Estimation of radar-based Area-Depth-Duration-Frequency curves with special focus on spatial sampling problems

Abstract. Radar-based Area-Depth-Duration-Frequency (ADDF) curves offer the possibility of incorporating a space dimension into analysis of rainfall extremes. This solves some shortcomings of the traditional point-based Depth-Duration-Frequency (DDF) curves which characterize design rainfall. In this study, ADDF curves are calculated from a radar-based rainfall data set, a product of the conditional merging of corrected radar data and station data, covering a large area in north part of Germany. The initial results show implausible behavior in the curves where the rainfall quantiles increase with increasing area. It is discussed in details in this paper that the implausible behavior persists due to the shortcoming of fixed-area sampling methods which is missing the most extreme annual maximum rainfall events within the area of interest. Three alternative sampling strategies are developed to address this issue. Among the introduced methods the Multiple-Location-Extreme-Sampling (MLES) and the Single-Location-Extreme-Sampling (SLES) methods successfully reduced the number of study locations with implausible behavior by 67 % and 43 % respectively. The SLES method is recommended as the best method for calculating areal design rainfall directly from high resolution radar-based data sets. This method tackles the spatial sampling issue and it can result in Area-Reduction-Factor values compatible with station-based point design rainfall values.