published_or_final_version / abstract / toc / China Area Studies / Master / Master of Arts

China has a long history of hydrological development. According to Chinese legends, famous projects of flood water diversion were developed by the Great Yu as early as the year two thousand B.C. The earliest hydrological record appeared in 256 B.C., when Mr. Lipin and his son constructed the Dujiangyan irrigation system in the upper reach of the Mingjiang River in Sichuan Province. At Baopingkao, the water intake point of the Dujiangyan irrigation system, a water staff gage was carved on a stone for the measurement of water levels. Although hydrological studies in China started early, hydrology and water resources as modern sciences have been developed only in the last several decades, particularly rapidly in the last 30 years. For instance, the number of hydrological stations has increased 45 times, from about 350 to more than 16,000. Of these, about 3300 stations also take flow velocity measurements. The average density of the hydrological stations is about one per 530 km2 and that of discharge measurement stations about one in 3,000 km2. These stations are highly concentrated in eastern China. The longest records of precipitation are maintained in the large cities in eastern China, including Beijing, Shanghai and Tianjing. Beijing has 140 years of precipitation records. The Hankao hydrological station on the Changjiang (Yangtze) River has the longest discharge record spanning 117 years (1865- 1982).

published_or_final_version / Architecture / Master / Master of Landscape Architecture

published_or_final_version / abstract / toc / China Area Studies / Master / Master of Arts

This paper analyzes the management problem of the conjunctive use of surface and ground water in an irrigation system on both sides of the Lower Yellow River. For this purpose, a stochastic dynamic programming model is developed. In the model, the statistical characteristics of seasonal rainfall within 2 years are considered; groundwater level control is also emphasized in order to prevent soil salinity and waterlogging. Through computer calculations, optimal operation policies are obtained for efficient conjunctive use of surface and groundwater. These policies take into account the interactions between pumping groundwater by farmers, canal diversions by irrigation system managers, and the physical response of the stream- aquifer system, and minimize the total operation costs. In this paper, we take an irrigation district, the People's Victory Canal System, as an example to illustrate the development and solution of the model. At the same time, the effects of system parameters, including surface irrigation efficiency and rainfall recharge coefficient, on the optimal policies or total operation costs, are discussed. The analytical results in this example indicate that the variation in optimal operation costs caused by the proportion of rainfall infiltrated is small, but the effect of surface irrigation efficiency on the costs is significant. Hence, the surface irrigation efficiency must be increased as much as possible. Then, efficient conjunctive use of surface and groundwater can be attained with the optimal policies.

Field measurements were combined with synthetic aperture radar (SAR) images to evaluate the use of C-band multi-polarized radar remote sensing for estimating plant parameters (plant height, fresh biomass, dry biomass and vegetation water content) of wetland vegetation, and mapping the dynamics of water bodies, wetlands (natural wetlands and rice paddies) and flooding extents in the Poyang Lake region. The capacity of L-band SAR in land cover mapping was also investigated by integrating with optical imagery. / Hydrological patterns in Poyang Lake are the dominant factor controlling the spatial and temporal variations of wetland species in Poyang Lake. Water levels in this region are primarily governed by five rivers (Ganjiang river, Xiushui river, Raohe river, Fuhe river, and Xinjiang river). Its northern region is also influenced by the backflow from Yangtze River. The above-ground total biomass increased steadily from March following the hydrological cycle. Wetland species colonizing at different altitudes were gradually flooded from late spring to summer. Carex spp. died during flooding periods and started another growth cycle in autumn after flooding receded. Canopy volume dominates the radar backscattering mechanism in Carex spp. wetlands during their growth period, but the temporal variation of radar backscatter from these wetlands is mainly influenced by flooding. Tall wetland species (Miscanthus sacchariflorus, Phragmites communis Trin., and others) still emerged above water surfaces during flooding peaks and started to senesce in autumn. Surface backscattering mechanism is dominant during the early growing stage and the senescent period of tall vegetation. Plant canopy variation controlled the temporal dynamics of radar backscatters from Phragmites communis Min. Radar backscattering mechanisms from Miscanthus sacchariflorus wetlands were more complicated during the flooding periods. The variations of ground water depth and plant structure of Miscanthus sacchariflorus during its growth period result in over 10 dB spatial and temporal variation in ASAR backscatter in HH- and HV-polarization. / Temporal profiles of C-band multi-polarized backscatter coefficients for individual land cover types over the period of December 2004 to November 2005 were studied and described in the context of the ecology and seasonal dynamics of biophysical parameters of individual land cover types. A knowledge-based hierarchical land cover mapping method was developed to quantify the dynamics of paddy rice, natural wetlands and floods using the time series of HH- and HV-backscatters. The specific phenological and ecological characteristics of wetlands including paddy rice are the most important data in mapping their spatial and temporal patterns. The classification accuracy is over 90% for water bodies, rice paddies and Carex spp. wetlands, but it is not high for tall wetlands (68%). A decision tree approach was adopted to evaluate the capacity of L-band SAR in land cover mapping by combining with optical imagery. Classification errors were mainly induced by the mixed spectrum between and covers, and lack of independent training data and validation data also caused uncertainty in the results. / The relationship of canopy height with ASAR backscattering coefficient is the most significant among the influencing factors (plant height, fresh biomass, dry biomass, vegetation water content) on radar backscattering mechanism (R2=0.9 for HH-polarization and R2=0.59 for HV-polarization) from Phragmites cummunis Trin. HH- and HV-backscatters are more sensitive to the variation of dry biomass (R2=0.76 for HH and R2=0.56 for HV) than to that of fresh biomass (R 2=0.07 for HV and R2=0.42 for HH). Plant water content plays a negative role and attenuates the backscattering signals in both polarizations. For Phragmites communis Trin. with tall stalks (over 2m) and long, blade-like leaves, HH-polarization is more sensitive to vegetation parameters than HV-polarization for C-band SAR signals. Similar to Phragmites communis Trin., ASAR backscattering coefficient in both polarizations is more sensitive to plant height and dry biomass of non-flooded Miscanthus sacchariflorus, and their regression coefficients (R2) are over 0.5 for HH-polarization and over 0.4 for HV-polarization. Plant water content has no evident effect on the variation of ASAR backscatter. HV-polarization is more sensitive to the variation of above-water canopy parameters than HH-polarization for flooded Miscanthus saccharifiorus. HH- and HV-polarized radar backscatters from Carex spp. wetlands increased significantly with the variation of plant height, fresh biomass and dry biomass, but they reach saturated when vegetation grows up to 30cm. Compared with those tall grass with stalks and long blade-like leaves, the correlation of fresh biomass with HV-polarization is more pronounced (R 2=0.78) than that with HH-polarization (R2=0.41) for Carex spp. Vegetation structure play a more important role in radar backscattering mechanism than plant water content for these three wetland species. / Sang, Huiyong. / "April 2008." / Adviser: Hui Lin. / Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1443. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 149-159). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Wong, Jefferson See. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 143-151). / Abstracts in English and Chinese. / LIST OF FIGURES --- p.i / LIST OF TABLES --- p.iv / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Research Background --- p.1 / Chapter 1.3 --- Study Area --- p.8 / Chapter 1.3.1 --- Geographical setting --- p.8 / Chapter 1.3.2 --- Climate --- p.9 / Chapter 1.3.3 --- Water demand and supply in Hong Kong --- p.10 / Chapter 1.4 --- Objectives of the Study --- p.16 / Chapter 1.5 --- Significance of the Study --- p.16 / Chapter 1.6 --- Outline of Thesis --- p.17 / Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.18 / Chapter 2.1 --- Introduction --- p.18 / Chapter 2.2 --- Concept of Urban Water Consumption / Water Use --- p.19 / Chapter 2.3 --- Urban Water Consumption Patterns --- p.19 / Chapter 2.4 --- Factors Influencing Urban Water Consumption --- p.22 / Chapter 2.5 --- Model Formulation of Urban Water Consumption --- p.29 / Chapter 2.6 --- Methods of Forecasting Urban Water Consumption --- p.37 / Chapter 2.7 --- Conclusion --- p.42 / Chapter CHAPTER THREE --- DATA AND METHODOLOGY --- p.44 / Chapter 3.1 --- Introduction --- p.44 / Chapter 3.2 --- Water Consumption and Climatic Data --- p.44 / Chapter 3.3 --- Modeling Framework and Procedure --- p.49 / Chapter 3.4 --- Base Water Use --- p.52 / Chapter 3.4.1 --- Long-term trend --- p.52 / Chapter 3.5 --- Seasonal Water Use --- p.53 / Chapter 3.5.1 --- Seasonal cycle --- p.54 / Chapter 3.5.2 --- Climatic effect --- p.58 / Chapter 3.6 --- Calendrical Water Use --- p.61 / Chapter 3.6.1 --- Day-of-the-week effect --- p.62 / Chapter 3.6.2 --- Holiday effect --- p.63 / Chapter 3.6.3 --- Persistence component --- p.64 / Chapter 3.7 --- Summary --- p.65 / Chapter CHAPTER FOUR --- RESULTS AND DISCUSSION --- p.67 / Chapter 4.1 --- Introduction --- p.67 / Chapter 4.2 --- Model Fitting and Parameterization --- p.68 / Chapter 4.3 --- Long-term Trend in Base Water Use --- p.69 / Chapter 4.4 --- Seasonal Water Use --- p.76 / Chapter 4.4.1 --- Seasonal cycle --- p.76 / Chapter 4.4.2 --- Climatic effect --- p.81 / Chapter 4.5 --- Calendrical Water Use --- p.86 / Chapter 4.5.1 --- Day-of-the-week effect --- p.86 / Chapter 4.5.2 --- Holiday effect --- p.90 / Chapter 4.5.3 --- Persistence component --- p.98 / Chapter 4.6 --- Evaluation of Model Performance --- p.112 / Chapter 4.7 --- Relative Contribution of Various Components of Water Consumption --- p.128 / Chapter CHAPTER FIVE --- CONCLUSION --- p.136 / Chapter 5.1 --- Introduction --- p.136 / Chapter 5.2 --- Summary of Findings --- p.137 / Chapter 5.3 --- Limitations of the Study --- p.141 / Chapter 5.4 --- Recommendations for Future Studies --- p.142 / REFERENCE LIST --- p.143