Effect of forest recovery on summer streamflow in small forested watersheds, Northeastern China
Bibliographic record
Abstract
Abstract The hydrological effect of forest recovery is receiving renewed interest globally because information on forest carbon–water relationship is critically needed to support carbon management through reforestation and sustainable water management. In Northeastern China, summer (June to August) streamflow accounts for about 50% of total annual streamflow and is vital to water supply and management in the region. Understanding how forest recovery may affect streamflow is important to both reforestation campaign and long‐term water sustainability. In this study, we analysed 33 years of summer hydrologic data (1970–2002) from two comparable small‐scale watersheds located in the Xiaoxing'anling, Northeastern China. Time series analysis and two graphic methods (double mass curve and flow duration curve) with statistical testing as well as long‐term data on forest cover changes and climate were used. Our results show that the significant streamflow reduction as a result of reforestation occurred when forest cover reached 70% or 10 years after planting. After forest cover reached 85%, water reduction became stabilized. The accumulative streamflow reduction in 2002 reached 8·61% of the total accumulative streamflow. Among those water reduced, high flows (from 5 to 25 percentiles) were mostly affected, demonstrating that northeastern forests have an important role in reducing high flows. Implications of these results are discussed in the context of climate change, reforestation and water resource management. Copyright © 2011 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 | 0.001 | 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 | 0.000 | 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".