Estimation of groundwater recharge in a shallow sandy aquifer using unsaturated zone modeling and water table fluctuation method
Bibliographic record
Abstract
Quantification of groundwater recharge is one of the most important issues in hydrogeology, especially in view of the ongoing changes in climate and land use. In this study, we use numerical models of 1D vertical flow in the vadose zone and the water table fluctuation (WTF) analysis to investigate local-scale recharge of a shallow sandy aquifer in the Brda outwash plain in northern Poland. We show that these two methods can be jointly used to improve confidence in recharge estimation. A set of preliminary numerical simulations based on soil water content measurements from 4 grassland and pine forest profiles provided a wide range of recharge estimates (263 mm to 839 mm for a 3-year period). Additional simulations were performed with the lower boundary condition specified as a functional relationship between the groundwater table elevation and the rate of groundwater outflow from the vertical profile (horizontal drains boundary condition). In this way, we could reproduce the water table fluctuations resulting from recharge and lateral discharge to nearby lakes. The agreement between simulated and observed groundwater levels differed depending on the specific set of parameters characterizing vadose zone flow, which allowed us to find the most representative parameter sets and refine the range of plausible recharge estimates (501 mm to 573 mm per 3 years). The recharge rates from WTF (410 mm to 606 mm per 3 years) were in good agreement with numerical simulations, providing that the effect of the natural recession of groundwater table due to lateral outflow was considered (master recession curve method). Our results show that: (i) the proposed approach combining 1D vadose zone modeling and WTF improves recharge estimation, (ii) multiple types of observations, including groundwater table positions, are needed to calibrate and validate vadose zone flow models, and (iii) extended periods of observations and simulations are necessary to capture year-to-year variability in the recharge rates.
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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 | 0.000 |
| 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) | 0.000 | 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".