An Analytical Method to Estimate Groundwater Depletion of an Aquitard Due To Variable Drawdowns in Adjacent Aquifers
Bibliographic record
Abstract
Abstract Computing aquitard depletion, which is often overlooked, is of great significance for the assessment of groundwater resources and land subsidence. The issue is viewed as troublesome because of the additional computational burden, the poorly known hydrogeological parameters of the aquitard, and the lack of drawdown history in pumped aquifers. In this study, an analytical solution is derived to describe the drawdown variation in a nonlinear‐consolidated aquitard under the condition of variable drawdowns in adjacent aquifers. Based on the analytical solution, we study the characteristics of groundwater dynamics and water balance under the conditions of linearly increasing drawdown of aquifers in adjacent aquifers. In addition, we put forward a method to calculate the depletion and hydrogeological parameters of an aquitard corresponding to variable drawdowns in adjacent aquifers, applicable even when historical drawdown data are lacking. The accuracy of the method is generally very good, but results improve when the drawdown history of pumped aquifers is divided into more periods for estimation. Groundwater depletion of the aquitard increases with increasing compression index, coefficient of consolidation, thickness of aquitard, rate of drawdown change in the adjacent aquifer, while decreasing with initial void ratio, and initial effective stress. The proposed approach is demonstrated at a field site in Shanghai city of China, and it would help for the effective management of groundwater resources and estimation of the global transfer from groundwater to surface water.
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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 | 0.001 | 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 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".