京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20681号 / 工博第4378号 / 新制||工||1680(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 角 哲也, 准教授 竹門 康弘, 准教授 Sameh Kantoush / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Student Number : 0413447J - MSc research report - School of Geography and Environmental Studies - Faculty of Science / This research analyses the vulnerability and adaptation of communities living along the River Kaduna floodplain at Shiroro Local Government in Niger State to flood occurrences. These communities are one of the most flood-prone areas in Niger State, with fertile alluvial deposits for agricultural production. The analysis of rainfall and flood flow into the Kaduna River System shows that there is an increasing flood frequency and flood magnitude along the River Kaduna for the past two decades because of slight increase in rainfall amount. Although there is a slight increase in rainfall amounts, the flooding of the Kaduna River could be regarded as normal. This is because there is no major change in rainfall amounts. Therefore, any slight increase in rainfall may cause flooding. The 1990s with slight increase in rainfall coincides with the period of abundant flood flow in the Kaduna River System and very significant runoff into the Kaduna reservoir. Floods have impacted negatively on the life of the people living on the floodplain resulting into food insecurity, poverty and vulnerability to malnutrition and other health problems among the communities in Shiroro Local Government Area. There are three vulnerable groups identified within the communities (the very poor, those residing on the floodplain and those that depend only on agriculture). These three groups identified lack accesses to good shelters and social amenities like electricity, good water, roads, health facilities and schools. Moreover, this research shows that the adaptive capacity of these communities is being severely compromised by factors such as poverty, poor infrastructure, weakening social networks and environmental degradation.

One of the most widespread causes of building deterioration is high moisture, which in extreme cases may even arrive as floods. The action of high moisture in a structure often results in damage or alteration of properties, but it can be mitigated with various protective measures, most commonly waterproofing. However, the efficacy of waterproofing depends of flawless implementation. Another effective form of protection against high moisture is the correct choice of location, but the number of suitable construction plots is rapidly decreasing or their price is too high. This is why new construction, particularly family homes, now occurs even in locations known for the increased risk of high moisture. This doctoral thesis focuses on the research and development of the most common masonry systems with structural clay tiles designed to withstand application in flood areas thanks to the special properties of the individual elements, components, and the system as a whole.

The quality of modern life is intrinsically tied to the development and maintenance of infrastructure systems. Modern energy and electricity infrastructure systems have high-reliability requirements, with people expecting power at the flip of a switch. The complex market structure and public-private partnerships at multiple levels in power generation and transmission systems make ensuring high reliability even more difficult. The 21st century brings with it multiple challenges and opportunities within these sectors. A large portion of the infrastructure fleet, like dams and fossil fuel generation plants, is old and needs replacement. Further, the decarbonization of the power sector is poised to result in the inclusion of large amounts of variable renewable energy sources, thereby introducing stochasticity in supply. The research presented in this dissertation seeks to assess and improve energy infrastructure resilience against regional spatiotemporal climate risk in the face of the upcoming decarbonization of the power sector. This dissertation seeks to develop our understanding of climate risk to energy infrastructure systems at a regional level. The analysis will be focused on the identification of organized modes of climate variability that lead to space-time clustering of risk.These investigations are accompanied by specific case studies in the contiguous United States and are applicable to electricity grids and river basins. Overall, I will focus on the ability to simulate and predict extreme climate events which pose reliability and failure risks to energy infrastructure systems. Since such events are rare, I propose methods that establish event excedance probabilities accounting for their underlying uncertainties. In chapter I, I present a novel statistical simulation model that can produce realistic, synthetic realizations of hydroclimatic fields across a large region. This k-nearest neighbor-based space-time simulator can be applied to single or multiple hydroclimatic fields across a large domain. The algorithm facilitates the estimation of the probability of extreme events that are not well represented in relatively short observational records. I apply this algorithm to wind and solar fields across the Texas Interconnection. Many regions plan to integrate more wind and solar generation into the energy grid, increasing power supply variability that can pose risks of under-supply. This simulation tool facilitates the estimation of the probability of regional wind and solar energy “droughts” and hence allows for the estimation of the storage needed to achieve desired supply-side reliability. In chapter II, I present a clustering based variant of the simulator developed in chapter I. I show how the algorithm developed in chapter I is a special case of a general class of algorithms. In Chapter II, I generalize the algorithm by introducing clustering on the neighbor likelihoods, thereby allowing for the identification of sub-regions with different state-space evolution characteristics. This allows for the application of the generalized algorithm to cases with greater heterogeneity, for example, increased temporal resolution. The clustering based k-nearest neighbor space-time simulator was developed to generate synthetic simulations of wind-solar data at an hourly timescale. I present an application of this algorithm to hourly wind-solar data across the Texas Interconnection. The application of this algorithm to estimate the underlying uncertainty and risk faced by power producers in entering short-term power supply contracts is demonstrated. In chapter III, I present a retrospective analysis of inferred energy demand trends across the contiguous United States. Future net zero scenarios generally require replacing all fossil-fuel heating with electric heat, thereby precipitating higher electricity peak loads during winter. Assuming 100% penetration of efficient electric space-heating and cooling, this chapter carries out a spatially explicit trend analysis of temperature-based proxies of electricity demand over the past 70 years. As expected, annual mean heating and cooling demand decreases and increases over most of the contiguous US, respectively. Peak thermal load is generally dominated by heating, showing large inter-annual and decadal variability, thus far not displaying statistically significant decreasing trends. In the south, the peak cooling demand has started to dominate the peak demand, but the possibility of an occasional high peak heating demand can not be discounted. Conversely, in the north, the average thermal loads are declining while the peak thermal loads are not. This points to the need for an improved pre-season forecast of peak winter heating loads. In chapter IV, I present a method for the diagnosis of low-frequency climate variability from multi-site data, which leads to spatiotemporal clustering of flooding risk at a regional level. Disruptions to energy infrastructure systems are often caused due to flooding, and the characterization of climate risk to energy infrastructure due to flooding is explored in this context. The approach is demonstrated using the Ohio River Basin as a case study. I show that the dominant timescales of flood risk within the Ohio River Basin are in the interannual (6-7 years), decadal (11-12 years), and long-term (secular) scales, with different sub-regions responding to different climate forcings. These leading modes are associated with El-Nino Southern Oscillation and secular trends. Further, the secular trend points to an east-to-west shift in flood incidence and changes in the storm track, which are consistent with certain climate change projections. Overall, the results point to the presence of compound climate risk inherent at regional levels, with the low-frequency climate variability translating into periods of increased and decreased flood risk, which all the stakeholders should consider.

Storm induced flooding presents a multifaceted threat to coastal communities across the world.With climate change and sea level rise this danger is expected to increase. As coastal communities become exposed to more frequent and more severe flooding, the need for protective measures will increase. To know how to optimally protect against coastal flooding requires an understanding of future flood risk, storms, and storm surge. These are challenging to estimate due to many sources of uncertainty. In this thesis I present a methodology to forecast this future flood risk. I combine multiple computational, physics and statistical models to accurately describe the fluid dynamics of flooding, the cyclones that drive surge, and how climate change will influence these different components in the future. These computational models must be fast so that they can be embedded into an optimization framework that makes many evaluations. To find an optimal protective measure I employ stochastic and derivative free optimization methods. A complete study is conducted on New York City and optimal protective strategies are found for minimizing the total cost from storm surge subject to different budget constraints.

No description available.

No description available.

Disasters, whether natural or man-made have become an issue of mounting concern all over the world. Natural disasters such as floods, earthquakes, landslides, cyclones, tsunamis and volcanic eruptions are yearly phenomena that have devastating effect on infrastructure and property and in most cases, results in the loss of human life. Floods are amongst the most prevalent natural disasters. The frequency with which floods occur, their magnitude, extent and the cost of damage are escalating all around the globe. Accra, the capital city of Ghana experiences the occurrence of flooding events annually with dire consequences. Past studies demonstrated that remote sensing and geographic information system (GIS) are very useful and effective tools in flood risk assessment and management.  This thesis research seeks to demarcate flood risk areas and create a flood risk map for the Greater Accra Metropolitan Area using remote sensing and Geographic information system. Multi Criteria Analysis (MCA) is used to carry out the flood risk assessment and Sentinel-1A SAR images are used to map flood extend and to ascertain whether the resulting map from the MCA process is a close representation of the flood prone areas in the study area.  The results show that the multi-criteria analysis approach could effectively combine several criteria including elevation, slope, rainfall, drainage, land cover and soil geology to produce a flood risk map. The resulting map indicates that over 50 percent of the study area is likely to experience a high level of flood.  For SAR-based flood extent mapping, the results show that SAR data acquired immediately after the flooding event could better map flooding extent than the SAR data acquired 9 days after.  This highlights the importance of near real-time acquisition of SAR data for mapping flooding extent and damages.  All parts under the study area experience some level of flooding. The urban land cover experiences very high, and high levels of flooding and the MCA process produces a risk map that is a close depiction of flooding in the study area.  Real time flood disaster monitoring, early warning and rapid damage appraisal have greatly improved due to ameliorations in the remote sensing technology and the Geographic Information Systems.

An accurate understanding of a population’s risk perception is important, as risk perception can influence attitudes to new policies and recommended health behaviours. There is a lack of research on the possible impact that Swedish news media could have on the Swedish population’s risk perception of natural hazards. This thesis has aimed to investigate the relationship between newspaper coverage and risk perception of natural hazards by quantifying newspaper coverage and comparing it with survey data on perceived likelihood and impact of several hazards (floods, droughts, wildfires, epidemics, and climate change). Direct experience which in this thesis meant direct involvement with a hazard was shown to be the main cause of increased risk perception for floods, droughts and wildfire, while no significant impact of newspaper coverage could be found. Survey data and newspaper coverage did however suggest that there is a significant impact on epidemics risk perception. Risk perception of climate change was found to be the hazard most tied to direct experience, but it was also correlated with newspaper coverage. This means that there is a possibility of newspaper coverage impacting risk perception of climate change. Climate change was also found to be mentioned frequently in connection with the other hazards, this could reinforce the idea that climate change intensifies the other hazards and thus increases risk perception. / En korrekt förståelse för en befolknings riskuppfattning är viktig, eftersom riskuppfattning kan påverka attityder till nya policyer och rekommenderade hälsobeteenden. Det saknas forskning om den möjliga inverkan som svenska nyhetsmedier kan ha på den svenska befolkningens riskuppfattning av naturkatastrofer. Denna uppsats ämnar att undersöka förhållandet mellan tidningstäckning och risk uppfattning av naturkatastrofer genom att kvantifiera mängden artiklar som nämner olika naturkatastrofer och jämföra dem med undersökningsdata om upplevd sannolikhet för inverkan av flera naturkatastrofer (översvämningar, torka, bränder, epidemier och klimatförändringar) ämnar denna uppsats att undersöka förhållandet mellan tidningstäckning och risk uppfattning. Resultaten från den här undersökningen visar att direkta erfarenheter av översvämningar, torka och skogsbränder var den främsta orsaken till ökad riskuppfattning, medan ingen betydande inverkan av tidningsnyheter kunde hittas. Undersökningsdata och tidningstäckning tyder dock på att det finns en betydande inverkan på riskuppfattningen av epidemier. Klimatförändringar visade sig vara den risk som var mest knuten till erfarenhet men, korrelerade också med mängden tidningsnyheter. Detta innebär att det är möjligt för tidningsartiklar att påverka riskuppfattningen av klimatförändringar. Klimatförändringar nämndes också ofta i samband med de andra farorna, vilket kan ha förstärkt idén att klimatförändringar intensifierar de andra naturkatastroferna och därmed ökat riskuppfattningen.

The severity of flood events is predicted to increase as a consequence of climate change and may lead to a higher burden of waterborne infectious diseases in the United States. Contaminated floodwater transports bacterial, protozoal, and viral pathogens that typically cause moderate intestinal or respiratory disease, but can also lead to more serious disseminated infections among immunocompromised, young, and older people. Hydroclimatology and drinking water infrastructure influence the transmission of disease, but their roles are not well-understood and may vary by pathogen-type or geographic region. Specific outbreaks of waterborne disease have been attributed to major floods and cases have been positively associated with some meteorological variables, but the association between infections and flooding has not been systematically examined. In this dissertation, we examine the association between seasonal and extreme floods and parasitic and bacterial infections using multiple flood-indicator variables and exposure definitions. In Chapter 2, we use multimodel inference and generalized linear mixed models to determine the effect of seasonal meteorology on hospitalizations across the US. We found that hospitalization rates were generally higher in rural areas and in places that relied on groundwater for drinking water sources. Soil moisture, precipitation, and runoff were associated with significant increases in hospitalizations for Legionnaires' disease, Cryptosporidiosis, and Campylobacteriosis, respectively. In Chapter 3, we use 23 years of weekly case data to examine the effect of cyclonic storms on six waterborne infections in a conditional quasi-Poisson statistical model. Storm exposure was defined separately for distinct storm hazards, namely wind speed and cumulative rainfall, and effects were examined over 3 weeks post-storm. We found that exposure to storm-related rainfall was associated with immediate and lagged increases in cases. In Chapter 4, we use a nonparametric bootstrap to examine the effect of anomalous meteorological conditions, i.e. extremes unrelated to cyclonic storms, on Legionnaires' disease hospitalizations. We also assess the effect of exposure to specific cyclonic storms in a GLMM framework and compare these approaches. Extreme precipitation and months with cyclonic storms were positively associated with Legionnaires' disease hospitalizations. Determining the effect of flooding on Legionnaires' disease is particularly important as it causes severe illness and has steadily increased in incidence for 20 years. An objective of this dissertation was to develop a framework for examining flood-disease dynamics in the context of hydrometeorological and infrastructure-related factors that may influence transmission. We demonstrated that drinking water source, rurality, and geography may play an important role in these dynamics; the analyses also underscored, however, the urgent need for more extensive epidemiological surveillance and water quality data. Climate change will likely place a considerable strain on aging water infrastructure in the US. A nuanced understanding of flood-disease dynamics is central to mitigating these effects.