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Establishing flood thresholds for sea level rise impact communication

Author

Listed:
  • Sadaf Mahmoudi

    (The University of Alabama
    The University of Alabama)

  • Hamed Moftakhari

    (The University of Alabama
    The University of Alabama)

  • David F. Muñoz

    (Virginia Tech)

  • William Sweet

    (NOAA/National Ocean Service)

  • Hamid Moradkhani

    (The University of Alabama
    The University of Alabama)

Abstract

Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States’ coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.

Suggested Citation

  • Sadaf Mahmoudi & Hamed Moftakhari & David F. Muñoz & William Sweet & Hamid Moradkhani, 2024. "Establishing flood thresholds for sea level rise impact communication," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48545-1
    DOI: 10.1038/s41467-024-48545-1
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    References listed on IDEAS

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