Diploma thesis deals with design of a flood protection a controlling structures with respect to problém of inner waters in the area of interest. The river reaches assessed are located mostly in the urban area of the city Letovice, more precisely from the junction of the Svitava and Křetínka river. The reaches are at kilometer 59,608 – 61,520 of the Svitava and kilometer 0-1,752 of the Křetínka river including the race between them. Program MIKE 11 (developed by Danish hydraulic institut) was used to calculate the water levels as nonuniform flow.

The heavy rainfall event in August 2002 in Saxony/Germany caused the break of a flood retaining basin in a valley of the Erzgebirge Mountains. The rainfall event with ists hyrologic characteristics and dam break event were analysed and an outflow hydrograph was determing. The propagation of the dam break flood wave in the valleyas of the Briesnitz and Müglitz Rivers has been simulated. Calculated values were compared with some observed data.

The Diploma Thesis consists of research and design part. The content of research part is description of dry reservoirs and theory of 2D modelling. The design part of the work deals with the hydrotechnical study of possibility of building a dry reservoir Blučina in two variants of designs of reservoir with objects. The design contains simulations of 2D numerical model of reservoir made in program SMS-SRH of both designed variants. The result is evaluation of both designed variants and recommendation of better option for a possibility of construction in the future in terms of the transformation of the century-flood wave.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18620号 / 情博第544号 / 新制||情||96(附属図書館) / 31520 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 多々納 裕一, 教授 矢守 克也, 教授 堀 智晴 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

付記する学位プログラム名: グローバル生存学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19975号 / 工博第4219号 / 新制||工||1653(附属図書館) / 33071 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 教授 寶 馨, 教授 堀 智晴 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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

Floods are the most common global natural disaster, and 1 billion people live in floodplains worldwide adding to the impactful damage that inundation causes. Disaster managers strive to mitigate damages to their communities but need to know what the impact of a potential flood may be. GEOGloWS ECMWF Streamflow Services estimates forecasted streamflow around the world. These forecasted streamflow's can be used to create predicted flood extent maps using Height Above Nearest Drainage (HAND) or Sedimentation and River Hydraulics - Two Dimension (SRH-2D). Another method to obtain a flood map is using Setinel-1 satellite Synthetic Aperture Radar (SAR) imagery. Flood maps alone will not demonstrate the impact of the flood, but some exposure data will provide needed impact metrics. In this research, I wanted to produce a general geoprocessing method for stakeholders to compute flood impact metrics over any flood extent map using any exposure dataset. Additionally, I sought to create similar geoprocessing workflows in ArcGIS Pro, QGIS, and stand-alone Python script so that the stakeholders can choose the best suited method that correlates with their access and familiarity. The general geoprocessing workflow was tested using three different global exposure datasets (Agriculture, Infrastructure, and Population). The three different geoprocessing implementations were tested in three areas that are of concern in the greater NASA SERVIR organization using the same flood map and exposure datasets for each area. This research produced a feasible, sustainable, successful, generalized geoprocessing workflow that computes flood impact metrics from a flood map and global exposure datasets. The global datasets can be interchanged with higher resolution exposure datasets specific to an area of interest generating more accurate results. The three geoprocessing methods performed similarly. The results were slightly different when the exposure dataset was a raster file as the conversion from raster to vector format produced differences in rounding values and programming implementation. However, this research's findings are such that the three geoprocessing methods are comparable and that any of the three geoprocessing implementations will produce reasonably similar flood impact results. Ongoing work by the Brigham Young University (BYU) Hydroinformatics lab is to create a Tethys web application that will allow stakeholders to view the flood map and flood impact of areas of interest. Future work may include investigating the workflow workability on a global scale, discovering and implementing global exposure data sources of better resolution, researching more data metrics that can contribute to a more robust flood impact results, and increasing the accuracy of flood impact results when compared among ArcGIS Pro, QGIS, and Python.

Floods are one of the most common types of natural disasters. They annually affect vast amounts of people and cause severe economic losses. While fluvial, coastal, and flash floods are well studied, pluvial floods (rain related) have received modest attention from researchers and decision-makers in comparison. There are several reasons for this, one is that it has been considered a fixed problem with infrastructure and other engineered solutions and another is that they are generally undramatic and small-scale. However, as cities expand, the environment’s ability to retain and dispose of excess water is inhibited and as the frequency of extreme weather events is expected to increase due to climate change, the risk associated with pluvial floods has become increasingly recognized. Commercial and open-source Urban pluvial flood models tend to require advanced modelling expertise, considerable computational power, large amounts of input data and are often expensive. Consequently, there is less knowledge of flood inundation caused by pluvial floods compared to other types. This thesis investigates the Bluespot model, which aims to provide an approachable tool to generate an overview of the effects of pluvial floods in urban areas. The model requires few input data and is relatively simple to perform. Results from the model are compared to the August 2021 flood event in Gävle, Sweden.The study finds that results ranged from accurate to over- and underestimated. Slope and incoming water were found to affect the outcome most. Blue spots without the influence of streams or other waterways, with a distinct slope were mapped with accuracy and showed consistency with coarser resolutions. Consequently, underpasses in the road network were mapped with especially good consistency. Further, blue spots within close distance to large flow accumulation were underestimated and the accuracy tended to decrease with a coarser resolution. The model cannot account for water outside blue spots, thus, when large volumes of water accumulate and spread beyond these boarders it generates poor results. These areas were found to be efficiently indicated by generating a heatmap from high-flow accumulation points. Thus, indicating low confidence and where a hydraulic flood model should be performed. Depending on the scope a 1-3m resolution is recommended for investigating effects on property etc and a 5-10m resolution is sufficient for investigating underpasses, however, a finer resolution will generate more accurate results.

The National Flood Insurance Program (NFIP) introduced the Community Rating System (CRS) to encourage community-level flood mitigation and increase individual-level flood insurance uptake. This study analyzes what factors influence communities to participate in the CRS and conditional on participation, what factors affect the type and intensity of participation over time? I used historical data of policies-in-force, claims, CRS participation, and U.S. Census American Community Survey for all NFIP communities in 11 key states to answer these questions. The results show socio-demographic factors significantly and positively predict communities' likelihood of participating in the CRS, whilst flood risk variables of claims and policies held do not. Further, flood risk variable (claims payment received) as well as socio-demographic variable (income) positively influence the intensity of communities' participation in the CRS over time.

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