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

In response to increased greenhouse gases and global temperatures, changes to the hydrologic cycle are projected to occur and new precipitation characteristics are expected to emerge. The study of these characteristics is facilitated by common indices to measure precipitation and temperature developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices can be used to describe the likely consequences of climate change such as increased daily precipitation intensity (SDII) and heavier rainfall events (R95p). This study calculates a subset of these indices from observed and modelled precipitation data in Portland, Oregon. Five rainfall gages from a high resolution rain gage network and projections from three downscaled global climate models including CanESM2, CESM1, CNRM-CM5 are used to calculate precipitation indices. Mann-Kendall's tau is used to detect monotonic trends in indices. The observational record is compared with models for the historic period (1977-2005) and these past trends are compared with projected future trends (2006-2100). The influence of study unit on trend detection is analyzed by computing trends at the annual and monthly scale. Study unit is shown to be important for trend detection. When the annual study unit is used, projected future trends towards increased precipitation intensity and event volumes are not observed in the historic data. However, when analyzed with a monthly study unit, trends towards increased precipitation intensity and event volumes are observed in the historic data. These trends are shown to be important for Portland area flooding, as precipitation indices are shown to significantly correlate with 40 maximum peak flow events that occurred during the period of study.

Diarrheal disease has long been known to be a burden to children worldwide. Although child mortality rates as a result of diarrhea have decreased over the past two decades, overall diarrhea incidence has remained relatively stable. In Cambodia, diarrhea was the third most prevalent outpatient diagnosis for children under five in 2012, with a reported incidence rate of 12 per 100 children, up from 7 per 100 children in 2008 (Ministry of Planning Cambodia, 2012). Cambodia is an agricultural country with 80% of the population living in rural areas (National Institute of Statistics Ministry of Planning, 2013). Many are also poor, with 20.5% of the population living at or below the poverty line (Sobrado et al., 2014). As a result, a large portion of the population is vulnerable to changing climate patterns and seasonal rainfalls. These patterns have been shown to have an effect on the incidence of diarrhea in regions throughout the world. This research evaluated the relationship between seasonality and the incidence of diarrheal disease in children under five years old in the Rumduol district, Svay Rieng province of Cambodia. Using monthly under-five diarrhea data from a local health center and meteorological data from the Ministry of Water Resources and Meteorology (MoWRAM), Spearman’s Correlation was used to find associations between monthly rainfall, heavy rainfall (defined by the 90th percentile), maximum average monthly temperature, and minimum average monthly temperature. Additionally, household surveys, interviews, and observations were used to understand how seasonal behavior, age/gender, household practices, and caregiver perception of the disease affect decisions surrounding diagnosis and treatment of childhood diarrhea throughout the year. Based on the results of the analysis, the number of heavy rainfall days had a weak negative association with monthly under-five diarrhea incidence in the initial month and following month, referred to as lag 0 and lag 1 (rs(96) = -.216, p = .035 and rs(95) = -.219, p = .033). Minimum average monthly temperature was also shown to have weak negative association with monthly under-five diarrhea incidence at lag 0 and lag 1 (rs(96) = -.208, p = .042 and rs(95) = -.212, p = .039). The negative correlation between heavy rainfall and under-five diarrhea indicates that heavy rain can have a washing effect on the environment at lag 0 and lag 1 months. Higher diarrhea incidence in the cooler months suggest a possible link to rotavirus, however more research must be done to make confirm this. Monthly rainfall was seen to have a positive association with diarrhea within the lag 3 and lag 4 months (rs(93) = .250, p = .015; rs(92) = .222, p = .034). This indicates that increased rainfall could have a delayed effect on diarrhea by three or four months. Maximum temperature did not have statistically significant results. These results show that heavy rainfall and minimum temperature likely play role in under-five child diarrhea in the study area at shorter lags times, while monthly rainfall has a greater effect at longer lag times. Results of survey and interview data showed participants had sufficient knowledge on personal and food hygiene practices but often practiced improper environmental hygiene behavior, especially as it related to the handling of child and animal feces. Additionally, there were statistically significant results when looking at the relationship between environmental hygiene knowledge and practice and households where mothers migrate for work. Households with migrant mothers was common within the study area, with 51.9% of households having a mother who was currently working away from home or had plans to return to work soon. More data is needed to further investigate this relationship; however, this result suggests that hygiene knowledge and practice could suffer in households where the mother is absent, as added burden is placed on grandmothers to do domestic and childcare work.

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.

published_or_final_version / Civil Engineering / Master / Master of Philosophy

published_or_final_version / Statistics / Master / Master of Social Sciences

published_or_final_version / Physics / Master / Master of Philosophy

No description available.

No description available.

Typescript. / Bibliography: leaves 64-69. / ix, 75 leaves ill., maps