Articles | Volume 17, issue 5
https://doi.org/10.5194/nhess-17-735-2017
https://doi.org/10.5194/nhess-17-735-2017
Research article
 | 
19 May 2017
Research article |  | 19 May 2017

Probabilistic flood extent estimates from social media flood observations

Tom Brouwer, Dirk Eilander, Arnejan van Loenen, Martijn J. Booij, Kathelijne M. Wijnberg, Jan S. Verkade, and Jurjen Wagemaker

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Cited articles

Aronica, G., Bates, P. D., and Horrit, M. S.: Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002.
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EA (Environment Agency): LIDAR Composite DTM – 2 m, available at: https://data.gov.uk/dataset/lidar-composite-dtm-2m1 (last access: 3 May 2016), 2014.
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Short summary
The increasing number and severity of floods, driven by e.g. urbanization, subsidence and climate change, create a growing need for accurate and timely flood maps. At the same time social media is a source of much real-time data that is still largely untapped in flood disaster management. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.
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