Fagnant, CarlynnSchedler, Julia C.Ensor, Katherine B.2024-10-082024-10-082024Fagnant, 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-JDS1133https://hdl.handle.net/1911/117916One 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.engExcept 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.Journal articlejds1133https://doi.org/10.6339/24-JDS1133