Tailings are finely ground waste rock produced as a by-product of standard mining projects as well as some industrial and power plant operations. Tailings are conventionally impounded behind a dam that is raised perpetually insofar as operations continue, thus amassing large volumes of materials (sometimes including supernatant pond water) in the process. The failures of some tailings impoundments have triggered downstream mass movements that have caused human, economic and environmental impacts, thus inviting considerable public attention and scrutiny. Developing a detailed inventory of these tailings flows facilitates a better understanding of the magnitude-frequency statistics, preconditioning and trigger variables, breach-outflow processes and downstream runout behaviour. Upon screening over 350 historical waste impoundment failure incidents in pre-existing secondary datasets, we have developed a comprehensive global database of 63 tailings flows from 1928-2020 while following strict case selection criteria with the support of satellite imagery, digital elevation models (DEMs) and source literature. Using a novel runout zonation method, the satellite images and DEMs were analyzed on geographic information systems (GIS) platforms to independently estimate runout distances, inundation areas and travel path angles of tailings flows. Depending on data availability or quality, we also summarized the background information, impoundment conditions and geotechnical indices to provide site-specific context to case histories. The collated data is aimed to (i) broaden the scholarly understanding of tailings breach-runout behaviour, (ii) provide comprehensive documentation while assessing the limitations of data availability and/or quality in the public domain and (iii) establish a consistent framework for reporting various properties of tailings dam failures and tailings flows. Lastly, we note that the data should be treated with prudence. Tailings impoundments are highly variable depending on the locality, and site-specific conditions exert strong controls on post-breach behaviour. As such, it is recommended that our database be used purely as a basis for screening-level assessments, case analog comparisons and academic research. For site-specific prediction studies undertaken by practitioners, targeted field observations, laboratory investigations and numerical models are essential.
CanBreach is a NSERC-funded university-industry research team whose purpose is to equip engineers with better tools, including computer simulations, to help manage the risks associated with tailings storage facilities. Using an integrated approach to research, the team will work to develop a better understanding of the mobility of tailings flow, breach mechanisms of tailings dams and the downstream behaviour of long runout tailings flow.
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Tailings flows result from the breach of tailings dams. Large-scale tailings flows can travel over substantial distances with high velocities and cause significant loss of life, environmental damage, and economic costs. Runout modelling and inundation mapping are critical components of risk assessment for tailings dams. In an attempt to develop consistency in reporting tailings data, we established a new tailings-flow runout classification system. Our data analysis applies to the zone corresponding to the extent of the main solid tailings deposit, which is characterized by visible or field-confirmed sedimentation, above typical surface water levels if extending into downstream water bodies. We introduced a new database of 33 tailings dam breaches by independently estimating the planimetric inundation area for each event using remote sensing data. This paper examines the applicability of a semi-physical areavolume relationship us[1]ing the new database. Our results indicate that the equation A = cV 2/3 , which has been used previously to characterize the mobility of other types of mass movements, is a statistically justifiable choice for the relationship between total released volume and planimetric inundation area. Our analysis suggests that, for a given volume, tailings flows are, on average, less mobile than lahars but more mobile than non-volcanic debris flows, rock avalanches, and waste dump failures.