A Nonstationary Multivariate Framework for Modelling Compound Flooding

Flood is widely regarded as one of the most dangerous natural hazards worldwide. It often arises from various sources either individually or combined such as extreme rainfall, storm surge, high sea level, large river discharge or the combination of them. However, the concurrence or close succession of these different source mechanisms can lead to compound floods, resulting in larger damages and even catastrophic consequences than those from the events caused by the individual mechanism. In this chapter, a modelling framework is presented aiming at supporting risk analysis of compound flooding in the context of climate change, where the nonstationary joint probability of multiple variables and their interactions need to be quantified. The framework uses the Block Bootstrapping Mann-Kendall test to detect the temporal changes of marginals, and the correlation test associated with the Rolling Window method to estimate whether the correlation structure varies with time; it then evaluates various combinations of marginals and copulas under stationary and nonstationary assumptions. Meanwhile, a Bayesian Markov Chain Monte Carlo method is employed to estimate the time-varying parameters of copulas. Finally, an illustrative case of Ho Chi Minh City of Vietnam is provided to demonstrate the application of the proposed framework. The result highlights the significance to incorporate non-stationarity into the statistical analysis when estimating the compound floods.