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Sensitivity of Subjective Decisions in the GLUE Methodology for Quantifying the Uncertainty in the Flood Inundation Map for Seymour Reach in Indiana, USA

1
Institute of Environmental Research, Kangwon National University, Chuncheon-si, Gangwon-do 200-701, Korea
2
School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA
3
Columbia Water Center, Earth Institute, Columbia University, New York, NY 10027, USA
4
Department of Civil Engineering, Inha University, Incheon-si 402-751, Korea
5
Water Resources Research Division, Water Resources and Environment Research Department, Korea Institute of Civil Engineering and Building Technology, Goyang-si, Gyeonggi-do 411-712, Korea
*
Author to whom correspondence should be addressed.
Water 2014, 6(7), 2104-2126; https://doi.org/10.3390/w6072104
Received: 30 May 2014 / Revised: 4 July 2014 / Accepted: 10 July 2014 / Published: 23 July 2014
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. View Full-Text
Keywords: generalized likelihood uncertainty estimation (GLUE); flood inundation mapping; uncertainty; likelihood measure; White River; Indiana generalized likelihood uncertainty estimation (GLUE); flood inundation mapping; uncertainty; likelihood measure; White River; Indiana
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MDPI and ACS Style

Jung, Y.; Merwade, V.; Kim, S.; Kang, N.; Kim, Y.; Lee, K.; Kim, G.; Kim, H.S. Sensitivity of Subjective Decisions in the GLUE Methodology for Quantifying the Uncertainty in the Flood Inundation Map for Seymour Reach in Indiana, USA. Water 2014, 6, 2104-2126.

AMA Style

Jung Y, Merwade V, Kim S, Kang N, Kim Y, Lee K, Kim G, Kim HS. Sensitivity of Subjective Decisions in the GLUE Methodology for Quantifying the Uncertainty in the Flood Inundation Map for Seymour Reach in Indiana, USA. Water. 2014; 6(7):2104-2126.

Chicago/Turabian Style

Jung, Younghun; Merwade, Venkatesh; Kim, Soojun; Kang, Narae; Kim, Yonsoo; Lee, Keonhaeng; Kim, Gilho; Kim, Hung S. 2014. "Sensitivity of Subjective Decisions in the GLUE Methodology for Quantifying the Uncertainty in the Flood Inundation Map for Seymour Reach in Indiana, USA" Water 6, no. 7: 2104-2126.

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