| dc.description.abstract | Although a number of nonparametric analysis
have appeared in the literature, traditional flood
frequency analysis has been approached primarily as
a problem in parametric statistical inference. Peak
annual streamflow data are assureed to come frore a
parent population whose distribution function is
known7 is analytically expressable and contains a
finite nurr.bre of parameters. A large number of peak
flow distributions have been studied, for example,
t.he normal, the lognormal) the Gumbel, the Carnrna and
Weibull distributions. Goodness of fit procedures
then test whether or not the data do indeed fit the
assumed distribution with a specified degree of
confidence. However, the use of the convetional
goodness of fit procedures in flood frequency analysis
has several disadvantages. Firstly, is the lack of
power of these goodness of fit tests with respect to
the typically skewed flood Peak distributions. This
generates considerable variability in the estimation
of design, events. Secondly, the conventional goodness
of fit tests are subjective in that the final results
drawn from such tests depend very much on the level of
confidence utilized. This me ans that different levels
of confidence can often lead to conflicting results.
Lastly, and probably the most serious disadvantage is | en |
