02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
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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 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 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.Item Open Access The use of personal weather station observations to improve precipitation estimation and interpolation(2021) Bárdossy, András; Seidel, Jochen; El Hachem, AbbasIn this study, the applicability of data from private weather stations (PWS) for precipitation interpolation was investigated. Due to unknown errors and biases in these observations, a two-step filter was developed that uses indicator correlations and event-based spatial precipitation patterns. The procedure was tested and cross validated for the state of Baden-Württemberg (Germany). The biggest improvement is achieved for the shortest time aggregations.Item Open Access Why do our rainfall-runoff models keep underestimating the peak flows?(2023) Bárdossy, András; Anwar, FaizanIn this paper, the question of how the interpolation of precipitation in space by using various spatial gauge densities affects the rainfall-runoff model discharge if all other input variables are kept constant is investigated. The main focus was on the peak flows. This was done by using a physically based model as the reference with a reconstructed spatially variable precipitation model and a conceptual model calibrated to match the reference model's output as closely as possible. Both models were run with distributed and lumped inputs. Results showed that all considered interpolation methods resulted in the underestimation of the total precipitation volume and that the underestimation was directly proportional to the precipitation amount. More importantly, the underestimation of peaks was very severe for low observation densities and disappeared only for very high-density precipitation observation networks. This result was confirmed by using observed precipitation with different observation densities. Model runoffs showed worse performance for their highest discharges. Using lumped inputs for the models showed deteriorating performance for peak flows as well, even when using simulated precipitation.Item Open Access A methodology to estimate flow duration curves at partially ungauged basins(2020) Ridolfi, Elena; Kumar, Hemendra; Bárdossy, AndrásThe flow duration curve (FDC) of streamflow at a specific site has a key role in the knowledge on the distribution and characteristics of streamflow at that site. The FDC gives information on the water regime, providing information to optimally manage the water resources of the river. In spite of its importance, because of the lack of streamflow gauging stations, the FDC construction can be a not straightforward task. In partially gauged basins, FDCs are usually built using regionalization among the other methods. In this paper we show that the FDC is not a characteristic of the basin only, but of both the basin and the weather. Different weather conditions lead to different FDCs for the same catchment. The differences can often be significant. Similarly, the FDC built at a site for a specific period cannot be used to retrieve the FDC at a different site for the same time window. In this paper, we propose a new methodology to estimate FDCs at partially gauged basins (i.e., target sites) using precipitation data gauged at another basin (i.e., donor site). The main idea is that it is possible to retrieve the FDC of a target period of time using the data gauged during a given donor time period for which data are available at both target and donor sites. To test the methodology, several donor and target time periods are analyzed and results are shown for different sites in the USA. The comparison between estimated and actually observed FDCs shows the reasonability of the approach, especially for intermediate percentiles.Item Open Access Changing correlations : a flexible definition of non-Gaussian multivariate dependence(2023) Bárdossy, AndrásDependencies between variables are often very complex, and may for high values, be different from that of the low values. As the normal distribution and the corresponding copula behave symmetrically for low and high values the frequent application of the normal copula for the description of the dependence may be inappropriate. In this contribution a new way of defining high dimensional multivariate distributions with changing correlations is presented. The method can also be used for a flexible definition of tail dependence. Examples of copulas with linear changing correlations illustrate the methodology. Parameter estimation methods and simulation procedures are discussed. A five dimensional example using groundwater quality data and another four dimensional one using air pollution data, are used to illustrate the methodology.