This session will focus on the application of modeling approaches to assess the impacts of global change on aquatic ecosystems, emphasizing the resolution of biological problems. By incorporating a range of modeling methodologies, including but not limited to biophysical modeling, biogeochemical modeling, and ecosystem modeling, the session will advance the understanding of how environmental stressors affect aquatic ecosystems. Additionally, it will discuss how these models can be utilized to develop better management strategies for policymakers facing the stressors of global change.
We will examine how modeling can elucidate the effects of multiple stressors — such as climate change, land use intensification, contaminants, nutrients, habitat destruction, and overfishing — on environmental and biological issues. These issues include, but are not limited to, algal blooms, water quality, shifts in species distributions, and changes in ecosystem functions. This session also aims to advance our understanding of the complex dynamics of global change impacts on aquatic systems and address pressing environmental challenges effectively through fostering interdisciplinary collaborations. On the one hand, the robustness and predictive capabilities of modeling can be enhanced by integrating real-time observational data, remote sensing, in situ measurements, and machine learning/data analysis techniques. On the other hand, modeling work can provide better guidance for observed fieldwork and public policymaking.
We invite contributions that demonstrate innovative modeling approaches, case studies, and practical applications to promote knowledge sharing and the development of effective solutions. Specific areas of focus will include but are not limited to, modeling aquatic greenhouse gas emissions, harmful algal blooms (HABs) occurrences, assessing changes in water quality indicators, and understanding nutrient dynamics under varying climatic conditions. This session will provide a platform for exchanging ideas and strategies to enhance the resilience of aquatic ecosystems in the face of various stressors, leveraging modeling approaches to resolve critical biological and environmental problems.
We encourage submissions not only from researchers but also from practitioners and policymakers to promote interdisciplinary collaborations and advance our understanding of climate change impacts on aquatic ecosystems. We particularly welcome submissions from early career researchers and those from BIPOC, LGBTQIA+, and other marginalized communities.
Lead Organizer: Chuyan Zhao, Michigan Technological University (zhaocy0516@gmail.com)
Co-organizers:
Pengfei Xue, Michigan Technological University (pexue@mtu.edu)
Mark Rowe, NOAA (mark.rowe@noaa.gov)
Xing Zhou, Georgia Institute of Technology (xzhou473@gatech.edu)
Reza Valipour, Environment and Climate Change Canada (reza.valipour@ec.gc.ca)
Presentations
09:00 AM
Modeling Study of Phytoplankton Response in Lake Michigan to a Changing Climate (9168)
Primary Presenter: Mark Rowe, NOAA Great Lakes Environmental Research Laboratory (mark.rowe@noaa.gov)
Climatic change is projected to significantly impact lake ecosystems by altering physical processes and biophysical dynamics. We applied a three-dimensional biophysical model to investigate the impacts of future climate scenarios on phytoplankton dynamics and primary production in Lake Michigan. With a storyline approach, we conducted both historical simulations for six representative years (2005, 2006, 2010, 2014, 2015, and 2016) and future projections for the mid-21st century (2040–2049) and late 21st century (2090–2099) under the high-emission Representative Concentration Pathway (RCP) 8.5 scenario. Our results indicate that changes in the lake’s thermal structure, transitioning from dimictic to monomictic mixing regimes, will alter seasonal phytoplankton patterns. The winter-spring phytoplankton blooms, characteristic of mid-depth (30–90 m) and offshore (>90 m) regions, are expected to disappear due to diminished winter stratification and reduced spring turnover. Late spring and early summer blooms are projected to shift earlier, driven by the earlier onset and extended duration of summer stratification. The deep chlorophyll layer is projected to develop 15–30 days earlier and last 20–45 days longer by the mid- and late-21st century. An overall increase in primary production was simulated, driven by rising water temperatures and increased photosynthetically active radiation, along with a significant shift in the seasonal patterns.
09:15 AM
Sensitivity Analysis of Phytoplankton Dynamics in an Ecosystem Model for the Great Lakes (8785)
Primary Presenter: Chuyan Zhao, Michigan Technological University (zhaocy0516@gmail.com)
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.
09:30 AM
Modeling microbial water quality in tropical waters using culture-dependent and molecular DNA- based assays for fecal indicator bacteria (9243)
Primary Presenter: Marirosa Molina, US Environmental Protection Agency (molina.marirosa@epa.gov)
Monitoring fecal indicator bacteria (FIB) dynamics in water systems is crucial for assessing water quality, identifying potential health risks, and implementing appropriate management strategies. There is a lack of information regarding the applicability of culture-dependent vs culture-independent microbial methods to develop predictions of FIB levels. The objective of this study is to compare the use of quantitative polymerase chain reaction (qPCR) assays and culture-dependent FIB measurements to assess microbial water quality (MWQ) and further the development of statistical models in tropical watersheds. Water samples were collected from April 2022-December 2023 from rural and urban basins in Puerto Rico and analyzed for E. coli and enterococci levels (culture-based and qPCR assays). Statistical models were developed to predict FIB levels based on environmental variables using Multi Linear Regression (MLR) and Gradient Boosting Machine (XGBoost) learning techniques. Enterococci predictions were highly accurate (0.92) using XGBoost with both methods across watersheds and performed better than the MLR models (R2=0.16 to 0.4). E. coli model accuracy was 0.84-0.96. Temperature, pH, turbidity, dissolve oxygen, and conductivity were all important parameters for FIB prediction, but their influence depended on the type of watershed (urban vs rural). Results indicate that these rivers are constantly exceeding local water quality criteria, and application of statistical modeling tools may provide a cost-effective methodology to perform MWQ assessments in tropical watersheds.
09:45 AM
GLARM-Proj1: A new dataset for evaluating climate change impacts on the Great Lakes ecosystem (9393)
Primary Presenter: Miraj Kayastha, Michigan Technological University (mkayasth@mtu.edu)
The Great Lakes form the largest surface freshwater system in the world and have become a climate change hotspot with disproportionate warming of water. Warming has led to expanding heatwaves, diminishing ice cover, redistribution of fish habitat, and promotion of harmful algal blooms. Aquatic species living near their thermal tolerance are especially vulnerable to a significant increase in temperature. As a result, the projection of the Great Lakes under climate change has been identified as a top research priority by the scientific community. Here, we introduce a new state-of-the-art publicly accessible dataset, GLARM-Proj1, that delivers a comprehensive outlook on the future of the Great Lakes under climate change. It is developed by dynamically downscaling global climate models using the Great Lakes-Atmosphere Regional Model (GLARM), which is a state-of-the-art regional climate modeling system that simulates the Great Lakes at a 1-4 km horizontal and a <5 m vertical resolution using a two-way coupled three-dimensional (3D) hydrodynamic lake and ice model. GLARM-Proj1 spans from 1981 to 2099 and considers two future emission scenarios, Representative Concentration Pathways 4.5 and 8.5. GLARM-Proj1 provides a 3D field of daily water temperature, and 2D fields of ice cover and 2-meter air temperature. We also showcase various potential use cases for GLARM-Proj1 in aquatic ecosystem studies, including projections of subsurface heatwaves and how thermal habitats and thermal refuge from abnormally warmer waters may shift for specific fish species in the future.
SS20A - Leveraging Modeling Approaches to Understand and Mitigate Global Change Impacts on Aquatic Ecosystems
Description
Time: 9:00 AM
Date: 28/3/2025
Room: W205CD