Projecting nuisance flooding in a warming climate using generalized linear models and Gaussian processes

Abstract

Nuisance flooding corresponds to minor and frequent flood events that have significant socio-economic and public health impacts on coastal communities. Yearly-averaged local mean sea level can be used as proxy to statistically predict the impacts of sea level rise (SLR) on the frequency of nuisance floods (NF). In this study, we use Generalized Linear Models (GLM) and Gaussian Process (GP) models combined to (i) estimate the frequency of NF associated with the change in mean sea level, and (ii) quantify the associated uncertainties via a novel and statistically robust approach. We calibrate our models to the water level data from eighteen tide gauges along the coasts of United States, and after validation, we estimate the frequency of NF associated with the SLR projections in year 2030 (under RCPs 2.6 and 8.5), along with their 90% bands, at each gauge. The historical NF-SLR data is very noisy, and shows large changes in variability (heteroscedasticity) with SLR. Prior models in the literature do not properly account for the observed heteroscedasticity, and thus their projected uncertainties are highly suspect. Among the models used in this study the Negative Binomial Distribution GLM with GP best characterizes the uncertainties associated with NF estimates; on validation data ≈ 93% of the points fall within the 90% credible limit, showing our approach to be a robust model for uncertainty quantification. This article is protected by copyright. All rights reserved.