The uncertain future of mountaintop-removal-mined landscapes 1: How mining changes erosion processes and variables

Abstract

Surface mining may be humanity's most tangible impact on Earth's surface, and will become more prevalent globally as the energy transition progresses. Prediction of post-mining landscape change can help mitigate environmental damage and allow effective reuse of mined lands, but requires understanding how mining changes geomorphic processes and variables. Here we investigate surface mining's complex influence on surface processes in a case study of mountaintop removal (MTR) coal mining in the Appalachian Coalfields, USA. The future evolution of MTR-influenced landscapes is unclear, largely because the ways that human changes to the landscape affect geomorphic processes are poorly understood. Here we use geospatial analysis—leveraging the existence of pre- and post-MTR elevation models—and synthesis of literature to ask how MTR alters topography, hydrology, and land-surface erodibility and how these changes could be incorporated into numerical models of post-MTR landscape evolution. MTR mining reduces slope and slope–area product, and dramatically rearranges drainage divides. Creation of large numbers of closed depressions alters flow routing and casts doubt on the utility, especially over human timescales, of models that assume steady, uniform flow. MTR mining creates two contrasting hydrologic domains, one in which overland flow is generated efficiently due to a lack of infiltration capacity, and one in which waste rock deposits act as extensive subsurface reservoirs. This dichotomy creates localized hotspots of overland flow and erosion. Loss of forest cover probably reduces cohesion in near-surface soils for at least the timescale of vegetation recovery, while human-made VFs and mine soils also likely experience reduced erosion resistance. Our analysis suggests three necessary—though potentially not sufficient—ingredients for numerical modeling of post-MTR landscape change in the Appalachians: 1) accurate routing and accumulation of unsteady, nonuniform overland flow across low-gradient, engineered landscapes, 2) separation of the landscape into cut, filled, and unmined regions, and 3) incorporation of vegetation recovery trajectories. Our companion paper explores this final ingredient in detail. Improved modeling of post-mining landscapes will mitigate environmental degradation from past mining and reduce the impacts of future mining that supports the energy transition.