Quantifying the impacts of climate change on groundwater in an unconfined aquifer that is strongly influenced by surface water
Bibliographic record
Abstract
Abstract A three-dimensional transient groundwater flow model, implemented in MODFLOW, is used to quantify the impacts of climate change on groundwater in an unconfined aquifer with demonstrated strong connection to surface water (Kettle and Granby Rivers). The Grand Forks aquifer is located in a semi-arid region of south-central British Columbia, Canada. Distributed recharge is modelled using HELP, driven by the LARS-WG stochastic weather generator, and stage–discharge curves for rivers are modelled using BRANCH and calibrated to historical data. For recharge modelling, three year-long climate scenarios were run, each representing one typical year in the present, and future (2020s and 2050s), by perturbing the historical weather according to the downscaled CGCM1 global climate model results. By the 2050s the largest increase in recharge relative to present occurs in late spring, by a factor of three or more, a 50% increase in summer months in most areas of the aquifer, a 10–25% increase in autumn, and a reduction in recharge in winter. CGCM1 downscaling was also used to predict basin-scale runoff for the Kettle River. Future climate scenarios suggest a shift in the hydrograph peak to an earlier date, although the peak flow remains the same, and baseflow level is lower and of longer duration. Groundwater levels near the river floodplain are predicted to be higher earlier in the year due to an earlier onset of peak flow, but considerably lower during the summer months. Away from rivers, groundwater levels increase slightly due to the predicted increase in recharge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".