Coastal marshes and wetlands are vital natural resources that offer habitats for plants and animals, serve as ecological filtration for soil and water pollutants, and act as protection for coastlines. Fishing, both commercial and sport, has a large economic impact in the study area – the Gulf Coast between Galveston Bay, TX and Oak Grove, LA. The objective of this research was to determine the contribution of Hurricane Ike storm surge sedimentation to long-term marsh aggradation in Texas and Louisiana coastal marshes. The research hypothesized that Hurricane Ike’s storm surge deposit would be equal to decades and possibly even a century’s worth of the average annual non-storm sedimentation. A quantitative field study was performed. The storm surge deposit was examined in a series of 15 transects covering approximately 180 km east of Hurricane Ike’s landfall. Nine of the 15 transects were re-surveyed a year after the initial measurement to assess preservation of the deposit. The results demonstrate that Hurricane Ike contributed between 10 to 135 years’ worth of sediment to coastal marshes along the coasts of Texas and Louisiana, and the sediment deposits have been preserved for over two years.

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The most critical hazards impacting the world today are the affects of climate change and global warming. Scientists have been studying the Earth's climate for centuries and have come to agreement that our climate is changing, and has changed, many times abruptly over the history of our planet. This research focuses on the impacts of global warming related to increased hurricane intensities and their surge responses along the coast of the State of Louisiana. Surge responses are quantified for storms that could potentially occur under present climate but 50 years into the future on a coast subjected to current erosion and local subsidence effects. Analyses of projected hurricane intensities influenced by an increase in sea surface temperatures (SSTs) are performed. Intensities of these storms are projected to increase by 5% per degree of increase in SSTs. A small suite of these storms influenced by global warming and potentially realized by abrupt climate changes are modeled. Simulations of these storms are executed using a storm surge model. The surges produced by these storms are significantly higher than surges produced by presentday storms. These surges are then compared to existing surge frequency distributions along the Louisiana coast.

The objective of this study was to determine if numerical models commonly used for large scale applications could also be used to model flow through flood control structures in the Rigolets Pass between Lake Borgne and Lake Pontchartrain. For this purpose a small scale physical model was built. It showed that bi-stable flow can develop downstream of a constriction. Small changes in the distribution of the approaching flow significantly impacted flows downstream of the constriction. This behavior could not be properly reproduced by a small scale 2-dimensional RMA2 model of identical dimensions. A large scale RMA2 model of the Rigolets testing possible locations and geometries of flood control structures showed that this pass is very sensitive to variations in the cross sectional flow area. Even minor reductions can significantly increase headlosses and velocities. To reduce negative impacts a flood control structure should be built in a wide and shallow area of the pass.

Deadwood has increased over the last 25 years, but it remains unclear to what extent this is driven by forestry practices or storms. Therefore, I wanted to study the change in volume, decay stage and tree species during a 22-year period, to see if there was a correlation between increase of deadwood and storm. This study included data from southern Sweden, collected by the Swedish National Forest Inventory between 1994-2016. Deadwood in production forest have doubled over the last 25 years and almost quadrupled in protected forest. The increase does not depend on storm since much of the fallen wood was probably removed following year. In protected forest there was an increase in deadwood of broadleaved trees and a drastic decrease in Pinus sylvetstris. While in production forest, conifer trees dominate and there was no lasting effect due to the storm Gudrun (2005) on Picea abies. Hard deadwood decreased in production forest, possibly due to increased removal of branches and treetops, used as forest fuel in forest management. Possible reasons for the increase in deadwood could be the awareness in forestry, especially certification system and voluntarily set asides. Though, there is still necessary to increase the volume of deadwood in production forest, since it covers the largest parts of Swedish forests and does not seem to reach the national environment objective in 2030.

The model equations for storm surge and tsunamis most commonly used are the shallow water equations with addition of appropriate source terms for bathymetry. Traditional approaches will need to resolve the mesh to discretize small-scale structure, which impacts the time-step size to be proportional to the size of cells. In this thesis, a novel approximate Riemann solver was developed in order to deal with the existence of barrier without restricting the time-step due to small cells. Because of the wave redistribution method and proper ghost cells setting, the novel Riemann solver maintained properties including mass and momentum conservation, the well-balancing properties and robustness at the wet-dry interface. The solver also preserves nonnegative water depth and prevents leakage. A modified h-box method is applied so the algorithm can overcome restrictions of small time-step sizes. The work has been done in the context of the GeoClaw platform with retaining the capabilities of GeoClaw solver. At the same time, the special developed Riemann solver extends the package to handle the sub-grid-scale effects of barriers. Incorporating the solver developed in this work into the GeoClaw framework has allowed to leverage GeoClaw’s ability to handle complex bathymetry and real applications.

The Mid-Atlantic coastal forests in Virginia are stressed by episodic disturbance from storms associated with hurricanes and nor'easters. Using annual tree ring data, we adopt a dendroclimatic and statistical modelling approach to understand the response and resilience of a coastal pine forest to slow progressive climate change and extreme storm surge events. Results indicate that radial growth of trees in the study area is influenced by age, vigor, competition, microsite variability, and regional climatic trends, but dominated periodically by disturbance due to storm surges. We evaluated seven local storm surge events to understand the effect of storm surges associated with nor'easters and hurricanes on radial growth. A general decline in radial growth was observed in the year of the storm and three years following it, after which the radial growth starts recovering. Given the projected increase in hurricanes and storm surge severity with changing global climate, this study contributes to understanding declining tree growth response and resilience of coastal forests to past disturbances. This can help predict vegetation response patterns to similar disturbances in the future.

Water chemistry in rivers is impacted by a variety of natural and anthropogenic processes including agricultural runoff, urban runoff, storm runoff, groundwater inputs, and the built environment. In this study we used trace element concentrations (including As, B, Ce, Co, Cu, Li, Mn, Rb, Sb, Sr, Tl, V, and Zn) and continuous measurements of flow rates and specific conductance to investigate dynamic processes affecting water quality in a rapidly urbanizing agricultural area typical of the western U.S. The middle Provo River, located in northern Utah, USA, was selected as the study area because it is well instrumented with water quality stations and streamflow gauges. We sampled 6 sites on the middle Provo River and 15 sites on tributaries in the watershed a minimum of 5 times between April 2014 and March 2015 to evaluate potential contributions from surface water and groundwater inputs to the Provo River. Additional water samples were collected at 13 cold, thermal, and mixed cold/thermal springs in Heber Valley during summer 2014 to evaluate regional groundwater chemistry. Samples were also collected during two storm events including high frequency sampling in a tributary and road-puddle samples to characterize potential storm runoff chemistry. Specific conductance data loggers were deployed in tributaries to monitor effects of precipitation and other runoff on the middle Provo River at 15-min intervals. See Table 1 for a summary of sampling events. Middle Provo River water chemistry is impacted by natural groundwater inputs as well as surface water tributaries. Li, B, Sr, As concentrations increased dramatically (3-10 fold) downstream of the confluence with a major tributary, Snake Creek. Snake Creek had average As concentrations of ~15 µg/L above the confluence with Provo River and accounted for roughly 20% of the flow to the middle Provo River, but increased the As concentration in Provo River ~4 fold. Thermal springs had ~20 and ~80 times higher concentrations of As and Li, respectively, relative to cold springs and was found to be a major contributor of trace elements to Snake Creek and the middle Provo River. Cl mixing calculations indicated that groundwater contributions increased downstream with up to 15% of the flow to the middle Provo River being contributed within the most downstream reach. Tributaries were found to impact the Provo River based on specific conductance fluxes in tributaries corresponding to fluxes in the river. Notably, Spring Creek, a dominantly agricultural tributary, accounts for >40% of the annual V load and >18% of the annual U, Mn, Pb, Ba, La, and Ce loads to the middle Provo River. The trace elements B, Li, As, and Sr which are found in high concentrations in groundwater, were strongly correlated with Provo River specific conductance and may indicate a potential method of predicting select trace element concentrations in the middle Provo River based on specific conductance data. Filtered puddle samples collected during a storm event had higher concentrations of Co, Cu, V, and Zn, but lower concentrations of major and select trace elements including As, Li, and Sr, relative to the middle Provo River. This study has implications for understanding water quality in complex coupled human-natural systems.

On June 13, 2008, after many days of rain, the Cedar River flooded the city of Cedar Rapids. With a peak discharge of 139,987 cfs and at 19.12 feet above flood stage, the 2008 flood of Cedar Rapids was the largest flood in the city’s historic record. As rivers rose, the city had received forecasts of an incoming flood as early as June 8. Then, on June 12, it began to rain in Cedar Rapids. Finally, on June 13, 2008, the Middle Cedar crested at 31.12 feet. This thesis project modeled a variety of rainfall patterns on June 12, 2008, to determine the effect of varying rainfall intensity and location on the magnitude of the 2008 flood of Cedar Rapids. Using a method known as Stochastic Storm Transposition (SST), I overwrote precipitation data in a hydrologic model of the Middle Cedar Watershed with rainfall data extracted from specific storm events that occurred in the Upper Midwest. We used a physically-based, semi-distributed hydrologic model known as GHOST (Generic Hydrologic Overland-Subsurface Toolkit) developed by Marcela Politano at the University of Iowa. Traditionally, hydrologic modeling for watersheds has used design storms to create rainfall inputs in flood modeling. These design storms have uniform rainfall timing and accumulation patterns across a watershed and are determined by designated equations for a geographic region. In large watersheds such as the Middle Cedar (2,400 square miles), design storms are not physically realistic because of their uniformity. Additionally, design storms fail to capture unique storm patterns such as high intensity periods or the movement of a storm across a watershed. By implementing SST into GHOST, we used physically realistic storm events that have unique rainfall patterns and intensities within a designated return period. SST extracts rainfall data from real storm events and transposes the storm patterns onto watersheds to provide physically realistic rainfall data for hydrologic modeling. A tool called RainyDay, developed by Professor Daniel Wright at the University of Wisconsin, provided the storm transpositions used in this research. We assigned the storm transpositions return periods created by RainyDay, corresponding to their average transposed rainfall across the Middle Cedar Watershed. Replacing the June 12 rainfall with RainyDay’s two-year transposed storm events (average rain accumulation 1.8 inches) resulted in modeled flood peaks larger than the unaltered June 12 flood peak. Storm transpositions of 5-, 10-, and 2,000-year return periods showed even larger peaks, illustrating the potential for floods much larger than the 2008 flood. In addition to the analysis of flood magnitude in 2008, we modeled a set of storm transposition scenarios for a variety of soil-moisture conditions. The increased discharge levels in scenarios with high soil moisture emphasize the importance of initial conditions in flooding scenarios. Finally, we modeled the effect that two-year RainyDay storms would have had on the 2016 flood of Cedar Rapids had they occurred on the day before the peak. The two-year transpositions showed that with an impending flood crest smaller than the 2008 crest, several two-year RainyDay scenarios would have resulted in floods nearly equal in magnitude to the 2008 flood event. Our manipulation of the rainfall in the Middle Cedar Watershed on June 12, 2008, using the GHOST model provided the opportunity to re-examine the influence that a specific day of rainfall had on the 2008 flood of Cedar Rapids. The potential for higher flooding under conditions of repeated rainfall and high soil moisture illustrates the susceptibility of the Middle Cedar Watershed to future flood events under similar conditions. Applying SST in hydrologic modeling also provided an opportunity to model a variety of rainfall scenarios and to better understand watershed responses to nuanced and physically realistic rainfall patterns.

Bridge scour is the leading cause of bridge damage nationwide. Successfully mitigating bridge scour problems depends on our ability to reliably estimate scour potential, design safe and economical foundation elements that account for scour potential, identify vulnerabilities related to extreme events, and recognize changes to the environmental setting that increase risk at existing bridges. This study leverages available information, gathered from several statewide resources, and adds watershed metrics to create a comprehensive, georeferenced dataset to identify parameters that correlate to bridges damaged in an extreme flood event. Understanding the underlying relationships between existing bridge condition, fluvial stresses, and geomorphological changes is key to identifying vulnerabilities in both existing and future bridge infrastructure. In creating this comprehensive database of bridge inspection records and associated damage characterization, features were identified that correlate to and discriminate between levels of bridge damage. Stream geomorphic assessment features were spatially joined to every bridge, marking the first time that geomorphic assessments have been broadly used for estimating bridge vulnerability. Stream power assessments and watershed delineations for every bridge and stream reach were generated to supplement the comprehensive database. Individual features were tested for their significance to discriminate bridge damage, and then used to create empirical fragility curves and probabilistic predictions maps to aid in future bridge vulnerability detection. Damage to over 300 Vermont bridges from a single extreme flood event, the August 28, 2011 Tropical Storm Irene, was used as the basis for this study. Damage to historic bridges was also summarized and tabulated. In some areas of Vermont, the storm rainfall recurrence interval exceeded 500 years, causing widespread flooding and damaging over 300 bridges. With a dataset of over 330 features for more than 2,000 observations to bridges that were damaged as well as not damaged in the storm, an advanced evolutionary algorithm performed multivariate feature selection to overcome the shortfalls of traditional logistic regression analysis. The analysis identified distinct combinations of variables that correlate to the observed bridge damage under extreme food events.