paper_2016.bib

@article{Bond-Lamberty_Ecosphere_20160627,
  title = {Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps},
  author = {Bond-Lamberty, Ben and Epron, Daniel and Harden, Jennifer and Harmon, Mark E. and Hoffman, Forrest and Kumar, Jitendra and David McGuire, Anthony and Vargas, Rodrigo},
  journal = {Ecosphere},
  year = {2016},
  note1 = {e01380},
  number = {6},
  volume = {7},
  doi = {10.1002/ecs2.1380},
  file = {pubs/Bond-Lamberty_Ecosphere_20160627.pdf},
  issn = {2150-8925},
  keywords = {carbon cycle, heterotrophic respiration, modeling},
  owner = {jkumar},
  timestamp = {2016.12.27},
  url = {http://dx.doi.org/10.1002/ecs2.1380},
  note = {\url{http://dx.doi.org/10.1002/ecs2.1380}}
}

@article{Kumar_TC_2016,
  title = {Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape},
  author = {Kumar, J. and Collier, N. and Bisht, G. and Mills, R. T. and Thornton, P. E. and Iversen, C. M. and Romanovsky, V.},
  journal = {The Cryosphere},
  year = {2016},
  number = {5},
  pages = {2241--2274},
  volume = {10},
  doi = {10.5194/tc-10-2241-2016},
  note = {\url{https://doi.org/10.5194/tc-10-2241-2016}},
  file = {pubs/Kumar_TC_2016.pdf},
  owner = {jkumar},
  timestamp = {2016.12.27},
  url = {http://www.the-cryosphere.net/10/2241/2016/}
}

@article{Kumar_ESSDD_2016,
  title = {Understanding the Representativeness of {FLUXNET} for Upscaling Carbon Flux from Eddy Covariance Measurements},
  author = {J. Kumar and F. M. Hoffman and W. W. Hargrove and N. Collier},
  journal = {Earth System Science Data Discussion},
  year = {2016},
  month = aug,
  pages = {1--25},
  volume = {2016},
  day = {23},
  doi = {10.5194/essd-2016-36},
  note = {\url{https://doi.org/10.5194/essd-2016-36}},
  file = {pubs/Kumar_ESSDD_2016.pdf}
}

@article{Langford_RemoteSensing_2016,
  title = {Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets},
  author = {Langford, Zachary and Kumar, Jitendra and Hoffman, Forrest M. and Norby, Richard J. and Wullschleger, Stan D. and Sloan, Victoria L. and Iversen, Colleen M.},
  journal = {Remote Sensing},
  year = {2016},
  number = {9},
  pages = {733},
  volume = {8},
  abstract = {Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June–August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m × 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy ( R 2 = 0.75 , R M S E = 9.94 ) than the single image upscaling ( R 2 = 0.63 , R M S E = 12.05 ) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model ( R 2 = 0.61 , R M S E = 13.78 ) than only using the original 48 plots for the phenology model ( R 2 = 0.23 , R M S E = 17.49 ). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems.},
  doi = {10.3390/rs8090733},
  note = {\url{https://doi.org/10.3390/rs8090733}},
  file = {pubs/Langford_RemoteSensing_2016.pdf},
  issn = {2072-4292},
  owner = {jkumar},
  timestamp = {2016.12.27},
  url = {http://www.mdpi.com/2072-4292/8/9/733}
}

@inproceedings{Prakash_IEEEBigData_2016,
  title = {{HPC infrastructure to support the next-generation ARM facility data operations}},
  author = {G. Prakash and J. Kumar and E. Rush and R. Records and A. Clodfelter and J. Voyles},
  booktitle = {2016 IEEE International Conference on Big Data (Big Data)},
  year = {2016},
  month = {Dec},
  pages = {4026-4028},
  doi = {10.1109/BigData.2016.7841098},
  note = {\url{https://doi.org/10.1109/BigData.2016.7841098}},
  file = {:pubs/Prakash_IEEEBigData_2016.pdf:PDF},
  keywords = {computer centres;geophysics computing;parallel processing;ARM Data Center;Decadal Vision;Department of Energy Atmospheric Radiation Measurement Climate Research Facility;HPC infrastructure;adaptive data operation architecture;adaptive data services;high-performance computing infrastructure;next-generation ARM facility data operations;Atmospheric measurements;Big data;Computational modeling;Computer architecture;Data models;Meteorology;Next generation networking;ARM Data Center;ARM clusters;Big Data Analytics;High-Performance Computing (HPC)}
}

@article{Tang_GMD_2016,
  title = {Addressing numerical challenges in
 introducing a reactive transport code into a land surface model: a
 biogeochemical modeling proof-of-concept with {CLM–PFLOTRAN
 1.0}},
  author = {Tang, G. and Yuan, F. and Bisht, G. and Hammond, G. E. and Lichtner, P. C. and Kumar, J. and Mills, R. T. and Xu, X. and Andre, B. and Hoffman, F. M. and Painter, S. L. and Thornton, P. E.},
  journal = {Geoscientific Model Development},
  year = {2016},
  number = {3},
  pages = {927--946},
  volume = {9},
  doi = {10.5194/gmd-9-927-2016},
  note = {\url{https://doi.org/10.5194/gmd-9-927-2016}},
  file = {:pubs/Tang_GMD_2016.pdf:PDF},
  owner = {jkumar},
  timestamp = {2016.03.04},
  url = {http://www.geosci-model-dev.net/9/927/2016/}
}