Hydrological processes in major types of Chinese forest
Bibliographic record
Abstract
Abstract Overexploitation of forest resources in China has caused serious concerns over its negative impacts on water resources, biodiversity, soil erosion, wildlife habitat and community stability. One key concern is the impact of forestry practices on hydrological processes, particularly the effect of forest harvest on water quality and quantity. Since the mid 1980s, a series of scientific studies on forest hydrology have been initiated in major types of forest across the country, including Korean pine (Pinus koraiensis), Chinese fir (Cunninghamia lanceolata), oak (Quercus mongolica), larch (Larix gmelinii), faber fir (Abies fabri), Chinese pine (Pinus tabulaeformis), armand pine (Pinus arandi), birch (Betula platyphylla) and some tropical forests. These studies measured rainfall interception, streamflow, evapotranspiration and impacts of forest management (clearcutting and reforestation). This paper reviews key findings from these forest hydrological studies conducted over the past 20 years in China. Forest canopy interception rates varied from 15 to 30% of total rainfall, depending on forest canopy and rainfall characteristics. Stemflow is generally a small percentage (<5%) of total rainfall, but it accounts for 15% in the oak forest in northeast China. The high amounts of stemflow, as well as higher amounts of nutrients contained in stemflow, may allow oak trees to adapt to a dry and nutrient‐poor environment. Evapotranspiration was a significant component of the water budget in these Chinese forests studied, ranging from 80–90% of total rainfall in the northern temperate forests to 40–50% in the southern tropical forests. Forests substantially reduced surface runoff and erosion. However, no consistent response on total streamflows was observed. The reason for the inconsistency may be due to complexities of streamflow processes and the utilization of different methodologies applied at the various spatial scales. Copyright © 2005 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 | 0.000 |
| 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".