Methodological approach for mapping the flood physical vulnerability index with geographical open-source data: an example in a small-middle city (Ponferrada, Spain)
[EN] To increase the resilience of communities against floods, it is necessary to develop method- ologies to estimate the vulnerability. The concept of vulnerability is multidimensional, but most flood vulnerability studies have focused only on the social approach. Nevertheless, in recent years, fol...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/17640 |
| Acceso en línea: | https://hdl.handle.net/10612/17640 |
| Access Level: | acceso abierto |
| Palabra clave: | Geodinámica Physical vulnerability indicators Flood hazard GIS Open data Cartography 2508.14 Aguas superficiales 2508.99 Otras (Inundaciones) |
| Sumario: | [EN] To increase the resilience of communities against floods, it is necessary to develop method- ologies to estimate the vulnerability. The concept of vulnerability is multidimensional, but most flood vulnerability studies have focused only on the social approach. Nevertheless, in recent years, following seismic analysis, the physical point of view has increased its rele- vance. Therefore, the present study proposes a methodology to map the flood physical vul- nerability and applies it using an index at urban parcel scale for a medium-sized town (Pon- ferrada, Spain). This index is based on multiple indicators fed by geographical open-source data, once they have been normalized and combined with different weights extracted from an Analytic Hierarchic Process. The results show a raster map of the physical vulnerability index that facilitates future emergency and flood risk management to diminish potential damages. A total of 22.7% of the urban parcels in the studied town present an index value higher than 0.4, which is considered highly vulnerable. The location of these urban par- cels would have passed unnoticed without the use of open governmental datasets, when an average value would have been calculated for the overall municipality. Moreover, the build- ing percentage covered by water was the most influential indicator in the study area, where the simulated flood was generated by an alleged dam break. The study exceeds the spatial constraints of collecting this type of data by direct interviews with inhabitants and allows for working with larger areas, identifying the physical buildings and infrastructure differ- ences among the urban parcels |
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