Classification of river networks for prediction in ungauged basins

Reungoat, Anne Françoise Jeanne (2004) Classification of river networks for prediction in ungauged basins. PhD thesis, University of Glasgow.

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Printed Thesis Information: https://eleanor.lib.gla.ac.uk/record=b2244578

Abstract

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.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
Colleges/Schools: College of Science and Engineering > School of Engineering
Supervisor's Name: Supervisor, not known
Date of Award: 2004
Depositing User: Ms Anikó Szilágyi
Unique ID: glathesis:2004-6331
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 07 May 2015 10:45
Last Modified: 07 May 2015 10:49
URI: http://theses.gla.ac.uk/id/eprint/6331

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