Dependence Between High Sea-Level And High River Discharge Increases Flood Hazard In Global Deltas And Estuaries

Keywords

Coastal flooding; Compound flood; Flood; Flood risk; River flooding

Abstract

When river and coastal floods coincide, their impacts are often worse than when they occur in isolation; such floods are examples of ‘compound events’. To better understand the impacts of these compound events, we require an improved understanding of the dependence between coastal and river flooding on a global scale. Therefore, in this letter, we: provide the first assessment and mapping of the dependence between observed high sea-levels and high river discharge for deltas and estuaries around the globe; and demonstrate how this dependence may influence the joint probability of floods exceeding both the design discharge and design sea-level. The research was carried out by analysing the statistical dependence between observed sea-levels (and skew surge) from the GESLA-2 dataset, and river discharge using gauged data from the Global Runoff Data Centre, for 187 combinations of stations across the globe. Dependence was assessed using Kendall’s rank correlation coefficient () and copula models. We find significant dependence for skew surge conditional on annual maximum discharge at 22% of the stations studied, and for discharge conditional on annual maximum skew surge at 36% of the stations studied. Allowing a time-lag between the two variables up to 5 days, we find significant dependence for skew surge conditional on annual maximum discharge at 56% of stations, and for discharge conditional on annual maximum skew surge at 54% of stations. Using copula models, we show that the joint exceedance probability of events in which both the design discharge and design sea-level are exceeded can be several magnitudes higher when the dependence is considered, compared to when independence is assumed. We discuss several implications, showing that flood risk assessments in these regions should correctly account for these joint exceedance probabilities.

Publication Date

1-1-2018

Publication Title

Environmental Research Letters

Volume

13

Issue

8

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1088/1748-9326/aad400

Socpus ID

85053050401 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85053050401

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