@article{MacdonaldMerzGuseetal.2022,
  author    = {Macdonald, Elena and Merz, Bruno and Guse, Bj{\"o}rn and Wietzke, Luzie and Ullrich, Sophie and Kemter, Matthias and Ahrens, Bodo and Vorogushyn, Sergiy},
  title     = {Event and catchment controls of heavy tail behavior of floods},
  series = {Water resources research},
  volume    = {58},
  journal   = {Water resources research},
  number    = {6},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2021WR031260},
  pages     = {25},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{MerzBassoFischeretal.2022,
  author    = {Merz, Bruno and Basso, Stefano and Fischer, Svenja and Lun, David and Bloeschl, Guenter and Merz, Ralf and Guse, Bjorn and Viglione, Alberto and Vorogushyn, Sergiy and Macdonald, Elena and Wietzke, Luzie and Schumann, Andreas},
  title     = {Understanding heavy tails of flood peak distributions},
  series = {Water resources research},
  volume    = {58},
  journal   = {Water resources research},
  number    = {6},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2021WR030506},
  pages     = {37},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@misc{GusePfannerstillGafurovetal.2017,
  author    = {Guse, Bj{\"o}rn and Pfannerstill, Matthias and Gafurov, Abror and Kiesel, Jens and Lehr, Christian and Fohrer, Nicola},
  title     = {Identifying the connective strength between model parameters and performance criteria},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {657},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41914},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-419142},
  pages     = {17},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@article{MetinNguyenVietDungSchroeteretal.2018,
  author    = {Metin, Ayse Duha and Nguyen Viet Dung, and Schr{\"o}ter, Kai and Guse, Bj{\"o}rn and Apel, Heiko and Kreibich, Heidi and Vorogushyn, Sergiy and Merz, Bruno},
  title     = {How do changes along the risk chain affect flood risk?},
  series = {Natural hazards and earth system sciences},
  volume    = {18},
  journal   = {Natural hazards and earth system sciences},
  number    = {11},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-18-3089-2018},
  pages     = {3089 -- 3108},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{MerzApelDungNguyenetal.2018,
  author    = {Merz, Bruno and Apel, Heiko and Dung Nguyen, Viet-Dung and Falter, Daniela and Guse, Bj{\"o}rn and Hundecha, Yeshewatesfa and Kreibich, Heidi and Schr{\"o}ter, Kai and Vorogushyn, Sergiy},
  title     = {From precipitation to damage},
  series = {Global flood hazard : applications in modeling, mapping and forecasting},
  volume    = {233},
  journal   = {Global flood hazard : applications in modeling, mapping and forecasting},
  publisher = {American Geophysical Union},
  address   = {Washington},
  isbn      = {978-1-119-21788-6},
  issn      = {0065-8448},
  doi       = {10.1002/9781119217886.ch10},
  pages     = {169 -- 183},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{WietzkeMerzGerlitzetal.2020,
  author    = {Wietzke, Luzie M. and Merz, Bruno and Gerlitz, Lars and Kreibich, Heidi and Guse, Bj{\"o}rn and Castellarin, Attilio and Vorogushyn, Sergiy},
  title     = {Comparative analysis of scalar upper tail indicators},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {65},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {10},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2020.1769104},
  pages     = {1625 -- 1639},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{GusePfannerstillGafurovetal.2017,
  author    = {Guse, Bj{\"o}rn and Pfannerstill, Matthias and Gafurov, Abror and Kiesel, Jens and Lehr, Christian and Fohrer, Nicola},
  title     = {Identifying the connective strength between model parameters and performance criteria},
  series = {Hydrology and earth system sciences : HESS},
  volume    = {21},
  journal   = {Hydrology and earth system sciences : HESS},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-21-5663-2017},
  pages     = {5663 -- 5679},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@article{MetinDungSchroeteretal.2020,
  author    = {Metin, Ayse Duha and Dung, Nguyen Viet and Schr{\"o}ter, Kai and Vorogushyn, Sergiy and Guse, Bj{\"o}rn and Kreibich, Heidi and Merz, Bruno},
  title     = {The role of spatial dependence for large-scale flood risk estimation},
  series = {Natural hazards and earth system sciences},
  volume    = {20},
  journal   = {Natural hazards and earth system sciences},
  number    = {4},
  publisher = {European Geosciences Union (EGU) ; Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-20-967-2020},
  pages     = {967 -- 979},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Guse2010,
  author    = {Guse, Bj{\"o}rn Felix},
  title     = {Improving flood frequency analysis by integration of empirical and probabilistic regional envelope curves},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49265},
  school      = {Universit{\"a}t Potsdam},
  year      = {2010},
  abstract  = {Flood design necessitates discharge estimates for large recurrence intervals. However, in a flood frequency analysis, the uncertainty of discharge estimates increases with higher recurrence intervals, particularly due to the small number of available flood data. Furthermore, traditional distribution functions increase unlimitedly without consideration of an upper bound discharge. Hence, additional information needs to be considered which is representative for high recurrence intervals. Envelope curves which bound the maximum observed discharges of a region are an adequate regionalisation method to provide additional spatial information for the upper tail of a distribution function. Probabilistic regional envelope curves (PRECs) are an extension of the traditional empirical envelope curve approach, in which a recurrence interval is estimated for a regional envelope curve (REC). The REC is constructed for a homogeneous pooling group of sites. The estimation of this recurrence interval is based on the effective sample years of data considering the intersite dependence among all sites of the pooling group. The core idea of this thesis was an improvement of discharge estimates for high recurrence intervals by integrating empirical and probabilistic regional envelope curves into the flood frequency analysis. Therefore, the method of probabilistic regional envelope curves was investigated in detail. Several pooling groups were derived by modifying candidate sets of catchment descriptors and settings of two different pooling methods. These were used to construct PRECs. A sensitivity analysis shows the variability of discharges and the recurrence intervals for a given site due to the different assumptions. The unit flood of record which governs the intercept of PREC was determined as the most influential aspect. By separating the catchments into nested and unnested pairs, the calculation algorithm for the effective sample years of data was refined. In this way, the estimation of the recurrence intervals was improved, and therefore the use of different parameter sets for nested and unnested pairs of catchments is recommended. In the second part of this thesis, PRECs were introduced into a distribution function. Whereas in the traditional approach only discharge values are used, PRECs provide a discharge and its corresponding recurrence interval. Hence, a novel approach was developed, which allows a combination of the PREC results with the traditional systematic flood series while taking the PREC recurrence interval into consideration. An adequate mixed bounded distribution function was presented, which in addition to the PREC results also uses an upper bound discharge derived by an empirical envelope curve. By doing so, two types of additional information which are representative for the upper tail of a distribution function were included in the flood frequency analysis. The integration of both types of additional information leads to an improved discharge estimation for recurrence intervals between 100 and 1000 years.},
  language  = {en}
}
@misc{TarasovaMerzKissetal.2019,
  author    = {Tarasova, Larisa and Merz, Ralf and Kiss, Andrea and Basso, Stefano and Bl{\"o}chl, G{\"u}nter and Merz, Bruno and Viglione, Alberto and Pl{\"o}tner, Stefan and Guse, Bj{\"o}rn and Schumann, Andreas and Fischer, Svenja and Ahrens, Bodo and Anwar, Faizan and B{\´a}rdossy, Andr{\´a}s and B{\"u}hler, Philipp and Haberlandt, Uwe and Kreibich, Heidi and Krug, Amelie and Lun, David and M{\"u}ller-Thomy, Hannes and Pidoto, Ross and Primo, Cristina and Seidel, Jochen and Vorogushyn, Sergiy and Wietzke, Luzie},
  title     = {Causative classification of river flood events},
  series = {Wiley Interdisciplinary Reviews : Water},
  volume    = {6},
  journal   = {Wiley Interdisciplinary Reviews : Water},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {2049-1948},
  doi       = {10.1002/wat2.1353},
  pages     = {23},
  year      = {2019},
  abstract  = {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},
  language  = {en}
}
@misc{MetinDungSchroeteretal.2018,
  author    = {Metin, Ayse Duha and Dung, Nguyen Viet and Schr{\"o}ter, Kai and Guse, Bj{\"o}rn and Apel, Heiko and Kreibich, Heidi and Vorogushyn, Sergiy and Merz, Bruno},
  title     = {How do changes along the risk chain affect flood risk?},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1067},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-46879},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-468790},
  pages     = {22},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}