ALBERT

Description: During and shortly after a disaster, data about the hazard and its consequences are scarce and not readily available. Information provided by eyewitnesses via social media is a valuable information source, which should be explored in a~more effective way. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in the case of floods. The novelty of this approach is the utilization of quantitative data that are derived from photos from eyewitnesses extracted from social media posts and their integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, areas affected by a flood, for example, can be determined quickly. The challenge is to filter the large number of posts to a manageable amount of potentially useful inundation-related information, as well as to interpret and integrate the posts into mapping procedures in a timely manner. To support rapid inundation mapping we propose a methodology and develop "PostDistiller", a tool to filter geolocated posts from social media services which include links to photos. This spatial distributed contextualized in situ information is further explored manually. In an application case study during the June 2013 flood in central Europe we evaluate the utilization of this approach to infer spatial flood patterns and inundation depths in the city of Dresden.

Description: At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, The Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real-time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about USD 4.2 billion in the Caribbean and between USD 78 and 97 billion in the US. Indirect economic losses from power outages is estimated in the order of USD 16.3 billion. Modelling sector-specific dependencies quantifies total business interruption losses between USD 10.8 and 15.5 billion. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.

Description: During and shortly after a disaster data about the hazard and its consequences are scarce and not readily available. Information provided by eye-witnesses via social media are a valuable information source, which should be explored in a more effective way. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in case of floods. The novelty of this approach is the utilization of quantitative data that are derived from photos from eye-witnesses extracted from social media posts and its integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, for example areas affected by a flood can be determined quickly. The challenge is to filter the large number of posts to a manageable amount of potentially useful inundation-related information as well as their timely interpretation and integration in mapping procedures. To support rapid inundation mapping we propose a methodology and develop a tool to filter geo-located posts from social media services which include links to photos. This spatial distributed contextualized in-situ information is further explored manually. In an application case study during the June 2013 flood in central Europe we evaluate the utilization of this approach to infer spatial flood patterns and inundation depths in the city of Dresden.

Description: At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about 4.2 billion US$ in the Caribbean and between 78 and 97 billion US$ for the US. Indirect economic losses from power outages is estimated in the order of 16.3 billion US$. Modelling sector-specific dependencies, quantifies total business interruption losses between 10.8 and 15.5 billion US$. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.

Description: The summer flood 2013 sets a new record for large-scale floods in Germany since at least 1952. In this paper we analyze the key hydro-meteorological factors using extreme value statistics as well as aggregated severity indices. For the long-term classification of the recent flood we draw comparisons to a set of past large-scale flood events in Germany, notably the high impact summer floods from August 2002 and July 1954. Our analysis shows that the combination of extreme initial wetness at the national scale – caused by a pronounced precipitation anomaly in the month of May 2013 – and strong, but not extraordinary event precipitation were the key drivers for this exceptional flood event. This provides new insights to the importance of antecedent soil moisture for high return period floods on a large-scale. The data base compiled and the methodological developments provide a consistent framework for the rapid evaluation of future floods.

Description: The summer flood of 2013 set a new record for large-scale floods in Germany for at least the last 60 years. In this paper we analyse the key hydro-meteorological factors using extreme value statistics as well as aggregated severity indices. For the long-term classification of the recent flood we draw comparisons to a set of past large-scale flood events in Germany, notably the high-impact summer floods from August 2002 and July 1954. Our analysis shows that the combination of extreme initial wetness at the national scale – caused by a pronounced precipitation anomaly in the month of May 2013 – and strong, but not extraordinary event precipitation were the key drivers for this exceptional flood event. This provides additional insights into the importance of catchment wetness for high return period floods on a large scale. The database compiled and the methodological developments provide a consistent framework for the rapid evaluation of future floods.

Description: This data set provides a set of residential flood loss maps (ESRI Shapefiles) for the German part of the Danube catchment for current and future climate based on a stochastic event set of flood hazard footprints (Schröter et al. 2017; http://doi.org/10.5880/GFZ.5.4.2017.003). The multi-polygon maps provide flood loss in EUR for residential land use areas according to the ATKIS (Authoritative Topographic Cartographic Information System) codes residential areas (2111) and areas of mixed use (2113), (BKG GEODATENZENTRUM: ATKIS-Basis-DLM, 2005). Loss values are calculated using the FloodLossEstimationMOdel for the residential sector (FLEMOps+r) developed by Elmer et al. (2010) in combination with exposure data based on total replacement costs for residential buildings (Kleist et al., 2006). Asset values with a spatial resolution corresponding to the underlying inundation depth maps of the stochastic event set (100x100 m) have been derived by applying a binary disaggregation method and using the digital basic landscape model ATKIS as ancillary information (Wünsch et al. 2009). The flood event sets are derived for the historical period (1970-1990) and two RCPs (4.5 and 8.5) for the near future (2020-2049) and far future (2070-2099) for four CORDEX models. These flood event sets are created within continuous long-term simulations of a coupled model chain including the IMAGE stochastic multi-variable, multi-site weather generator, the eco-hydrological model SWIM and 1D river network coupled with 2D hydro-numeric hinterland inundation model, see Schröter et al. (2017) for further details The data have been produced within the OASIS+ demonstrator project 'Future Danube Multi Hazard and Risk Model' funded by Climate-KIC in the period from January 2016 to December 2017. Key features: • Flood loss maps for residential areas in the German part of the Danube catchment from stochastic flood event sets for current and future climate. • High spatial resolution for ATKIS residential land use areas intersected with 100x100 m inundation depth maps. • Flood loss scenarios for historical period (1970-1990) and two RCPs (4.5 and 8.5) for the near future (2020-2049) and far future (2070-2099) from four CORDEX models Key usage: • Large-scale flood risk assessment • Future flood risk assessment • Flood risk management with long-term perspective A full description of the data provenance and specification is given in the README_Schroeter-et-al-2017-004.txt file available in the data download section at this DOI Landing Page.

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).