Statistical framework for assessing trends in sub-daily and daily precipitation extremes
(English)Manuscript (preprint) (Other academic)
Extreme precipitation events vary with regard to duration, and hence sub-daily data do not necessarily exhibit the same trends as daily data. Here, we present a framework for a comprehensive yet easily undertaken statistical analysis of long-term trends in daily and sub-daily extremes. A parametric peaks-over-threshold model is employed to estimate annual percentiles for data of different temporal resolution. Moreover, a trend-durationfrequency table is used to summarize how the statistical significance of trends in annual percentiles varies with the temporal resolution of the underlying data and the severity of the extremes. The proposed framework also includes nonparametric tests that can integrate information about nonlinear monotonic trends at a network of stations. To illustrate our methodology, we use climate model output data from Kalmar, Sweden, and observational data from Vancouver, Canada. In both these cases, the results show different trends for moderate and high extremes, and also a clear difference in the statistical evidence of trends for daily and sub-daily data.
Rainfall extremes; precipitation; sub-daily, temporal trend; generalized Pareto distribution; climate indices; global warming
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-71296OAI: oai:DiVA.org:liu-71296DiVA: diva2:447067