Modelling the Wieringermeer effect observed in the Champagne Chalk aquifer
Bibliographic record
Abstract
Using the water-table-fluctuation method (WTF) in the Chalk unconfined aquifer of northern Europe leads to low specific yield estimations ( ∼ 2 % ) despite the high porosity of the chalk matrix ( ∼ 40 % ). The reason for this is the Wieringermeer effect, a phenomenon caused by capillarity that has rarely been mentioned in previous studies. In this paper, we highlight the role of the mostly overlooked Wieringermeer effect in explaining the water level records and present a 2D model capable of correctly reproducing the low specific yield in a 40%-porosity matrix. This study focuses on a 10 km 2 experimental site located in the Champagne region of France and is based on six years of water level records. We derived the specific yield and response time using the WTF method and correlograms. The acquired data were then used to construct a distributed numerical model based on physics and the coupling of groundwater flow in unsaturated and saturated zones, using the Metis code. In particular, we tested different retention laws to highlight the effects of capillarity. The piezometric records show that the water table under the hills strongly reacts to precipitation, whereas it reacts only to seasonal signals in the valleys. The specific yields observed are between 0.7 and 5.6%, including matrix and fracture effects. Our model shows that in a 40% porosity matrix, such low specific yields are only possible with high retention, thereby highlighting the Wieringermeer effect within the simulation. • The Chalk is a porous and fractured aquifer with a high retention capacity. • The vadose zone retains a large quantity of capillary water. • This leads to important water-level fluctuations in response to climate forcing. • This corresponds to the Wieringermeer effect.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".