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

TL;DR

This paper introduces a novel spatial-temporal modeling approach called PARE for estimating extreme environmental events at larger areas based on point-level data, aiding flood risk assessment and policy making.

Contribution

The paper develops a point-to-area random effects model that converts point measurements into areal-scale extreme value estimates, addressing a key gap in environmental risk modeling.

Findings

Effective modeling of spatial-temporal extremes at the areal level.

Improved understanding of flood risk over large regions.

Potential for better policy and hazard management.

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.