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Representativeness Of Eddy-Covariance Flux Footprints For Areas Surrounding Ameriflux Sites
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., Zona, D.
Journal: Agricultural And Forest Meteorology, Volume 301-302: 108350 (2021), ISBN . DOI: 10.1016/j.agrformet.2021.108350 Sites: CA-ARB, CA-ARF, CA-Ca1, CA-Ca2, CA-Ca3, CA-Cbo, CA-DBB, CA-ER1, CA-Gro, CA-Let, CA-Man, CA-MR3, CA-MR5, CA-Na1, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-NS6, CA-NS7, CA-Oas, CA-Obs, CA-Ojp, CA-Qc2, CA-Qcu, CA-Qfo, CA-SCC, CA-SF1, CA-SF2, CA-SF3, CA-SJ2, CA-SJ3, CA-TP1, CA-TP3, CA-TP4, CA-TPD, CA-WP1, US-A03, US-A10, US-A32, US-A74, US-ADR, US-AR1, US-AR2, US-ARb, US-ARc, US-ARM, US-Aud, US-Bar, US-Bi1, US-Bi2, US-Bkg, US-Blk, US-Blo, US-Bn1, US-Bn2, US-Bn3, US-Bo1, US-Bo2, US-Br3, US-CaV, US-Ced, US-CF1, US-CF2, US-CF3, US-CF4, US-ChR, US-Cop, US-CPk, US-CRT, US-Ctn, US-Dia, US-Dix, US-Dk1, US-Dk2, US-Dk3, US-EDN, US-Elm, US-EML, US-Fmf, US-FPe, US-FR2, US-FR3, US-Fuf, US-Fwf, US-GLE, US-GMF, US-Goo, US-Ha1, US-Ha2, US-Hn2, US-Hn3, US-Ho1, US-Ho2, US-Ho3, US-IB1, US-IB2, US-Ivo, US-KFS, US-KLS, US-Kon, US-KS1, US-KS2, US-KUT, US-Lin, US-Los, US-LPH, US-LWW, US-Me1, US-Me2, US-Me3, US-Me4, US-Me5, US-Me6, US-MMS, US-MOz, US-Mpj, US-MRf, US-MtB, US-Myb, US-NC1, US-NC2, US-NC3, US-NC4, US-Ne1, US-Ne2, US-Ne3, US-NGB, US-NR1, US-Oho, US-ORv, US-PHM, US-Pon, US-Prr, US-RC1, US-RC2, US-RC3, US-RC4, US-RC5, US-Rls, US-Rms, US-Ro1, US-Ro2, US-Ro5, US-Ro6, US-Rpf, US-Rws, US-SdH, US-Seg, US-Ses, US-SFP, US-Shd, US-Skr, US-Slt, US-Snd, US-Sne, US-Snf, US-SO2, US-SO3, US-SO4, US-SP1, US-SP2, US-SP3, US-SRC, US-SRG, US-SRM, US-Srr, US-Sta, US-StJ, US-Syv, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Tw4, US-Tw5, US-Twt, US-Uaf, US-UMB, US-UMd, US-Var, US-Vcm, US-Vcp, US-Vcs, US-WBW, US-WCr, US-Wdn, US-Wgr, US-Whs, US-Wi0, US-Wi1, US-Wi3, US-Wi4, US-Wi5, US-Wi6, US-Wi7, US-Wi8, US-Wi9, US-Wjs, US-Wkg, US-Wlr, US-Wpp, US-WPT, US-Wrc, US-xBR, US-xCP, US-xDL, US-xHA, US-xKA, US-xKZ, US-xRM, US-xSR, US-xWD
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., Zona, D.
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 …
Journal: Agricultural And Forest Meteorology, Volume 301-302: 108350 (2021), ISBN . DOI: 10.1016/j.agrformet.2021.108350 Sites: CA-ARB, CA-ARF, CA-Ca1, CA-Ca2, CA-Ca3, CA-Cbo, CA-DBB, CA-ER1, CA-Gro, CA-Let, CA-Man, CA-MR3, CA-MR5, CA-Na1, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-NS6, CA-NS7, CA-Oas, CA-Obs, CA-Ojp, CA-Qc2, CA-Qcu, CA-Qfo, CA-SCC, CA-SF1, CA-SF2, CA-SF3, CA-SJ2, CA-SJ3, CA-TP1, CA-TP3, CA-TP4, CA-TPD, CA-WP1, US-A03, US-A10, US-A32, US-A74, US-ADR, US-AR1, US-AR2, US-ARb, US-ARc, US-ARM, US-Aud, US-Bar, US-Bi1, US-Bi2, US-Bkg, US-Blk, US-Blo, US-Bn1, US-Bn2, US-Bn3, US-Bo1, US-Bo2, US-Br3, US-CaV, US-Ced, US-CF1, US-CF2, US-CF3, US-CF4, US-ChR, US-Cop, US-CPk, US-CRT, US-Ctn, US-Dia, US-Dix, US-Dk1, US-Dk2, US-Dk3, US-EDN, US-Elm, US-EML, US-Fmf, US-FPe, US-FR2, US-FR3, US-Fuf, US-Fwf, US-GLE, US-GMF, US-Goo, US-Ha1, US-Ha2, US-Hn2, US-Hn3, US-Ho1, US-Ho2, US-Ho3, US-IB1, US-IB2, US-Ivo, US-KFS, US-KLS, US-Kon, US-KS1, US-KS2, US-KUT, US-Lin, US-Los, US-LPH, US-LWW, US-Me1, US-Me2, US-Me3, US-Me4, US-Me5, US-Me6, US-MMS, US-MOz, US-Mpj, US-MRf, US-MtB, US-Myb, US-NC1, US-NC2, US-NC3, US-NC4, US-Ne1, US-Ne2, US-Ne3, US-NGB, US-NR1, US-Oho, US-ORv, US-PHM, US-Pon, US-Prr, US-RC1, US-RC2, US-RC3, US-RC4, US-RC5, US-Rls, US-Rms, US-Ro1, US-Ro2, US-Ro5, US-Ro6, US-Rpf, US-Rws, US-SdH, US-Seg, US-Ses, US-SFP, US-Shd, US-Skr, US-Slt, US-Snd, US-Sne, US-Snf, US-SO2, US-SO3, US-SO4, US-SP1, US-SP2, US-SP3, US-SRC, US-SRG, US-SRM, US-Srr, US-Sta, US-StJ, US-Syv, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Tw4, US-Tw5, US-Twt, US-Uaf, US-UMB, US-UMd, US-Var, US-Vcm, US-Vcp, US-Vcs, US-WBW, US-WCr, US-Wdn, US-Wgr, US-Whs, US-Wi0, US-Wi1, US-Wi3, US-Wi4, US-Wi5, US-Wi6, US-Wi7, US-Wi8, US-Wi9, US-Wjs, US-Wkg, US-Wlr, US-Wpp, US-WPT, US-Wrc, US-xBR, US-xCP, US-xDL, US-xHA, US-xKA, US-xKZ, US-xRM, US-xSR, US-xWD
Tidal Wetland Gross Primary Production Across The Continental United States, 2000–2019
Feagin, R. A., Forbrich, I., Huff, T. P., Barr, J. G., Ruiz‐Plancarte, J., Fuentes, J. D., Najjar, R. G., Vargas, R., Vázquez‐Lule, A., Windham‐Myers, L., Kroeger, K. D., Ward, E. J., Moore, G. W., Leclerc, M., Krauss, K. W., Stagg, C. L., Alber, M., Knox, S. H., Schäfer, K. V., Bianchi, T. S., Hutchings, J. A., Nahrawi, H., Noormets, A., Mitra, B., Jaimes, A., Hinson, A. L., Bergamaschi, B., King, J. S., Miao, G.
Journal: Global Biogeochemical Cycles, Volume 34 (2): (2020), ISBN . DOI: 10.1029/2019GB006349 Sites: US-GCE, US-HPY, US-LA1, US-NC4, US-PHM, US-PLM, US-Skr, US-Srr, US-StJ, US-VFP
Feagin, R. A., Forbrich, I., Huff, T. P., Barr, J. G., Ruiz‐Plancarte, J., Fuentes, J. D., Najjar, R. G., Vargas, R., Vázquez‐Lule, A., Windham‐Myers, L., Kroeger, K. D., Ward, E. J., Moore, G. W., Leclerc, M., Krauss, K. W., Stagg, C. L., Alber, M., Knox, S. H., Schäfer, K. V., Bianchi, T. S., Hutchings, J. A., Nahrawi, H., Noormets, A., Mitra, B., Jaimes, A., Hinson, A. L., Bergamaschi, B., King, J. S., Miao, G.
We mapped tidal wetland gross primary production (GPP) with unprecedented detail for multiple wetland types across the continental United States (CONUS) at 16‐day intervals for the years 2000–2019. To accomplish this task, we developed the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field‐based …
Journal: Global Biogeochemical Cycles, Volume 34 (2): (2020), ISBN . DOI: 10.1029/2019GB006349 Sites: US-GCE, US-HPY, US-LA1, US-NC4, US-PHM, US-PLM, US-Skr, US-Srr, US-StJ, US-VFP
Constraining marsh carbon budgets using long‐term C burial and contemporary atmospheric CO2 fluxes
I. Forbrich, A.E. Giblin, C.S. Hopkinson
Journal: JGR-Biogeosciences, Volume : (2018), ISBN . DOI: https://doi.org/10.1002/2017JG004336 Sites: US-PHM
I. Forbrich, A.E. Giblin, C.S. Hopkinson
Journal: JGR-Biogeosciences, Volume : (2018), ISBN . DOI: https://doi.org/10.1002/2017JG004336 Sites: US-PHM
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