Data: 17/09/2014 à 19/09/2014
Local: São Paulo - Brazil
Computationally Efficient, Yet Robust, Coastal Flood Risk Analysis Using a Reduced Complexity Inundation Model and Metamodel (PAP014366)
Floods in a changing climate
To effectively manage coastal flood risk it is necessary to be able to quantify it. This quantification can however, be a challenging undertaking. Coastal flooding risk is defined as the probability of flooding multiplied by the consequences. However, both the probability of flooding and the consequences can vary significantly over broad spatial and temporal scales. The probability component of coastal flood risk it is usually calculated by the application of multivariate extreme value models that extrapolate the joint probability density of the historical data, normally defined by the offshore sea condition (wave height, wave period, wave direction, wind intensity and direction, astronomical tide, storm surge level, mean sea level), to extreme values. Executing the corresponding hydrodynamic and inundation models for the full set of stochastically generated events is often not viable in computational terms. In the study described here, a computationally efficient, and hence practical, coastal flood risk analysis modelling system has been developed. The system applies the multivariate extreme value model of Heffernan and Tawn (2004) to high resolution offshore sea condition data (Camus et al, 2013). The resulting monte-carlo simulation data are then transferred inshore using a meta-modelling approach based on data mining techniques and non-linear interpolation functions (Camus et al 2011). These transformed nearshore data then form the boundary conditions of a reduced complexity (and hence computationally efficient) flood inundation model (RFSM-EDA).The modelling system has been applied to an urban coastal area located at Northern Spain mainly affected by wave-induced overtopping events.