University of Technology, Sydney. Faculty of Engineering. / Although extensive worldwide literature on urban stormwater runoff exists, very few publications describe runoff development in terms of its basic building blocks or processes and their individual and accumulative significance in response to varying inputs and boundary conditions. Process algorithms should respond accurately to varying input magnitudes and characteristics as well as to changes in antecedent conditions. The present state of estimation errors involved in many current numerical simulation techniques has been reviewed in this thesis. A significant amount of errors that are presently encountered for have been explained in terms of undefined process response not explicitly included within many modelling methodologies. Extensive field monitoring of intra-catchment rainfall and runoff within an urban catchment at Giralang in Canberra, which is typical of Australian urban catchments, was carried out over a 3-year period to define and measure individual runoff processes. This monitoring work led to a greater understanding of the processes driving the aggregation of local runoff from many sub-areas into the runoff observed at full catchment scale. The results from the monitoring process prompted a number of approaches to potentially reduce standard errors of estimate from model-attributable errors based on improvements to definable catchment response mechanisms. The research isolated a number of basic building blocks associated with typical residential allotments, that can be grouped into roof drainage, yard drainage and adjacent road drainage. A proposed modelling approach was developed that allowed these building blocks at an allotment scale to be simply computed using storage routing techniques. This then aggregated via the total catchment’s public drainage system isochronal characteristics utilising a “process tree” approach to provide full catchment scale runoff response. The potential reduction in estimation errors utilising the developed procedure was assessed using a large number of recorded events from the Giralang catchment monitoring data. The proposed numerical modelling approach was found to provide significant improvements over current methods and offered a scale-independent and stormindependent methodology to model catchments of any size without the need for changes to any of the runoff routing parameters. Additionally the approach permits the flexible sequencing and inclusion of a wide range of different urban drainage structures within a catchment that are representative of the local characteristics. The developed procedure also includes a spatially varied water balance approach to infiltration estimation that is more suited to future continuous simulation models. The developed “flexible process tree” approach provides an important step forward in the numerical modelling of complex urban drainage systems. This can reduce errors of estimate by improving intra-catchment process representation.

This thesis presents an approach for generating long synthetic sequences of single-site daily rainfall which can incorporate low-frequency features such as drought, while still accurately representing the day-to-day variations in rainfall. The approach is implemented in a two-stage process. The first stage is to generate the entire sequence of rainfall occurrence (i.e. whether each day is dry or wet). The second stage is to generate the rainfall amount on all wet days in the sequence. The models used in both stages are nonparametric (they make minimal general assumptions rather than specific assumptions about the distributional and dependence characteristics of the variables involved), and ensure an appropriate representation of the seasonal variations in rainfall. A key aspect in formulation of the models is selection of the predictor variables used to represent the historical features of the rainfall record. Methods for selection of the predictors are presented here. The approach is applied to daily rainfall from Sydney and Melbourne. The models that are developed use daily-level, seasonal-level, annual-level, and multi-year predictors for rainfall occurrence, and daily-level and annual-level predictors for rainfall amount. The resulting generated sequences provide a better representation of the variability associated with droughts and sustained wet periods than was previously possible. These sequences will be useful in catchment water management studies as a tool for exploring the potential response of catchments to possible future rainfall.

Design flood estimation is often required in hydrologic practice. For catchments with sufficient streamflow data, design floods can be obtained using flood frequency analysis. For catchments with no or little streamflow data (ungauged catchments), design flood estimation is a difficult task. The currently recommended method in Australia for design flood estimation in ungauged catchments is known as the Probabilistic Rational Method. There are alternatives to this method such as Quantile Regression Technique or Index Flood Method. All these methods give the flood peak estimate but the full streamflow hydrograph is required for many applications. The currently recommended rainfall based flood estimation method in Australia that can estimate full streamflow hydrograph is known as the Design Event Approach. This considers the probabilistic nature of rainfall depth but ignores the probabilistic behavior of other flood producing variables such as rainfall temporal pattern and initial loss, and thus this is likely to produce probability bias in final flood estimates. Joint Probability Approach is a superior method of design flood estimation which considers the probabilistic nature of the input variables (such as rainfall temporal pattern and initial loss) in the rainfall-runoff modelling. Rahman et al. (2002) developed a simple Monte Carlo Simulation technique based on the principles of joint probability, which is applicable to gauged catchments. This thesis extends the Monte Carlo Simulation technique to ungauged catchments. The Joint Probability Approach/ Monte Carlo Simulation Technique requires identification of the distributions of the input variables to the rainfall-runoff model e.g. rainfall duration, rainfall intensity, rainfall temporal pattern, and initial loss. For gauged catchments, these probability distributions are identified from observed rainfall and/or streamflow data. For application of the Joint Probability Approach to ungauged catchments, the distributions of the input variables need to be regionalised. This thesis, in particular, investigates the regionalisation of the distribution of rainfall duration and intensity. In this thesis, it is hypothesised that the distribution of storm duration can be described by Exponential distribution. The developed new technique of design flood estimation can provide the full hydrograph rather than only peak value as with the Probabilistic Rational Method and Quantile Regression Technique. The developed new technique can further be improved by addition of new and improved regional estimation equations for the initial loss, continuing loss and storage delay parameter (k) as and when these are available. / (M. Eng.) (Hons)

Increased urbanization causes pervious greenfields to be converted to impervious areas increasing stormwater runoff. Most of the urban floods occur because existing drainage systems are unable to handle peak flows during rainfall events. During a storm event, flood runoff will carry contaminants to receiving waters such as rivers and creeks. Engineers and scientists have combined their knowledge to introduce innovative thinking to manage the quality of urban runoff and harvest stormwater for productive purposes. The introduction of pervious pavements addresses all the principles in Water Sensitive Urban Design. A pervious pavement is a load bearing pavement structure that is permeable to water. The pervious layer sits on the top of a reservoir storage layer. Pervious pavements reduce the flood peak as well as improve the quality of stormwater at source before it is transported to receiving waters or reused productively. To be accepted as a viable solution, understanding of the influence of design parameters on the infiltration rate (both from the bedding and the sub-base) as well as strength of the pavement requires to be established. The design of a particular pavement will need to be customized for different properties of sub layer materials present in different sites. In addition, the designs will have to meet local government stormwater discharge standards. The design of drainage systems underneath pervious pavements will need to be based on the permeability of the whole pervious system. The objectives of the research project are to: • Understand the factors influencing infiltration capacities and percolation rates through the pervious surface as well as the whole pavement structure including the bedding and the sub-base using a laboratory experimental setup. • Obtain relationships between rainfall intensity, infiltration rate and runoff quantity based on the sub-grade material using a computational model to assist the design of pervious pavements. A laboratory scale pavement was constructed to develop relationships between the surface runoff and the infiltration volume from a pervious pavement with an Eco-Pavement surface. 2 to 5mm crushed gravel and 5 to 20mm open graded gravel were chosen as the bedding and sub-base material. Initial tests such as dry and wet density, crushing values, hydraulic conductivity, California Bearing Ratio tests for aggregate material were conducted before designing and constructing the pavement model. A rainfall simulator with evenly spaced 24 sprays was set up above the pervious pavement surface. The thesis presents design aspects of the laboratory scale pavement and the tests carried out in designing the pavement and the experimental procedure. The Green and Ampt model parameters to calculate infiltration were obtained from the laboratory test results from aggregate properties. Runoff results obtained from rainfall simulator tests were compared with the Green and Ampt infiltration model results to demonstrate that the Green and Ampt parameters could be successfully calculated from aggregate properties. The final infiltration rate and the cumulative infiltration volume of water were independent of the rainfall intensity once the surface is saturated. The model parameters were shown to be insensitive to the final infiltration capacity and to the total amount of infiltrated water. The Green and Ampt infiltration parameters are the most important parameters in designing pervious pavements using the PCSWMMPP model. The PCSWMMPP model is a Canadian model built specially for designing pervious pavements. This is independent of the type of sub-grade (sand or clay) determining whether the water is diverted to the urban drainage system (clay sub-grade) or deep percolation into the groundwater system (sand sub-grade). The percolation parameter in Darcy's law is important only if the infiltrated water recharges the groundwater. However, this parameter is also insensitive to the final discharge through the subgrade to the groundwater. The study concludes by presenting the design characteristics influencing runoff from a pervious pavement depending on the rainfall intensity, pavement structure and sub-grade material and a step-by step actions to follow in the design.

Rainfall data are usually gathered at daily timescales due to the availability of daily rain-gauges throughout the world. However, rainfall data at fine timescale are required for certain hydrologic modellings such as crop simulation modelling, erosion modelling etc. Limited availability of such data leads to the option of daily rainfall disaggregation. This research investigates the use of a stochastic rainfall disaggregation model on a regional basis to disaggregate daily rainfall into any desired fine timescale in the State of Queensland, Australia. With the incorporation of seasonality into the variance relationship and capping of the fine timescale maximum intensities, the model was found to be a useful tool for disaggregating daily rainfall in the regions of Queensland. The degree of model complexity in terms of binary chain parameter calibration was also reduced by using only three parameters for Queensland. The resulting rainfall Intensity-Frequency-Duration (IFD) curves better predicted the intensities at fine timescale durations compared with the existing Australian Rainfall and Runoff (ARR) approach. The model has also been linked to the SILO Data Drill synthetic data to disaggregate daily rainfall at sites where limited or no fine timescale observed data are available. This research has analysed the fine timescale rainfall properties at various sites in Queensland and established sufficient confidence in using the model for Queensland.

The Madden-Julian Oscillation is a tropical atmospheric phenomenon detected as anomalies in zonal winds, convection and cloudiness. This perturbation has a definitive timescale of about thirty to sixty days, allowing its signal to be extracted from background data. The Madden-Julian Oscillation originates over the western Indian Ocean and generates a convective region which moves east along the equatorial region. This perturbation is thought to contribute to the timing and intensity of the eastern hemisphere monsoons, the El Niño/ Southern Oscillation and tropical storms and cyclones. The current understanding of the Madden-Julian Oscillation is that it restricts the bulk of its' influence to the tropics, however some evidence suggested that the impact is more extensive. Analysis of about 30 years of data showed significant modulation of rainfall by the equatorial passage of the MJO. The real-time multivariate Madden-Julian Oscillation Index was used to estimate the location and amplitude of the Madden-Julian Oscillation, and forms the basis of the basic rainfall prediction tool developed. The method developed here clearly linked the low latitude passage of the Madden-Julian Oscillation with suppressed and enhanced rainfall events in the Australasian region and beyond. A rudimentary forecasting capability at the intraseasonal time scale has been developed suitable for assisting Australian agricultural sector. A subsequent and independent analysis of global mean sea level pressure anomalies provided evidence of teleconnections between the Madden-Julian Oscillation and higher latitude atmospheric entities. These anomalies confirm the existence of teleconnections capable of producing the rainfall pattern outputs. The MJO is strongly influenced by the season. However the seasonally dependant analysis of rainfall with respect to the Madden Julian Oscillation conducted was inconclusive, suggesting aspects of the MJO influence still require clarification. Considering the importance of rainfall variability to the Australian agricultural sector the forecasting tool developed, although basic, is significant.

In first part of the study we discuss impact of dynamical core in simulation of tropical rainfall. Over years many new dynamical cores have been developed for atmospheric models to increase efficiency and reduce numerical errors. CAM3.1 gives an opportunity to study the impact of the dynamical core on simulations with its three dynamical cores namely Eulerian spectral(EUL) , Semilagrangian dynamics(SLD) and Finite volume(FV) coupled to a single parametrization package. A past study has compared dynamical cores of CAM3 in terms on tracer transport and has showed advantages using FV in terms of tracer transport. In this study we compare the dynamical cores in climate simulations and at their optimal configuration, which is the intended use of the model. The model is forced with AMIP type SST and rainfall over seasonal, interannual scales is compared. The significant differences in simulation of seasonal mean exist over tropics and over monsoon regions with observations and among dynamical cores. The differences among EUL and SLD, which use spectral transform methods are lesser compared that of with FV clearly indicating role of numerics in differences. There exist major errors in simulation of seasonal cycle in all dynamical cores and errors in simulation of seasonal means over many regions are associated with errors in simulation of seasonal cycle such as over south china sea. Seasonal cycle in FV is weaker compared to SLD and EUL. The dynamical cores exhibit different interannual variability of rainfall over Indian monsoon region, the period of maximum power corresponding to a dynamical core differs substantially with another. From this study there seems no superiority associated with FV dynamical core over all climate scales as seen in tracer transport. The next part of the study deals with impact of diurnal ocean atmosphere coupling in an AGCM,CAM3.1. Due to relatively low magnitude of diurnal cycle of SST and lack of SST observations over diurnal scales current atmospheric models are forced with SSTs of periods grater than a day. CAM 3.1 standalone model is forced with monthly SSTs but the interpolation is linear to every time step between any two months and this linear interpolation implies a linear diurnal and intraseasonal variation of SST which is not true in nature. To test the sensitivity of CAM3.1 to coupling of SST on diurnal scales, we prescribed over tropics(20S20N) a diurnal cycle of SST over daily mean interpolated SST of different magnitudes and phase comparable to observations. This idea of using a diurnal cycle of SST retaining seasonal mean SST in an atmospheric model is novel and provides an interesting frame work to test sensitivity of model to interpolations used in coupling of boundary conditions. Our analysis shows a high impact of using diurnal cycle of SST on simulation of mean rainfall over tropics. The impact in a case where diurnal cycle of SST is fixed and retained to daily mean SST implies that changes associated with a coupled model are to some extent due to change in representation of diurnal cycle of SST. A decrease of excess rainfall over western coast of Bay of Bengal and an increase of rainfall over northern bay of Bengal in such case is similar to the improvement due to coupling atmospheric model to a slab ocean model. This also implies that problems with current AMIP models in simulation of seasonal mean Indian monsoon rainfall could be due to erroneous representation of diurnal cycle of SST in models over this region where the diurnal cycle of SST is high in observations. The high spatial variability of the impact in various cases over tropics implies that a similar spatial variation of diurnal cycle could be important for accurate simulation of rainfall over tropics. Preliminary analysis shows that impact on rainfall was due to changes in moisture convergence. We also hypothesized that diurnal cycle of SST could trigger convection over regions such as northern Bay of Bengal and rainfall convergence feedback sustains it. The impact was also found on simulation of internal interannual variability of rainfall

There have been reports that rainfall in East Africa is changing or becoming more variable. This can have significant implications for conservation initiatives and the food security of this populace region that is heavily reliant on the rain fed agricultural system. The perceptions of farmers regarding rainfall along with 30 years of satellite data and 16 years of ground level observations were analyzed in order to characterize rainfall in and around Kibale National Park, a protected area in the Ugandan portion of the Albertine Rift. Two homogenous rainfall regions exist in the area, and the onset, cessation, and amount of rainfall during seasons is highly variable. The perceptions of farmers align with the analysis of rainfall data, indicating that the season beginning in March shows the highest degree of variability. Decreases in the amount of rainfall are found for both rainy seasons.

This study presents an unsteady-state hydraulic model analysis of hurricane storm surge and rainfall-runoff interactions in the Clear Creek Watershed, a basin draining into Galveston Bay and vulnerable to flooding from both intense local rainfalls and storm surge. Storm surge and rainfall-runoff have historically been modeled separately, and thus the linkage and interactions between the two during a hurricane are not completely understood. This study simulates the two processes simultaneously by using storm surge stage hydrographs as boundary conditions in the Hydrologic Engineering Center’s – River Analysis System (HEC-RAS) hydraulic model. Storm surge hydrographs for a severe hurricane were generated in the Advanced Circulation Model for Oceanic, Coastal, and Estuarine Waters (ADCIRC) model to predict the flooding that could be caused by a worst-case scenario. Using this scenario, zones have been identified to represent areas in the Clear Creek Watershed vulnerable to flooding from storm surge, rainfall, or both.

The role of remote sensing data for monitoring different parameters in the study of ecosystems has been increasing. Particularly the development of different indices has played a great role to study the properties of vegetation and vegetation dynamics in large countries. In addition to this, satellite rainfall estimate data has been used to study the pattern of precipitation in areas where station rain-gauge data is not available. The Normalized Difference Vegetation Index (NDVI) and rainfall estimates data from the National Oceanic and Atmospheric Administration (NOAA) satellites were used to investigate the spatio-tempotal pattern of precipitation and the response of vegetation to precipitation in Ethiopia from 1996 to 2008. The patterns were studied in different land cover classes using data from the Global Land Cover Network (GLCN). The spatial patternof NDVI and precipitation showed that vegetation responded directly to precipitation. The seasonal patterns showed that there was between 0 to 3 months lag between precipitationand vegetation. However it was not possible to draw conclusion regarding the annual trendsof precipitation and NDVI because of the nature of the NDVI data, which was produced using the 10 day maximum composite values.