Award Abstract # 1532013
MRI Track 1: Acquisition of High Performance Computing to Model Coastal Responses to a Changing Environment

NSF Org: OCE
Division Of Ocean Sciences
Recipient: UNIVERSITY OF MAINE SYSTEM
Initial Amendment Date: August 14, 2015
Latest Amendment Date: August 14, 2015
Award Number: 1532013
Award Instrument: Standard Grant
Program Manager: Kandace Binkley
kbinkley@nsf.gov
 (703)292-7577
OCE
 Division Of Ocean Sciences
GEO
 Directorate for Geosciences
Start Date: August 15, 2015
End Date: July 31, 2018 (Estimated)
Total Intended Award Amount: $266,309.00
Total Awarded Amount to Date: $266,309.00
Funds Obligated to Date: FY 2015 = $266,309.00
History of Investigator:
  • Damian Brady (Principal Investigator)
    damian.brady@maine.edu
  • Bruce Segee (Co-Principal Investigator)
  • Huijie Xue (Co-Principal Investigator)
  • Fei Chai (Co-Principal Investigator)
  • Qingping Zou (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Maine
5717 CORBETT HALL
ORONO
ME  US  04469-5717
(207)581-1484
Sponsor Congressional District: 02
Primary Place of Performance: University of Maine
5717 Corbett Hall
Orono
ME  US  04469-5717
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): PB3AJE5ZEJ59
Parent UEI:
NSF Program(s): Major Research Instrumentation
Primary Program Source: 01001516DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9150
Program Element Code(s): 118900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.050

ABSTRACT

The Gulf of Maine is undergoing unprecedented change due to a combination of influences from climatic and anthropogenic sources. By some measures, the Gulf of Maine is among the fastest warming regions of the world's oceans. This warming trend has been concurrent with a 73% increase in extreme events, defined as the top 1% of storms, over the past century. The potential impact of these changes are significant for an economy tightly linked to marine resources and infrastructure. Maine's aquaculture industry (comprising mostly salmon and shellfish) has doubled in value from 2005 to 2013 and is now second only to the lobster industry in the state. Maine's commercial fisheries were valued at a record $585 million dollars in 2014. The effective management of natural resources and infrastructure in the coastal zone requires modeling tools that not only help us understand the ecological and physical consequences of natural and human-induced changes in environmental drivers, but also help us predict those changes and weigh the costs and benefits of adaptation or mitigation. The accurate and timely forecasts of severe storms and coastal inundation are critical to ensure safe maritime activities, protect life and property along the coast. To further the development of these modeling tools for the coastal economy of Maine, the University of Maine will significantly increase its high performance computing to meet the rigors of problems faced by the coastal zone. Specifically, increasing the computing capacity at the University of Maine will allow coastal modelers to increase the spatial resolution of models to inform decisions made on local scales (e.g., aquaculture leases) and significantly increase access to high performance computing for undergraduate and graduate students. Consequently, a new generation of scientists familiar with advanced computing can be trained to develop the environmental decision support infrastructure needed in changing coastal ecosystems.

The University of Maine is an established international and national leader in marine science research and education and as such, the community that relies on coastal resources and infrastructure looks to the University to inform them of impending changes. Maine is uniquely positioned physically and economically to be affected by climate change. The state sits on one of the sharpest latitudinal gradients in temperature in the world and has one of the longest coastlines in the country. What the future holds for fisheries and aquaculture systems along the coast is difficult to predict. However, the capacity to forecast multiple scenarios of potential future change will allow managers and decision makers the ability to adapt. There are four reasons for an investment in increased high performance computing: (1) an increase in spatial resolution will allow models currently under development to resolve finer scale features and inform many decisions made on the scale of less than 100 m, (2) the current high performance computing cluster runs at near full capacity, reducing educational opportunities for graduate and undergraduate students, (3) linking climate change projections to the regional scale using dynamic downscaling as opposed to simpler methods requires more processing capacity, and (4) the ability to run multiple scenarios of future change will more adequately characterize uncertainty. The cluster system will have 28 nodes, each with two 12 core Intel Xeon 2.6 GHz CPU's, the latest Haswell v3 chips. This will comprise 672 cores and will yield about 28 peak TFlops. This nearly triples the high performance computing power at the University of Maine.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 17)
Bayer, S.R., Wahle, R.A., Brady, D.C., Jumars, P.A., Stokesbury, K.D.E., & Carey, J.D. "Fertilization dynamics in scallop aggregations: reconciling model predictions with field measurements" Ecosphere , v.9 , 2018
Du Clos, K.T., Jones, I.T., Carrier, T.J., Brady, D.C., and Jumars, P.A. "Model-assisted measurements of suspension-feeding flow velocities" Journal of Experimental Biology , v.220 , 2017 , p.2096
Du Clos, K.T., Jones, I.T., Carrier, T.J., Brady, D.C., and Jumars, P.A. "Model-assisted measurements of suspension-feeding flow velocities" Journal of Experimental Biology , v.220 , 2017
Frederick, C., Brady, D.C., & Bricknell, I. "Landing strips: Model development for estimating body surface area of farmed Atlantic salmon (Salmo salar)" Aquaculture , v.473 , 2017 , p.299
Frederick, C., Brady, D.C., & Bricknell, I. "Landing strips: Model development for estimating body surface area of farmed Atlantic salmon (Salmo salar)" Aquaculture , v.473 , 2017
Friedland, K.D., Mouw, C.B., Asch, R.G., Ferreira, A.S.A., Henson, S., Hyde, K.J., Morse, R.E., Thomas, A.C., & Brady, D.C. "Phenology and time series trends of the dominant seasonal phytoplankton bloom across global scales" Global Ecology and Biogeography , v.27 , 2018
Gray, M.W., Chapparo, O., O?Neill, S.P., Couture, T. , Moreira, A., & Brady, D.C. "Does brooding prepare young for tomorrow?s acidic oceans and estuaries?" Special Issue of Journal of Shellfish Research , 2018
Li, B., Tanaka, K..R., Chen, Y., Brady, D.C., Thomas, A.C. "Assessing the quality of modeled bottom water temperatures from the Finite-Volume Community Ocean Model (FVCOM) in the Northwest Atlantic Region" Journal of Marine Systems , v.173 , 2017 , p.21
Li, B., Tanaka, K.R., Chen, Y., Brady, D.C., & Thomas, A.C. "Assessing the quality of modeled bottom water temperatures from the Finite-Volume Community Ocean Model (FVCOM) in the Northwest Atlantic Region" Journal of Marine Systems , v.173 , 2017
McHenry, J., Steneck, R., & Brady, D.C. "Abiotic proxies for predictive mapping of near-shore benthic assemblages: Implications for marine spatial planning" Ecological Applications , 2016
Snyder, J. ?, Boss, E., Weatherbee, R., Thomas, A., Brady, D.C., and Newell, C. "Oyster aquaculture site selection using Landsat 8-derived sea surface temperature, turbidity, and chlorophyll a" Frontiers in Marine Science , v.4 , 2018
(Showing: 1 - 10 of 17)

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.

The major goal of the project was to expand and enhance University of Maine researcher efforts to understand climate impacts on coastal ecosystems. During the course of this project, coastal modeling activities consumed over 17 million core hours, a significant expansion of computational capacity to answer climate related changes along the coast. Since that time, this MRI proposal has more than tripled the high performance computing power at the University of Maine. There is considerable diversity in the questions these models have been used to answer. For example, three high resolution near shore ocean circulation models have been developed that simulate current speed, temperature, and salinity. The output from these models has been used to characterize American lobster thermal habitat expansion. We have linked these findings to surveys of juvenile American lobster to demonstrate that even as the density of juvenile lobsters has decreased, the overall abundance may have been stabilized by the warming Gulf of Maine expanding their available habitat. There have also been significant gains in the ability of the University of Maine to utilize remote sensing products to monitor the coast. We now serve images of the coast from LandSat and Sentinel 2. These high resolution satellite products can for the first time in human history resolve temperature, chlorophyll, and turbidity at spatial scales that aquaculture growers can use to make better decisions regarding farm siting. In conclusion, this NSF Major Research Instrumentation investment has unlocked the ability to forecast and monitor the very jagged coast of Maine on spatial scales that address local problems, processes, and industries.


Last Modified: 12/07/2018
Modified by: Damian C Brady

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