Declining Runoff Sensitivity to Precipitation Following Permafrost Degradation: Insights From Event‐Scale Runoff Response in the Yellow River Source Region
Bibliographic record
Abstract
Abstract Frozen ground, including permafrost and seasonally frozen ground (SFG), is a critical element of the cryosphere that strongly regulates hydrological processes in cold regions. It has been debated whether frozen ground degradation will make landscapes more, or less, sensitive to precipitation inputs; either outcome has profound implications for water resources and climate resilience. Using a data‐driven approach based solely on observations, we quantify four decades of changes in event‐scale runoff responses to daily precipitation in the source region of the Yellow River on the northeastern Tibetan Plateau. We apply Ensemble Rainfall‐Runoff Analysis (ERRA), which infers hydrologic impulse responses directly from precipitation and streamflow time series without relying on model assumptions. This enables the assessment of nonlinear, nonstationary, and spatially heterogeneous hydrologic behavior across different frozen ground types and precipitation intensities. Results show that, relative to 1979–1998, the permafrost‐dominant zone experienced a 47% reduction in peak runoff response per unit precipitation during 1999–2018 and a 32% decrease in the 25‐day runoff coefficient, while the SFG‐dominant region showed no substantial changes. The weakened runoff response in the permafrost‐dominant zone, particularly under high‐intensity precipitation (>10 mm d −1 ), likely reflects enhanced infiltration and subsurface storage driven by active‐layer deepening and weakened near‐surface seasonal freezing. These findings highlight the power of data‐driven approaches in detecting hydrological regime shifts and provide critical insights for drought mitigation and flood risk assessment in permafrost‐affected regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".