Probabilistic projections of multidimensional flood risks at a convection-permitting scale

Abstract

Understanding future river flood risk is a prerequisite for developing climate change adaptation strategies and enhancing disaster resilience. Previous flood risk assessments can barely take into account future changes of fine-scale hydroclimatic characteristics and hardly quantify multivariate interactions among flood variables, thereby resulting in an unreliable assessment of flood risk. In this study, for the first time, we develop probabilistic projections of multidimensional river flood risks at a convection-permitting scale through the Weather Research and Forecasting (WRF) climate simulations with 4-km horizontal grid spacing. Vine copula has been widely used to assess the multidimensional dependence structure of hydroclimate variables, but the commonly used frequentist approach may fail to identify the correct vine model and to obtain the uncertainty interval. Thus, a Bayesian vine copula approach is proposed to explicitly address the multidimensional dependence of flood characteristics (i.e., flood peak, volume, and duration) and underlying uncertainties. The proposed approach enables a robust assessment of return periods of future floods for Guadalupe and Mission river basins located in South Texas of the United States. Our findings reveal that the South Texas region is projected to experience more flood events with longer duration and greater discharge volume. The flood peak, however, will not necessarily increase even though precipitation extremes are expected to become more frequent. The projected flood return periods over the Guadalupe river basin do not show an obvious increase while the Mission river basin is projected to face a dramatic increase in flood risk with exposed to 100-year and even severer floods nearly every 2 years, on average, when considering the combined effects of flood peak, volume, and duration.