Flood Risk and Vulnerability Analysis Project - Atlantic Climate ...

Development of Projected Intensity-Duration-Frequency Curves
for Corner Brook and Goulds/Petty Harbour, Newfoundland May 16, 2012
considerable uncertainty regarding those projections, and this uncertainty should always
be considered when making long-term decisions about investment, policy or
infrastructure.
4.2 Selected Approach
Based on a review of available methods, and considering the objectives and constraints
of the current work, an approach employing extreme value statistics was judged to be
the best approach for estimating the sensitivity of short-term precipitation to projected
climate. This approach, described in Towler, et al., (2010) (published after compilation
of the Technical Guide), involves fitting linear models of the parameters of an extreme
value distribution of a short-term variable to predictor variables, usually long term
variables, referred to as ―covariates‖. We refer to this statistical model as the ―intensity
model‖. The method, which is sometimes referred to as generalized linear models
(GLM) (Furrer and Kaatz, 2007), allows modeling of variables that do not have a normal
distribution as well as discrete variables, such as precipitation occurrence.
In the approach described below the climate predictor variables used to force the GLM
model of climate sensitivity are developed using a delta approach and the final adjusted
values of precipitation intensity are in turn calculated by a second application of the
delta approach to the output of the GLM model. The delta approach and its application
are described below.
4.3 Data Diagnostics
A diagnostic analysis was conducted to determine the strength of available long-term
climate variables for predicting short-term precipitation. The available projected climate
variables were monthly precipitation depth and monthly average temperature. These
variables could also be calculated from the local historical climate record. Three
variables, monthly total precipitation, monthly average temperature and the product of
those two variables were tested for their strength as a predictor of extreme precipitation.
The strength of a predictor was assessed using the coefficient of determination
estimated using linear regression. These analyses were made using the statistical
package ―R‖ (http://www.r-project.org/).
The results of this analysis revealed that statistically significant predictors could not be
found for annual precipitation extremes. However, significant predictors were found for
many durations for many months. Table 4-1 and Table 4-2 show the occurrence of
extreme precipitation at Deer Lake and St. John’s A respectively: shaded cells indicate
a month and duration for which snow was a possibility and, due to the lack of accurate
snowfall recording at the stations, extreme precipitation data could not be verified. On
this basis we elected to model the occurrence of monthly extremes between May and
October, the maximum of which would represent the annual extreme.
AMEC Environment & Infrastructure 19