JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1133 10.6339/24-JDS1133 Statistical Data Science Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE) FagnantCarlynn1 https://orcid.org/0000-0003-1242-0048 SchedlerJulia C.1 EnsorKatherine B.ensor@rice.edu1∗ Department of Statistics, Rice University, Houston, TX 77005, U.S.A. Corresponding author. Email: ensor@rice.edu. 20242452024222221238 Supplementary Material

A pdf file containing the Supplemental Tables is included in the Supplemental Materials. Code to reproduce the PARE analysis for the three windows considered in this paper is provided as a Quarto project available for download on Github (https://github.com/juliaSchedler/ST_PARE), or viewing as a rendered vignette at https://juliaschedler.github.io/ST_PARE/. Due to large file sizes, additional resources on the GPD fitting process for all 80 years’ worth of rolling windows and models used for comparison to PARE are provided in another Github Repository (https://github.com/carly-fagnant/Spatial_Extreme_Value_Modeling).

31720231442024 2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2024 Open access article under the CC BY license.

One 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.

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