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An Integrated Approach in Developing Flood Vulnerability Index of India using Spatial Multi-Criteria Evaluation Technique


Affiliations
1 Disaster Management Support Division, National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, India
 

In recent years, flash floods took place in various parts of the country that are not under floodplains due to high rainfall events, causing damage to rail, road and urban infrastructure. There is a need to develop a flood vulnerable index map of the country for precautionary measures in such vulnerable areas. Developing flood vulnerability index (FVI) at country level in India is a multifaceted job due to huge variations in topographic, meteorological and hydrological conditions over space and time. The paper focuses on developing a scientific approach in preparing FVI map of the country in a spatial decision support system environment by using space-based inputs, topographic data and long-term meteorological data. Probable maximum precipitation (PMP) and high rainfall frequency were computed using 100 years daily rainfall data of the country. Runoff potential of the country was prepared using high resolution landuse, soils, and digital elevation model grids. Probable maximum run-off was further computed at national level using PMP and run-off potential grids. Morphometric analysis was done using topographic and drainage information. All these layers were normalized and integrated in SDSS environment to compute the flood vulnerability index of the country. Suitable weights were given for all layers using the knowledge base reviewed across the globe. Sensitivity analysis and validation were done using the previous flood incidents.

Keywords

Flood Vulnerability Index, Morphometric Analysis, Probable Maximum Precipitation, Probable Maximum Run-Off, Run-off Potential, Spatial Decision Support System.
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  • An Integrated Approach in Developing Flood Vulnerability Index of India using Spatial Multi-Criteria Evaluation Technique

Abstract Views: 377  |  PDF Views: 126

Authors

K. H. V. Durga Rao
Disaster Management Support Division, National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, India
Shravya Alladi
Disaster Management Support Division, National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, India
Amanpreet Singh
Disaster Management Support Division, National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, India

Abstract


In recent years, flash floods took place in various parts of the country that are not under floodplains due to high rainfall events, causing damage to rail, road and urban infrastructure. There is a need to develop a flood vulnerable index map of the country for precautionary measures in such vulnerable areas. Developing flood vulnerability index (FVI) at country level in India is a multifaceted job due to huge variations in topographic, meteorological and hydrological conditions over space and time. The paper focuses on developing a scientific approach in preparing FVI map of the country in a spatial decision support system environment by using space-based inputs, topographic data and long-term meteorological data. Probable maximum precipitation (PMP) and high rainfall frequency were computed using 100 years daily rainfall data of the country. Runoff potential of the country was prepared using high resolution landuse, soils, and digital elevation model grids. Probable maximum run-off was further computed at national level using PMP and run-off potential grids. Morphometric analysis was done using topographic and drainage information. All these layers were normalized and integrated in SDSS environment to compute the flood vulnerability index of the country. Suitable weights were given for all layers using the knowledge base reviewed across the globe. Sensitivity analysis and validation were done using the previous flood incidents.

Keywords


Flood Vulnerability Index, Morphometric Analysis, Probable Maximum Precipitation, Probable Maximum Run-Off, Run-off Potential, Spatial Decision Support System.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi1%2F80-86