Sarah Preheim (Department of Environmental Health and Engineering)

Anand Gnanadesikan ( Department of Earth and Planetary Sciences)

Environmental policy is increasingly based on results from computer simulations, but more integration between models and observations is needed to make sound decisions. For example, the Environmental Protection Agency (EPA)regularly uses models to set the total maximum daily load (TMDL) limits for nutrients entering watersheds, such as the Chesapeake Bay, with the goal of making all waterways in the US fishable and swimmable under the Clean Water Act. Predictions used for policy decisions are typically informed by a series of models, refined by observations and represent input from a variety of scientists.

We propose to optimize the integration of sequence-based approaches into biogeochemical models, with specific application to ChesROMs, a model of the Chesapeake Bay Dead-zone. Run-off from agricultural and urban areas pollutes the Bay surface waters with nitrogen and phosphorous. This pollution drives harmful algal blooms that have devastating consequences on the ecosystems and threaten public health. One major consequence of pollution is the development of oxygen-free (anoxic) or reduced oxygen (hypoxic) dead-zones that deteriorate the habitat for many aquatic animals. An interdisciplinary approach to this problem is essential as the physical environment and microbial processes components are inextricably linked. Physical stratification within the water column, based on salinity and temperature gradients, determine the extent of vertical mixing between the upper and lower water bodies. Microbial processes are sensitive to mixing, adjusting not only growth, but the specific metabolic pathways, based on the amount of mixing. Denitrification and dissimilatory nitrate reduction to ammonia are two processes that can be very sensitive to the physical environment, yet which determines the fate of nitrogen that fuels algal growth. Integrating an understanding of the physical environmental and microbial processes is vital for improved predictions.