A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

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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“…A companion paper (Liu et al. ) applies this approach to nationally available data using high performance computing.…”

Section: Resultsmentioning

confidence: 99%

River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage

Zheng

Tarboton

Maidment

et al. 2018

J American Water Resour Assoc

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129

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“…NFIE features multidisciplinary collaboration for innovating national‐scale flood forecasting and mapping capabilities, conducted by the U.S. National Water Center in partnership with pertinent research communities . A key effort is to produce a high‐resolution (10m) geospatial reference dataset, called Height Above Nearest Drainage (HAND), as the hydrologic terrain for mapping flood inundation extent for the conterminous U.S. (CONUS). The HAND experiment in year 2016 is a representative example of transforming hydrological research from studying local watersheds by a single research group to continental hydrology research involving multiple research teams in cyberGIS, hydrology, hydraulics, and emergency management.…”

Section: Discussionmentioning

confidence: 99%

CyberGIS‐Jupyter for reproducible and scalable geospatial analytics

Yin

Liu

et al. 2018

Concurrency and Computation

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Summary The interdisciplinary field of cyberGIS (geographic information science and systems (GIS) based on advanced cyberinfrastructure) has a major focus on data‐ and computation‐intensive geospatial analytics. The rapidly growing needs across many application and science domains for such analytics based on disparate geospatial big data poses significant challenges to conventional GIS approaches. This paper describes CyberGIS‐Jupyter, an innovative cyberGIS framework for achieving data‐intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on ROGER, the first cyberGIS supercomputer. The framework adapts the Notebook with built‐in cyberGIS capabilities to accelerate gateway application development and sharing while associated data, analytics, and workflow runtime environments are encapsulated into application packages that can be elastically reproduced through cloud‐computing approaches. As a desirable outcome, data‐intensive and scalable geospatial analytics can be efficiently developed and improved and seamlessly reproduced among multidisciplinary users in a novel cyberGIS science gateway environment.

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“…The paper by Liu et al. () describes the geoprocessing methodology and advanced computational and cyberinfrastructure approaches they developed in order to calculate this national‐scale 10‐m HAND database. The HAND dataset can be used to map flood‐inundation extent by identifying the cells whose HAND values are less than the water depth at their nearest stream.…”

Section: Overviewmentioning

confidence: 99%