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by Sin Tak Wah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1981. / Bibliography: leaves 208-215.

Wong Chun Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves [132-143]). / Abstracts in English and Chinese. / List of Tables --- p.i / List of Figures --- p.iv / List of Symbols --- p.v / Chapter CHAPTER ONE: --- INTRODUCTION --- p.1 / Chapter 1.1 --- Objectives and Significance of the Study --- p.4 / Chapter 1.2 --- Physical Setting of Hong Kong --- p.5 / Chapter 1.3 --- Climate of Hong Kong --- p.9 / Chapter 1.4 --- Data Acquisition --- p.11 / Chapter 1.4.1 --- Raingauges in Hong Kong --- p.11 / Chapter 1.4.2. --- Database for the Spatial Variation Analyses --- p.14 / Chapter 1.4.2.1. --- Data Selection for the Analyses for Factors Affecting Rainfall ´ؤ Elevation and Aspect --- p.15 / Chapter 1.4.2.2. --- Data Selection for the Classification of Stations and Inter-station Correlation Analysis --- p.17 / Chapter 1.4.3 --- Database for the Temporal Variation Analyses --- p.20 / Chapter CHAPTER TWO : --- LITERATURE REVIEW --- p.22 / Chapter 2.1 --- Spatial Variation of Rainfall --- p.22 / Chapter 2.2 --- Detection of Temporal Changes in Rainfall --- p.28 / Chapter 2.3 --- Urban Influence on Rainfall --- p.29 / Chapter 2.4 --- Studies in Hong Kong --- p.33 / Chapter CHAPTER THREE : --- METHODOLOGY --- p.33 / Chapter 3.1 --- Preliminary Processing of the Data --- p.38 / Chapter 3.2 --- Data Analysis --- p.40 / Chapter 3.2.1 --- General Pattern of Rainfall Distribution --- p.40 / Chapter 3.2.2 --- Data Analyses of Spatial Variation --- p.41 / Chapter 3.2.2.1 --- Correlation between Rainfall and Elevation --- p.41 / Chapter 3.2.2.2 --- Correlation between Rainfall and Aspect --- p.42 / Chapter 3.2.2.3 --- Classification of Stations --- p.43 / Chapter 3.2.2.4 --- Inter-Station Correlation Analysis --- p.46 / Chapter 3.2.3 --- Data Analysis of Temporal Variation --- p.46 / Chapter 3.2.3.1 --- The Running Mean Method --- p.47 / Chapter 3.2.3.2 --- The 'Standard Error of the Difference' Test --- p.49 / Chapter CHAPTER FOUR: --- RESULTS AND DISCUSSION --- p.50 / Chapter 4.1 --- Graphical Representation of Spatial Rainfall Pattern --- p.50 / Chapter 4.1.1 --- Annual Rainfall Pattern --- p.50 / Chapter 4.1.2 --- Monthly Rainfall Pattern --- p.56 / Chapter 4.1.3 --- Frequency Distribution of Raindays --- p.59 / Chapter 4.1.4 --- Pentade Rainfall Pattern --- p.64 / Chapter 4.1.5 --- Diurnal Rainfall Pattern --- p.67 / Chapter 4.1.6 --- Implications of the Spatial Rainfall Pattern --- p.70 / Chapter 4.2 --- Analyses of Spatial Variation in Rainfall --- p.78 / Chapter 4.2.1 --- Relationship between Rainfall and Elevation --- p.78 / Chapter 4.2.2 --- Relationship between Rainfall and Aspect --- p.82 / Chapter 4.2.3 --- Classification of Stations --- p.85 / Chapter 4.2.3.1 --- Principal Components Interpretation --- p.87 / Chapter 4.2.3.2 --- Result of Classification --- p.90 / Chapter 4.2.4 --- Inter-Station Correlation Analysis --- p.98 / Chapter 4.2.5 --- Discussion of the Rainfall Spatial Variation --- p.103 / Chapter 4.3 --- Analyses of Temporal Variation in Rainfall --- p.107 / Chapter 4.3.1 --- Annual Rainfall --- p.107 / Chapter 4.3.2 --- Monthly Rainfall --- p.110 / Chapter 4.3.3 --- Pentade Rainfall --- p.112 / Chapter 4.3.4 --- Diurnal Rainfall --- p.117 / Chapter 4.3.5 --- Discussion of the Rainfall Temporal Variation --- p.118 / Chapter CHAPTER FIVE: --- CONCLUSIONS AND RECOMMENDATIONS --- p.126 / Chapter 5.1 --- Summary of Findings --- p.126 / Chapter 5.2 --- Limitation of this Research --- p.129 / Chapter 5.3 --- Prospects of this Research --- p.130 / Bibliography

Har, Tsoen Hei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 53-58). / Abstract also in Chinese. / List of Figures --- p.i / List of Tables --- p.iii / List of Acronyms --- p.iv / Chapter Chapter 1: --- Introduction --- p.1 / Chapter Chapter 2: --- Background / Chapter 2.1 --- Tropical Rainfall --- p.4 / Chapter 2.2 --- Aerosol-rainfall interactions --- p.7 / Chapter Chapter 3: --- Land-sea difference in tropical rainfall / Chapter 3.1 --- Introduction --- p.11 / Chapter 3.2 --- Method / Chapter 3.2.1 --- Linear Trend Analysis --- p.12 / Chapter 3.2.2 --- Empirical Mode Decomposition --- p.13 / Chapter 3.3 --- Result --- p.16 / Chapter Chapter 4: --- Possible relation with aerosols / Chapter 4.1 --- Introduction --- p.28 / Chapter 4.2 --- Area division according to Aerosol Optical Depth (AOD) --- p.29 / Chapter 4.3 --- High aerosol areas / Chapter 4.3.1 --- Southeast China --- p.33 / Chapter 4.3.2 --- Northern India --- p.36 / Chapter 4.3.3 --- Sahara Desert --- p.38 / Chapter 4.4 --- Multifractal Detrended Fluctuation Analysis (MF-DFA) --- p.42 / Chapter Chapter 5: --- Conclusion --- p.50 / Bibliography --- p.53

Rainfall is a natural process, which has a high degree of variability in both space and time. Information on the spatial and temporal variability of rainfall plays an important role in the process of surface runoff generation. Hence it is important for a variety of applications in hydrology and water resources management. The spatial variability of rainfall can be substantial even for very small catchments and an important factor in the reliability of rainfall-runoff simulations. Catchments in urban areas usually are small, and the management problems often require the numerical simulation of catchment processes and hence the need to consider the spatial and temporal variability of rainfall. A need exists, therefore, to analyse the sensitivity of rainfall-runoff behaviour of catchment modelling systems (CMS) to imperfect knowledge of rainfall input, in order to judge whether or not they are reliable and robust, especially if they are to be used for operational purposes. Development of a methodology for identification of storm events according to the degree of heterogeneity in space and time and thence development of a detailed spatial and temporal rainfall model within a hydroinformatic environment utilising real-time data has been the focus of this project. The improvement in runoff prediction accuracy and hence the importance of the rainfall input model in runoff prediction is then demonstrated through the application of a CMS for differing variability of real storm events to catchments with differing orders of scale. The study identified both spatial and temporal semi-variograms, which were produced by plotting the semi-variance of gauge records in space and time against distance and time respectively. These semi-variograms were utilised in introducing estimators to measure the degree of heterogeneity of each individual storm events in their space and time scale. Also, the proposed estimators use ground based gauge records of the real storm events and do not rely on delicate meteorological interpretations. As the results of the investigation on the developed semi-variogram approach, real storm events were categorised as being High Spatial-High Temporal (HS-HT); High Spatial-Low Temporal; (HS-LT); Low Spatial-High Temporal (LS-HT); and Low Spatial-Low Temporal variability.A comparatively detailed rainfall distribution model in space and time was developed within the Geographical Information Systems (GIS). The enhanced rainfall representation in both space and time scale is made feasible in the study by the aid of the powerful spatial analytic capability of GIS. The basis of this rainfall model is an extension of the rainfall model developed by Luk and Ball (1998) through a temporal discretisation of the storm event. From this model, improved estimates of the spatially distributed with smaller time steps hyetographs suited for especially the urban catchments could be obtained. The importance of the detailed space-time rainfall model in improving the robustness of runoff prediction of CMS was investigated by comparing error parameters for predictions from CMS using alternate rainfall models, for various degrees of spatiotemporal heterogeneity events. Also it is appropriate to investigate whether the degree of this improvement to be dependent on the variability of the storm event which is assessed by the adopted semi-variogram approach. From the investigations made, it was found that the spline surface rainfall model, which considered the spatial and temporal variability of the rainfall in greater detail than the Thiessen rainfall model resulted in predicted hydrographs that more closely duplicated the recorded hydrograph for the same parameter set. The degree of this improvement in the predicted hydrograph was found to be dependent on the spatial and temporal variability of the storm event as measured by the proposed semi-variogram approach for assessing this feature of a storm event. The analysis is based on forty real events recorded from the Centennial Park Catchment (1.3km2) and the Upper Parramatta River Catchment (110km2) in Sydney, Australia. These two case study catchments were selected to ensure that catchment scale effects were incorporated in the conclusions developed during the study.