Abstract:
Modelling of extreme rainfall events is the fundamental part of
flood hazard estimation. Establishing a probability distribution to represent
the precipitation depth at various durations has long been a topic of interest
in hydrology, meteorology and others. The objective of this paper is to fit a
probability model to describe the frequency variation of annual extreme
rainfall events in Colombo region in order to predict the probability of
occurrence and return periods. Annual extreme rainfall events for a period of
110 years (1900-2009) have been used for the analysis. Early study into the
distribution of daily rainfall has identified the Two Parameter Gamma, Log
Normal, Two Parameter Log Normal, Three Parameter Inverse Gaussian,
Generalized Extreme Value, Gumbel Max, Log Pearson Type III and Pearson
Type V distributions as the most likely candidate distributions. As such,
these eight probability distribution models were considered in this study.
Model parameters were estimated using by the maximum likelihood method.
The comparative assessment of the explanatory ability of each model was
based on the graph of cumulative distribution function combined with the
empirical distribution function, Kolmogorov-Smirnov test and Q-Q Plot. On
the basis of these comparisons, it is concluded that the Log Pearson Type III
distribution is the most appropriate distribution for describing the annual
maximum daily rainfall events in Colombo. The fitted model has been
efficiently used to estimate the probability of occurrence and return periods
for various return levels. The model reveals that for the 200mm or more of
annual maximum daily rainfall return period is seven years and 4 months
with 95% confidence interval (6.27, 8.91). In similar manner, the paper
concentrated on developing models for extreme rainfall events during the
four seasons of a year. Relevant estimates of probability of occurrence return
periods and its corresponding confidence intervals for extreme rainfalls are
reported against return levels.
