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Thesis (Ph. D.)--Ohio State University, 1982. / Includes bibliographical references (leaves 121-124). Available online via OhioLINK's ETD Center

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Supervisor: John M. Sharp. Vita. Includes bibliographical references. Also available from UMI.

Thesis (M.S. - Hydrology and Water Resources)--University of Arizona, 1986. / Includes bibliographical references (leaves 102-104).

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.

An examination of research and information needs in urban hydrology suggested the investigation of urban water balances and micro- hydrological processes. This should facilitate more accurate modelling of the rainfall-runoff process from urban impermeable surfaces. Greater London data produced annual water balances for 5 heavily urbanized Thames tributaries and estimates of the annual yield ranging from 12-72%. Mean annual runoff for largely rural basins in South East England in comparison was 15-44% of rainfall. The inadequacy of the data for water balance studies led to the instrumentation of a small urbanized catchment at Redbourn, Hertfordshire. Standard meteorological measures were recorded. New instrumentation was designed to measure runoff from shallow pitched roofs while commercially produced instruments were adapted and installed to monitor runoff from a block of flat asphalt-and-chippings garage roofs, and runoff from asphalt roads and pavements at the highway drain outfall. Runoff from these impermeable surfaces is less than 100% even during winter months when evaporation is low. Percentage runoff is 76% for both the pitched and flat roofs while that from the paved surfaces is only 17%. Despite differences in slope, runoff volumes from the pitched and flat roofs are almost identical suggesting that the flat roof does not afford much greater depression storage and evaporation losses. The flat roof does however attenuate storm runoff producing lower flow rates and longer runoff duration than the pitched roofs. Road runoff is very low because of infiltration. The calculated depression storage is 0.25 mm for both roof types and 1.00 mm for the road surface. An average water balance compiled for the roofs gave evaporation as the residual 19% of rainfall. Using an average roof evaporation rate in the road surface water balance gave infiltration as 36% of rainfall with 17% runoff, 21% evaporation and 26% depression storage. Runoff from metre-square roof samples produced slightly different percentage runoff figures for the same winter period. Average percent runoff from red Redland 49 tiles (set at 30°) was 98%, grey Stonewold tiles (set at 17½°) produced 85% and asphalt roofing felt produced 38% runoff. These results are evaluated in the light of probable errors in measurements.

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.

Thesis (Ph. D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 127-140).

Thesis (Ph.D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 127-140) Also available in print.

Published Article / To assess and predict runoff and soil loss on different tillage methods coupled with alternative cropping systems in the central highland vertisols of Ethiopia, a study was carried out at Ginchi, Agricultural Research Sub-Center during 1996. The experiment was conducted on runoff plots of 4 meter wide by 22 meter long, on surface slopes that range between 0.1% and 2.3%. The data collected was analyzed using regression models and an empirical formula developed by the Soil Conservation Service of America (SCS, 1964; 1972), known as curve number (CN). Both the regression model and the SCS simulated the mean daily runoff reasonably well with R2 93% and 83%, respectively. The overall results obtained explain that the improved tillage practice, BBF could drain the excess surface water safely.