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.
Highlights
The 0.2 m of approximate global sea level rise (SLR) over the past 100 years is the result of higher global temperatures due to increased atmospheric CO2 levels
Uncertainty is inherent to spatial data and spatial analysis and it is of paramount importance to effectively communicate it, when dealing with decision-making in a changing climate
The standard deviation of 0.18 m was consistent with commonly reported errors in light detection and ranging (LiDAR)-derived digital elevation model (DEM)
Summary
The 0.2 m of approximate global sea level rise (SLR) over the past 100 years is the result of higher global temperatures due to increased atmospheric CO2 levels. Most SLR vulnerability assessments are elevation-based, where areas adjacent to the sea and below a given elevation (e.g. representing an SLR forecast or storm surge level) are mapped using a deterministic line dividing potentially inundated from dry areas [10]. This approach, known as the bathtub method, is implemented and commonly used, only requiring an elevation dataset usually in the form of a digital elevation model (DEM) [11]
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