Assessing the dependence structure between oceanographic, fluvial, and pluvial flooding drivers along the United States coastline

Abstract

Abstract. Flooding is of particular concern in low-lying coastal zones that are prone to flooding impacts from multiple drivers: oceanographic (storm surge and wave), fluvial (excessive river discharge), and/or pluvial (surface runoff). In this study, we analyse for the first time the compound flooding potential along the contiguous United States (CONUS) coastline from all flooding drivers, using observations and reanalysis datasets. We assess the overall dependence from observations by using Kendall’s rank correlation coefficient (τ) and tail (extremal) dependence (χ). Geographically, we find highest dependence between different drivers at locations in the Gulf of Mexico, southeast, and southwest coasts. Regarding different driver combinations, the highest dependence exists between surge-waves, followed by surge-precipitation, surge-discharge, waves-precipitation, and waves-discharge. We also perform a seasonal dependence analysis (tropical vs extra-tropical season), where we find higher dependence between drivers during the tropical season along the Gulf and parts of the East coast and stronger dependence during the extra-tropical season on the West coast. Finally, we compare the dependence structure of different combinations of flooding drivers using observations and reanalysis data and use the Kullback–Leibler (KL) Divergence to assess significance in the differences of the tail dependence structure. We find, for example, that models underestimate the tail dependence between surge-discharge on the East and West coasts and overestimate tail dependence between surge-precipitation on the East coast, while they underestimate it on the West coast. The comprehensive analysis presented here provides new insights on where compound flooding potential is relatively higher, which variable combinations are most likely to lead to compounding effects, during which time of the year (tropical versus extra-tropical season) compound flooding is more likely to occur, and how well reanalysis data captures the dependence structure between the different flooding drivers.

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

“…Exceedance probability estimates of CF hazards, for example, require joint probability analysis based on multivariate probability distribution functions, including multivariate parametric distributions and Copulas ( Hao and Singh, 2020 ; Salvadori et al., 2015 , 2016 ). Joint occurrence analysis of extreme events via multivariate probabilistic methods enables researchers to conduct assessments at regional and global scale ( Camus et al., 2021 ; Eilander et al., 2020 ; Nasr et al., 2021 ). The probabilistic approaches, though useful, require long overlapping observation records (e.g., >30 years of nearly complete data for estimating a 100-year return level) and if based on point measurements (i.e., gauges) fail to provide information regarding the spatial distribution of hazards, their dependencies and their local patterns.…”

Compound Effects of Flood Drivers, Sea Level Rise, and Dredging Protocols on Vessel Navigability and Wetland Inundation Dynamics

“…The procedure for sampling flood-driver pairs consists of peakover-threshold, with twice the length of the available records, i.e., 80 total compound samples from two-sided sampling and/or 40 samples each side. Since compound effects of flood drivers do not necessarily coincide in time, we consider a maximum lag-time of 7 days between sampled events as suggested in compound flood (CF) studies conducted at local, regional, and global scales (Klerk et al, 2015;Moftakhari et al, 2017;Ward et al, 2018;Nasr et al, 2021).…”

“…Moreover, SLR is expected to increase the intensity and frequency of compound flood (CF) hazards in coastal areas where about 190 million people are currently living below high tide lines (Kulp and Strauss, 2019;Arns et al, 2020). Also, the nonlinear interactions among SLR, terrestrial and coastal flood drivers, and anthropogenic activities can exacerbate the impacts of CF hazards, escalate flood risks in coastal communities, and cause wetland loss (Eilander et al, 2020;Rezaie et al, 2020;Nasr et al, 2021). In that regard, Muñoz et al (2021) analyzed the effects of SLR, urbanization, and hurricane impacts on long-term wetland change dynamics in Mobile Bay (MB), AL.…”

Daily synoptic conditions associated with occurrences of compound events in estuaries along North Atlantic coastlines

“…This led Zscheischler et al (2020) to propose a typology of these events in which flooding is considered to be caused by the interaction between multiple climate drivers and/or hazards within the same geographical region, that may not be extreme themselves, but whereby their joint occurrence causes an extreme impact. The lack of data of the hazard or impact at regional and global scales have resulted in the application of two-sided conditional sampling to bivariate drivers to identify compound flooding potential events (Wahl et al, 2015;Ward et al, 2018;Couasnon et al, 2020;Camus et al, 2021;Nasr et al, 2021). Recently, Eilander et al (2020) simulated water levels at river mouths generated by the interaction between oceanographic and riverine drivers using a global coupled river-coast flood model framework.…”