Future spatial and temporal variation of blue and green water in the Jing River Basin of central China under changing climatic conditions
Bibliographic record
Abstract
The blue and green water resource distribution of the Jing River Basin, a cradle of Chinese civilization on the Loess Plateau, was studied using the Soil and Water Assessment Tool and the Sequential Uncertainty Fitting algorithm (vers. 2). Understanding these resources aids in the ecological preservation of the Yellow River Basin and its high-quality development. Future climate conditions were simulated using the Statistical Downscaling Model (SDSM). The data came from the Shared Socioeconomic Pathway (SSP) projections (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the Canadian Earth System Model (vers. 5), covering the 2030s, 2060s, and 2090s (based on 2015–2045, 2046–2075, and 2076–2100 climate projections, respectively). The SDSM accurately simulated temperature and precipitation trends, with its temperature predictions being more accurate. The results show that the maximum temperature, minimum temperature, and precipitation tend to increase under the three future climate scenarios, and the amount of blue and green water continues to increase in the future, with the SSP5-8.5 scenario showing the highest amount of blue and green water, and the SSP1-2.6 scenario showing the lowest amount of blue and green water, in terms of the climate scenarios. In terms of temporal distribution, 2090s has the most abundant blue and green water and 2030s has the least blue and green water content. Forecasting blue and green water changes due to climate change is vital for regional water management and risk assessment.
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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 | 0.001 | 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".