Incorporating DEM uncertainty in coastal inundation mapping

• Published in: PloS one Vol. 9; no. 9; p. e108727
• Main Authors: Leon, Javier X, Heuvelink, Gerard B M, Phinn, Stuart R
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.09.2014; Public Library of Science (PLoS)
• Subjects: adaptation; airborne lidar; Climate Change; Coastal management; Coastal zone management; Computer and Information Sciences; Computer simulation; Computers; Conservation of Natural Resources; Data processing; Decision making; Digital Elevation Models; Earth Sciences; Elevation; elevation data; Engineering and Technology; error; Flood mapping; Geostatistics; Hydrology; Impact analysis; Kriging interpolation; Land cover; Land use; Mapping; Normal distribution; Regression analysis; Regression models; Remote Sensing Technology; Risk assessment; Sea level; Sea level changes; Sea level rise; simulation; Spatial analysis; Spatial data; Spatial discrimination; spatial prediction; Spatial resolution; Spatial variability; Statistical analysis; Studies; System reliability; topography; Uncertainty; Australia; St Lucia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0108727

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.