Quantifying Retrogressive Thaw Slump Mass Wasting and Carbon Mobilisation on the Qinghai-Tibet Plateau Using Multi-Modal Remote Sensing
Bibliographic record
Abstract
Abstract. Retrogressive Thaw Slumps (RTS) are slope failures triggered by permafrost thaw, occurring in ground-ice rich regions in the Arctic and on 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 40 % permafrost-covered area holds 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 increasing decomposition. Despite this, regional remote sensing studies for quantifying RTS5 mass wasting, including material erosion volumes and SOC mobilisation, are lacking. Analysing time-series data from digital elevation models (DEM) enables direct observation of RTS activity by measuring changes in active area, volume of eroded material, and the overall magnitude of surface change. However, most available DEM sources lack sufficient spatial resolution and temporal frequency for comprehensive RTS monitoring. In contrast, optical data provides higher spatial resolution and more frequent observations, but lacks elevation information. We evaluated the mass wasting of RTS throughout the QTP from 2011 to 2020 by combining DEMs from bistatic Interferometric Synthetic Aperture Radar (InSAR) observations of the TanDEM-X mission with annual RTS inventories derived from high-resolution optical satellite images and geophysical soil property data to estimate erosion volume, ground ice loss, and SOC mobilisation. By combining modelled soil property datasets with multi-modal remote sensing data, we estimated that RTS activity in the QTP between 2011 and 2020 relocated 5.0225.350.75 × 107 m3 formerly frozen material, contributed to 3.5828.200.28 × 106 m3 loss of ground ice and mobilised 2.787.980.11 × 108 kg C organic carbon. We found a reliable power law scaling between the RTS area in the optical RTS inventory and the calculated volume change with α = 1.30 ± 0.01 (R2 = 0.88, p < 0.001) that potentially allows future research to transform the planimetric RTS area into volume estimates for large-scale and comprehensive investigations on RTS mass wasting and SOC mobilisation in QTP during the last decade. Despite the comparably recent initiation and smaller size of RTS in QTP, material erosion and SOC mobilisation in the past decade in QTP surpassed some regions in the Siberian Arctic, but remained up to 10 times lower than hotspots in the Canadian High Arctic. Although the current impact of RTS in QTP is relatively modest, affecting only 0.006 % of the total permafrost area and contributing less than 1 % to the regional carbon budget, the increasing rates of RTS activity suggest that this phenomenon could become more significant in the future. Our study underscores the importance of regional studies in understanding the impact of permafrost thaw on the carbon dynamics of QTP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".