Potential Impact of Afforestation on Water Yield in the Subalpine Region of Southwestern China<sup>1</sup>
Bibliographic record
Abstract
Abstract: To combat its growing ecological problems, China has implemented a large‐scale Natural Forest Protection Program (NFPP). Under the umbrella of this program, the Sloping Land Conversion Program (SLCP) was established in 1999 to return cultivated land with slopes of 25° or more to perennial vegetation. However, the regional impacts on water resource management that are incurred by afforestation have not been carefully evaluated, especially in the subalpine region of southwestern China. The purpose of the present study was to provide reference values for the SLCP by evaluating the potential impact of afforestation on water yield under different climatic regimes. Accordingly, evapotranspiration (ET) in cropland (CL), shrubland, and general forest was calculated using a modification of Thornthwaite’s method, and in coniferous forest, broad‐leaved forest (BF), and mixed coniferous and broad‐leaved forest (MF) using the Surface Energy Balance Algorithm for Land (SEBAL) model. The results of both approaches showed that afforestation reduces water yield by 9.6‐24.3% depending on the types of conversion and climatic conditions. Water‐yield reduction is greatest (>143.4 mm, or 24.3%) when CL is converted to BF in dry climate conditions. Compared with the other forest types studied, coniferous plantations prevented water‐yield reduction by as much as 9.6% because of their relatively low levels of ET. It is expected that implementation of the SLCP, together with continuing climate change, will further pressure regional water resources. Thus, the effectiveness of afforestation must be evaluated in a broader context while taking into account its positive ecological aspects, such as soil‐erosion control, the preservation of biodiversity, and the significant carbon sequestration provided by forests.
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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 | 0.000 | 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".