Modeling Nonstationary Extreme Water Levels Considering Local Covariates in Ho Chi Minh City, Vietnam

Abstract

Recently, human intervention and climate change have been proposed as the causes of changes in extreme water levels, which impact the likelihood of flooding, especially in coastal areas. In many studies, frequency analysis of extreme water levels has been conducted under nonstationary conditions, in which the parameters of a given distribution vary with time or several climatological variables. However, water levels possess unique characteristics in that they are strongly impacted by local influences, so the covariates used for nonstationary extreme water level modeling should be chosen with respect to the area of interest. For these reasons, it is important to consider local variables that have strong physical associations with flooding in the study of nonstationary extreme water levels. This study uses four local covariates, i.e., rainfall, sea level, urbanization growth, and outflows from upstream reservoirs to develop 92 nonstationary extreme water level models. The stationary models are also developed for comparison purposes. The results indicate that the nonstationary approach using local covariates is suitable for modeling extreme water levels in Ho Chi Minh City. Additionally, based on the best chosen statistical models, a significant influence of sea level and urbanization on nonstationarity in extreme water levels was found at all surveyed stations. Moreover, it was found that the extreme water levels derived from the stationary models were underestimated relative to the best nonstationary models for all stations, with the differences in magnitude reaching approximately 47%, 35%, 31%, and 25% at the Bienhoa, Nhabe, Phuan, and TDM stations, respectively.