@article{160327f245ac4041b12eedae2552019b,
title = "A CyberGIS Integration and Computation Framework for High-Resolution Continental-Scale Flood Inundation Mapping",
abstract = "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.",
keywords = "computational methods, cyberGIS, data management, geospatial analysis, height above nearest drainage (HAND), inundation mapping, streamflow",
author = "Liu, {Yan Y.} and Maidment, {David R.} and Tarboton, {David G.} and Xing Zheng and Shaowen Wang",
note = "Funding Information: Our cyberGIS framework addresses CFIM computational challenges through collaboration among NFIE, the National Science Foundation (NSF) cyberGIS software project (Wang et al. 2013), NSF HydroShare (Tarboton et al. 2014; Horsburgh et al. 2016), USGS, the NSF cyberGIS Facility (that houses the Resourcing Open Geospatial Education and Research [ROGER] supercomputer) (Wang 2017), and the Extreme Science and Engineering Discovery Environment (XSEDE) (Towns et al. 2014). Our open source software solution constructs a cyberGIS workflow that couples the scalable and high-performance TauDEM software (Tesfa et al. 2011) for DEM-based hydrologic analysis and a collection of open source geospatial software for pre-and postprocessing of geospatial data. ROGER, which has a hybrid supercomputing architecture, provides an integrated high-performance data handling, analysis, modeling, and visualization platform for CFIM by coupling HTC, high-performance computing (HPC), and cloud computing. HAND for CONUS was computed in 1.5 days on ROGER. Funding Information: This work is part of the ECSS project (award number ENG140009) of XSEDE that is supported by National Science Foundation (NSF) under grant number 1053575. This research is supported in part by U.S. Geological Survey (USGS) under grant number G14AC00244 and NSF under grant numbers 1047916 and 1343785. The work used the Resourcing Open Geospatial Education and Research (ROGER) supercomputer, which is supported by NSF under grant number 1429699. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing HPC and storage resources that have contributed to the research results reported within this paper. HydroShare is being developed under NSF grants 1148453 and 1148090. TauDEM was enhanced to support parallel computing and integrate with GDAL libraries with support from the US Army Corps of Engineers contract numbers W912HZ-11-P-0338 and W91238-15-P-0033 and XSEDE ECSS allocation EAR130008. The authors are grateful for the insightful discussions with Steve Kopp and Dean Djokic at Esri, and Larry Stanislawski at USGS. The authors thank Dandong Yin at the University of Illinois at Urbana-Champaign for developing the HAND Jupyter notebook. Funding Information: This work is part of the ECSS project (award number ENG140009) of XSEDE that is supported by National Science Foundation (NSF) under grant number 1053575. This research is supported in part by U.S. Geological Survey (USGS) under grant number G14AC00244 and NSF under grant numbers 1047916 and 1343785. The work used the Resourcing Open Geospatial Education and Research (ROGER) supercomputer, which is supported by NSF under grant number 1429699. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing HPC and storage resources that have contributed to the research results reported within this paper. Hydro-Share is being developed under NSF grants 1148453 and 1148090. TauDEM was enhanced to support parallel computing and integrate with GDAL libraries with support from the US Army Corps of Engineers contract numbers W912HZ-11-P-0338 and W91238-15-P-0033 and XSEDE ECSS allocation EAR130008. The authors are grateful for the insightful discussions with Steve Kopp and Dean Djokic at Esri, and Larry Stanislawski at USGS. The authors thank Dandong Yin at the University of Illinois at Urbana-Champaign for developing the HAND Jupyter notebook. Publisher Copyright: {\textcopyright} 2018 American Water Resources Association",
year = "2018",
month = aug,
doi = "10.1111/1752-1688.12660",
language = "English (US)",
volume = "54",
pages = "770--784",
journal = "Journal of the American Water Resources Association",
issn = "1093-474X",
publisher = "Wiley-Blackwell",
number = "4",
}