- 1NOAA, Physical Sciences Laboratory , Boulder, United States of America
- 2National Ocean Service, National Oceanic and Atmospheric Administration, Silver Spring, MD, USA
- 3CIRES, University of Colorado, Boulder, CO, USA
Communities globally are experiencing an increase in high tide flood (HTF) frequency. The present-day impact of HTF for communities is expansive and recurrent, ranging from disrupted activities for infrastructure, inundated stormwater and wastewater systems, and increased public health hazards. Accurate estimates of the probability density functions (PDFs), especially for extreme water levels, are essential for quantifying risks of coastal flooding. In this work, we decompose still water levels measured at 148 tide gauge stations along the United States’ coasts and evaluate the characteristics of the nontidal residual (NTR) distributions. We compare the distribution of high-pass filtered water levels (hourly anomalies) to PDFs of a first-order autoregressive (AR1) process resulting in a Gaussian (normal) distribution and a non-Gaussian (skewed and heavy tailed) ‘Stochastically Generated Skewed’ (SGS) distribution that includes correlated additive and multiplicative noise (CAM noise). We find that the overall error computed between the PDFs and the observed anomalies is reduced at most stations when using the non-Gaussian PDF compared to the AR1 for both the bulk of the distribution and extreme values. We also show that the non-Gaussian SGS distribution is more robust at capturing extreme values in the case of sparse observations, compared to other distributions (kernel density) and extreme value analysis methods (i.e., Generalized Extreme Value and Generalized Pareto Distribution). Our non-Gaussian PDF allows us to diagnose how the shape of the distribution may evolve with climate change. Findings from this work will be implemented in the National Oceanic and Atmospheric Administration’s HTF monthly predictions and used to evaluate changes in forecast skill. This work has relevance for high tide flooding forecasts along the coast and inundation mitigation strategies, as well as estimating PDFs for other physical variables that exhibit heavy-tailed skewed distributions.
How to cite: Hovenga, P., Newman, M., Albers, J., Dusek, G., Sweet, W., Xu, T., Callahan, J., and Shin, S.-I.: Assessing the Characteristics of Nontidal Residual Water Level Distributions for High Tide Flooding Predictions and Projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13607, https://doi.org/10.5194/egusphere-egu25-13607, 2025.