THE INFLUENCE OF SURFACE MINING ON RUNOFF TIMING AND FLOW PATHWAYS IN ELK VALLEY, BRITISH COLUMBIA
Bibliographic record
Abstract
Surface mining is a common method of accessing coal. In high-elevation environments, vegetation and soils are typically removed prior to the blasting of overburden rock thereby allowing access to mineable ore. The removed waste rock is deposited in adjacent valleys where it may bury existing streams. Previous research, predominantly in Appalachia, has focused on downstream water quality impacts with less focus on how streamflow response and flow pathways are affected by surface mining. This study reports on how surface mining affects streamflow hydrological and chemical responses at the headwater catchment scale in the Elk Valley, British Columbia. A paired catchment approach was utilized between May and October 2012, where a reference catch- ment (Dry Creek - DC) was compared to an impacted catchment (West Line Creek - WLC), whose area is 30% covered by deposited waste rock. Hydrometrically, WLC had considerably lower flows and exhibited a damped, slower response to precipitation events than DC. Dissolved ions were an order of magnitude greater in WLC, with conductivity (SpC) ranging between 400 μS/cm at high flow to 1300 μS/cm at low flow. A strong hysteretic pattern was observed between SpC and flow and with specific ions at WLC, suggesting dilution or changing flowpaths as the season progressed. In contrast, patterns of SpC and flow at DC did not exhibit hysteresis. Major ion hydrochemistry at WLC shows dilution affecting ion concentrations whereas results at DC are consistent with chemostatic behavior. Stable isotopes were more depleted at DC compared with WLC, suggesting different sources and timing of water contributing to streamflow. Future research will work towards a conceptual model of surface mining impacts on catchment scale processes in montane environments through increased understanding of residence time and flowpath distributions at a number of impacted and reference catchments.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".