Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast
Precipitation exceedance probabilities are widely used in engineering design, risk assessment, and floodplain management. While common approaches like National Oceanographic and Atmospheric Administration (NOAA) Atlas 14 assume that extreme precipitation characteristics are stationary over time, thi...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research: Climate |
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| Online Access: | https://doi.org/10.1088/2752-5295/adf56e |
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| author | Yuchen Lu Benjamin Seiyon Lee James Doss-Gollin |
| author_facet | Yuchen Lu Benjamin Seiyon Lee James Doss-Gollin |
| author_sort | Yuchen Lu |
| collection | DOAJ |
| description | Precipitation exceedance probabilities are widely used in engineering design, risk assessment, and floodplain management. While common approaches like National Oceanographic and Atmospheric Administration (NOAA) Atlas 14 assume that extreme precipitation characteristics are stationary over time, this assumption may underestimate current and future hazards due to anthropogenic climate change. However, the incorporation of nonstationarity in statistical modeling of extreme precipitation has faced practical challenges, which have restricted its applications. In particular, random sampling variability challenges the reliable estimation of trends and parameters, especially when observational records are limited. To address this methodological gap, we propose the Spatially Varying Covariates Model, a hierarchical Bayesian spatial framework that integrates nonstationarity and regionalization for robust frequency analysis of extreme precipitation. This model draws from extreme value theory, spatial statistics, and Bayesian statistics, and is validated through cross-validation and multiple performance metrics. Applying this framework to a case study of daily rainfall in the Western Gulf Coast, we identify robustly increasing trends in extreme precipitation intensity and variability throughout the study area, with notable spatial heterogeneity. This flexible model accommodates stations with varying observation records, yields smooth return level estimates, and can be straightforwardly adapted to the analysis of precipitation frequencies at different durations and for other regions. |
| format | Article |
| id | doaj-art-e8351f4f052b4615a090ee2ebe2849d2 |
| institution | Kabale University |
| issn | 2752-5295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research: Climate |
| spelling | doaj-art-e8351f4f052b4615a090ee2ebe2849d22025-08-22T16:09:42ZengIOP PublishingEnvironmental Research: Climate2752-52952025-01-014303501610.1088/2752-5295/adf56eBayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf CoastYuchen Lu0https://orcid.org/0000-0002-3607-0364Benjamin Seiyon Lee1https://orcid.org/0000-0003-0658-7458James Doss-Gollin2https://orcid.org/0000-0002-3428-2224Department of Civil and Environmental Engineering, Rice University , Houston, TX 77005, United States of AmericaDepartment of Statistics, George Mason University , Fairfax, VA 22030, United States of AmericaDepartment of Civil and Environmental Engineering, Rice University , Houston, TX 77005, United States of America; Ken Kennedy Institute, Rice University , Houston, TX 77005, United States of AmericaPrecipitation exceedance probabilities are widely used in engineering design, risk assessment, and floodplain management. While common approaches like National Oceanographic and Atmospheric Administration (NOAA) Atlas 14 assume that extreme precipitation characteristics are stationary over time, this assumption may underestimate current and future hazards due to anthropogenic climate change. However, the incorporation of nonstationarity in statistical modeling of extreme precipitation has faced practical challenges, which have restricted its applications. In particular, random sampling variability challenges the reliable estimation of trends and parameters, especially when observational records are limited. To address this methodological gap, we propose the Spatially Varying Covariates Model, a hierarchical Bayesian spatial framework that integrates nonstationarity and regionalization for robust frequency analysis of extreme precipitation. This model draws from extreme value theory, spatial statistics, and Bayesian statistics, and is validated through cross-validation and multiple performance metrics. Applying this framework to a case study of daily rainfall in the Western Gulf Coast, we identify robustly increasing trends in extreme precipitation intensity and variability throughout the study area, with notable spatial heterogeneity. This flexible model accommodates stations with varying observation records, yields smooth return level estimates, and can be straightforwardly adapted to the analysis of precipitation frequencies at different durations and for other regions.https://doi.org/10.1088/2752-5295/adf56eprecipitation frequency analysisnonstationary risk assessmentstatistical hydrologyextreme rainfallBayesian statistics |
| spellingShingle | Yuchen Lu Benjamin Seiyon Lee James Doss-Gollin Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast Environmental Research: Climate precipitation frequency analysis nonstationary risk assessment statistical hydrology extreme rainfall Bayesian statistics |
| title | Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast |
| title_full | Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast |
| title_fullStr | Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast |
| title_full_unstemmed | Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast |
| title_short | Bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the Western Gulf Coast |
| title_sort | bayesian spatiotemporal nonstationary model quantifies robust increases in daily extreme rainfall across the western gulf coast |
| topic | precipitation frequency analysis nonstationary risk assessment statistical hydrology extreme rainfall Bayesian statistics |
| url | https://doi.org/10.1088/2752-5295/adf56e |
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