7/2/2023 0 Comments Radar extreme![]() For most durations, significant regional differences in extreme rainfall in the Netherlands are found. GEV distributions are fitted to these annual maxima. The climatological radar rainfall data set is utilized to obtain annual rainfall maxima for durations of 15 min to 24 h and the size of a radar pixel. This hampers the study of regional variability in extreme rainfall and the estimation of extreme areal rainfall, which can be overcome by using weather radar. In general, only few digitized time series from rain gauges are available for subdaily durations. Verification shows that the radar data set has a high quality. Since radar data can be vulnerable to a number of errors, they are adjusted using rain gauges. A 10-year radar-based climatology of rainfall depths for durations of 15 min to 24 h is derived for the Netherlands. Weather radars are widely used in real-time quantitative precipitation estimation over large areas with high temporal and spatial resolutions not achieved by conventional rain gauge networks. Therefore, uncertainties in the DDF curves are estimated as well. Often, uncertainties are not incorporated in the design of hydraulic structures, which can lead to a risk of under design. Using a new methodology, rain gauge data from 12 stations in the Netherlands are employed to derive rainfall depth-duration-frequency (DDF) curves, which describe rainfall depth as a function of duration for given return periods. ![]() In this thesis, the Generalized Extreme Value (GEV) distribution is used to model annual rainfall maxima. Subsequently, a probability distribution is fitted to the selected maxima, so that rainfall depths can be estimated for a chosen return period, which can be longer than the rainfall record. Such a climatology can be obtained by abstracting maxima from long rainfall records. Therefore, a reliable climatology of extreme rainfall is of importance, for instance, for the design of hydraulic structures. Extreme rainfall events can have a large impact on society and can lead to loss of life and property. ![]()
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