Vadose zone modeling to identify controls on groundwater recharge in an unconfined granular aquifer in a cold and humid environment with different meteorological data sources
Bibliographic record
Abstract
Abstract Groundwater recharge (GR) is a complex process that is difficult to quantify. Increasing attention has been given to unsaturated zone modeling to estimate GR and better understand the processes controlling it. Continuous soil-moisture time series have been shown to provide valuable information in this regard. The objectives of this study were to (i) analyze the processes and factors controlling GR in an unconfined granular aquifer in a cold and humid environment and (ii) assess the uncertainties associated with the use of data from different sources. Soil moisture data monitored over three years at three experimental sites in southern Quebec (Canada) were used to calibrate the HYDRUS-1D model and to estimate ranges of possible GR in a region where groundwater is increasingly used as a source of fresh water. The simulations identified and quantified important factors responsible for the near-surface water balance that leads to GR. The resulting GR estimates from 2016 to 2018 showed marked differences between the three sites, with values ranging from 347 to 735 mm/y. Mean GR for the three sites was 517 mm/y for 2016–2018 and 455 mm/y for the previous 12-year period. GR was shown to depend on monthly variations in precipitation and on soil textural parameters in the root zone, both controlling soil-water retention and evapotranspiration. Monthly recharge patterns showed distinct preferential GR periods during the spring snowmelt (38–45% of precipitation) and in the fall (29% of precipitation). The use of different meteorological datasets was shown to influence the GR estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".