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AmeriFlux Management Project (funded by US DOE) collaborates with AmeriFlux scientists to ensure the quality and availability of the continuous, long-term ecosystem measurements necessary to understand these ecosystems and to build effective models and multisite syntheses. To achieve these goals, AMP has established technical, data, and outreach services, held annual meetings and workshops, and provided operational support to 13–14 flux site clusters (Core sites) to ensure public access to high-quality and long-term flux-met datasets.
AmeriFlux Management Project (funded by US DOE) collaborates with AmeriFlux scientists to ensure the quality and availability of the continuous, long-term ecosystem measurements necessary to understand these ecosystems and to build effective models and multisite syntheses. To achieve these goals, AMP has established technical, data, and outreach services, held annual meetings and workshops, and provided operational support to 13–14 flux site clusters (Core sites) to ensure public access to high-quality and long-term flux-met datasets.
Working on the AmeriFlux Management Project, I collaborated with interdisciplinary and multi-institutional teams to build a reproducible and scalable data pipeline to quality-control, process, and synthesize the extensive and extended data products of ecosystem-level carbon, water, and energy fluxes across hundreds of eddy-covariance sites (Chu et al., 2023). As a data curator of AmeriFlux BASE datasets, I quality-checked and analyzed flux and meteorological data submitted from 300+ sites across the Americas. I also devoted myself to the following research areas.
Working on the AmeriFlux Management Project, I collaborated with interdisciplinary and multi-institutional teams to build a reproducible and scalable data pipeline to quality-control, process, and synthesize the extensive and extended data products of ecosystem-level carbon, water, and energy fluxes across hundreds of eddy-covariance sites (Chu et al., 2023). As a data curator of AmeriFlux BASE datasets, I quality-checked and analyzed flux and meteorological data submitted from 300+ sites across the Americas. I also devoted myself to the following research areas.
Value-added data products based on large-scale flux datasets. I developed a flux-derived canopy aerodynamic data product to track the seasonal and interannual dynamics of vegetation canopies, including growth, harvest, land-use change, and disturbance, providing a new benchmark for Earth system models and remotely sensed canopy structures (Chu et al., 2018).
Flux data’s footprint variability and representativeness. I initiated and led a synthesis evaluating flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within a fixed extent around flux towers) that are often used in large-scale flux-data synthesis and modeling studies (Chu at al., 2021).
Flux data time-series clustering: We used data from more than 300 research sites to evaluate the harmonic similarity among sites based on site data characteristics. By quantifying unique or under-sampled areas within the network, our analysis helps inform which regions within the networks require increased attention to support research syntheses and computer model simulations better to project the effects of climate change on ecosystems (Reed, Chu, et al., in prep).
Relevant Publication
Relevant Publication
Khand, K., G.B. Senay, M. Friedrichs, K. Yi, J.B. Fisher, L. Wang, K. Suvočarev, A. Ahmadi, H. Chu, S. Good, K. Mallick, J. Missik, J.A. Nelson, D.E. Reed, T. Wang, and X. Xiao. (2025) A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints. Journal of Hydrology, 662A, 133824, https://doi.org/10.1016/j.jhydrol.2025.133824
Reed, E.D., H. Chu, B.G. Peter, J. Chen, M. Abraha, B. Amiro, R.G. Anderson, M.A. Arain, P. Arruda, G.A. Barron-Gafford, C. Bernacchi, D.P. Beverly, S.C. Biraud, T.A. Black, P.D. Blanken, G. Bohrer, R. Bowler, D.R. Bowling, M.S. Bret-Harte, M. Bretfeld, N.A. Brunsell, S.H. Bullock, G. Celis, X. Chen, A.T. Classen, D.R. Cook, A. Cueva, H.J. Dalmagro, K. Davis, A. Desai, A.J. Duff, A.L. Dunn, D. Durden, C.W. Edgar, E. Euskirchen, R. Bracho, B. Ewers, L.B. Flanagan, C. Florian, V. Foord, I. Forbrich, B.R. Forsythe, J. Frank, J. Garatuza-Payan, S. Goslee, C. Gough, M. Green, T. Griffis, M. Helbig, A.C. Hill, R. Hinkle, J. Horne, E. Humphreys, H. Ikawa, G. Iwahana, R. Jassal, B. Johnson, M. Johnson, S.A. Kannenberg, E. Kelsey, J. King, J.F. Knowles, S. Knox, H. Kobayashi, T. Kolb, R. Kolka, K.W. Krauss, L. Kutzbach, B. Lamb, B. Law, S.-C. Lee, X. Lee1, H. Liu, H.W. Loescher, S.L. Malone, R. Matamala, M. Mauritz, S. Metzger, G. Meyer, B. Mitra, J.W. Munger, Z. Nesic, A. Noormets, T.L. O'Halloran, P.T. O'Keeffe, S.F. Oberbauer, W. Oechel, P. Oikawa, P.C. Olivas, A. Ouimette, G. Pastorello, J.F. Perez-Quezada, C. Phillips, G. Posse, B. Qu, W.L. Quinton, M.L. Reba, A.D. Richardson, V. Picasso, A.V. Rocha, J.C. Rodriguez, R. Ruzol, S. Saleska, R.L. Scott, A.P. Schreiner-McGraw, E.A.G. Schuur, M. Silveira, O. Sonnentag, D.L. Spittlehouse, R. Staebler, G. Starr, C. Staudhammer, C. Still, C. Sturtevant, R.C. Sullivan, A. Suyker, D. Trejo, M. Ueyama, R. Vargas, B. Viner, E.R. Vivoni, D. Wang, E.J. Ward, S. Wiesner, L. Windham-Myers, D. Yannick, E.A. Yepez, T. Zenone, J. Zhao, and D. Zona. (2025) Network of networks: Time-series clustering of AmeriFlux sites. Agricultural and Forest Meteorology, 372 (15), 110686. https://doi.org/10.1016/j.agrformet.2025.110686
Luo, X., R. Zhao, H. Chu, A. Collalti, S. Fatichi, T.F. Keenan, X. Lu, N. Nguyen, I.C. Prentice, W. Sun, K. Yu, and L. Yu. (2025) Global variation in vegetation carbon use efficiency inferred from eddy covariance observations. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-025-02753-0.
Yi, K., M. L. Barnes, H. Chu, K. B. Delwiche, J. B. Fisher, T. F. Keenan, X. Luo, K. Mallick, J. E. Missik, G. W. Moore, J. A. Nelson, K. A. Novick, G. B. Senay, K. Suvočarev, L. Wang, M. Litvak, C. Oishi, D. Reed, G. Bohrer, M. S. Torn, D. Baldocchi. (2024) Challenges and future directions in quantifying terrestrial evapotranspiration. Water Resources Research, 60 (10), e2024WR037622. https://doi.org/10.1029/2024WR037622
Chen, J., C. Lei, H. Chu, X. Li, M. Torn, Y. Wang, and P. Sciusco, and G.P. Robertson. (2024) Overlooked cooling effects of albedo in terrestrial ecosystems. Environmental Research Letters, 19, 093001. https://doi.org/10.1088/1748-9326/ad661d
Chu, H., D. Christianson, Y.-W. Cheah, G. Pastorello, F. O’Brien, J. Geden, S.-T. Ngo, R. Hollowgrass, K. Leibowitz, N.F. Beekwilder, M. Sandesh, S. Dengel, S.W. Chan, A. Santos, K. Delwiche, K. Yi, C. Buechner, D. Baldocchi, D. Papale, T.F. Keenan, S.C. Biraud, D.A. Agarwal, and M.S. Torn. (2023) AmeriFlux BASE data pipeline to support network growth and data sharing. Scientific Data, 10: 614, https://doi.org/10.1038/s41597-023-02531-2.
Young, A.M., M.A. Friedl, B. Seyednasrollah, E. Beamesderfer, C.M. Carrillo, X. Li; M. Moon, M.A. Arain, D.D. Baldocchi, P.D. Blanken, G. Bohrer, S.P. Burns, H. Chu, A.R. Desai, T.J. Griffis, D.Y. Hollinger, M.E. Litvak, K. Novick, R.L. Scott, A.E. Suyker, J. Verfaillie, J.D. Wood, A.D. Richardson (2021) Seasonality in aerodynamic resistance across a range of North American ecosystems. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2021.108613
Chu, H., X. Luo, Z. Ouyang, S. Chan, S. Dengel, S. C. Biraud, M. Torn, S. Metzger, J. Kumar, M. A. Arain, T. J. Arkebauer, D. Baldocchi, C. Bernacchi, D. Billesbach, T. A. Black, P. Blanken, G. Bohrer, R. Bracho, S. Brown, N. Brunsell, J. Chen, X. Chen, K. Clark, A Desai, T. Duman, D. Durden, E. Humphreys, S. Fares, I. Forbrich, J. Gamon, C. Gough, T. Griffis, D. Hollinger, M. Helbig, H. Ikawa, H. Iwata, Y. Ju, J. F. Knowles, S. Knox, H. Kobayashi, T. Kolb, B Law, X. Lee, M. Litvak, H. Liu, J. W. Munger, A Noormets, K. Novick, S. Oberbauer, W. Oechel, P. Oikawa, S. Papuga, E. Pendall, P. Prajapati, J. Prueger, W. L Quinton, A. D. Richardson, E. Russell, R. L. Scott, G. Starr, R. Staebler, P. Stoy, E. Stuart-Haëntjens, O. Sonnentag, R. Sullivan, A. Suyker, M. Ueyama, R. Vargas, J. D. Wood, and D. Zona. (2021) Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology. 301-302, 108350, https://doi.org/10.1016/j.agrformet.2021.108350.
Durden, D.J., S. Metzger, H. Chu, N. Collier, K.J. Davis, A.R. Desai, J. Kumar, W.R. Wieder, M. Xu, and F.M. Hoffman. (2020). Automated integration of continental-scale observations in near-real time for simulation and analysis of biosphere–atmosphere interactions. In Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data, and AI. Edited by J. Nichols, B. Verastegui, A.B. Maccabe, O. Hernandez, S. Parete-Koon, and T. Ahearn. pp. 204–225. 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC2020 (August 26–28, 2020), Springer. https://doi.org/10.1007/978-3-030-63393-6_14
Pastorello, G., C. Trotta, E. Canfora, H. Chu, D. Christianson, Y.-W. Cheah, C. Poindexter, J. Chen, A. Elbashandy, M. Humphrey, P. Isaac, D. Polidori, A. Ribeca, C. van Ingen, L. Zhang, B. Amiro, C. Ammann, A. Arain, J. Ardo, T. Arkebauer, S. Arndt, N. Arriga, M. Aubinet, M. Aurela, D. Baldocchi, A. Barr, E. Beamesderfer, L. B. Marchesini, O. Bergeron, J. Beringer, C. Bernhofer, D. Berveiller, D. Billesbach, T. A. Black, P. Blanken, G. Bohrer, J. Boike, P. V. Bolstad, D. Bonal, J.-M. Bonnefond, D. Bowling, R. Bracho, J. Brodeur, C. Brümmer, N. Buchmann, B. Burban, S. Burns, P. Buysse, P. Cale, M. Cavagna, P. Cellier, S. Chen, I. Chini, T. Christensen, J. Cleverly, A. Collalti, C. Consalvo, B. Cook, D. Cook, C. Coursolle, E. Cremonese, P. Curtis, E. D'andrea, H. Rocha, X. Dai, K. Davis, B. de Cinti, A. de Grandcourt, A. de Ligne, R. Oliveira Junior, N. Delpierre, A. Desai, C. Di Bella, P. Di Tommasi, A. J. Dolman, F. Domingo, G. Dong, S. Dore, P. Duce, E. Dufrêne, A. Dunn, J. Dušek, D. Eamus, U. Eichelmann, H. ElKhidir, W. Eugster, C. Ewenz, B. E. Ewers, D. Famulari, S. Fares, I. Feigenwinter, A. Feitz, R. Fensholt, G. Filippa, M. Fischer, J. Frank, M. Galvagno, M. Gharun, D. Gianelle, B. Gielen, B. Gioli, A. Gitelson, I. Goded, M. Gockede, A. Goldstein, C. Gough, M. Goulden, A. Graf, A. Griebel, C. Gruening, T. Grünwald, A. Hammerle, S. Han, X. Han, B. Hansen, C. Hanson, J. Hatakka, Y. He, M. Hehn, B. Heinesch, N. Hinko-Najera, L. Hörtnagl, L. Hutley, A. Ibrom, H. Ikawa, M. Jackowicz-Korczynski, D. Janouš, W. Jans, R. Jassal, S. Jiang, and T. Kato. (2020) The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data. 7(1), 225, https://doi.org/10.1038/s41597-020-0534-3
Xu, B, M. A. Arain, T. A. Black, B. E. Law, G. Z. Pastorello, and H. Chu. (2019) Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems. Global Change Biology. 2019; 00:1-18. https://doi.org/10.1029/2019JD032255
Chu, H., D. D. Baldocchi, C. Poindexter, M. Abraha, A. R. Desai, G. Bohrer, M. A. Arain, T. Griffis, P. D. Blanken, T. L. O'Halloran, R. Q. Thomas, Q. Zhang, S. Burns, J. M. Frank, D. Christian, S. Brown, T. A. Black, C. M. Gough, B. E. Law, X. Lee, J. Chen, D. E. Reed, W. J. Massman, K. Clark, J. Hatfield, J. Prueger, R. Bracho, T. A. Martin, and J. M. Baker. (2018) Temporal dynamics of aerodynamic canopy height derived from eddy covariance momentum flux data across North American Flux Networks. Geophysical Research Letters. 45, 9275–9287. https://doi.org/10.1029/2018GL079306
Pastorello, G.Z., D.K. Gunter, H. Chu, D.S. Christianson, B. Faybishenko, Y. Cheah, S.W. Chan, S. Dengel, T.F. Keenan, F.L. O'Brien, A. Elbashandy, D.A. Agarwal, C. Trotta, D. Papale, N.F. Beekwilder, M. Humphrey, E. Canfora, and C.M. Poindexter (2017) Hunting data rogues at scale: data quality control for observational data in research infrastructures. Proceedings of the IEEE 12th International conference on E-Science, October 24, 2017. https://doi.org/10.1109/ESCIENCE.2017.64
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