Abstract
Study region:Germany. Study focus:Estimation of precipitation return levels on various temporal scales and with high spatial resolution is crucial for risk management and hydrological applications. Weather radars may provide this information, but design precipitation estimates from these instruments suffer from estimation biases and from the limited length of the available records. Here, the performance of two statistical methods for deriving design precipitation from 20 years of radar data for Germany is investigated: (a) a method based on peaks-over-threshold and an exponential distribution, operationally used for decades to compute design precipitation in Germany (DWA); and (b) a non-asymptotic approach that was recently shown to reduce estimation uncertainties related to short records (SMEV). The most recent official design precipitation for Germany derived from station data (KOSTRA-DWD-2020) are used as a benchmark. New Hydrological Insights for the RegionDesign precipitation from radar data tends to be lower than those derived from stations, due to the scale mismatch (point scale versus ∼1km2 of radar) and to biases in radar estimates of extremes. SMEV tends to underestimate more than DWA, especially for short durations. Larger uncertainties are reported for the DWA method, while SMEV estimates tend to be more stable and less influenced by statistical outliers. Application of the KOSTRA-DWD-2020 method on radar data leads to results closer to SMEV.
Published Version
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