NSF Org:	RISE Div of Res, Innovation, Synergies, & Edu
Recipient:	UNIVERSITY OF ALASKA FAIRBANKS
Initial Amendment Date:	August 26, 2020
Latest Amendment Date:	October 13, 2020
Award Number:	2022438
Award Instrument:	Standard Grant
Program Manager:	Jonathan G Wynn jwynn@nsf.gov  (703)292-4725 RISE  Div of Res, Innovation, Synergies, & Edu GEO  Directorate For Geosciences
Start Date:	January 1, 2021
End Date:	December 31, 2024 (Estimated)
Total Intended Award Amount:	$125,000.00
Total Awarded Amount to Date:	$125,000.00
Funds Obligated to Date:	FY 2020 = $125,000.00
History of Investigator:	Margaret Darrow (Principal Investigator) mmdarrow@alaska.edu Louise Farquharson (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Alaska Fairbanks Campus 2145 N TANANA LOOP FAIRBANKS AK  US  99775-0001 (907)474-7301
Sponsor Congressional District:	00
Primary Place of Performance:	University of Alaska Fairbanks 1764 Tanana Loop Fairbanks AK  US  99775-5910
Primary Place of Performance Congressional District:	00
Unique Entity Identifier (UEI):	FDLEQSJ8FF63
Parent UEI:
NSF Program(s):	NNA-Navigating the New Arctic
Primary Program Source:	01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	9150
Program Element Code(s):	104Y00
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.050

ABSTRACT

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by supporting planning activities with clear potential to develop novel, leading edge research ideas and approaches to address NNA goals. It integrates aspects of the natural and built environments to address important societal challenges at this intersection, and engages internationally and with local communities.

Changing climate conditions have increased the occurrence of mass-movement hazards in the cryosphere, such as landslides, debris flows, and slope failures resulting from thawing of ice-rich permafrost. Mass-movement hazards across the cryosphere pose a significant risk to people and infrastructure, such as highways and pipelines. This project brings together experts from diverse backgrounds, including engineers, geoscientists, computer scientists, and officials from a variety of academic institutions, public and government agencies, and industry, to discuss key challenges and formulate research priorities in 1) characterizing mass-movement hazards in the cryosphere, 2) mapping such hazards using machine learning, 3) forecasting such hazards using artificial intelligence, and 4) building climate-change-resilient infrastructure through flexible and adaptive approaches to reduce costs. The project outcomes enable planners and policy makers to identify critical infrastructure that is most threatened by mass-movement hazards in the Arctic, sub-Arctic, and mountainous regions, and high- and low-hazard areas when planning future infrastructure near urban and rural sites.

This award funds the development and planning activities of a convergent research team to address infrastructure resilience and adaptation to increasing mass-movement risks across the cryosphere as the climate warms. Planning activities center around two annual workshops that will identify gaps in our current understanding and existing methodologies to develop: 1) more automated hazard mapping tools through remote sensing and machine learning, including well-established tools like convolutional neural networks; 2) compilations of datasets and databases and interactions with potential users; 3) more reliable artificial intelligence models including time series machine learning for hazard forecasting; and 4) more flexible and adaptive approaches to reduce costs for infrastructure resiliency and adaptation.

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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