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http://hdl.handle.net/10023/2666
Title:  Applications of statistics in flood frequency analysis 
Authors:  Ahmad, Muhammad Idrees 
Supervisors:  Sinclair, C. D. 
Issue Date:  1989 
Abstract:  Estimation of the probability of occurrence of future flood events at one
or more locations across a river system is frequently required for the design of
bridges, culverts, spillways, dams and other engineering works. This study
investigates some of the statistical aspects for estimating the flood frequency
distribution at a single site and on regional basis.
It is demonstrated that generalized logistic (GL) distribution has many
properties well suited for the modelling of flood frequency data. The GL
distribution performs better than the other commonly recommended flood frequency
distributions in terms of several key properties. Specifically, it is capable of
reproducing almost the same degree of skewness typically present in observed
flood data. It appears to be more robust to the presence of extreme outliers in the
upper tail of the distribution. It has a relatively simpler mathematical form. Thus all
the well known methods of parameter estimation can be easily implemented.
It is shown that the method of probability weighted moments (PWM)
using the conventionally recommended plotting position substantially effects the
estimation of the shape parameter of the generalized extreme value (GEV)
distribution by relocating the annual maximum flood series. A location invariant
plotting position is introduced to use in estimating, by the method of PWM, the
parameters of the GEV and the GL distributions.
Tests based on empirical distribution function (EDF) statistics are
proposed to assess the goodness of fit of the flood frequency distributions. A
modified EDF test is derived that gives greater emphasis to the upper tail of a
distribution which is more important for flood frequency prediction. Significance
points are derived for the GEV and GL distributions when the parameters are to be
estimated from the sample data by the method of PWMs. The critical points are
considerably smaller than for the case where the parameters of a distribution are
assumed to be specified. Approximate formulae over the whole range of the
distribution for these tests are also developed which can be used for regional
assessment of GEV and GL models based on all the annual maximum series
simultaneously in a hydrological region.
In order to pool atsite flood data across a region into a single series for
regional analysis, the effect of standardization by atsite mean on the estimation of
the regional shape parameter of the GEV distribution is examined. Our simulation
study based on various synthetic regions reveals that the standardization by the atsite
mean underestimates the shape parameter of the GEV by about 30% of its true
value and also contributes to the separation of skewness of observed and simulated
floods. A two parameter standardization by the atsite estimates of location and
scale parameters is proposed. It does not distort the shape of the flood frequency
data in the pooling process. Therefore, it offers significantly improved estimate of
the shape parameter, allows pooling data with heterogeneous coefficients of
variation and helps to explain the separation of skewness effect.
Regions on the basis of flood statistics LCV and USKEW are derived
for Scotland and North England. Only about 50% of the basins could be correctly
identified as belonging to these regions by a set of seven catchment characteristics.
The alternative approach of grouping basins solely on the basis of physical
properties is preferable. Six physically homogeneous groups of basins are
identified by WARD's multivariate clustering algorithm using the same seven
characteristics. These regions have hydrological homogeneity in addition to their
physical homogeneity. Dimensionless regional flood frequency curves are produced
by fitting GEV and GL distributions for each region. The GEV regional growth
curves imply a larger return period for a given magnitude flood. When floods are
described by GL model the respective return periods are considerably smaller. 
URI:  http://hdl.handle.net/10023/2666 
Type:  Thesis 
Publisher:  University of St Andrews 
Appears in Collections:  Applied Mathematics Theses

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