Regional groundwater recharge estimation in the Assiniboine Delta Aquifer (ADA)
Bibliographic record
Abstract
This study aimed to accurately estimate groundwater recharge in the Assiniboine Delta Aquifer (ADA) by utilizing a comprehensive approach that involved configuring 3916 HYDRUS-1D models for each grid cell with a dimension of 1 km by 1 km in the ADA, based on input factors such as soil textures, land use characteristics, meteorological data, and groundwater levels. The impact of vegetation cover on regional groundwater recharge was also considered using the Penman-Monteith equation to calculate potential evapotranspiration (Monteith, 1981), which was then partitioned into potential evaporation and potential transpiration to be used as inputs in each HYDRUS-1D model. Groundwater recharge was found to be highest during the months of April and May, coinciding with the snow melt season, and during late summer and fall months, specifically in September and October. Soil characteristics and groundwater levels were also found to significantly impact groundwater recharge, with sandy soil textures exhibiting the highest groundwater recharge rates. The average groundwater recharge for the years 2019, 2020, and 2021 was calculated to be approximately 79 mm/year, 74 mm/year, and 54 mm/year, respectively, with an overall average recharge of 69 mm/year. This value was consistent with the results reported by Stafford et al. (2022) and almost double the recharge value estimated by Render (1988), which was 34 mm/year. Additionally, the recharge to precipitation (R/P) ratio for each year was found to be 15%, 25%, and 21% for 2019, 2020, and 2021, respectively. These findings provide valuable insights into the dynamics of regional groundwater recharge in the ADA over time and highlight the importance of considering the spatial distribution of soil characteristics, land use characteristics, and groundwater levels when estimating groundwater recharge in the region.
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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 | 0.000 |
| 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 | 0.001 | 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".