Eutrophication Studies

Identified light extinction and algal growth parameters as most sensitive for cyanobacteria modeling.

Simulated algal dynamics governed by phosphorus and temperature.

Modeled sediment-water phosphate flux feeding algae growth.

Nakdong River bloom prediction model.

Found Trophic State Index above 50, mesotrophic-to-eutrophic conditions.

A 3D hydrodynamic and water quality model was applied to evaluate how weir gate operations affect water quality and harmful algal bloom occurrences across three major Korean rivers. Open-gate scenarios reduced upstream chlorophyll-a and HABs in the Geum and Yeongsan Rivers but increased downstream concentrations, while the Nakdong River saw increases at all locations — reflecting the differing physical, chemical, and biological conditions at each site.

EFDC was used to model toxic cyanobacterial blooms along a 71 km section of the lower Han River in Seoul, Korea, following excessive cyanobacterial growth in 2015. Using chlorophyll-a as the primary bloom indicator and accounting for three algal groups — diatoms, green algae, and cyanobacteria — the model was calibrated and verified against three years of field data (2013–2015). Results showed that more than three phytoplankton groups, or region-specific maximum growth rates, are needed to accurately reproduce observed bloom dynamics.

Projected algal blooms advancing ~6 days/decade.

The U.S. EPA’s Environmental Fluid Dynamics Code (EFDC) was applied to simulate the eutrophication process in Daoxiang Lake, Beijing, using chlorophyll-a concentration as the indicator for algal bloom prediction. After calibration and validation against field sampling conducted from March to October 2008, simulated chlorophyll-a generally agreed with observations, achieving an average algal bloom prediction accuracy of 63.43% and confirming EFDC’s suitability for chlorophyll-a simulation and bloom prediction in the lake.

EFDC was applied to simulate dissolved oxygen, salinity, water temperature, and nutrients in the Caloosahatchee River Estuary, Florida. Sensitivity analyses identified river discharge, wind, and tidal forcing as the primary drivers of spatial and temporal DO distributions — with wind-driven mixing increasing bottom-layer DO and river discharge enhancing stratification in deeper areas of the estuary.

Directly documents updates to the model’s eutrophication module itself.

Case study modeling cyanobacteria (harmful algal bloom species) behavior in a lake.

Same cyanobacteria/algal-bloom modeling focus, applied to a different waterbody.

Phytoplankton dynamics are the core biological driver of eutrophication and algal blooms.

Feature post extending the same phytoplankton/algae modeling capability referenced above.

Directly covers a real-world algal bloom event.

EFDC+ was used to model sediment, nutrient, and heavy metal dynamics in Lake Taihu, one of China’s largest and most heavily eutrophic lakes. The study examined contaminant exchange between the water column and the sediment bed, contributing to the scientific understanding of this internationally significant water body.

EFDC+ was applied to Daoxiang Lake to simulate eutrophication and forecast algal blooms, using chlorophyll-a concentration as the key indicator and 2008 field data for calibration. The study demonstrated EEMS as a proactive tool for managing bloom risk in urban lakes across Asia.

After a toxic algal bloom struck the Han River in 2015, EFDC+ was used to model bloom dynamics along a 71 km reach using a 2D approach, with chlorophyll-a as the principal variable. The application showcased EEMS’s value for early warning and protective water management in dense urban river systems. See also the free grid.

Lake Mendota has served as a benchmark site for EEMS water quality capabilities, with demonstrations spanning cyanobacteria behavior, turbulence, thermal dynamics, water quality calibration, and ice formation. These validated applications showcase the breadth of the EFDC+ water quality toolkit.

EEMS was applied to West Lake to simulate water quality, including toxin dynamics and wind-wave interactions. A real fish die-off event provided validation data, demonstrating that EEMS supports both emergency response and longer-term water management in Asian urban lakes. Demo models: DM-05 (Toxics) and DM-03 (Windwave).