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Are Feature Agreement Statistics Alone Sufficient to Validate Modelled Flood Extent Quality?: A Study on Three Swedish Rivers Using Different Digital Elevation Model Resolutions
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences. (Geospatial Information Science)ORCID iD: 0000-0002-3906-6088
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences. (Geospatial Information Science)ORCID iD: 0000-0002-3884-3084
2019 (English)In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, Vol. 2019, article id 9816098Article in journal (Refereed) Published
Abstract [en]

Hydraulic modelling is now, at increasing rates, used all over the world to provide flood risk maps for spatial planning, flood insurance, etc. This puts heavy pressure on the modellers and analysts to not only produce the maps but also information on the accuracy and uncertainty of these maps. A common means to deliver this is through performance measures or feature statistics. These look at the global agreement between the modelled flood area and the reference flood that is used. Previous studies have shown that the feature agreement statistics do not differ much between models that have been based on digital elevation models (DEMs) of different resolutions, which is somewhat surprising since most researchers agree that high-resolution DEMs are to be preferred over poor resolution DEMs. Hence, the aim of this study was to look into how and under which conditions the different feature agreement statistics differ, in order to see when the full potential of high-resolution DEMs can be utilised. The results show that although poor resolution DEMs might produce high feature agreement scores (around F > 0.80), they may fail to provide good flood extent estimations locally, particularly when the terrain is flat. Therefore, when high-resolution DEMs (1 to 5 m) are used, it is important to carefully calibrate the models by the use of the roughness parameter. Furthermore, to get better estimates on the accuracy of the models, other performance measures such as distance disparities should be considered.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2019. Vol. 2019, article id 9816098
National Category
Oceanography, Hydrology and Water Resources Physical Geography Remote Sensing Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hig:diva-30966DOI: 10.1155/2019/9816098OAI: oai:DiVA.org:hig-30966DiVA, id: diva2:1370221
Available from: 2019-11-14 Created: 2019-11-14 Last updated: 2019-11-20Bibliographically approved

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Lim, Nancy J.Brandt, S. Anders

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Mathematical problems in engineering (Print)
Oceanography, Hydrology and Water ResourcesPhysical GeographyRemote SensingProbability Theory and Statistics

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