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RESEARCH> Biological-Physical Interactions

Biological-Physical Interactions 

We are interested in studying how physical processes after plankton production in the ocean. Our approach is to develop simple coupled biological/physical models and explore the interactions between physical transport and biological production processes. 

Chesapeake Bay

Large interannual fluctuations in river runoff result in highly variable nutrient loading to estuaries. To understand the effects of interannual climate variability on plankton productivity and water quality, we have conducted hindcast model simulations over a six-year period between 1995 and 2000, spanning years of highly variable hydrological and meteorological forcing conditions. As an example, we compare the surface distribution of spring phytoplankton biomass between the high runoff year of 1996 and normal runoff year of 1997 in Figure 1. The spring bloom shifts to the mid and lower Bay regions during 1996 as the fresh water carries nutrient further downstream while high sediment loading inhibits phytoplankton production in the upper bay. In contrast, the spring bloom occurs in the upper and mid bay during 1997, since nutrients are exhausted before reaching the lower bay and light field is favorable for phytoplankton growth in the upper Bay. These model predictions are in agreement with observed interannual variations of plankton biomass distributions (Harding et al., 2002).

Figure 1. Comparison of spring bloom between high (1996, a) and normal (1997, b) runoff years.

Another good example of biophysical interactions is the shift in cross-estuary tilting of Chesapeake Bay’s pycnocline induced by strong southerly winds during the summer, which drives deep O2-depleted water up into shallow areas toward the western shore (Malone et al., 1986) and produces episodic hypoxia in shoal regions that are normally well-oxygenated (Breitburg, 1990). These events can have severe ecological consequences that impact local fishers who routinely operate in these shallow waters. As shown in Figure 2, the model can simulate the cross-estuary pycnocline tilting that drive the hypoxic events. Before the southerly wind events, the isohalines were tilted downward on the western shore because the Coriolis force acting on the seaward-moving plume caused downwelling there (Figure 2b). However, the southerly winds on days 194 and 195 drove east-directed Ekman transports and upwelling on the western shore, driving deep hypoxia water to the shallow shoal regions.


Figure 2. Shoaling of deep hypoxia water due to southerly summer winds. (a) Time series of wind stress vector. Cross-channel salinity distribution before (b) and after (c) the wind event.

Georgia-Fuca estuary

As Figure 3 shows below, both phytoplankton and zooplankton populations in the Georgia-Fuca estuary show large year-year fluctuations. To understand this interannual variability, we coupled a NPZ model with the box model for the Strait of Georgia and Juan de Fuca Strait. We found that plankton production is insensitive to interannual variability in River runoff but is sensitive to small changes in intrinsic biological parameters. 

Figure 3. Interannual variation of algal volume and zooplankton biomass observed at monitoring stations in Georgia-Fuca estuary.

Related Publications:

Gargett, A., M. Li and R. Brown. 2001. Testing mechanistic explanations of observed correlations between environmental factors and marine fisheries. Can. J. Fish. Aquat. Sci., 58, 208-219.

Li, M., A. Gargett and K.L. Denman. 2000. What determines seasonal and interannual variability of phytoplankton and zooplankton in strongly estuarine system? Estuarine, Coastal and Shelf Sci., 50, 467-488.

Li, M., A. Gargett and K.L. Denman. 1999. Seasonal and interannual variability of estuarine circulation in a box model of the Strait of Georgia and Juan de Fuca Strait. Atmos-Ocean, 37, 1-19.