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    Integrating Erosion Model Predictions into Mine Rehabilitation: A Data-Driven Approach to Predicting Rill and Gully Erosion at the Landform Scale

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    Name:
    azu_etd_22203_sip1_m.pdf
    Embargo:
    2026-05-16
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    8.093Mb
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    Author
    Abramson, Nathan S.
    Issue Date
    2025
    Keywords
    erosion modeling
    hillslope hydrology
    landform design
    mine closure
    mine rehabilitation
    Advisor
    Pelletier, Jon
    
    Metadata
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    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Embargo
    Release after 05/16/2026
    Abstract
    Waste material resulting from surface and underground mining operations create large stockpiles or repositories of waste material which must be managed due to its sensitive chemical or material properties. Rehabilitation of these post-mining waste landforms aims to mitigate the risks associated with the transport of the waste material by surface water, groundwater or wind. One goal of mine rehabilitation, and the focus of this study, is to minimize the risk of runoff-driven erosion. Predictive mathematical models of rill and gully erosion can estimate the likely erosional performance of rehabilitated post-mining landforms. While predictive erosion models used in mine rehabilitation applications vary in their complexity, model architecture, and predictive accuracy, they all fundamentally assess the balance between erosive forces (e.g., from rainfall or runoff), and the resistance of the cover materials for a given landform design (i.e., topography). In this dissertation, I present three studies that advance the prediction of rill and gully erosion at rehabilitated mine sites and provide guidance for integrating erosion model predictions into landform and cover designs. The first study (Appendix A) focuses on the forcing side of the problem, presenting a method to predict runoff-driven shear stress at every location across a hillslope or small (≲10 ha) catchment using input data for rainfall and topography. The second study (Appendix B) evaluates the ability of commonly used predictive erosion models to retrodict the observed rill and gully erosion at a rehabilitated mine site in southern Arizona where rock-armored covers provide the primary resistance to erosion. The third study (Appendix C) tests the ability of a newly developed predictive model of rill and gully erosion, Rillgen2D, to retrodict the observed rill/gully erosion at three rehabilitated mine sites in Queensland, Australia where covers with soil and/or vegetation provide the primary resistance to erosion.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Geosciences
    Degree Grantor
    University of Arizona
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