Thesis (Ph. D.)--University of Hong Kong, 1971. / List of published papers in meteorology by the author in pocket. Offset from typescript.

Thesis (M.S.-Renewable Natural Resources)--University of Arizona, 1980. / Includes bibliographical references.

Thesis (M. S. - Hydrology and Water Resources)--University of Arizona, 1998. / Includes bibliographical references (leaves 78-80).

Thesis (M.Soc.Sc.)--University of Hong Kong, 1989. / Also available in print.

Thesis (M.Soc.Sc.)--University of Hong Kong, 1989. / Also available in print.

Thesis (M. Phil.)--University of Hong Kong, 1984.

Thesis (Ph. D.)--Texas State University-San Marcos, 2007. / Vita. Appendices: leaves 194-207. Includes bibliographical references (leaves 208-209).

Thesis (Ph. D.)--Texas State University-San Marcos, 2007. / Vita. Appendices: leaves 194-207. Includes bibliographical references (leaves 208-209).

This dissertation describes spatio-temporal properties of rainfall. Rainfall in space was modeled by a precipitation areal reduction factor (ARF) using a NEXRAD image. The storms are represented as ellipses, which are determined by maximizing the volume of rainfall. The study investigated 18,531 storms of different durations that took place in different seasons and regions of Texas. Statistical analysis was carried out to find a relationship between ARFs and predictor variables (storm duration, area, season, region, and precipitation depth). The stochastic model for temporal disaggregation of rainfall data was evaluated across Texas. The hourly historic data from the selected 531 hourly gauges in Texas were used to evaluate the model’s performance to reproduce hourly rainfall statistics. Spatial trends in performance statistics or spatial patterns among gauge characteristics (e.g. period of record, precipitation statistics) were examined by cluster analysis. Since no spatial trends or patterns were identified, the state database is used and verified for a selection of gauges. The method was further applied to estimate intensity-duration curves for hydrologic applications. To obtain basic information on the spatial and dynamic patterns of rainfall over an area, it is necessary to identify and track a storm objectively. Automated algorithms are needed to process a large amount of radar images. A methodology was presented to overcome the identification and tracking difficulties of one-hour accumulated distributed rainfall data and to extract the characteristics of moving storms (e.g., size, intensity, orientation, propagation speed and direction, etc.). The method presented in this dissertation allows the user to better understand the precipitation patterns in any given area of the United States, and yields parameters that describe storm dynamic characteristics. These parameters can then be used in the definition of synthetic dynamic storms for hydrologic modeling.

Climatic fluctuation is putting Nigeria’s agriculture system under serious threat and stress. The study of the effect of climate change on agricultural productivity is critical given its impact in changing livelihood patterns in the country. Descriptive and co-integration analysis are the techniques used to analyze the Time series data used in this work. The finding demonstrates that the rate in agricultural productivity is persistently higher between 1981 and 1995, followed by a much lower growth rate in the 1996–2000 sub period. There was variation in the trend pattern of rainfall. Temperature was not relatively constant either. The Augmented Dickey-Fuller test for unit root revealed that agricultural productivity is not stationary and likewise the annual rainfall but became stationary after the differencing. Annual temperature on the other hand is stationary at its level. Temperature change was revealed to exert negative effect while rainfall change exerts positive effect on agricultural productivity. However previous year rainfall was negatively significant in affecting current year agricultural productivity. It is recommended that if agricultural productivity was to be increased and sustained, environmentally and agricultural sensitive technologies and innovations that can prevent climate fluctuation should be encouraged.