Estimation of tropical cyclone (TC) rainfall risk in South Korea using the integrated TC track and semi-physical TC rainfall models

Utilizing historical records is limited in estimating the full distribution of plausible tropical cyclone (TC) risk due to insufficient samples. To alleviate this limitation, this study presents a semi-physical TC rainfall model to produce spatially and temporally resolved TC rainfall sequences for a proper TC risk assessment. The model incorporates a statistical-based track model that follows the Markov renewal process to generate synthetic TC tracks for any number of years, with a physics-based model that acknowledges the interaction between TCs and the environment to generate tropical cyclone rainfall. After simulating the TC rainfall data, we then fit it to a probability distribution function to explore the spatially heterogeneous risk induced by landfalling TCs. The procedure is applied in South Korea as a case study to conduct a country-scale-based vulnerability evaluation from damaging TC impacts. As a result, we show that the proposed TC model is promising in producing sufficient numbers of TC tracks that do not only follow the spatial distribution of the historical data but also reveal hidden paths that could be utilized in examining scenarios outside what have been historically observed. Results also demonstrate that the model is suitable for properly estimating the amount of TC-induced rainfall across various locations over the study area. The generated TC rainfall sequences allow us to reliably characterize higher return periods that are often underestimated or overlooked when the observed data is employed due to sampling limitations. Moreover, our model can properly capture the distribution of rainfall extremes that follow a heterogeneous pattern throughout the study area and vary per return period. Overall, our results show that the proposed approach is effective in providing sufficient TC sequences that could help in improving TC risk assessment.