Abstract
Sea level rise will increase the frequency and severity of coastal flooding events. Compound coastal flooding is characterized by multiple flooding pathways (i.e., high offshore water levels, streamflow, energetic waves, precipitation) acting concurrently. This study explores the joint flood risks caused by the co-occurrence of high marine water levels and precipitation in a highly urbanized semi-arid, tidally dominated region. A novel structural function developed from the multivariate analysis is proposed to consider the implications of flood control infrastructure in compound coastal flood risk assessments. Univariate statistics are analyzed for individual sites and events. Conditional, and joint probabilities are developed using a range of copulas and sampling methods. The Independent, and Cubic copulas produced poor results while the Fischer-Kock, and Roch-Alegre generally produced robust results across a range of sampling methods. The impacts of sampling are considered using annual maximum, annual coinciding, wet season monthly coinciding, and wet season monthly maximum sampling. Although, annual maximum sampling is commonly recommended for characterizing compound events, this work suggests annual maximum sampling does not produce “worst-case” event pairs and substantially underestimates marine water levels for extreme events. Wet season coinciding water level and precipitation pairs benefit from a dramatic increase in available data, improved goodness of fit statistics, and provide a range of physically realistic pairs. Wet season coinciding sampling may provide a more accurate compound flooding risk characterization for long return periods in semi-arid regions.
Highlights
Coastal flooding is a significant human hazard (Leonard et al, 2014; Wahl et al, 2015) and is considered a primary health hazard by the U.S Global Change Research Program (Bell et al, 2016)
Compound coastal flooding is characterized by multiple flooding pathways acting concurrently
Two marginal distributions do not pass the chi square test at the standard 0.05 level of significance (San Diego Annual Maximum (AM) OWL and Santa Monica Wet Season Monthly Maximum (WMM) OWL). 200 These distributions pass at reduced significance levels of 0.01
Summary
Coastal flooding is a significant human hazard (Leonard et al, 2014; Wahl et al, 2015) and is considered a primary health hazard by the U.S Global Change Research Program (Bell et al, 2016). Climate changeinduced sea level rise will substantially increase flood risk (Church et al, 2013; Horton et al, 2014), and negatively impact 20 coastal populations (Bell et al, 2016). Even relatively modest sea level rise will significantly increase flood frequencies through the US (e.g., Tebaldi et al, 2012; Taherkhani et al, 2020). Small changes in sea level (∼5 cm) double the odds of the 50-year flooding event (Taherkhani et al, 2020) and the 100year event is expected to become annual by 2050 (Tebaldi et al, 2012). Regional research has explored flood risks caused by sea level rise and coastal forcing (e.g., Heberger et al, 2011; Hanson et al, 2011; Gallien et al, 2015).
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