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The thesis builds upon the hypothesis that the spatial arrangement of topographic features, such as buildings, roads and other land cover parcels, indicates how land is used. The aim is to make this kind of high-level semantic information explicit within topographic data. There is an increasing need to share and use data for a wider range of purposes, and to make data more definitive, intelligent and accessible. Unfortunately, we still encounter a gap between low-level data representations and high-level concepts that typify human qualitative spatial reasoning. The thesis adopts an ontological approach to bridge this gap and to derive functional information by using standard reasoning mechanisms offered by logic-based knowledge representation formalisms. It formulates a framework for the processes involved in interpreting land use information from topographic maps. Land use is a high-level abstract concept, but it is also an observable fact intimately tied to geography. By decomposing this relationship, the thesis correlates a one-to-one mapping between high-level conceptualisations established from human knowledge and real world entities represented in the data. Based on a middle-out approach, it develops a conceptual model that incrementally links different levels of detail, and thereby derives coarser, more meaningful descriptions from more detailed ones. The thesis verifies its proposed ideas by implementing an ontology describing the land use ‘residential area’ in the ontology editor Protégé. By asserting knowledge about high-level concepts such as types of dwellings, urban blocks and residential districts as well as individuals that link directly to topographic features stored in the database, the reasoner successfully infers instances of the defined classes. Despite current technological limitations, ontologies are a promising way forward in the manner we handle and integrate geographic data, especially with respect to how humans conceptualise geographic space.

This dissertation examines the interaction between a series of progressive gravity waves and an overlying airflow. A critical review of the existing literature is made, from which it is concluded that although a large number of wind-wave interactions have been proposed, few have been verified experimentally. Furthermore, virtually no consideration has been given to the effect of the airflow on the underlying wave motion. A comprehensive experimental investigation of the interaction between wind and waves has therefore been undertaken. In respect of the airflow, a complete set of kinematics measurements, involving both horizontal and vertical velocity data, is presented. The mean, wave-induced and fluctuating velocities are presented in a wave-following frame of reference. The flow streamlines are deduced from the measured velocity data, and the structure of the airflow examined in detail. Analysis of the velocity profiles using boundary layer theory enables the effects of the flow structure identified above to be quantified and an assessment of the surfece stresses made. A corresponding set of kinematics measurements were undertaken in the underlying water flow. The wind-induced currents and wave kinematics are examined. Numerical models to predict the wave motion in combined wind-wave field are developed. The models incorporate the effects of a wind-induced current and the varying surface stresses, and allow the relative importance of each of these effects to be investigated. Furthermore, measurements of the water surface elevation are used to investigate the modification to the underlying long waves caused by the airflow, and the nature of the superposed wind-waves. The variation in the properties of the wind-generated waves with the phase of the long waves are also examined. Finally, the fundamental mechanisms of wind-wave interaction are considered, and the relative importance of the various mechanisms assessed. The implications for the oflfehore engineer, in terms of wind loading, gas ventilation and wave loading are outlined.

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Understanding turbulent flow structure in open channel flows is an important issue for Civil Engineers who study the transport of water, sediments and contaminants in rivers. In the present study, turbulent flows over rough impermeable and porous beds are studied numerically using the Smoothed Particle Hydrodynamics (SPH) method. A comprehensive review is carried out on the methods of turbulence modelling and treatment of bed boundary in open channel flows in order to identify the limitations of the existing particle models developed in this area. 2D macroscopic SPH models are developed for simulating turbulent free surface flows over rough impermeable and porous beds under various flow conditions. For the case of impermeable beds, a drag force model is proposed to take the effect of bed roughness into account, while for the case of porous beds, macroscopic governing equations are developed based on the SPH formulation, incorporating the effects of drag and porosity. To simulate the effect of turbulence on the average flow field, a Macroscopic SPH-mixing-length (MSPH-ML) model is proposed based on the Large Eddy Simulation (LES) concept where the mixing-length approach is applied to estimate the eddy-viscosity rather than employing the standard Smagorinsky model. The difficulty in reproducing steady uniform free surface flow is tackled by introducing novel inflow/outflow techniques for the situations in which the flow quantities are unknown at the inflow and outflow boundaries. The performance of these models is tested by simulating different engineering problems with an insight developed into turbulence modelling and bed/interface boundary treatment. The accuracy of the models is tested by comparing the predicted quantities such as flow velocity, water surface elevation, and turbulent shear stress with existing experimental data. The limitations of the models are mainly attributed to the macroscopic representation of the roughness layer and porous bed, difficulty in the determination of the values of the empirical coefficients in the closure terms, and limitations with the use of fine computational resolution. On the other hand, the main strength of the model is describing the complicated processes occuring at the bed using simple and practical computational treatments so that the momentum transfer is estimated accurately. It is shown that if the closure terms in the momentum equation which represent the effect of bed drag and flow turbulence are determined carefully based on the physical conditions of bed and flow, the model is capable of being employed for different civil engineering applications.

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Urban surface sediment is a major source of pollution as it acts as a transport medium for many contaminants. Accurate modelling of sediment wash-off from urban surfaces requires an understanding of the effect of variability in the external drivers such as rainfall on the wash-off process. This study investigates the uncertainty created due to the urban-scale variability of rainfall, in sediment wash-off predictions. Firstly, a rigorous geostatistical method was developed that quantifies uncertainty due to spatial rainfall variability of rainfall at an urban scale. The new method was applied to a unique high-resolution rainfall dataset collected with multiple paired gauges for a study designed to quantify rainfall uncertainty. Secondly, the correlation between calibration parameters and external drivers - rainfall intensity, surface slope and initial load- was established for a widely used exponential wash-off model using data obtained from new detailed laboratory experiments. Based on this, a new wash-off model where the calibration parameters are replaced with functions of these external drivers was derived. Finally, this new wash-off model was used to investigate the propagation of rainfall uncertainty in wash-off predictions. This work produced for the first time quantitative predictions of the variation in wash-off load that can be linked to the rainfall variability observed at an urban scale. The results show that (1) the assumption of constant spatial rainfall variability across rainfall intensity ranges is invalid for small spatial and temporal scales, (2) wash-off load is sensitive to initial loads and using a constant initial load in wash-off modelling is not valid, (3) the level of uncertainty in predicted wash-off load due to rainfall uncertainty depends on the rainfall intensity range and the “first-flush” effect. The maximum uncertainty in the prediction of peak wash-off load due to rainfall uncertainty within an 8-ha catchment was found to be ~15%.

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