Bibliographic record
Abstract
River hydrographs generally exhibit intense flood events during which the discharge increases quickly during rainfall, and decreases slowly afterwards. In this manuscript, we show that the dynamics of groundwater in an unconfined aquifer can account for these features.In the frame of the Dupuit-Boussinesq (shallow-water) approximation, the discharge increase rate $\dot{Q}$ is a non-linear function of the rainfall rate $R$: $\dot{Q} \propto R^{3/2}$. After the rain, two consecutive asymptotic regimes compose the drought flow. During the early drought flow, the discharge decreases as the inverse square root of time ($Q \sim 1/ \sqrt{t}$ (Polubarinova-Kochina (1962)). Later, the discharge decreases as the inverse square of time ($Q \sim 1/t^2$ (Boussinesq, 1903)).A laboratory aquifer (homogeneous and bidimensional) submitted to artificial rainfall confirms the existence of these asymptotic regimes. This simplified experimental setup generates a realistic flood signal, in the absence of surface runoff.Field observation in the catchment of the Quiock Creek, Guadeloupe reveals a similar behaviour. The water table and the river discharge evolve simultaneously during rainfall, and conform to theory. Like in our laboratory experiment, this aquifer reacts non-linearly to forcing by rainfall.The river discharge from three other catchments (Plynlimon, Wales and Laval, France) confirms this non-linear reaction: $\dot{Q} \propto R^n$, with $n > 1$. The exponent, however, is different from $3/2$. A preliminary laboratory experiment suggests that this breakdown of the Dupuit-Boussinesq theory is due to vertical groundwater flow.
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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 | 0.000 | 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) | 0.000 | 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".