paper_2021.bib
@techreport{Kumar2021_AI4ESP,
note = {\url{https://doi.org/10.2172/1769772}},
doi = {10.2172/1769772},
url = {https://doi.org/10.2172/1769772},
year = {2021},
month = apr,
institution = {DOE BER AI4ESP 2021},
publisher = {Office of Scientific and Technical Information ({OSTI})},
author = {Jitendra Kumar and Forrest Hoffman and Joel Rowland},
title = {Representing the Unrepresented Impact of River Ice on Hydrology, Biogeochemistry, Vegetation, and Geomorphology: A Hybrid Physics-Machine Learning Approach}
}
@techreport{Hoffman2021_AI4ESP,
doi = {10.2172/1769668},
note = {\url{https://doi.org/10.2172/1769668}},
url = {https://doi.org/10.2172/1769668},
year = {2021},
month = apr,
institution = {DOE BER AI4ESP 2021},
publisher = {Office of Scientific and Technical Information ({OSTI})},
author = {Forrest Hoffman and Jitendra Kumar and Zheng Shi and Anthony Walker and Jiafu Mao and Yaoping Wang and Abigail Swann and James Randerson and Umakant Mishra and Gariel Kooperman and Hchonggang Xu and Charles Koven and David Lawrence and Megan Fowler and Belinda Medlyn and Lianhong Gu and Liz Agee and Jeff Warren and Shawn Serbin and Alistair Rogers and Trevor Keenan and Nate McDowell and Nathan Collier and Sarat Sreepathi and Juan Restrepo and Rick Archibald and Feng Bao and Richard Mills},
title = {{AI}-Constrained Bottom-Up Ecohydrology and Improved Prediction of Seasonal, Interannual, and Decadal Flood and Drought Risks}
}
@techreport{Prakash2021_AI4ESP,
doi = {10.2172/1769667},
note = {\url{https://doi.org/10.2172/1769667}},
url = {https://doi.org/10.2172/1769667},
year = {2021},
month = apr,
institution = {DOE BER AI4ESP 2021},
publisher = {Office of Scientific and Technical Information ({OSTI})},
author = {Giri Prakash and Nicki Hickmon and Adam Theisen and Debjani Singh and Cory Stuart and Ranjeet Devarakonda and Jitendra Kumar},
title = {{AI}-Based Upgrades to Observational Data Centers to Facilitate Data Interoperability}
}
@techreport{Devarakonda2021_AI4ESP,
doi = {10.2172/1769671},
note = {\url{https://doi.org/10.2172/1769671}},
url = {https://doi.org/10.2172/1769671},
year = {2021},
month = apr,
institution = {DOE BER AI4ESP 2021},
publisher = {Office of Scientific and Technical Information ({OSTI})},
author = {Ranjeet Devarakonda and Jitendra Kumar and Dalton Lunga and Jong Choi and Giri Prakash},
title = {{AI}-Driven Data Discovery to Improve Earth System Predictability}
}
@techreport{Mills2021_AI4ESP,
doi = {10.2172/1769690},
note = {\url{https://doi.org/10.2172/1769690}},
url = {https://doi.org/10.2172/1769690},
year = {2021},
month = apr,
institution = {DOE BER AI4ESP 2021},
publisher = {Office of Scientific and Technical Information ({OSTI})},
author = {Richard Mills and Forrest Hoffman and Jitendra Kumar and Robert Jacob and Zachary Langford and Sarat Sreepathi and Nathan Collier},
title = {Computationally Tractable High-Fidelity Representation of Global Hydrology in {ESMs} via Machine Learning Approaches to Scale-Bridging}
}
@techreport{Dafflon2021_AI4ESP,
doi = {10.2172/1769774},
note = {\url{https://doi.org/10.2172/1769774}},
url = {https://doi.org/10.2172/1769774},
year = {2021},
month = apr,
institution = {DOE BER AI4ESP 2021},
publisher = {Office of Scientific and Technical Information ({OSTI})},
author = {Baptiste Dafflon and S. Wielandt and S. Uhlemann and Haruko Wainwright and K. Bennett and Jitendra Kumar and Sebastien Biraud and Susan Hubbard and Stan Wullschleger},
title = {Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded {AI}}
}
@article{CHU2021108350,
title = {Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites},
journal = {Agricultural and Forest Meteorology},
volume = {301-302},
pages = {108350},
year = {2021},
issn = {0168-1923},
note = {\url{https://doi.org/10.1016/j.agrformet.2021.108350}},
doi = {https://doi.org/10.1016/j.agrformet.2021.108350},
url = {https://www.sciencedirect.com/science/article/pii/S0168192321000332},
author = {Housen Chu and Xiangzhong Luo and Zutao Ouyang and W. Stephen Chan and Sigrid Dengel and Sébastien C. Biraud and Margaret S. Torn and Stefan Metzger and Jitendra Kumar and M. Altaf Arain and Tim J. Arkebauer and Dennis Baldocchi and Carl Bernacchi and Dave Billesbach and T. Andrew Black and Peter D. Blanken and Gil Bohrer and Rosvel Bracho and Shannon Brown and Nathaniel A. Brunsell and Jiquan Chen and Xingyuan Chen and Kenneth Clark and Ankur R. Desai and Tomer Duman and David Durden and Silvano Fares and Inke Forbrich and John A. Gamon and Christopher M. Gough and Timothy Griffis and Manuel Helbig and David Hollinger and Elyn Humphreys and Hiroki Ikawa and Hiroki Iwata and Yang Ju and John F. Knowles and Sara H. Knox and Hideki Kobayashi and Thomas Kolb and Beverly Law and Xuhui Lee and Marcy Litvak and Heping Liu and J. William Munger and Asko Noormets and Kim Novick and Steven F. Oberbauer and Walter Oechel and Patty Oikawa and Shirley A. Papuga and Elise Pendall and Prajaya Prajapati and John Prueger and William L Quinton and Andrew D. Richardson and Eric S. Russell and Russell L. Scott and Gregory Starr and Ralf Staebler and Paul C. Stoy and Ellen Stuart-Haëntjens and Oliver Sonnentag and Ryan C. Sullivan and Andy Suyker and Masahito Ueyama and Rodrigo Vargas and Jeffrey D. Wood and Donatella Zona},
keywords = {Flux footprint, Spatial representativeness, Landsat EVI, Land cover, Sensor location bias, Model-data benchmarking},
abstract = {Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.}
}
@article{Bennett_TC_2021,
author = {Bennett, K. E. and Miller, G. and Busey, R. and Chen, M. and Lathrop, E. R. and Dann, J. B. and Nutt, M. and Crumley, R. and Dafflon, B. and Kumar, J. and Bolton, W. R. and Wilson, C. J.},
title = {Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic},
journal = {The Cryosphere Discussions},
volume = {2021},
year = {2021},
pages = {1--44},
url = {https://tc.copernicus.org/preprints/tc-2021-341/},
note = {\url{https://tc.copernicus.org/preprints/tc-2021-341/}},
doi = {10.5194/tc-2021-341}
}