The majority of the world's river basins remain ungauged and, therefore, the triedand- tested empirical techniques for predicting floods and droughts cannot be applied. An alternative approach, which is currently receiving a great deal of attention from research hydrologists, is to develop continuous simulation models whose parameters pertain to physical or hydrological properties of the river basins. However, difficulties related to scale, heterogeneity and complexity of real river basins have made a priori estimation of such parameters impossible: their estimation has always required calibration using river flow data. Therefore, estimating hydrological model parameters in ungauged river basins is one of the greatest challenges currently facing research hydrologists. In this thesis research advances towards this goal have been made at three different levels. First, at a conceptual level, a novel method for classifying river basins according to their physical properties is proposed. It is specifically designed for transferring hydrological model parameters from gauged river basins, where calibration is possible, to ungauged river basins. This approach relies on recognising that river basins can be similar in parts of their hydrological cycle but not in others. Thus, basins go through three independent classifications, one relative to each of the major components of the land phase hydrological cycle: interaction of soil water/vegetation and atmosphere; surface flow; and groundwater flow. This requires the ability to characterise the response of the components of the hydrological cycle independently, which leads to a second conceptual advance; rather than relying entirely on measured river flow data, from which it is difficult to separate out the effects of the three components, classification rules are devised on the basis of synthetic data produced by comprehensive, distributed, physically-based models. This thesis focuses on the surface flow component, applying the methodology to the identification of the best classifiers for surface flow through river networks. This required simulating river flow through a large number of Scottish river basins, which led to more practical research advances; all available commercial flow routing models were too cumbersome and required an impractical level of detail to be applied in such a large study. Therefore, a new flow routing modelling system was developed that extracts river network detail from digital databases and numerically solves a distributed flow routing model. Finally, on a detailed scientific level, significant insights have been made into the relationship between river network geomorphologic structure and stream flow response. In particular, it is shown that: a downstream hydraulic geometry relationship exists for Scottish rivers; although channel conveyance is a key factor in dictating network response, the features of the response hydro graph - namely the percentage attenuation of the flood peak and the lag in time to peak - scale linearly with both roughness and hydraulic geometry coefficients; much publicised invariant power law scaling rules for flood peaks in fact vary as a function of storm duration; statistical multivariate analysis of the simulated network flow responses demonstrated the low capacity of the network descriptors commonly used in regionalisation studies for characterising flow response. Four variables are shown to have significantly higher classifying power than the majority of the commonly used classifiers. Of these, two are entirely new to this thesis.

Flow mechanisms of compound meandering channels are recognised to be far more complicated than compound straight channels. The compound meandering channels are mainly characterised by the continuous variation of mean and turbulent flow parameters along a meander wavelength; the existence of horizontal shear layer at the bankfull level and the presence of strong helical secondary flow circulations in the streamwise direction. The secondary flow circulations are very important as they govern the advection of flow momentum, distort isovels, and influence bed shear stress, thus producing a complicated and fully three-dimensional turbulent flow structures. A great deal of experiments has been conducted in the past, which explains flow mechanisms, mixing patterns and the behaviour of secondary flow circulations. However, a complete understanding of secondary flow structures still remains far from conclusive mainly because the secondary flow structures are influenced by the host of geometrical and flow parameters, which are yet to be investigated in detail. The three-dimensional Reynolds-averaged Navier-Stokes and continuity equations were solved using a standard Computational Fluid Dynamics solver to predict mean velocity, secondary flow and turbulent kinetic energy. Five different flow cases of various model scales and relative depths were considered. Detailed analyses of the measured and predicted flow variables were carried out to understand mean flow mechanisms and turbulent secondary flow structures in compound meandering channels. The streamwise vorticity equation was used to quantify the complex and three-dimensional behaviour of secondary flow circulations in terms of their generation, development and decay along the half-meander wavelength. The turbulent kinetic energy equation was used to understand energy expense mechanisms of secondary flow circulations. The strengths of secondary flow circulations were calculated and compared for different flow cases considered. The main findings from this research are as follows. The shearing of the main channel flow as the floodplain flow plunges into and over the main channel influences the mean and turbulent flow structures particularly in the crossover region. The horizontal shear layer at the inner bankfull level generates secondary flow circulations. As the depth of flow increases, the point of generation of secondary flow circulations moves downstream. The secondary shear stress significantly contributes towards the generation of streamwise vorticity and the production of turbulent kinetic energy. The rate of turbulence kinetic energy production was found to be higher than the rate of its dissipation in the crossover region, which demonstrates that the turbulence extracts more energy from the mean flu\\' than what is actually dissipated. This also implies that, in the crossover region, the turbulence is always advected downstream by the mean and secondary flows, The strength of geometry induced secondary flow circulation increases with the increase in the relative depth.