Multidimensional Model for Vulnerability Assessment of Urban Flooding: An Empirical Study in Pakistan

• Main Authors: Irfan Ahmad Rana, Jayant K. Routray
• Format: Article
• Language: English
• Published: SpringerOpen 2018-08-01
• Series: International Journal of Disaster Risk Science
• Subjects: Pakistan; Punjab; Urban flooding; Vulnerability assessment
• Online Access: http://link.springer.com/article/10.1007/s13753-018-0179-4

Abstract Urban flooding has become a regular phenomenon in many towns and cities in the Asia Pacific region over the past years. Professionals associated with disaster management and climate change are at the forefront of addressing urban flooding. To reduce flood risks, vulnerability and its components must be understood. Vulnerability assessment methods are diverse and complex, with a varied nature of understanding the key terms used in various contexts, and this diversity ultimately reflects on the interpretation of results in research settings. Diverse interpretations and definitions exist in the disaster risk and climate change literature, complicating the process of astute and comprehensive vulnerability assessment. The main purpose of this study was to quantify vulnerability indicators and develop a multidimensional model for vulnerability assessment. Vulnerability is explored through the lens of five dimensions: social, economic, physical/infrastructural, institutional, and attitudinal. This methodology is applied to urban flooding in Pakistan, to verify the proposed model. Three study sites in urban areas with different population sizes, situated in high-risk flood zones in the Punjab Province of Pakistan were selected for empirical investigation. A household survey was conducted, and indices were developed for each dimension based on well-defined indicators. The proposed methodology for vulnerability assessment was tested and found operational. This method can be replicated irrespective of spatial scales and can be modified for other disasters by streamlining hazard specific indicators.