Identifying the runoff variation in the Naryn River Basin under multiple climate and land-use change scenarios
Bibliographic record
Abstract
Abstract A multiple scenario-based ensemble prediction (MSEP) method is developed for exploring the impacts of climate and land-use changes on runoff in the Naryn River Basin. MSEP incorporates multiple global climate models, Cellular Automata–Markov and Soil and Water Assessment Tool (SWAT) within a general framework. MSEP can simultaneously analyze the effects of climate and land-use changes on runoff, as well as providing multiple climate and land-use scenarios to reflect the associated uncertainties in runoff simulation and prediction. A total of 96 scenarios are considered to analyze the trend and range of future runoff. Ensemble prediction results reveal that (i) climate change plays a leading role in runoff variation; (ii) compared to the baseline values, peak flow would increase 36.6% and low flow would reduce 36.8% by the 2080s, which would result in flooding and drought risks in the future and (iii) every additional hectare of arable land would increase the water deficit by an average of 10.9 × 103 m3, implying that the arable land should be carefully expanded in the future. Results suggest that, to mitigate the impact of climate change, the rational control of arable land and the active promotion of irrigation efficiency are beneficial for water resources management and ecological environmental recovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".