An evaluation of an enhanced soil erosion and landscape evolution model: a case study assessment of the former Nabarlek uranium mine, Northern Territory, Australia

Abstract

AbstractThe assessment of post‐mining landscapes as case studies is an important part of the evaluation of current rehabilitation practices. A necessary part of this assessment is to predict the surface stability of the landform using erosion and landform evolution modelling techniques. In the short term, erosion on a rehabilitated mine site can lead to increased sediment loads and transport of other mine related contaminants in downstream waterways. It is well recognized that in many mine areas the erodibility of surface materials can, and does, vary. This is a particularly significant issue on mine sites, where the surface conditions may range from areas of undisturbed natural surface materials, waste rock dumps constructed with materials exhumed from the sub‐surface, and other areas that have a mix of waste rock and soil to enhance the growth of vegetation. A further significant issue is that when the subsurface materials are exposed to surface conditions they can weather rapidly, changing their erodibility. This paper uses a new version of the SIBERIA landscape evolution and soil erosion model to evaluate the former Nabarlek uranium mine site in the Northern Territory, Australia. This new version of SIBERIA uses spatially variable erosion and hydrology parameters across the study domain to represent different erodibilities of surface materials, thus allowing better representation of catchment heterogeneity. The results demonstrate that the model predicts erosion rates similar to that of other modelled results and independent field data, providing confidence in the model and its parameterization. The tailings, deposited in the mined out pit and capped with waste rock, appear to be safely encapsulated for the modelled period. Copyright © 2008 John Wiley & Sons, Ltd and Commonwealth of Australia (Department of the Environment and Water Resources Supervising Scientist).