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  • 1
    Publication Date: 2013-10-08
    Description: Collection and investigation of flood information are essential to understand the nature of floods, but this has proved difficult in data-poor environments, or in developing or under-developed countries due to economic and technological limitations. The development of remote sensing data, GIS, and modeling techniques have, therefore, proved to be useful tools in the analysis of the nature of floods. Accordingly, this study attempts to estimate a flood discharge using the generalized likelihood uncertainty estimation (GLUE) methodology and a 1D hydraulic model, with remote sensing data and topographic data, under the assumed condition that there is no gauge station in the Missouri river, Nebraska, and Wabash River, Indiana, in the United States. The results show that the use of Landsat leads to a better discharge approximation on a large-scale reach than on a small-scale. Discharge approximation using the GLUE depended on the selection of likelihood measures. Consideration of physical conditions in study reaches could, therefore, contribute to an appropriate selection of informal likely measurements. The river discharge assessed by using Landsat image and the GLUE Methodology could be useful in supplementing flood information for flood risk management at a planning level in ungauged basins. However, it should be noted that this approach to the real-time application might be difficult due to the GLUE procedure.
    Electronic ISSN: 2073-4441
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI Publishing
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  • 2
    Publication Date: 2014-03-01
    Description: A need for more accurate flood inundation maps has recently arisen due to the increasing frequency and extremity of flood events. The accuracy of flood inundation maps is determined by the uncertainty propagated from all of the variables involved in the overall process of flood inundation modeling. Despite our advanced understanding of flood progression, it is impossible to eliminate the uncertainty due to the constraints involving cost, time, knowledge, and technology. Nevertheless, uncertainty analysis in flood inundation mapping can provide useful information for flood-risk management. The twin objectives of this study were firstly to estimate the propagated uncertainty rates of key variables in flood inundation mapping by using the first-order approximation (FOA) method and secondly to evaluate the relative sensitivities of the model variables by using the Hornberger–Spear–Young (HSY) method. Monte Carlo simulations using the Hydrologic Engineering Center's River Analysis System (HEC–RAS) and triangle-based interpolation were performed to investigate the uncertainty arising from discharge, topography, and Manning's n in the East Fork of the White River near Seymour, Indiana, and in Strouds Creek in Orange County, North Carolina. We found that the uncertainty of a single variable is propagated differently to the flood inundation area depending on the effects of other variables in the overall process. The uncertainty was linearly/nonlinearly propagated corresponding to valley shapes of the reaches. In addition, the HSY sensitivity analysis revealed the topography of Seymour reach and the discharge of Strouds Creek to be major contributors to the change of flood inundation area. This article is protected by copyright. All rights reserved.
    Print ISSN: 0885-6087
    Electronic ISSN: 1099-1085
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Wiley
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  • 3
    Publication Date: 2014-07-24
    Description: Generalized likelihood uncertainty estimation (GLUE) is one of the widely-used methods for quantifying uncertainty in flood inundation mapping. However, the subjective nature of its application involving the definition of the likelihood measure and the criteria for defining acceptable versus unacceptable models can lead to different results in quantifying uncertainty bounds. The objective of this paper is to perform a sensitivity analysis of the effect of the choice of likelihood measures and cut-off thresholds used in selecting behavioral and non-behavioral models in the GLUE methodology. By using a dataset for a reach along the White River in Seymour, Indiana, multiple prior distributions, likelihood measures and cut-off thresholds are used to investigate the role of subjective decisions in applying the GLUE methodology for uncertainty quantification related to topography, streamflow and Manning’s n. Results from this study show that a normal pdf produces a narrower uncertainty bound compared to a uniform pdf for an uncertain variable. Similarly, a likelihood measure based on water surface elevations is found to be less affected compared to other likelihood measures that are based on flood inundation area and width. Although the findings from this study are limited due to the use of a single test case, this paper provides a framework that can be utilized to gain a better understanding of the uncertainty while applying the GLUE methodology in flood inundation mapping.
    Electronic ISSN: 2073-4441
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI Publishing
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