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The main focus of this study is the update of rainfall IDF curves for the City of London under the conditions of changed climate. Predicted future climate change impacts for Southwestern Ontario include higher temperatures and increases in precipitation, leading to an intensification of the hydrologic cycle. One of the expected consequences of change is an increase in the magnitude and frequency of extreme events (e.g. high intensity rainfall, flash flooding, severe droughts, etc.). Changes in extreme events are of particular importance for the design, operation and maintenance of municipal water management infrastructure. Management of municipal water infrastructure (sewers, storm water management ponds or detention basins, street curbs and gutters, catchbasins, swales, etc) is based on the use of local rainfall Intensity Duration Frequency (IDF) curves developed using historical rainfall time series data. Annual extreme rainfall is fitted to a theoretical probability distribution from which rainfall intensities, corresponding to particular durations, are obtained. In the use of this procedure an assumption is made that historic hydro meteorological conditions can be used to characterize the future (i.e., the historic record is assumed to be stationary). This assumption is not valid under changing climatic conditions. Potential shifts in extreme rainfall at the local level demand revisions of the existing water infrastructure management regulations as well as changes in design practices.
The objective of this report is to assess the change in IDF curves for use by the City of London under changing climatic conditions. This assessment is completed using (a) only data collected at the London Airport (b) for the period 1961 - 2002. This is all the information that is available from the Environment Canada (EC).
An original methodology is developed in this study to update the rainfall intensity duration frequency (IDF) curves under changing climatic conditions. A non-parametric K-Nearest Neighbour weather generator algorithm operating on a daily time step is used to synthetically create long time series of weather data. The weather generator algorithm is developed to employ data collected by the Environment Canada for use in IDF analysis, including eight for-the-day-maximums of 5, 10, 15, 30 minutes, 1, 2, 6 and 12 hour, along with daily rainfall time series. The weather generator uses (a) a sophisticated shuffling mechanisms to produce synthetic data similar to the observed record; and (b) a perturbation mechanism that pushes the simulated data outside of their historic bounds, thereby generating sequences of extreme rainfall that are likely, but not yet been observed.
Two climate scenarios are used in the analysis: (i) historic climate change scenario (that reshuffles and perturbs the observed data), and (ii) wet scenario (that modifies the observed record according to Global Circulation Model simulation outputs and then uses this data as the weather generator input). Results of the study include tabular and graphical presentation of updated IDF curves for the London Airport. Results are generated for return periods of 2, 5, 10, 25, 50, 100 and 250 years.
The study presents the results of three simulations that differ in the historic input data. The first simulation analysis is based on the original London Airport data set for the period 1961 – 2001 obtained from the EC (eight for-the-day-maximums of 5, 10, 15, 30 minutes, 1, 2, 6 and 12 hour, and daily rainfall time series). Due to limitations of the original data set in correctly representing daily rainfall, the second simulation analysis is based on the combination of the original for-the-day-maximums for the period 1961 – 2002 (eight for-the-day-maximums of 5, 10, 15, 30 minutes, 1, 2, 6 and 12 hour) with hourly data collected at London Airport. Since the hourly data set also had some deficiencies, the third simulation analysis is performed that used the same combination of input data as the second analysis with modifications added to the last three years of observations. It is recommended that the modified data set be used for drawing conclusions of the study.
The simulation results indicate that rainfall magnitude will increase under climate change for all durations and return periods. The outputs of the study indicate that: (i) the rainfall magnitude will be different in the future, (ii) the wet climate scenario reveals significant increase in rainfall intensity for a range of durations and return periods, and (iii) the increase in rainfall intensity and magnitude may have major implications on ways in which current (and future) municipal water management infrastructure is designed, operated, and maintained. Our recommendation is that the current IDF curves should be revised to reflect the potential impact of climate change.
Results of comparison between the updated IDF curves for modified data set indicate small difference between the historic and wet climate change scenarios. This difference ranges between 0.1% and 12.2% with average value of approximately 4.5%. Therefore the recommendation is to proceed with potential revisions of the standards using the historic climate change scenario.
Comparison between the updated IDF curves for modified data set (historic climate change scenario) and the EC IDF curves shows a difference that ranges between 10.7 % and 34.9% with average value of approximately 21%. Based on this comparison our recommendation to the City of London is to proceed with change of IDF curves in the range of 20%. Detailed economic analyses should be performed to justify the necessary investment that this change will require.
Department of Civil and Environmental Engineering, The University of Western Ontario
London, Ontario, Canada
Intensity-Duration-Frequency, IDF, Curves, Climate change impact modelling, Weather generation algorithm, Synthetic generation of rainfall
Civil and Environmental Engineering
Simonovic, Slobodan P. and Peck, Angela, "Updated Rainfall Intensity Duration Frequency Curves for the City of London under the Changing Climate" (2009). Water Resources Research Report. 29.