Calibration of Linked Hydrodynamic and Water Quality Model for Santa Margarita Lagoon
Space and Naval Warfare Systems Center Pacific San Diego United States
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The Santa Margarita Lagoon SML is a water body that forms where the Santa Margarita River and its tributaries meet the Pacific Ocean. The lagoon and lower part of the river are within Marine Corps Base Camp Pendleton Camp Pendleton. The Regional Water Quality Control Board, San Diego listed the lagoon as an impaired water body for eutrophication with excessive nutrients and issued an Investigative Order R9-2006-076 in 2007 to evaluate the extent of impairment. A watershed stakeholder group, including Camp Pendleton, was established to develop and implement a water quality monitoring effort to evaluate the impairment in 2008. Those data along with measurements made by the Southern California Coastal Water Research Project SCCWRP in 2009 provided the first relatively comprehensive evaluation of lagoon water quality. We have identified multiple sources of uncertainties and data gaps. For example, the p-load obtained by calibration could be attributed to uncertainty associated with watershed groundwater load or benthic flux or other sources between Stuart Mesa Bridge and the model upstream boundary. However, no existing data could support either assumed sources. Despite these uncertainties in the data and the model, overall calibration of the linked Environmental Fluid Dynamics Code EFDC hydrodynamic model was selected and linked with the Water Quality Analysis Simulation ProgramEutrophication WASP-Eutro models provided a sufficiently reasonable match to field datasets collected between 2008 and 2009. The model should be able to simulate and compare the relative water quality responses among different scenarios, such as load reduction and effect of different benthic flux scenarios and others. To fully characterize and evaluate model performance, a sensitivity analysis should be conducted. In this analysis, key processes that govern the dynamics of the water quality parameters can be identified, which should help design and plan load management scenarios.