Accounting digital elevation uncertainty for flood consequence assessment. (17th February 2017)

• Record Type: Journal Article
• Authors: Bhuyian, Md. N.M.; Kalyanapu, A.
• Is Part Of: Journal of flood risk management. Volume 11(2018)Supplement 2
• Page Start: S1051
• Page End: S1062
• Publication Date: 2017-02-17
• Subjects: Digital terrain modelling -- hydraulic modelling -- mapping of hazard and risk -- risk analysis; Flood control -- Periodicals; Floods -- Risk assessment -- Periodicals; Flood damage prevention -- Periodicals; Flood warning systems -- Periodicals; 627.405
• DOI: 10.1111/jfr3.12293
• Languages: English
• ISSNs: 1753-318X

Abstract

Abstract : A digital elevation model (DEM) is commonly used as a substitute for surveyed topographic data. Selection of suitable DEM and optimum spatial resolution is, thus, a key for achieving expected accuracy within sufficient simulation time. This study compared DEMs from different sources (i.e. Shuttle Radar Topography Mission, Advanced Spaceborne Thermal Emission and Reflection Radiometer, National Elevation Dataset, and Light Detection and Ranging) with various spatial resolutions for a 35 km long stretch of the American River downstream of Folsom Dam in California. The study period was the 1997 'New Year's Flood' used to estimate downstream flood consequences, especially in urban areas near Sacramento. The objective of this study was to quantify the comparative deviation of model accuracy for each specific set of topographic data. This study also looked into developing correlations between consequences and flood magnitude. The hydrodynamic model furnished input for flood damage assessment. The Hydrologic Engineering Center's Flood Impact Analysis (HEC‐FIA) was employed to estimate flood losses for each scenario. This analysis will assist decision‐makers in selecting the appropriate DEM for flood consequence assessment to get reasonable results within a convenient amount of time. It is also expected that this study will be useful for estimating consequences in absence of high‐quality terrain data, which will be especially helpful in remote study areas.