Linking dynamics of transport timescale and variations of hypoxia in the Chesapeake Bay

Hong, Bo; Shen, Jian

doi:10.1002/2013jc008859

Cited by 33 publications

(40 citation statements)

References 27 publications

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“…The thickness of the bottom layer below the pycnocline and the hypoxic estimations for these two regions are also included. Rabalais et al (2007); c Hong and Shen (2013); d Rabalais et al (2010) 6 mg L −1 . Based on this result, we will continue to study the reasons for such low DO S concentrations at the bottom layer of the HFZ.…”

Section: Why Is the Hypoxia In The Pre Not Severe?mentioning

confidence: 99%

A numerical analysis of biogeochemical controls with physical modulation on hypoxia during summer in the Pearl River estuary

Wang

et al. 2017

Biogeosciences

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Abstract.A three-dimensional (3-D) physicalbiogeochemical coupled model was applied to explore the mechanisms controlling the dissolved oxygen (DO) dynamics and bottom hypoxia during summer in the Pearl River estuary (PRE). By using the numerical oxygen tracers, we proposed a new method (namely the physical modulation method) to quantify the contributions of boundary conditions and each source and sink process occurring in local and adjacent waters to the DO conditions. A mass balance analysis of DO based on the physical modulation method indicated that the DO conditions at the bottom layer were mainly controlled by the source and sink processes, among which the sediment oxygen demand (SOD) at the water-sediment interface and the re-aeration at the air-sea interface were the two primary processes determining the spatial extent and duration of bottom hypoxia in the PRE. The SOD could cause a significant decrease in the bottom DO concentrations (averaged over July-August 2006) by over 4 mg L −1 on the shelf off the Modaomen sub-estuary, leading to the formation of a high-frequency zone of hypoxia (HFZ). However, the hypoxia that occurred in the HFZ was intermittent and distributed in a small area due to the combined effects of re-aeration and photosynthesis, which behaved as sources for DO and offset a portion of the DO consumed by SOD. The bottom DO concentrations to the west of the lower Lingdingyang Bay (i.e. the western shoal near Qi'ao Island) were also largely affected by high SOD, but there was no hypoxia occurring there because of the influence of re-aeration. Specifically, re-aeration could lead to an increase in the bottom DO concentrations by ∼ 4.8 mg L −1 to the west of the lower Lingdingyang Bay. The re-aeration led to a strong vertical DO gradient between the surface and the lower layers. As a result, the majority (∼ 89 %) of DO supplemented by re-aeration was transported to the lower layers through vertical diffusion and ∼ 28 % reached the bottom eventually. Additional numerical experiments showed that turning off re-aeration could lead to an expansion of the hypoxic area from 237 to 2203 km 2 and result in persistent hypoxia (hypoxic frequency > 80 %) to the west of the lower Lingdingyang Bay. Compared to re-aeration and SOD, photosynthesis and water column respiration had relatively small impacts on the DO conditions; turning off these two processes increased the hypoxic area to 591 km 2 . In summary, our study explicitly elucidated the interactive impacts of physical and biogeochemical processes on the DO dynamics in the PRE, which is critical to understanding hypoxia in this shallow and river-dominated estuarine system.

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Section: Why Is the Hypoxia In The Pre Not Severe?mentioning

confidence: 99%

A numerical analysis of biogeochemical controls with physical modulation on hypoxia during summer in the Pearl River estuary

Wang

et al. 2017

Biogeosciences

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“…Many 3-D hydrodynamic-oxygen models of varying complexity stemming from the academic research community have also been used to simulate DO concentrations throughout the Chesapeake Bay (Scully, 2010(Scully, , 2013Hong and Shen, 2013;Feng et al, 2015;Y. Li et al, 2015).…”

Section: D Irby Et Al: Multiple Modeling Low-oxygen Waters 2013mentioning

confidence: 99%

Challenges associated with modeling low-oxygen waters in Chesapeake Bay: a multiple model comparison

Irby

Friedrichs

et al. 2016

Biogeosciences

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Abstract. As three-dimensional (3-D) aquatic ecosystem models are used more frequently for operational water quality forecasts and ecological management decisions, it is important to understand the relative strengths and limitations of existing 3-D models of varying spatial resolution and biogeochemical complexity. To this end, 2-year simulations of the Chesapeake Bay from eight hydrodynamic-oxygen models have been statistically compared to each other and to historical monitoring data. Results show that although models have difficulty resolving the variables typically thought to be the main drivers of dissolved oxygen variability (stratification, nutrients, and chlorophyll), all eight models have significant skill in reproducing the mean and seasonal variability of dissolved oxygen. In addition, models with constant net respiration rates independent of nutrient supply and temperature reproduced observed dissolved oxygen concentrations about as well as much more complex, nutrient-dependent biogeochemical models. This finding has significant ramifications for short-term hypoxia forecasts in the Chesapeake Bay, which may be possible with very simple oxygen parameterizations, in contrast to the more complex full biogeochemical models required for scenario-based forecasting. However, models have difficulty simulating correct density and oxygen mixed layer depths, which are important ecologically in terms of habitat compression. Observations indicate a much stronger correlation between the depths of the top of the pycnocline and oxycline than between their maximum vertical gradients, highlighting the importance of the mixing depth in defining the region of aerobic habitat in the Chesapeake Bay when low-oxygen bottom waters are present. Improvement in hypoxia simulations will thus depend more on the ability of models to reproduce the correct mean and variability of the depth of the physically driven surface mixed layer than the precise magnitude of the vertical density gradient.

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“…The mean differences of phase and amplitude were estimated based on CBP model outputs and measured tidal elevations at Sewells Point. The hourly salinity boundary conditions used for the 2006 to 2013 period are outputs from the large domain Chesapeake Bay model [21,22]. The temperature boundary condition used hourly measurements at Sewells Point at the surface and monthly temperature data measured at CBP Station CB8.1.…”

Section: Model Configurationmentioning

confidence: 99%

“…Any change of hydrodynamic conditions will result in a change of transport processes, which is more important for transporting both nutrients and phytoplankton [21,22]. Because it is difficult to evaluate the change of dynamic conditions (e.g., change of velocity, surface elevation) and their impact on algae and nutrient transport, we can use the transport timescale to evaluate the impact of dynamics on water quality because it shows a cumulative effect.…”

Section: Age Calculationmentioning

confidence: 99%

“…We computed the freshwater age along the James It is interesting to know if the change of salinity, which is within the accepted error range due to flow or other forcings, will affect the long-term transport or export of nutrients, as the retention of nutrients and eutrophication are highly determined by the residence time [27][28][29]. The transport property of a substance can be quantified by the transport timescales, such as residence time and water age [21][22][23][24]. The age of water is defined as the time elapsed since it leaves the headwaters.…”

Section: Freshwater Dischargementioning

confidence: 99%

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Development of the Hydrodynamic Model for Long-Term Simulation of Water Quality Processes of the Tidal James River, Virginia

Shen

Sisson

2016

JMSE

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Harmful algal blooms (HABs) have frequently occurred in the James River. The State has convened a Scientific Advisory Panel (SAP) to review the James River chlorophyll-a standards. The SAP will conduct a scientific study to review the basis for setting the chlorophyll-a standards. To support the SAP study of chlorophyll-a standards, the State of Virginia has decided to develop a numerical modeling system that is capable of simulating phytoplankton and HABs. The modeling system includes a watershed model, a three-dimensional hydrodynamic model and water quality models. The focus of this study will be on the development and verification of the hydrodynamic model. In order to simulate the complex geometry of the James River, a high-resolution model has been implemented. The model has been calibrated for a long-term period of 23 years. A series of model experiments was conducted to evaluate the impact of forcings on dynamic simulation and transport time. It was found that freshwater discharge is the most sensitive for an accurate simulation of salinity and transport time. The water age predicted by the model in the tidal freshwater region represents the fluctuation of transport processes, and it has a good correlation with the algal bloom, while at the downstream, the transport time simulation agrees with the delay of the HAB in the mesohaline of the James after the HAB occurred in the Elizabeth River due to the transport processes.The results indicate that the hydrodynamic model is capable of simulating the dynamic processes of the James and driving water quality models in the James River.

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Linking dynamics of transport timescale and variations of hypoxia in the Chesapeake Bay

Cited by 33 publications

References 27 publications

A numerical analysis of biogeochemical controls with physical modulation on hypoxia during summer in the Pearl River estuary

A numerical analysis of biogeochemical controls with physical modulation on hypoxia during summer in the Pearl River estuary

Challenges associated with modeling low-oxygen waters in Chesapeake Bay: a multiple model comparison

Development of the Hydrodynamic Model for Long-Term Simulation of Water Quality Processes of the Tidal James River, Virginia

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