Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)
Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 221–238
Pub. online: 24 May 2024
Type: Statistical Data Science
Open Access
Open Access
Received
31 July 2023
31 July 2023
Accepted
14 April 2024
14 April 2024
Published
24 May 2024
24 May 2024
Abstract
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.
Supplementary material
Supplementary MaterialA 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/carly-fagnant/Spatial_Extreme_Value_Modeling).
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