A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty1. (7th June 2018)

• Record Type: Journal Article
• Authors: Zarzar, Christopher M.; Hosseiny, Hossein; Siddique, Ridwan; Gomez, Michael; Smith, Virginia; Mejia, Alfonso; Dyer, Jamie
• Is Part Of: Journal of the American Water Resources Association. Volume 54:Number 4(2018:Aug.)
• Page Start: 807
• Page End: 819
• Publication Date: 2018-06-07
• Subjects: flooding -- uncertainty mapping -- risk assessment -- decision support system -- ensemble modeling -- uncertainty analysis; Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/1752-1688.12656
• Languages: English
• ISSNs: 1093-474X

Abstract

Abstract: While deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.