Three-dimensional biogeochemical modeling of eutrophication in Edmonton stormwater ponds

A three-dimensional coupled hydrodynamic-biogeochemical model was applied to simulate and investigate causes of eutrophication in two stormwater ponds in the City of Edmonton (Alberta, Canada). The ponds differ in shape, surface area, depth and trophic state. Strong spatial gradients, in water column thermal structure and water quality state variables were observed (e.g., similar to 10 degrees C; 0.1 mg L-1 PO4, 0.5 mg L-1 total P and 50 mu g L-1 chlorophyll-a over similar to 3m), showing the ponds were incompletely mixed systems. Using a single set of calibration parameters, which differed from calibrations to larger systems, the model accurately simulated these gradients, with errors being highest for NO3 and total chlorophyll-a (RMSE <64.3 mu g L-1 and NRMSE<0.94. Simulation of total chlorophyll-a was at times visually inaccurate, particularly in the more eutrophic pond, but the normalized metrics (e.g., NRMSE) were consistent between ponds and with literature values. The model accurately predicted the trophic state (in more than 60% of the observations) and the distribution of phytoplankton community structure was simulated with marginal error (average similar to 7%). Chlorophyll-a alone was not a suitable index to define trophic state in the ponds; suspended algae and macro-algae reduced pond aesthetics, while competing with phytoplankton for nutrients and was coincident with lower phytoplankton biomass and increased water clarity. Simulated remediation strategies, involving reduction of influent phosphorus and nitrogen fractions, showed that the trophic state of each pond could be improved from mesotrophic/eutrophic to oligotrophic/mesotrophic states by reducing nutrient loads at least 50%.

Automated summary

A three-dimensional coupled hydrodynamic-biogeochemical model was used to study eutrophication in stormwater ponds in Edmonton, Alberta. The model accurately simulated spatial gradients and predicted trophic state, showing potential for improving pond conditions by reducing nutrient loads.