Potential of LiDAR and Structure-from-Motion Photogrammetry for High-Resolution Monitoring of Snowmelt Water Balance in Tailings Storage Facilities

Water management represents a critical challenge for the mining industry, as large volumes of surface water must be controlled and treated during operations to ensure the stability of geotechnical structures and protect the environment. This extends beyond the operational phase, as it is essential to ensure the physical stability of tailings storage facilities and to limit the potential transport of contaminants on reclaimed mine sites. The water balance of tailings storage facilities is regulated by surface water management and often treatment infrastructures. In addition, the water balance is also closely linked to the performance of several engineered cover systems used to reclaim tailings storage facilities. Because tailings storage facilities generally occupy large areas, snow accumulation in winter and rapid melting in spring generate substantial volumes of meltwater within a short period. Thus, the spring freshet is a critical phase for water inventory control, from operation to post-reclamation. In this context, developing tailored monitoring tools is essential to ensure effective spring water management on tailings storage facilities.

This work aims to develop a high spatial resolution drone-based sensing approach for semi-real-time monitoring of the snow water balance in tailings storage facilities during snowmelt. This study is based on the results of several drone-based Structure-from-Motion photogrammetry and LiDAR surveys conducted during snowmelt on a reclaimed tailings storage facility. The site presents two major challenges for these sensing techniques: a flat, featureless area prone to oversaturated whites when covered with snow, and sections of dense low vegetation that reduce LiDAR signal penetration and hinder the generation of accurate digital elevation models. The accuracy and precision of the two remote sensing technologies to evaluate the snow depth were assessed based on manual measurements and conventional GNSS surveys. The impact of the reconstruction software/algorithms and parameters, as well as the number of ground control points (between 3 and 21) used in the reconstructions, on accuracy was also assessed. Finally, a preliminary snow-water equivalent model was developed and integrated within the data processing scheme to provide the changes of snow-water equivalent during snowmelt. Results show that LiDAR is the most accurate and reliable approach to monitor the snow depth. Photogrammetry-derived digital elevation models resulted in an error up to 66 cm. The quality and accuracy of photogrammetric surveys depend on the number of ground control points, the reconstruction algorithm used, and the absence of aerotriangulation tie points in certain areas. A snow-water equivalent model was integrated with LiDAR-derived snow depth data to characterize the temporal evolution of the tailings storage facility water balance during snowmelt. This presents an incremental improvement towards effective spring-water management on tailings storage facilities.