Abstract
Water balance models in operating mines are essential decision-making tools that monitor the effectiveness of water management plans and assess risks associated with climate variability, deposition models, operational changes, and other information. Despite their importance, the reliability of these models is often not disclosed or well understood by users, leading to uncertainties—such as how much confidence to place in water level predictions or which assumptions most affect accuracy. KCB is evaluating an excel-based operational water balance for the Lake Shore Gold Bell Creek Tailings Facility near Timmins, Ontario, which has been informed over four years by climate data, water level and flow measurements, deposition modeling, and bathymetric surveys. To validate the model, KCB removed historical data and compared model-predicted water levels against recorded values. This process aims to (1) identify critical inputs that most influence the model’s accuracy, (2) guide monitoring efforts to strengthen reliability, and (3) quantify model error, enabling users to better assess the dependability of deterministic (i.e., excel-based) water balance projections. This approach has the potential to benefit the mining community significantly by providing clearer insights into model reliability, enhancing decision-making confidence, and enabling targeted monitoring of influential data sources. Additionally, by fostering a deeper understanding of model error, mining operations can more effectively adapt to climate variability, better meet regulatory requirements, and potentially establish industry-wide standards for water balance validation—ultimately promoting more sustainable water management practices across the mining sector.