ALBERT

Description: Increasing flood losses over the last decades emphasize the need towards significantly improved and more efficient flood risk management. One key requirement is reliable risk assessment in conjunction with consistent flood loss modeling. Current risk assessments and flood loss estimations for Europe are until now based on regional approaches using deterministic depth-damage function and do rarely report associated uncertainties. To reduce these shortcomings, we present the results of a novel, consistent approach based on the Bayesian Network Flood Loss Estimation MOdel for the private sector (BN-FLEMOps). The dataset is consistent in terms of the input data used to drive the model and because we use the same vulnerability model to derive the flood loss estimation. Essential inputs for any flood loss estimation are hazard (usually water depth), asset (value of objects at risk) and flood experience parameters. The hazard input was given by a European inundation scenario for a continent-wide flood with 100 years return period (Alfieri et al., 2014). Asset values were computed following the the approach by Huizinga et al. (2017) and the flood experience was derived using the database of the Dartmouth Flood Observatory (DFO) (Brakenridge, 2018).

Description: The dataset comprises a range of variables describing characteristics of flood events and river catchments for 480 gauging stations in Germany and Austria. The event characteristics are asscoiated with annual maximum flood events in the period from 1951 to 2010. They include variables on event precipitation, antecedent catchment state, event catchment response, event timing, and event types. The catchment characteristics include variables on catchment area, catchment wetness, tail heaviness of rainfall, nonlinearity of catchment response, and synchronicity of precipitation and catchment state. The variables were compiled as potential predictors of heavy tail behaviour of flood peak distributions. They are based on gauge observations of discharge, E-OBS meteorological data (Haylock et al. 2008), mHM hydrological model simulations (Samaniego et al., 2010), 4DAS climate reanalysis data (Primo et al., 2019), and the 25x25 m resolution EU-DEM v1.1. A short description of the data processing is included in the file inventory and more details can be found in Macdonald et al. (2022).

Description: floodsimilarity provides classes and methods to conduct a similarity analysis between multiple flood events. The library mainly consists of two parts: (1) algorithms to compute indices and other statistics based on pandas and xarray (2) well-defined data structures for data exchange (e.g. through the Similarity Backend Module) floodsimilarity is used by the Digital Earth Similarity Backend Module (Eggert, 2021) as part of the Digital Earth Flood Event Explorer. It is developed at the GFZ German Research Centre for Geosciences and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project.

Description: The Digital Earth Flood Event Explorer supports geoscientists and experts to analyse flood events along the process cascade event generation, evolution and impact across atmospheric, terrestrial, and marine disciplines. It applies the concept of scientific workflows and the component-based Data Analytics Software Framework (DASF, Eggert and Dransch, 2021) to an exemplary showcase. It aims at answering the following geoscientific questions: - How does precipitation change over the course of the 21st century under different climate scenarios over a certain region? - What are the main hydro-meteorological controls of a specific flood event? - What are useful indicators to assess socio-economic flood impacts? - How do flood events impact the marine environment? - What are the best monitoring sites for upcoming flood events? The Flood Event Explorer developed scientific workflows for each geoscientific question providing enhanced analysis methods from statistics, machine learning, and visual data exploration that are implemented in different languages and software environments, and that access data form a variety of distributed databases. The collaborating scientists are from different Helmholtz research centers and belong to different scientific fields such as hydrology, climate-, marine-, and environmental science, and computer- and data science. It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/).

Description: As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2022-002_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

Description: The Socio-Economic Flood Impacts Workflow is part of the Flood Event Explorer (FEE, Eggert et al., 2022), developed at the GFZ German Research Centre for Geosciences . It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). The Socio-Economic Flood Impacts Workflow aims to support the identification of relevant controls and useful indicators for the assessment of flood impacts. It should support answering the question What are useful indicators to assess socio-economic flood impacts?. Floods impact individuals and communities and may have significant social, economic and environmental consequences. These impacts result from the interplay of hazard - the meteo-hydrological processes leading to high water levels and inundation of usually dry land, exposure - the elements affected by flooding such as people, build environment or infrastructure, and vulnerability - the susceptibility of exposed elements to be harmed by flooding. In view of the complex interactions of hazard and impact processes a broad range of data from disparate sources need to be compiled and analysed across the boundaries of climate and atmosphere, catchment and river network, and socio-economic domains. The workflow approaches this problem and supports scientists to integrate observations, model outputs and other datasets for further analysis in the region of interest. The workflow provides functionalities to select the region of interest, access hazard, exposure and vulnerability related data from different sources, identifying flood periods as relevant time ranges, and calculate defined indices. The integrated input data set is further filtered for the relevant flood event periods in the region of interest to obtain a new comprehensive flood data set. This spatio-temporal dataset is analysed using data-science methods such as clustering, classification or correlation algorithms to explore and identify useful indicators for flood impacts. For instance, the importance of different factors or the interrelationships among multiple variables to shape flood impacts can be explored. The added value of the Socio-Economic Flood Impacts Workflow is twofold. First, it integrates scattered data from disparate sources and makes it accessible for further analysis. As such, the effort to compile, harmonize and combine a broad range of spatio-temporal data is clearly reduced. Also, the integration of new datasets from additional sources is much more straightforward. Second, it enables a flexible analysis of multivariate data and by reusing algorithms from other workflows it fosters a more efficient scientific work that can focus on data analysis instead of tedious data wrangling.

Description: Climate change manifests in terms of changing frequency and magnitude of extreme hydro-meteorological events and thus drives changes in urban flood hazard. Flood risk oriented urban planning is key to derive smart adaptation strategies, strengthen resilience and achieve sustainable development. 3D city models offer detailed spatial information which is useful to describe the exposure and to characterize the susceptibility of buildings at risk.This web-based application presents the 3d-city flood damage module (3DCFD) prototype which has been developed and implemented within a pathfinder projected funded by Climate-KIC during 2015-2016. The presentation illustrates the results of the 3DCFD-module exemplarily for the demonstration case in the City of Dresden. Relative damage to residential buildings which results from various flooding scenarios is shown for the focus area Pieschen in Dresden.The application allows the user to browse through the virtual city model and to colour the residential buildings regarding their relative damage values caused by different flooding scenarios. To do so click on 'Content', then on the brush-icon next to 'Buildings' and select a certain style from the drop-down menu. A style represents a specific combination of loss model and flooding scenario. Flooding scenarios provide spatially detailed inundation depth information according to different water stages at the gauge Dresden. Currently two flood loss models are implemented: a simple stage-damage-function (sdf) which related inundation depth to relative loss and the 3DCFD-module which uses additional information about building characteristics available from the virtual city model. A click on a coloured building will display additional information. The loss estimation module has been developed by the German Research Centre for Geosciences (GFZ), Section Hydrology. The web-application has been developed by virtualcitySYSTEMS GmbH. The data consisting of flood scenarios, a virtual 3D city model, and a terrain model were provided by the City of Dresden.

Description: The European exposure data for BN-FLEMO models contains three datasets that can be used with BN-FLEMO models for the estimation of flood loss. The dataset contains: (1) European asset map with unit area values of residential and commercial buildings in EURO per square meter based on reconstruction cost and NUTS-3 regions or national GDP per capita. The values are mapped on CORINE land cover classes for urban areas (111 and 112). (2) Residential building areas in Europe with building area sizes in square meter for each NUTS-3 region. The building area sizes were calculated based on the building geometries extracted from the OSM database. (3) Flood experience in Europe with geometries of historic flood events (1985- 2015) with start date of the events. This dataset can be used to calculate the number of past flood events in an area.

Description: As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2023-001_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.