@article{KellermannSchroeterThiekenetal.2020, author = {Kellermann, Patric and Schr{\"o}ter, Kai and Thieken, Annegret and Haubrock, S{\"o}ren-Nils and Kreibich, Heidi}, title = {The object-specific flood damage database HOWAS 21}, series = {Natural hazards and earth system sciences}, volume = {20}, journal = {Natural hazards and earth system sciences}, number = {9}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1561-8633}, doi = {10.5194/nhess-20-2503-2020}, pages = {2503 -- 2519}, year = {2020}, abstract = {The Flood Damage Database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. The main purpose of development of HOWAS 21 was to support forensic flood analysis and the derivation of flood damage models. HOWAS 21 was first developed for Germany and currently almost exclusively contains datasets from Germany. However, its scope has recently been enlarged with the aim to serve as an international flood damage database; e.g. its web application is now available in German and English. This paper presents the recent advancements of HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data. The data applications indicate a large potential of the database for fostering a better understanding and estimation of the consequences of flooding.}, language = {en} } @phdthesis{Schroeter2020, author = {Schr{\"o}ter, Kai}, title = {Improved flood risk assessment}, doi = {10.25932/publishup-48024}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-480240}, school = {Universit{\"a}t Potsdam}, pages = {408}, year = {2020}, abstract = {Rivers have always flooded their floodplains. Over 2.5 billion people worldwide have been affected by flooding in recent decades. The economic damage is also considerable, averaging 100 billion US dollars per year. There is no doubt that damage and other negative effects of floods can be avoided. However, this has a price: financially and politically. Costs and benefits can be estimated through risk assessments. Questions about the location and frequency of floods, about the objects that could be affected and their vulnerability are of importance for flood risk managers, insurance companies and politicians. Thus, both variables and factors from the fields of hydrology and sociol-economics play a role with multi-layered connections. One example are dikes along a river, which on the one hand contain floods, but on the other hand, by narrowing the natural floodplains, accelerate the flood discharge and increase the danger of flooding for the residents downstream. Such larger connections must be included in the assessment of flood risk. However, in current procedures this is accompanied by simplifying assumptions. Risk assessments are therefore fuzzy and associated with uncertainties. This thesis investigates the benefits and possibilities of new data sources for improving flood risk assessment. New methods and models are developed, which take the mentioned interrelations better into account and also quantify the existing uncertainties of the model results, and thus enable statements about the reliability of risk estimates. For this purpose, data on flood events from various sources are collected and evaluated. This includes precipitation and flow records at measuring stations as well as for instance images from social media, which can help to delineate the flooded areas and estimate flood damage with location information. Machine learning methods have been successfully used to recognize and understand correlations between floods and impacts from a wide range of data and to develop improved models. Risk models help to develop and evaluate strategies to reduce flood risk. These tools also provide advanced insights into the interplay of various factors and on the expected consequences of flooding. This work shows progress in terms of an improved assessment of flood risks by using diverse data from different sources with innovative methods as well as by the further development of models. Flood risk is variable due to economic and climatic changes, and other drivers of risk. In order to keep the knowledge about flood risks up-to-date, robust, efficient and adaptable methods as proposed in this thesis are of increasing importance.}, 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} }