US-Var: Vaira Ranch- Ione
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Tower_team: | |
PI: | Dennis Baldocchi Baldocchi@berkeley.edu - University of California, Berkeley |
AncContact: | Joe Verfaillie jverfail@berkeley.edu - University of California, Berkeley |
Technician: | Daphne Szutu daphneszutu@berkeley.edu - UC Berkeley |
Lat, Long: | 38.4133, -120.9508 |
Elevation(m): | 129 |
Network Affiliations: | AmeriFlux, Phenocam |
Vegetation IGBP: | GRA (Grasslands: Lands with herbaceous types of cover. Tree and shrub cover is less than 10%. Permanent wetlands lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water.) |
Climate Koeppen: | Csa (Mediterranean: mild with dry, hot summer) |
Mean Annual Temp (°C): | 15.8 |
Mean Annual Precip. (mm): | 559 |
Flux Species Measured: | CO2, H2O |
Years Data Collected: | 2000 - Present |
Years Data Available: | AmeriFlux BASE 2000 - 2024 Data Citation AmeriFlux FLUXNET 2000 - 2021 Data Citation |
Data Use Policy: | AmeriFlux CC-BY-4.0 Policy1 |
Description: | Located in the lower foothills of the Sierra Nevada Mountains on privately owned land, the Vaira Ranch site is classified as a grassland dominated by C3 ... Located in the lower foothills of the Sierra Nevada Mountains on privately owned land, the Vaira Ranch site is classified as a grassland dominated by C3 annual grasses. Managed by local rancher, Fran Vaira, brush has been periodically removed for cattle grazing. Species include a variety of grasses and herbs, including purple false brome, smooth cat's ear, and rose clover. Growing season is confined to the wet season only, typically from October to early May. See MoreShow Less |
URL: | http://nature.berkeley.edu/biometlab/sites.php?tab=US-Var |
Research Topics: | The research approach of the University of California, Berkeley Biometeorology Laboratory involves the coordinated use of experimental measurements and ... The research approach of the University of California, Berkeley Biometeorology Laboratory involves the coordinated use of experimental measurements and theoretical models to understand the physical, biological, and chemical processes that control trace gas fluxes between the biosphere and atmosphere and to quantify their temporal and spatial variations. The research objectives of the Vaira Ranch site span from overall project goals to publication-specific topics. They are as follows: 1) Comparing radiative, convective and latent energy flux densities of an annual grassland over the course of multiple growing seasons; 2) The relative contributions of vegetation and the soil on CO2 and water vapor exchange; 3) Spatial variability of understory fluxes; 4) The impact of sloping terrain on the interpretation of flux covariances; 5) Characterize evapotranspiration, energy fluxes and related bulk parameters in monthly and annual timescales; 6) Investigate which abiotic and biotic factors control the interannual variability of water and energy fluxes; 7) The influence of growing season length on annual evapotranspiration amount; 8) Transition timing of energy-limited to water-limited modulate annual amount of evapotranspiration; 9) The controlling factors of evapotranspiration and atmospheric demand or stomatal regulation; 10) The response of evapotranspiration to changes in solar radiation amounts in water-limited and energy-limited periods. See MoreShow Less |
Acknowledgment: | This research was supported in part by the Office of Science (BER), U.S. Department of Energy, Grant No. DE-FG02-03ER63638 |
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US-Var: Vaira Ranch- Ione
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Use the information below for citation of this site. See the Data Policy page for more details.
DOI(s) for citing US-Var data
Data Use Policy: AmeriFlux CC-BY-4.0 License
This site’s data can also be used under the more restrictive AmeriFlux Legacy Policy.
The AmeriFlux Legacy Policy must be followed if US-Var data are combined with data from sites that require the AmeriFlux Legacy Policy.
- AmeriFlux BASE: https://doi.org/10.17190/AMF/1245984
Citation: Siyan Ma, Liukang Xu, Joseph Verfaillie, Dennis Baldocchi (2025), AmeriFlux BASE US-Var Vaira Ranch- Ione, Ver. 23-5, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/1245984 - AmeriFlux FLUXNET: https://doi.org/10.17190/AMF/1993904
Citation: Siyan Ma, Liukang Xu, Joseph Verfaillie, Dennis Baldocchi (2023), AmeriFlux FLUXNET-1F US-Var Vaira Ranch- Ione, Ver. 3-5, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/1993904
Find global FLUXNET datasets, like FLUXNET2015 and FLUXNET-CH4, and their citation information at fluxnet.org.
To cite BADM when downloaded on their own, use the publications below for citing site characterization. When using BADM that are downloaded with AmeriFlux BASE and AmeriFlux FLUXNET products, use the DOI citation for the associated data product.
Publication(s) for citing site characterization
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US-Var: Vaira Ranch- Ione
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This page displays the list of downloads of data for the site US-Var.
Note: Results are the number of downloads to distinct data users. The Download Count column indicates the number of times the data user downloaded the data. The Version column refers to the version of the data product for the site that was downloaded by the data user.
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US-Var: Vaira Ranch- Ione
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AmeriFlux Images | Add Image |

2017.US-Var.sitevisit.IMG_0808
2017.US-Var.sitevisit.IMG_0808
Keywords: —
Location:
View in Original Size
To download, right-click photo (Mac: control-click) and choose Save Image As

Oak Tree at Vaira
Keywords: —
Location: California, United States
View in Original Size
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US-Var: Vaira Ranch- Ione
- Overview
- Windroses
- Data Citation
- Data Use Log
- Image Gallery
- Remote Sensing Data
- MODIS
- PhenoCam
- GeoNEX
- Publications
- BADM
MODIS NDVI
The time series shows the 16-day Normalized Difference Vegetation Index (NDVI) average from the MOD13Q1 data product.
Use the slider below the time series to zoom in and out.
To view / download these data and other MOD13Q1 products for this site, visit MODIS/Terra Vegetation Indices.
For other related products, visit MODIS/VIIRS Fixed Sites Subsets Tool.
Citation:
ORNL DAAC. 2018. Terrestrial Ecology Subsetting & Visualization Services (TESViS) Fixed Sites Subsets. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1567
MODIS NDVI subsetted data is not yet available for this site.
For a complete list of AmeriFlux sites, visit ORNL DAAC's MODIS/VIIRS Fixed Sites Subsets Tool.
PhenoCam Images and Derived Time Series Data
PhenoCams are high-resolution digital cameras that take repeated images of studied ecosystems and provide quantitative information about the canopy phenology. The PhenoCam Network coordinates the camera installation and data reporting/analyses across sites in the Americas, providing automated, near-surface remote sensing of canopy phenology across a range of ecosystems and climate zones. Use of PhenoCam images / data should follow the PhenoCam Data Use Policy .
PhenoCam sites for US-Var:
Use the links below to explore camera images and interactive timeseries for these sites.
Citation:
B. Seyednasrollah, A. M. Young, K. Hufkens, T. Milliman, M. A. Friedl, S. Frolking, and A. D. Richardson. Tracking vegetation phenology across diverse biomes using version 2.0 of the phenocam dataset. Scientific Data, 6(1):222, 2019. doi:10.1038/s41597-019-0229-9Camera Imagery
Milliman, T., B. Seyednasrollah, A.M. Young, K. Hufkens, M.A. Friedl, S. Frolking, A.D. Richardson, M. Abraha, D.W. Allen, M. Apple, M.A. Arain, J.M. Baker, D. Baldocchi, C.J. Bernacchi, J. Bhattacharjee, P. Blanken, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, D.M. Browning, N. Brunsell, S.P. Burns, M. Cavagna, H. Chu, P.E. Clark, B.J. Conrad, E. Cremonese, D. Debinski, A.R. Desai, R. Diaz-Delgado, L. Duchesne, A.L. Dunn, D.M. Eissenstat, T. El-Madany, D.S.S. Ellum, S.M. Ernest, A. Esposito, L. Fenstermaker, L.B. Flanagan, B. Forsythe, J. Gallagher, D. Gianelle, T. Griffis, P. Groffman, L. Gu, J. Guillemot, M. Halpin, P.J. Hanson, D. Hemming, A.A. Hove, E.R. Humphreys, A. Jaimes-Hernandez, A.A. Jaradat, J. Johnson, E. Keel, V.R. Kelly, J.W. Kirchner, P.B. Kirchner, M. Knapp, M. Krassovski, O. Langvall, G. Lanthier, G.l. Maire, E. Magliulo, T.A. Martin, B. McNeil, G.A. Meyer, M. Migliavacca, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, W. Oechel, A.C. Oishi, W.W. Oswald, T.D. Perkins, M.L. Reba, B. Rundquist, B.R. Runkle, E.S. Russell, E.J. Sadler, A. Saha, N.Z. Saliendra, L. Schmalbeck, M.D. Schwartz, R.L. Scott, E.M. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, J.E. Thom, R.Q. Thomas, A.K. Van den berg, R. Vargas, J. Verfaillie, C.S. Vogel, J.J. Walker, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, T.G. Whitham, G. Wohlfahrt, J.D. Wood, J. Yang, X. Yang, G. Yenni, Y. Zhang, Q. Zhang, and D. Zona. 2019. PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1689
Green Chromatic Coordinate Time Series
Seyednasrollah, B., A.M. Young, K. Hufkens, T. Milliman, M.A. Friedl, S. Frolking, A.D. Richardson, M. Abraha, D.W. Allen, M. Apple, M.A. Arain, J. Baker, J.M. Baker, D. Baldocchi, C.J. Bernacchi, J. Bhattacharjee, P. Blanken, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, D.M. Browning, N. Brunsell, S.P. Burns, M. Cavagna, H. Chu, P.E. Clark, B.J. Conrad, E. Cremonese, D. Debinski, A.R. Desai, R. Diaz-Delgado, L. Duchesne, A.L. Dunn, D.M. Eissenstat, T. El-Madany, D.S.S. Ellum, S.M. Ernest, A. Esposito, L. Fenstermaker, L.B. Flanagan, B. Forsythe, J. Gallagher, D. Gianelle, T. Griffis, P. Groffman, L. Gu, J. Guillemot, M. Halpin, P.J. Hanson, D. Hemming, A.A. Hove, E.R. Humphreys, A. Jaimes-Hernandez, A.A. Jaradat, J. Johnson, E. Keel, V.R. Kelly, J.W. Kirchner, P.B. Kirchner, M. Knapp, M. Krassovski, O. Langvall, G. Lanthier, G.l. Maire, E. Magliulo, T.A. Martin, B. McNeil, G.A. Meyer, M. Migliavacca, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, T.L. O'Halloran, W. Oechel, A.C. Oishi, W.W. Oswald, T.D. Perkins, M.L. Reba, B. Rundquist, B.R. Runkle, E.S. Russell, E.J. Sadler, A. Saha, N.Z. Saliendra, L. Schmalbeck, M.D. Schwartz, R.L. Scott, E.M. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, J.E. Thom, R.Q. Thomas, A.K. Van den berg, R. Vargas, J. Verfaillie, C.S. Vogel, J.J. Walker, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, T.G. Whitham, G. Wohlfahrt, J.D. Wood, S. Wolf, J. Yang, X. Yang, G. Yenni, Y. Zhang, Q. Zhang, and D. Zona. 2019. PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1674
GeoNEX Data Products
GeoNEX led by NASA Earth eXchange (NEX) is a collaborative effort for generating Earth monitoring products from the new generation of geostationary satellite sensors. GeoNEX has produced a suite of geostationary data products including surface reflectance, land surface temperature, surface solar radiation, and many others.
The GeoNEX Common Grid locates GeoNEX data in the geographic (latitude/longitude) projection. Pixels (grid cells) are created at regular 0.005°, 0.01°, and 0.02° resolutions.
GeoNEX pixels below cover the area 0.06° x 0.06° around and including site US-Var, 38.4133, -120.9508.
Click a square in the grid at left to display its data below.
Coordinates for selected GeoNEX Pixel
Lat: 38.4300 Long: -120.9800
US-Var
Graph controls:
- Zoom: click-drag
- Pan: shift-click-drag
- Restore zoom level: double-click
- Use the slider below the time series to zoom in and out.
All download requests will be logged.
NDVI: Normalized Difference Vegetation Index
Resolution: 0.01° x 0.01° & 10 minutes
Coordinates for pixel: Lat: 38.4300 Long: -120.9800
NIRv Near-Infrared Reflectance of vegetation
Resolution: 0.01° x 0.01° & 10 minutes
Coordinates for pixel: Lat: 38.4300 Long: -120.9800
DSR: Surface downward shortwave radiation
Resolution: 0.01° x 0.01° & Hourly
Coordinates for pixel: Lat: 38.4300 Long: -120.9800
LST: Land Surface Temperature
Resolution: 0.02° x 0.02° & Hourly
Coordinates for pixel: Lat: 38.4200 Long: -120.9800
Citation
This material can be used without obtaining permission from NASA. NASA should be acknowledged as the source of this material.
Subset Data Citation:
- Hashimoto, H., Wang, W., Park, T., Khajehei, S., Ichii, K., Michaelis, A.R., Guzman, A., Nemani, R.R., Torn, M., Yi, K., Brosnan, I.G. (in preparation). Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles.
Relevant Science Publication Citation:
GeoNEX Surface Reflectance for Vegetation Indices (NDVI & NIRv)- Wang, W., Wang, Y., Lyapustin, A., Hashimoto, H., Park, T., Michaelis, A., & Nemani, R. (2022). A novel atmospheric correction algorithm to exploit the diurnal variability in hypertemporal geostationary observations. Remote Sensing, 14(4), 964.
- Li, R., Wang, D., Wang, W., & Nemani, R. (2023). A GeoNEX-based high-spatiotemporal-resolution product of land surface downward shortwave radiation and photosynthetically active radiation. Earth System Science Data, 15(3), 1419-1436.
- Jia, A., Liang, S., & Wang, D. (2022). Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data. Remote Sensing of Environment, 278, 113105.
US-Var: Vaira Ranch- Ione
- Overview
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- Data Citation
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- Image Gallery
- Remote Sensing Data
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AmeriFlux Publications | Add Publication |
Year | Publication |
---|---|
2021 | 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. (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 |
2021 | Baldocchi, D., Ma, S., Verfaillie, J. (2021) On The Inter‐ And Intra‐Annual Variability Of Ecosystem Evapotranspiration And Water Use Efficiency Of An Oak Savanna And Annual Grassland Subjected To Booms And Busts In Rainfall, Global Change Biology, 27(2), 359-375. https://doi.org/10.1111/gcb.15414 |
2020 | 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. (2020) Outgoing Near‐Infrared Radiation From Vegetation Scales With Canopy Photosynthesis Across A Spectrum Of Function, Structure, Physiological Capacity, And Weather, Journal Of Geophysical Research: Biogeosciences, 125(7), . https://doi.org/10.1029/2019jg005534 |
2020 | Ma, S., Eichelmann, E., Wolf, S., Rey-Sanchez, C., Baldocchi, D. D. (2020) Transpiration And Evaporation In A Californian Oak-Grass Savanna: Field Measurements And Partitioning Model Results, Agricultural And Forest Meteorology, 295, 108204. https://doi.org/10.1016/j.agrformet.2020.108204 |
2019 | Sullivan, R. C., Cook, D. R., Ghate, V. P., Kotamarthi, V. R., Feng, Y. (2019) Improved Spatiotemporal Representativeness And Bias Reduction Of Satellite-Based Evapotranspiration Retrievals Via Use Of In Situ Meteorology And Constrained Canopy Surface Resistance, Journal Of Geophysical Research: Biogeosciences, 124(2), 342-352. https://doi.org/10.1029/2018JG004744 |
2019 | Sullivan, R. C., Kotamarthi, V. R., Feng, Y. (2019) Recovering Evapotranspiration Trends From Biased CMIP5 Simulations And Sensitivity To Changing Climate Over North America, Journal Of Hydrometeorology, 20(8), 1619-1633. https://doi.org/10.1175/JHM-D-18-0259.1 |
2019 | Zhang, Q., Ficklin, D. L., Manzoni, S., Wang, L., Way, D., Phillips, R. P., Novick, K. A. (2019) Response Of Ecosystem Intrinsic Water Use Efficiency And Gross Primary Productivity To Rising Vapor Pressure Deficit, Environmental Research Letters, 14(7), 074023. https://doi.org/10.1088/1748-9326/ab2603 |
2019 | Novick, K. A., Konings, A. G., Gentine, P. (2019) Beyond Soil Water Potential: An Expanded View On Isohydricity Including Land–Atmosphere Interactions And Phenology, Plant, Cell & Environment, 42(6), 1802-1815. https://doi.org/10.1111/pce.13517 |
2018 | Schmidt, A., Creason, W., Law, B. E. (2018) Estimating Regional Effects Of Climate Change And Altered Land Use On Biosphere Carbon Fluxes Using Distributed Time Delay Neural Networks With Bayesian Regularized Learning, Neural Networks, 108, 97-113. https://doi.org/10.1016/j.neunet.2018.08.004 |
2018 | 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. (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 |
2018 | Baldocchi, D., Penuelas, J. (2018) The Physics And Ecology Of Mining Carbon Dioxide From The Atmosphere By Ecosystems, Global Change Biology, . https://doi.org/10.1111/gcb.14559 |
2017 | Filippa, G, Cremonesea, E, Migliavacca M., Galvagno, M., Sonnentag, O., Humphrey, E., Hufkens,K., Ryu, Y. Verfaillie, J., Morra di Cella, U., Richardson, A. (2017) NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types, Agricultural and Forest Meteorology, . https://doi.org/https://doi.org/10.1016/j.agrformet.2017.11.003 |
2017 | Liu, Y., Hill, M. J., Zhang, X., Wang, Z., Richardson, A. D., Hufkens, K., Filippa, G., Baldocchi, D. D., Ma, S., Verfaillie, J., Schaaf, C. B. (2017) Using Data From Landsat, Modis, Viirs And Phenocams To Monitor The Phenology Of California Oak/Grass Savanna And Open Grassland Across Spatial Scales, Agricultural And Forest Meteorology, 237-238, 311-325. https://doi.org/10.1016/j.agrformet.2017.02.026 |
2016 | Ma, S, Baldocchi, D.D., Wolf, S., Verfaillie, J. (2016) Slow ecosystem responses conditionally regulate annual carbon balance over 15 years in Californian oak-grass savanna, Agricultural and Forest Meteorology, 252-264. https://doi.org/10.1016/j.agrformet.2016.07.016 |
2016 | Wolf, S., Keenan, T.F., Fisher, J.B., Baldocchi, D.D., Desai, A.R., Richardson, A.D., Scott, R.L., Law, B.E., Litvak, M.E., Brunsell, N.A., Peters, W., van der Laan-Luijkx, I.T. (2016) Warm spring reduced carbon cycle impact of the 2012 US summer drought, Proceedings of the National Academy of Sciences, 113(21), 5880-5885. https://doi.org/10.1073/pnas.1519620113 |
2016 | Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., Scott, R. L., Wang, L., Phillips, R. P. (2016) The Increasing Importance Of Atmospheric Demand For Ecosystem Water And Carbon Fluxes, Nature Climate Change, 6(11), 1023-1027. https://doi.org/10.1038/nclimate3114 |
2015 | Dennis Baldocchi, Cove Sturtevant (2015) Does day and night sampling reduce spurious correlation between canopy photosynthesis and ecosystem respiration?, Agricultural and Forest Meteorology, 207, 117-126. https://doi.org/10.1016/j.agrformet.2015.03.010 |
2015 | Toomey, M., Friedl, M. A., Frolking, S., Hufkens, K., Klosterman, S., Sonnentag, O., Baldocchi, D. D., Bernacchi, C. J., Biraud, S. C., Bohrer, G., Brzostek, E., Burns, S. P., Coursolle, C., Hollinger, D. Y., Margolis, H. A., McCaughey, H., Monson, R. K., Munger, J. W., Pallardy, S., Phillips, R. P., Torn, M. S., Wharton, S., Zeri, M., Richardson, A. D. (2015) Greenness Indices From Digital Cameras Predict The Timing And Seasonal Dynamics Of Canopy-Scale Photosynthesis, Ecological Applications, 25(1), 99-115. https://doi.org/http://doi.org/10.1890/14-0005.1 |
2014 | Matheny, A. M., Bohrer, G., Stoy, P. C., Baker, I. T., Black, A. T., Desai, A. R., Dietze, M. C., Gough, C. M., Ivanov, V. Y., Jassal, R. S., Novick, K. A., Schäfer, K. V., Verbeeck, H. (2014) Characterizing The Diurnal Patterns of Errors in The Prediction of Evapotranspiration by Several Land-Surface Models: An Nacp Analysis, Journal Of Geophysical Research: Biogeosciences, 119(7), 1458-1473. https://doi.org/10.1002/2014JG002623 |
2013 | 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. (2013) Use Of Change-Point Detection For Friction–Velocity Threshold Evaluation In Eddy-Covariance Studies, Agricultural And Forest Meteorology, 171-172, 31-45. https://doi.org/10.1016/j.agrformet.2012.11.023 |
2012 | Grant, R., Baldocchi, D., Ma, S. (2012) Ecological Controls On Net Ecosystem Productivity Of A Seasonally Dry Annual Grassland Under Current And Future Climates: Modelling With Ecosys, Agricultural And Forest Meteorology, 152, 189-200. https://doi.org/10.1016/j.agrformet.2011.09.012 |
2008 | Ryu, Y, Baldoicchi, D. D., Ma, S., Hehn, T. (2008) Interannual Variability Of Evapotranspiration And Energy Exchange Over An Annual Grassland In California, Journal Of Geophysical Research, 113(D09104), n/a-n/a. https://doi.org/10.1029/2007jd009263 |
2007 | Ma, S., Baldocchi, D. D., Xu, L., Hehn, T. (2007) Inter-Annual Variability In Carbon Dioxide Exchange Of An Oak/Grass Savanna And Open Grassland In California, Agricultural And Forest Meteorology, 147(3-4), 157-171. https://doi.org/10.1016/j.agrformet.2007.07.008 |
2006 | Kim, J., Guo, Q., Baldocchi, D., Leclerc, M., Xu, L., Schmid, H. (2006) Upscaling Fluxes From Tower To Landscape: Overlaying Flux Footprints On High-Resolution (IKONOS) Images Of Vegetation Cover, Agricultural And Forest Meteorology, 136(3-4), 132-146. https://doi.org/10.1016/j.agrformet.2004.11.015 |
2005 | Gu, L., Falge, E. M., Boden, T., Baldocchi, D. D., Black, T., Saleska, S. R., Suni, T., Verma, S. B., Vesala, T., Wofsy, S. C., Xu, L. (2005) Objective Threshold Determination For Nighttime Eddy Flux Filtering, Agricultural And Forest Meteorology, 128(3-4), 179-197. https://doi.org/10.1016/j.agrformet.2004.11.006 |
2005 | Sims, D. A., Rahman, A. F., Cordova, V. D., Baldocchi, D. D., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., Schmid, H. P., Wofsy, S. C., Xu, L. (2005) Midday Values Of Gross CO2 Flux And Light Use Efficiency During Satellite Overpasses Can Be Used To Directly Estimate Eight-Day Mean Flux, Agricultural And Forest Meteorology, 131(1-2), 1-12. https://doi.org/10.1016/j.agrformet.2005.04.006 |
2004 | Xu, L., Baldocchi, D. D., Tang, J. (2004) How Soil Moisture, Rain Pulses, And Growth Alter The Response Of Ecosystem Respiration To Temperature, Global Biogeochemical Cycles, 18(4), n/a-n/a. https://doi.org/10.1029/2004gb002281 |
2004 | Baldocchi, D. D., Xu, L., Kiang, N. (2004) How Plant Functional-Type, Weather, Seasonal Drought, And Soil Physical Properties Alter Water And Energy Fluxes Of An Oak–Grass Savanna And An Annual Grassland, Agricultural And Forest Meteorology, 123(1-2), 13-39. https://doi.org/10.1016/j.agrformet.2003.11.006 |
2004 | Xu, L., Baldocchi, D. D. (2004) Seasonal Variation In Carbon Dioxide Exchange Over A Mediterranean Annual Grassland In California, Agricultural And Forest Meteorology, 123(1-2), 79-96. https://doi.org/10.1016/j.agrformet.2003.10.004 |
US-Var: Vaira Ranch- Ione
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BADM for This Site
Access the Biological, Ancillary, Disturbance and Metadata (BADM) information and data for this site.
BADM contain information for many uses, such as characterizing a site’s vegetation and soil, describing disturbance history, and defining instrumentation for flux processing. They complement the flux/met data.
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* Online updates are shown on the Overview tab real time. However, downloaded BADM files will not reflect those updates until they have been reviewed for QA/QC.
US-Var: Vaira Ranch- Ione
- Overview
- Windroses
- Data Citation
- Data Use Log
- Image Gallery
- Remote Sensing Data
- MODIS
- PhenoCam
- GeoNEX
- Publications
- BADM
Wind Roses
Wind Speed (m/s)
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- Wind Direction Scale (%): AmeriFlux