Abstract

Abstract. 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.

Highlights

  • Information provided by citizens via the internet can improve the information basis for disaster response after natural disasters (Poser and Dransch, 2010; Dransch et al, 2013)

  • Within scenario (a), the water level observation for the flood peak at the Dresden gauge retrieved online is intersected with the DEM10

  • We assume that the gradient of the water level along the river is approximately parallel to the bottom slope

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Summary

Introduction

Information provided by citizens via the internet can improve the information basis for disaster response after natural disasters (Poser and Dransch, 2010; Dransch et al, 2013). During a wildfire event in 2007 in California, affected people posted information about the wildfires in their own neighborhood to an internet page to inform others about the expansion and behavior of the fires (Sutton et al, 2008; Goodchild and Glennon, 2010). Another popular example is “Ushahidi” (http://www.ushahidi.com/), a content-sharing platform that collects and provides eyewitness reports of natural disasters, like earthquakes (Haiti and Chile 2010, Christchurch 2011), winter storms (Washington, D.C. 2010), wildfires (Russia 2010) and floods (Missouri 2011 and Thailand 2011). Information from citizens can be conducive to a rapid description of the extent and intensity of the hazardous event as well as the resulting impacts

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