| Description |
Various processes and factors contribute to the occurrence, timing, magnitude, and extent of algal blooms in the Great Salt Lake (GSL) system (including the Great Salt Lake, Farmington Bay, and Utah Lake). While this system is recognized for its role in local and global ecosystems, recreation, and industry, the practices of monitoring, assessing, and planning for changing water quality conditions are severely limited in their ability to describe relationships between the many contributing factors and algal bloom conditions. The aim of this work is to use traditional field sampling and remotely sensed records to explore patterns and trends and identify some of the key factors that influence or contribute to algal bloom conditions in the lakes of the GSL system. Factors explored in this study include local weather, seasonal climate, and hydrologic variables, which have particular relevance to the nearby developing urban area that is experiencing uncertain and changing climate conditions. The study is divided into three distinct bodies, which enables a more complete examination of historical algal bloom patterns, the processes that influence them, and uses this information to guide monitoring and management practices in the future. This research brings together a wide breadth of data types and sources to gain a more holistic view of the complex lake system. The three major objectives of this dissertation are to: 1) evaluate historical patterns and trends using remotely sensed estimates of algal biomass; 2) describe the complex relationships between climate and hydrologic variables and algal blooms through a data-driven modeling and analysis approach; 3) use these relationships to develop a decision support framework that can be used to forecast conditions within the lake system. Primary impacts of this work include an improved understanding of historical water quality conditions, context for evaluating ongoing conditions, knowledge of how external factors contribute to and influence these conditions, and tools for better planning and monitoring practices in the future. |
