<p> To provide a logical framework for the comparison of various methods of kinematic flood routing a general method of kinematic flood routing is developed.</p> <p> After presenting the general framework, the properties of the numerical model are investigated by: l. Algebraic examination of the finite difference scheme. 2.Numerical experiments using a high speed digital computer. 3. Comparison of the kinematic flood routing results with results of simulations using the complete one dimensional dynamic representation.</p> <p> Particular facets of the numerical kinematic model that were studied included: 1. The stability of the numeric schematizations. 2. The degree of approximation with the finite difference system. 3. The applicability of kinematic methods to unsteady flow systems. 4. Methods of extending the kinematic solutions to predict attenuation as well as translation of the flood wave through the channel systems. </p> <p> The results indicate that kinematic flood routing methods differ primarily in the point about which the finite difference equation is formulated, hereafter termed the nucleus, and that the general framework is capable of emulating such methods as the Muskinghum Method, other non-linear kinematic methods and reservoir routing. By varying the location of the nucleus the stability and degree of approximation is significantly altered. This results in the outflow hydrograph being sensitive to the location of the nucleus and the size of the finite difference steps.</p> <p> To facilitate further research and application of the methods outlined in the thesis, a computer program was developed to enable kinematic flood routing to be performed in a natural channel with arbitrary geometry. Furthermore, the data is compatible with a program that is capable of performing a flood routing analysis using a numerical solution of the complete Saint-Venant equations. Documentation of the computer program for kinematic analysis is included with this thesis.</p> / Thesis / Master of Engineering (MEngr)

Rivers are integral parts of Bangladesh & Bengali people’s life. The whole country is criss-crossed by more than 700 rivers including their tributaries. In other words, rivers formed this country as a delta and have been contributing to spring life to this land with agriculture, food, electricity, transportation, and tranquil beauty, creating opportunity for different occupations, and providing very comfortable moderate climate. Due to over population and urban sprawl, the current development pattern of the country is culminating in filling up wetlands, changing the course of lakes, narrowing down and in some cases killing rivers for urban development leaving great impact on environment. As a consequence, the country is being affected by frequent flood, deforestation, surge, cyclones, and rise of sea level due to global warming. The concern for introducing an adoptive sustainable architecture that interacts with rivers and water, protects environment as well as facilitates new developments has been noticed by scientists and scholars from every sector of development. This thesis will be emphasizing on establishing some unique architectural features that would especially be applicable for riverfront architectures, that leaves least impact on nature and respects the country’s tradition, heritage and lifestyle which are inseparable from rivers.

No description available.

Effective prediction of localized flash flood regions for an approaching rainfall event requires an in-depth knowledge of the land surface and stream characteristics of the forecast area. Flash Flood Guidance (FFG) is currently formulated once or twice a day at the county level by River Forecast Centers (RFC) in the U.S. using modeling systems that contain coarse, generalized land and stream characteristics and hydrologic runoff techniques that often are not calibrated for the forecast region of a given National Weather Service (NWS) office. This research investigates the application of embedded geographic information systems (GIS) modeling techniques to generate a localized flash flood model for individual small watersheds at a five minute scale and tests the model using historical case storms to determine its accuracy in the FFG process. This model applies the Soil Conservation Service (SCS) curve number (CN) method and synthetic dimensionless unit hydrograph (UH), and Muskingum stream routing modeling technique to formulate flood characteristics and rapid update FFG for the study area of interest. The end result of this study is a GIS-based Flash Flood Forecasting system for ungaged small watersheds within a study area of the Blacksburg NWS forecast region. This system can then be used by forecasters to assess which watersheds are at higher risk for flooding, how much additional rainfall would be needed to initiate flooding, and when the streams of that region will overflow their banks. Results show that embedding these procedures into GIS is possible and utilizing the GIS interface can be helpful in FFG analysis, but uncertainty in CN and soil moisture can be problematic in effectively simulating the rainfall-runoff process at this greatly enhanced spatial and temporal scale. / Master of Science

Cemeteries have a lot of different values, both to people but also to society. Besides from being a burial place where survivors can go to be close to their deceased, they can also function as restorative places or cultural and historical places. This study’s aim is to investigate how future changes in the climate may potentially have impacts on cemeteries in Sweden in forms of flooding and to make a rough estimation of how many cemeteries that would be affected by this. This study will also investigate how many individuals that would be affected by this. In order to investigate this an overlay analysis was done in a Geographical Information System (GIS). The results showed that there are some cemeteries that would potentially have 10% or more of the total area flooded, given the scenarios in this study. It also shows that there could potentially be a lot of individuals that would be affected, in different ways.

This study addresses flood risk assessment in the Catalpa Creek watershed, located in northeast Mississippi, USA. Employing the Hydrological Modeling System (HEC-HMS) and the River Analysis System (HEC-RAS), integrated models were developed and calibrated, to predict flood behavior within the watershed. The study conducted flood frequency analyses for return periods ranging from 2 to 100 years and generated flood inundation maps, pinpointing flood-prone areas. Mitigation measures for flood risk management were recommended. The results underscore the effectiveness of the integrated modeling approach for simulating and understanding the complex dynamics of flood events. The research identified critical flood-prone zones, emphasizing the importance of proactive flood risk management. The calibrated hydrological model serves as a valuable tool for stormwater management, water resource planning, and watershed assessment. The study provides insights into flood risk in the Catalpa Creek watershed, offering valuable guidance to regional decision-makers. This study lays the foundation for future investigations in floodplain encroachment, sediment transport, stream restoration, and flood inundation hazard mapping.

Compared with other types of floods, timely and accurately predicting flash floods is particularly challenging due to the small spatiotemporal scales in which the hydrologic and hydraulic processes tend to develop, and to the short lead time between the causative event and the inundation scenario. With continuous increased availability of data and computational power, the interest in applying techniques based on machine learning for hydrologic purposes in the context of operational forecasting has also been increasing. The primary goal of the research activities developed in the context of this thesis is to explore the use of emerging machine learning techniques for enhancing flash flood forecasting. The studies presented start with a review on the state-of-the-art of documented forecasting systems suitable for flash floods, followed by an assessment of the potential of using multiple concurrent precipitation estimates for early prediction of high-discharge scenarios in a flashy catchment. Then, the problem of rapidly producing realistic highresolution flood inundation maps is explored through the use of hybrid machine learning models based on Non-linear AutoRegressive with eXogenous inputs (NARX) and SelfOrganizing Maps (SOM) structures as surrogates of a 2D hydraulic model. In this context, the use of k-fold ensemble is proposed and evaluated as an approach for estimating uncertainties related to the surrogating of a physics-based model. The results indicate that, in a small and flashy catchment, the abstract nature of data processing in machine learning models benefits from the presentation of multiple concurrent precipitation products to perform rainfall-runoff simulations when compared to the business-as-usual single-precipitation approach. Also, it was found that the hybrid NARX-SOM models, previously explored for slowly developing flood scenarios, present acceptable performances for surrogating high-resolution models in rapidly evolving inundation events for the production of both deterministic and probabilistic inundation maps in which uncertainties are adequately estimated. / Thesis / Doctor of Science (PhD) / Flash floods are among the most hazardous and impactful environmental disasters faced by different societies across the globe. The timely adoption of mitigation actions by decision makers and response teams is particularly challenging due to the rapid development of such events after (or even during) the occurrence of an intense rainfall. The short time interval available for response teams imposes a constraint for the direct use of computationally demanding components in real-time forecasting chains. Examples of such are high-resolution 2D hydraulic models based on physics laws, which are capable to produce valuable flood inundation maps dynamically. This research explores the potential of using machine learning models to reproduce the behavior of hydraulic models designed to simulate the evolution of flood inundation maps in a configuration suitable for operational flash flood forecasting application. Contributions of this thesis include (1) a comprehensive literature review on the recent advances and approaches adopted in operational flash flood forecasting systems with the identification and the highlighting of the main research gaps on this topic, (2) the identification of evidences that machine learning models have the potential to identify patterns in multiple quantitative precipitation estimates from different sources for enhancing the performance of rainfall-runoff estimation in urban catchments prone to flash floods, (3) the assessment that hybrid data driven structures based on self-organizing maps (SOM) and nonlinear autoregressive with exogenous inputs (NARX), originally proposed for large scale and slow-developing flood scenarios, can be successfully applied on flashy catchments, and (4) the proposal of using k-folding ensemble as a technique to produce probabilistic flood inundation forecasts in which the uncertainty inherent to the surrogating step is represented.

No description available.

This research explores geospatial patterns in social vulnerability to flooding and experimentally examines the effect of coverage maximums on flood insurance demand. In the first chapter, I analyze census data for the City of Calgary from 1991-2016 to identify trends in social vulnerability based on flood hazard level. Using a quasi-experimental design, I estimate the short-term changes in social vulnerability attributable to the 2013 Calgary flood. The results show that the Calgary flood was associated with a 2.6% increase in postsecondary education, a 1.4% decrease in the immigrant population, a 1.7% decrease in the visible minority population, a $7,100 increase in median family income, 2.8% decrease in home ownership, 3.7% increase in housing construction and 2.2% increase in recent movers. Together, these findings suggest that the highest flood hazard areas in Calgary are generally comprised of lower vulnerability populations; absolute loss potential from floods is getting higher over time due to higher property wealth in high flood hazard areas; and flooding events are associated with a decline in social vulnerability over the short-term. In the second chapter, I examine flood insurance coverage preferences through the use of a hypothetical choice experiment. The experiment was designed to examine the effect of dwelling value and coverage limit on the probability of flood insurance purchase, while holding the probability of flooding and insurance price constant. Controlling for income, the results indicate that amount of coverage is negatively related to flood insurance demand, however, for people in high-value dwellings the opposite is observed. This may suggest an approach to flood insurance as an investment into high-value properties as a financial asset, but the trade-off in higher yearly premiums may not seem worth the investment for lower-valued dwellings. This research shows an inconsistent demand for flood insurance, dependent on dwelling value and independent of income. / Thesis / Master of Science (MSc) / In this research I analyze the population characteristics of the City of Calgary geographically in order to determine if more vulnerable populations are exposed to the hazard of flooding. I also look at the before-and-after flood population characteristics of the flooded and non-flooded areas to see if flooding makes communities more or less vulnerable following an event. My aim is to provide context for the flooding hazard in Calgary and see if a flood changes the population vulnerability of the affected areas afterwards. I also conduct a choice experiment where I provide participants with a devised flood insurance scenario. I keep some of the variables constant, such as flood probability and insurance price, but change the amount of coverage and dwelling value randomly to see if they influence the likelihood that people buy insurance. The goal of this is to understand how insurance maximums can influence consumer demand.

The intention of this paper is to argue a position for the use of floodplain linear parks as a means of urban flood mitigation. Current approaches often focus on protecting existing and future structures via the use of costly-engineered solutions such as dams and floodwalls. My argument is that the same money can be used to restore the floodplain by removing such structures and establishing a park system that will serve as a valuable public amenity, while allowing flooding to occur with minimal damage produced. In the long run, such a park will provide a greater return on the investment than other potential solutions. A discussion of the "Proximate Principle" will describe how this works. From an environmental perspective, the importance of such a park will be discussed by placing it in the context of the green infrastructure concept, which is essentially an umbrella term for ongoing efforts to better integrate human and natural systems. Three case studies are presented that demonstrate examples of such park systems and the effects they had on local economies and communities. These studies begin demonstrating the social connotations for such a project as well. Throughout this paper, ties are made to the city of Roanoke, Virginia (where the project portion of this thesis takes place) to demonstrate the relevance of floodplain linear parks to the city. All arguments made are supported by a conceptual floodplain park plan for the city of Roanoke. / Master of Landscape Architecture