TY - JOUR A1 - Metin, Ayse Duha A1 - Dung, Nguyen Viet A1 - Schröter, Kai A1 - Vorogushyn, Sergiy A1 - Guse, Björn A1 - Kreibich, Heidi A1 - Merz, Bruno T1 - The role of spatial dependence for large-scale flood risk estimation JF - Natural hazards and earth system sciences N2 - Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments. Y1 - 2020 U6 - https://doi.org/10.5194/nhess-20-967-2020 SN - 1561-8633 SN - 1684-9981 VL - 20 IS - 4 SP - 967 EP - 979 PB - European Geosciences Union (EGU) ; Copernicus CY - Göttingen ER - TY - JOUR A1 - Guse, Björn A1 - Pfannerstill, Matthias A1 - Gafurov, Abror A1 - Kiesel, Jens A1 - Lehr, Christian A1 - Fohrer, Nicola T1 - Identifying the connective strength between model parameters and performance criteria JF - Hydrology and earth system sciences : HESS N2 - In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.& para;& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.& para;& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.& para;& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling. Y1 - 2017 U6 - https://doi.org/10.5194/hess-21-5663-2017 SN - 1027-5606 SN - 1607-7938 VL - 21 SP - 5663 EP - 5679 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Guse, Björn A1 - Pfannerstill, Matthias A1 - Gafurov, Abror A1 - Kiesel, Jens A1 - Lehr, Christian A1 - Fohrer, Nicola T1 - Identifying the connective strength between model parameters and performance criteria T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.& para;& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.& para;& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.& para;& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 657 KW - flow-duration curves KW - hydrological models KW - multiobjective calibration KW - sensitivity-analysis KW - landscape controls KW - lowland catchment KW - regional patterns KW - physical controls KW - water-balance KW - part 1 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419142 SN - 1866-8372 IS - 657 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 - Metin, Ayse Duha A1 - Nguyen Viet Dung, 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? JF - Natural hazards and earth system sciences 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. Y1 - 2018 U6 - https://doi.org/10.5194/nhess-18-3089-2018 SN - 1561-8633 SN - 1684-9981 VL - 18 IS - 11 SP - 3089 EP - 3108 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Merz, Bruno A1 - Apel, Heiko A1 - Dung Nguyen, Viet-Dung A1 - Falter, Daniela A1 - Guse, Björn A1 - Hundecha, Yeshewatesfa A1 - Kreibich, Heidi A1 - Schröter, Kai A1 - Vorogushyn, Sergiy T1 - From precipitation to damage BT - a coupled model chain for spatially coherent, large-scale flood risk assessment JF - Global flood hazard : applications in modeling, mapping and forecasting N2 - Flood risk assessments for large river basins often involve piecing together smaller-scale assessments leading to erroneous risk statements. We describe a coupled model chain for quantifying flood risk at the scale of 100,000 km(2). It consists of a catchment model, a 1D-2D river network model, and a loss model. We introduce the model chain and present two applications. The first application for the Elbe River basin with an area of 66,000 km(2) demonstrates that it is feasible to simulate the complete risk chain for large river basins in a continuous simulation mode with high temporal and spatial resolution. In the second application, RFM is coupled to a multisite weather generator and applied to the Mulde catchment with an area of 6,000 km(2). This approach is able to provide a very long time series of spatially heterogeneous patterns of precipitation, discharge, inundation, and damage. These patterns respect the spatial correlation of the different processes and are suitable to derive large-scale risk estimates. We discuss how the RFM approach can be transferred to the continental scale. Y1 - 2018 SN - 978-1-119-21788-6 SN - 978-1-119-21786-2 U6 - https://doi.org/10.1002/9781119217886.ch10 SN - 0065-8448 VL - 233 SP - 169 EP - 183 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Macdonald, Elena A1 - Merz, Bruno A1 - Guse, Björn A1 - Wietzke, Luzie A1 - Ullrich, Sophie A1 - Kemter, Matthias A1 - Ahrens, Bodo A1 - Vorogushyn, Sergiy T1 - Event and catchment controls of heavy tail behavior of floods JF - Water resources research N2 - In some catchments, the distribution of annual maximum streamflow shows heavy tail behavior, meaning the occurrence probability of extreme events is higher than if the upper tail decayed exponentially. Neglecting heavy tail behavior can lead to an underestimation of the likelihood of extreme floods and the associated risk. Partly contradictory results regarding the controls of heavy tail behavior exist in the literature and the knowledge is still very dispersed and limited. To better understand the drivers, we analyze the upper tail behavior and its controls for 480 catchments in Germany and Austria over a period of more than 50 years. The catchments span from quickly reacting mountain catchments to large lowland catchments, allowing for general conclusions. We compile a wide range of event and catchment characteristics and investigate their association with an indicator of the tail heaviness of flood distributions, namely the shape parameter of the GEV distribution. Following univariate analyses of these characteristics, along with an evaluation of different aggregations of event characteristics, multiple linear regression models, as well as random forests, are constructed. A novel slope indicator, which represents the relation between the return period of flood peaks and event characteristics, captures the controls of heavy tails best. Variables describing the catchment response are found to dominate the heavy tail behavior, followed by event precipitation, flood seasonality, and catchment size. The pre-event moisture state in a catchment has no relevant impact on the tail heaviness even though it does influence flood magnitudes. KW - heavy tail behavior KW - floods KW - event characteristics KW - catchment KW - characteristics KW - catchment response Y1 - 2022 U6 - https://doi.org/10.1029/2021WR031260 SN - 0043-1397 SN - 1944-7973 VL - 58 IS - 6 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Wietzke, Luzie M. A1 - Merz, Bruno A1 - Gerlitz, Lars A1 - Kreibich, Heidi A1 - Guse, Björn A1 - Castellarin, Attilio A1 - Vorogushyn, Sergiy T1 - Comparative analysis of scalar upper tail indicators JF - Hydrological sciences journal = Journal des sciences hydrologiques N2 - Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness - the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution. KW - upper tail behaviour KW - heavy-tailed distributions KW - extremes KW - diagnostics KW - surprise Y1 - 2020 U6 - https://doi.org/10.1080/02626667.2020.1769104 SN - 0262-6667 SN - 2150-3435 VL - 65 IS - 10 SP - 1625 EP - 1639 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Tarasova, Larisa A1 - Merz, Ralf A1 - Kiss, Andrea A1 - Basso, Stefano A1 - Blöchl, Günter A1 - Merz, Bruno A1 - Viglione, Alberto A1 - Plötner, Stefan A1 - Guse, Björn A1 - Schumann, Andreas A1 - Fischer, Svenja A1 - Ahrens, Bodo A1 - Anwar, Faizan A1 - Bárdossy, András A1 - Bühler, Philipp A1 - Haberlandt, Uwe A1 - Kreibich, Heidi A1 - Krug, Amelie A1 - Lun, David A1 - Müller-Thomy, Hannes A1 - Pidoto, Ross A1 - Primo, Cristina A1 - Seidel, Jochen A1 - Vorogushyn, Sergiy A1 - Wietzke, Luzie T1 - Causative classification of river flood events JF - Wiley Interdisciplinary Reviews : Water N2 - A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large-scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph-based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space-time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods. This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes Science of Water > Methods KW - flood genesis KW - flood mechanisms KW - flood typology KW - historical floods KW - hydroclimatology of floods Y1 - 2019 U6 - https://doi.org/10.1002/wat2.1353 SN - 2049-1948 VL - 6 IS - 4 PB - Wiley CY - Hoboken ER -