River channel geometry is an important input to hydraulic and hydrologic models. Traditional approaches to quantify river geometry have involved surveyed river cross sections, which cannot be extended to ungaged basins. In this paper, we describe a method for developing a synthetic rating curve to relate flow to water level in a stream reach based on reach‐averaged channel geometry properties developed using the Height above Nearest Drainage (HAND) method. HAND uses a digital elevation model (DEM) of the terrain and computes the elevation difference between each land surface cell and the stream bed cell to which it drains. Taking increments in water level in the stream, HAND defines the inundation zone and a water depth grid within this zone, and the channel characteristics are defined from this water depth grid. We apply our method to the Blanco River (Texas) and the Tar River (North Carolina) using 10‐m terrain data from the United States Geological Survey (USGS) 3D Elevation Program (3DEP) dataset. We evaluate the method's performance by comparing the reach‐average stage‐river geometry relationships and rating curves to those from calibrated Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models and USGS gage observations. The results demonstrate that after some adjustment, the river geometry information and rating curves derived from HAND using national‐coverage datasets are comparable to those obtained from hydraulic models or gage measurements. We evaluate the inundation extent and show our approach is able to capture the majority of the Federal Emergency Management Agency (FEMA) 100‐year floodplain.

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Recent floods from intense storms in the southern United States and the unusually active 2017Atlantic hurricane season have highlighted the need for real-time flood inundation mapping using high-resolution topography. High-resolution topographic data derived from lidar technology reveal unprecedented topographic details and are increasingly available, providing extremely valuable information for improving inundation mapping accuracy. The enrichment of terrain details from these data sets, however, also brings challenges to the application of many classic approaches designed for lower-resolution data. Advanced methods need to be developed to better use lidar-derived terrain data for inundation mapping. We present a new workflow, GeoFlood, for flood inundation mapping using high-resolution terrain inputs that is simple and computationally efficient, thus serving the needs of emergency responders to rapidly identify possibly flooded locations. First, GeoNet, a method for automatic channel network extraction from high-resolution topographic data, is modified to produce a low-density, high-fidelity river network. Then, a Height Above Nearest Drainage (HAND) raster is computed to quantify the elevation difference between each land surface cell and the stream bed cell to which it drains, using the network extracted from high-resolution terrain data. This HAND raster is then used to compute reach-average channel hydraulic parameters and synthetic stage-discharge rating curves. Inundation maps are generated from the HAND raster by obtaining a water depth for a given flood discharge from the synthetic rating curve. We evaluate our approach by applying it in the Onion Creek Watershed in Central Texas, comparing the inundation extent results to Federal Emergency Management Agency 100-yr floodplains obtained with detailed local hydraulic studies. We show that the inundation extent produced by GeoFlood overlaps with 60%~90% of the Federal Emergency Management Agency floodplain coverage demonstrating that it is able to capture the general inundation patterns and shows significant potential for informing real-time flood disaster preparedness and response.Plain Language Summary Simple and computationally efficient flood inundation mapping methods are needed to take advantage of increasingly available high-resolution topography data. In this work, we present a new approach, called GeoFlood, for flood inundation mapping using high-resolution topographic data. This approach combines GeoNet, an advanced method for high-resolution terrain data analysis, and the Height Above Nearest Drainage. GeoFlood can rapidly convert real-time forecasted river flow conditions to corresponding flood maps. A case study in central Texas demonstrated that the flood maps generated with our approach capture the majority of the inundated extent reported by detailed Federal Emergency Management Agency flood studies. Our results show that GeoFlood is a valuable solution for rapid inundation mapping. ZHENG ET AL.10,013

We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies.

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