Sabatine, Shaina M., authorNiemann, Jeffrey D., advisorGreimann, Blair, committee memberHoeting, Jennifer, committee member2007-01-032007-01-032011http://hdl.handle.net/10217/70825This paper aims to quantify parameter and model uncertainty in simulations from a 1D sediment transport model using two methods from Bayesian statistics. The first method, Multi-Variable Shuffled Complex Evolution Metropolis - Uncertainty Analysis (MSU), is an algorithm that identifies the most likely parameter values and estimates parameter uncertainty for models with multiple outputs. The other method, Bayesian Model Averaging (BMA), determines a combined prediction based on three sediment transport equations and evaluates the uncertainty associated with the selection of a transport equation. These tools are applied to simulations of three flume experiments. Results show that MSU's ability to consider correlation between parameters improves its estimate of the uncertainty in the model forecasts. Also, BMA results suggest that a combination of transport equations usually provides a better forecast than an individual equation, and the selection of a single transport equation substantially increases the overall uncertainty in the model forecasts.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Bayesian model averagingsediment transport uncertaintyparameter uncertaintyparameter optimizationmodel uncertaintyText