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

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No description available.

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

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.

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.

Thesis (M.S.)----University of North Carolina Wilmington, 2008. / Title from PDF title page (viewed May 26, 2009) Includes bibliographical references (p. 61)

The study presents the results of applying two isolated event, constant runoff proportion, conceptual models to a range of catchments drawn from various climatic and physiographic regions of South Africa and the USA. The models can be operated in either lumped or semi-distributed modes. The research progressed through the following stages. The initial stage involved the calibration of both models on two sets of catchments so that an initial evaluation of the performance of the models could be carried out and any deficiencies in the model structure identified, and where practical, corrected. The models were then calibrated on a further 8 catchments. An important result of the calibration is that for both models to produce reasonably acceptable simulations, at least one parameter has to vary between storms on the same catchment to account for variations in storm or antecedent moisture characteristics. The next stage consisted of compiling quantitative descriptions of the physical characteristics of the catchments and rainfall events and an attempt to relate the calibrated parameter values to relevant physical characteristics for the purpose of estimating parameter values when calibration is not possible. Despite the difficulties encountered in quantifying some of the hydrological characteristics the general trends exhibited by many of the relationships are encouraging and the format of the combinations of physical variables used, do make sense with respect to the original parameter conceptualisations. The relationships between storm characteristics and parameters of both models are less satisfactory. There is a high degree of scatter and the between-catchment variation in the form of the relationships, indicates that the derived relationships are likely to be of little use for parameter estimation purposes. The final stage involved a validation exercise in which new parameters were estimated from the physical variable-parameter relationships for all the catchments previously used, as well as a further four. The new parameters were used to re-simulate all the storms and comparison of these results were made with the original calibration results. Both models produced poor results and are unlikely to give reliable results where calibration is not possible. The parameter relationships for the parameters related to storm characteristics are so catchment specific that transfer to other areas will produce unpredictable results. Foot note:- For compatability with computer printouts decimal full stops are used in the format of real numbers in tables etc