Abstract
Testing the homogeneity in extreme rainfall data series is an important step to be performed before applying the frequency analysis method to obtain quantile values. In this work, six homogeneity tests were applied in order to check the existence of break points in extreme annual 24-h rainfall data at eight stations located in the Umbria region (Central Italy). Two are parametric tests (the standard normal homogeneity test and Buishand test) whereas the other four are non-parametric (the Pettitt, Sequential Mann–Kendal, Mann–Whitney U, and Cumulative Sum tests). No break points were detected at four of the stations analyzed. Where inhomogeneities were found, the multifractal approach was applied in order to check if they were real or not by comparing the split and whole data series. The generalized fractal dimension functions Dq and the multifractal spectra f(α) were obtained, and their main parameters were used to decide whether or not a break point existed.
Highlights
Frequency analysis of extreme rainfall data is a common application in hydrologic engineering
The Buishand range (BR) and standard normal homogeneity (SNH) tests were applied to all the extreme annual daily rainfall data series for each station selected in the Umbria region
The multifractal character of rainfall data has been widely studied for extreme annual data of different durations and it is useful when dealing with rainfall predictive modeling or IDF estimation
Summary
Frequency analysis of extreme rainfall data (defined as yearly maxima of a certain duration) is a common application in hydrologic engineering. It should be applied to recorded series that follow the characteristics of independency, stationarity and homogeneity [1]. Inhomogeneities in station data records can be due to observational routines (station relocations or changes in measuring techniques) and in that case, statistical methods and metadata information are effective for identifying these [2]. Regardless of the reason, it is important to interpret past variability and recognize the shifts in climatic data in order to make adequate future projections. It is crucial to investigate the effects of climate change on hydrologic time series, and especially on extreme rainfall data in order to accurately estimate or even update the intensity-duration-frequency (IDF) curves in a certain place [8]
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