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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
Chu, H., Baldocchi, D. D., Poindexter, C., Abraha, M., Desai, A. R., Bohrer, G., Arain, M. A., Griffis, T., Blanken, P. D., O'Halloran, T. L., Thomas, R. Q., Zhang, Q., Burns, S. P., Frank, J. M., Christian, D., Brown, S., Black, T. A., Gough, C. M., Law, B. E., Lee, X., Chen, J., Reed, D. E., Massman, W. J., Clark, K., Hatfield, J., Prueger, J., Bracho, R., Baker, J. M., Martin, T. A.
Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface‐atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance …
Journal: Geophysical Research Letters, Volume 45: 9275–9287 (2018). DOI: 10.1029/2018GL079306 Sites: BR-Sa1, BR-Sa3, CA-Ca1, CA-Ca2, CA-Ca3, CA-Cbo, CA-ER1, CA-Gro, CA-Man, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-Oas, CA-Obs, CA-Ojp, CA-Qfo, CA-TP1, CA-TP3, CA-TP4, CA-TPD, US-Blo, US-Bn1, US-Bn2, US-Br1, US-Br3, US-Ced, US-CPk, US-CRT, US-Dix, US-Dk2, US-Dk3, US-Fmf, US-Fuf, US-GBT, US-GLE, US-GMF, US-Ha1, US-Ha2, US-Ho2, US-Ho3, US-IB1, US-IB2, US-KL1, US-KL2, US-KL3, US-KM1, US-KM2, US-KM3, US-KM4, US-Me2, US-Me3, US-Me4, US-Me5, US-Me6, US-MMS, US-MRf, US-NC1, US-NC2, US-Ne1, US-Ne2, US-Ne3, US-NR1, US-Oho, US-Prr, US-Ro1, US-Ro3, US-SB1, US-Shd, US-Skr, US-Slt, US-SP1, US-SP2, US-SP3, US-SRM, US-Srr, US-Syv, US-Ton, US-Tw3, US-Twt, US-UMB, US-UMd, US-Var, US-Vcm, US-WBW, US-Wi0, US-Wi1, US-Wi3, US-Wi4, US-Wi5, US-Wi8, US-Wi9, US-Wrc
Barr, A., Richardson, A., Hollinger, D., Papale, D., Arain, M., Black, T., Bohrer, G., Dragoni, D., Fischer, M., Gu, L., Law, B., Margolis, H., McCaughey, J., Munger, J., Oechel, W., Schaeffer, K.
Journal: Agricultural And Forest Meteorology, Volume 171-172: 31-45 (2013). DOI: 10.1016/j.agrformet.2012.11.023 Sites: CA-Ca1, CA-Ca2, CA-Ca3, CA-Gro, CA-Let, CA-Mer, CA-NS1, CA-Oas, CA-Obs, CA-Ojp, CA-Qfo, CA-SJ1, CA-SJ2, CA-SJ3, CA-TP4, CA-WP1, US-ARM, US-Dk3, US-Ha1, US-Ho1, US-IB1, US-Los, US-Me3, US-Me5, US-MMS, US-MOz, US-Ne1, US-Ne2, US-Ne3, US-NR1, US-Shd, US-SO2, US-Syv, US-Ton, US-UMB, US-Var, US-WCr
F. A. Heinsch, M. S. Zhao, S. W. Running, J. S. Kimball, R. R. Nemani, K. J. Davis, P. V. Bolstad, B. D. Cook, A. R. Desai, D. M. Ricciuto, B. E. Law, W. C. Oechel, H. Kwon, H. Luo, S. C. Wofsy, A. L. Dunn, J. W. Munger, D. D. Baldocchi, L. Xu, D. Y. Hollinger, A. D. Richardson, P. C. Stoy, M. B. S. Siqueira, R. K. Monson, S. P. Burns, L. B. Flanagan
The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO2 flux-based estimates across diverse land cover types and climate regimes. We …
Journal: IEEE Transactions On Geoscience And Remote Sensing, Volume 44 (7): 1908-1925 (2006). DOI: 10.1109/tgrs.2005.853936 Sites: US-Me4, US-Me5
Hibbard, K. A., Law, B. E., Reichstein, M., Sulzman, J.
Over two-thirds of terrestrial carbon is stored belowground and a significant amount of atmospheric CO2 is respired by roots and microbes in soils. For this analysis, soil respiration (Rs) data were assembled from 31 AmeriFlux and CarboEurope sites representing deciduous broadleaf, evergreen needleleaf, grasslands, mixed …
Journal: Biogeochemistry, Volume 73 (1): 29-70 (2005). DOI: 10.1007/s10533-004-2946-0 Sites: US-Dk3, US-Ho1, US-Me2, US-Me4, US-Me5, US-NR1, US-Ton, US-UMB, US-WCr
Irvine, J., Law, B. E., Kurpius, M. R.
The strength of coupling between canopy gas exchange and root respiration was examined in ~15-yr-old ponderosa pine (Pinus ponderosa Doug. Ex Laws.) growing under seasonally drought stressed conditions. By regularly watering …
Journal: Biogeochemistry, Volume 73 (1): 271-282 (2005). DOI: 10.1007/s10533-004-2564-x Sites: US-Me2, US-Me5
Turner, D. P., Ritts, W. D., Cohen, W. B., Maeirsperger, T. K., Gower, S. T., Kirschbaum, A. A., Running, S. W., Zhao, M., Wofsy, S. C., Dunn, A. L., Law, B. E., Campbell, J. L., Oechel, W. C., Kwon, H. J., Meyers, T. P., Small, E. E., Kurc, S. A., Gamon, J. A.
Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site-level studies across a range of biomes, with close …
Journal: Global Change Biology, Volume 11 (4): 666-684 (2005). DOI: 10.1111/j.1365-2486.2005.00936.x Sites: US-Me4, US-Me5
Coops, N. C., Waring, R. H., Law, B. E.
Climate variability at decadal scales influences not only the growth of widely distributed species such as Pinus ponderosa, but also can have an effect on the timing and severity of fire and insect outbreaks that may alter species distributions. In this paper, we present a spatial modelling technique to assess the influence …
Journal: Ecological Modelling, Volume 183 (1): 107-124 (2005). DOI: 10.1016/j.ecolmodel.2004.08.002 Sites: US-Me2, US-Me4, US-Me5
Campbell, J., Law, B.
To assess the relative influence of edaphoclimatic gradients and stand replacing disturbance on the soil respiration of Oregon forests, we measured annual soil respiration at 36 independent forest plots arranged as three replicates of four age classes in …
Journal: Biogeochemistry, Volume 73 (1): 109-125 (2005). DOI: 10.1007/s10533-004-5165-9 Sites: US-Me2, US-Me3, US-Me4, US-Me5
Williams, M., Schwarz, P. A., Law, B. E., Irvine, J., Kurpius, M. R.
There are two broad approaches to quantifying landscape C dynamics – by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key …
Journal: Global Change Biology, Volume 11 (1): 89-105 (2005). DOI: 10.1111/j.1365-2486.2004.00891.x Sites: US-Me5