Antony Ray Berthelote, The University of Montana


This dissertation contains three papers describing groundwater system responses to dam removals and presents current and new methodologies that managers can use to proactively forecast and mitigate those impacts. The three papers bring together literature, data gathering, data analysis, testing, and modeling techniques that apply groundwater science to forecasting the response of groundwater systems to dam removal actions. Conceptual and numerical models developed as part of this work provide scientists, environmental consultants, regulators and managers with tools to assess the consequences of the removal of stream reservoirs on the adjacent and underlying groundwater system The first chapter/paper, Responses of Groundwater Systems to Dam Removal is a review paper on the connection between groundwater systems and artificial impoundments. The synthesis of materials was compiled into a general conceptual model of the effects of dam and reservoir emplacement and removal on associated groundwater systems. Additionally, a method is proposed and tested to forecast the magnitude of impacts to groundwater levels using a generalized lumped parameter approach that allows the ratio of aquifer discharges to hydraulic conductivity to vary depending on the hydrogeological setting. The second chapter/paper, Proactive Mitigation of Domestic and Municipal Groundwater Supplies During Dam Removal Actions, Milltown Reservoir, Western Montana is an applied case study and outlines the process for mitigating water supplies from engineering actions associated with the removal of the Milltown Dam in western Montana between 2006 and 2009. This paper is a summary of four technical reports completed and submitted by these authors to the EPA ( outlining the groundwater mitigation processes in detail. The paper summarizes data collection and modeling approaches undertaken to provide practical forecasts of groundwater level changes. A range of forecasts are compared to completed mitigation actions and model performance is evaluated. A risk management framework is proposed and tested. The third chapter/paper, The Role of Drawdown Data in ANN Forecasting of Water Table Responses to Dam and Reservoir Removals examines the applicability of using Artificial Neural Networks (ANNs) to forecast groundwater levels changes resulting from a dam removal. Two specific ANN models were developed and analyzed to specifically examine the need for training data inclusive of a temporary or partial drawdown. Results for the Milltown Dam removal are compared to observed water levels and results of standard numerical techniques (presented in the second paper). ANN modeling shows promise as a tool to forecast likely groundwater responses to dam removals as it requires less detailed hydrogeological data sets and is executed more efficiently than standard numerical models.


© Copyright 2013 Antony Ray Berthelote