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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
Figure 3.
Interannual variation of algal volume and zooplankton
biomass observed at monitoring stations in Georgia-Fuca
estuary.
Publications:
Gargett, A., M. Li and R. Brown. 2001. Testing mechanistic explanations of
observed correlations between environmental factors and marine fisheries.
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