  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.
