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001009014 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-02574
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001009014 1001_ $$0P:(DE-Juel1)188805$$aSaadi, Mohamed$$b0$$eCorresponding author
001009014 245__ $$aComparison of Three Radar-Based Precipitation Nowcasts for the Extreme July 2021 Flooding Event in Germany
001009014 260__ $$aBoston, Mass.$$bAMS$$c2023
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001009014 520__ $$aQuantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts, especially for lead times up to 12 h, but their evaluation depends on a variety of factors, namely, the choice of the hydrological model and the benchmark. We tested three precipitation nowcasting techniques based on radar observations for the disastrous mid-July 2021 event in seven German catchments (140–1670 km2). Two deterministic [advection-based and spectral prognosis (S-PROG)] and one probabilistic [Short-Term Ensemble Prediction System (STEPS)] QPN with a maximum lead time of 3 h were used as input to two hydrological models: a physically based, 3D-distributed model (ParFlowCLM) and a conceptual, lumped model (GR4H). We quantified the hydrological added value of QPN compared with hydrological persistence and zero-precipitation nowcasts as benchmarks. For the 14 July 2021 event, we obtained the following key results. 1) According to the quality of the forecasted hydrographs, exploiting QPN improved the lead times by up to 4 h (8 h) compared with adopting zero-precipitation nowcasts (hydrological persistence) as a benchmark. Using a skill-based approach, obtained improvements were up to 7–12 h depending on the benchmark. 2) The three QPN techniques obtained similar performances regardless of the applied hydrological model. 3) Using zero-precipitation nowcasts instead of hydrological persistence as benchmark reduced the added value of QPN. These results highlight the need for combining a skill-based approach with an analysis of the quality of forecasted hydrographs to rigorously estimate the added value of QPN.
001009014 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
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001009014 7001_ $$0P:(DE-HGF)0$$aFurusho-Percot, Carina$$b1
001009014 7001_ $$0P:(DE-Juel1)179108$$aBelleflamme, Alexandre$$b2$$ufzj
001009014 7001_ $$0P:(DE-HGF)0$$aTrömel, Silke$$b3
001009014 7001_ $$0P:(DE-Juel1)151405$$aKollet, Stefan$$b4$$ufzj
001009014 7001_ $$0P:(DE-HGF)0$$aReinoso-Rondinel, Ricardo$$b5
001009014 773__ $$0PERI:(DE-600)2042176-X$$a10.1175/JHM-D-22-0121.1$$gVol. 24, no. 7, p. 1241 - 1261$$n7$$p1241 - 1261$$tJournal of hydrometeorology$$v24$$x1525-755X$$y2023
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