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A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty
Author(s) -
Zarzar Christopher M.,
Hosseiny Hossein,
Siddique Ridwan,
Gomez Michael,
Smith Virginia,
Mejia Alfonso,
Dyer Jamie
Publication year - 2018
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12656
Subject(s) - flood myth , environmental science , flood forecasting , visualization , ensemble forecasting , uncertainty analysis , streamflow , hydrological modelling , hydrology (agriculture) , realization (probability) , computer science , meteorology , data mining , geology , statistics , simulation , climatology , drainage basin , machine learning , geography , mathematics , cartography , geotechnical engineering , archaeology
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.