| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | September 16, 2010 |
| Latest Amendment Date: | September 16, 2010 |
| Award Number: | 1028163 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Kenneth Whang
kwhang@nsf.gov (703)292-5149 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 15, 2010 |
| End Date: | August 31, 2015 (Estimated) |
| Total Intended Award Amount: | $150,000.00 |
| Total Awarded Amount to Date: | $150,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
77 MASSACHUSETTS AVE CAMBRIDGE MA US 02139-4301 (617)253-1000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
77 MASSACHUSETTS AVE CAMBRIDGE MA US 02139-4301 |
| 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): | CDI TYPE II |
| 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
This project will advance systems modeling approaches by developing a suite of stochastic modeling approaches, coupled with geostatistical and machine learning techniques. The new system modeling approach will utilize both in situ and satellite remotely sensed data to improve system model parameters and model structure. These novel developments, together with observed data, will advance ecosystem and environmental sciences through computational thinking. The proposed approach will be used to develop a cyber-enabled stochastic carbon-weather system to provide more adequate quantification of regional carbon exchanges, which is critical to better understanding carbon-climate-atmosphere feedbacks and facilitating climate-policy making.
The proposed approach will transform the current system modeling approach by (1) developing a stochastic version of the deterministic differential equation models of ecosystems and environmental systems; (2) developing geospatial statistical techniques to fully exploit multifaceted observational data to improve model parameterization; (3) developing advanced statistical and machine learning techniques to further utilize observational data to improve model structure; and (4) applying the improved model to examine the societal and biogeochemical impacts of land use change. Advantages of the proposed cyber-enabled terrestrial ecosystem model will include: (1) Efficiently quantifying regional net carbon exchanges and associated uncertainty and (2) Improving system model parameters and structure using advanced statistical and machine learning techniques and spatiotemporal data acquired over the U.S. Project deliverables include: (1) An innovative, cyber-enabled carbon-weather system that can quantify net carbon exchanges and associated probabilistic information at high spatial and temporal resolution for the continental U.S. and (2) a suite of transformative advanced mathematical, statistical and system modeling techniques that could be applied to other complex modeling fields (e.g., hydrological modeling). This project will significantly advance ecosystem sciences with computational thinking and will provide a unique opportunity to train a new generation of scientists in a highly interdisciplinary research environment.
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.
Land use change and carbon emissions associated with it are an important societal concern. A particular consideration is whether expansion of biomass energy production in combination with increased demand for conventional agriculture and forestry products in a changing environment would lead to excessive CO2 emissions from land use change. To investigate this issue, the MIT Joint Program developed in greater detail a model of human activity and land use change, which can be linked to a terrestrial ecosystem model. This work therefore covers a unique intersection of economics and ecosystem research. To the MIT Economic Projection and Policy Analysis (EPPA) Model we added multiple bioenergy technologies, feedstocks and uses in the economy; developed a more realistic representation of the relationship between food consumption and income; updated our land use model component; and developed new ways to represent complex policies such as biofuel mandates. We then explored the interactions among climate, CO2, concentrations, tropospheric ozone, food prices, land use change, and carbon storage in land with and without bioenergy.
We found that carbon emissions from land use change due to bioenergy expansion are often indirect, occurring because bioenergy expansion is typically on existing crop land, thereby causing crops to shift to natural land, causing “indirect” emissions. Expansion of bioenergy can enhance land carbon sinks when it occurs on grass or pastureland and enhanced management of bioenergy production increases productivity and carbon storage. With carbon pricing of land use change emissions, even with significant bioenergy expansion, land use change can be a significant carbon sink, avoiding as much as 0.5 degrees C of future global warming. The information we provide on land-use CO2 emissions associated with bioenergy production is useful in informing policies that are directed toward assessing direct and indirect emissions associated with bioenergy, which is particularly important in relation to a scaling up of the bioenergy industry.
This research provided training opportunities for students and junior researchers, and the results were widely disseminated through talks, presentations, forums and events. In addition, a book chapter describing this work and its modelling approach will provide guidance to other students and researchers.
Last Modified: 11/30/2015
Modified by: John M Reilly
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