With the impact of global climate change, the speedy, intelligent and accessible dissemination of coastal flood predictions from a number of modelling tools at a range of temporal and spatial scales becomes increasingly important for policy decision makers. This thesis provides a novel approach to integrate the coastal flood data into an ArcFLOOD data model to improve the analysis, assessment and mitigation of the potential flood risk in coastal zones. This novel methodology has improved the accessibility, dissemination and visualisation of coastal flood risk. The results were condensed into spatial information flows, data model schematic diagrams and XML schema for end-user extension, customisation and spatial analysis. More importantly, software developers with these applications can now develop rich internet applications with little knowledge of numerical flood modelling systems. Specifically, this work has developed a coastal flooding geodatabase based upon the amalgamation, reconditioning and analysis of numerical flood modelling. In this research, a distinct lack of Geographic Information Systems (GIS) data modelling for coastal flooding prediction was identified in the literature. A schema was developed to provide the linkage between numerical flood modelling, flood risk assessment and information technology (IT) by extending the ESRI ArcGIS Marine Data Model (MDM) to include coastal flooding. The results of a linked hybrid hydrodynamic-morphological numerical flood model were used to define the time-series representation of a coastal flood in the schema. The results generated from GIS spatial analyses have improved the interpretation of numerical flood modelling output by effectively mapping the flood risk in the study site, with an improved definition according to the time-series duration of a flood. The improved results include flood water depth at a point and flood water increase which equates to the difference in significant wave height for each time step of coastal flooding. The flood risk mapping provided has indicated the potential risk to infrastructure and property and depicted the failure of flood defence structures. In the wider context, the results have been provided to allow knowledge transfer to a range of coastal flooding end-users.

Coastal flooding, caused by sea level rise (SLR), storm surge, and tropical cyclones, is a growing threat. Previous studies have documented mortality associated with historical coastal flooding and developed predictions of mortality risk based on SLR and human development. This study updates those estimates and provides a new model by including new mortality data from events between 2010 and 2020 and an updated method for estimating the population exposed to coastal flooding events. Primary data sources include the Emergency Events Database (EM-DAT) and the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) model. We first characterize trends in exposed populations and mortality associated with coastal flooding between 1990 and 2020. A mixed effect regression model estimates mortality associated with coastal flooding and investigates the influence of variables including Human Development Index (HDI), country population, and event frequency. The frequency of coastal flooding events between 1990 and 2020 has increased, while there was an overall decrease in recorded deaths associated with coastal flooding events. The association between mortality and coastal flood exposure is reduced in countries with higher populations. This result suggests countries with larger populations may buffer risks in exposed regions. Results showed significant reduction in mortality risk, by approximately 34% (95% CI, 17-47%), associated with an increase of approximately 61 million in country-level population. Additionally, a 7% increase (95% CI, 3-11%) in mortality risk with each additional occurrence of coastal flooding events was observed. By leveraging this knowledge, decision-makers can develop targeted policies and interventions to enhance community preparedness, reduce vulnerability, and ultimately save lives in the face of increasing coastal flooding risks. / Master of Science / This study aims to explore the association between coastal flooding deaths and socio-economic variables globally. Additionally, it seeks to analyze trends in coastal flooding mortality, exposed populations, and flooding frequency across global regions, as well as income regions differentiated by the World Bank, from 1990 to 2020. Coastal flooding mortality data for every coastal flooding event were sourced from EM-DAT, a widely utilized disaster database. We utilized a climate model to retrieve the population exposed to coastal flooding for every event. Human Development Index (HDI) data and country population from 1990 to 2020 were taken from United Nations Development Programme (UNDP) and World Bank databases, respectively. A statistical model was used to estimate mortality risk associated with coastal flooding events and to investigate the influence of variables including Human Development Index (HDI), population, and event frequency. The frequency of coastal flooding events between 1990 and 2020 has increased, while there was an overall decrease in recorded deaths associated with coastal flooding events. The association between mortality and coastal flood exposure is reduced in countries with higher populations. This result suggests countries with larger populations may buffer risks in exposed regions. Results showed significant reduction in mortality risk, by approximately 34% (95% CI, 17-47%), associated with an increase of approximately 61 million in country population. Additionally, a 7% increase (95% CI, 3-11%) in mortality risk with each additional occurrence of a coastal flooding event was observed.

Reliable and robust methods of extreme value based hurricane surge prediction, such as the Joint Probability Method (JPM), are critical in the coastal engineering profession. The JPM has become the preferred surge hazard assessment method in the United States; however, it has a high computational cost: one location can require hundreds of simulated storms, and more than ten thousand computational hours to complete. Optimal sampling methods that use physics based surge response functions (SRFs), can reduce the required number of simulations. This study extends the development of SRFs to bay interior locations at Panama City, Florida. Mean SRF root-mean-square (RMS) errors for open coast and bay interior locations were 0.34 m and 0.37 m, respectively; comparable to expected ADCIRC model errors (~0.3 m—0.5 m). Average uncertainty increases from open coast and bay SRFs were 10% and 12%, respectively. Long-term climate trends, such as rising sea levels, introduce nonstationarity into the simulated and historical surge datasets. A common approach to estimating total flood elevations is to take the sum of projected sea-level rise (SLR) and present day surge (static approach); however, this does not account for dynamic SLR effects on surge generation. This study demonstrates that SLR has a significant dynamic effect on surge in the Panama City area, and that total flood elevations, with respect to changes in SLR, are poorly characterized as static increases. A simple adjustment relating total flood elevation to present day conditions is proposed. Uncertainty contributions from these SLR adjustments are shown to be reasonable for surge hazard assessments. / Master of Science

Coastal flood impacts are increasing in severity with the rising sea levels, causing damage to ecological and human systems. Climate-hazards may also result in cascading impacts, where an initial disaster sets off a chain of events that extends beyond the initial spatiotemporal point of impact. Coastal flood events may result in consecutive disasters in which the initial flood event results in a secondary technological disaster, prompting disruptions to socio-economic systems and resulting in a public health crisis. Flood events that trigger technological emergencies through the inundation and dispersion of hazardous materials are known as Natech disasters. However, current research on the cascading impacts of Natech events is limited. Hampton Roads, Virginia, is experiencing an accelerated rate of sea level rise and a proportionally higher risk of storm surge, potentially leading to a greater risk of Natech disasters. The main objective of this study is to evaluate the impact of Natech events on surrounding communities in Hampton Roads. This study uses geospatial analysis to identify the current (2021) and future (2051) threats of flood-induced Natech disaster and assess its exposure to different coastal populations and ecosystems. The present study calculated the Flood Hazard Density Index (FHDI), using a 1-mile radius around the significantly flooded facilities to determine the spatial dispersion of Natech disasters. The flood risks were determined using the 100-year flood plain and intermediate (RCP 4.5) climate scenario. The risk of a Natech disaster was identified by combining the spatial extent of flood risk with the location of Toxics Release Inventory (TRI) facilities and National Priorities List (NPL) designated Superfund sites. The exposed environmental and social systems to Natech events were chosen through the literature gap analysis. Sociodemographic data from the American Community Survey were collected to examine its correlation with 2021 and 2051 FHDI-affected block groups. Findings reveal that block groups with higher proportions of minorities, people in poverty, and people without a vehicle experience significant exposure to a Natech disaster compared to those who are living further away from the TRI and Superfund facilities. Additionally, open water and wetland environments will also experience significant exposure to Natech events, which could indicate a loss of ecosystem services. This study suggests a need for proactive policy and programmatic interventions to minimize the potential impacts of Natech events on the surrounding communities, such as the remediation of Superfund sites and the development of hazard mitigation plans for TRI facilities. / Master of Science / Coastal flood impacts are increasing in severity with the rising sea levels, causing damage to ecological and human systems. Climate-hazards may also result in cascading impacts, where an initial disaster sets off a chain of events that extends beyond the initial spatial origin of impact, prolonging the effects of the initial disaster. Coastal flood events may result in consecutive disasters, where an initial flood event results in a secondary technological disaster, prompting disruptions to socio-economic systems and resulting in a public health crisis. Flood events that trigger technological emergencies causing the inundation and dispersion of hazardous materials are known as Natech disasters. However, current research on the cascading impacts of Natech events is limited. Hampton Roads, Virginia, is experiencing accelerated sea level rise and a proportionally higher risk of storm surge, potentially leading to a greater risk of Natech disasters. The main objective of this study is to evaluate the impact of Natech events on surrounding communities in Hampton Roads. This study uses geospatial analysis to identify the current (2021) and future (2051) threats of flood-induced Natech disaster and assess its exposure to different coastal populations and ecosystems. The present study used a 1-mile radius around the significantly flooded facilities to determine the spatial dispersion of Natech diasters. The flood risks were determined using the 1 in 100 annual flood risk and an intermediate climate projection. The risk of a Natech disaster was identified by combining the spatial extent of flood risk with the location of U.S. Environmental Protection Agency (U.S. EPA) regulated Toxics Release Inventory (TRI) facilities and National Priorities List (NPL) designated Superfund sites. The most susceptible social, economic, and environmental subsystems to Natech events were identified using a literature gap analysis. Sociodemographic data were collected from the American Community Survey to examine its relationship to the 2021 and 2051 Natech affected census block groups. Findings reveal that block groups with higher proportions of minorities, people in poverty, and people without a vehicle experience significant exposure to a Natech disaster compared to those who are living further away from the TRI and Superfund facilities. Additionally, open water and wetland environments will also experience significant exposure to Natech events, which may indicate a loss of ecosystem services. This study suggests a need for proactive policy and programmatic interventions to minimize the potential impacts of Natech events on the surrounding communities, such as the remediation of Superfund sites and the development of hazard mitigation plans for TRI facilities.

Coastal flooding has been increasing in frequency and severity across the US East Coast, adversely impacting the human population. Preferred adaptation strategies, such as protection and accommodation, may prove insufficient under current climate change scenarios and projected future sea level rise, prompting the coastal population to consider relocation as a more efficient disaster risk reduction strategy. This study focuses on the flood-prone urban areas along the US East Coast where residents are more willing to relocate due to coastal flooding. Using the survey data, it evaluates the flood experiences, considerations toward relocation, and preferences for relocation destinations. The extent of top concerns influencing respondents' willingness to relocate, such as crime rate, buyout programs, access to critical services and amenities, and availability of comparable housing, were further explored as indirect relocation drivers. Four study locations with heightened relocation potential were identified across urban areas on the US East Coast. Relocation drivers such as crime and limited access to services and amenities are not significantly present in these study locations. However, the absence of buyout programs and affordable housing options in similar communities leaves low-income households trapped in high-risk zones, exacerbating socioeconomic disparities, and increasing the disproportionate risk faced by marginalized populations. The findings have important implications for policymakers, urban planners, and stakeholders involved in climate adaptation and disaster risk reduction efforts. They highlight the need for targeted interventions to address socioeconomic vulnerabilities, promote equitable access to housing, and enhance the resilience of communities facing coastal hazards. / Master of Science / Coastal flooding is increasing in both frequency and severity along the US East Coast, significantly impacting local populations. Traditional adaptation strategies, such as building protective structures and making accommodations, may not be sufficient under current climate change scenarios and projected sea level rise. Consequently, some coastal residents are considering relocation as a more effective strategy for reducing disaster risk. This study focuses on flood-prone urban areas along the US East Coast, where residents are more inclined to relocate due to coastal flooding. Using survey data, it assesses their flood experiences, considerations towards relocation, and preferred relocation destinations. The study also examines indirect factors influencing the willingness to relocate, such as concerns about crime rates, buyout programs, access to essential services and amenities, and the availability of comparable housing. The research identifies four study locations of urban areas with a high potential for relocation. In these study locations, issues such as crime and limited access to services and amenities are less significant. However, the absence of buyout programs and affordable housing options in similar communities traps low-income households in high-risk zones, exacerbating socioeconomic disparities and increasing the disproportionate risk faced by marginalized populations. These findings have significant implications for policymakers, urban planners, and stakeholders involved in climate adaptation and disaster risk reduction. They highlight the need for targeted interventions to address socioeconomic vulnerabilities, promote equitable access to housing, and enhance the resilience of communities facing coastal hazards.

Cette thèse sur travaux vise à rassembler et unifier les travaux réalisés sur le sujet des évènements météo-océaniques extrêmes depuis 2009, dans le cadre de mon travail à SOGREAH, devenu depuis ARTELIA. À mesure que progressaient ces travaux, un thème central a progressivement apparu : la notion d'évènement, tel qu'une tempête. Ce concept fournit un cadre robuste et pertinent, en particulier dans le cas des extrêmes multivariés (par exemple, la probabilité d'occurrence conjointe des vagues et des niveaux marins), ainsi qu'une meilleure compréhension de la notion de période de retour, très utilisée dans le domaine de l'ingénierie.Les principaux résultats des travaux réalisés au cours de la décennie écoulée sont les suivants :- mise à jour de la méthodologie de détermination des houles ou vents extrêmes :- développement et justification d'un cadre en deux étapes pour la modélisation sup-seuil des extrêmes univariés (méthode du renouvellement), introduisant la notion d'évènement et la séparation des seuils physique et statistique,- proposition d'outils pratiques pour le choix du seuil statistique,- introduction de la méthode du bootstrap paramétrique pour le calcul des intervalles de confiance,- identification d'un comportement problématique de l'Estimateur du Maximum de Vraisemblance et proposition d'une solution : utilisation de distributions à trois paramètres avec l'estimateur des L-moments,- application du cadre POT (Peaks-Over-Threshold) à la Méthode des Probabilités Jointes (JPM) pour la détermination des niveaux marins extrêmes :- distinction entre les valeurs séquentielles et les pics des évènements à l'aide d'indices extrémaux pour les surcotes et les niveaux marins,- construction d'un modèle mixte pour la distribution des surcotes,- raffinements pour le traitement de la dépendance marée-surcote,- application du cadre POT-JPM pour l'analyse conjointe des hauteurs de vagues et des niveaux marins :- proposition d'une procédure alternative d'échantillonnage,- analyse séparée de la marée et de la surcote dans le but de modéliser la dépendance entre la hauteur de vagues et la surcote ; avec incorporation dans la distribution conjointe de la hauteur de vagues et du niveau marin à l'aide d'une opération de convolution 2D1D,- utilisation de copules des valeurs extrêmes,- présentation améliorée du chi-plot,- introduction d'une nouvelle classification pour les analyses multivariées :- Type A : un phénomène unique décrit par différentes grandeurs physiques qui ne sont pas du même type,- Type B : un phénomène fait de différentes composantes, décrits par des grandeurs physiques du même type d'un composant à l'autre,- Type C : plusieurs phénomènes décrits par des grandeurs physiques qui ne sont pas du même type,- interprétation de la signification des évènements multivariés :- lien avec l'échantillonnage,- lien avec les différentes définitions de la période de retour,- dans le cas bivarié : transformation d'une distribution conjointe de variables descriptives de l'évènement vers la distribution des couples de variables séquentielles,- génération de graphes de srotie alternatifs tels que les contours d'iso-densité pour les couples de variables séquentielles,- un package R dédié, artextreme, pour l'implémentation des méthodes ci-dessus / This PhD on published works aims at unifying the works carried out on the topic of extreme metocean events since 2009, while working for SOGREAH then ARTELIA.As these works went along, a leading theme progressively appeared: the notion of event, such as a storm. This concept provides a sound and relevant framework in particular in the case of multivariate extremes (such as joint probabilities of waves and sea levels), as well as a better understanding of the notion of return period, much used for design in the field of engineering.The main results of the works carried out in the last decade are as follows:- updating of the methodology for determining extreme wave heights or wind speeds:- development and justification of a two-step framework for extreme univariate over-threshold modelling introducing the concept of event and the separation of the physical and statistical thresholds,- proposal of practical tools for choosing the statistical threshold,- introduction of the parametric bootstrap approach for computing confidence intervals,- identification of a problematic issue in the behaviour of the Maximum Likelihood Estimator and proposal of a solution: use of 3-parameter distributions along with the L-moments estimator,- application of the POT framework to the Joint Probability Method for determining extreme sea levels:- distinction between sequential values and event peaks through extremal indexes for surge and sea level,- construction of a mixture model for the surge distribution,- refinements for handling tide-surge dependence,- application of the POT-JPM framework for the joint analysis of wave height and sea level:- proposal of an alternative sampling procedure,- separate analysis of tide and surge in order to model the dependence between wave height and surge to be incorporated in the joint distribution of wave height and sea level thanks to a 2D1D convolution operation,- use of extreme-value copulas,- improved presentation of the chi-plot,- introduction of a new classification for multivariate analyses:- Type A: a single phenomenon described by different physical quantities that are not of the same kind,- Type B: a phenomenon made of different components, described by physical quantities of the same kind between one component and another,- Type C: several phenomena described by physical quantities that are not of the same kind,- interpretation of the meaning of multivariate events:- link with the sampling procedure,- link with the different definitions of the return period,- in the bivariate case: transformation of the joint distribution of event-describing variables into the joint distribution of sequential pairs,- generation of alternative output plots such as contours of density for sequential pairs;- a dedicated R package, artextreme, for implementing the methodologies presented above

Flood risk increases with rising sea levels and coastal settlements need to adapt to this increasing risk. For that, hazard and risk assessments are an important step. Coastal floods have caused problems in Iceland in the past and are thought to do so in the future as well. Therefore, a coastal flooding risk as- sessment needs to be made for Iceland. A risk assessment is currently in the early steps of preparation and a fitting method needs to be developed. To facilitate the process, an overview of the methods used in neighbouring countries is provided here and the suitability of the methods for Iceland is discussed. Building on these methods, a coastal flood scenario is produced for both present and future conditions as a preliminary hazard assessment for the country. The scenario produced is an upper bound scenario, highly unlikely but still possible. As a result, flooded areas are mapped and areas that need to be studied further in regard to flood hazard and risk are identified. It is shown that hazard estimation can be per- formed for Iceland through scenario production and that scenario results can be used in risk assessments. / De nuvarande klimatförändringar i världen kommer påverka människor på många olika sätt. En av de många saker som förändras är havsnivån. Havsnivån har stigit allt snabbare sedan i början av 1900 talet och kommer nästan säkert att fortsätta stiga i flera århudraden. Förhöjda havsnivåer föl- jer ökad översvämningsrisk som vi måste anpassa oss efter (Church, Clark, et al., 2013). Därför är riskbedömningar, alltså systematiska förfarande för att värdera risk, viktiga så att passande förebyggande åtgärder kan användas för att minska negativa påverkan från havsöversvämningar. En bedömning av översvämningsrisk från havet fattas för Island men för närvarande förbereder Is- ländska Meteorologiska Byrån att genomföra en. En tillämplig method behövs hittas och för att un- derlätta arbetet beskrivas i denna rapport metoder för preliminära bedömningar av översvämningsrisk från några av Islands grannländer; Danmark, Norge, Sverige och Storbrittanien. I huvudsak använder alla dessa länder liknande metoder, även om de har olika fysiska förutsättningar. De använder statis- tiska återkomsttider från mareograf data och informationer om historiska översvämningar för att bedöma faran. Sårbarhet identifieras inom fyra sårbarhets klasser, ofta genom ett index. Till slut sammanställs faro- och sårbarhetskartor för att bedöma risken och utpeka områden med översvämningsrisk. Eftersom Island har inte tillräckligt mycket data för att använda samma metoder som grannländerna, produceras i den här rapporten ett scenario för att värdera översvämningsfaran. Scenariot bygger på idéer från grannländerna och ska vara osannolikt men möjligt. Det är beräknad för både nuvarande och framtida förhållanden. Genom att subtrahera landhöjden från scenario havsnivån är översvämningsdju- pet beräknat. Några områden vart vattnet sannolikt skulle flöda och måste vara grundligt forskade är identiferade. Många påverkande faktorer är inte inkluderade i scenariot och därför anger resultatet inte noggranna översvämningskartor utan grovt överblick över översvämningsfaran. Resultaten ger alltså en idé om vart faran från havsöversvämningar är som störst och i vilka områden framtidiga havsnivåförän- dringar kommer bli som största. De visar också att ett scenario kan användas för farobedömning på Island, som sen kan kombineras med sårbarhetsbedömning via en index för att bedöma översvämn- ingsrisken på samma sätt som i grannländerna.

Over 3.7 million people are in high risk of coastal flooding and live within 1 mile of high tide in the US alone. The Atlantic coast is one of the most vulnerable areas due to its low elevation, large population, and economic importance (Bray, et. al, 2016). Coastal municipalities in the region of Southeast Florida, such as the cities of Miami, Miami Beach, Fort Lauderdale, etc., are at especial risk from coastal flooding related to sea level rise. The US National Climate Assessment has named Miami, Florida as the economically most vulnerable city impacted by this sea level rise in the world (Melillo et. al, 2014). Virtually all coastal communities in Southeast Florida are now experiencing increased incidents of coastal tidal flooding and coastal storm flooding related to sea level rise. This has led to a variety of responses by coastal communities in how to address this issue. In the case of the City of Miami Beach, the city has [CS1] come up with an ambitious but expensive plan to help combat the increased urban coastal flooding that is now occurring multiple times a year. They invested over 500 million dollars into replacing the increasingly less-effective gravity-based drainage system with a pump-based system (Bray, et. al, 2016). With these influences, we hypothesized that microbial communities would significantly differ between three years (2014-2016) and that the potential pathogens would increase over the past years . Genetic analyses of the 16S rRNA V4 region yielded a total of 77,346 unique bacterial OTUs from a total of 96 samples collected monthly for three years from 2014-2016. The most abundant OTU within the whole sample set was New.ReferenceOTU407 or Arcobacter in the Campylobacter family with an overall abundance of 0.008232535481%.The second most abundant organism in the sample set was Bacillus, or OTUNew.CleanUp.ReferenceOTU121132, with an overall abundance of .007797807097%. Bacillusmay cause many more foodborne illness than is known and one main reason that there is not more reported cases is because people do not seek medical attention (FDA 2012). The remaining pathogens except for Serratia, Pleisomonas, and Cronobacter were all with an abundance over .001%, with Salmonella, Yersinia,andListeria not being identified at all within the data set. By showing that genetic signatures for this bacterium, especially Arcobacter,was present in more than half of the samples stresses the importance of better understanding of the microbial population within South Florida waters and how to prevent or reduce future outbreaks by making sure the water is treated correctly before use, and to better identify potential exposure sources in water.

The increasing rate of global sea mean level rise, one of many effects of climate change, will most likelyproduce heightened risk of coastal flooding. Cities located along coastlines have to adapt to thesecircumstances that otherwise will increase the magnitude and frequency of coastal flooding. The purposeof this study is to evaluate the current and future risk of coastal flooding in central Stockholm in relationto global sea level rise. The overall coastal flood risk of central Stockholm is assessed using the currentsea level as well as with possible future sea level rise. Wave modelling is carried out in Saltsjön toestimate the possible addition to extreme sea levels from wave action. The wave characteristics isdetermined according to the physical properties of inner Saltsjön and local meteorological conditions.In-depth case studies regarding flood risk and flood prevention measures are carried out on Stadsholmenand Hammarby Sjöstad, two areas with very different physical characteristics. Areas most prone toflooding in these districts are identified using flood vulnerability maps. Suggestions for flood defencemeasures for the identified vulnerable areas are presented. It is evident that implementation of flooddefence should be adapted after the physical and social properties of the locality. Results of this studycorrelates with well with similar research claiming that there is a small risk of significant coastalflooding in central Stockholm. The flood risk will not increase significantly in the near future, primarilydue to the effect of regional uplift counteracting the global sea level rise. The accelerating rate of globalsea level rise in combination with possible trend changes of meteorological extremes will however mostlikely generate larger problems with coastal flooding in a longer time span. / En av de mest påtagliga effekterna av pågående klimatförändringar är global havsnivåhöjning. Ökademedel- samt extremnivåer kommer innebära ökad risk för översvämningar längs med kuststräckor.Städer belägna längs med kuster behöver anpassa sig efter dessa framtida förhållanden för att undvikaomfattande skador och förluster. Syftet med denna studie är att utvärdera den nuvarande och framtidarisken för kustöversvämning i centrala Stockholm med koppling till global havsnivåhöjning. Dengenerella risken för kustöversvämning i centrala Stockholm bedöms för den nuvarande havsnivån såvälsom för möjliga framtida havsnivåer. Modellering och beräkning av möjliga våghöjder för Saltsjönutförs för att uppskatta det potentiella tillägget till extrema havsnivåer från vågeffekter. Den potentiellavåghöjden i inre Saltsjön kontrolleras främst av de rådande meteorologiska och fysiskaförutsättningarna, så som vindens styrka och riktning. Mer ingående fallstudier gällandeöversvämningsrisk och åtgärder för att förhindra översvämningar utförs för Stadsholmen (Gamla Stan)och Hammarby Sjöstad, två områden med distinkt olika stadsbild och förutsättningar. Områden mestsårbara för kustöversvämning i dessa distrikt identifieras med hjälp av översvämningssårbarhetskartor.Förslag till åtgärder för att förhindra översvämningar i dessa områden presenteras. Hur och vad för typav översvämningsskydd som anläggs bör anpassas efter den specifika plats fysiska och estetiskaförutsättningar. Resultaten i denna studie stämmer överens med tidigare forskning vad gällande den lågarisk för kustöversvämning som finns i centrala Stockholm. Översvämningsrisken kommer inte ökanämnvärt i den närmaste framtiden, främst på grund av den regionala landhöjningens motverkandeeffekt till den globala havsnivåhöjningen. Däremot kommer hastigheten hos den globalahavsnivåhöjningen att öka. Detta i kombination med möjliga förändringar i meteorologiska extremerkommer troligtvis utgöra större risk för kustöversvämningar i ett längre tidsperspektiv.

Coastal socio-ecological systems face unprecedented challenges due to climate change, with impacts encompassing long-term, chronic changes and short-term extreme events. These events will impact society in many ways and prompt human responses that are extremely challenging to predict. This dissertation employs complex systems methods of agent-based modeling and machine learning to simulate the interactions between climatic stressors such as increased flooding and extreme weather and socio-economic aspects of coastal human systems. Escalating sea-level rise and intensified flooding has the potential to prompt relocation from flood-prone coastal areas. This can reduce flood exposure but also disconnect people from their homes and communities, sever longstanding social ties, and lower the tax base leading to difficulties in providing government services. Chapter 2 demonstrates a stochastic agent-based model to simulate human relocation influenced by flooding events, particularly focusing on the responses of rural and urban communities in coastal Virginia and Maryland. The findings indicate that a stochastic, bottom-up social system simulator is able to replicate top-down population projections and provide a baseline for assessing the impact of increasingly intense flooding. Chapter 3 leverages this model to assess how incorporating heterogeneity in relocation decisions across socio-economic groups impacts flood-induced relocation patterns. The results demonstrate how this heterogeneity leads to a decrease in low-income households, yet a rise in the proportion of elderly individuals in flood-prone regions by the end of the simulation period. Flood-prone areas also exhibit distinct income clusters at the end of simulation time horizon compared to simulations with a homogenous relocation likelihood. Lastly, Chapter 4 explores relationships between extreme weather and agricultural losses in the Delmarva Peninsula. Existing research on climatic impacts to agriculture largely focuses on changes to major crop yields, providing limited insights into impacts on diverse regional agricultural systems where human management and adaptation play a large role. By comparing various multistep modeling configurations and machine learning techniques, this work demonstrates that machine learning methods can accurately simulate and predict agricultural losses across the complex agricultural landscape that exists on the Delmarva peninsula. The multistep configurations developed in this work are able to address data imbalance and improve models' capacity to classify and estimate damage occurrence, which depends on multiple geographical, seasonal, and climatic factors. Collectively, this work demonstrates the potential for advanced modeling techniques to accurately replicate and simulate the impacts of climate on complex socio-ecological systems, providing insights that can ultimately support coastal adaptation. / Doctor of Philosophy / Coastal areas are facing increasing challenges from climate change, including rising sea levels and extreme weather conditions. This dissertation explores socio-economic consequences of these adverse environmental changes for coastal communities. Disruptive repetitive flooding due to exacerbated rise in sea levels is one of these consequences that may eventually leave some highly exposed coastal communities no alternative but migrating from their residences. Focusing on coastal Virginia and Maryland, Chapter 2 develops a data-informed model that can simulate individual relocation decisions and assess how they impact population changes and migration patterns. Chapter 3 employs this model to investigate how future changes in sea levels affect diverse socio-economic groups, their relocation decisions, and the resulting collective migration flows in flood-prone areas. We found that considering demographic differences leaves highly flood-prone areas with less low-income households, higher elderly individuals, and more economic clusters compared to simulations where these differences are not accounted for. Chapter 4 uses machine learning models to simulate the economic impact of extreme weather events as another manifestation of climate change on the agriculture in the Delmarva Peninsula. Through data-based modeling techniques, we identify the climatic conditions most responsible for agricultural losses and recognize modeling choices that enhance our predictive ability. Collectively, this dissertation demonstrates how sophisticated modeling techniques can be used to better understand the complex ways in which climate change will impact human society, with the ultimate goal of supporting adaptation strategies that can better address these impacts.