02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
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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 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 Simultaneous calibration of hydrological models in geographical space(2016) Bárdossy, András; Huang, Yingchun; Wagener, ThorstenHydrological models are usually calibrated for selected catchments individually using specific performance criteria. This procedure assumes that the catchments show individual behavior. As a consequence, the transfer of model parameters to other ungauged catchments is problematic. In this paper, the possibility of transferring part of the model parameters was investigated. Three different conceptual hydrological models were considered. The models were restructured by introducing a new parameter η which exclusively controls water balances. This parameter was considered as individual to each catchment. All other parameters, which mainly control the dynamics of the discharge (dynamical parameters), were considered for spatial transfer. Three hydrological models combined with three different performance measures were used in three different numerical experiments to investigate this transferability. The first numerical experiment, involving individual calibration of the models for 15 selected MOPEX catchments, showed that it is difficult to identify which catchments share common dynamical parameters. In the second numerical experiment, a common spatial calibration strategy was used. It was explicitly assumed that the catchments share common dynamical parameters. In the third numerical experiment, the common calibration methodology was applied for 96 catchments. Another set of 96 catchments was used to test the transfer of common dynamical parameters. The results show that even a large number of catchments share similar dynamical parameters. The performance is worse than those obtained by individual calibration, but the transfer to ungauged catchments remains possible. The performance of the common parameters in the second experiment was better than in the third, indicating that the selection of the catchments for common calibration is important.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 Assessing rainfall radar errors with an inverse stochastic modelling framework(2024) Green, Amy C.; Kilsby, Chris; Bárdossy, AndrásWeather radar is a crucial tool for rainfall observation and forecasting, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources - including attenuation, ground clutter, beam blockage and drop-size distribution - with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods and imposing path-integrated attenuation effects, a stochastic drop-size-distribution field, and sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field and the corrected “best-guess” rainfall field which would be obtained if they were observed in a real-world case. The structure of these errors is then investigated, with a focus on the frequency and behaviour of “rainfall shadows”. Half of the simulated weather radar images have at least 3 % of their significant rainfall rates shadowed, and 25 % have at least 45 km 2 containing rainfall shadows, resulting in underestimation of the potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well in generating realistic weather radar images visually for a large range of event types.Item Open Access Probabilistic downscaling of EURO-CORDEX precipitation data for the assessment of future areal precipitation extremes for hourly to daily durations(2025) El Hachem, Abbas; Seidel, Jochen; Bárdossy, AndrásThis work presents a methodology to inspect the changing statistical properties of precipitation extremes with climate change. Data from regional climate models for the European continent (EURO-CORDEX 11) were used. The use of climate model data first requires an inspection of the data and a correction of the biases of the meteorological model. Corrections to the biases of the point precipitation data and those of the spatial structure were both performed. For this purpose, a quantile–quantile transformation of the point precipitation data and a spatial recorrelation method were used. Once corrected for bias, the data from the regional climate model were downscaled to a finer spatial scale using a stochastic method with equally probable outcomes. This allows for the assessment of the corresponding uncertainties. The downscaled fields were used to derive area–depth–duration–frequency (ADDF) curves and areal reduction factors (ARFs) for selected regions in Germany. The estimated curves were compared to those derived from a reference weather radar dataset. While the corrected and downscaled data show good agreement with the observed reference data over all temporal and spatial scales, the future climate simulations indicate an increase in the estimated areal rainfall depth for future periods. Moreover, the future ARFs for short durations and large spatial scales increase compared to the reference value, while for longer durations the difference is smaller.