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Title: ESIF 2017 Annual Report


This annual report highlights research done at the Energy Systems Integration Facility (ESIF) in Fiscal Year 2017.

Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Country of Publication:
United States
24 POWER TRANSMISSION AND DISTRIBUTION; 29 ENERGY PLANNING, POLICY, AND ECONOMY; ESIF; Energy Systems Integration Facility; 2017; FY17; Fiscal Year 2017; annual report; ESI; Energy Systems Integration

Citation Formats

. ESIF 2017 Annual Report. United States: N. p., 2018. Web.
. ESIF 2017 Annual Report. United States.
. 2018. "ESIF 2017 Annual Report". United States. doi:.
title = {ESIF 2017 Annual Report},
author = {},
abstractNote = {This annual report highlights research done at the Energy Systems Integration Facility (ESIF) in Fiscal Year 2017.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2018,
month = 2
  • The 2017 Annual Terrestrial Sampling Plan for Sandia National Laboratories/New Mexico on Kirtland Air Force Base has been prepared in accordance with the “Letter of Agreement Between Department of Energy, National Nuclear Security Administration, Sandia Field Office (DOE/NNSA/SFO) and 377th Air Base Wing (ABW), Kirtland Air Force Base (KAFB) for Terrestrial Sampling” (signed January 2017), Sandia National Laboratories, New Mexico (SNL/NM). The Letter of Agreement requires submittal of an annual terrestrial sampling plan.
  • The overall goal of my Early Career research is to constrain belowground carbon turnover times for tropical forests across a broad range in moisture regimes. My group is using 14C analysis and modeling to address two major objectives: quantify age and belowground carbon turnover times across tropical forests spanning a moisture gradient from wetlands to dry forest; and identify specific areas for focused model improvement and data needs through site-specific model-data comparison and belowground carbon modeling for tropic forests.
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