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Keywords = FVCOM-ICM

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17 pages, 3957 KB  
Article
Factors Controlling Hypoxia Occurrence in Estuaries, Chester River, Chesapeake Bay
by Richard Tian
Water 2020, 12(7), 1961; https://doi.org/10.3390/w12071961 - 10 Jul 2020
Cited by 12 | Viewed by 3515
Abstract
The Chester River, a tributary of Chesapeake Bay, provides critical habitats for numerous living species and oyster aquaculture, but faces increasing anthropogenic stresses due to excessive nutrient loading and hypoxia occurrence. An application of the Integrated Compartment Water Quality Model (ICM), coupled with [...] Read more.
The Chester River, a tributary of Chesapeake Bay, provides critical habitats for numerous living species and oyster aquaculture, but faces increasing anthropogenic stresses due to excessive nutrient loading and hypoxia occurrence. An application of the Integrated Compartment Water Quality Model (ICM), coupled with the Finite-Volume Community Ocean Model (FVCOM), was carried out to study the controlling mechanisms and interannual variability in hypoxia occurrence from 2002 to 2011. Our study shows that hypoxia occurs mostly in the main stem in July, followed by August and June. On an interannual scale, 2005 had the highest hypoxia occurrence with an accumulative hypoxia volume of about 10 km3-days, whereas 2008 had the lowest occurrence with an accumulative hypoxia volume of about 1 km3-days. Nutrient loading is the predominant factor in determining the intensity and interannual variability in hypoxia in the Chester River estuary, followed by stratification and saltwater intrusion. Phosphorus has been found to be more efficient in controlling hypoxia occurrence than nitrogen due to their different limiting extent. On a local scale, the Chester River estuary is characterized by several meanders, and at certain curvatures helical circulation is formed due to centrifugal forces, leading to better reaeration and dissolved oxygen (DO) supply to the deeper layers. Our study provides valuable information for nutrient management and restoration efforts in the Chester River. Full article
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23 pages, 7891 KB  
Article
Application of an Unstructured Grid-Based Water Quality Model to Chesapeake Bay and Its Adjacent Coastal Ocean
by Meng Xia and Long Jiang
J. Mar. Sci. Eng. 2016, 4(3), 52; https://doi.org/10.3390/jmse4030052 - 1 Sep 2016
Cited by 29 | Viewed by 7996
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 [...] Read more.
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. Full article
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