The impacts of global tailing dam failures

The impacts of global tailing dam failures

The impacts of global tailing dam failures

Lead Supervisor: Dr A Winson

Location: British Geological Survey, Shallow Geohazards and Earth Observation: Geodesy and Remote Sensing.

Duration: 6 weeks

Suitable undergraduate degrees: Earth Science / Environmental Science

Project background

There are currently at least 1700 dams storing mine waste globally.  Whilst these structures remain stable they present little risk, but when they fail the impacts can be catastrophic.  This was highlighted on the 25th of January 2019 when a tailings dam at the Córrego de Feijão mine, failed releasing 12 million m3 of material that travelled 7km downstream and ultimately lead to the death of 267 people.  In this study we would like to conduct a comprehensive literature and grey literature review to capture the multi-hazard impacts of these events globally. Critically we will review impacts not just at the point of the disaster but will assess multi-hazard impacts that can continue long beyond the initial event.  We will also endeavour to catalogue any policies that aim to mitigate these effects.  This review will link to work at the BGS that is being conducted in Philippines, regarding the stability of tailings dams at nickel laterite mines.  By including a review of international approaches to mitigate impacts we hope to identify examples of good practice for recovery after these events.

Further, there will be a possibility to conduct scaled runout experiments in a debris flow flume using materials analogous to tailings. The scaled experiments are essential to support numerical modelling of flow characteristics (e.g., velocity, depth, travel distance, and inundation area) to assess the tailings dam failure impact. For this study, experiments using high speed camera will be carried out to capture the flow dynamics including estimation of flow velocity. These experiments will feed into a database established with the aim of testing or calibrating numerical models for better process understanding and impact assessment.

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