How do climate and forest changes affect long‐term streamflow dynamics? A case study in the upper reach of Poyang River basin
Bibliographic record
Abstract
Abstract In forested watersheds, forest changes and climatic variability have been commonly recognized as two major drivers for streamflow variations. Previous research has separated their relative contributions but mainly focused on either deforestation and climate or reforestation and climate, but rarely with single studies on both. This study used the Meijiang watershed (6983·2 km2), situated in the upper reach of the Poyang Lake basin, as an example to quantify how climate and forest changes (both deforestation and reforestation) consecutively affect streamflow dynamics. Two methods, namely modified double‐mass curves and sensitivity‐based approach, were used in this study. Two breakpoints (years 1968 and 1985) with significant annual streamflow changes were detected, and together with the control period, they were then used to define three distinct periods: the control (1957–1967), deforestation (1968–1984) and reforestation (1985–2006) periods. Our results show that in the deforestation period, the average annual streamflow increment attributed to deforestation was 112·78 mm year−1, while the annual streamflow variation attributed to climate variability was −111·39 mm year−1. In the reforestation period, the average annual streamflow decrease caused by reforestation was −51·04 mm year−1, while the annual streamflow variation attributed to climate variability was 52·52 mm year−1. The sensitivity‐based approach also provided similar results. The positive and negative values in the streamflow changes suggest offsetting effects between forest changes and climate variability in both deforestation and reforestation periods. The similar magnitudes of streamflow changes demonstrate that the hydrological effects of forest changes can be as great as those caused by climate change. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".