Natural disasters often result in large-scale power outages. Real-time tracking of the extent, distribution, and timelines of electrical service loss and recovery can play an important role in minimizing disaster impacts. Using NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB), the extent and duration of disrupted electric utility infrastructure in the Florida Panhandle following Hurricane Michael were estimated. The percent loss of electrical service was downscaled to a neighborhood level using the 2013-2017 American Community Survey (ACS) data at the block group level. Two ordinary least square models were estimated to examine the association between socioeconomic characteristics and the extent and duration of the power outages as well as recovery rates. The study found that block groups with higher percent minorities, multi-family housing units, rural areas, and a higher percentage of households receiving public assistance were experiencing slower power restoration rates than urban and more affluent neighborhoods. The findings have implications for disaster preparedness and recovery planning. / Includes bibliography. / Thesis (MURP)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

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<p dir="ltr">Tropical Cyclones (TCs) are intense low-pressure weather systems that acts as a meteorological monster causing severe rainfall and widespread freshwater flooding, leading to extensive damage and disruption. Quantitative precipitation estimates (QPEs) are crucial for accurately understanding and evaluating the impacts of TCs. However, QPEs derived from various modalities, such as rain gauges, ground-based merged radars, and satellites, can differ significantly and require thorough comparison. Understanding the limitations/advantages of using each QPE is essential to simulate a hydrological model especially to estimate extreme events like TCs. The objective of the dissertation is to 1) characterize the tropical cyclone precipitation (TCP) using three gridded products, 2) characterize the impact of using different QPEs in estimation of hydrological variables using a hydrology model, and 3) understand the usability of satellite-derived QPEs for eight cases of TC and its impact on the estimate of hydrological variables. The QPEs include near real-time and post-processed satellite data from NASA’s Global Precipitation Mission-Integrated Multi-sensor Retrievals for GPM Rainfall Product (IMERG), merged ground radar observations (Stage IV) from the National Centers for Environmental Prediction (NCEP), and interpolated gauge observations from the National Weather Service Cooperative Observer Program (GCOOP). The study quantifies how differences in rainfall intensity and location, as derived from these gridded precipitation datasets, impact surface hydrology. The Variable Infiltration Capacity (VIC) model and the geographic information system (GIS) routing assess the propagation of bias in the daily rainfall rate to total runoff, evapotranspiration, and flooding. The analysis covers eight tropical cyclones, including Hurricane Charley (2004), Hurricane Frances (2004), Hurricane Jeanne (2004), Tropical Storm Fay (2008), Tropical Storm Beryl (2012), Tropical Storm Debby (2012), Hurricane Irma (2017) and Hurricane Michael (2018) focusing on different regions in South-Atlantic Gulf region and land uses. The findings indicate that IMERG underpredicts precipitation at higher quantiles but aligns closely with ground-based and radar-based products at lower quantiles. IMERG reliably estimates total runoff and evapotranspiration in 90% of TC scenarios along the track and in agricultural and forested regions. There is substantial overlap ~ 70% between IMERG and GCOOP/Stage IV for the 90th percentile rainfall spatially for the case of TC Beryl 2012. Despite previous perceptions of underestimation, the study suggests that satellite-derived rainfall products can be valuable in simulating streamflow, particularly in data-scarce regions where ground estimates are lacking. The relative error in estimation is 12% and 22% when using IMERG instead of Stage IV and GCOOP rainfall data. The findings contribute to a broader perspective on usability of IMERG in estimating near real-time hydrological characteristics, paving the way for further research in this area. This analysis demonstrates that IMERG can be a reliable data product for hydrological studies even in the extreme events like landfalling TCs. This will be helpful in improving the preparedness of vulnerable communities and infrastructure against TC-induced flooding in data scare regions.</p>