Future flood riverine risk analysis considering the heterogeneous impacts from tropical cyclone and non-tropical cyclone rainfalls: Application to daily flows in the Nam River Basin, South Korea

Alcantara, Angelika L.; Ahn, Kuk‐Hyun

doi:10.1016/j.advwatres.2021.103983

Cited by 5 publications

(2 citation statements)

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“…It is important to investigate and understand the drivers of this phenomenon to diagnose whether it recently emerged due to climate change or it is just one of the natural extreme events. For the purpose of water resource management, our model can also be incorporated for flood riverine risk analysis with the novel approach recently proposed by Alcantara and Ahn (2021) to consider the heterogeneous impacts of landfalling TCs driven by the changing climate. To be specific, we can adjust the atmospheric conditions based on the possible impacts of climate change, as well as increase the number of TC occurrences in a particular year to present several probable climate change scenarios.…”

Section: Discussionmentioning

confidence: 99%

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

Alcantara

Ahn

2023

Intl Journal of Climatology

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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.

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Section: Discussionmentioning

confidence: 99%

“…Here, we define TC rainfall as any rainfall that occurs within a 500‐km radial distance from the TC centre to a certain point in the gridded dataset during the onslaught of a TC. This approach is similar to previous studies (Gu et al ., 2017; Alcantara and Ahn, 2020; Alcantara and Ahn, 2021).…”

Section: Study Area and Datamentioning

confidence: 99%