Simulation of the hydrological processes on reconstructed watersheds using system dynamics
Bibliographic record
Abstract
Reconstruction of disturbed watersheds is a common practice by the oil sands industry in northern Alberta, Canada. The reconstruction and restoration of the watershed hydrology are required as part of the reclamation mandated by Alberta Environment for mine closure. Assessment of the hydrological performance of the reconstructed watersheds is essential to ensure a sustainable reclamation strategy. A conceptual lumped system dynamics watershed (SDW) model is developed and calibrated in this study. The model, built within an object-based simulation environment, is capable of simulating the various hydrological processes in the reconstructed watersheds with good accuracy. STELLA Software is used as an object-based simulation environment that allows visual computations. The SDW model developed combines both physically-based and empirical formulations to replicate the hydrological system mathematically. The system dynamics approach along with the visual simulation environment help in developing a simulation-for-learning model, not only simulation for prediction. The model is successfully calibrated and validated; the results show that the SDW model is capable of simulating the various hydrological processes (soil moisture, evapotranspiration and runoff) with good accuracy. The SDW model can help in the assessment of the short- and long-term performances of the reconstructed watersheds, thus providing a useful decision-aid tool for the mining industry.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".