Generally urban drainage systems are built to protect urban property and control runoff. Moreover, these systems collect the runoff for storage purposes to serve society through sufficient water supply to meet the needs of demand, irrigation, and drainage. Urban environments are exposed to risks of extreme hydrological events. Therefore, urban water systems and their management are critical. Precipitation data are crucial, but may be prone to errors due to the lack of information e.g., short length of records. In this thesis, a Monte Carlo simulation and regional frequency analysis based on L-moments approach were utilized during the research in order to estimate the uncertainty in the Intensity Duration Frequency (IDF) curves by using historical precipitation data from Environment Canada (EC) weather stations and simulating a new series of data through a weather generator (WG) model. The simulations were then disaggregated from daily into hourly data for extraction of the annual maximum precipitation for different durations in hours (1, 2, 6, 10, 12, and 24). Regional frequency analysis was used to form the sites into groups based on homogeneity test results, and the quantile values were computed for various sites and durations with the return periods (T) in years (2, 10, 20, and 100). As a result, the regional frequency analysis was used to estimate the regional quantile values based on L-moment approach. Moreover, the box and whisker plots were utilized to display the results. When the return periods and durations increased, the uncertainty slightly increased. The historical IDF curves of London site falls within the regional simulated IDF curves. Furthermore, 1000 runs have been generated by using the weather generator.
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An Approach to Quantifying Uncertainty in Estimates of Intensity Duration Frequency (IDF) Curves
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