This paper presents a robust cost-effective framework for assessment of coastal-fluvial flooding due to compound action of multivariate dependent drivers. The methodology is an 8-step process that links statistical and hydrodynamic models to determine probabilities of multiple-driver flood events and associated hazards. The method involves individual and combined extreme value analysis, assessment of dependencies and interactions between flood drivers, multivariate joint probability determination accounting for dependencies, high-resolution hydrodynamic modelling of flood scenarios derived from multivariate statistical analysis, and ultimately mapping of inundation.

Using Cork City, on the south coast of Ireland, as a study case, the research shows that the interactions and dependencies between tides, surges and river flows affect flood severity when they occur jointly. Tide-surge interactions have a damping effect on the total water level, while dependence between the surge residual and river flow amplifies the risk of flooding. The multivariate joint exceedance probability occurrence of high discharges and water levels represents a more realistic representation of the spatially variable water surface profiles then the combined univariate marginal scenarios. Multivariate analysis allows also considering multiple combinations of joint probability solutions along RP iso-curves. The results show that the quantification of compound flood impacts must be performed along the entire RP probability curve. This is because the physical/hydrological impacts of multiple-driver same-RP flood events can be very different leading to substantially different characteristics of flooding. The multi-scale nested flood model (MSN_Flood) was used to simulate flood wave propagation over urban floodplains for an ensemble of statistically derived flood scenarios. The hydrodynamic runs provide inundation maps that can be used to draw inferences about flood mechanisms and impacts.