Modelling Water Balance Components in Reconstructed Ecosystems and their Sensity to Climate Change in the Athabasca Oil Sands Region
Bibliographic record
Abstract
The Western Boreal Plains in Alberta are identified as one of the most susceptible landscapes to disturbance in Canada as it experiences anthropogenic pressures from surface mining in the Athabasca Oil Sands Region (AOSR). While oil production in the AOSR significantly contributes to the Canadian economy, surface mining for oil extraction leads to irreversible damage to a previously thriving landscape. Oil companies are legally bound to recover disturbed landscapes into functioning ecosystems, and this requires complete reconstruction of a natural ecosystem. These reconstructed sites are continuously monitored to track the progress, successes, and limitations of man-made ecosystems. Sites are reconstructed to mimic their pre-disturbed conditions using a framework that considers ecosystem water use, climate, and hydrologic fluxes. However, studies that explore the impacts of climate change on these reconstructed ecosystems are limited. This study employs the HYDRUS 1D numerical modelling software to develop and calibrate a hydrological model designed to simulate the reaction of reconstructed ecosystems (W1 and 30T) to a range of climate change scenarios, including alterations in temperature (increases of 1.5 and 3.0 C) and changes in precipitation (increases or decreases of 15% and 30%). Using 2018 as a baseline for calibration, the results of the simulations revealed a consistent trend of decreasing water storage as temperatures increased and precipitation levels decreased. W1 demonstrated a higher level of resilience to future climate change events compared to 30T, attributed to its lower vegetation density and reduced water stress. The baseline storage in 2018 was -41.2 mm for W1 and -70 mm for 30T. Among the various climate change scenarios, the most significant impact was observed with a 30% reduction in precipitation, resulting in a water deficit of 63.5 mm at W1 and 93.6 mm at 30T. These findings underscore the importance of informed vegetation planting and management in rehabilitated sites, particularly evident at 30T. The long-term sustainability of upland sites like these is crucial for the overall watershed system, emphasizing the need to integrate climate change considerations into land rehabilitation conceptual models. This study lays the groundwork for future research to explore more intricate climate change conditions in numerical modelling.
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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 | 0.001 | 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".