TY - GEN A1 - Murawski, Aline A1 - Bürger, Gerd A1 - Vorogushyn, Sergiy A1 - Merz, Bruno T1 - Can local climate variability be explained by weather patterns? BT - a multi-station evaluation for the Rhine basin T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 525 KW - athmospheric circulation patterns KW - stochastic rainfall model KW - within-type variability KW - river Rhine KW - precipitation KW - temperature KW - trends KW - classification KW - Europe KW - scenarios Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-410155 SN - 1866-8372 IS - 525 ER - TY - GEN A1 - Metin, Ayse Duha A1 - Dung, Nguyen Viet A1 - Schröter, Kai A1 - Guse, Björn A1 - Apel, Heiko A1 - Kreibich, Heidi A1 - Vorogushyn, Sergiy A1 - Merz, Bruno T1 - How do changes along the risk chain affect flood risk? T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1067 KW - global sensitivity analysis KW - climate change KW - river floods KW - frequency KW - Europe KW - model KW - vulnerability KW - adaptation KW - strategies KW - catchment Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-468790 SN - 1866-8372 IS - 1067 ER - TY - JOUR A1 - Duethmann, Doris A1 - Bolch, Tobias A1 - Farinotti, Daniel A1 - Kriegel, David A1 - Vorogushyn, Sergiy A1 - Merz, Bruno A1 - Pieczonka, Tino A1 - Jiang, Tong A1 - Su, Buda A1 - Güntner, Andreas T1 - Attribution of streamflow trends in snow and glacier melt-dominated catchments of the Tarim River, Central Asia JF - Water resources research N2 - Observed streamflow of headwater catchments of the Tarim River (Central Asia) increased by about 30% over the period 1957-2004. This study aims at assessing to which extent these streamflow trends can be attributed to changes in air temperature or precipitation. The analysis includes a data-based approach using multiple linear regression and a simulation-based approach using a hydrological model. The hydrological model considers changes in both glacier area and surface elevation. It was calibrated using a multiobjective optimization algorithm with calibration criteria based on glacier mass balance and daily and interannual variations of discharge. The individual contributions to the overall streamflow trends from changes in glacier geometry, temperature, and precipitation were assessed using simulation experiments with a constant glacier geometry and with detrended temperature and precipitation time series. The results showed that the observed changes in streamflow were consistent with the changes in temperature and precipitation. In the Sari-Djaz catchment, increasing temperatures and related increase of glacier melt were identified as the dominant driver, while in the Kakshaal catchment, both increasing temperatures and increasing precipitation played a major role. Comparing the two approaches, an advantage of the simulation-based approach is the fact that it is based on process-based relationships implemented in the hydrological model instead of statistical links in the regression model. However, data-based approaches are less affected by model parameter and structural uncertainties and typically fast to apply. A complementary application of both approaches is recommended. KW - trend analysis KW - data-based KW - simulation-based KW - multiobjective calibration KW - hydrological modeling KW - glacier melt Y1 - 2015 U6 - https://doi.org/10.1002/2014WR016716 SN - 0043-1397 SN - 1944-7973 VL - 51 IS - 6 SP - 4727 EP - 4750 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Merz, Bruno A1 - Viet Dung Nguyen, A1 - Vorogushyn, Sergiy T1 - Temporal clustering of floods in Germany: Do flood-rich and flood-poor periods exist? JF - Journal of hydrology N2 - The repeated occurrence of exceptional floods within a few years, such as the Rhine floods in 1993 and 1995 and the Elbe and Danube floods in 2002 and 2013, suggests that floods in Central Europe may be organized in flood-rich and flood-poor periods. This hypothesis is studied by testing the significance of temporal clustering in flood occurrence (peak-over-threshold) time series for 68 catchments across Germany for the period 1932-2005. To assess the robustness of the results, different methods are used: Firstly, the index of dispersion, which quantifies the departure from a homogeneous Poisson process, is investigated. Further, the time-variation of the flood occurrence rate is derived by non-parametric kernel implementation and the significance of clustering is evaluated via parametric and non-parametric tests. Although the methods give consistent overall results, the specific results differ considerably. Hence, we recommend applying different methods when investigating flood clustering. For flood estimation and risk management, it is of relevance to understand whether clustering changes with flood severity and time scale. To this end, clustering is assessed for different thresholds and time scales. It is found that the majority of catchments show temporal clustering at the 5% significance level for low thresholds and time scales of one to a few years. However, clustering decreases substantially with increasing threshold and time scale. We hypothesize that flood clustering in Germany is mainly caused by catchment memory effects along with intra- to inter-annual climate variability, and that decadal climate variability plays a minor role. (C) 2016 Elsevier B.V. All rights reserved. KW - Climate variability KW - Flooding KW - Temporal clustering KW - Index of dispersion KW - Kernel occurrence rate Y1 - 2016 U6 - https://doi.org/10.1016/j.jhydrol.2016.07.041 SN - 0022-1694 SN - 1879-2707 VL - 541 SP - 824 EP - 838 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - He, Zhihua A1 - Unger-Shayesteh, Katy A1 - Vorogushyn, Sergiy A1 - Weise, Stephan M. A1 - Kalashnikova, Olga A1 - Gafurov, Abror A1 - Duethmann, Doris A1 - Barandun, Martina A1 - Merz, Bruno T1 - Constraining hydrological model parameters using water isotopic compositions in a glacierized basin, Central Asia JF - Journal of hydrology N2 - Water stable isotope signatures can provide valuable insights into the catchment internal runoff processes. However, the ability of the water isotope data to constrain the internal apportionments of runoff components in hydrological models for glacierized basins is not well understood. This study developed an approach to simultaneously model the water stable isotopic compositions and runoff processes in a glacierized basin in Central Asia. The fractionation and mixing processes of water stable isotopes in and from the various water sources were integrated into a glacio-hydrological model. The model parameters were calibrated on discharge, snow cover and glacier mass balance data, and additionally isotopic composition of streamflow. We investigated the value of water isotopic compositions for the calibration of model parameters, in comparison to calibration methods without using such measurements. Results indicate that: (1) The proposed isotope-hydrological integrated modeling approach was able to reproduce the isotopic composition of streamflow, and improved the model performance in the evaluation period; (2) Involving water isotopic composition for model calibration reduced the model parameter uncertainty, and helped to reduce the uncertainty in the quantification of runoff components; (3) The isotope-hydrological integrated modeling approach quantified the contributions of runoff components comparably to a three-component tracer-based end-member mixing analysis method for summer peak flows, and required less water tracer data. Our findings demonstrate the value of water isotopic compositions to improve the quantification of runoff components using hydrological models in glacierized basins. KW - Water stable isotope KW - Isotope-hydrological integrated modeling KW - Quantification of runoff components KW - Glacierized basins Y1 - 2019 U6 - https://doi.org/10.1016/j.jhydrol.2019.01.048 SN - 0022-1694 SN - 1879-2707 VL - 571 SP - 332 EP - 348 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Murawski, Aline A1 - Vorogushyn, Sergiy A1 - Bürger, Gerd A1 - Gerlitz, Lars A1 - Merz, Bruno T1 - Do changing weather types explain observed climatic trends in the rhine basin? BT - an analysis of within- and between-type changes JF - Journal of geophysical of geophysical research-atmosheres N2 - For attributing hydrological changes to anthropogenic climate change, catchment models are driven by climate model output. A widespread approach to bridge the spatial gap between global climate and hydrological catchment models is to use a weather generator conditioned on weather patterns (WPs). This approach assumes that changes in local climate are characterized by between-type changes of patterns. In this study we test this assumption by analyzing a previously developed WP classification for the Rhine basin, which is based on dynamic and thermodynamic variables. We quantify changes in pattern characteristics and associated climatic properties. The amount of between- and within-type changes is investigated by comparing observed trends to trends resulting solely from WP occurrence. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901-2010 with a minimum length of 31 years are analyzed. Increasing frequency is found for some patterns associated with high precipitation, although the trend sign highly depends on the considered period. Trends and interannual variations of WP frequencies are related to the long-term variability of large-scale circulation modes. Long-term WP internal warming is evident for summer patterns and enhanced warming for spring/autumn patterns since the 1970s. Observed trends in temperature and partly in precipitation are mainly associated with frequency changes of specific WPs, but some amount of within-type changes remains. The classification can be used for downscaling of past changes considering this limitation, but the inclusion of thermodynamic variables into the classification impedes the downscaling of future climate projections. KW - attribution KW - weather pattern KW - trend analysis KW - downscaling KW - hypothetical trend Y1 - 2018 U6 - https://doi.org/10.1002/2017JD026654 SN - 2169-897X SN - 2169-8996 VL - 123 IS - 3 SP - 1562 EP - 1584 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - He, Zhihua A1 - Vorogushyn, Sergiy A1 - Unger-Shayesteh, Katy A1 - Gafurov, Abror A1 - Kalashnikova, Olga A1 - Omorova, Elvira A1 - Merz, Bruno T1 - The Value of Hydrograph Partitioning Curves for Calibrating Hydrological Models in Glacierized Basins JF - Water resources research N2 - This study refines the method for calibrating a glacio-hydrological model based on Hydrograph Partitioning Curves (HPCs), and evaluates its value in comparison to multidata set optimization approaches which use glacier mass balance, satellite snow cover images, and discharge. The HPCs are extracted from the observed flow hydrograph using catchment precipitation and temperature gradients. They indicate the periods when the various runoff processes, such as glacier melt or snow melt, dominate the basin hydrograph. The annual cumulative curve of the difference between average daily temperature and melt threshold temperature over the basin, as well as the annual cumulative curve of average daily snowfall on the glacierized areas are used to identify the starting and end dates of snow and glacier ablation periods. Model parameters characterizing different runoff processes are calibrated on different HPCs in a stepwise and iterative way. Results show that the HPC-based method (1) delivers model-internal consistency comparably to the tri-data set calibration method; (2) improves the stability of calibrated parameter values across various calibration periods; and (3) estimates the contributions of runoff components similarly to the tri-data set calibration method. Our findings indicate the potential of the HPC-based approach as an alternative for hydrological model calibration in glacierized basins where other calibration data sets than discharge are often not available or very costly to obtain. KW - hydrograph partitioning curves KW - model calibration KW - glacierized basins Y1 - 2018 U6 - https://doi.org/10.1002/2017WR021966 SN - 0043-1397 SN - 1944-7973 VL - 54 IS - 3 SP - 2336 EP - 2361 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Ullrich, Sophie Louise A1 - Hegnauer, Mark A1 - Nguyen, Dung Viet A1 - Merz, Bruno A1 - Kwadijk, Jaap A1 - Vorogushyn, Sergiy T1 - Comparative evaluation of two types of stochastic weather generators for synthetic precipitation in the Rhine basin JF - Journal of hydrology N2 - Stochastic modeling of precipitation for estimation of hydrological extremes is an important element of flood risk assessment and management. The spatially consistent estimation of rainfall fields and their temporal variability remains challenging and is addressed by various stochastic weather generators. In this study, two types of weather generators are evaluated against observed data and benchmarked regarding their ability to simulate spatio-temporal precipitation fields in the Rhine catchment. A multi-site station-based weather generator uses an auto-regressive model and estimates the spatial correlation structure between stations. Another weather generator is raster-based and uses the nearest-neighbor resampling technique for reshuffling daily patterns while preserving the correlation structure between the observations. Both weather generators perform well and are comparable at the point (station) scale with regards to daily mean and 99.9th percentile precipitation as well as concerning wet/dry frequencies and transition probabilities. The areal extreme precipitation at the sub-basin scale is however overestimated in the station-based weather generator due to an overestimation of the correlation structure between individual stations. The auto-regressive model tends to generate larger rainfall fields in space for extreme precipitation than observed, particularly in summer. The weather generator based on nearest-neighbor resampling reproduces the observed daily and multiday (5, 10 and 20) extreme events in a similar magnitude. Improvements in performance regarding wet frequencies and transition probabilities are recommended for both models. KW - Rainfall generation KW - Rainfall occurrence KW - Multi-site stochastic weather KW - generator KW - Resampling weather generator KW - Time series analysis Y1 - 2021 U6 - https://doi.org/10.1016/j.jhydrol.2021.126544 SN - 0022-1694 SN - 1879-2707 VL - 601 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Merz, Bruno A1 - Basso, Stefano A1 - Fischer, Svenja A1 - Lun, David A1 - Bloeschl, Guenter A1 - Merz, Ralf A1 - Guse, Bjorn A1 - Viglione, Alberto A1 - Vorogushyn, Sergiy A1 - Macdonald, Elena A1 - Wietzke, Luzie A1 - Schumann, Andreas T1 - Understanding heavy tails of flood peak distributions JF - Water resources research N2 - Statistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails. KW - extreme events KW - flood frequency KW - flood risk KW - upper tail Y1 - 2022 U6 - https://doi.org/10.1029/2021WR030506 SN - 0043-1397 SN - 1944-7973 VL - 58 IS - 6 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Sairam, Nivedita A1 - Brill, Fabio Alexander A1 - Sieg, Tobias A1 - Farrag, Mostafa A1 - Kellermann, Patric A1 - Viet Dung Nguyen, A1 - Lüdtke, Stefan A1 - Merz, Bruno A1 - Schröter, Kai A1 - Vorogushyn, Sergiy A1 - Kreibich, Heidi T1 - Process-based flood risk assessment for Germany JF - Earth's future / American Geophysical Union N2 - Large-scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process-based Regional Flood Model (RFM) to simulate a 5000-year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D-2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector-wise exposure data and empirical multi-variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5% confidence for Germany to be euro0.529 bn and euro8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. The overall EAD is comparable to other large-scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large-scale assessments with homogeneous return periods. Thus, the process-based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German-wide flood risk assessments. KW - risk model chain KW - continuous simulation KW - expected annual damage KW - risk KW - curves KW - multi-sector risk Y1 - 2021 U6 - https://doi.org/10.1029/2021EF002259 SN - 2328-4277 VL - 9 IS - 10 PB - Wiley-Blackwell CY - Hoboken, NJ ER -