Modeling Efforts Aim to Improve Chesapeake Bay Seasonal Hypoxia Forecasts
Ecological forecasts help resource managers better understand their management options, the likely effects of their decisions, and consequences of their actions. In the Chesapeake Bay, deep portions provide more habitats for fish, shellfish and crabs. However, during the summer, deeper waters are too dark for plants to grow and create oxygen by photosynthesis so oxygen levels decline to the point where only bacteria can survive. Under the low oxygen (hypoxia) and no oxygen (anoxic) conditions, valuable fish, blue crabs and oysters cannot survive. Reducing and eliminating hypoxia in Chesapeake Bay is a long-term goal of the state of Maryland and the Federal government.
Forecasting hypoxia in the Chesapeake Bay has historically been a good faith but somewhat ad hoc effort. The National Centers for Coastal Ocean Science funds two projects under our Coastal Hypoxia Research Program (CHRP) that are being considered for a Chesapeake Bay operational seasonal hypoxia forecast.
One annual hypoxia forecast is run by scientists at the University of Michigan using a hybrid model that combines three existing watershed/water quality models. A team of researchers from the University of Maryland, University of Delaware and Dalhousie University are developing a second forecast. It’s based on the Regional Ocean Model (“ROMS”) physical hydrodynamic model and a generalized water quality model called “RCA.” Together, they improve the quantitative understanding of mechanisms causing Chesapeake Bay hypoxia.
Ultimately, the long-term goal is to use these models, and perhaps others, for an “ensemble” forecast similar to the methodology used to predict hurricane storm tracks. Additionally, these two models may be useful for updating components of the Chesapeake Bay Program 3-dimensional watershed/water quality model.
A reliable and consistent hypoxia forecast in Chesapeake Bay will provide information to guide restoration, improve communication of conditions, and help prioritize future research.