Sustainable Groundwater Decisions: Hydro-Economic Model Applications to Irrigation in Contrasting Environmental Conditions
Bibliographic record
Abstract
Groundwater is important for global agriculture but increasing populations and rising food demand are placing significant pressure on its sustainable use for irrigation. Effective, financially viable irrigation management strategies are urgently needed. This study applies a farm-level hydro-economic model to two contrasting sites: the High Plains Aquifer (HPA), a deep, overexploited, unconfined aquifer where overlying regions are almost entirely reliant on groundwater for irrigation; and the Saskatchewan River Basin (SRB), a relatively undepleted basin where overlying regions use both groundwater and surface water for irrigation. The model estimates groundwater availability and long-term land values while accounting for climate change impacts on crop production and irrigation practices. Using Conditional Value-at-Risk to assess economic risks, it offers robust recommendations for sustainable groundwater use. Results demonstrate distinct irrigation strategies: the HPA site faces greater potential economic and environmental impacts and requires increased irrigation to maintain productivity in the future; the SRB site experiences moderate impacts with little change needed to adapt to future climate scenarios. This divergence highlights how climate and water source variability shape trade-offs between economic returns and resource sustainability. This framework provides practical guidance for tailoring irrigation policies to regional conditions while managing risk under uncertain futures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".