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https://doi.org/10.5194/hess-2024-177
https://doi.org/10.5194/hess-2024-177
27 Jun 2024
 | 27 Jun 2024
Status: this preprint is currently under review for the journal HESS.

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

Golbarg Goshtasbpour and Uwe Haberlandt

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.

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Golbarg Goshtasbpour and Uwe Haberlandt

Status: open (until 23 Aug 2024)

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Golbarg Goshtasbpour and Uwe Haberlandt
Golbarg Goshtasbpour and Uwe Haberlandt

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Short summary
We provide a method how to estimate extreme rainfall from radar observations. Extreme value statistic is applied for observed radar rainfall covering different areas from point size up to about 1000 km2. The rainfall extremes are supposed to be as higher as smaller the area is. This behaviour could not be confirmed by the radar observations. The reason is the limited single point sampling approach of the radar data. New multiple point sampling strategies are proposed to mitigate this problem.