Date of Award

Spring 2012

Degree Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Civil Engineering

Supervisor

Yiping Guo

Co-Supervisor

Altaf Arain

Language

English

Committee Member

Brian Baetz

Abstract

Hourly archived rainfall records are separated into individual rainfall events with

an Inter-Event Time Denition. Individual storms are characterized by their depth,

duration, and peak intensity. Severe events are selected from among the events for

a given station. A lower limit, or threshold depth is used to make this selection,

and an upper duration limit is established. A small number of events per year are

left, which have relatively high depth and average intensity appropriate to small

to medium catchment responses. The Generalized Pareto Distributions are tted

to the storm depth data, and a bounded probability distribution is tted to storm

duration. Peak storm intensity is bounded by continuity imposed by storm depth

and duration. These physical limits are used to develop an index measure of peak

storm intensity, called intensity peak factor, bounded on (0; 1), and tted to the Beta

distribution. The joint probability relationship among storm variables is established,

combining increasing storm depth, increasing intensity peak factor, with decreasing

storm duration as being the best description of increasing rainstorm severity. The

joint probability of all three variables can be modelled with a bivariate copula of

the marginal distributions of duration and intensity peak factor, combined simply

with the marginal distribution of storm depth. The parameters of the marginal

distributions of storm variables, and the frequency of occurrence of threshold-excess

events are used to assess possible shifts in their values as a function of time and

temperature, in order to evaluate potential climate change eects for several stations.

Example applications of the joint probability of storm variables are provided that

illustrate the need to apply the methods developed.

The overall contributions of this research combine applications of existing probabilistic

tools, with unique characterizations of rainstorm variables. Relationships

between these variables are examined to produce a new description of storm severity,

and to begin the assessment of the eects of climate change upon severe rainstorm

events.

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