Decoupling the influence of biological and physical processes on the dissolved oxygen in the Chesapeake Bay

Abstract

It is instructive and essential to decouple the effects of biological and physical processes on the dissolved oxygen condition, in order to understand their contribution to the interannual variability of hypoxia in Chesapeake Bay since the 1980s. A conceptual bottom DO budget model is applied, using the vertical exchange time scale (VET) to quantify the physical condition and net oxygen consumption rate to quantify biological activities. By combining observed DO data and modeled VET values along the main stem of the Chesapeake Bay, the monthly net bottom DO consumption rate was estimated for 1985-2012. The DO budget model results show that the interannual variations of physical conditions accounts for 88.8% of the interannual variations of observed DO. The high similarity between the VET spatial pattern and the observed DO suggests that physical processes play a key role in regulating the DO condition. Model results also show that long-term VET has a slight increase in summer, but no statistically significant trend is found. Correlations among southerly wind strength, North Atlantic Oscillation index, and VET demonstrate that the physical condition in the Chesapeake Bay is highly controlled by the large-scale climate variation. The relationship is most significant during the summer, when the southerly wind dominates throughout the Chesapeake Bay. The seasonal pattern of the averaged net bottom DO consumption rate (B

The competing impacts of climate change and nutrient reductions on dissolved oxygen in Chesapeake Bay

“…By increasing estuarine circulation and decreasing residence time (Hong and Shen, 2012;Du and Shen, 2015), rising sea levels can actually increase bottom oxygen concentrations in the most anoxic portions of the deep main stem trench. At the same time, increasing estuarine circulation increases stratification (Chua and Xu, 2014), which serves to further decrease oxygen concentrations (Lennartz et al, 2014).…”

“…2650 I. D. Irby et al: Climate change impacts on low-oxygen waters in Chesapeake Bay teractive, as increasing volume and circulation can bring in high-oxygen water from the coastal ocean, while increased stratification inhibits downward mixing of the high-oxygen water from the surface waters. Stronger estuarine circulation generally also leads to shorter residence times that typically increase oxygen concentrations (Hong and Shen, 2012;Du and Shen, 2015). In addition, over much of the mid-Atlantic region, annual precipitation, and thus river discharge, has been increasing Yang et al, 2015a, b).…”

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

“…Model D employs a ROMS implementation for the Chesapeake Bay based on M. Li et al (2005), while Model G uses the ROMS-based Chesapeake Bay Operational Forecast System (CBOFS; Lanerolle et al, 2011). Models A, E, and H each use a different hydrodynamic base model: the Curvilinear Hydrodynamics in Three Dimensions model (CH3D; Cerco et al, 2010), the FiniteVolume Community Ocean Model (FVCOM; Jiang and Xia, 2016), and the Hydrodynamic Eutrophication Model -Environmental Fluid Dynamics Code (EFDC; Park et al, 1995;Hong and Shen, 2012;Du and Shen, 2015), respectively. The only model that employs a non-sigma vertical grid is Model A and the only model utilizing an unstructured horizontal grid is Model E. While Model E contains 10 sigma vertical layers, all of the other sigma grids use 20 layers.…”

“…At the open boundary with the Atlantic Ocean, Models B, C, D, F, G, and H utilize a sub-tidal elevation extrapolated from tidal stations on either side of the open boundary. Model E uses the TPXO tidal model, while Model A uses a mix of observational and model forcing (Cerco et al, 2010 Du and Shen (2015) els C through H use wind estimates from the North American Regional Reanalysis (NARR).…”

Partial ages: diagnosing transport processes by means of multiple clocks

“…The age concentration is an extensive variable; it satisfies a budget equation that can be used to compute the mean age of the constituent. This approach has been applied in a large number of studies (e.g., Gourgue et al 2007;Meier 2007;Plus et al 2009;Bendtsen et al 2009;de Brye et al 2012;Bendtsen and Hansen 2013;Ren et al 2014;Du and Shen 2015).…”