Pumping Optimization Under Uncertainty in an Island Freshwater Lens Using a Sharp‐Interface Seawater Intrusion Model
Bibliographic record
Abstract
Abstract Pumping optimization under uncertainty is a powerful approach for the management of groundwater resources, and its implementation would be valuable in island aquifers where freshwater lenses are affected by seawater intrusion. Sharp‐interface numerical models are especially well suited for the task as they offer fast simulation times, but to date they have not been used because of a lack of guidelines and due to the specific challenges associated with this approach. This study presents a methodology for pumping optimization under uncertainty for island freshwater lenses using a sharp‐interface model (MODFLOW‐SWI2) and demonstrates it for a real case (Magdalen Islands, Quebec, Canada). The total pumping in a well field was maximized while avoiding well salinization due to upconing. To do so, the sharp interface simulated below the pumping wells was corrected successively for cell‐to‐well upconing and for dispersion. Pumping optimization under uncertainty was then conducted using PESTPP‐OPT, considering parameter and observation uncertainty, and was repeated for 23 reliability levels to illustrate a large range of risk‐averse, tolerant and neutral stances. The maximum pumping was obtained as a function of risk of well salinization. This approach enabled quantification of the tradeoffs between pumping and risk, allocation of pumping amongst wells, and an examination of the ability of the well field to meet the water demand while maintaining an acceptable level of risk. Ultimately, this framework allows groundwater managers to select the final pumping scenario themselves, depending on their attitude toward risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".