Spatial and temporal hydrodynamic and water quality modeling analysis of a large reservoir on the South Carolina (USA) coastal plain |
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| Institution: | 1. Toxicology Center, University of Saskatchewan, 44 Campus Drive, Saskatoon, SK, S7N 5B3, Canada;2. Global Institute for Water Security, University of Saskatchewan, 11 Innovation Boulevard, Saskatoon, SK, S7N 3H5, Canada;3. Department of Biology, Limnology Laboratory, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada;4. School of Environment and Sustainability, University of Saskatchewan, 117 Science Place, Saskatoon, SK, S7N 5C3, Canada;5. Institute of Loess Plateau, Shanxi University, Taiyuan, Shanxi, PR China;6. Department of Veterinary Biomedical Sciences, University of Saskatchewan, Saskatoon, SK, S7N 5B4, Canada;1. Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China;2. Center for Ecological Research (CER), Kyoto University, Otsu, Shiga 5202113, Japan;3. College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China;4. National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China |
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| Abstract: | Two-dimensional, 31-segment, 61-channel hydrodynamic and water quality models of Lake Marion (surface area 330.7 km2; volume 1548.3×106 m3) were developed using the WASP5 modeling system. Field data from 1985 to 1990 were used to parameterize the models. Phytoplankton kinetic rates and constants were obtained from a related in situ study; others from modeling literature. The hydrodynamic model was calibrated to estimates of daily lake volume; the water quality model was calibrated for ammonia, nitrate, ortho-phosphate, dissolved oxygen, chlorophyll-a, biochemical oxygen demand, organic nitrogen, and organic phosphorus. Water quality calibration suggested the model characterized phytoplankton and nutrient dynamics quite well. The model was validated (Kolmogorov–Smirnov two-sample goodness-of-fit test at P<0.05) by reparameterizing the nutrient loading functions using an independent set of field data. The models identified several factors that may contribute to the spatial variability previously reported from other research in the reservoir, despite the superficial absence of complex structure. Sensitivity analysis of the phytoplankton kinetic rates suggest that study site-specific estimates were important for obtaining model fit to field data. Sediment sources of ammonia (10–60 mg m?2 day?1) and phosphate (1–6 mg m?2 day?1) were important to achieve model calibration, especially during periods of high temperatures and low dissolved oxygen. This sediment flux accounted for 78% (nitrogen) and 50% (phosphorus) of the annual load. Spatial and temporal variability in the lake, reflected in the calibrated and validated models, suggest that ecological factors that influence phytoplankton productivity and nutrient dynamics are different in various parts of the lake. The WASP5 model as implemented here does not fully accommodate the ecological variability in Lake Marion due to model constraints on the specification of rate constants. This level of spatial detail may not be appropriate for an operational reservoir model, but as a research tool the models are both versatile and useful. |
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