A quantitative risk framework to assess the impact of climate change on coal tip stability.

Wales has approximately 2500 disused coal tips, including 327 considered high risk. In February 2020, extreme rainfall during Storm Dennis resulted in several large coal tip landslides. Although no casualties were reported, there was extensive damage to property. These events have highlighted the low safety margins of these tips and raised concerns about their long-term stability considering climate change. This project seeks to address this by improving current risk assessments which are currently qualitative and independent of triggering actions. Qualitative risk frameworks are useful for identifying safe regions, but not very good at distinguishing risk at the other end of the scale. As all regions with certain traits are generally just labelled as high risk with no indication of relative risk within the high-risk category. To safely remediate these tips, we need to develop a risk register enabling the ranking of tips thereby enabling investment and subsequent remediation where it is needed most. Such an approach necessitates a quantitative framework, which to be fit for purpose needs to be dynamic and account for climate triggers. Aims of this project: 1. The development of an advanced dynamic quantitative coal tip assessment strategy which can produce real-time responses to weather forecasts, enabling active risk management to ensure safety. 2.Mapping and forecasting the risk of landslides in the South Wales valleys by integrating geospatial mapping techniques, geotechnical data, and current and predicted climate data. 3.Establish rainfall thresholds for the region to understand the rainfall levels that trigger landslides in the area in terms of both magnitude and duration of the rainfall event. 4.Establish whether there are any corelations between landslides and the antecedent conditions of the other environmental variables such as vegetation, slope aspect, soil water index etc. and use these correlations as a heuristic to inform a slope stability model.