Evaluation of a Stream-Aquifer Analysis Test for Deriving Reach-Scale Streambed Conductance
Bibliographic record
Abstract
Extracting groundwater from pumping wells located adjacent to streams can reduce streamflow, a result that is known as alluvial well depletion. Numerous analytical solutions have been developed for alluvial well depletion that vary in their mathematical complexity. Predicted drawdown by the analytical solutions can be matched to observed drawdown from a stream-aquifer analysis (SAA) test (i.e., a pumping test adjacent to a stream) to simultaneously estimate aquifer and streambed hydrologic parameters. However, only a few SAA tests have been documented in the literature and compared to field-measured streambed parameters. Therefore, the objective of this research was to perform an SAA test for the purpose of evaluating the ability of analytical solutions, when applied to the SAA test data, to estimate reach-scale streambed conductance. The SAA test was performed at a well site located adjacent to the North Canadian River in central Oklahoma. Observation wells were installed between the stream and the pumping well and were instrumented with automated water level loggers. The pumping well, located approximately 85 m from the North Canadian River, discharged at a constant rate (2180 m3 d-1) for 90 h. Predicted drawdown by an analytical solution was fit to the observed drawdown to inversely estimate the transmissivity (790 to 950 m2 d-1), specific yield (0.19 to 0.28), and streambed conductance (600 to 1500 m d-1), which was compared to values derived from in-stream measurements (i.e., grain-size analyses on streambed sediment samples and in-stream falling-head permeameter tests). Estimated streambed conductance from the in-stream measurements and the SAA test were both on the order of 1000 m d-1. The similarity in estimates supported the use of SAA tests to derive reach-scale streambed conductance. Both the SAA test and in-stream conductivity measurements suggested minimal streambed hydraulic resistance. Therefore, for this and other streams, simpler analytical solutions may be adequate to inversely estimate the aquifer and streambed hydrologic parameters.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".