Role of rainwater induced subsurface flow in water-level dynamics and thermoerosion of shallow thermokarst ponds on the Northeastern Qinghai–Tibet Plateau
Bibliographic record
Abstract
Abstract. Understanding hydrological and thermal regimes of thermokarst lakes is of great importance for predicting their responses to climate change. However, mechanism of water-level dynamics and associated thermal effects on thermoerosion of thermokarst lakes are still not well understood on the Qinghai–Tibet Plateau (QTP). In this study, we investigate two typical shallow thermokarst ponds (namely small lakes) in a warm permafrost region with thick active layer on the northeastern QTP through quantifying water budget. Results demonstrate that, rainfall induced subsurface lateral flow dominates pond water-level regime. Annual variation of pond water-level relies on areal water budget of surrounding active layer, particularly the high variable of precipitation. Besides, it is worth noting the extraordinary warming during the late ice-cover period, because marked air gap between upper ice-cover and underlying water, led by the upward thawing of thick ice-cover, might result in greenhouse-like condition due to the unique weather that strong solar radiation and little snowpack. This hydrological mechanism also exerts evident impacts on thermal regime and thermoerosion of the shallow thermokarst ponds, and they are closely related to retreat of thermokarst pondshore and underlying permafrost degradation. These findings imply a localized model addressing the unique hydrological and thermal regimes of thermokarst lakes would be essential to study the evolution of these shallow rainwater dominated thermokarst ponds on the 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".