Sensitivity Analysis of Phytoplankton Dynamics in an Ecosystem Model for the Great Lakes

The Great Lakes is one of the largest freshwater systems globally, providing essential ecological and socio-economic services. However, the increasing frequency of cyanobacterial harmful algal blooms (cyanoHABs) poses significant challenges to water quality and ecosystem health. The LEEM is a Lake Erie ecosystem model designed to simulate phytoplankton dynamics and understand the drivers of harmful algal blooms (HABs) in the Great Lakes. To enhance LEEM’s predictive performance, this study focuses on a set of sensitivity analysis of key phytoplankton parameters. Phytoplankton are critical to aquatic ecosystems, and their growth and behavior are influenced by environmental variables such as light, temperature, nutrient availability, and selective grazing. This study systematically evaluates which of these mechanisms has the most influence on LEEM’s ability to predict bloom onset and severity. The results will provide critical insights into the robustness of LEEM’s parameterization, guiding future adjustments and enhancing its reliability. This sensitivity analysis will also contribute to advancing LEEM as a tool for forecasting HABs and supporting water quality management efforts in the Great Lakes.

Presentation Preference: Oral

Primary Presenter: Chuyan Zhao, Michigan Technological University (zhaocy0516@gmail.com)

Authors:

Chuyan Zhao, Michigan Technological University  (zhaocy0516@gmail.com)

Pengfei Xue, Michigan Technological University  (pexue@mtu.edu)

Mark Rowe, NOAA - Great Lakes Environmental Research Laboratory  (Mark.Rowe@noaa.gov)

Todd Redder, LimnoTech  (tredder@limno.com)

Chenfu Huang, Michigan Technological University  (chenfuh@mtu.edu)