Assessment of Drivers of Algal Biomass in North American Great Lakes via Satellite Remote Sensing

• Main Author: Dallosch, Michael
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: University of Waterloo 2024
• Subjects: great lakes; limnology; algae; phytoplankton; remote sensing; time series; machine learning; bayesian networks; chlorophyll-a; lake ice; temperature; algal biomass dynamics; Great Bear Lake; Great Slave Lake; Lake Athabasca
• Online Access: http://hdl.handle.net/10012/20412

Lakes are regarded as sentinels of change, where shifts in environmental conditions significantly affect lake phenology. A significant consequence of the change is the perceived increase in the frequency, magnitude, and severity of algal blooms in lakes globally. Algal blooms/increased productivity in lakes pose significant ecological, economic and health risks, impacting fisheries, tourism, and freshwater access. The impacts of external nutrient loading from anthropogenic sources are well documented; however, blooms have been observed to occur in even remote lakes. Climate change is a hypothesized driver of these recent algal bloom trends, such as increasing global air temperatures, water temperatures, lake ice loss, precipitation intensity, and drought. Past research on the impact of climatic drivers on algal biomass dynamics has often been limited to lab, mesocosm, or short termed observations, due to limited in situ data. New remote sensing data products make use of historic multispectral satellite image archives to provide greater spatial and temporal coverage of algal biomass concentrations, allowing for longer time series observational studies to be conducted over large areas. Using data provided by the European Space Agency (ESA) Climate Change Initiative (CCI) Lakes project (product version 2.0.0), daily chlorophyll-a (chl-a; proxy of algal biomass), Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) from 2002 to 2020 were derived from five North American Great Lakes: Great Bear Lake (GBL), Great Slave Lake (GSL), Lake Athabasca (LA), Lake Winnipeg (LW), and Lake Erie (LE). Additional atmospheric and lake physical variables were provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land data as part of the ERA5 climate reanalysis product including: 2-m air temperature (T2m), Total Precipitation (PPT), Surface Net Solar Radiation (SNSR), Surface Runoff (SR) and Subsurface Runoff (SSR), Wind Speed (WS) and Lake Mix-Layer Depth (LMLD). Such data products allow for ...