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Development of a drought vulnerability index using MCDM and GIS: study case in São Paulo and Ceará, Brazil

Author

Listed:
  • Gabriela Reis

    (Federal University of Ceará)

  • Francisco Assis Souza Filho

    (Federal University of Ceará)

  • Donald Robert Nelson

    (University of Georgia)

  • Renan Vieira Rocha

    (Federal University of Ceará)

  • Samiria Maria Oliveira Silva

    (Federal University of Ceará)

Abstract

Although climate change vulnerability research in general has increased over the last decade, Latin American countries have more directed more limited efforts toward vulnerability and its social aspects. To respond to this gap, the authors developed a method to quantify drought vulnerability, which is key climate risk in Brazil. The iSECA model uses MCDM techniques to calculate vulnerability indexes by applying weighting schemes to indicators that represent climatological, social, economic, and water management factors. GIS software was used to classify and to map vulnerability. The model output is a drought vulnerability index, displayed through maps and graphs, including a vulnerability triangle and frequency curves. The results provide a clear understanding for water managers and non-specialists and can serve as an indispensable tool for water management in drought-prone regions. A sensitivity analysis confirms the model's robustness. iSECA was applied to Ceará and São Paulo, two states with distinct climatological and socio-economic contexts. The application demonstrated how the model works well across different spatial scales within these different contexts. The study found that in São Paulo more than 30 million people are living with very high vulnerability to drought primarily due to water management characteristics. However, in Fortaleza, Ceará, the climatic factors are the most critical. Even the state’s robust interbasin water infrastructure system does not significantly reduce the drought risk. The ability to identify vulnerability hotspots and the underlying characteristics that influence the rankings provides the necessary input of policy decisions.

Suggested Citation

  • Gabriela Reis & Francisco Assis Souza Filho & Donald Robert Nelson & Renan Vieira Rocha & Samiria Maria Oliveira Silva, 2020. "Development of a drought vulnerability index using MCDM and GIS: study case in São Paulo and Ceará, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1781-1799, November.
  • Handle: RePEc:spr:nathaz:v:104:y:2020:i:2:d:10.1007_s11069-020-04247-7
    DOI: 10.1007/s11069-020-04247-7
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    Cited by:

    1. Larissa Zaira Rafael Rolim & Francisco de Assis Souza Filho, 2025. "Machine learning strategies for multiannual rainfall prediction and drought early warning: insights from Ceará, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(6), pages 7229-7263, April.
    2. Luiz Martins Araújo Júnior & Francisco de Assis Souza Filho & Guilherme Alencar Barreto & Daniel Antônio Camelo Cid & João Dehon Araújo Pontes Filho, 2023. "A drought risk management system for early drought alert and proactive actions in large semi-arid areas," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 2297-2324, July.

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