Impacts of forest changes on hydrology: a case study of large watersheds in the upper reaches of Minjiang River watershed in China
Bibliographic record
Abstract
Abstract. Quantifying the effects of forest changes on hydrology in large watersheds is important for designing forest or land management and adaptation strategies for watershed ecosystem sustainability. Minjiang River watershed, located in the upper reach of the Yangtze River basin, plays a strategic role in the environmental protection and economic and social well-being for both the watershed and the entire Yangtze River basin. The watershed lies in the transition zone from Sichuan Basin to Qinghai-Tibet Plateau with a size of 24 000 km2. Due to its strategic significance, severe historic deforestation and high sensitivity to climate change, the watershed has long been recognized as one of the highest priority watersheds in China for scientific research and resource management. The purpose of this review paper is to provide a state-of-the-art summary on what we have learned from several recently completed research programs (one of them known as "973 of the China National Major Fundamental Science" from 2002 to 2008). This summary paper focused on how land cover or forest change affected hydrology at both forest stand and watershed scales in this large watershed. Inclusion of two different spatial scales is useful, because the results from a small spatial scale (e.g. forest stand level) can help interpret the findings on a large spatial scale. Our review suggests that historic forest harvesting or land cover change has caused significant water yield increase due to reduction of forest canopy interception and evapotranspiration caused by removal of forest vegetation on both spatial scales. The impact magnitude caused by forest harvesting indicates that the hydrological effects of forest or land cover changes can be as important as those caused by climate change, while the opposite impact directions suggest their offsetting effects on water yield in the Minjiang River watershed. In addition, different types of forests have different magnitudes of evapotranspiration (ET), with the lowest in old-growth natural coniferous forests (Abies faxoniana Rehd. et Wils.) and the highest in coniferous plantations (e.g. Picea asperata Mast.) among major forest types in the study watershed. This suggests that selection of different types of forests can have an important role in ET and consequently water yield. Our synthesis indicates that future reforestation and climate change would likely produce the hydrological effects in the same direction and thus place double the pressure on water resource as both key drivers may lead to water yield reduction. The findings can support designing management strategies for protection of watershed ecological functions in the context of future land cover and climate changes.
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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 | 0.003 | 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".