Conjunctive Water Management for Agriculture With Groundwater Salinity
Bibliographic record
Abstract
Abstract Salt accumulations in aquifers significantly affect and transform the conjunctive use of surface water and groundwater supporting irrigated agriculture. Salt accumulates in aquifers under many semi‐arid irrigated lands where pumping has lowered water levels enough to prevent drainage of saline groundwater from the basin. This paper provides new insights into optimal conjunctive management of groundwater pumping, recharge, surface water, and cropping patterns with groundwater salinity and hydrologic variability in an irrigated semi‐arid region, such as California's western San Joaquin Valley, reducing agricultural crop yields and revenues. A two‐stage stochastic quadratic model explores this problem to prescribe economically optimal crop mix and conjunctive water operation policies over a 10‐year period with probabilistic annual surface water availability, considering groundwater salinity's harm to crop yields. At low groundwater salinity, agricultural conjunctive use usually pumps most groundwater in drier years, supplied by additional recharge in wetter years. In contrast, at higher groundwater salinity, optimal conjunctive use pumps less in drier years while pumping more in wetter years, when more surface water allows more dilution of saltier groundwater. Reduced pumping in drier years substantially reduces a region's ability to support higher‐value perennial crops and reduces or eliminates lower‐value annual crops in dry years. Artificial recharge with fresh surface water in wetter years can have economic value from slowing groundwater salination which allows more groundwater use in drier years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".