This study investigates the direct influence of urban areas on storm events (heavy rainfall above 17 mm/hr). An 11‐year climatology of summertime precipitation was performed to capture and describe storm events for Berlin, Germany. An hourly radar composite product from the German Weather Service with a 1‐km grid resolution was used for the analysis. Berlin was chosen due to the relatively modest influences of topography and distinct urban‐rural landscape heterogeneity potentially affecting storms. The analysis identified 96 storm events for the period from 2006 to 2016. Individual storm characteristics were evaluated before the city (upwind), over the city, and behind the city (downwind). Both visual and statistical estimations quantified storm modifications caused by the urban area. Around 60% of the events showed typical patterns of storm alteration by the city. In conformance to earlier studies, we found evidence of urban rainfall intensification about 20 to 50 km downwind of the urban center. Most storms were suppressed, deflected, or split by the city, whereas intensification over the city typically occurred at night or when storms formed over the city. In the present study, results indicate an urban influence on storms in and around Berlin. But, unlike other studies, downwind intensification of storm rainfall was not a typical feature. This study on urban rainfall modification marks one of the first observations‐based climatological assessments for the European region.

We investigated the depth–duration relationship of maximum rainfall over all of Germany based on 16 years of radar-derived quantitative precipitation estimates (namely, RADKLIM-YW, German Meteorological Service) with a space–time resolution of 1 km² and 5 min. Contrary to the long-term historic records that identified a smooth power law scaling behaviour between the maximum rainfall depth and duration, our analysis revealed three distinct scaling regimes of which boundaries are approximately 1 h and 1 d. A few extraordinary events dominated a wide range of durations and deviate to the usual power law. Furthermore, the shape of the depth–duration relationship varied with the sample size of randomly selected radar pixels. A smooth scaling behaviour was identified when the sample size was small (e.g. 10 to 100), but the original three distinct scaling regimes became more apparent as the sample size increases (e.g. 1000 to 10 000). Lastly, a pixel-wise classification of the depth–duration relationship of the maximum rainfall at all individual pixels in Germany revealed three distinguishable types of scaling behaviour, clearly determined by the temporal structure of the extreme rainfall events at a pixel. Thus, the relationship might change with longer time series and can be improved once available.

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The flash flood guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information are required to issue warnings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 h, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996–2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The proposed adjusted FFG approach has the potential to provide reliable support in flash flood forecasting.

Highly-resolved data on water balance components (such as runoff or storage) are crucial to improve water management, for example, in drought or flood situations. As regional observations of these components cannot be acquired adequately, a feasible solution is to apply water balance models. We developed an innovative approach using the physically-based lumped-parameter water balance model BROOK90 (R version) integrated into a sensor network platform to derive daily water budget components for catchments in the Free State of Saxony. The model is not calibrated, but rather uses available information on soil, land use, and precipitation only. We applied the hydro response units (HRUs) approach for 6175 small and medium-sized catchments. For the evaluation, model output was cross-evaluated in ten selected head catchments in a low mountain range in Saxony. The mean values of Kling–Gupta efficiency (KGE) for the period 2005–2019 to these catchments are 0.63 and 0.75, for daily and monthly discharge simulations, respectively. The simulated evapotranspiration and soil wetness are in good agreement with the SMAP_L4_GPH product in April 2015–2018. The study can be enhanced by using different data platforms as well as available information on study sites.

The sampling adjustment factor (SAF) can correct the underestimation of fixed time interval maxima (F-Maxima) relative to maxima of moving time windows (M-Maxima) as a direct consequence of temporal discretization of time series. Radar data can help to understand the significance of using moving window aggregation rather than fixed window. Here, we investigate SAFs for two gridded radar quantitative precipitation estimates from the German Meteorological Service (RADKLIM-RW and RADKLIM-YW, DWD 2018) with different temporal resolutions (5 min and hourly) and a spatial resolution of 1 km $$\times $$× 1 km from 2001 to 2016. For each grid cell’s time series within Germany, the overall maximum intensities per chosen duration were derived with (1) moving and (2) fixed window aggregation. Correction factors (SAF) from fixed to moving window were retrieved. The findings of this study partly match previous findings, more for the 5-min product than for the hourly product. No clear dependency on topography or season could be derived from the analysis. It showed that high SAF are usually related to high(er) actual rainfall values. The study emphasises the probabilistic nature of the rainfall maxima correction and shows that it is important to not only consider average SAFs, but also take an in-depth look at the distribution when correcting maxima. As a consequence, design precipitation as used, e.g., in context of flash or urban floods could profit from spatially adjusted or uniform scaling, depending on the characteristics found in the spatial distribution of the SAFs.

We investigate the depth–duration relationship of maximum rainfall over the whole of Germany based on 16 yrs of radar derived Quantitative Precipitation Estimates (namely, RADKLIM–YW, German Meteorological Service) with a space–time resolution of 1 km and 5 min. Contrary to the long–term historic records that identified a smooth power law scaling behavior between the maximum rainfall depth and duration, our analysis revealed three distinct scaling regimes of which boundaries are approximately 1.5 h and 1 d. Few extraordinary events dominate a wide range of durations and deviate to the usual power law. Furthermore, the shape of the depth–duration relationship varies with the sample size of randomly selected radar pixels. A smooth scaling behavior were identified when the sample size is small (e.g. 10 to 100), but the original three distinct scaling regimes became more apparent as the sample size increases (e.g. 1000 to 10 000). Lastly, a pixel wise classification of the depth–duration relationship of the maximum rainfall at all individual pixels in Germany revealed three distinguishable types of scaling behavior, clearly determined by the temporal structure of the extreme rainfall events at a pixel. Thus, the relationship might change with longer time series and can be improved once available.