Annual runoff and evapotranspiration of forestlands and non‐forestlands in selected basins of the Loess Plateau of China
Bibliographic record
Abstract
Abstract Large‐scale forestation has been undertaken over decades principally to control the serious soil erosion in the Loess Plateau of China. A quantitative assessment of the hydrological effects of forestation, especially on basin water yield, is critical for the sustainable forestry development within this dry region. In this study, we constructed the multi‐annual water balances to estimate the respective grand average of annual evapotranspiration (ET) and runoff for forestlands and non‐forestlands of 57 basins. The overall annual runoff and corresponding runoff/precipitation ratio were low, with a mean of 33 mm (7%) ranging from 10 (2%) to 56 mm (15%). Taking the grand average of annual precipitation of 463 mm for all basins, the corresponding grand averages of annual ET and runoff were 447 and 16 mm for forestlands, 424 and 39 mm for non‐forestlands, respectively. Thus, the corresponding ratios of annual ET and runoff to precipitation were 91·7 and 8·3% for non‐forestlands, 96·6 and 3·4% for forestlands, respectively. Although the absolute difference in grand average of annual runoff was only 23 mm, it represents a large difference in relative terms, as it equates up to 58% of annual runoff from non‐forestlands. We argue that the large‐scale forestation may have serious consequences for water management and sustainable development in the dry region of NW China because of a runoff reduction. This study highlights the importance of quantifying the ET of forests and other land uses and to examine how land cover change may affect the water balances in an arid environment. Copyright © 2011 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".