Browsing by Author "Fagnant, Carlynn"

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    Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)
    (Center for Applied Statistics, School of Statistics, Renmin University of China, 2024) Fagnant, Carlynn; Schedler, Julia C.; Ensor, Katherine B.
    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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    Spatiotemporal Extreme Value Modeling with Environmental Applications
    (2021-10-06) Fagnant, Carlynn; Ensor, Katherine B.
    Extreme value analysis (EVA) is essential to evaluate the extreme events brought on by natural hazards in the environment. Particularly, EVA informs risk assessment for communities, which is crucial to protecting people and property. This work focuses on an application to extreme rainfall in the Houston, TX region and Harris County, and performs spatiotemporal extreme value modeling in order to assess the evolution of extremes over time for the region. Rainfall extreme values are compared to previous standards in order to demonstrate the need for updated policies. In addition to the temporal evolution of EVA, a key component of this work is the introduction of new methods to extend extreme value modeling at the point observation level to the areal level. The methods employ spatial statistics change-of-support concepts and the use of the extended Hausdorff distance to provide estimates of the extreme value distribution at the region level. Regional inference provides insight to support policy decisions for communities and cities.