Climate Change Impacts on Groundwater Recharge in Gulf Islands, Canada
Bibliographic record
Abstract
With significant changes expected in the earth's global climate over the next century, due to global warming, and its resulting changes in the frequency and amount of precipitation, there has been growing concerns on climate change impacts on water resources. Especially concerned to water managers and governments is the potential impacts on groundwater aquifers since they are the main available source of potable water supply worldwide. This research, therefore, investigates the potential impacts of climate change on groundwater recharge to the fractured bedrock aquifers, which serve as the main source of potable water supply to the inhabitants of Gulf Islands in BC, Canada. Using Statistical DownScaling Model (SDSM) in combination with the LARS-WG stochastic weather generator, daily current and future (i.e., 2010-2039, 2040-2069, and 2070-2099) climate data were generated from CGCM1 predictions of the study location. These predictions were used as input to the HELP hydrologic model for estimation of recharge for the different climate change periods. The main properties of the aquifer -soil permeability, aquifer permeability and water table depth- used for recharge modeling were linked to ArcGIS for generating recharge zones, which allowed spatial and temporal integration of the recharge results. The combination of SDSM and LARS-WG in downscaling and predicting both the observed monthly temperature and precipitation was very successful. Mean annual precipitation downscaling with SDSM is predicted to increase by 52%, 65% and 88% relative to the observed for 2020s, 2050s and 2080s, respectively. On the other hand, the mean monthly temperature is predicted to rise by 1.14 ºC in 2020s, 2.05 ºC in the next 30 years and up to 3.5 ºC by the end of the century. According to HELP, the current mean annual recharge is about 44 % of the annual precipitation and is predicted to increase progressively by 7%, 8% and 9% in the 2020s, 2050s and 2080s, respectively, from the current. (Detta examensarbete är utfört vid Avd Teknisk vattenresurslära, TVRL)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".