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Incorporating DEM Uncertainty in Coastal Inundation Mapping

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  • Javier X Leon
  • Gerard B M Heuvelink
  • Stuart R Phinn

Abstract

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

Suggested Citation

  • Javier X Leon & Gerard B M Heuvelink & Stuart R Phinn, 2014. "Incorporating DEM Uncertainty in Coastal Inundation Mapping," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0108727
    DOI: 10.1371/journal.pone.0108727
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    References listed on IDEAS

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    1. Megan I. Saunders & Javier X. Leon & David P. Callaghan & Chris M. Roelfsema & Sarah Hamylton & Christopher J. Brown & Tom Baldock & Aliasghar Golshani & Stuart R. Phinn & Catherine E. Lovelock & Ove , 2014. "Interdependency of tropical marine ecosystems in response to climate change," Nature Climate Change, Nature, vol. 4(8), pages 724-729, August.
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    5. Hannah Cooper & Qi Chen, 2013. "Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment: A case study in Kahului, Maui," Climatic Change, Springer, vol. 121(4), pages 635-647, December.
    6. Keqi Zhang & Yuepeng Li & Huiqing Liu & Hongzhou Xu & Jian Shen, 2013. "Comparison of three methods for estimating the sea level rise effect on storm surge flooding," Climatic Change, Springer, vol. 118(2), pages 487-500, May.
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    1. Hannah Cooper & Caiyun Zhang & Donna Selch, 2015. "Incorporating uncertainty of groundwater modeling in sea-level rise assessment: a case study in South Florida," Climatic Change, Springer, vol. 129(1), pages 281-294, March.
    2. Morena Mills & Konar Mutafoglu & Vanessa M. Adams & Carla Archibald & Justine Bell & Javier X. Leon, 2016. "Perceived and projected flood risk and adaptation in coastal Southeast Queensland, Australia," Climatic Change, Springer, vol. 136(3), pages 523-537, June.
    3. M. Karamouz & F. Fooladi Mahani, 2021. "DEM Uncertainty Based Coastal Flood Inundation Modeling Considering Water Quality Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3083-3103, August.

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