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A Multiple Linear Regression-Based Approach for Storm Surge Prediction Along South Brazil

  • Arthur OhzEmail author
  • Antonio H. F. Klein
  • Davide Franco
Chapter
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Part of the Climate Change Management book series (CCM)

Abstract

Knowledge of the characteristics of storm surges is of paramount importance for an effective coastal hazard risk assessment. The vast Majority of storm surges in southern Brazil are generated by extratropical cyclones. In this context, this study focuses on spatial and temporal analysis of storm surges caused by the cyclones within a 30-year period. The wind and pressure from CFSv2 data were analyzed from 1979 to 2010 time span, with a time lag of 9 h identified for pressure and 26 h for wind stress. A multiple linear regression model was then employed to estimate the expected intensity of the storm surge. The results suggest that the model represented 82% of the variations in the storm surge levels. The accuracy of the surge predictions was validated by the recorded storm surges using tide gauge data for Itajaí port. The agreement between the modelled and the observed values were considered good by explaining 74% of the variations in the storm surge. Finally, Analysis of future scenarios of 1-, 10-, 50- and 100-return period revealed that positive storm surge events may be higher, increasing coastal risks in the coastal zone of southern Brazil.

Keywords

Storm surge simulation Multiply linear regression model Statistical forecasting Risk assessment 

Notes

Acknowledgements

The authors express their gratitude to UNIVALI for making the data on Itajaí port available and to Chicago Bridge and Iron (CBI) for their participation in the project. The research was supported by CAPES (Rede Riscos Costeiros 09/2009), CNPQ (Universal/2008 [Proc. No. 471068/2008-0] and CTTrans/44-2008 [Proc. No. 575008/2008-3] and Scholarship for Research Productivity (Bolsa de Produtividade em Pesquisa – Nível 2) [Proc. No. 303550/2012-0]), PFRH-PB240 (PETROBRAS) and the Postgraduate Geography Program of the Federal University of Santa Catarina.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Arthur Ohz
    • 1
    Email author
  • Antonio H. F. Klein
    • 1
  • Davide Franco
    • 2
  1. 1.Coastal Oceanography Laboratory, Department of GeosciencesFederal University of Santa CatarinaFlorianópolisBrazil
  2. 2.Maritime Hydraulic Laboratory, Department of Sanitary and Environmental EngineeringFederal University of Santa CatarinaFlorianópolisBrazil

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