Quantifying the impact of climate change on groundwater recharge to fractured-rock aquifers: a case study from Canada
Bibliographic record
Abstract
This study investigates the potential impact of climate change on groundwater recharge to a fractured bedrock aquifer. A case study area on the west coast of Canada is used to demonstrate a methodology that can be applied to estimate recharge under scenarios of climate change in other regions, including Africa. The Hydrologic Evaluation of Landfill Performance (HELP) hydrological model is used. This water-balance model derives estimates of vertical flux (recharge) at the base of a percolation column. Different percolation profiles, representative of the different combinations of soil type and thickness, depth to water table, and vadose zone fractured media are developed. Average estimates of media properties (thickness, hydraulic conductivity, field capacity, wilting point, and porosity), surface slope, and leaf area index are mapped in ArcGIS to generate recharge zones that allow spatial and temporal integration of the recharge results. The recharge model is driven by daily weather data downscaled from current and future global climate model (here Canadian Global Coupled Model 1) predictions using Statistical DownScaling Model (SDSM) and the Long Ashton Research Station Weather Generator (LARS-WG) stochastic weather generator that is calibrated to the observed local climate data. In our Canadian case study, recharge varies from 184 to 537 mm·year -1 and is projected to increase by up to 8% by 2070.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".