Varying hydrological response to climate change in three neighborhood plateau lake basins: Localized climate change feature matters
Bibliographic record
Abstract
Climate change and its impact on plateau lakes are of wide concern in China owing to their diverse yet fragile ecosystems. The three largest and most concerning plateau lakes (Dianchi, Erhai, and Fuxian lakes) in southwestern China were selected as case studies to demonstrate their different hydrological responses attributing to the local climatic and watershed characteristics. We processed 27 climate change scenarios according to the local climate characteristics and simulated the daily runoff of each lake basin under the historical and the 27 climate change scenarios. Then we analyzed the change of mean annual and seasonal runoff, and hydrological extremes of each lake basin. The results indicate a great risk of socio-economic and ecological for these plateau lakes as climate change will significantly alter the horological regimes of each lake basin. The mean annual runoff of the three lake basins will change from–65.24 to 54.17 %, when the air temperature increases by 1–2 °C and precipitation changes from –20 to + 20 %. Climatic and topographic heterogeneities caused each lake basin responded differently to climate change. Among them, the LFB was more sensitive to climate change than the LDB and LEB. Changes in the annual and seasonal runoff for the LFB were approximately 1.5-fold higher than that of the LDB and LEB. The hydrological extremes in the LFB also had the most significant changes. To cope with future climate change, each lake requires reasonable and effective mitigation measures.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".