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Record W4415419720 · doi:10.5194/tc-19-4855-2025

Quantifying retrogressive thaw slump mass wasting and carbon mobilisation on the Qinghai-Tibet Plateau using multi-modal remote sensing

2025· article· en· W4415419720 on OpenAlexaffabout
Kathrin Maier, Zhuoxuan Xia, Lin Liu, Mark J. Lara, Jurjen van der Sluijs, Philipp Bernhard, Irena Hajnsek

Bibliographic record

Venue˜The œcryosphere · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGovernment of Northwest Territories
FundersNuclear Safety and Security CommissionChinese University of Hong KongLanzhou UniversityResearch Grants Council, University Grants CommitteeNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPermafrostMass wastingDigital elevation modelPlateau (mathematics)Soil carbonErosionSynthetic aperture radarThermokarstArctic

Abstract

fetched live from OpenAlex

Abstract. Retrogressive Thaw Slumps (RTS) are slope failures triggered by permafrost thaw that occur in ground-ice-rich regions of the Arctic and the Qinghai-Tibet Plateau (QTP). A strong warming trend has amplified RTS activity on the QTP in recent years. Although the region currently acts as a carbon sink, its permafrost-covered area (40 %) contains substantial soil organic carbon (SOC) stocks. Intensifying thaw-driven mass wasting may transform the QTP into a net carbon source by mobilising previously frozen SOC and enhancing decomposition. Yet, regional remote sensing studies have not yet quantified RTS mass wasting, including material erosion volumes and associated SOC mobilisation. Analysing time-series data from digital elevation models (DEMs) enables direct observation of RTS activity by measuring changes in active area, eroded material volume, and the overall magnitude of surface change. However, most available DEM sources lack the spatial resolution and temporal frequency required for comprehensive RTS monitoring. In contrast, optical data provide higher spatial resolution and more frequent observations, but lack elevation information. Here, we evaluated RTS mass wasting across the QTP from 2011 to 2020 by combining DEMs derived from bistatic Interferometric Synthetic Aperture Radar (InSAR) observations of the TanDEM-X mission with annual RTS inventories generated from high-resolution optical satellite imagery and geophysical soil property data to estimate erosion volume, ground ice loss, and SOC mobilisation. We estimated that RTS activity on the QTP during 2011–2020 relocated 5.020.7525.35×107 m3 previously frozen material, resulting in a loss of 3.580.2828.20×106 m3 of ground ice, and mobilised 2.780.117.98×108 kg C of organic carbon. We found a reliable power-law scaling relationship between RTS area in the optical RTS inventory and calculated volume change, with α values ranging from 1.20±0.01 to 1.30±0.01 (R2=0.87, p<0.001) depending on the regression model used, which may readily transform planimetric RTS area into volume estimates at scale on the QTP. Despite the relatively recent initiation and smaller size of RTSs on the QTP, material erosion and SOC mobilisation over the past decade exceeded levels in some Siberian Arctic regions, but remained up to 10 times lower than hotspots in the Canadian High Arctic. While current RTS impacts on the QTP are relatively modest, affecting < 0.01 % of the total permafrost area and contributing approximately 0.1 % to the regional carbon budget, the accelerating rates of RTS activity indicate that this phenomenon could become increasingly significant in the future. Our findings highlight the importance of regional studies in advancing our understanding of permafrost thaw-driven changes to the carbon dynamics of rapidly changing permafrost ecosystems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.282
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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