Integrated Statistical and Optimization Analysis for Computationally Expensive Models of Complex Environmental Systems

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The investigators will develop an integrated and computationally efficient general method for utilizing field and laboratory data coupled with environmental models to provide a site-specific basis for incorporating uncertainty in parameter and input values into scientific investigations and decisions related to environmental analysis, protection, and remediation. The research will use recently developed methods of global optimization based on response surfaces that provide an efficient method for calibration of model parameters when, as is typical for complex environmental models, the model is expensive to run. Efficiency (computational and statistical) will also be improved by using, within a Bayesian framework, data transformations and weightings that reflect nonnormal distributions, heteroscedasticity, and correlation in the data, to address different sources of uncertainty.Future states of the environmental system will be forecast by simulating the underlying environmental model for a number of sets of parameters selected according to the posterior distribution of parameter values and using a response surface approximation of each output variable of interest. Sensitivity and uncertainty analyses will be done and their accuracies will be investigated. The research will be applied to the Cannonsville reservoir in New York and the bioremediation of toxic chlorinated ethenes in groundwater used for drinking water.