Nonstationary frequency analysis of extreme daily precipitation amounts in Southeastern Canada using a peaks-over-threshold approach

Abstract

In this paper, a statistical inference of Southeastern Canada extreme daily precipitation amounts is proposed using a classical nonstationary peaks-over-threshold model. Indeed, the generalized Pareto distribution (GPD) is fitted to excess time series derived from annual averages of independent precipitation amount events above a fixed threshold, the 99th percentile. Only the scale parameter of the fitted distribution is allowed to vary as a function of a covariate. This variability is modeled using B-spline function. Nonlinear correlation and cross-wavelet analysis allowed identifying two dominant climate indices as covariates in the study area, Arctic Oscillation (AO) and Pacific North American (PNA). The nonstationary frequency analysis showed that there is an east-west behavior of the AO index effects on extreme daily precipitation amounts in the study area. Indeed, the higher quantiles of these events are conditional to the AO positive phase in Atlantic Canada, while those in the more southeastern part of Canada, especially in Southern Quebec and Ontario, are negatively related to AO. The negative phase of PNA also gives the best significant correlation in these regions. Moreover, a regression analysis between AO (PNA) index and conditional quantiles provided slope values for the positive phase of the index on the one hand and the negative phase and on the other hand. This statistic allows computing a slope ratio which permits to sustain the nonlinear relation assumption between climate indices and precipitation and the development of the nonstationary GPD model for Southeastern Canada extremes precipitation modeling.