HR: 09:45h
AN: NB41E-06 INVITED     [Abstracts]
TI: A Bayesian Network of Eutrophication Models for Synthesis, Prediction, and Uncertainty Analysis
AU: * Stow, C A
EM: cstow@sc.edu
AF: Deparment of Environmental Health Sciences University of South Carolina, Sumter Street, Columbia, SC 29208 United States
AU: Borsuk, M E
EM: mark.borsuk@eawag.ch
AF: SIAM EAWAG, PO Box 611, Dubendorf, Switzerland
AU: Reckhow, K H
EM: reckhow@duke.edu
AF: Nicholas School of the Environment Duke University, LSRC, Durham, NC 27708 United States
AB: Accurate prediction in complex adaptive systems is difficult. Even in a simple programmed system where all the rules of interaction are known exactly, unexpected behavior can arise. Thus, forecasting in real ecological systems, where system behavior is often poorly understood, requires characterization of the inherent prediction uncertainty. We developed a Bayesian probability network to predict the effect of nitrogen load reductions on eutrophication symptoms in the Neuse River Estuary, NC. The model consists of a set of sub-models, each independently estimated, and capable of probabilistic prediction. Probabilistic forecasts are propagated through the network in accordance with fundamental probability rules. This approach is extremely flexible as it can accommodate information from detailed process-based models, empirically-based models, probabilistic knowledge elicited from experts, or any combination of these sources. Additionally, the Bayesian framework facilitates model updating, in an Adaptive Management context. As management actions to reduce nitrogen inputs become effective the response of system can be monitored and the new data rigorously assimilated into the model via Bayes Theorem. In the Neuse Estuary this latter feature should be particularly informative because the influences of river flow and nitrogen input are highly confounded making their independent effects difficult to estimate.
DE: 1845 Limnology
DE: 4235 Estuarine processes
DE: 4842 Modeling
DE: 4845 Nutrients and nutrient cycling
DE: 4857 Pollution
SC: North American Benthological Society [NB]
MN: 2005 Joint Assembly