Response of flow regimes to deforestation and reforestation in a rain‐dominated large watershed of subtropical China
Bibliographic record
Abstract
Abstract Flow regime refers to five elements of streamflow including flow magnitude, frequency, timing, duration and change rate. In spite of wide recognition of its critical significance in aquatic functions and ecosystem integrity, its responses to forest or land use change are rarely and quantitatively assessed. This paper used the Meijiang watershed (6983.2 km 2 ), situated in the upper reach of the Poyang Lake basin, as an example to first demonstrate how flow regimes were altered by deforestation and then show if the altered flow regimes were possibly recovered by consequent reforestation. Two breakpoints (year 1968 and year 1985) with significant annual streamflow changes were detected, and they were then used to define three distinct periods including the reference or control period (1957 to 1967), deforestation (1968 to 1984) and reforestation (1985–2006). The paired year approach was then applied to quantitatively analyse the responses of flow regimes to forest cover changes. Both high flows (daily flows ≧ Q 5% ) and low flows (daily flows ≦ Q 95% ) were assessed. For high flows, the deforestation significantly increased the averaged magnitudes by 10.4%, increased the return periods (5–10 year) by 23.4%, advanced the averaged timings by 10.7 h and extended the averaged durations over the thresholds by 4 days. In contrast, reforestation delayed the averaged timings by 10.5 h, reduced the averaged duration by 5 days and decreased the averaged magnitude by 17.9%. Regarding low flows, the deforestation decreased the flow magnitudes by 30.1% with 30.5 day advancing in the average timings. To our surprise, however, low flows were not significantly changed by reforestation. All above results clearly demonstrate that flow regimes of both high and low flows were significantly altered by deforestation, and recovery of those alterations through reforestation may take much longer time than expected probably because of severe soil erosion and resultant loss of soil infiltration capacity after deforestation. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".