Hydrologic response of catchments to precipitation: Quantification of mechanical carriers and origins of water
Bibliographic record
Abstract
Precipitation‐induced overland and groundwater flow and mixing processes are quantified to analyze the temporal (event and pre‐event water) and spatial (groundwater discharge and overland runoff) origins of water entering a stream. Using a distributed‐parameter control volume finite‐element simulator that can simultaneously solve the fully coupled partial differential equations describing 2‐D Manning and 3‐D Darcian flow and advective‐dispersive transport, mechanical flow (driven by hydraulic potential) and tracer‐based hydrograph separation (driven by dispersive mixing as well as mechanical flow) are simulated in response to precipitation events in two cross sections oriented parallel and perpendicular to a stream. The results indicate that as precipitation becomes more intense, the subsurface mechanical flow contributions tend to become less significant relative to the total pre‐event stream discharge. Hydrodynamic mixing can play an important role in enhancing pre‐event tracer signals in the stream. This implies that temporally tagged chemical signals introduced into surface‐subsurface flow systems from precipitation may not be strong enough to detect the changes in the subsurface flow system. It is concluded that diffusive/dispersive mixing, capillary fringe groundwater ridging, and macropore flow can influence the temporal sources of water in the stream, but any sole mechanism may not fully explain the strong pre‐event water discharge. Further investigations of the influence of heterogeneity, residence time, geomorphology, and root zone processes are required to confirm the conclusions of this study.
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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 | 0.002 | 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 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".