A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people
This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quanti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/16/1323/2016/nhess-16-1323-2016.pdf |
| Summary: | This article presents a novel methodology to assess flood risk to people by integrating
people's vulnerability and ability to cushion hazards through coping and
adapting. The proposed approach extends traditional risk assessments beyond
material damages; complements quantitative and semi-quantitative data with
subjective and local knowledge, improving the use of commonly available
information; and produces estimates of model uncertainty by providing probability
distributions for all of its outputs. Flood risk to people is modeled using
a spatially explicit Bayesian network model calibrated on expert opinion.
Risk is assessed in terms of (1) likelihood of non-fatal physical injury,
(2) likelihood of post-traumatic stress disorder and (3) likelihood of death.
The study area covers the lower part of the Sihl valley (Switzerland)
including the city of Zurich. The model is used to estimate the effect of
improving an existing early warning system, taking into account the
reliability, lead time and scope (i.e., coverage of people reached by the
warning). Model results indicate that the potential benefits of an improved
early warning in terms of avoided human impacts are particularly relevant in
case of a major flood event. |
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| ISSN: | 1561-8633 1684-9981 |