A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United States
The nonstationarity of extreme precipitation is now well established in the presence of climate change and low-frequency variability. Consequently, the implications for urban flooding, for which there are not long flooding records, need to be understood better. The vulnerability is especially high i...
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MDPI AG
2025-01-01
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| author | Lei Yan Yuhan Zhang Mengjie Zhang Upmanu Lall |
| author_facet | Lei Yan Yuhan Zhang Mengjie Zhang Upmanu Lall |
| author_sort | Lei Yan |
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| description | The nonstationarity of extreme precipitation is now well established in the presence of climate change and low-frequency variability. Consequently, the implications for urban flooding, for which there are not long flooding records, need to be understood better. The vulnerability is especially high in coastal cities, where the flat terrain and impervious cover present an additional challenge. In this paper, we estimate the time-varying probability distributions for hourly and daily extreme precipitation using the Generalized Additive Model for Location Scale and Shape (GAMLSS), employing different climate indices, such as Atlantic Multi-Decadal Oscillation (AMO), the El Niño 3.4 SST Index (ENSO), Pacific Decadal Oscillation (PDO), the Western Hemisphere Warm Pool (WHWP) and other covariates. Applications to selected coastal cities in the USA are considered. Overall, the AMO, PDO and WHWP are the dominant factors influencing the extreme rainfall. The nonstationary model outperforms the stationary model in 92% of cases during the fitting period. However, in terms of its predictive performance over the next 5 years, the ST model achieves a higher log-likelihood in 86% of cases. The implications for the time-varying design rainfall in coastal areas are considered, whether this corresponds to a structural design or the duration of a contract for a financial instrument for risk securitization. The opportunity to use these time-varying probabilistic models for adaptive flood risk management in a coastal city context is discussed. |
| format | Article |
| id | doaj-art-a56f1c87a9c44741b07e208e7708f14a |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-a56f1c87a9c44741b07e208e7708f14a2025-01-24T13:21:56ZengMDPI AGAtmosphere2073-44332025-01-011617510.3390/atmos16010075A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United StatesLei Yan0Yuhan Zhang1Mengjie Zhang2Upmanu Lall3Department of Earth and Environmental Engineering, & Columbia Water Center, Columbia University, New York, NY 10027, USASchool of Complex Adaptive Systems, & Water Institute, Arizona State University, Tempe, AZ 85281, USADepartment of Earth and Environmental Engineering, & Columbia Water Center, Columbia University, New York, NY 10027, USADepartment of Earth and Environmental Engineering, & Columbia Water Center, Columbia University, New York, NY 10027, USAThe nonstationarity of extreme precipitation is now well established in the presence of climate change and low-frequency variability. Consequently, the implications for urban flooding, for which there are not long flooding records, need to be understood better. The vulnerability is especially high in coastal cities, where the flat terrain and impervious cover present an additional challenge. In this paper, we estimate the time-varying probability distributions for hourly and daily extreme precipitation using the Generalized Additive Model for Location Scale and Shape (GAMLSS), employing different climate indices, such as Atlantic Multi-Decadal Oscillation (AMO), the El Niño 3.4 SST Index (ENSO), Pacific Decadal Oscillation (PDO), the Western Hemisphere Warm Pool (WHWP) and other covariates. Applications to selected coastal cities in the USA are considered. Overall, the AMO, PDO and WHWP are the dominant factors influencing the extreme rainfall. The nonstationary model outperforms the stationary model in 92% of cases during the fitting period. However, in terms of its predictive performance over the next 5 years, the ST model achieves a higher log-likelihood in 86% of cases. The implications for the time-varying design rainfall in coastal areas are considered, whether this corresponds to a structural design or the duration of a contract for a financial instrument for risk securitization. The opportunity to use these time-varying probabilistic models for adaptive flood risk management in a coastal city context is discussed.https://www.mdpi.com/2073-4433/16/1/75nonstationary frequency analysissub-daily extreme precipitationGAMLSSAMOENSOIOD |
| spellingShingle | Lei Yan Yuhan Zhang Mengjie Zhang Upmanu Lall A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United States Atmosphere nonstationary frequency analysis sub-daily extreme precipitation GAMLSS AMO ENSO IOD |
| title | A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United States |
| title_full | A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United States |
| title_fullStr | A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United States |
| title_full_unstemmed | A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United States |
| title_short | A Nonstationary Daily and Hourly Analysis of the Extreme Rainfall Frequency Considering Climate Teleconnection in Coastal Cities of the United States |
| title_sort | nonstationary daily and hourly analysis of the extreme rainfall frequency considering climate teleconnection in coastal cities of the united states |
| topic | nonstationary frequency analysis sub-daily extreme precipitation GAMLSS AMO ENSO IOD |
| url | https://www.mdpi.com/2073-4433/16/1/75 |
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