The integration of extreme historical floods in contemporary flood protection contributes towards improved risk management and safer handling of floods in the future. As a case study within the "Xfloods" project at the University of Freiburg (Germany), the discharges of the extreme flood in 1824 in the Neckar River basin (Baden-Württemberg/southwest Germany) were reconstructed using historical data. Quantitative and qualitative historical sources were applied to model the regional atmospheric circulation pattern, the weather conditions and the precipitation distribution associated with the event. Discharges were simulated using the water-balance model LARSIM (Large Area Runoff Simulation Model), the operational flood forecasting model in Baden-Württemberg. The developed methodology shows potential for wider use in assessing extreme historical floods and for application to contemporary flood management.Reconstitution hydro-météorologique de la crue 1824 dans le bassin du Neckar (sud-ouest de l'Allemagne) Résumé L'intégration des crues historiques dans la prévention actuelle des risques d'inondation contribue à établir une meilleure estimation des risques et à faire un traitement plus sûr des futures crues. La crue extrême de 1824 dans le bassin du Neckar (Bade-Wurtemberg/sud-ouest de l'Allemagne) a été reconstituée en exploitant les informations des sources historiques en tant qu'étude de cas pour le projet "Xfloods" mené à l'Université de Freiburg (Allemagne). Des sources historiques quantitatives et qualitatives ont été utilisées pour modéliser la circulation atmosphérique à grande échelle spatiale, ainsi que la situation météorologique et la distribution des précipitations qui ont causé cet événement. Les écoulements ont été alors simulés en utilisant le modèle hydrologique LARSIM, qui est le modèle opérationnel de la prédiction des crues au Bade-Wurtemberg. La méthode développée montre un potentiel pour une utilisation plus importante en vue d'évaluer les crues historiques extrêmes et pour son application à la gestion contemporaine des risques d'inondation.
We present a 1-km 2 gridded German dataset of hourly surface climate variables covering the period 1995 to 2012. The dataset comprises 12 variables including temperature, dew point, cloud cover, wind speed and direction, global and direct shortwave radiation, down-and up-welling longwave radiation, sea level pressure, relative humidity and vapour pressure. This dataset was constructed statistically from station data, satellite observations and model data. It is outstanding in terms of spatial and temporal resolution and in the number of climate variables. For each variable, we employed the most suitable gridding method and combined the best of several information sources, including station records, satellite-derived data and data from a regional climate model. A module to estimate urban heat island intensity was integrated for air and dew point temperature. Owing to the low density of available synop stations, the gridded dataset does not capture all variations that may occur at a resolution of 1 km 2 . This applies to areas of complex terrain (all the variables), and in particular to wind speed and the radiation parameters. To achieve maximum precision, we used all observational information when it was available. This, however, leads to inhomogeneities in station network density and affects the long-term consistency of the dataset. A first climate analysis for Germany was conducted.
Abstract. This paper presents a hydrometeorological reconstruction of the flood triggering meteorological situation and the simulation of discharges of the flood event of December 1882 in the Neckar catchment in Baden-Württemberg (southwest Germany). The course of the 1882 flood event in the Neckar catchment in southwest Germany and the weather conditions which led to this flood were reconstructed by evaluating the information from various historical sources. From these historical data, daily input data sets were derived for run-off modeling. For the determination of the precipitation pattern at the end of December 1882, the sparse historical data were modified by using a similar modern day precipitation pattern with a higher station density. The results of this run-off simulation are compared with contemporary historical data and also with 1-D hydraulic simulations using the HEC-RAS model.
Abstract. This paper presents a case study on the estimation of peak discharges of extreme flood events during the 19th century of the Neckar River located in south-western Germany. It was carried out as part of the BMBF (German Federal Ministry of Education and Research) research project RIMAX (Risk Management of Extreme Flood Events). The discharge estimations were made for the 1824 and 1882 flood events, and are based on historical cross profiles. The 1-D model Hydrologic Engineering Centers River Analysis System (HEC-RAS) was applied with different roughness coefficients to determine these estimations. The results are compared (i) with contemporary historical calculations for the 1824 and 1882 flood events and (ii) in the case of the flood event in 1824, with the discharge simulation by the water balance model LARSIM (Large Area Runoff Simulation Model). These calculations are matched by the HEC-RAS simulation based on the standard roughness coefficients.
For the period 1978 to 2001, the physically based model BROOK90 has been applied to simulate the water balance of the Scots pine forest (Pinus sylvestris L.) at the forest meteorological experimental site Hartheim with emphasis on drought. The forest is located in the southern upper Rhine plain, which shows climate conditions similar to those predicted by regional climate models for Central Europe. The transpiration index (ratio of actual transpiration and potential transpiration) has been chosen as an ecophysiologically based drought index on a daily basis. Simulations exhibit that the transpiration index depends not only on the weather conditions but also on forest characteristics like maximum leaf conductance and projected leaf area index. Taking into account different time scales for the transpiration index (daily, monthly and annual basis), a tendency of the occurrence of drought significant to the forest could not be determined for the investigation period. August turned out to be the month with the highest drought risk. Drought for the forest was most pronounced in the year 1991 (annual transpiration index: 0.53), whereas the year 2000 was the only one with an optimal water supply of the Scots pine forest (annual transpiration index: 1.0). Zusammenfassung Mit Hilfe des physikalisch begründeten Modells BROOK90 wurde der Wasserhaushalt des Kiefernwaldes (Pinus sylvestris L.) an der Forstmeteorologischen Messstelle Hartheim in der südlichen Oberrheinebene im Zeitraum 1978 bis 2001 simuliert, um Trockenheit auf Tagesbasis zu untersuchen. Dieser Wald befindet sich in einer Region mit klimatischen Bedingungen, die denjenigen nahe kommen, die von regionalen Klimamodellen für Mitteleuropa prognostiziert werden. Als Kenngröße für Trockenheit wurde derökophysiologisch basierte Transpirationsindex (Verhältnis von potentieller zu aktueller Transpiration) gewählt. Simulationsberechnungen zeigen, dass der Transpirationsindex nicht nur von Witterungsbedingungen, sondern auch von forstlichen Kenngrößen wie maximaler stomatärer Leitfähigkeit und projiziertem Blattflächenindex abhängt. Unter Berücksichtigung verschiedener Zeitskalen für den Transpirationsindex (Tages-, Monatsund Jahresbasis) konnte im Untersuchungszeitraum für diesen Trockenstandort keine Tendenz im Auftreten waldrelevanter Trockenheit festgestellt werden. Als Monat mit dem größten Trockenheitsrisiko wurde der August ermittelt. Als Jahr mit ausgeprägtester Trockenheit stellte sich 1991 (jährlicher Transpirationsindex: 0,53) heraus. Im Gegensatz dazu war das Jahr 2000 das einzige in der Untersuchungsperiode, in dem der Kiefernwald eine optimale Wasserversorgung hatte (jährlicher Transpirationsindex: 1,0).
Abstract. This paper presents a case study to estimate peak discharges of extreme flood events of Neckar River in south-western Germany during the 19th century. It was carried out within the BMBF research project RIMAX (Risk Management of Extreme Flood Events). The discharge estimations were made for the flood events of 1824 and 1882 based on historical cross profiles. The 1-D model Hydrologic Engineering Centers River Analysis System (HEC-RAS) was applied with different roughness coefficients. The results are compared (i) with contemporary historical calculations and (ii) in the case of a flood event in 1824 with the discharge simulation by the water balance model LARSIM (Large Area Runoff Simulation Model). These calculations are matched by the HEC-RAS simulation based on the standard roughness coefficients.
Abstract. Precise quantification of climate change depends on long time series of meteorological variables. Such time series should be as homogeneous as possible but some changes of measurement conditions cannot be prevented. At German climate reference stations, parallel measurements are used to analyze the effects of changes in measurement systems for example for the transition from manual to automatic instruments. These parallel measurements aim to identify measurement uncertainties and to analyze the comparability of measurement systems to investigate the homogeneity. In this study, we investigate daily sunshine duration. Traditionally, manual measurements of daily sunshine duration are taken with Campbell-Stokes sunshine recorders. For automatic measurements the SONIe or SCAPP instrument is used. The different measurement principles (glass sphere and photodiode) cause systematic differences between the observations. During summer, values for manual observations are larger especially in case of frequent alternations between sunny and cloudy conditions. Furthermore, the standard deviation of the differences between the two measurement systems is larger during summer because of the greater day length. To adjust the automatic measurements a linear regression model is suggested based on parallel measurements from 13 climate reference stations in Germany. To validate the regression coefficients, a leave-one-out cross validation was performed (by leaving out data of individual stations). The regression coefficients (derived from different sets of stations) are similar, thereby indicating a robust data set for the estimation of the linear model. With this method we want to prevent breaks in long time series of daily sunshine duration caused by the transition from manual to automatic instruments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.