Quantifying spatiotemporal inconsistencies in runoff responses to forest logging in a subtropical watershed, China
Bibliographic record
Abstract
Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances, profoundly impacting hydrological processes. However, the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales, challenging comprehensive assessment of logging impacts at the watershed scale. Here, we developed multiple forest logging scenarios using the soil and water assessment tool (SWAT) model for the Le'an River watershed, a 5,837 km2 subtropical watershed in China, to quantify the hydrological effects of forest logging across different spatiotemporal scales. Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity. However, sensitivity varied across spatiotemporal scales, with the rainy season (15.30%–15.81%) showing higher sensitivity than annual (11.56%–12.07%) and dry season (3.38%–5.57%) periods. Additionally, the ecohydrological sensitivity of logging varied significantly across the watershed, with midstream areas exhibiting the highest sensitivity (13.13%–13.25%), followed by downstream (11.87%–11.98%) and upstream regions (9.96%–10.05%). Furthermore, the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas, with attenuated runoff changes due to scale effects. Scale effects were more pronounced during dry seasons ((−8.13 to −42.13) × 104 m3⋅month−1) than in the rainy season ((−11.11 to −26.65) × 104 m3⋅month−1). These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions, providing valuable insights for forest management under increasing anthropogenic activities and climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".