Award Abstract # 1761673
RAPID:Archiving and Enabling Community Access to Data from Recent US Hurricanes

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: UTAH STATE UNIVERSITY
Initial Amendment Date: September 20, 2017
Latest Amendment Date: September 20, 2017
Award Number: 1761673
Award Instrument: Standard Grant
Program Manager: Rob Beverly
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2017
End Date: September 30, 2018 (Estimated)
Total Intended Award Amount: $200,000.00
Total Awarded Amount to Date: $200,000.00
Funds Obligated to Date: FY 2017 = $200,000.00
History of Investigator:
  • David Tarboton (Principal Investigator)
    david.tarboton@usu.edu
  • David Maidment (Co-Principal Investigator)
  • Ray Idaszak (Co-Principal Investigator)
  • Jerad Bales (Co-Principal Investigator)
Recipient Sponsored Research Office: Utah State University
1000 OLD MAIN HL
LOGAN
UT  US  84322-1000
(435)797-1226
Sponsor Congressional District: 01
Primary Place of Performance: Utah State University
4110 Old Main Hill
Logan
UT  US  84322-4110
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): SPE2YDWHDYU4
Parent UEI:
NSF Program(s): Hurricane Harvey 2017
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7433, 7914, 8004
Program Element Code(s): 071Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Hurricanes Harvey and Irma were large, highly impactful, weather events.  In particular, Harvey was a unique storm event that exceeded all previously recorded storms in terms of precipitation. Information that helps the research community understand such events better will enable better forecasting techniques and designs of critical facilities design that can better withstand severe weather events.The objective of this project is to collect, document and archive data from hurricanes Harvey and Irma within HydroShare, a data service provided by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) that supports the sharing and publication of a broad class of hydrologic data and models. This data will serve as a key resource for understanding how such large and sustained flooding and rainfall events could have occurred, and how critical infrastructures should be designed. Further, HydroShare is used by over 130 institutions, and having the information related to Hurricanes Harvey and Irma centrally available will have allow research related to these events to be more easily conducted, as well as facilitate creative and unanticipated use of this data.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Hurricanes Harvey and Irma were large, highly impactful, weather events.  In particular, Harvey was a unique storm event that exceeded all previously recorded storms in terms of precipitation. Information that helps the research community understand such events better enables better forecasting techniques and designs of critical facilities design that can better withstand severe weather events.


Intellectual merit

This project collected, documented and archived data from hurricanes Harvey and Irma within HydroShare, a data service provided by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) that supports the sharing and publication of a broad class of hydrologic data and models. Assembling this archive was enabled by the team at UT Austin having been embedded in the Texas State Operations Center helping with the initial emergency response to Harvey.  They thus had knowledge of and access to a treasure trove of data that has been published in this archive. This data will serve as a key resource for understanding such large and sustained flooding and rainfall events and for testing flood forecast models, including coupled storm surge and upland flood models, under such extreme circumstances.


The Harvey collection in HydroShare includes 17 resources that are organized into two sets: (1) Basemap data collections and (2) Flood data collections.  All of these are discoverable in HydroShare using the keyword "Harvey2017" or any other word that matches text in their title, abstract or other metadata.  

The Irma collection in HydroShare includes 13 resources organized into the base collection that includes basemap data and flood data collections.  All of these are discoverable in HydroShare using the keyword "Irma2017" or any other word that matches text in their title, abstract or other metadata.  


Broader Impacts

ESRI story maps hosted on a CUAHSI server provide easy-to-understand graphical overviews of each of these events that include interactive maps for access to flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Harvey.  They serve as a visual entry point to much of the data assembled (Figures 1-3).


HydroShare is a domain-specific repository for hydrologic data operated by CUAHSI.  The hurricane archive data are openly accessible and the HydroShare user base of over 3000 users and 130 institutions ensures that the data are accessible and prominent within the within the hydrology research community.  CUAHSI has a project web page that draws attention to this collection and staff to assist with access and interpretation where needed. Having the information related to Hurricanes Harvey and Irma centrally available will have allow research related to these events to be more easily conducted, as well as facilitate creative and unanticipated use of this data.

 

 

 

 

 


Last Modified: 02/11/2019
Modified by: David G Tarboton

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