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An Ensemble Modeling Approach to Assess Lake Erie’s Response to Changes in Nutrient Loads

An Ensemble Modeling Approach to Assess Lake Erie’s Response to Changes in Nutrient Loads

An Ensemble Modeling Approach to Assess Lake Erie’s Response to Changes in Nutrient Loads

Lake Erie

Lake Erie. Credit: skypics.com

Historical Background

The Canada-U.S. Great Lakes Water Quality Agreement (GLWQA) was originally signed in 1972 and updated several times in the decades since, including most recently in 2012. The GLWQA sets water quality goals and objectives for the Great Lakes. These are meant to address the impacts of urban development, industrial growth, and agricultural land use practices that result in altered habitat and toxic chemicals and nutrients entering the lakes. In 2012, the two countries agreed to set targets for nutrient loading, specifically phosphorus, to each lake. In 2015, GLWQA’s Nutrient Annex (Annex 4) Objectives and Targets Task Team recommended phosphorus loading targets for Lake Erie. Canada and the U.S. agreed to these targets in 2016.

To mitigate the impacts of cyanobacteria algal blooms in Lake Erie’s western basin and hypoxic zones in its central basin, the Task Team set maximum phosphorus loading targets for all major tributaries entering Lake Erie.

A Decade Later

These phosphorus reduction goals were developed to meet Environmental Response Indicator thresholds guided by a collection of computer models developed and evaluated with nutrient loading and environmental response data collected through 2014. With the addition of 10 years of new data, a suite of new and updated harmful algal bloom and hypoxia models, and an increased understanding of the impacts of weather and climate on Environmental Response Indicators, it is time to evaluate and update the modeling approach.

Ensemble Model Approach

Using the most recent data and building on existing computer models, ten experts will update these computer models to develop and compare environmental response curves in Lake Erie including harmful algal blooms and hypoxia. They will compare their results and combine their models into a single, more accurate “ensemble" model. Using the ensemble model, the team will develop updated phosphorus-load response curves for how harmful algal blooms, hypoxia, chlorophyll-a, and fisheries respond to nutrient loads, weather, and climate conditions.

There are three 3D mechanistic HAB/hypoxia models of the entire lake (LimnoTech, Environment and Climate Change Canada, and University of Waterloo) and one model that focuses on the diagenesis of sedimentary material to explore the impact on lake sediments (University of Toronto). The 3D models are capable of simulating western basin HABs and the dissolved oxygen properties that lead to central basin hypoxia. There is also a statistical model that predicts central basin hypoxia as a function of loads and air temperature (University of Michigan/North Carolina State University/Stanford). There are 3 models that explore the effects of loads and/or hypoxia on fish and fisheries (Ohio State University, Eureka Aquatic Research). There are also 3 statistical models that predict western basin HABs as a function of Maumee River loads (NOAA, Stanford University, University of Michigan), and they are looking at the potential impact of temperature. All of these models and their derivative publications are listed below.

After calibrating their models and demonstrating their capabilities at an in-person meeting in January 2025, the modelers will use meteorology data from a typical year and/or generate a load-response curve that accounts for recent climate to generate potential load response curves predicting HABs and hypoxia under current climate conditions. At this meeting, the team will also make decisions about how future climate response curves will be determined.

The team will ultimately meet three times in-person to discuss response curves with today’s climate and mid-century climates. The team will meet again in April to finalize mid-century climate scenarios and inform fisheries models, and in August to finalize response curves and begin analysis. The project is expected to conclude in February 2026.

Modeling Team

  • Don Scavia - University of Michigan
  • Stuart Ludsin - The Ohio State University
  • Daniel Obenour - North Carolina State University
  • Anna Michalak - Carnegie Institution for Science/Stanford University
  • Serghei Bocaniov - University of Waterloo
  • Richard Stumpf - National Oceanic and Atmospheric Administration
  • Reza Valipour - Environment and Climate Change Canada
  • Todd Redder - Limnotech
  • George Arhonditsis - University of Toronto
  • Hongyan Zhang - Eureka Aquatic Research, LLC.

Policy-oriented Stakeholders

The University of Michigan Water Center will convene this group of stakeholders at several moments through the project. They will ensure the modeling team addresses questions that will result in useful information relevant to Great Lakes policy decision-making. They will provide advice on framing results in meaningful contexts and formats for sharing with various groups in the public and private sectors as the project is completed.

These agencies and organizations are represented as stakeholders:

  • National Oceanic and Atmospheric Administration
  • Environmental Protection Agency
  • Environment and Climate Change Canada
  • Ministry of the Environment, Conservation and Parks
  • Michigan Department of Agriculture and Rural Development
  • Michigan Department of Natural Resources
  • Great Lakes Fishery Commission
  • Ohio Lake Erie Commission
  • National Wildlife Federation
  • Alliance for the Great Lakes
  • The Nature Conservancy

Contact

Alison Bressler - [email protected] - (864) 243-7914

Details About Models

3D HAB/Hypoxia Models:

This is a 3D linked hydrodynamic, wind-wave, and water quality model. It uses a hydrodynamic model to simulate water transport, mixing, and thermal regimes; a wave model for wind-driven waves and sediment resuspension; and an advanced aquatic ecosystem model to simulate lower food web dynamics, water quality, sediment diagenesis, and sediment transport. The model includes nutrient loads from more than 70 tributaries, as well as minor and direct drainage inputs and point sources. Meteorological inputs include air temperature, solar radiation, cloud cover and wind forcings at the air-water interface. The model was calibrated for 2014-2016, and its performance confirmed for 2011-2020. Calibration was focused on nutrients, suspended solids, dissolved oxygen, and chlorophyll-a at several long-term monitoring locations in the lake.

  • Publication: Redder et al. in prep

This is a coupled hydrodynamic (ELCOM) and biogeochemical (CAEDYM) model. The hydrodynamic model predicts the velocity, transport, mixing, temperature, and salinity distribution subjected to inflows, outflows, wind stress, surface heating or cooling. CAEDYM simulates inorganic particles, dissolved oxygen, organic and inorganic nutrients, phytoplankton, macroalgae and macrophytes, zooplankton, fish, mussels and clams, bacteria, and metals. The model was calibrated for 2002 and then validated for 2005, 2008 and 2014. It has been applied to Lake Erie for estimates of seasonal and spatial dynamics of water quality and phytoplankton, impacts of mussel grazing on phytoplankton biomass and its seasonal dynamics, effects of external nutrient loads and atmospheric forcings on seasonal hypoxia.

Links to Publications:

This coupled hydrodynamic-ecosystem model has been applied successfully to several small and large lakes and shown to resolve the predominant nearshore and offshore physical processes such coastal upwelling events and internal waves. It has also been shown to be capable of simulating water quality conditions in Lake Erie. The model includes nutrient cycling, exchange of dissolved oxygen to and from the atmosphere, decomposition of organic material, abiotic and biotic oxidation of reduced species, and photosynthetic oxygen production and respiratory oxygen by phytoplankton community.

This model simulates nutrient-phytoplankton-zooplankton-fish-detritus dynamics using phosphorus as the main currency. A recent augmentation of the model introduced two fish groups and three phytoplankton groups to simulate shifts among types and understand their potential effects on fish productivity.

Statistical Models:

This model predicts maximum summer harmful algal bloom (HAB) extent as a function of the 9-year cumulative bioavailable phosphorus load from the Maumee River. This model explains 84% of interannual HAB variability. This model continues to be used as part of the NOAA ensemble annual HAB forecasts.

  • Scavia, D., Wang, Y. C., & Obenour, D. R. (2023). Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie. Science of The Total Environment, 856, 158959.
  • Link to information about the model (Don Scavia's Website)

This model uses April-July and 9-year cumulative dissolved reactive phosphorus loading from the Maumee River to predict the seasonal maximum bloom area. This simple two-term model explains 75% of the observed variability over 1985-2015 and has been used in seasonal forecasting since that time and continues to perform well.

This model predicts, with uncertainty, the summer seasonal average hypoxic extent as a function of March-April average air temperature and the six-year cumulative total phosphorus load from the Detroit, Maumee, Raisin, Sandusky, and Cuyahoga rivers (78% of the total load to the central basin). This model augments a similar model (Del Giudice et al. 2018) by adding the Detroit River loads and extending the calibration period to 46 years (between 1959 and 2022). This model explains 80% of the interannual variability in hypoxia extent. In addition to predicting hypoxic extent as a function of phosphorus load, it considers the influence of climate change through the spring air temperature term.

This model determines the maximum harmful algal bloom (HAB) biomass associated with the March - July phosphorus load from the Maumee River. It was initially developed in 2012, and then refined to consider seasonal temperature as a factor. The model uses measurements of HAB biomass and bioavailable phosphorus loads from the Maumee River as a key input.

Steady State Model:

This model is used to derive total phosphorus load–response curves for annual, spring, and summer conditions. The relationships predict Chlorophyll-a, water transparency, primary production, fish production, and standing biomass. These relationships and matrices provide a simple but robust framework to gauge the potential long-term changes in response to total phosphorus reduction interventions.

Fish and Fisheries Models:

This model is capable of identifying potential hypoxic area and nutrient load thresholds above which they negatively impact commercial harvests. The model has suggested that Lake Whitefish were most negatively affected by increased total phosphorus loads and hypoxic extent and that Walleye had slightly higher thresholds. Yellow Perch showed the opposite effects, with little negative impact on extensive hypoxic areas.

This is a food web model for the central basin of Lake Erie, including dynamic simulations of detritus, bacteria, algae, zooplankton, benthic invertebrates, and fish. The model was calibrated to the biomass dynamics of 23 model groups spanning the years 1996 to 2020. The model uses phosphorus levels and hypoxia conditions as forcing factors and steering the dynamics of the food web over time. By changing the phosphorus and hypoxia conditions as forcings, the model generates the responses of fish biomass to varying nutrient and hypoxia.