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Lake Minnetonka (44◦54N; 93◦41W; MN, USA) has been a major regional water resource since 1800s. Its hotels and resorts were replaced by large homes and estates by 1860s and its 175km shoreline well developed by 1930s. The lake has a complex morphometry with 26 bays and coves that has created a varying water quality regions and bottom sediment types. The lake and its catchment have been well studied; however, a lake water quality model had been lacking due to the lake's complex morphology. The goals of this project, Lake Minnetonka 3D water quality modeling, were to apply a model robust enough to capture the system's large water quality heterogeneity, conduct sensitivity analysis to increase confidence in the model results, and provide engineering application of the model to aid in management of the lake. We have successfully applied a coupled 3D hydrodynamic (ELCOM) and ecological (CAEDYM) model to a number of bays of Lake Minnetonka, including: • parameter calibration and model confirmation (validation), • model simulations and the insights gained about the biogeochemical processes, • the sensitivity and uncertainty analyses by two methods and the model parameter ranking, and • examples of ecological application of the model in Lake Minnetonka. The results reflect a lake system with significant spatial and temporal water quality changes that requires the use of a 3D model. Spatial and temporal dynamics were well simulated and model output results of water temperature (T), dissolved oxygen (DO), total phosphorus (TP) and one group of algae (Cyanobacteria) as represented as chlorophyll a (Chla) compared well with an extensive field data in the bays under two very meteorologically different growing seasons. The model was able to capture water quality variations including significant sudden (days) water quality changes caused by stream inflow events that would not have been otherwise detected by typical summer monitoring. Model simulations indicate that total phosphorus variations in deeper water columns were more likely the consequences of physical perturbations caused by storm events flows. The model captured the heterogeneity of the lake and showed that the use of 3D model along with an accurate bathymetry, and a systematic calibration and confirmation process can help to analyze the hydrodynamics of a morphologically complex lake. Much effort was made in setting up and configuring the model, carrying out calibration, and confirmation. Two sensitivity and uncertainty methods were used to improve understanding of the model, identify influential model parameters, explore spatial and temporal variabilities of model prediction, and to rank 40 of the model parameters. 70% of model output uncertainty was explained by 7 parameters or less. T, DO, TP, and Chla model outputs contributed 3, 13, 26, and 58% to total model variance respectively. Results identified the need for a better understanding of biological model parameters and the need to include bacteria and zooplankton simulations in future work. The spatial and temporal variations of model outputs were found to be sensitive to the hydrodynamics of physical perturbations such as those caused by stream inflows generated from storm events. 3D sensitivity-uncertainty analyses must include periods of physical perturbations. The model has been used to examine and compare the coolwater fish habitat analysis in 3D and under a scenario where spatial heterogeneity has been eliminated by horizontally averaging T and DO. The model is currently used to investigate the relationship between climate change, shoreline plant survival, lake hydrodynamics, lake water level changes, sediment nutrient flux, and water quality.

3D water quality modeling of a morphologically complex lake, sensitivity and uncertainty analyses, and examples of model applications