Streamflow depletion from groundwater pumping in contrasting hydrogeological landscapes: Evaluation and sensitivity of a new management tool
Bibliographic record
Abstract
Groundwater pumping can reduce streamflow by reducing groundwater discharge and/or inducing streamflow infiltration, which together are referred to as streamflow depletion. Recently, analytical depletion functions (ADFs) have been suggested as rapid and accurate tools for streamflow depletion assessment, but their performance has only been tested in a few hydrogeological settings. To evaluate whether they will be useful tools for other regions with contrasting stream network and hydrogeological characteristics, we compared ADFs to calibrated MODFLOW models in BX Creek and Peace Region with distinct hydrogeological settings (interior plateaus & highlands, and boreal plains, respectively) and spatial scales (165 km2 and 1952 km2, respectively) in British Columbia, Canada. Results showed that ADFs can accurately identify streams most affected by pumping for 100% and 83% of wells in the BX Creek and Peace Region, respectively, and had small prediction errors compared with MODFLOW. Specifically, the mean absolute error of predicted depletion ranged from 2% to 14% of the highest simulated pumping rate over the study period of 30 years, with improved accuracy during the pumping season. We also found contrasting responses of ADF performance to hydrostratigraphic properties such as hydraulic conductivity, aquifer thickness, streambed conductance, and well depth across two domains, indicating that different drivers control ADF accuracy in different hydrogeological settings. Therefore, we conclude that ADFs are useful tools for conjunctive water management, but a good understanding of local hydrogeological conditions is needed to address the potential uncertainty of ADFs for decision-making.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".