Application of the non-stationary peak-over-threshold methods for deriving rainfall extremes from temperature projections

Abstract

Concerns about climate change are amplifying interest in future rainfall extremes. However, there are big differences between the statistics of rainfall extremes obtained using future rainfall time series produced from various climate models. Such large uncertainties create a lot of confusion in establishing climate change adaptation measures. Looking at future rainfall extremes at a particular site yields increasing trends in some climate models and decreasing trends in others. The spatial patterns of rate of change in rainfall extremes also vary widely, depending on the climate model. As a result, they often do not gain the public’s trust. We believe that this difficulty in obtaining credibility does not come from a lack of theory or technique, but from an approach that persuades the public of uncertain future rainfall extremes. In this study, we employed a novel approach to integrate a co-variate of the not-stationary Peak-Over-Threshold (POT) – Generalized Pareto distribution (GPD) model identified at each site with its future projection information for obtaining future rainfall extreme ensembles. Rainfall extremes are obtained from the observed rainfall time series using the POT method, and the scale parameter among GPD parameters are applied as a function of surface air temperature (SAT) or dew-point temperature (DPT). At this time, the threshold of the POT series is set to match the results of frequency analysis of the annual maximum series and the POT series for each site as much as possible. The behavior of future rainfall extremes is analyzed by inputting the future SAT or DAT produced from various climate models into the non-stationary frequency model using the co-variate. As a result of comparing the rainfall extremes obtained using the future rainfall time series directly with the future rainfall extremes obtained indirectly using the proposed method, it was found that the proposed approach projected future design rainfall depths with much less variation between climate models. The spatial pattern of rate of change was also consistent regardless of climate model. The proposed method is expected to contribute to the public’s confidence in future rainfall extremes under climate change scenarios and to be of practical help in formulating reasonable climate change adaptation policies.