Kane Hammond

Date of Award

January 2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Earth System Science & Policy

First Advisor

Sean Hammond

Abstract

Lake Sakakawea, located in central North Dakota, is a 286-kilometer-long reservoir with over 2,462 kilometers of shoreline (Garrison Power Plant, 2011). This reservoir is maintained by the United States Army Corps of Engineers (USACE). Each summer, the USACE employs a crew of approximately 12 individuals to monitor the Piping Plover (Charadrius melodus, hereafter; plover) and Least Tern (Sternula antillarum, hereafter; tern) at this location. Given that these species frequently return to the same breeding areas, monitoring efforts are largely focused on those areas. However, this is a dynamic system and amount of available habitat can change in response to precipitation. Additionally, precipitation patterns also have the potential change in response to climate change, making seasonal precipitation and habitat formation irregular. In order to find where new habitat is forming under varying conditions, crews would be required to search large areas of the reservoir which have historically been unproductive. Therefore, the purpose of this study was to first introduce an automated habitat model, supported by machine learning, to provided relevant habitat predictions to assist exploration efforts. Second, the model was developed using available data to support its utilization under current monitoring practices. Third, this study analyzed local precipitation trends and their seasonal effect on reservoir elevation to better understand how it has changed over time.

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