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

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.