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Current and future extreme analysis of hourly precipitation from short records by metastatistical extreme value : master thesis = Sedanja in prihodnja ekstremna analiza urnih padavin iz kratkih zapisov z metastatistično ekstremno vrednostjo
Jia, Yue
Extremely intensive hourly precipitation is one of the major cause of floods. Hence, it is necessary to investigate hourly precipitation extremes to be proactive at flood risk management. This paper ...applys Metastatistical Extreme Value (MEV) for estimation of current and future 1 in 10 to 100 year return values of hourly precipitation from short records with the duration around 10 years. Extreme Value Theory (EVT), including Generalized Extreme Value (GEV) and Peak-Over-Threshold (POT), has been prevalent in hydrology frequency analysis since last century. However, the asymptotic assumption of EVT and very often not-large-enough data records in practical cases limit the application and development of EVT. MEV is a novel method in extreme value analysis by considering ordinary events as well as the extremes. Compared with traditional EVT, MEV is a non-asymptotic approach and it estimates high quantiles by considering all or most of independent ordinary events in the process of estimation return values. In this paper, MEV is applied to three different datasets to investigate the mechanism of MEV from various aspects and to assess the performance under different thresholds. The first is KNMI dataset. With up to 95 years of historical hourly rainfall observations in the Netherlands, it is used to analyze the influence of sample data and thresholds on the MEV estimates, as well as for error analysis. Subsequently, a DWD dataset, with 1035 station observations covering Germany, is used for the analysis of the spatial distribution of the hourly rainfall extremes. Finally, an EUCP dataset of kilometre-scale gridded data covering Europe generated by the UKMO Connective Permitting Regional Climate Model (CPRCM) is used. It includes an evaluation run forced by ERA interim reanalysis, a historical run and two future runs downscaled from CMIP5 GCM. The evaluation run is also used for spatial analysis to support the applicability of MEV to climate model simulation data. The historical run and two future runs were used to investigate the mid-century and end-of-century variability of 10-year, 50-year and 100-year hourly precipitation. MEV, with 75 percentile as threshold, is more advantageous than traditional extreme value analysis methods in the case of small sample data. This is reflected in the fact that MEV has smaller errors, less uncertainty and better spatial expressiveness. Applying MEV to the EUCP dataset provides a glimpse of the variation of hourly rainfall extremes in the future: by mid-century, the increase of hourly rainfall extremes is not significant, while the second half of the century faces a significant increase. This increase is also related to the topography, with more pronounced in the Alpine and Mediterranean coastal regions.
Type of material - master's thesis
; adult, serious
Publication and manufacture - Ljubljana : [Y. Jia], 2022
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