Abstract
To provide insightful information on water quality management, it is crucial to improve the understanding of the complex biogeochemical cycles of Chesapeake Bay (CB), so a three-dimensional unstructured grid-based water quality model (ICM based on the finite-volume coastal ocean model (FVCOM)) was configured for CB. To fully accommodate the CB study, the water quality simulations were evaluated by using different horizontal and vertical model resolutions, various wind sources and other hydrodynamic and boundary settings. It was found that sufficient horizontal and vertical resolution favored simulating material transport efficiently and that winds from North American Regional Reanalysis (NARR) generated stronger mixing and higher model skill for dissolved oxygen simulation relative to observed winds. Additionally, simulated turbulent mixing was more influential on water quality dynamics than that of bottom friction: the former considerably influenced the summer oxygen ventilation and new primary production, while the latter was found to have little effect on the vertical oxygen exchange. Finally, uncertainties in riverine loading led to larger deviation in nutrient and phytoplankton simulation than that of benthic flux, open boundary loading and predation. Considering these factors, the model showed reasonable skill in simulating water quality dynamics in a 10-year (2003–2012) period and captured the seasonal chlorophyll-a distribution patterns. Overall, this coupled modeling system could be utilized to analyze the spatiotemporal variation of water quality dynamics and to predict their key biophysical drivers in the future.
Highlights
As the largest and most biologically-diverse coastal plain estuary in North America [1], Chesapeake Bay (CB) is highly influenced by its vast watershed with a land-to-water ratio of 14.3 [2].Following the population growth, industrial and agricultural development, CB has undergone severe eutrophication with symptoms of excessive nutrient loading, nuisance algal blooms, extensive summer hypoxia and declined seagrass coverage since the mid-1900s [3,4]
We tried to achieve a comprehensive understanding of various uncertainties for the model development and answer the following questions: (1) how will increased model resolution improve the simulation of water quality variables; (2) how sensitive is the model to different wind sources; and (3) what is the most significant physical and biological sources of uncertainty in our model? Sections 2 and 3 introduce the model frame and sensitivity experiments; model calibration results are depicted in Section 4; Section 5 lists the major conclusions
It was found that the model performance of all water quality variables was better in the fine-grid model, as revealed by higher correlation coefficients and lower root mean squared errors, and that dissolved inorganic nitrogen (DIN) and total suspended solids (TSS) were among the variables with a large deviation between the two models (Figure 4 and Table 3)
Summary
As the largest and most biologically-diverse coastal plain estuary in North America [1], Chesapeake Bay (CB) is highly influenced by its vast watershed with a land-to-water ratio of 14.3 [2]. Statistical empirical models and simplified oxygen models omitting nutrient cycles have been developed as substitutes for specific research goals, but it is impossible to reproduce the detailed internal spatiotemporal water quality variation and comparatively evaluate biophysical drivers [14]. The three-dimensional physical-biogeochemical model could better resolve the biophysical interactions between circulation and water quality kinetics [11,12] and facilitate mechanistic analysis of internal water-column dynamics [15,16,17], making it an ideal tool to investigate nutrient dynamics and algal variability in eutrophic estuaries [18], synoptically assess estuarine biophysical processes and project future scenarios [19,20]. Other key challenges and sources of uncertainty in configuring biophysical models lay in meteorological forcing, hydrodynamic simulation and nutrient loading [20]. We tried to achieve a comprehensive understanding of various uncertainties for the model development and answer the following questions: (1) how will increased model resolution improve the simulation of water quality variables; (2) how sensitive is the model to different wind sources; and (3) what is the most significant physical and biological sources of uncertainty in our model? Sections 2 and 3 introduce the model frame and sensitivity experiments; model calibration results are depicted in Section 4; Section 5 lists the major conclusions
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