No description available.

A millimetric wave transmission link of the radar type has been established at the University of British Columbia campus. It operates at 74 GHz and has a 1.8 km total transmission length. The RF source is an unmodulated klystron and a phase-locked receiver is used to detect the received signal. The main objective is to measure the excess path attenuation due to precipitation. For this purpose a weather station has been constructed. It consists of 5 tipping-bucket rain gauges distributed along the path and apparatus for the measurement of temperature and wind velocity. The level of the microwave signal and the outputs of the weather sensors are transmitted to a central station. There, initial processing and data recording on magnetic tape are performed on a continuous basis by a Nova 840 minicomputer. Subsequently, data is further processed on an IBM 370. Of the 1400 hours of data recorded during a four month period, approximately 25 hours included simultaneous rain and microwave data and were sufficiently "reliable" to analyse. The processed experimental data are compared with the theory of Ryde and Ryde over the range of rain rates observed (0 to 10 mm/hour). / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

No description available.

Title page, abstract and table of contents only. The complete thesis in print form is available from the University of Adelaide Library. / This thesis describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique is demonstrated using a high-resolution point rainfall model based on wet-dry alternating storm events. A model has been produced that is well defined and can be vigorously compared against numerous observed statistics in a quantative manner. The rainfall model presented in this thesis can be used to provide accurate synthetic rainfall data at sites with minimal historical rainfall data providing a powerful tool for application in hydrological risk analysis across Australia. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1282259 / Thesis (Ph.D.) -- University of Adelaide, Dept. of Civil and Environmental Engineering, 2007

Title page, abstract and table of contents only. The complete thesis in print form is available from the University of Adelaide Library. / This thesis describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique is demonstrated using a high-resolution point rainfall model based on wet-dry alternating storm events. A model has been produced that is well defined and can be vigorously compared against numerous observed statistics in a quantative manner. The rainfall model presented in this thesis can be used to provide accurate synthetic rainfall data at sites with minimal historical rainfall data providing a powerful tool for application in hydrological risk analysis across Australia. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1282259 / Thesis (Ph.D.) -- University of Adelaide, Dept. of Civil and Environmental Engineering, 2007

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 90-93).

Thesis (Ph. D.)--University of Hong Kong, 1983.

Thesis (M.S.)--University of Missouri-Columbia, 2008. / 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 August 13, 2009) Includes bibliographical references.

The world is undergoing rapid changes and the future is uncertain. The changes are related to modification of the landscape due to human activities, such as large and small scale irrigation, afforestation and changes to the climate system. Understanding and predicting hydrologic change is one of the challenges facing hydrologists today. Part of this understanding can be developed from observed data, however, there often too few observations and those that are available are frequently affected by uncertainties. Hydrological models have become essential tools for understanding historical variations of catchment hydrology and for predicting future possible trends. However, most developing countries are faced with poor spatial distributions of rainfall and evaporation stations that provide the data used to force models, as well as stream flow gauging stations to provide the data for establishing models and for evaluating their success. Hydrological models are faced with a number of challenges which include poor input data (data quality and poorly quantified human activities on observed stream flow data), uncertainties associated with model complexity and structure, the methods used to quantify model parameters, together with the difficulties of understanding hydrological processes at the catchment or subbasin. Within hydrological modelling, there is currently a trend of dealing with equifinality through the evaluation of parameter identifiability and the quantification of uncertainty bands associated with the predictions of the model. Hydrological models should not only focus on reproducing the past behaviour of a basin, but also on evaluating the representativeness of the surface and subsurface model components and their ability to simulate reality for the correct reasons. Part of this modelling process therefore involves quantifying and including all the possible sources of uncertainty. Uncertainty analysis has become the standard approach to most hydrological modelling studies, but has yet to be effectively used in practical water resources assessment. This study applied a hydrological modelling approach for understanding the hydrology of a large Tanzanian drainage basin, the Great Ruaha River that has many areas that are ungauged and where the available data (climate, stream flow and existing water use) are subject to varying degrees of uncertainty. The Great Ruaha River (GRR) is an upstream tributary of the Rufiji River Basin within Tanzania and covers an area of 86 000 km2. The basin is drained by four main tributaries; the Upper Great Ruaha, the Kisigo, the Little Ruaha and the Lukosi. The majority of the runoff is generated from the Chunya escarpment, the Kipengere ranges and the Poroto Mountains. The runoff generated feeds the alluvial and seasonally flooded Usangu plains (including the Ihefu perennial swamp). The majority of the irrigation water use in the basin is located where headwater sub‐basins drain towards the Usangu plains. The overall objective was to establish uncertain but behavioural hydrological models that could be useful for future water resources assessments that are likely to include issues of land use change, changes in patterns of abstraction and water use, as well the possibility of change in future climates.

The recent high profile flooding events – that have occurred in many parts of the world – have drawn attention to the need for new and improved methods for water resources assessment, water management and the modelling of large-scale flooding events. In the case of the Zambezi Basin, a review of the 2000 and 2001 floods identified the need for tools to enable hydrologists to assess and predict daily stream flow and identify the areas that are likely to be affected by flooding. As a way to address the problem, a methodology was set up to derive catchment soil moisture statistics from Earth Observation (EO) data and to study the improvements brought about by an assimilation of this information into hydrological models for improving reservoir management in a data scarce environment. Rainfall data were obtained from the FEWSNet Web site and computed by the National Oceanic and Atmospheric Administration Climatic Prediction Center (NOAA/CPC). These datasets were processed and used to monitor rainfall variability and subsequently fed into a hydrological model to predict the daily flows for the Zambezi River Basin. The hydrological model used was the Geospatial Stream Flow Model (GeoSFM), developed by the United States Geological Survey (USGS). GeoSFM is a spatially semi-distributed physically-based hydrological model, parameterised using spatially distributed topographic data, soil characteristics and land cover data sets available globally from both Remote Sensing and in situ sources. The Satellite rainfall data were validated against data from twenty (20) rainfall gauges located on the Lower Zambezi. However, at several rain gauge stations (especially those with complex topography, which tended to experience high rainfall spatial variability), there was no direct correlation between the satellite estimates and the ground data as recorded in daily time steps. The model was calibrated for seven gauging stations. The calibrated model performed quite well at seven selected locations (R2=0.66 to 0.90, CE=0.51 to 0.88, RSR=0.35 to 0.69, PBIAS=−4.5 to 7.5). The observed data were obtained from the National Water Agencies of the riparian countries. After GeoSFM calibration, the model generated an integration of the flows into a reservoir and hydropower model to optimise the operation of Kariba and Cahora Bassa dams. The Kariba and Cahora Bassa dams were selected because this study considers these two dams as the major infrastructures for controlling and alleviating floods in the Zambezi River Basin. Other dams (such as the Kafue and Itezhi-Thezi) were recognised in terms of their importance but including them was beyond the scope of this study because of financial and time constraints. The licence of the reservoir model was limited to one year for the same reason. The reservoir model used was the MIKE BASIN, a professional engineering software package and quasi-steady-state mass balance modelling tool for integrated river basin and management, developed by the Denmark Hydraulic Institute (DHI) in 2003. The model was parameterised by the geometry of the reservoir basin (level, area, volume relationships) and by the discharge-level (Q-h) relationship of the dam spillways. The integrated modelling system simulated the daily flow variation for all Zambezi River sub-basins between 1998 and 2008 and validated between 2009 and 2011. The resulting streamflows have been expressed in terms of hydrograph comparisons between simulated and observed flow values at the four gauging stations located downstream of Cahora Bassa dam. The integrated model performed well, between observed and forecast streamflows, at four selected gauging stations (R2=0.53 to 0.90, CE=0.50 to 0.80, RSR=0.49 to 0.69, PBIAS=−2.10 to 4.8). From the results of integrated modelling, it was observed that both Kariba and Cahora Bassa are currently being operated based on the maximum rule curve and both remain focused on maximising hydropower production and ensuring dam safety rather than other potential influences by the Zambezi River (such as flood control downstream – where the communities are located – and environmental issues). In addition, the flood mapping analysis demonstrated that the Cahora Bassa dam plays an important part in flood mitigation downstream of the dams. In the absence of optimisation of flow releases from both the Kariba and Cahora Bassa dams, in additional to the contribution of any other tributaries located downstream of the dams, the impact of flooding can be severe. As such, this study has developed new approaches for flood monitoring downstream of the Zambezi Basin, through the application of an integrated modelling system. The modelling system consists of: predicting daily streamflow (using the calibrated GeoSFM), then feeding the predicted streamflow into MIKE BASIN (for checking the operating rules) and to optimise the releases. Therefore, before releases are made, the flood maps can be used as a decision-making tool to both assess the impact of each level of release downstream and to identify the communities likely to be affected by the flood – this ensures that the necessary warnings can be issued before flooding occurs. Finally an integrated flood management tool was proposed – to host the results produced by the integrated system – which would then be accessible for assessment by the different users. These results were expressed in terms of water level (m). Four discharge-level (Q-h) relationships were developed for converting the simulated flow into water level at four selected sites downstream of Cahora Bassa dam – namely: Cahora Bassa dam site, Tete (E-320), Caia (E-291) and Marromeu (E-285). However, the uncertainties in these predictions suggested that improved monitoring systems may be achieved if data access at appropriate scale and quality was improved.