Thesis (Ph. D.)--Florida State University, 2006. / Advisor: James B. Elsner, Florida State University, College of Social Sciences, Dept. of Geography. Title and description from dissertation home page (Sept. 19, 2006). Document formatted into pages; contains x, 90 pages. Includes bibliographical references.

Thesis (M.S.)--Rhodes University, 2004. / Title from PDF t.p. (viewed on Apr. 30, 2006). Includes bibliographical reference (p. 171-182).

Thesis (M.S.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on October 25, 2007) Vita. Includes bibliographical references.

Thesis (M.S.)--University of Nevada, Reno, 2006. / "December, 2006." Includes bibliographical references (leaves 104-108). Online version available on the World Wide Web.

The objective of the thesis is to make a critical assessment of the performance of three relatively simple deterministic models of the rainfall-runoff process. The need to evaluate and compare deterministic models arises because of the large number of models which are available in the literature. A number of the available models would appear to be equally suitable for a given situation whereas many models are found to be valid only under the specific range of conditions for which they were developed. Therefore there is a need for guidelines to allow the most judicious selection of a model for a particular set of circumstances. The models used in the study will be tested in a semi-arid catchment to determine their applicability under ephemeral flow conditions

This study gives a phenomenologically based stochastic model of space -time rainfall. Specifically, two random variables on the spatial rainfall, e.g. the cumulative rainfall within a season and the maximum cumulative rainfall per rainfall event within a season are considered. An approach is given to determine the cumulative distribution function (c.d.f.) of the cumulative rainfall per event, based on a particular random structure of space -time rainfall. Then the first two moments of the cumulative seasonal rainfall are derived based on a stochastic dependence between the cumulative rainfall per event and the number of rainfall events within a season. This stochastic dependence is important in the context of the spatial rainfall process. A theorem is then proved on the rate of convergence of the exact c.d.f. of the seasonal cumulative rainfall up to the ith year, i > 1, to its limiting c.d.f. Use of the limiting c.d.f. of the maximum cumulative rainfall per rainfall event up to the ith year within a season is given in the context of determination of the 'design rainfall'. Such information is useful in the design of hydraulic structures. Special mathematical applications of the general theory are developed from a combination of empirical and phenomenological based assumptions. A numerical application of this approach is demonstrated on the Atterbury watershed in the Southwestern United States.

Includes bibliography. / The objective of this thesis is to review existing methods for estimating missing values in rainfall records and to propose a number of new procedures. Two classes of methods are considered. The first is based on the theory of variable selection in regression. Here the emphasis is on finding efficient methods to identify the set of control stations which are likely to yield the best regression estimates of the missing values in the target station. The second class of methods is based on the EM algorithm, proposed by Dempster, Laird and Rubin (1977). The emphasis here is to estimate the missing values directly without first making a detailed selection of control stations. All "relevant" stations are included. This method has not previously been applied in the context of estimating missing rainfall values.

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