Hurricane hazards result from the combined processes of wind, waves, storm surge, and overwash (Lennon et al., 1996). Predicting the severity of these hazards requires immense effort to quantify the processes and then predict how different coastal regions respond to them. A somewhat simpler, but no less daunting task is to begin to predict the hazards due to potential erosion of barrier islands. A four-part scale has been developed by Sallenger (2000) to provide a framework for understanding how barrier islands might respond during extreme storm events. These four regimes describe how beach and dune elevations interact with surge and wave runup. This study will produce estimates of potential hazards through combining lidar surveys of dune elevation with modeled elevations of storm water levels. Direct measurements of maximum wave heights during hurricanes are rare. We evaluated three simple equations proposed by Kjerfve (1986), Young (1988), and Hsu (1998) to forecast the maximum wave height (Hmax) generated by three 1999 hurricanes. Model results were compared to wave data recorded by the National Oceanic and Atmospheric Administration (NOAA) wave rider buoys. The radius of maximum winds, wind speed, forward velocity, distance from buoy to the storm's eye-wall (r), and buoy's position relative to the quadrant of the storm (Q) were found to have significant and direct roles in evaluating recorded hurricane induced wave heights (H) and thus, were individually examined for each comparison. The implications of the r and Q on H were assessed when determining the overall effectiveness of the modelers' equations. Linear regression analyses tested the accuracy of each modeled prediction of the Hmax, comparing it to the observed wave heights. Three statistical criteria were used to quantify model performance. Hsu's model was the most reliable and useful forecasting technique. Despite the predictive skill of Hsu's model, direct observations of the maximum wave conditions, when available and appropriate, are preferred as inputs for SWAN, a 3rd generation shoaling wave model. Outputs from SWAN are used to calculate the empirical relationships for wave runup. For our test case, pre and post-storm topographies were surveyed as part of a joint USGS-NASA program using lidar technology. These data sets were used to calculate changes in the elevation and location of the dune crest (Dhigh) and dune base (Dlow) for the North Carolina Outer Banks. We hindcast potential coastal hazards (erosional hot spots) using the pre-storm morphology and modeled wave runup and compare those estimates to the measured results from the post-storm survey. Links among the existing topography and spatial variations in wave runup were found to be 95% correlated for the north-south and east-west facing barrier islands. Application of Sallenger's (2000) four-part Storm Impact Scale to the pre-storm Dhigh elevation survey and wave runup extremes (Rhigh and Rlow) were found to accurately predict zones of overwash and showed potential to forecast the inundation regime.

Utvecklingen av den digitala världen och sociala medier har gjort att konsumenter fått större makt i hur ett företag uppfattas då de direkt och indirekt påverkar företagets image genom det de delar, kommenterar och recenserar (Alam och Khan 2019). Ji, Li, North och Liu (2017) menar att en enda negativ kommentar på sociala medier kan växa till en allvarlig kris, som i denna studie definieras som en onlinestorm. Studiens syfte är att undersöka hur köpintentionen kan påverkas utifrån den onlinestorm som företaget NA-KD fick erfara i december 2020. Fallet uppmärksammades i sociala medier utifrån hur tidigare praktikanter och anställda hos NA-KD hade upplevt utskällningar från chefer, mobbning och ohållbar stress på arbetsplatsen (Hansson 2020).För att köpintentioner ska kunna förutsägas måste konsumenternas förändrade varumärkesattityd och känslornas påverkan på konsumentbeteende undersökas. För att besvara vårt syfte formulerades två frågeställningar samt två hypoteser utifrån tidigare forskning och teori och en kvantitativ undersökning i form av en enkät genomfördes. SOR-teorin av Jacoby (2002) användes som teoretiskt ramverk för studien. Resultatet av studien påvisar att konsumenterna upplevt ilska och därmed antagit ett negativt beteende som svar vilket gör att varumärkesattityden försämrats. Detta påverkar i sin tur köpintentionen negativt. Studiens resultat visar även att i detta fall har engagemanget på sociala medier varit relativt lågt hos respondenterna, men trots det har varumärkesattityden försämrats hos det allra flesta. Detta indikerar på att konsumentengagemang på sociala medier inte är en betydande faktor för att varumärkesattityden och köpintentionen ska bli påverkade i en onlinestorm. Studien är skriven på svenska. / The development of the digital world and social media has given consumers greater power in how an organisation is perceived as they have a direct and indirect impact on an organisation's image through what they share, comment on and review (Alam & Khan 2019). Ji, Li, North and Liu (2017) argue that one single negative comment on social media can develop into a crisis, defined in this study as an online storm. The purpose of this study is to examine how purchase intention can be affected by analysing the online storm that NA-KD experienced in December 2020. This case was highlighted on social media where former interns and employees at NA-KD shared that they had experienced verbal abuse from bosses, bullying and untenable stress at the workplace (Hansson 2020).Consumers changing brand attitudes and the role that emotions play in consumer behaviour must be examined to predict purchase intentions. To attain the purpose of this study, two research questions were formulated as well as two hypotheses, based on previous research and theory, and a quantitative study in the form of a questionnaire was conducted. The SOR theory proposed by Jacoby (2002) was used as a theoretical framework for the study. The results show that consumers experienced anger and therefore assumed a negative behaviour towards the organisation, which worsened brand attitude. This in return negatively affects purchase intention. The results of the study also show that in this case consumer engagement on social media had been relatively low. Despite this, brand attitude had been affected negatively with the majority. This indicates that consumer engagement on social media is not a significant factor in affecting brand attitude and purchase intention by an online storm.The study is written in Swedish.

Tropical cyclone (TC)-spawned tornadoes in Florida were analyzed to determine patterns of occurrence based on storm and geographic features. Tornadoes were determined to be associated with a landfalling or nearby TC if a tornado occurred within 800 km of the TC’s center of circulation. TC-tornadoes were analyzed for patterns based on distance and angle from TC’s center, topographic influences, population biases, and influence based on time of landfall. Most TC-Tornadoes tend to occur more often before landfall than after. It was discovered that tornadoes have occurred in different areas with respect to the bearing from the center depending on the landfall location and time of landfall. It was also discovered that land use type, and elevation had little to do with TC-Tornado occurrence. The results do suggest some population bias. The findings will be a guide for operational meteorologists to aid in forecasting likely tornadogenesis from TCs.

This research paper fulfills the requirements of Geography 4C6. / This paper is an introductory study on the ability of a detention pond to reduce pollutant loading to a receiving water body. Three forms of water pollution are analysed in this study, trace metal (V, Ti and Mn in the water and on suspended solids and bottom sediments), organic and bacterial (bacterial indicators of fecal coliform and fecal streptococci are utilized) pollutants. Each pollutant type requires a different form of analysis to obtain concentrations for targeted pollutants. V, Ti and Mn concentrations were obtained from Instrumental Neutron Activation Analysis (INAA), organic concentrations were acquired by Electron Capture Gas Chromatography (ECGC) and bacterial concentrations were obtained from various laboratory techniques performed by technicians in the Microbiology Lab at McMaster University and in the Provincial Health Laboratories in Hamilton, Ontario. Suspended solid concentration are also analysed to determine the pond's effectiveness in reducing suspended solids load and thus the pollutants they carry. The Storm Water Management Model was used to estimate total pollutant loading into the pond via a combined sewer overflow (CSO). The pollutant concentrations obtained were analysed spatially through the sampling network and temporally between sampled dry and wet weather periods. The result of this study has led to the disturbing conclusion that the detention pond appears to have little or no effect on enhancing water quality. / Thesis / Bachelor of Science (BSc)

This dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness parameterization schemes, an application of a cutting edge remotely sensed inundation detection method to assess the performance of a tidal model, and a preliminary investigation into using 3-dimensional airborne laser scanning data to parameterize surface roughness. A thorough academic review of wetting and drying algorithms employed by contemporary numerical tidal models was conducted. Since nearly all population centers and valuable property are located in the overland regions of the model domain, the coastal models must adequately describe the inundation physics here. This is accomplished by techniques that generally fall into four categories: Thin film, Element removal, Depth extrapolation, and Negative depth. While nearly all wetting and drying algorithms can be classified as one of the four types, each model is distinct and unique in its actual implementation. The use of spatial elevation data is essential to accurate coastal modeling. Remotely sensed LiDAR is the standard data source for constructing topographic digital terrain models (DTM). Hydrographic soundings provide bathymetric elevation information. These data are combined to form a seamless topobathy surface that is the foundation for distributed coastal models. A three-point inverse distance weighting method was developed in order to account for the spatial variability of bathymetry data referenced to tidal datums. This method was applied to the Tampa Bay region of Florida in order to produce a seamless topobathy DTM. Remotely sensed data also contribute to the parameterization of surface roughness. It is used to develop land use / land cover (LULC) data that is in turn used to specify spatially distributed bottom friction and aerodynamic roughness parameters across the model domain. However, these parameters are continuous variables that are a function of the size, shape and density of the terrain and above-ground obstacles. By using LULC data, much of the variation specific to local areas is generalized due to the categorical nature of the data. This was tested by comparing surface roughness parameters computed based on field measurements to those assigned by LULC data at 24 sites across Florida. Using a t-test to quantify the comparison, it was proven that the parameterizations are significantly different. Taking the field measured parameters as ground truth, it is evident that parameterizing surface roughness based on LULC data is deficient. In addition to providing input parameters, remotely sensed data can also be used to assess the performance of coastal models. Traditional methods of model performance testing include harmonic resynthesis of tidal constituents, water level time series analysis, and comparison to measured high water marks. A new performance assessment that measures a model's ability to predict the extent of inundation was applied to a northern Gulf of Mexico tidal model. The new method, termed the synergetic method, is based on detecting inundation area at specific points in time using satellite imagery. This detected inundation area is compared to that predicted by a time-synchronized tidal model to assess the performance of model in this respect. It was shown that the synergetic method produces performance metrics that corroborate the results of traditional methods and is useful in assessing the performance of tidal and storm surge models. It was also shown that the subject tidal model is capable of correctly classifying pixels as wet or dry on over 85% of the sample areas. Lastly, since it has been shown that parameterizing surface roughness using LULC data is deficient, progress toward a new parameterization scheme based on 3-dimensional LiDAR point cloud data is presented. By computing statistics for the entire point cloud along with the implementation of moving window and polynomial fit approaches, empirical relationships were determined that allow the point cloud to estimate surface roughness parameters. A multi-variate regression approach was chosen to investigate the relationship(s) between the predictor variables (LiDAR statistics) and the response variables (surface roughness parameters). It was shown that the empirical fit is weak when comparing the surface roughness parameters to the LiDAR data. The fit was improved by comparing the LiDAR to the more directly measured source terms of the equations used to compute the surface roughness parameters. Future work will involve using these empirical relationships to parameterize a model in the northern Gulf of Mexico and comparing the hydrodynamic results to those of the same model parameterized using contemporary methods. In conclusion, through the work presented herein, it was demonstrated that incorporating remotely sensed data into coastal models provides many benefits including more accurate topobathy descriptions, the potential to provide more accurate surface roughness parameterizations, and more insightful performance assessments. All of these conclusions were achieved using data that is readily available to the scientific community and, with the exception of the Synthetic Aperture Radar (SAR) from the Radarsat-1 project used in the inundation detection method, are available free of charge. Airborne LiDAR data are extremely rich sources of information about the terrain that can be exploited in the context of coastal modeling. The data can be used to construct digital terrain models (DTMs), assist in the analysis of satellite remote sensing data, and describe the roughness of the landscape thereby maximizing the cost effectiveness of the data acquisition.

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