| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | August 25, 2014 |
| Latest Amendment Date: | June 17, 2019 |
| Award Number: | 1442735 |
| Award Instrument: | Standard Grant |
| Program Manager: |
David Corman
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2014 |
| End Date: | September 30, 2019 (Estimated) |
| Total Intended Award Amount: | $1,196,295.00 |
| Total Awarded Amount to Date: | $1,196,295.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
701 S NEDDERMAN DR ARLINGTON TX US 76019-9800 (817)272-2105 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
TX US 76019-0045 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CyberSEES |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Many cities face tremendous water-related challenges due to urban population growth and climate fluctuations. Even moderate rainfall can quickly fill and overflow urban water reserves. Urban areas are particularly susceptible not only to excesses and shortages of water but also to variations in water quality. This project protects urban areas from the shocks of extreme precipitation cycles and urbanization by advancing our understanding of the urban water cycle through the integration of advanced computing and cyber-infrastructure, environmental modeling, geoscience, and information science.
This project utilizes high-resolution precipitation information from the network of Collaborative Adaptive Sensing of the Atmosphere (CASA) radars available in the Dallas-Fort Worth area, crowdsourced water observations for ubiquitous sensing of surface water over a large urban area, and new innovative wireless sensors for water quantity, water quality and soil moisture to close the observation gaps. Cloud computing is then used for advanced high-resolution modeling, data optimization, and predictive analytics to assess water quantity and quality in both the short and long term. This project advances our understanding of urban sustainability and the associated challenges through environmental, social and economic responses of a large city as an uncertain dynamic system.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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.
Many cities face a wide range of water-related challenges from too much or too little water. With urbanization, population growth, aging infrastructure and climate change, these challenges are mounting in many communities. This research sought to improve urban sustainability from transient shocks of heavy-to-extreme precipitation by synergistically integrating advances in computing and cyber-infrastructure, environmental sensing and modeling, geoscience, and information science. To improve the analysis and prediction of flooding and water supply for short to long ranges, we utilized very high-resolution (1 min, 500 m) precipitation information from a network of weather radars uniquely available in the Dallas-Fort Worth area, developed the flood reporting app, iSeeFlood, for crowdsourcing of flood observations, and deployed innovative wireless sensors for water level and soil moisture to help close the observation gaps in the urban water cycle. We then used high-performance and cloud computing for street-resolving high-resolution modeling, data assimilation for optimal fusion of model predictions and observations, ensemble prediction for quantifying uncertainty in the predictions, and data-driven discovery for improving prediction of surprise events. To engage the stakeholders and users of the research outcomes, we organized workshops and developed demonstration projects. To develop professional workforce for the new methods, products and services, we held workshops and developed training resources. To nurture future sustainability scientists and engineers, we developed internships and educational materials. The research outcomes advance general understanding of urban sustainability and associated challenges, and allow risk-based decision making related to hazards and stresses associated with urban water to a wide spectrum of users and stakeholders. We are hopeful that they will help the communities plan for and manage water-related hazards, water resources and water infrastructure better for more sustainable cities.
Last Modified: 12/29/2019
Modified by: Dong-Jun Seo
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