Application of Automated Remote Sensing and Deep Learning to Small Reservoir Identification and Water Quality Modeling in Lake Michigan Watersheds
Major Goals and Objectives
The overarching goal of this graduate student scholars project is to better evaluate the effects of small dams and reservoirs on changing the flow of nutrients to downstream water bodies and water quality across Lake Michigan Watersheds. There are two primary objectives associated with this goal: (1). Small reservoir identification through a combination of remote-sensing and deep-learning approaches and reservoir dataset development with associated information (reservoir location, surface area, storage volume, catchment drainage area, and residence time). 2). Using hydrologic modelling and USGS water quality data collected above and below reservoirs to quantify the spatially and temporally varying effects of small reservoirs on water quality (nutrient runoff and retention). This proposed research is of pressing concern due to increased release of legacy contaminants to surface and groundwater around Lake Michigan.