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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF ARIZONA
Doing Business As Name:University of Arizona
PD/PI:
  • Laura Condon
  • (720) 771-6885
  • lecondon@email.arizona.edu
Co-PD(s)/co-PI(s):
  • Nirav C Merchant
  • Reed M Maxwell
  • Peter M Melchior
Award Date:09/15/2021
Estimated Total Award Amount: $ 5,000,000
Funds Obligated to Date: $ 2,610,166
  • FY 2021=$2,610,166
Start Date:10/01/2021
End Date:09/30/2023
Transaction Type: Cooperative Agreements
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.083
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Track D: Hidden Water and Extreme Events: HydroGEN, A Physically Rigorous Machine Learning Platform for Hydrologic Scenario Generation
Federal Award ID Number:2134892
DUNS ID:806345617
Parent DUNS ID:072459266
Program:Convergence Accelerator Resrch
Program Officer:
  • Mike Pozmantier
  • (703) 292-4475
  • mpozmant@nsf.gov

Awardee Location

Street:888 N Euclid Ave
City:Tucson
State:AZ
ZIP:85719-4824
County:Tucson
Country:US
Awardee Cong. District:03

Primary Place of Performance

Organization Name:University of Arizona
Street:888 Euclid Ave
City:Tucson
State:AZ
ZIP:85719-4824
County:Tucson
Country:US
Cong. District:03

Abstract at Time of Award

Water is the driving force behind extreme events like floods, droughts and wildfires. These events have cost the US $234.3B in damages just in the past three years, and this figure is projected to increase. Recent events like the record setting wildfires in California and the mega drought on the Colorado river are merely the latest illustrations. Historical data are no longer a reliable guide for the risks we will face in the future. This project addresses the uncertainty that poses a huge challenge for decision makers. HydroGEN is a web-based machine learning (ML) platform that generates custom hydrologic scenarios on demand. It combines powerful physics-based simulations with ML and observations to provide customizable scenarios from the bedrock through the treetops. Without any prior modeling experience, water managers and planners can directly manipulate state-of-the-art tools to explore scenarios that matter to them. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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