Doctor of Philosophy

dissertationThe goal of this dissertation is to improve flood risk management by enhancing the computational capability of two-dimensional models and incorporating data and parameter uncertainty to more accurately represent flood risk. Improvement of computational performance is accomplished by using the Graphics Processing Unit (GPU) approach, programmed in NVIDIA's Compute Unified Development Architecture (CUDA), to create a new two-dimensional hydrodynamic model, Flood2D-GPU. The model, based on the shallow water equations, is designed to execute simulations faster than the same code programmed using a serial approach (i.e., using a Central Processing Unit (CPU)). Testing the code against an identical CPU-based version demonstrated the improved computational efficiency of the GPU-based version (approximate speedup of more than 80 times). Given the substantial computational efficiency of Flood2D-GPU, a new Monte Carlo based flood risk modeling framework was created. The framework developed operates by performing many Flood2D-GPU simulations using randomly sampled model parameters and input variables. The Monte Carlo flood risk modeling framework is demonstrated in this dissertation by simulating the flood risk associated with a 1% annual probability flood event occurring in the Swannanoa River in Buncombe County near Asheville, North Carolina. The Monte Carlo approach is able to represent a wide range of possible scenarios, thus leading to the identification of areas outside a single simulation inundation extent that are susceptible to flood hazards. Further, the single simulation results underestimated the degree of flood hazard for the case study region when compared to the flood hazard map produced by the Monte Carlo approach. The Monte Carlo flood risk modeling framework is also used to determine the relative benefits of flood management alternatives for flood risk reduction. The objective of the analysis is to investigate the possibility of identifying specific annual exceedance probability flood events that will have greater benefits in terms of annualized flood risk reduction compared to an arbitrarily-selected discrete annual probability event. To test the hypothesis, a study was conducted on the Swannanoa River to determine the distribution of annualized risk as a function of average annual probability. Simulations of samples of flow rate from a continuous flow distribution provided the range of annual probability events necessary. The results showed a variation in annualized risk as a function of annual probability. And as hypothesized, a maximum annualized risk reduction could be identified for a specified annual probability. For the Swannanoa case study, the continuous flow distribution suggested targeting flood proofing to control the 12% exceedance probability event to maximize the reduction of annualized risk. This suggests that the arbitrary use of a specified risk of 1% exceedance may not in some cases be the most efficient allocation of resources to reduce annualized risk

A model of delta frequency neuronal network activity and theta-gamma interactions in rat sensorimotor cortex in vitro

In recent decades, advances in electrophysiological techniques have enabled understanding of neuronal network activity, with in vitro brain slices providing insights into the mechanisms underlying oscillations at various frequency ranges. Understanding the electrical and neuro-pharmacological properties of brain networks using selective receptor modulators in native tissue allows to compare such properties with those in disease models (e.g. epilepsy and Parkinson’s). In vivo and in vitro studies have implicated M1 in execution of voluntary movements and, from both local network in vitro and whole brain in vivo perspectives. M1 has been shown to generate oscillatory activity at various frequencies, including beta frequency and nested theta and gamma oscillations similar to those of rat hippocampus. In vivo studies also confirmed slow wave oscillations in somatosensory cortex including delta and theta band activity. However, despite these findings, non-thalamic mechanisms underlying cortical delta oscillations remain almost unexplored. Therefore, we determined to explore these oscillations in vitro in M1 and S1. Using a modified sagittal plane slice preparation with aCSF containing neuroprotectants, we have greatly improved brain slice viability, enabling the generation and study of dual rhythms (theta and gamma oscillations) in deep layers (LV) of the in vitro sensorimotor slice (M1 and S1) in the presence of KA and CCh. We found that theta-gamma activity in M1 is led by S1 and that the amplitude of gamma oscillations was (phase-amplitude) coupled to theta phase in both regions. Oscillations were dependent on GABAAR, AMPAR and NMDAR and were augmented by DAR activation. Experiments using cut/reduced slices showed both M1 and S1 could be intrinsic generators of oscillatory activity. Delta oscillations were induced in M1 and S1 by maintaining a neuromodulatory state mimicking deep sleep, characterised by low dopaminergic and low cholinergic tone, achieved using DAR blockade and low CCh. Delta activity depends on GABAAR, GABABR and AMPAR but not NMDAR, and once induced was not reversible. Unlike theta-gamma activity, delta was led by M1, and activity took >20mins to develop in S1 after establishement of peak power in M1. Unlike M1, S1 alone was unable to support delta activity. Dopamine modulates network activity in M1 and it is known that fast-spiking interneurons are the pacemakers of network rhythmogenesis. Recent studies reported that dopamine (DA) controled Itonic in medium spiny, ventrobasal thalamus and nucleus accumbens neurons by modulation of GABARs or cation channels. In the current study, voltage-clamp whole cell recordings were performed in fast spiking interneurons (FS cells) in Layer V of M1. These recordings revealed tonic and phasic GABAAR inhibition and when DA was bath applied, a slow inward current (IDA) was induced. IDA was mediated by non-specific cationic TRPC channels following D2R-like receptor activation. Overall, my studies show the strong interdependence of theta-gamma rhythmogenesis between M1 and S1, dominanace of M1 at delta frequency and the crucial role of dopamine in controlling FS cell activity. Further exploration of these rhythms in models of pathological conditions such as Parkinson`s disease and Epilepsy may provide insights into network changes underlying these disease conditions

Integration of SWMM into a Dam Break, Hurricane, and Extreme Flood Modeling and Damage Assessment Framework

SWMM5 has been seamlessly integrated with a Geographic Information System (GIS) for simulation of inundation and analysis of consequences resulting from extreme flood events. The GIS-based environment processes digital elevation models, land use/cover data, stream networks and soils to create stream network, sub-basins, and cross-section shapefiles for river basins selected for analysis. The following readily-available public-domain datasets are utilized: 30-m topographical data from the United States Geological Survey (USGS), 30-m NLCD, Natural Resources Conservation Service (NRCS) (STATSGO), and National Hydrography Dataset (NHD). Rainfall predictions are made by a numerical weather model and ingested in gridded format into the simulation environment. Runoff hydrographs are estimated using Green-Ampt infiltration excess runoff prediction and a onedimensional diffusive wave overland flow routing approach. The hydrographs and the channel morphology are used to generate a SWMM5 compatibl

Development of a Hydrologic Modeling System for the Dry Valley Catchment

The natural geology and topography of Putnam and surrounding counties have created a drainage system reliant on karst features such as sinkholes and caves. One limiting feature of such a system is the potential for greater flow demand than the capacity of these often-narrow swallets and cave openings, which can lead to increased flooding scenarios. Within Putnam County, the Dry Valley Watershed southeast of Cookeville, Tennessee, which spans roughly 7580 acres, experienced massive flash flooding due to this effect in July of 2015. The objective of this study is to explore the karst drainage flooding problem in the Dry Valley area and develop a HEC-HMS model to simulate the 10-year, 50-year, 100-year and 500-year floods for this region while also analyzing previous storm events. To achieve this objective, the project team collected survey and meteorological data and imported these data into the ArcGIS and HEC-HMS models to develop basin and meteorological models using Soil Conservation Service (SCS) Technical Release 55 (TR-55) methods in order to force HEC-HMS solutions for the various expected flood recurrence intervals. These solutions were used to estimate potential storm runoff hydrographs at points of interest within the study region, and potential engineering solutions were explored to aid in ameliorating the flooding situation within this watershed

A review of modelling methodologies for flood source area (FSA) identification

Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises