Water resource vulnerability: simulation and optimisation models
Bibliographic record
Abstract
Approaches to adaptation to a changing climate in water resource planning have relied on both simulation and optimisation models. Simulation models project the impacts of climate change on water system performance, while optimisation models show the optimal system performance under climate change conditions. This study uses two water resource models to analyse a water resource system in Sussex (south-east England) under climatic and socio-economic uncertainty. Overall, the simulation and optimisation models show structural model uncertainty. The simulation model highlights potential vulnerability in current operational practice, while the optimisation model shows that the current system could be vulnerable to climate change and demand growth even under the best-case scenario. The integrated scenarios in this study combine both climate scenarios from four different climate products over the periods of 2020s, 2030s and 2050s and socio-economic scenarios represented by different demand profiles. The results show that water demand quickly becomes a controlling factor once it increases by more than 35% from the 2007 baseline level. Both models demonstrate a gradual increasing risk of supply deficit in the 2020s and the 2030s. Water deficit risks vary widely in the 2050s and are highly dependent on the socio-economic scenarios.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".