Articles | Volume 16, issue 3
Nat. Hazards Earth Syst. Sci., 16, 833–853, 2016
https://doi.org/10.5194/nhess-16-833-2016
Nat. Hazards Earth Syst. Sci., 16, 833–853, 2016
https://doi.org/10.5194/nhess-16-833-2016

Research article 23 Mar 2016

Research article | 23 Mar 2016

Regional prioritisation of flood risk in mountainous areas

María Carolina Rogelis et al.

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Cited articles

Akbas, S., Blahut, J., and Sterlacchini, S.: Critical assessment of existing physical vulnerability estimation approaches for debris flows, in: Landslide processes: from geomorphological mapping to dynamic modelling, 229–233, 2009.
Albano, R., Sole, A., Adamowski, J., and Mancusi, L.: A GIS-based model to estimate flood consequences and the degree of accessibility and operability of strategic emergency response structures in urban areas, Nat. Hazards Earth Syst. Sci., 14, 2847–2865, https://doi.org/10.5194/nhess-14-2847-2014, 2014.
Balica, S. F., Wright, N. G., and van der Meulen, F.: A flood vulnerability index for coastal cities and its use in assessing climate change impacts, Nat. Hazards, 64, 73–105, https://doi.org/10.1007/s11069-012-0234-1, 2012.
Barrenechea, J., Gentile, E., González, S., and Natenson, C.: Una propuesta metodológica para el estudio de la vulnerabilidad social en el marco de la teoría social del riesgo, in: IV Jornadas de Sociología, edited by: Facultad de Ciencias Sociales Facultad de Ciencias Sociales, UBA, Buenos Aires, Argentina, 1–13, 2000.
Barroca, B., Bernardara, P., Mouchel, J. M., and Hubert, G.: Indicators for identification of urban flooding vulnerability, Nat. Hazards Earth Syst. Sci., 6, 553–561, https://doi.org/10.5194/nhess-6-553-2006, 2006.
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Short summary
A method to identify mountainous watersheds with the highest flood risk at the regional level is proposed and applied in Bogotá (Colombia). Vulnerability at the regional level was assessed and combined with an existing flood susceptibility indicator, thus providing an index that allows the watersheds to be prioritised. Results show that vulnerability can be expressed in terms of four constituent indicators and a sensitivity analysis shows that the classification of vulnerability is robust.
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