Articles | Volume 16, issue 6
https://doi.org/10.5194/nhess-16-1323-2016
https://doi.org/10.5194/nhess-16-1323-2016
Research article
 | 
08 Jun 2016
Research article |  | 08 Jun 2016

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

Stefano Balbi, Ferdinando Villa, Vahid Mojtahed, Karin Tessa Hegetschweiler, and Carlo Giupponi

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Cited articles

Addor, N., Jaun, S., Fundel, F., and Zappa, M.: An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios, Hydrol. Earth Syst. Sci., 15, 2327–2347, https://doi.org/10.5194/hess-15-2327-2011, 2011.
Adger, W. N. and Vincent, K.: Uncertainty in adaptive capacity, C. R. Geosci., 337, 399–410, 2005.
Amendola, A., Ermoliev, Y., Ermolieva, T. Y., Gitis, V., Koff, G., and Linnerooth-Bayer, J.: A systems approach to modeling catastrophic risk and insurability, Nat. Hazards, 21, 381–393, 2000.
Antonucci, A., Salvetti, A., and Zaffalon, M.: Hazard assessment of debris flows by credal networks, in: iEMSs, 98–103, 2004.
AWEL: Amt für Abfall, Wasser, Energie und Luft: Hochwasserschutz an Sihl, Zürichsee und Limmat: Integrales Risikomanagement und Massnahmenziel-Konzept, available at: http://www.hochwasserschutz-zuerich.zh.ch (last access: 7 June 2016), 2013.
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Short summary
This study develops a novel methodology to assess flood risk to people by integrating people’s vulnerability and ability to cushion hazards through coping and adapting. The model is used to estimate the effect of improving an existing early warning system. The proposed approach extends traditional risk assessments beyond material damages, complements quantitative and semi-quantitative data with subjective and local knowledge, and improves the use of commonly available information.
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