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Baldocchi, D., Ma, S., Verfaillie, J.
Journal: Global Change Biology, Volume 27 (2): 359-375 (2021). DOI: 10.1111/gcb.15414 Sites: US-Ton, US-Var
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). 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
Baldocchi, D. D., Ryu, Y., Dechant, B., Eichelmann, E., Hemes, K., Ma, S., Sanchez, C. R., Shortt, R., Szutu, D., Valach, A., Verfaillie, J., Badgley, G., Zeng, Y., Berry, J. A.
Journal: Journal Of Geophysical Research: Biogeosciences, Volume 125 (7): (2020). DOI: 10.1029/2019jg005534 Sites: US-Bi1, US-Bi2, US-Ton, US-Tw1, US-Var
Ma, S., Eichelmann, E., Wolf, S., Rey-Sanchez, C., Baldocchi, D. D.
Journal: Agricultural And Forest Meteorology, Volume 295: 108204 (2020). DOI: 10.1016/j.agrformet.2020.108204 Sites: US-Ton, US-Var
Novick, K. A., Konings, A. G., Gentine, P.
Journal: Plant, Cell & Environment, Volume 42 (6): 1802-1815 (2019). DOI: 10.1111/pce.13517 Sites: US-ARM, US-Blo, US-GLE, US-KS2, US-MMS, US-Ne3, US-NR1, US-SRG, US-SRM, US-Ton, US-Var, US-WCr, US-Whs, US-Wkg
Zhang, Q., Ficklin, D. L., Manzoni, S., Wang, L., Way, D., Phillips, R. P., Novick, K. A.
Journal: Environmental Research Letters, Volume 14 (7): 074023 (2019). DOI: 10.1088/1748-9326/ab2603 Sites: CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS6, CA-NS7, US-AR1, US-AR2, US-ARM, US-Blo, US-GLE, US-KS2, US-Me2, US-MMS, US-Ne1, US-Ne2, US-Ne3, US-NR1, US-SRG, US-SRM, US-Syv, US-Ton, US-UMB, US-Var, US-WCr, US-Whs
Sullivan, R. C., Kotamarthi, V. R., Feng, Y.
Future projections of evapotranspiration (ET) are of critical importance for agricultural and freshwater management and for predicting land–atmosphere feedbacks on the climate system. However, ET from phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations exhibits substantial biases, bolstering little confidence …
Journal: Journal Of Hydrometeorology, Volume 20 (8): 1619-1633 (2019). DOI: 10.1175/JHM-D-18-0259.1 Sites: US-AR1, US-AR2, US-ARM, US-Blo, US-Cop, US-GLE, US-Ha1, US-Los, US-Me2, US-Me6, US-MMS, US-Myb, US-Ne1, US-Ne2, US-Ne3, US-NR1, US-ORv, US-PFa, US-SRG, US-SRM, US-Syv, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Tw4, US-Twt, US-UMB, US-UMd, US-Var, US-WCr, US-Whs, US-Wkg
Sullivan, R. C., Cook, D. R., Ghate, V. P., Kotamarthi, V. R., Feng, Y.
Evapotranspiration (ET) is a key component of the atmospheric and terrestrial water and energy budgets. Satellite‐based vegetation index approaches have used remotely sensed vegetation and reanalysis meteorological properties with surface energy balance models to estimate global ET (MOD16 ET). We reconstructed satellite retrievals …
Journal: Journal Of Geophysical Research: Biogeosciences, Volume 124 (2): 342-352 (2019). DOI: 10.1029/2018JG004744 Sites: US-AR1, US-AR2, US-ARM, US-Blo, US-Cop, US-GLE, US-Ha1, US-Los, US-Me2, US-Me6, US-MMS, US-Myb, US-Ne1, US-Ne2, US-Ne3, US-NR1, US-ORv, US-PFa, US-SRG, US-SRM, US-Syv, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Tw4, US-Twt, US-UMB, US-UMd, US-Var, US-WCr, US-Whs, US-Wkg
Schmidt, A., Creason, W., Law, B. E.
he ability to accurately predict changes of the carbon and energy balance on a regional scale is of great importance for assessing the effect of land use changes on carbon sequestration under future climate conditions. Here, a suite of land cover-specific Distributed Time Delay Neural Networks with a parameter adoption algorithm …
Journal: Neural Networks, Volume 108: 97-113 (2018). DOI: 10.1016/j.neunet.2018.08.004 Sites: US-Var
Baldocchi, D., Penuelas, J.
Reforesting and managing ecosystems have been proposed as ways to mitigate global warming and offset anthropogenic carbon emissions. The intent of our opinion piece is to provide a perspective on how well plants and ecosystems sequester carbon. The ability of individual plants and ecosystems to mine carbon dioxide from the atmosphere, …
Journal: Global Change Biology, Volume : (2018). DOI: 10.1111/gcb.14559 Sites: CA-Man, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-NS6, CA-NS7, CA-Qfo, CA-SF1, CA-SF2, CA-SF3, US-AR1, US-AR2, US-ARc, US-ARM, US-Blo, US-GBT, US-GLE, US-HAR, US-KS2, US-Me1, US-MMS, US-Myb, US-NC1, US-NC2, US-PFa, US-Snd, US-SRG, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Twt, US-Var, US-WCr