Quantifying the water balance of two northeastern boreal watersheds, British Columbia.
Bibliographic record
Abstract
Northeastern British Columbia (BC) is undergoing steady development for oil and gas extraction, mainly due to subsurface hydraulic fracturing (fracking), which requires significant quantities of water. Thus, it is of vital importance to obtain accurate long-term water balance information in the complex wetlands of northeastern BC to assist regulators to balance multiple priorities in a way that will not compromise the long-term sustainability of water resources, while minimizing ecological impacts. At the initial phase of this study, all fluxes of the Coles Lake water balance were measured for the 2013_2014 hydrological year. The total storage change was negative (-8.3 mm), and 2013_2014 was considered a relatively dry year. This study also quantifies the water balance fluxes within two boreal watersheds, the Coles Lake and Tsea Lake watersheds, through a combination of observational data analysis and numerical modelling using the MIKE SHE hydrological model for 1979_2014. MIKE SHE model calibration was performed manually based on snowmelt, pressure head, and streamflow, using a trial-and-error parameter adjustment procedure. Similar trends were observed for the Coles Lake and Tsea Lake watersheds although average of actual evapotranspiration (AET = 472.9 mm year-1) was higher while overland flow (OL = 26.3 mm year-1) was lower at the Coles Lake watershed compared to the Tsea Lake watershed (AET= 405.5 mm year-1 and OL = 48.5 mm year-1). Sensitivity simulations with the MIKE SHE model whereby the leaf area index was modified uniformly across the Coles Lake watershed to represent fully open, mixed and closed canopies provided further insights on the role of vegetation on the water balance. Simulated AET = 515, 529, and 558 mm year-1 and OL = 59, 46, and 11 mm year-1 for open, mixed, and closed canopies, respectively. Further, the Coles Lake forcing data were applied for the Tsea Lake watershed as a sensitivity test while other parameters remained unchanged. The variability of the vegetation canopies, and land cover including wetland distribution were the main contributors for different hydrological responses in these two watersheds. Baseline information generated by this study will support the assessment of the sustainability of current strategies for freshwater extraction.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".