Today's HWR seminar will be given by Larry Winter.
"A Reduced Complexity Model for Probabilistic Risk Assessment of Groundwater Contamination"
Abstract:
I'll discuss a model of reduced complexity for assessing the risk of groundwater pollution from a point-source that Daniel Tartakovsky and I have been developing. An important feature of the model is that it's based on observable parameters like solute concentration at an observation well. The progress of contamination is represented as a sequence of transitions among coarsely resolved states corresponding to simple statements like "a spill has occurred", "natural attenuation has failed", etc. Transitions between states are modeled as a Markov jump process. This introduces the element of time in the state transition model which is missing in many risk assessment models. A general expression for the probability of aquifer contamination is obtained. I'll discuss the assumptions the model is based on and the data it requires, and I'll apply the model to a simple example to illustrate the method and fix ideas. I'll also say something about the uses of models, and the restrictions that sparse data imposes on them, to motivate the usefulness of reduced models.
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