An empirical test of groundwater pumping effects on river flow and temperature: Chemainus River case study
Bibliographic record
Abstract
Groundwater abstraction from pumping wells can reduce the flows and water levels of hydraulically connected surface waters, which can affect aquatic communities and water rights of other users. Four pump tests were implemented in the 2016 summer low-flow period to measure groundwater pumping effects to Chemainus River surface water flow and temperature. The effect of pumping on surface flow was estimated using four metrics based on differences between measured and predicted flow at a hydrometric gauge located 340 m from the groundwater wells. Differences were estimated as residuals from a regression model relating flow at this gauge to flow at a gauge 1.3 km upstream of the wells. The effect of pumping on surface water temperature was assessed using daily maximum and daily average water temperature at gauges located 340 m and 1.4 km downstream of the wells. An effect of pumping on river flow was measurable when the pumps were turned on and shut off. The consistency of measured effects across all tests and the absence of discernable trends within and across tests indicate that the average effect of pumping across a broader period is equivalent to the average of all before and after comparisons, which was estimated as 9.9 L/s ± 12.1 L/s, or 5.5% of river discharge at the time of the tests. Groundwater pumping did not result in a detectable shift in water temperature. The results indicate that changes in water temperature in relation to the pump tests were smaller in magnitude or spatial scale than the detection limits of the implemented experimental design. This case study provides an example of quantification of pumping effects on stream flow and temperature, as required by the Water Sustainability Act for licensing of groundwater abstraction. We suggest this approach could be used elsewhere to quantify effects of groundwater abstraction.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".