One challenge that faces hydrologists in water resources planning is to predict the catchment???s response to a given rainfall. Estimation of parameter uncertainty (and model uncertainty) allows assessment of the risk in likely applications of hydrological models. Bayesian statistical inference, with computations carried out via Markov Chain Monte Carlo (MCMC) methods, offers an attractive approach to model specification, allowing for the combination of any pre-existing knowledge about individual models and their respective parameters with the available catchment data to assess both parameter and model uncertainty. This thesis develops and applies Bayesian statistical tools for parameter estimation, comparison of model performance and hierarchical model aggregation. The work presented has three main sections. The first area of research compares four MCMC algorithms for simplicity, ease of use, efficiency and speed of implementation in the context of conceptual rainfall-runoff modelling. Included is an adaptive Metropolis algorithm that has characteristics that are well suited to hydrological applications. The utility of the proposed adaptive algorithm is further expanded by the second area of research in which a probabilistic regime for comparing selected models is developed and applied. The final area of research introduces a methodology for hydrologic model aggregation that is flexible and dynamic. Rigidity in the model structure limits representation of the variability in the flow generation mechanism, which becomes a limitation when the flow processes are not clearly understood. The proposed Hierarchical Mixtures of Experts (HME) model architecture is designed to do away with this limitation by selecting individual models probabilistically based on predefined catchment indicators. In addition, the approach allows a more flexible specification of the model error to better assess the risk of likely outcomes based on the model simulations. Application of the approach to lumped and distributed rainfall runoff models for a variety of catchments shows that by assessing different catchment predictors the method can be a useful tool for prediction of catchment response.

Two catchments were investigated to determine whether the "first flush" effect was likely to occur in metropolitican Adelaide, South Australia. The residential catchments chosen were located in at Halsey Road, Henly Beach South and Riverway, Fulham Gardens; their areas were 155 ha and 18 ha respectively. / Thesis (MEng(CivilEngineering))--University of South Australia, 2001.

Thesis (M.S. - Watershed Management)--University of Arizona. / Includes bibliographical references.

Thesis (Ph. D. - Civil Engineering and Engineering Mechanics)--University of Arizona. / Includes bibliographical references.

Thesis (M.S. - Watershed Management)--University of Arizona. / Includes bibliographical references.

Thesis (M. S. - Renewable Natural Resources)--University of Arizona, 1998. / Known page missing: Pg. #135 Includes bibliographical references (leaves 211-213).

Thesis (Ph. D.) -- University of Maryland, College Park, 2007. / Thesis research directed by: Civil Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

Thesis (M.S.)--Ohio University, August, 2004. / Title from PDF t.p. Includes bibliographical references (leaves 116-119).

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 191-201). Also available via the Internet.

Thesis (M.S.)--Ohio University, November, 1997. / Title from PDF t.p.