Ruark, Morgan D., authorNiemann, Jeffrey D., advisorKampf, Stephanie, committee memberGriemann, Blair, committee member2022-09-282022-09-282009https://hdl.handle.net/10217/235795Covers not scanned.Print version deaccessioned 2022.Numerical sediment transport models are widely used to evaluate impacts of water management activities on endangered species, to identify appropriate strategies for dam removal, and many other applications. The SRH-1D (Sedimentation and River Hydraulics - One Dimension) numerical model, formerly known as GST ARS, is used by the U.S. Bureau of Reclamation for many such evaluations. The predictions from models such as SRH-1D include uncertainty due to assumptions embedded in the model 's mathematical structure, uncertainty in the values of parameters, and various other sources. In this paper, we aim to develop a method that quantifies the degree to which parameter values are constrained by calibration data and determines the impacts of the remaining parameter uncertainty on model forecasts. Ultimately, this method could be used to assess how well calibration exercises have constrained model behavior and to identify data collection strategies that improve parameter certainty. The method uses a new multi-objective version of Generalized Likelihood Uncertainty Estimation (GLUE). In this approach, the likelihoods of parameter values are assessed using a function that weights different output variables using their first order global sensitivities, which are obtained from the Fourier Amplitude Sensitivity Test (FAST). The method is applied to SRH-1D models of two flume experiments: an erosional case described by Ashida and Michiue (1971) and a depositional case described by Seal et al. (1997). Overall, the results suggest that the sensitivities of the model outputs to the parameters can be rather different for erosional and depositional cases and that the outputs in the depositional case can be sensitive to more parameters. The results also suggest that the form of the likelihood function can have a significant impact on the assessment of parameter uncertainty and its implications for the uncertainty of model forecasts.masters 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.Sediment transport -- Mathematical modelsText