Determining vulnerabilities and risks in water management through water balances and water accounting analysis
Bibliographic record
Abstract
This study emphasizes the importance of site-wide water balances in mining operations, focusing on water accumulation, excess discharge, and deficits. Two mining sites in Indonesia and Canada are analyzed by their water balances to identify and mitigate water-related risks. Creating a water balance helps mine site water managers understand operational water needs, storage limitations, and potential risks, offering practical solutions for sustainable water resource management. This thesis examines vulnerabilities in site water management at two nickel sites by analyzing their site water balances and water accounts. The aim is to review water inputs and outputs, ensure accountability for water entering site boundaries, and explore mitigation options. A probabilistic GoldSim model was used to create a site water balance model, calibrated and validated using publicly available and site-specific data collected through field visits. These simulations incorporate historical data, establishing probability distributions for operational risk assessment, including high precipitation years as simulations for wet conditions and discrete variables like land use, infrastructure, and water management variations. Model refinement involves sensitivity analysis and calibration to enhance accuracy. The base case, high rainfall, and future scenarios were run, and the results were analyzed using event probability and risk analysis. The results reveal a water surplus for both sites in all scenarios. The water risks include flooding, infrastructure vulnerability, and storage capacities. The models suggest water risk mitigations such as reducing site surface area, increasing water flow of the bottlenecked areas, and increasing discharge rates. Climate change affects the site by changing the precipitation patterns, affecting high rainfall periods, and affecting the Sudbury site more due to changes to the periods of snowmelt. More infrastructure should be added for redundancies to prevent the risk of overtopping or inundation. Future work can look at risk strategies by creating hydro-economic analysis to determine the best course of action for companies to reduce their risk, contaminant analysis, or data improvement using remote sensing. In conclusion, this research highlights the pivotal role of water balances in managing water resources, with implications for areas such as stakeholder collaboration, sustainability, and risk mitigation in 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".