In recent years, there has been a growing interest from cities to understand how climate change will affect the management of urban drainage. The performance assessment of urban stormwater infrastructure requires precipitation data on a spatial scale of tens of square kilometres and on range of time scales from 15 minutes to 24 hours. However, the spatial and temporal averaging methods that are used by Global Climate Models (GCMs) to predict precipitation are typically of the order of a few hundred square kilometres and of months or seasons, respectively. This mismatch of spatial and temporal scales has prompted research to bridge the gap and produce rainfall Intensity-Duration-Frequency (IDF) relationships that can be used to assess the impacts of climate change on stormwater infrastructure.
In this presentation, a method is described that takes a different approach to address this gap. Instead of looking at the effects of climate change on the return period (frequency) of different intensity-duration rainfall events, the effects of climate change on component reliability are examined. This is accomplished by relating component reliability to intensity-duration values using the hydraulic risk function, the Extreme Value (Type I) Distribution (EVD-I), and historical rainfall data. To examine the effects of climate change on component reliability, the historical rainfall data is used to ascertain correlations between the mean and standard deviation of the EVD-I and long-term rainfall depths (monthly, seasonal, or annual). With these correlations and the intensity-duration-reliability functions, one can examine how changes in long-term rainfall depths, which are provided by GCMs, influence changes in component reliability. This is demonstrated using hourly rainfall data from six cities on the Canadian prairies.