Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: one is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise. / Master of Science / Storm surge flooding, which is the rise in sea level caused by tropical cyclones and other storms, is a devastating threat to coastal regions, and its impact is increasing due to sea-level rise (SLR). This poses a considerable risk to communities living near the coast. Therefore, it is crucial to accurately and quickly predict the potential for storm surge flooding. This study aimed to develop a new way that can rapidly estimate peak storm surges under different sea-level rise scenarios for any random synthetic storms of interest and assess the likelihood of their occurrence. The approach is based on historical tropical cyclone datasets and a machine learning model trained on high-quality simulations provided by the US Army Corps of Engineers (USACE). The study focused on the Chesapeake Bay area of the US and estimated the probabilistic tropical cyclone flood hazard in two locations, an urban site in Virginia and a rural site in Maryland. This new approach has the potential to assist in reducing coastal storm risks in vulnerable communities by providing a quick and reliable assessment of the hazard that takes into account the effects of future sea-level rise.

In this study, stage, streamflow, and precipitation data was collected from small watersheds in the Swanton Pacific Ranch for the first two hydrologic years following the 2020 CZU Lightning Complex. The Little Creek watershed was setup for high-resolution data collection with four separate stage gauge sites (Main Stem, North Fork, South Fork, and Upper North Fork) and four rain gauge sites (Al Smith House, Ridgeline, Upper North Fork, and Landing 23). Stage gauge sites were also established at Queseria, Archibald, and Mill creeks. Preliminary post-fire rating curves were developed for the four sites of Little Creek. The Main Stem (MS) and North Fork (NF) post-fire curves showed some flattening of the slope indicating channel filling, while the South Fork (SF) curve displayed a steepening indicating channel scouring. The Upper North Fork (UNF) rating curve did not indicate any shifts. However, at the time of this study the rating curves were incomplete due to limitations in streamflow measurements. Linear regression models were fit to pre-fire data (hydrologic years 2000-2008) to predict peak flows and storm flow volumes. Antecedent precipitation index (API) and total storm precipitation depth were found to be significant predictors while peak 1-hour rainfall intensity was not. Comparison of post-fire observations to pre-fire model predictions indicated that there were increases in both peak flow and storm flow volumes in Little Creek. However, these findings are not statistically significant due to the limited post-fire observations (n

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The movement and fate of heavy metal inputs (Cd, Zn, Mn, Cu, Al, Fe, Pb, Ni and Cr) from highway runoff were investigated in a three-year study on 1.3 hectare retention facility near the Maitland Interchange on Interstate 4, north of Orlando, Florida. Physical characteristics of the retention pond and surrounding watershed were defined and field instrumentation was installed. Stormwater samples were collected over a one-year period, representing a wide range of intensities and antecedent dry periods. Stormwater characteristics were compared with average retention pond water quality to determine removal efficiencies for heavy metals within the pond. A total of 138 core samples were collected in the pond over a three-year period to investigate the horizontal and vertical migrations of heavy metals within the pond. Sediment core samples were also carried through a series of sequential extraction procedures to examine the type of chemical associations and stability of each metal in the sediments. An apparatus was built which allowed sediments to be incubated under various conditions of redox potential and pH to investigate the effects of changes in sediment conditions on the stability of metal-sediment associations. Five groundwater monitoring wells were also installed to monitor metal movement and accumulations under stormwater management systems. Heavy metal inputs from highway runoff were found to be predominantly particulate in nature, with dissolved fractions for most metals of only 25 percent. Upon entering the retention pond, most metal species settled into the sediments within 60-90 m of the inlet. Removal efficiencies for metals after entering the pond averaged 70-90 percent for particulate species and about 50 percent for dissolved species. Sediment concentrations of heavy metals were highest near the surface, with rapidly decreasing concentrations with increasing depth. Metal-sediment associations appear to be very strong for most metals, with the vast majority of metal inputs into the pond over the eight-year life still remaining in the top 10 cm. Concentrations of all heavy metals measured were higher in groundwaters beneath the pond that in the pond water; but for most metals, the increases only extended to depths of 1-3 m beneath the pond. In general, metal concentrations beneath swale areas were significantly higher than concentrations beneath the retention pond. Due to slow groundwater movement in the area, the effects of increased metal concentrations are very localized. Evidence was presented to suggest that mobilization of metals into groundwaters could substantially increase with time if maintenance procedures are not conducted.