026

Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Preprint (PDF, 13204 KB)

Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Preprint (13204 KB)

Supplement (14926 KB)

Download & links

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Journal article(s) based on this preprint

17 Jul 2026

Long-term InSAR and streamflow recession analysis reveal accelerated permafrost degradation in the mining area of the Qilian Mountains

Tian Chang, Yonghong Yi, Masato Furuya, Huiru Jiang, Tao Che, Youhua Ran, Lin Liu, and Rongxing Li

The Cryosphere, 20, 3933–3957, https://doi.org/10.5194/tc-20-3933-2026,https://doi.org/10.5194/tc-20-3933-2026, 2026

Short summary

Tian Chang, Yonghong Yi, Masato Furuya, Huiru Jiang, Tao Che, Youhua Ran, Lin Liu, and Rongxing Li

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5611', Anonymous Referee #1, 29 Apr 2026

General comments:

This manuscript integrates multi-source InSAR observations with hydrological recession analysis to investigate permafrost-related surface deformation and its hydrological implications in a disturbed alpine river basin. The study addresses an important topic in cryosphere–hydrology interactions, and the development of a long-term deformation dataset is a valuable contribution to the community. The joint analysis of InSAR deformation and hydrology recession analysis is novel, and overall it is well done. I have some concerns on the methodology development and analysis that need to be clarified or discussed before the publication. Details are provided as below.
1. InSAR processing

-The deformation analysis relies primarily on descending-orbit SAR acquisitions. While limited data availability may justify this choice for earlier sensors, Sentinel-1 provides systematic coverage in both ascending and descending tracks over most land areas. The authors should clarify the rationale for restricting the Sentinel-1 analysis to descending data.

The SAR datasets used in this study also differ in temporal coverage and observation periods. The potential impact of these inconsistencies on the long-term deformation estimates, as well as the associated uncertainties, need to be more explicitly discussed.
-The study addresses the poor quality of C-band interferograms during winter, likely caused by snow cover and freeze–thaw-related decorrelation, by applying a linear–periodic temporal constraint within the NSBAS-InSAR framework and by calibrating the Sentinel-1 time series using ALOS-2-derived deformation rates. The NSBAS time-series inversion is constrained using long-term deformation rates derived from ALOS-2 data. While this approach is practical, the imposed constraints and simplified temporal model may not fully capture complex or transient deformation signals. A discussion of the limitations of this methodology, and how they may influence the reconstructed long-term deformation time series, would help clarify the strength and advantages of the method. In addition, previous studies have attempted to mitigate winter decorrelation in C-band InSAR over permafrost regions by using long-temporal-baseline interferograms acquired during summer-to-summer periods, which can avoid snow-contaminated winter acquisitions and has been shown to perform well in Alaska (Guan, S.; Wang, C.; Tang, Y.; Zou, L.; Yu, P.; Li, T.; Zhang, H. (2024). North American Circum-Arctic Permafrost Degradation Observation Using Sentinel-1 InSAR Data. Remote Sensing, 16(15), 2809. DOI: 10.3390/rs16152809). It would be useful for the authors to discuss this alternative strategy and compare its advantages and limitations with the ALOS-2-constrained approach used in this study. Such a discussion would better clarify the methodological applicability, and potential limitations of the proposed approach.
2. Uncertainty of the thaw-season deformation scaling

The thaw-season deformation scaling approach assumes that the relative proportions of deformation across different sub-periods remain stable across years. While practical, this assumption may be influenced by interannual variability in the climate conditions. Assessing the variability of the scaling factor across Sentinel-1 observational period, and providing an estimate of the associated uncertainty, would help evaluate the robustness of this approach.
3. Recession analysis

The streamflow recession analysis provides some interesting insights. It is based on the power-law recession curve (Eq. 5), while the analysis focuses primarily on the recession rate (a). The other recession parameter (i.e. b) is also a key parameter controlling the nonlinearity of catchment drainage process. The authors may consider examining b, assessing its variability, and discussing its potential implications alongside with changes in a.
4. Quantitative attribution of environmental controls on deformation variability

The manuscript includes several environmental variables to interpret deformation patterns; however, their relative contributions are not well quantitatively assessed. It would be helpful if the authors could further clarify the relative importance of these factors, for example, by providing a ranking of explanatory power or applying a feature importance analysis.
Specific comments:

1. Section 3.1.1: It is unclear whether a consistent reference point is used across different InSAR data processing, which may affect the comparability of deformation estimates.

2. Line 255: Please clarify the temporal coverage of the datasets used in this analysis.

3. Figure 9c: There is a spelling error in the labels.

4. Figure 14: Is the statistical analysis of environmental factors and deformation limited to the study area? How do the authors explain why the northeastern part (outside the study area) shows low albedo values but does not exhibit strong subsidence signals (e.g. as shown in Fig. 13)?

Citation: https://doi.org/10.5194/egusphere-2025-5611-RC1
- RC2:
  'Reply on RC1', Neelarun Mukherjee, 06 Jun 2026
  
  This manuscript integrates multi-decadal InSAR observations with streamflow recession analysis to investigate permafrost degradation in a mining-disturbed alpine basin. The topic is timely and the combination of surface deformation monitoring with a hydrological signal is novel. The long-term deformation dataset spanning five SAR sensors is a genuine contribution. I have a some comments:
  
  1. Attribution of Ks changes to mining versus climate: The paper fits a multiple linear regression (MLR) using pre-mining data (1973–2002) and applies it to 2003–2022 to estimate climate-driven Ks changes. The authors do acknowledge uncertainty in attribution generally, but they do not quantify it.
  
  2. he recession parameter b and the interpretation of Ks: The paper defines Ks = 1/a, where a is the coefficient of the power-law recession −dQ/dt = aQ^b (Kirchner, 2009). In this framework, a and b are estimated jointly: the magnitude of a depends on the value of b, and both parameters change when the shape of the recession curve changes. If b shifts systematically with permafrost degradation, which is physically plausible, since deeper active-layer drainage may alter the storage–discharge nonlinearity, then a time trend in Ks reflects a combination of changes in recession timescale and recession shape, not timescale alone. The analysis focuses exclusively on a/Ks and does not report b or its temporal variability. The authors should examine whether b shows a trend over the study period and discuss how any such trend affects the interpretation of Ks as a proxy for active-layer water storage.
  
  3. Temporal constraint on the deformation onset (~2005): Section 4.1.2 states that significant permafrost degradation "likely initiated around 2005." The available SAR data between 2003 and 2007 consists of a single Envisat interferogram spanning 2003–2005, which shows minimal change. The next constraining observation is ALOS-1 from 2007 onward. The 2003–2007 gap means the onset could be anywhere from 2005 to 2007: a 2-year window, and the "~2005" date is effectively the midpoint of an unconstrained interval. The authors should frame this more carefully, acknowledging the unobserved gap and presenting the onset estimate as a range rather than a point date.
  
  Citation: https://doi.org/10.5194/egusphere-2025-5611-RC2
  - AC2: 'Reply on RC2', Yonghong Yi, 27 Jun 2026
    
    We thank the reviewer for the positive assessment and constructive comments. In response, we have: (1) quantified the uncertainty in the climate-versus-mining attribution of Ks by calculating the climate-unexplained residual and its 95% uncertainty bounds; (2) examined the temporal variability of the recession parameter b and performed a fixed-b sensitivity analysis; and (3) discussed the unobserved temporal gap in the SAR record. Detailed responses to each comment are provided in the attached file.
    
    Citation: https://doi.org/10.5194/egusphere-2025-5611-AC2
- AC1: 'Reply on RC1', Yonghong Yi, 27 Jun 2026
  
  We thank the reviewer for the positive assessment and constructive comments. In response, we have: (1) discussed the limitations of the linear–periodic temporal constraint and the ALOS-2 derived velocity reference and evaluated interannual Sentinel-1 interferograms and compared their performance with that of the ALOS-2 constrained NSBAS approach; (2) quantified the interannual stability of the thaw-season deformation scaling factors; (3) examined the temporal variability of the recession parameter b; and (4) applied Geodetector factor analysis to rank the explanatory power of environmental controls on seasonal deformation spatial variability. The relevant methods, results, discussion, figures, and supplementary materials have been revised accordingly. Detailed responses to each comment are provided in the attached file.
  
  Citation: https://doi.org/10.5194/egusphere-2025-5611-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5611', Anonymous Referee #1, 29 Apr 2026

General comments:

This manuscript integrates multi-source InSAR observations with hydrological recession analysis to investigate permafrost-related surface deformation and its hydrological implications in a disturbed alpine river basin. The study addresses an important topic in cryosphere–hydrology interactions, and the development of a long-term deformation dataset is a valuable contribution to the community. The joint analysis of InSAR deformation and hydrology recession analysis is novel, and overall it is well done. I have some concerns on the methodology development and analysis that need to be clarified or discussed before the publication. Details are provided as below.
1. InSAR processing

-The deformation analysis relies primarily on descending-orbit SAR acquisitions. While limited data availability may justify this choice for earlier sensors, Sentinel-1 provides systematic coverage in both ascending and descending tracks over most land areas. The authors should clarify the rationale for restricting the Sentinel-1 analysis to descending data.

The SAR datasets used in this study also differ in temporal coverage and observation periods. The potential impact of these inconsistencies on the long-term deformation estimates, as well as the associated uncertainties, need to be more explicitly discussed.
-The study addresses the poor quality of C-band interferograms during winter, likely caused by snow cover and freeze–thaw-related decorrelation, by applying a linear–periodic temporal constraint within the NSBAS-InSAR framework and by calibrating the Sentinel-1 time series using ALOS-2-derived deformation rates. The NSBAS time-series inversion is constrained using long-term deformation rates derived from ALOS-2 data. While this approach is practical, the imposed constraints and simplified temporal model may not fully capture complex or transient deformation signals. A discussion of the limitations of this methodology, and how they may influence the reconstructed long-term deformation time series, would help clarify the strength and advantages of the method. In addition, previous studies have attempted to mitigate winter decorrelation in C-band InSAR over permafrost regions by using long-temporal-baseline interferograms acquired during summer-to-summer periods, which can avoid snow-contaminated winter acquisitions and has been shown to perform well in Alaska (Guan, S.; Wang, C.; Tang, Y.; Zou, L.; Yu, P.; Li, T.; Zhang, H. (2024). North American Circum-Arctic Permafrost Degradation Observation Using Sentinel-1 InSAR Data. Remote Sensing, 16(15), 2809. DOI: 10.3390/rs16152809). It would be useful for the authors to discuss this alternative strategy and compare its advantages and limitations with the ALOS-2-constrained approach used in this study. Such a discussion would better clarify the methodological applicability, and potential limitations of the proposed approach.
2. Uncertainty of the thaw-season deformation scaling

The thaw-season deformation scaling approach assumes that the relative proportions of deformation across different sub-periods remain stable across years. While practical, this assumption may be influenced by interannual variability in the climate conditions. Assessing the variability of the scaling factor across Sentinel-1 observational period, and providing an estimate of the associated uncertainty, would help evaluate the robustness of this approach.
3. Recession analysis

The streamflow recession analysis provides some interesting insights. It is based on the power-law recession curve (Eq. 5), while the analysis focuses primarily on the recession rate (a). The other recession parameter (i.e. b) is also a key parameter controlling the nonlinearity of catchment drainage process. The authors may consider examining b, assessing its variability, and discussing its potential implications alongside with changes in a.
4. Quantitative attribution of environmental controls on deformation variability

The manuscript includes several environmental variables to interpret deformation patterns; however, their relative contributions are not well quantitatively assessed. It would be helpful if the authors could further clarify the relative importance of these factors, for example, by providing a ranking of explanatory power or applying a feature importance analysis.
Specific comments:

1. Section 3.1.1: It is unclear whether a consistent reference point is used across different InSAR data processing, which may affect the comparability of deformation estimates.

2. Line 255: Please clarify the temporal coverage of the datasets used in this analysis.

3. Figure 9c: There is a spelling error in the labels.

4. Figure 14: Is the statistical analysis of environmental factors and deformation limited to the study area? How do the authors explain why the northeastern part (outside the study area) shows low albedo values but does not exhibit strong subsidence signals (e.g. as shown in Fig. 13)?

Citation: https://doi.org/10.5194/egusphere-2025-5611-RC1
- RC2:
  'Reply on RC1', Neelarun Mukherjee, 06 Jun 2026
  
  This manuscript integrates multi-decadal InSAR observations with streamflow recession analysis to investigate permafrost degradation in a mining-disturbed alpine basin. The topic is timely and the combination of surface deformation monitoring with a hydrological signal is novel. The long-term deformation dataset spanning five SAR sensors is a genuine contribution. I have a some comments:
  
  1. Attribution of Ks changes to mining versus climate: The paper fits a multiple linear regression (MLR) using pre-mining data (1973–2002) and applies it to 2003–2022 to estimate climate-driven Ks changes. The authors do acknowledge uncertainty in attribution generally, but they do not quantify it.
  
  2. he recession parameter b and the interpretation of Ks: The paper defines Ks = 1/a, where a is the coefficient of the power-law recession −dQ/dt = aQ^b (Kirchner, 2009). In this framework, a and b are estimated jointly: the magnitude of a depends on the value of b, and both parameters change when the shape of the recession curve changes. If b shifts systematically with permafrost degradation, which is physically plausible, since deeper active-layer drainage may alter the storage–discharge nonlinearity, then a time trend in Ks reflects a combination of changes in recession timescale and recession shape, not timescale alone. The analysis focuses exclusively on a/Ks and does not report b or its temporal variability. The authors should examine whether b shows a trend over the study period and discuss how any such trend affects the interpretation of Ks as a proxy for active-layer water storage.
  
  3. Temporal constraint on the deformation onset (~2005): Section 4.1.2 states that significant permafrost degradation "likely initiated around 2005." The available SAR data between 2003 and 2007 consists of a single Envisat interferogram spanning 2003–2005, which shows minimal change. The next constraining observation is ALOS-1 from 2007 onward. The 2003–2007 gap means the onset could be anywhere from 2005 to 2007: a 2-year window, and the "~2005" date is effectively the midpoint of an unconstrained interval. The authors should frame this more carefully, acknowledging the unobserved gap and presenting the onset estimate as a range rather than a point date.
  
  Citation: https://doi.org/10.5194/egusphere-2025-5611-RC2
  - AC2: 'Reply on RC2', Yonghong Yi, 27 Jun 2026
    
    We thank the reviewer for the positive assessment and constructive comments. In response, we have: (1) quantified the uncertainty in the climate-versus-mining attribution of Ks by calculating the climate-unexplained residual and its 95% uncertainty bounds; (2) examined the temporal variability of the recession parameter b and performed a fixed-b sensitivity analysis; and (3) discussed the unobserved temporal gap in the SAR record. Detailed responses to each comment are provided in the attached file.
    
    Citation: https://doi.org/10.5194/egusphere-2025-5611-AC2
- AC1: 'Reply on RC1', Yonghong Yi, 27 Jun 2026
  
  We thank the reviewer for the positive assessment and constructive comments. In response, we have: (1) discussed the limitations of the linear–periodic temporal constraint and the ALOS-2 derived velocity reference and evaluated interannual Sentinel-1 interferograms and compared their performance with that of the ALOS-2 constrained NSBAS approach; (2) quantified the interannual stability of the thaw-season deformation scaling factors; (3) examined the temporal variability of the recession parameter b; and (4) applied Geodetector factor analysis to rank the explanatory power of environmental controls on seasonal deformation spatial variability. The relevant methods, results, discussion, figures, and supplementary materials have been revised accordingly. Detailed responses to each comment are provided in the attached file.
  
  Citation: https://doi.org/10.5194/egusphere-2025-5611-AC1

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Submit a revised manuscript (29 Jun 2026) by Heather Reese

AR by Yonghong Yi on behalf of the Authors (30 Jun 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (30 Jun 2026) by Heather Reese

Dear authors,
I think you have addressed most of the reviewers' concerns adequately. A few more small changes are needed, mainly technical except for this first one.

One of the concerns of reviewer 1 was the need to be cautious regarding the drivers of the permafrost thaw. Please look over your manuscript again and soften claims such as “mining activities have triggered significant surface subsidence” to “subsidence intensified after mining began and is consistent with mining-amplified permafrost degradation.” Although your work supports a strong temporal and spatial association, it is not providing an absolute causal attribution, and can be linked as well to other drivers. Although you can state that the evidence is strong.

In a few places the new text needs to be looked over for grammar, for example this sentence (is there maybe an “and” missing so that it would read, “... study area and the reference velocity...”?): "Moreover, ALOS-2 covers only part of the Sentinel-1 study area, the reference velocity outside the overlapping region was obtained by extending the CDF matching correction, which may not fully represent local deformation variability.”

Please also make sure that the Figure, Table and Equation numbers are all correct and in the right order, as well as referenced in the text (I believe they are, but after the changes it is good to double check).

In some places “Qilian Mountain” is used, but “Qilian Mountains” appears elsewhere. Use one form consistently.

The text cites Li et al., 2022 in one place, while the reference list contains Li et al., 2022a and Li et al., 2022b. The in-text citation should specify the correct letter.

Please check over your references to the MODIS data product names to make sure they are correct, and where necessary that they are consistent.

Fig. 3 caption begins: “Workflow for differential interferogram generation and (b)…” It should probably be “Workflow for (a) differential interferogram generation and (b)…”

Note that Fig. 8 and Fig. 12 captions misspell Sentinel-1 as Sentienl-1.

Hide

AR by Yonghong Yi on behalf of the Authors (04 Jul 2026) Author's response Manuscript

Post-review adjustments

AA – Author's adjustment | EA – Editor approval

AA by Yonghong Yi on behalf of the Authors (14 Jul 2026) Author's adjustment Manuscript

EA: Adjustments approved (16 Jul 2026) by Heather Reese

Journal article(s) based on this preprint

17 Jul 2026

Long-term InSAR and streamflow recession analysis reveal accelerated permafrost degradation in the mining area of the Qilian Mountains

Tian Chang, Yonghong Yi, Masato Furuya, Huiru Jiang, Tao Che, Youhua Ran, Lin Liu, and Rongxing Li

The Cryosphere, 20, 3933–3957, https://doi.org/10.5194/tc-20-3933-2026,https://doi.org/10.5194/tc-20-3933-2026, 2026

Short summary

Tian Chang, Yonghong Yi, Masato Furuya, Huiru Jiang, Tao Che, Youhua Ran, Lin Liu, and Rongxing Li

Supplement

https://doi.org/10.5194/egusphere-2025-5611-supplement

Tian Chang, Yonghong Yi, Masato Furuya, Huiru Jiang, Tao Che, Youhua Ran, Lin Liu, and Rongxing Li

Viewed

Total article views: 2,402 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
1,352	954	96	2,402	253	83	110

HTML: 1,352
PDF: 954
XML: 96
Total: 2,402
Supplement: 253
BibTeX: 83
EndNote: 110

Views and downloads (calculated since 14 Jan 2026)

Month	HTML	PDF	XML	Total
Jan 2026	604	346	42	992
Feb 2026	257	155	15	427
Mar 2026	316	287	23	626
Apr 2026	76	61	3	140
May 2026	54	62	8	124
Jun 2026	15	18	1	34
Jul 2026	30	25	4	59

Cumulative views and downloads (calculated since 14 Jan 2026)

Month	HTML	PDF	XML	Total
Jan 2026	604	346	42	992
Feb 2026	257	155	15	427
Mar 2026	316	287	23	626
Apr 2026	76	61	3	140
May 2026	54	62	8	124
Jun 2026	15	18	1	34
Jul 2026	30	25	4	59

Viewed (geographical distribution)

Total article views: 2,416 (including HTML, PDF, and XML) Thereof 2,416 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 25 Jul 2026

Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (13204 KB)
Metadata XML

Short summary

We combined a long-term InSAR derived surface deformation dataset with streamflow recession analysis to assess potential destruction effects of human activities on regional permafrost in the largest mining area of Qinghai-Tibet Plateau. We found that enhanced surface deformation coincides with significant recession slowdown after mining, indicating a strong signal of permafrost thaw. Our results highlight accelerated effects of human disturbance on permafrost degradation under regional warming.


Total:	0
HTML:	0
PDF:	0
XML:	0