13 Oct 2023
 | 13 Oct 2023
Status: this preprint is currently under review for the journal NHESS.

Influence of data source and copula statistics on estimates of compound extreme water levels in a river mouth environment

Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson

Abstract. Coastal and riverine floods are major concerns worldwide as they can impact highly populated areas and result in significant economic losses. In a river mouth environment, interacting hydrological and oceanographical processes can enhance the severity of floods. The compound flood risks from high sea levels and high river runoff levels are often estimated using statistical copulas. Here, we systematically investigate the influence of different data sources and the choice of statistical copula on extreme water level estimates. While we focus on the river mouth at Halmstad city (Sweden), the approach presented is easily transferable to other sites. Our results show that the compound occurrence of high sea levels and river runoff may lead to heightened flood risks as opposed to considering them as independent processes and that in the current study, this is dominated by the hydrological driver. We also show that the choice of data sources and copula can strongly influence the outcome of such analyses. Our findings contribute to framing existing studies, which typically only consider selected copulas and data sets, by demonstrating the importance of considering uncertainties. The choice of data sources as initial input influences strongly the results of the copula analysis.

Kévin Dubois et al.

Status: open (until 29 Dec 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-176', Anonymous Referee #1, 24 Oct 2023 reply
  • RC2: 'Comment on nhess-2023-176', Anonymous Referee #2, 20 Nov 2023 reply

Kévin Dubois et al.

Kévin Dubois et al.


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Short summary
Both extreme river discharge and storm surges can interact at the coast and lead to flooding. However, it is difficult to predict flood levels during such compound events because they are rare and complex. Here, we focus on the quantification of uncertainties; and we investigate the sources of limitations while carrying out such analyses at Halmstad city (Sweden). Based on a sensitivity analysis, we emphasize that both the choice of data source and statistical methodology influence the results.