Stochastische Simulation großflächiger, hochwasserrelevanter Niederschlagsereignisse

Die Bestimmung von Wahrscheinlichkeiten für Niederschlagsereignisse mit hoher Wiederkehrperiode ist wegen der kurzen Zeitspanne, in der Messdaten verfügbar sind, schwierig. Auch eine homogene Abdeckung durch Messstationen insbesondere in Gebirgen ist nur schwer realisierbar. In dieser Arbeit wird ein räumlich hoch aufgelöstes, analytisches Modell zur stochastischen Simulation von Starkniederschlägen in Verbindung mit großräumigen Flusshochwassern entwickelt, evaluiert und angewendet

Flood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation model

Various fields of application, such as risk assessments of the insurance industry or the design of flood protection systems, require reliable precipitation statistics in high spatial resolution, including estimates for events with high return periods. Observations from point stations, however, lack of spatial representativeness, especially over complex terrain. Current numerical weather models are not capable of running simulations over thousands of years. This paper presents a new method for the stochastic simulation of widespread precipitation based on a linear theory describing orographic precipitation and additional functions that consider synoptically driven rainfall and embedded convection in a simplified way. The model is initialized by various statistical distribution functions describing prevailing atmospheric conditions such as wind vector, moisture content, or stability, estimated from radiosonde observations for a limited sample of observed heavy rainfall events. The model is applied for the stochastic simulation of heavy rainfall over the complex terrain of southwestern Germany. It is shown that the model provides reliable precipitation fields despite its simplicity. The differences between observed and simulated rainfall statistics are small, being of the order of only ±10 % for return periods of up to 1000 years

Long-term variance of heavy precipitation across central Europe using a large ensemble of regional climate model simulations

Widespread flooding events are among the major natural hazards in central Europe. Such events are usually related to intensive, long-lasting precipitation over larger areas. Despite some prominent floods during the last three decades (e.g., 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, decadal hindcasts, and also predictions for the upcoming decade combined to a new large ensemble. Global reanalyses for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (Consortium for Small-scale Modeling – CLimate Mode; COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. Evaluation focuses on intensive widespread precipitation events and related temporal variabilities and trends. The presented ensemble data are within the range of observations for both statistical distributions and time series. The temporal evolution during the past 60 years is captured. The results reveal some long-term variability with phases of increased and decreased precipitation rates. The overall trend varies between the investigation areas but is mostly significant. The predictions for the upcoming decade show ongoing tendencies with increased areal precipitation. The presented regional climate model (RCM) ensemble not only allows for more robust statistics in general, it is also suitable for a better estimation of extreme values

What causes a heavy precipitation period to become extreme? The exceptional October of 2018 in the Western Mediterranean

The Mediterranean region is particularly exposed to heavy precipitation and flash flooding. Every autumn the region is affected by these weather-related hazards, frequently with immense costly and deadly consequences. What makes an already potentially damaging period in terms of heavy precipitation, even more intense? This is the underlying question in this study, in which the atmosphere and the ocean conditions in October 2018 are examined to identify anomalies favoring this intensification. Furthermore, the model representativity of the over-averaged precipitation period and underlying anomalies is analyzed across scales using climatological, seasonal, and event-based COSMO high-resolution model simulations. Our investigation shows that October-2018, in the context of the climatological series from 1982 to 2018, could be marked as an unprecedented period because of the presence of intense and numerous low-pressure systems. Additionally, atmospheric moisture values placed this time above the climatological average, mainly for the high percentiles of the TCWV hourly anomalies. Specific humidity showed similar behaviour as TCWV except for pressure levels lower than 700 hPa, probably in relation to the evolution of the former Hurricane Leslie. The atmosphere-ocean interaction presented combined strong sea surface temperature (SST) and evaporation anomalies. April to October SST clearly exceeds climatological values while October-2018 presents both strong monthly anomaly and intense evaporation peaks preceding the most intense precipitation events. These large-scale features’ anomalies were in general well captured by the high-resolution regional climate model simulations at climatic and seasonal scales leading to an accurate representation of accumulated precipitation for the October period. However, the numerical weather prediction simulations on an event scale revealed low predictability, in agreement with former investigations, due to differences at the location and intensity of the cut-off lows and particularly at the atmospheric moisture field. The conclusions of this study show that it is not the most extreme period in terms of single anomalies which lead to extreme wet seasons, but the synergy of atmospheric and oceanic anomaly conditions with a constant interplay which made Autumn/October 2018 an extreme season/month

Exceptional sequence of severe thunderstorms and related flash floods in May and June 2016 in Germany – Part 1: Meteorological background

Abstract. During a 15-day episode from 26 May to 9 June 2016, Germany was affected by an exceptionally large number of severe thunderstorms. Heavy rainfall, related flash floods and creek flooding, hail, and tornadoes caused substantial losses running into billions of euros (EUR). This paper analyzes the key features of the severe thunderstorm episode using extreme value statistics, an aggregated precipitation severity index, and two different objective weather-type classification schemes. It is shown that the thunderstorm episode was caused by the interaction of high moisture content, low thermal stability, weak wind speed, and large-scale lifting by surface lows, persisting over almost 2 weeks due to atmospheric blocking. For the long-term assessment of the recent thunderstorm episode, we draw comparisons to a 55-year period (1960–2014) regarding clusters of convective days with variable length (2–15 days) based on precipitation severity, convection-favoring weather patterns, and compound events with low stability and weak flow. It is found that clusters with more than 8 consecutive convective days are very rare. For example, a 10-day cluster with convective weather patterns prevailing during the recent thunderstorm episode has a probability of less than 1 %

Long-term Variances of Heavy Precipitation across Central Europe using a Large Ensemble of Regional Climate Model Simulations

Widespread flooding events are among the major natural hazards in Central Europe. Such events are usually related to intensive, long-lasting precipitation. Despite some prominent floods during the last three decades (e.g. 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, a large ensemble of decadal hindcasts, and also projections for the upcoming decade. Global reanalysis for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. The simulations show a good agreement with observations for both statistical distributions and time series. Differences mainly appear in areas with sparse observation data. The temporal evolution during the past 60 years is well captured. The results reveal some long-term variability with phases of increased and decreased heavy precipitation. The overall trend varies between the investigation areas but is significant. The projections for the upcoming decade show ongoing tendencies with increased precipitation for upper percentiles. The presented RCM ensemble not only allows for more robust statistics in general, in particular it is suitable for a better estimation of extreme values

Adaptation and application of the large LAERTES-EU regional climate model ensemble for modeling hydrological extremes: a pilot study for the Rhine basin

Enduring and extensive heavy precipitation events associated with widespread river floods are among the main natural hazards affecting central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to running hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12 000 simulated years. LAERTES-EU is adapted for use in an HM to calculate discharges for large river basins by applying quantile mapping with a parameterized gamma distribution to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU also improves the statistical representativeness for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in central Europe

Adaptation and application of the large LAERTES-EU RCM ensemble for modeling hydrological extremes: A pilot study for the Rhine basin

Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12.000 simulated years. LAERTES-EU is adapted for the use in an HM to calculate discharges for large river basins by applying a quantile mapping with a fixed density function to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in Central Europe

Hochwasser Mitteleuropa, Juli 2021 (Deutschland) : 21. Juli 2021 – Bericht Nr. 1 „Nordrhein-Westfalen & Rheinland-Pfalz”

Am 13. und 14. Juli 2021 fielen über dem Westen Deutschlands sowie in Teilen Belgiens und in Luxemburg enorme Regenmengen von 100 bis 150 mm. Ein Großteil des Niederschlags ging innerhalb von 15 bis 18 Stunden nieder. Besonders betroffen waren die beiden Bundesländer Nordrhein-Westfalen und Rheinland-Pfalz (Abbildung 2). Die Folge war, dass beispielsweise der Pegel an der Ahr (Altenahr) seinen bisherigen Rekord von 2016 (3,71 m, Durchfluss: 236 m³/s) deutlich übertraf, wobei die Messstation überflutungsbedingt bei einem Wert von 5,05 m (Durchfluss: 332 m³/s) komplett ausfiel. Aktuelle Schätzungen vermuten für dieses Ereignis einen Pegelstand zwischen 7 bis 8 m mit einem Durchfluss zwischen 400 bis 700 m³/s. Aus meteorologischer Sicht führten verschiedene Faktoren zu den extrem hohen Niederschlagssummen. Außerdem verstärkte das stark gegliederte Gelände der betroffenen Region mit teils tief eingeschnittenen Flusstälern den Oberflächenabfluss, der bereits annähernd gesättigte Boden unterstütze zudem die Situation. All dies zusammen führte letztlich zu einer verheerenden Katastrophe, die mindestens 170 Todesopfer und 820 Verletzte forderte (Stand: 21.07.2021) und katastrophale Schäden an Wohngebäuden und der Infrastruktur hinterließ. Erste grobe Schätzungen liegen bei einem versicherten Schaden von mehr als 10 Mrd. €, wobei der Gesamtschaden deutlich höher ausfallen dürfte, da nur rund 37 bis 47 % der Gebäude eine Elementarversicherungen aufweisen. Zudem ist die Infrastruktur massiv betroffen; der Bund rechnet allein an der Verkehrsinfrastruktur mit einem Schaden von 2 Mrd. €. In der Vergangenheit gab es im Ahrtal bereits zwei besonders bedeutende Hochwasserereignisse: 1804 und 1910. Ein Vergleich mit historischen Aufzeichnungen lässt vermuten, dass die Werte des diesjährigen Ereignisses niedriger einzuordnen sind als für das Hochwasserereignis von 1804 (Schätzung: ~ 1100 m³/s). Zudem wird abgeschätzt, dass das Ereignis von 2021 hydrologisch betrachtet ein ähnliches Ausmaß wie das Hochwasserereignis von 1910 (~ 500 m³/s) gehabt haben könnte. Da die Gefährdung in aktuellen Hochwasserkarten für das Ahrtal auf einer Abflussstatistik basierend auf zeitlich homogen verfügbaren Messreihen beruht (in dem Fall ab 1947), werden allerdings die beiden historischen Ereignisse bei der Gefährdungsabschätzung bisher nicht berücksichtigt und die aktuelle Schätzung des 100-jährliches Hochwasser (HQ100) für die Ahr liegt „nur“ bei 241 m³/s