02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
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Item Open Access Hochwasser – Staatsfeind Nr. 1(2002) Ehret, Uwe; Bárdossy, AndrásIn diesem Beitrag wird zusammen mit einer Erläuterung der Entstehung und der verschiedenen Arten von Hochwasser ein kurzer Überblick über die Arten des Hochwasserschutzes und der Hochwasservorhersage gegeben. Während sich die staatlichen Vorhersageinstitutionen momentan hauptsächlich auf große Flüsse wie Donau, Rhein und Neckar konzentrieren, wurde im Rahmen eines Forschungsprojekts am Institut für Wasserbau (IWS) ein Vorhersage- und Warnsystem für ein kleines Flusseinzugsgebiet, den Goldersbach bei Tübingen, entwickelt.Item Open Access Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS(2022) Gyawali, Dhiraj Raj; Bárdossy, AndrásGiven the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution, including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snowmelt estimation in mountainous regions is, however, further exacerbated by data scarcity. This study attempts to develop relatively simple extended degree-day snow models driven by freely available snow-cover images. This approach offers relative simplicity and a plausible alternative to data-intensive models, as well as in situ measurements, and has a wide range of applicability, allowing for immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snowmelt models on spatial snow distribution in contrast to the traditional snow-water-equivalent-based calibration. The spatial distribution of snow-cover is simulated using different extended degree-day models with parameters calibrated against individual MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg (Germany) and in Switzerland. The simulated snow-cover data show very good agreement with MODIS snow-cover distribution, and the calibrated parameters exhibit relative stability across the time domain. Furthermore, different thresholds that demarcate snow and no-snow pixels for both observed and simulated snow cover were analyzed to evaluate these thresholds' influence on the model performance and identified for the study regions. The melt data from these calibrated snow models were used as standalone inputs to a modified Hydrologiska Byråns Vattenbalansavdelning (HBV) without the snow component in all the study catchments to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show an overall increase in Nash–Sutcliffe efficiency (NSE) performance and a reduction in uncertainty in terms of model performance. This can be attributed to the reduction in the number of parameters available for calibration in the modified HBV and an added reliability of the snow accumulation and melt processes inherent in the MODIS calibrated snow model output. This paper highlights that the calibration using readily available images used in this method allows for a flexible regional calibration of snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data and globally available inputs. Likewise, the study concludes that simpler specific alterations to processes contributing to snowmelt can contribute to reliably identify the snow distribution and bring about improvements in hydrological simulations, owing to better representation of the snow processes in snow-dominated regimes.Item Open Access Clustering simultaneous occurrences of the extreme floods in the Neckar catchment(2021) Modiri, Ehsan; Bárdossy, AndrásFlood protection is crucial for making socioeconomic policies due to the high losses of extreme floods. So far, the synchronous occurrences of flood events have not been deeply investigated. In this paper, multivariate analysis was implemented to reveal the interconnection between these floods in spatiotemporal resolution. The discharge measurements of 46 gauges with a continuous daily time series for 55 years were taken over the Neckar catchment. Initially, the simultaneous floods were identified. The Kendall correlation between the pair sets of peaks was determined to scrutinize the similarities between the simultaneous events. Agglomerative hierarchical clustering tree (AHCT) and multidimensional scaling (MDS) were employed, and obtained clusters were compared and evaluated with the Silhouette verification method. AHCT shows that the Average and Ward algorithms are appropriate to detect reasonable clusters. The Neckar catchment has been divided into three major clusters: the first cluster mainly covers the western part and is bounded by the Black Forest and Swabian Alps. The second cluster is mostly located in the eastern part of the upper Neckar. The third cluster contains the remaining lowland areas of the Neckar basin. The results illustrate that the clusters act relatively as a function of topography, geology, and anthropogenic alterations of the catchment.Item Open Access Is precipitation responsible for the most hydrological model uncertainty?(2022) Bárdossy, András; Kilsby, Chris; Birkinshaw, Stephen; Wang, Ning; Anwar, FaizanRainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain-physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input-precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.Item Open Access Technical note: Space-time statistical quality control of extreme precipitation observations(2022) El Hachem, Abbas; Seidel, Jochen; Imbery, Florian; Junghänel, Thomas; Bárdossy, AndrásInformation about precipitation extremes is of vital importance for many hydrological planning and design purposes. However, due to various sources of error, some of the observed extremes may be inaccurate or false. The purpose of this investigation is to present quality control of observed extremes using space–time statistical methods. To cope with the highly skewed rainfall distribution, a Box–Cox transformation with a suitable parameter was used. The value at the location of a potential outlier is estimated using the surrounding stations and the calculated spatial variogram and compared to the suspicious observation. If the difference exceeds the threshold of the test, the value is flagged as a possible outlier. The same procedure is repeated for different temporal aggregations in order to avoid singularities caused by convection. Detected outliers are subsequently compared to the corresponding radar and discharge observations, and finally, implausible extremes are removed. The procedure is demonstrated using observations of sub-daily and daily temporal resolution in Germany.Item Open Access Indirect downscaling of hourly precipitation based on atmospheric circulation and temperature(2013) Beck, Ferdinand; Bárdossy, AndrásThe main source of information on future climate conditions are global circulation models (GCMs). While the various GCMs agree on an increase of surface temperature, the predictions for precipitation exhibit high spread among the models, especially in shorter-than-daily temporal resolution. This paper presents a method to predict regional distributions of the hourly rainfall depth based on daily mean sea level pressure and temperature data. It is an indirect downscaling method avoiding uncertain precipitation data from the GCM. It is based on a fuzzy logic classification of atmospheric circulation patterns (CPs) that is further subdivided by means of the average daily temperature. The observed empirical distributions at 30 rain gauges to each CP-temperature class are assumed as constant and used for projections of the hourly precipitation sums in the future. The method was applied to the CP-temperature sequence derived from the 20th-century run and the scenario A1B run of ECHAM5. For the study region in southwestern Germany ECHAM5 predicts that the summers will become progressively drier. Nevertheless, the frequency of the highest hourly precipitation sums will increase. According to the predictions, estival water stress and the risk of extreme hourly precipitation will both increase simultaneously during the next decades. However, the results are yet to be confirmed by further investigation based on other GCMs.Item Open Access Hydrological modelling in data sparse environment : inverse modelling of a historical flood event(2020) Bárdossy, András; Anwar, Faizan; Seidel, JochenWe dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.Item Open Access Grundlagenbericht Niederschlags-Simulator (NiedSim3)(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2017) Müller, Thomas; Mosthaf, Tobias; Gunzenhauser, Sarah; Seidel, Jochen; Bárdossy, AndrásDas Programmsystem NiedSim3 (Niederschlags-Simulation) ist ein stochastischer Generator, mit dem für einen beliebigen, frei wählbaren Punkt in einer Modellregion Niederschlagszeitreihen erzeugt werden können, deren statistische Eigenschaften denen des natürlichen Niederschlags an diesem Ort entsprechen.Item Open Access Grundlagenbericht Niederschlags-Simulator (NiedSim3)(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2017) Müller, Thomas; Mosthaf, Tobias; Gunzenhauser, Sarah; Seidel, Jochen; Bárdossy, AndrásItem Open Access Technical note: a guide to using three open-source quality control algorithms for rainfall data from personal weather stations(2024) El Hachem, Abbas; Seidel, Jochen; O'Hara, Tess; Villalobos Herrera, Roberto; Overeem, Aart; Uijlenhoet, Remko; Bárdossy, András; de Vos, LotteThe number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.