Recharge estimation from return flow on irrigated land in the Assiniboine Delta Aquifer
Bibliographic record
Abstract
The Assiniboine Delta Aquifer (ADA) is the largest unconfined aquifer and the main water supply for agricultural production in Manitoba, Canada. Groundwater has a critical role in irrigated agriculture to ensure food security, and aquifer recharge is a key to groundwater management. While aquifers are primarily recharged from rainfall and surface sources, return flow from irrigation water can recharge during cropping seasons. The previous studies on the ADA used the water budget method to estimate the recharge but not return flow through irrigation. In this study, both the groundwater recharge and return flow through irrigation water to the ADA were estimated numerically by using HYDRUS-1D. The data were retrieved from soil sensors and weather stations installed at each selected site. This study used three stations on cropland, and three on pastureland. The soil properties used to construct the numerical models were determined by laboratory experiments and retrieved from the Canada-Manitoba Crop Diversification Centre (CMCDC). The numerical models were calibrated by altering the van Genuchten-Mualem (VGM) parameters to match the observed and estimated soil water content. This modelling step was carried out by coupling HYDRUS-1D with Pareto Archived Dynamically Dimensioned Search (PA-DDS) using MATLAB. The results of this research showed that the estimated groundwater recharge based on simulations for the 2019 and 2020 cropping seasons was significantly higher than reported in previous studies and that the return flow on the irrigated cropland positively impacted the groundwater recharge. Return flow was higher in 2019 which was considered a normal year and reduces strongly in the dry year of 2020. The outcomes would be beneficial for new water licensing, an increase in agricultural production, and more importantly, sustainable groundwater management of the Assiniboine Delta Aquifer by limiting the environmental impact from the irrigation withdrawals .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".