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Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages

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Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages. / Schumann, Guy; Cutler, Mark; Black, Andrew; Matgen, Patrick; Pfister, Laurent; Hoffmann, Lucien; Pappenberger, Florian.

In: International Journal of River Basin Management, Vol. 6, No. 3, 2008, p. 187-199.

Research output: Contribution to journalArticle

Harvard

Schumann, G, Cutler, M, Black, A, Matgen, P, Pfister, L, Hoffmann, L & Pappenberger, F 2008, 'Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages' International Journal of River Basin Management, vol 6, no. 3, pp. 187-199., 10.1080/15715124.2008.9635347

APA

Schumann, G., Cutler, M., Black, A., Matgen, P., Pfister, L., Hoffmann, L., & Pappenberger, F. (2008). Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages. International Journal of River Basin Management, 6(3), 187-199. 10.1080/15715124.2008.9635347

Vancouver

Schumann G, Cutler M, Black A, Matgen P, Pfister L, Hoffmann L et al. Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages. International Journal of River Basin Management. 2008;6(3):187-199. Available from: 10.1080/15715124.2008.9635347

Author

Schumann, Guy; Cutler, Mark; Black, Andrew; Matgen, Patrick; Pfister, Laurent; Hoffmann, Lucien; Pappenberger, Florian / Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages.

In: International Journal of River Basin Management, Vol. 6, No. 3, 2008, p. 187-199.

Research output: Contribution to journalArticle

Bibtex - Download

@article{83c7081475194533ac6a17f55a569edb,
title = "Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages",
keywords = "Remote sensing-derived water stages, Non error-free data, Flood inundation model, Extended GLUE philosophy, Model uncertainty",
author = "Guy Schumann and Mark Cutler and Andrew Black and Patrick Matgen and Laurent Pfister and Lucien Hoffmann and Florian Pappenberger",
note = "dc.publisher: Taylor & Francis dc.description.sponsorship: ‘Ministère de la Culture, de l’Enseignement Supérieur et de la Recherche’ of Luxembourg",
year = "2008",
doi = "10.1080/15715124.2008.9635347",
volume = "6",
number = "3",
pages = "187--199",
journal = "International Journal of River Basin Management",
issn = "1571-5124",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages

A1 - Schumann,Guy

A1 - Cutler,Mark

A1 - Black,Andrew

A1 - Matgen,Patrick

A1 - Pfister,Laurent

A1 - Hoffmann,Lucien

A1 - Pappenberger,Florian

AU - Schumann,Guy

AU - Cutler,Mark

AU - Black,Andrew

AU - Matgen,Patrick

AU - Pfister,Laurent

AU - Hoffmann,Lucien

AU - Pappenberger,Florian

PY - 2008

Y1 - 2008

N2 - On January 2 2003 the Advanced Synthetic Aperture Radar (ASAR) instrument onboard ENVISAT captured a high magnitude flood event on a reach of the Alzette River (G.D. of Luxembourg) at the time of flood peak. This opportunity enables hydraulic analyses with spatially distributed information. This study investigates the utility of uncertain (i.e. non error-free) remotely sensed water stages to evaluate uncertain flood inundation predictions. A procedure to obtain distributed water stage data consists of an overlay operation of satellite radar-extracted flood boundaries with a LiDAR DEM followed by integration of flood detection uncertainties using minimum and maximum water stage values at each modelled river cross section. Applying the concept of the extended GLUE methodology, behavioural models are required to fall within the uncertainty range of remotely sensed water stages. It is shown that in order to constrain model parameter uncertainty and at the same time increase parameter identifiability as much as possible, models need to satisfy the behavioural criterion at all locations. However, a clear difference between the parameter identifiability and the final model uncertainty estimation exists due to ‘secondary’ effects such as channel conveyance. From this, it can be argued that it is necessary not only to evaluate models at a high number of locations using observational error ranges but also to examine where the model would require additional degrees of freedom to generate low model uncertainty at every location. Remote sensing offers this possibility, as it provides highly distributed evaluation data, which are however not error-free, and therefore an approach like the extended GLUE should be adopted in model evaluation.

AB - On January 2 2003 the Advanced Synthetic Aperture Radar (ASAR) instrument onboard ENVISAT captured a high magnitude flood event on a reach of the Alzette River (G.D. of Luxembourg) at the time of flood peak. This opportunity enables hydraulic analyses with spatially distributed information. This study investigates the utility of uncertain (i.e. non error-free) remotely sensed water stages to evaluate uncertain flood inundation predictions. A procedure to obtain distributed water stage data consists of an overlay operation of satellite radar-extracted flood boundaries with a LiDAR DEM followed by integration of flood detection uncertainties using minimum and maximum water stage values at each modelled river cross section. Applying the concept of the extended GLUE methodology, behavioural models are required to fall within the uncertainty range of remotely sensed water stages. It is shown that in order to constrain model parameter uncertainty and at the same time increase parameter identifiability as much as possible, models need to satisfy the behavioural criterion at all locations. However, a clear difference between the parameter identifiability and the final model uncertainty estimation exists due to ‘secondary’ effects such as channel conveyance. From this, it can be argued that it is necessary not only to evaluate models at a high number of locations using observational error ranges but also to examine where the model would require additional degrees of freedom to generate low model uncertainty at every location. Remote sensing offers this possibility, as it provides highly distributed evaluation data, which are however not error-free, and therefore an approach like the extended GLUE should be adopted in model evaluation.

KW - Remote sensing-derived water stages

KW - Non error-free data

KW - Flood inundation model

KW - Extended GLUE philosophy

KW - Model uncertainty

U2 - 10.1080/15715124.2008.9635347

DO - 10.1080/15715124.2008.9635347

M1 - Article

JO - International Journal of River Basin Management

JF - International Journal of River Basin Management

SN - 1571-5124

IS - 3

VL - 6

SP - 187

EP - 199

ER -

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