@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} } @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} } @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} }