Societal awareness and general understanding of the diversity of geosciences environments and phenomena across regions is vital. The primary method to obtain information about geosciences is through mass media. However, there is little coverage of geosciences information and the public may not trust it because of low geosciences awareness. Geosciences are rarely included as part of K-12 curricula and are optional at the collegiate level, contributing to the public deficiency regarding awareness and understanding of the geosciences. This research study investigated methods utilizing outdoor resources in an effort to increase region-specific understanding and awareness of geosciences among diverse groups. A Mississippi K-12 educator professional development session and two middle-high schools were assessed for impact on understanding and interest related to geosciences. All participants utilized outdoor resources to model erosional processes and potential natural hazard events. Both participating schools have high underrepresented minority populations. Pre assessment evaluated participant awareness of Mississippi natural hazards. Teachers and student participants all demonstrated a low awareness of erosion processes specific to the region. Teachers completed erosion models indoors or outdoors, with indoor participants having a significant increase in earth science interest. All student participants completed the erosion model outside, with pre-post erosion comprehension resulting in significant increases for both the middle and high schools. The middle school had significant gains in earth science interest while the high school had a significant decrease regarding careers in geosciences. Virtual field guides developed by online graduate students demonstrating personal understanding of broad geosciences concepts in their local region were evaluated pre-post for impact on geosciences awareness, understanding, and confidence. Awareness factors included geographical community size and locations included in the field guide to demonstrate participant understanding. Significant increases in awareness regarding geoscience resources, including those outdoors, occurred. Significant increases also occurred in confidence utilizing geosciences resources and communicating about geosciences. The majority agreed that the experience of creating the field guide enhanced understanding of geosciences and interest in outdoor activities. Geographical size of participants’ residential communities was significantly related to awareness of regional locations, with urban residents including fewer outdoor locations in the field guide.

Coastal areas are vulnerable to different natural hazards, including hurricanes, cyclones, tsunami, floods, coastal erosion, and saltwater intrusion. These hazards cause extensive social, ecological, economic, and human losses. Continued climate change and sea-level rise is expected to substantially impact the people living in coastal areas. Sea level rise poses serious threats for the people living in the coastal zone, which leads to coastal erosion, inundations in the low-lying areas, tidal water encroachment and subsequent salt-water intrusion, as well as the displacement of the people living along the coast. Coastal erosion is one of the biggest environmental threats in the coastal areas globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. For a lower middle-class country, such as Bangladesh, with limited internal resources, it is hard to cope with catastrophic natural hazards, such as coastal erosion and its related consequences. This research aims to advance the scientific understanding of past and future coastal erosion risk and associated changes in land change and land cover using geospatial analysis techniques. It also aims to understand the patterns and drivers of human perception of coastal erosion risk. To place the research questions and objectives in content, Chapter 1 includes a brief introduction and literature review of the coastal erosion context in Bangladesh. Chapter 2 assesses different methods of prediction to investigate the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Chapter 3 evaluates historical land loss and how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion. Chapter 4 focuses on the patterns and drivers of erosion risk perception using data from spatially explicit measures of coastal erosion risk derived from satellite imagery and a random sample survey of residents living in the coastal communities. In summary, this research advances our scientific understanding of past and future coastal erosion risk and associated changes in land change and land cover using geospatial analysis techniques. It also enhances the understanding of the patterns and drivers of human perception of coastal erosion risk by combining satellite imagery and social survey data. Compared to much of the coastal erosion literature, this work draws from a 35-year time series of satellite-derived shorelines at annual temporal resolution. This time depth enables us to employ a temporal design strategy expected to yield a robust characterization of space-time erosion patterns. This study also enabled us to assess how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion by using long-term historical data. The innovative we use has potential applications to other deltas and vulnerable shorelines globally. While empirical results are specific to the project's study area, results can inform the region's shoreline forecasting ability and associated mitigation and adaptation strategies. / Doctor of Philosophy / Coastal erosion is a global problem. Coastal Bangladesh has one of the highest rates of erosion in the world. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to advance our understanding of coastal erosion hazard, this study assessed past and future coastal erosion risk and associated changes in land change and land cover and human risk perceptions using different geospatial and statistical analysis techniques. First, different methods of coastal erosion prediction were evaluated to investigate the performance of future shoreline position predictions. Second, the historical land loss was estimated and how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion were assessed. Finally, the patterns and drivers of human perception of coastal erosion risk were explored.

This study aims to empirically explore if there is an association between climate-related disasters and climate mitigation policy change in middle-income countries. This is important for understanding future climate mitigation policy, as disasters are predicted to increase in both frequency and severity. I explore this by studying climate mitigation policy activity change among countries in Latin America and the Caribbean. The region is chosen due to a lack of previous studies in both the region specifically, as well as in middle-income countries generally. Due to uncertainties in the exact time lag between the disaster and policy response, different methods are used: one 5-year normalized scatterplot and two fixed effects (FE) regressions with 1- and 2-year time lags respectively. The results show no statistically significant effects between the variables when looking at a 1-year FE regression or a 5-year normalized scatterplot, however, it does find a negative correlation between extreme-impact events and affected people when using a 2-year FE regression. A possible interpretation of the results is that severe climate-related disasters pause climate mitigation policy development, but not the process of the policies already developed pre-disaster and that these are short-term effects. However, further studies are needed to confirm this.

Since ancient times, mankind has faced a constant battle against natural hazards. From the earlieststages of human communication, when human beings communicate by some sort of harsh gruntsand unintelligible words, people were struck by the awe-inspiring power of nature as floods, storms,earthquakes, and other events which reshaped their environment and way of living. However,among these events, droughts have been particularly devastating, and have left a profound mark onhuman history. Throughout centuries, droughts have forced communities to adapt and evolve in response tochanging environmental conditions. In California, the frequency and severity of droughts haveincreased in recent decades, leading to significant changes in land use patterns. These changes haveimpacted the economy, social structure, and ecology of the region, making the study of land usechanges after drought periods a crucial area of research. In this master thesis, the effects of droughts on land use in California will be deeply explored,analysing the outcome and impacts resulting from extreme dry events in the state, and identifyingpotential climate adaptation actions that can be implemented based on an enhanced understandingof the consequences of droughts on land use. By understanding the complex relationship betweendroughts and land use, mankind may better prepare and adapt to the challenges posed by futureclimate change and other environmental stressors. / Sedan urminnes tider har mänskligheten stått inför en ständig kamp mot naturliga faror. Ända frånde tidigaste stadierna av mänsklig kommunikation, när människor kommunicerade genom någonform av hårda grymtningar och obegripliga ord, har människor imponerats av naturens mäktigakraft i form av översvämningar, stormar, jordbävningar och andra händelser som omformat derasmiljö och levnadssätt. Bland dessa händelser har torka varit särskilt förödande och har satt djupaspår i mänsklighetens historia. Under århundraden har torkor tvingat samhällen att anpassa sig och utvecklas som svar påförändrade miljöförhållanden. I Kalifornien har frekvensen och allvaret av torkor ökat under desenaste decennierna, vilket har lett till betydande förändringar i markanvändningsmönster. Dessaförändringar har påverkat ekonomin, den sociala strukturen och ekologin i regionen, vilket görstudiet av markanvändningsförändringar efter torkperioder till ett avgörande forskningsområde. I denna masteruppsats kommer effekterna av torkor på markanvändningen i Kalifornien attutforskas ingående. Resultat och påverkan av extrema torkhändelser i delstaten kommer attanalyseras, och potentiella åtgärder för klimatanpassning kommer att identifieras baserat på en ökadförståelse för konsekvenserna av torkor för markanvändningen. Genom att förstå det komplexaförhållandet mellan torkor och markanvändning kan mänskligheten bättre förbereda sig ochanpassa sig till utmaningarna som framtidens klimatförändringar och andra miljömässigapåfrestningar medför.

In the light of the increased frequency of natural hazards in Europe alongside the high impact a hazardous event has on real estate value, real estate owners need to manage and strengthen assets within their entire portfolio to increase resilience and minimize the vulnerability to future negative impact. Further, a research gap exists in how to incorporate climate variables with real estate specifics which makes it challenging for property managers and other stakeholders to interpret the climatic impact on a particular asset. To understand how these climatic impacts could implicate assets in the future, this research study aims to investigate the risk profiles for five commercial buildings in Milan, Italy, by performing a risk rating assessment. The study is collecting historical weather data for strong wind, heatwave, and heavy precipitation and performs future estimations for expected frequency and probability in Milan for the upcoming 20 years. The study also involves a practical part where five assets have been analyzed based on a developed vulnerability checklist to capture climatic variables with asset characteristics and the final risk score is calculated with a risk rating technique. Findings show that the expected frequency of each hazard during a 10-year period will by the end of 2041 in a worst- case scenario reach 95, 156, and 78 times (strong wind, heatwave, and heavy precipitation). This result indicates almost a doubled number of heatwaves in Milan compared to 1981. Results from the risk rating show that assets located in very exposed areas and with a high risk of negative impact to the foundation or fundamental installations tend to score high. This study provides a methodology to build upon for how to evaluate and calculate risk for an asset that could be used for estimating future climatic and financial impact to assets. This will help improve decision-making regarding climate change for property managers, real estate owners, and other stakeholders for parts of, or, the entire portfolio. / Som en följd av klimatförändringarna så ökar frekvensen av extremväder i Europa vilket har stora negativa konsekvenser på en fastighet och dess värde. Genom att som fastighetsägare kunna estimera och förutspå riskerna som extremväder medför, går det att öka byggnadernas motståndskraft mot extremväder och minska de negativa konsekvenserna. Vidare finns det idag bristfällig forskning om huruvida det är möjligt att kombinera klimatrelaterade variabler med fastighetsspecifika parametrar samt vilka negativa konsekvenser dessa medför för en specifik fastighet. Denna studie syftar till att öka kunskapen för hur man kan estimera framtida klimatpåverkan på specifika fastigheter. Forskningsstudien undersöker fem kommersiella byggnader i Milano, Italien, genom att utföra en riskbedömning av dessa. Studien inkluderar framtida uppskattningar och förväntad frekvens av extremväder i Milano för de kommande 20 åren. Dessa är baserade på historiska väderdata för tre olika extremväder; storm, värmebölja och kraftig nederbörd. Studien omfattar också en praktisk del där en utvecklad metod för fastigheters sårbarhet kopplat till klimatvariabler har applicerats på de fem kommersiella fastigheterna i Milano för att ta fram en fastighetsspecifik riskklassning. Resultaten visar på att den förväntade frekvensen av storm, värmebölja och kraftig nederbörd under en 10-årsperiod kommer att nå respektive 95-, 156-, och 78 gånger fram till 2041. Dessa resultat indikerar på en nästintill fördubbling av antalet värmeböljor i Milano jämfört med år 1981. Resultaten från risk-klassificeringen av visar på att fastigheter som ligger i väderutsatta och exponerade områden tenderar att få en högre riskpoäng. Grundläggningen samt nödvändiga installationer tenderade att vara mer känslig och medföra en större negativ påverkan på byggnaden. Vidare tillhandahåller denna studie en metod att bygga vidare på kring hur fastighetsägare kan utvärdera och beräkna risken för en fastighet. Detta kan bli en grundsten att använda för att uppskatta framtida ekonomisk påverkan av en fastighet eller ett helt bestånd. Studien syftar till att underlätta beslutsfattandet för fastighetsägare-, förvaltare och andra intressenter gällande klimatförändringars påverkan på fastigheter.

Communities around the world are encountering unprecedented rates of change due to population growth, land use change, development, and increased social vulnerability to natural hazards. Understanding how physical processes and human vulnerability to natural hazards interact is a primary objective of researchers, policy makers, and disaster risk reduction practitioners in order to combat increases in natural hazard frequency and intensity. Nepal, a landlocked mountainous country spanning the central Himalayan region, has about 28 million inhabitants in 147,181 square kilometers. Nepal is exposed to a multitude of natural hazards, requiring individuals and communities to interact with and make decisions on risk acceptability on a day-to-day basis. In many cases, Nepal's geographic location, available resources (human, economic, and capital), and limited government capacity coalesce to turn natural hazards into disasters, resulting damaged infrastructure, economic disruptions, and death. This dissertation evaluates the geographic distribution of natural hazard mortality, quantifies social vulnerability to natural hazards, and models multi-hazard risk in the data deficient environment of Nepal. Chapter 1 conceptualizes relevant terms such as natural hazards, disaster, vulnerability, and risk before discussing the challenges associated with multi-hazard risk assessment in Nepal. Chapter 2 evaluates the spatial and temporal distribution of natural hazard mortalities at the village level using a publicly available disaster database. Results reveal that landslides were the deadliest disasters between 1971-2011. Chapter 3 identifies major social factors and processes that contribute to the vulnerability of individuals and communities using census data. Adapting the Social Vulnerability Index (SoVI) method developed for the US context, this chapter investigates the spatial distribution and clustering of various social vulnerabilities across the country. 'Renter and Occupation', 'Poverty and Poor Infrastructure', and 'Favorable Social Conditions' are three major components that influence social vulnerability in Nepal. Results indicate an interesting regional difference: the eastern and central Tarai are more vulnerable than western Tarai, whereas the eastern Hills and Mountains are less vulnerable than western Hills and Mountains. In Chapter 4, a model of risk from multiple natural hazards in the city of Dharan, Nepal, is presented. Freely available geospatial data in combination with socio-economic data collected from local government and secondary sources are used. Multi-hazard risk assessment is data intensive and requires considerable financial and human resources, which are lacking in Nepal. Results show that geospatial modeling techniques can be used to fill the gap and assist local officers and emergency managers in risk management. Cumulatively, this work offers new insights on natural hazards, vulnerability, risk, the use of geospatial technologies, and their inter-relationships. Research findings advance scholarly understandings of multi-hazard risk in general and particularly in the Nepali context. Additionally, this work is valuable to disaster practitioners who seek to implement more effective disaster risk reduction programs and policies. / Ph. D. / Natural hazards are earth system processes that pose threats to people and have the capacity to disrupt social and ecological processes. Thus, a consideration of both physical and social dimensions is required to better understand natural hazards. This research evaluates social factors and processes that have significant roles in enhancing the vulnerability of individuals and communities. First, this dissertation explores spatial and temporal patterns of natural hazard fatalities at the village level in Nepal. Research findings identified that landslides were the highest contributor to natural hazard fatalities from 1971-2011. Second, this dissertation assesses which social factors and processes contribute most to social vulnerability in Nepal. Additionally, the spatial distribution and clustering of social vulnerability is explored. Finally, geospatial modeling was performed to analyze cumulative risk to floods, landslides, and earthquakes in the municipality of Dharan, Nepal.

Ethiopia like other developing countries depends on domestic agricultural production, struggling with infrastructure and healthcare is also at a low level. Among these factors there is also added impact of catastrophic natural processes. Landslides in this area have caused enormous material damage and also human deaths. Effects of those losses are huge and locals are dealing with them difficuly. Therefore, prevention should be a priority concern. In theoretical part the basic facts about modeling and simulation of landslides susceptibility is presented. Those facts are complemented with specific researches from Ethiopian highlands. Furthermore, it describes the methodology that was used. It compares the real distribution of slope movements against the expected occurrence across the classes of input layers. An essential part of the model is mapping work, supported with terrain work, during which 242 landslides was mapped. Model parameters are: slope, altitude, lithology, land cover, distance from geological boundary and distance to river. Slope was determined as most significant factor. Used methodology is universally applicable and the results it generates match reality well. Key words: slope movements, natural hazards, susceptibility analysis, geomorphology, Ethiopian Highland

En cas d’alerte aux inondations, l'information en temps réel et sa diffusion à un large public sont des éléments cruciaux pour limiter les comportements à risque, détecter les premiers dégâts ou préparer les acteurs locaux à gérer la crise. Face à ces enjeux, les technologies smartphones apparaissent, deplus en plus, comme des solutions logicielles et matérielles qui pourraient compléter de façon positiveles dispositifs d’alerte institutionnels, notamment grâce à la dynamique des contenus partagés et auxnombreuses interactions dont ils sont le support. Alors que plusieurs pays (États-Unis, Norvège, Pays-Bas, Suède, Philippines) utilisent déjà de tels outils, et même si le Ministère de l'Intérieur a lancé uneapplication urgentiste (SAIP) le 8 juin 2016, la population et les services de l’État semblent beaucoupplus réticents en France. La défense de la propriété individuelle, le manque de crédit attribué auxmessages postés et les verrous technologiques sont les principaux facteurs de blocage avancés. Aprèsavoir mené une enquête dans plusieurs communes rurales des départements du Var et du Vaucluse(qui confirme la méconnaissance des applications déjà existantes alors que les besoins sont bien réelsface aux crues rapides), une nouvelle application a été développée (Al’in). Le prototype créé vise àréduire les écarts entre une approche étatique descendante (top-down), qui positionne et impose l’Étatcomme le seul lanceur d’alerte, et une initiative citoyenne (approche « Bottom-Up »), qui érigel’individu au rang de « citoyen capteur », à la fois capable de remonter ou de diffuser une alerte àtravers l’usage de son smartphone. Répondre à ce défi ne s’avère toutefois pas si simple au regard descontraintes juridiques et techniques préexistantes en France. / The information given on flash floods in real time should undoubtedly answers to the citizens needs aswell as to the politic and the safety services. Face to such objective, Smartphone could positivelycomplete the official institutional systems, especially thanks to the dynamic and nature of postedmessages, and to the social interactions they support. This study questions the opportunity ofdeveloping a proactive and innovative alert system using the Smartphone technologies to face therisks due to flash flood hazards. A first study focuses on several alert systems existing actually inFrance but we quickly aim at identifying limits (legal rules, technologies and scientific problems) andchallenges usefulness and opportunities in the numeric sphere) of our objective in general manner.Finally, a local study in Var and Vaucluse permits us to see if population and local stakeholdersshould be interested (or not) by a smartphone application. The proposed solution tries to achieve amajor challenge: reducing the gap between a state approach (top-down), which positions and imposedthe state as the main official alert source, and the citizen action (bottom-up) which establishes theindividual as “Citizen sensor” (both in go up or disseminate information) through the use of issmartphone. Meeting this challenge is indispensable if we hope effectively minimizing material andhuman damages especially when high temporal acuity phenomena such as flash floods happen.

This research integrates the theoretical implications of risk perception, the social amplification of risk, and the role of place-specific contexts, in order to explore the various perceptions surrounding Florida red tides. Florida red tides are a naturally-occurring event, yet most scientists agree that they are increasing in frequency, duration, and severity. This has profound implication for public health, the local economy, and the biological community. While many of the negative impacts are not easily controllable at this time, some of the secondary impacts can be mitigated through individuals' responses. Unfortunately, public perceptions and consequent reactions to red tides have not been investigated. This research uses questionnaire surveys, semi-structured interviews, and newspaper content analysis to explore the various perceptions of risk surrounding red tides. Surveys and interviews were conducted along two Florida west coast beaches, Fort De Soto Park and Siesta Key. Results indicate that the underlying foundations of the social amplification of risk framework are applicable to understanding how individuals form perceptions of risk relative to red tide events. There are key differences between the spatial locations of individuals and corresponding perceptions, indicating that place-specific contexts are essential to understanding how individuals receive and interpret risk information. The results also suggest that individuals may be lacking efficient and up-to-date information about red tides and their impacts due to inconsistent public outreach. Overall, particular social and spatial factors appear to be more influential as to whether individuals amplify or attenuate the risks associated with red tides.

Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. / Obwohl Naturgefahren in ihren Ursachen, Erscheinungen und Auswirkungen grundlegend verschieden sind, teilen sie doch viele Gemeinsamkeiten und Herausforderungen, wenn es um ihre Modellierung geht. Fehlendes Wissen über die zugrunde liegenden Kräfte und deren komplexes Zusammenwirken erschweren die Wahl einer geeigneten Modellstruktur. Hinzu kommen ungenaue und unvollständige Beobachtungsdaten sowie dem Naturereignis innewohnende Zufallsprozesse. All diese verschiedenen, miteinander interagierende Aspekte von Unsicherheit erfordern eine sorgfältige Betrachtung, um fehlerhafte und verharmlosende Einschätzungen von Naturgefahren zu vermeiden. Dennoch sind deterministische Vorgehensweisen in Gefährdungsanalysen weit verbreitet. Bayessche Netze betrachten die Probleme aus wahrscheinlichkeitstheoretischer Sicht und bieten somit eine sinnvolle Alternative zu deterministischen Verfahren. Alle vom Zufall beeinflussten Größen werden hierbei als Zufallsvariablen angesehen. Die gemeinsame Wahrscheinlichkeitsverteilung aller Variablen beschreibt das Zusammenwirken der verschiedenen Einflussgrößen und die zugehörige Unsicherheit/Zufälligkeit. Die Abhängigkeitsstrukturen der Variablen können durch eine grafische Darstellung abgebildet werden. Die Variablen werden dabei als Knoten in einem Graphen/Netzwerk dargestellt und die (Un-)Abhängigkeiten zwischen den Variablen als (fehlende) Verbindungen zwischen diesen Knoten. Die dargestellten Unabhängigkeiten veranschaulichen, wie sich die gemeinsame Wahrscheinlichkeitsverteilung in ein Produkt lokaler, bedingter Wahrscheinlichkeitsverteilungen zerlegen lässt. Im Verlauf dieser Arbeit werden verschiedene Naturgefahren (Erdbeben, Hochwasser und Bergstürze) betrachtet und mit Bayesschen Netzen modelliert. Dazu wird jeweils nach der Netzwerkstruktur gesucht, welche die Abhängigkeiten der Variablen am besten beschreibt. Außerdem werden die Parameter der lokalen, bedingten Wahrscheinlichkeitsverteilungen geschätzt, um das Bayessche Netz und dessen zugehörige gemeinsame Wahrscheinlichkeitsverteilung vollständig zu bestimmen. Die Definition des Bayesschen Netzes kann auf Grundlage von Expertenwissen erfolgen oder - so wie in dieser Arbeit - anhand von Beobachtungsdaten des zu untersuchenden Naturereignisses. Die hier verwendeten Methoden wählen Netzwerkstruktur und Parameter so, dass die daraus resultierende Wahrscheinlichkeitsverteilung den beobachteten Daten eine möglichst große Wahrscheinlichkeit zuspricht. Da dieses Vorgehen keine Expertenwissen voraussetzt, ist es universell in verschiedenen Gebieten der Gefährdungsanalyse einsetzbar. Trotz umfangreicher Forschung zu diesem Thema ist das Bestimmen von Bayesschen Netzen basierend auf Beobachtungsdaten nicht ohne Schwierigkeiten. Typische Herausforderungen stellen die Handhabung stetiger Variablen und unvollständiger Datensätze dar. Beide Probleme werden in dieser Arbeit behandelt. Es werden Lösungsansätze entwickelt und in den Anwendungsbeispielen eingesetzt. Eine Kernfrage ist hierbei die Komplexität des Algorithmus. Besonders wenn sowohl stetige Variablen als auch unvollständige Datensätze in Kombination auftreten, sind effizient arbeitende Verfahren gefragt. Die hierzu in dieser Arbeit entwickelten Methoden ermöglichen die Verarbeitung von großen Datensätze mit stetigen Variablen und unvollständigen Beobachtungen und leisten damit einen wichtigen Beitrag für die wahrscheinlichkeitstheoretische Gefährdungsanalyse.