Uncertainty in Coastal Inundation Mapping: A Probabilistic Approach

Abstract:

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly as extreme high sea levels and associated erosion are forecasted to increase in magnitude. 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 propagate into the inundation mapping. Error propagation within spatial modelling can be appropriately analysed using, for example, a probabilistic framework based on geostatistical simulations. Geostatistical modelling 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 aim of this study was to elaborate probability maps incorporating the impacts of spatially variable and spatially correlated elevation errors in high-resolution DEMs combined with sea level rise uncertainties. 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. Sea level rise uncertainty was non-parametrically modelled using 1000 Monte Carlo estimations which were processed to provide the probability density function numerically. The sea level rise uncertainties were modelled using a Weibull distribution with 0.95 scale and 2.2 shape parameters. These uncertainties were combined through addition (i.e., assuming they are independent), and when using probability density distributions, requires a convolution. 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 ~11% larger than mapped using the deterministic bathtub approach.