An alternative approach to the extreme value analysis of rainfall data

Abstract

We describe an Extreme Value (EV) analysis of rainfall data for 8 Canadian Atmospheric Environment Service Stations covering a range of northern mid‐latitude climate regimes. Our approach is to find a de‐seasonalizing transformation for each site that transforms the tails of the distribution of daily rainfall amounts into unit exponential distributions. An EV‐I distribution is then fitted to the collection of monthly maxima of the transformed daily rainfall amounts via the method of maximum likelihood. Subsequently, the de‐seasonalization transform is inverted to derive a distribution for the annual maximum of daily rainfall amounts, and this derived distribution is used to estimate return values. A bootstrap technique is used to assess the uncertainty of the estimates. Diagnostic statistics of the goodness of the tail transformation and of the fitted EV‐I distribution are computed and discussed. The return values are compared with those obtained using conventional maximum likelihood and method of moments techniques based on annual extremes. A sensitivity analysis designed to assess the robustness of the three EV analysis techniques considered is also described. © 1991 Taylor & Francis Group, LLC.