Date of Award

5-2008

Degree Type

Report

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Mevin Hooten

Abstract

Physical models in the hydrological sciences are often calibrated using methods that do not formally quantify uncertainty in the model parameters. Additionally, many competing hydrological models exist and are used to model the same processes. Considering existing mechanistic models of rainfall-run off in a statistical context can assist hydrologists in understanding the true physical process taking place. This paper introduces a data assimilation mixture model of runoff that yields statistical estimates of hydrological mode l parameters and predictions. This statistical model incorporates two commonly used hydrological models, each with strengths and weaknesses. The mixture framework allows comparisons between models as well as combines the strengths of both. Results from three implementations of the mixture model are summarized and additional generalizations of the models are suggested.

Included in

Mathematics Commons

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