Research Project Full Title: Estimating Inundation Extent and Depth from National Water Model Outputs and High Resolution Topographic Data
Principal Investigator(s): Paola Passalacqua
Researcher(s): Fernando R. Salas
Sponsor(s): National Ocean and Atmospheric Administration (NOAA)
Full Abstract: Flood disasters resulting from recent hurricanes (e.g., Harvey and Irma in 2017 and Florence in 2018) have emphasized the need for rapid estimation of flood inundation extent and depth over large areas, as reflected in NOAA’s priority JTTI-2 and the Department of Commerce (DOC) priority goal on flood inundation mapping. During these events, National Water Model (NWM) outputs were used to forecast peak flows several days in advance, but the forecast system does not have the capability to fully address these mapping needs. Since 2018, OWP’s Water Prediction and Operations Division supports some major flood events with estimation of flood inundation from NWM outputs. This operation is performed with terrain data at 10 m resolution and an approach that was developed and tested by our group during the National Flood Interoperability Experiment through the OWP Innovators Program and the NWC Summer Institute [Zheng et al., 2018b; Liu et al., 2018]. The NHDPlus MR centerlines, however, show lack of accuracy once overlaid on high resolution terrain (HRT) data (resolution < 3m), resulting in incorrect inundation prediction both in extent and depth. This project addresses NOAA’s JTTI-2 and the DOC/NOAA priority by enhancing the current system with our workflow GeoFlood [Zheng et al., 2018a] that estimates flood inundation extent and depth from NWM outputs and HRT data. With GeoFlood, flood inundation estimation from NWM outputs will take advantage of the information contained in HRT data, where available, to improve the accuracy of the river network used for the forecast and of the terrain elevation. As the coverage of HRT data increases, GeoFlood will allow a seamless transition from the current 10m resolution to 1m products.
GeoFlood retraces the river network centerlines in between NHDPlus network nodes by using geodesic minimization principles and topographic attributes computed on HRT data. Since channels are characterized by positive terrain curvature and high flow accumulation, these topographic attributes are used in a cost function that allows tracing centerlines as curves of minimum cost between network nodes. This operation corrects any misplacement of the NHDPlus centerlines with respect to the terrain. GeoFlood then applies the Height Above Nearest Drainage (HAND) method to compute synthetic rating curves and relate the NWM forecast discharge to a corresponding water depth, which is then used to compute the inundation extent and depth based on the calculated HAND value. The locations whose HAND values are below or equal to the reach’s forecasted depth will be flooded, analogously to what is currently done at 10 m resolution.
GeoFlood has been already applied to several areas of different characteristics and compared to FEMA inundation maps, for validating the inundation extent, and to HEC-RAS modeling outputs, for validating the estimated inundation depth [Zheng et al., 2018a]. The Readiness Level of our workflow is 6.5 and we expect to reach Readiness Level 8 by the completion of this project. Given the high spatial resolution of HRT data and the high temporal resolution of the NWM flood forecast data, High Performance Computing may be needed for processing large areas in a short time. Our workflow currently leverages the advanced computing facility at UT Austin (TACC) which we will continue to use as part of this project.
*Godbout, L., J.Y. Zheng, S. Dey, D. Eyelade, D. Maidment, P. Passalacqua, Error assessment for Height Above the Nearest Drainage inundation mapping, Journal of the American Water Resources Association, in revision
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*Sangireddy, H., Stark, C. P., Kladzyk, A., and Passalacqua, P. (2016). GeoNet: An open source software for the automatic and objective extraction of channel heads, channel network, and channel morphology from high resolution topography data. Environmental Modelling and Software, 83, 58-73.
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Zheng, X., D. Maidment, D. Tarboton, Y. Liu, P. Passalacqua (2018a), GeoFlood: Large scale flood inundation mapping based on high resolution terrain analysis, Water Resources Research, doi:10.1029/2018WR023457.
Zheng, X., D. Tarboton, D. Maidment, Y.Y. Liu, P. Passalacqua (2018b), River channel geometry and rating curve estimation using Height Above the Nearest Drainage, Journal of the American Water Resources Association, 54 (4), 785-806, doi:10.1111/1752-1688.12661.
Zheng, X., J. Zheng, D. Maidment, P. Passalacqua (2018c), Validation of National Water Model: Height Above Nearest Drainage flood maps during hurricane Harvey, NH31B-0976, American Geophysical Union AGU Fall Meeting, Washington D.C., December 2018.