Integrated Surface Water and Groundwater Analysis under the Effects of Climate Change, Hydraulic Fracturing and its Associated Activities: A Case Study from Northwestern Alberta, Canada
Bibliographic record
Abstract
This study assessed how hydraulic fracturing (HF) (water withdrawals from nearby river water source) and its associated activities (construction of well pads) would affect surface water and groundwater in 2021–2036 under changing climate (RCP4.5 and RCP8.5 scenarios of the CanESM2) in a shale gas and oil play area (23,984.9 km2) of northwestern Alberta, Canada. An integrated hydrologic model (MIKE-SHE and MIKE-11 models), and a cumulative effects landscape simulator (ALCES) were used for this assessment. The simulation results show an increase in stream flow and groundwater discharge in 2021–2036 under both RCP4.5 and RCP8.5 scenarios with respect to those under the base modeling period (2000–2012). This occurs because of the increased precipitation and temperature predicted in the study area under both RCP4.5 and RCP8.5 scenarios. The results found that HF has very small (less than 1%) subtractive impacts on stream flow in 2021–2036 because of the large size of the study area, although groundwater discharge would increase minimally (less than 1%) due to the increase in the gradient between groundwater and surface water systems. The simulation results also found that the construction of well pads related to HF have very small (less than 1%) additive impacts on stream flow and groundwater discharge due to the non-significant changes in land use. The obtained results from this study provide valuable information for effective long-term water resources decision making in terms of seasonal and annual water extractions from the river, and allocation of water to the oil and gas industries for HF in the study area to meet future energy demand considering future climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".