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• W4324138941 abstract "Abstract. Rainfall depth–duration–frequency (DDF) curves are required for the design of several water systems and protection works. For the reliable estimation of such curves, long and dense observation networks are necessary, which in practice is seldom the case. Usually observations with different accuracy, temporal resolution and density are present. In this study, we investigate the integration of different observation datasets under different methods for the local and regional estimation of DDF curves in Germany. For this purpose, two competitive DDF procedures for local estimation (Koutsoyiannis et al., 1998; Fischer and Schumann, 2018) and two for regional estimation (kriging theory vs. index based) are implemented and compared. Available station data from the German Weather Service (DWD) for Germany are employed, which includes 5000 daily stations with more than 10 years available, 1261 high-resolution (1 min) recordings with an observation period between 10 and 20 years, and finally 133 high-resolution (1 min) recordings with 60–70 years of observations. The performance of the selected approaches is evaluated by cross-validation, where the local DDFs from the long sub-hourly time series are considered the true reference. The results reveal that the best approach for the estimation of the DDF curves in Germany is by first deriving the local extreme value statistics based on Koutsoyiannis et al.'s (1998) framework and later using the kriging regionalisation of long sub-hourly time series with the short sub-hourly time series acting as an external drift. The integration of the daily stations proved to be useful only for DDF values of a low return period (T[a] < 10 years) but does not introduce any improvement for higher return periods (T[a] ≥ 10 years)." @default.
• W4324138941 created "2023-03-15" @default.
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• W4324138941 date "2023-03-14" @default.
• W4324138941 modified "2023-09-29" @default.
• W4324138941 title "Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany" @default.
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• W4324138941 doi "https://doi.org/10.5194/hess-27-1109-2023" @default.
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