Incorporating uncertainty of groundwater modeling in sea-level rise assessment: a case study in South Florida

Abstract

Researchers can assist in effective decision making by reducing uncertainty in marine and groundwater inundation due to Sea-level Rise (SLR). A majority of studies consider marine inundation, but only recently has groundwater inundation been incorporated into the SLR mapping. However, the effect of including uncertainty in groundwater modeling is still not well understood. In this study, we evaluate the effect of considering groundwater modeling uncertainty in assessing land area inundated vulnerable to marine and groundwater inundation in South Florida. Six Water Table Elevation Model (WTEM) techniques (Multiple Linear Regression (MLR), Geographic Weighted Regression (GWR), Global Polynomial Interpolation (GPI), Inverse Distance Weighted (IDW), Ordinary Kriging (OK), and Empirical Bayesian Kriging (EBK)) are tested to identify the best approach. Simple inundation models excluding uncertainty with and without WTEM are examined. Refined inundation models using Monte Carlo simulation that include uncertainty in future SLR estimates, LiDAR elevation, vertical datums and the transformations made between datums with and without WTEM uncertainty are evaluated. GPI and EBK are recognized as the best for producing WTEMs in two primary physiographic regions (the Southern Slope and Atlantic Coastal Ridge). Excluding uncertainty without WTEM underestimates total land area by 14 %, while including uncertainty without WTEM overestimates total vulnerable land area by 16 % at the 95 % probability threshold. It is significant to include WTEM uncertainty in SLR vulnerability analysis for more effective adaptation decisions.