Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)

Simple item page
dc.citation.firstpage221en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitleJournal of Data Scienceen_US
dc.citation.lastpage238en_US
dc.citation.volumeNumber22en_US
dc.contributor.authorFagnant, Carlynnen_US
dc.contributor.authorSchedler, Julia C.en_US
dc.contributor.authorEnsor, Katherine B.en_US
dc.date.accessioned2024-10-08T13:27:47Zen_US
dc.date.available2024-10-08T13:27:47Zen_US
dc.date.issued2024en_US
dc.description.abstractOne measurement modality for rainfall is a fixed location rain gauge. However, extreme rainfall, flooding, and other climate extremes often occur at larger spatial scales and affect more than one location in a community. For example, in 2017 Hurricane Harvey impacted all of Houston and the surrounding region causing widespread flooding. Flood risk modeling requires understanding of rainfall for hydrologic regions, which may contain one or more rain gauges. Further, policy changes to address the risks and damages of natural hazards such as severe flooding are usually made at the community/neighborhood level or higher geo-spatial scale. Therefore, spatial-temporal methods which convert results from one spatial scale to another are especially useful in applications for evolving environmental extremes. We develop a point-to-area random effects (PARE) modeling strategy for understanding spatial-temporal extreme values at the areal level, when the core information are time series at point locations distributed over the region.en_US
dc.identifier.citationFagnant, C., Schedler, J. C., & Ensor, K. B. (2024). Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE). Journal of Data Science, 22(2), 221–238. https://doi.org/10.6339/24-JDS1133en_US
dc.identifier.digitaljds1133en_US
dc.identifier.doihttps://doi.org/10.6339/24-JDS1133en_US
dc.identifier.urihttps://hdl.handle.net/1911/117916en_US
dc.language.isoengen_US
dc.publisherCenter for Applied Statistics, School of Statistics, Renmin University of Chinaen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleSpatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)en_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
jds1133.pdf
Size:
876.03 KB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
Statistics Publications