US-Twt: Twitchell Island
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| Tower_team: | |
| PI: | Dennis Baldocchi baldocchi@berkeley.edu - University of California, Berkeley |
| AncContact: | Daphne Szutu daphneszutu@berkeley.edu - UC Berkeley |
| AncContact: | Joe Verfaillie jverfail@berkeley.edu - University of California, Berkeley |
| Lat, Long: | 38.1087, -121.6531 |
| Elevation(m): | -7 |
| Network Affiliations: | AmeriFlux, Phenocam |
| Vegetation IGBP: | CRO (Croplands: Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type.) |
| Climate Koeppen: | Csa (Mediterranean: mild with dry, hot summer) |
| Mean Annual Temp (°C): | 15.6 |
| Mean Annual Precip. (mm): | 421 |
| Flux Species Measured: | CO2, CH4, H2O |
| Years Data Collected: | 2009 - 2017 |
| Years Data Available: | AmeriFlux BASE 2009 - 2017 Data Citation |
| Data Use Policy: | AmeriFlux CC-BY-4.0 Policy1 |
| Description: | The Twitchell Island site is a rice paddy that is owned by the state and managed by the California Department of Water Resources. While Bare Peat field ... The Twitchell Island site is a rice paddy that is owned by the state and managed by the California Department of Water Resources. While Bare Peat field was leveled for rice planting, the tower was installed on April 3, 2009. The rice paddy was converted from corn in 2007. In Summer 2009, Bispyribac-sodium and Pendimethalin herbicides were applied to the fields prior to rice planting and flooding, then pesticide and fertilizer application took place. Each year after rice is planted in the spring by drilling, the field is flooded. Then, the field is drained in early fall, rice is harvested, and the field site is moved. See MoreShow Less |
| URL: | http://nature.berkeley.edu/biometlab/fielddescription.html |
| 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. They are also responsible for making eddy covariance measurements of H2O, CO2, sensible heat, and CH4 exchange between a rice paddy and the atmosphere. The research objectives of the Mayberry Wetland, Sherman Island, and Twitchell Island sites are as follows: 1) Describe differences in the fluxes of CO2, CH4, H2O, and energy between different land uses; 2) Understand the mechanisms controlling these fluxes; 3) Use ecosystem modeling to understand controls on these mechanisms under different environmental scenarios. These three sites were selected to capture a wide range of inundated conditions within the Sacramento-San Joaquin River Delta. The research focuses on the eddy covariance technique to measure CH4, CO2, H2O, and energy fluxes and works to combine measurements of both net fluxes and partitioned fluxes in order to achieve a mechanistic understanding of the ecological controls on current and future carbon flux in the Delta. See MoreShow Less |
| Acknowledgment: | California Department of Water Resources; USDA/AFRI |
Site Tasks
- This site’s data can also be used under the more restrictive AmeriFlux Legacy Policy.
The AmeriFlux Legacy Policy must be followed if this site’s data are combined with data from sites that require the AmeriFlux Legacy Policy.
US-Twt
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Knox, S. H., J. H. Matthes, C. Sturtevant, P. Y. Oikawa, J. Verfaillie, and D. Baldocchi. 2016.
Biophysical controls on interannual variability in ecosystem-scale CO2 and CH4 exchange in a California rice paddy.,
Journal of Geophysical Research-Biogeosciences, 121, 978-1001.
US-Twt: Twitchell Island
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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-Twt 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-Twt data are combined with data from sites that require the AmeriFlux Legacy Policy.
- AmeriFlux BASE: https://doi.org/10.17190/AMF/1246140
Citation: Sara Knox, Jaclyn Hatala Matthes, Joseph Verfaillie, Dennis Baldocchi (2023), AmeriFlux BASE US-Twt Twitchell Island, Ver. 7-5, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/1246140
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-Twt: Twitchell Island
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This page displays the list of downloads of data for the site US-Twt.
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-Twt: Twitchell Island
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Search Images for this Site Images found : 4
| AmeriFlux Images | Add Image |
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US-Twt Twitchell Island Rice Field
Description:
flux tower and veg and fetch
Keywords: —
Credit: Dennis Baldocchi
Site ID: US-Twt
Location: California, United States
Location: California, United States
—
Journal: —
Article Title: —
Copyright Permissions: —
Right to Use: Open use
View in Original Size
To download, right-click photo (Mac: control-click) and choose Save Image As
US-Twt Deb Agarwal, AMP Data Lead, measures instrument distances at US-Twt.
Keywords: —
Credit: Marilyn Saarni
Site ID: US-Twt
Location: California, United States
Location: California, United States
Image Date: 2014/09/09
Journal: —
Article Title: —
Copyright Permissions: —
Right to Use: Open use
View in Original Size
To download, right-click photo (Mac: control-click) and choose Save Image As
US-Twt Twitchell Island Rice Field and Egrets
Keywords: —
Credit: Joe Verfaillie
Site ID: US-Twt
Location: California, United States
Location: California, United States
Image Date: 2013/06/29
Journal: —
Article Title: —
Copyright Permissions: —
Right to Use: As long as credit is given
View in Original Size
To download, right-click photo (Mac: control-click) and choose Save Image As
US-Twt Delta Sunset
Keywords: —
Credit: Joe Verfaillie
Site ID: US-Twt
Location: California, United States
Location: California, United States
Image Date: 2013/10/31
Journal: —
Article Title: —
Copyright Permissions: —
Right to Use: As long as credit is given
View in Original Size
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US-Twt: Twitchell Island
- Overview
- Windroses
- Data Citation
- Data Use Log
- Image Gallery
- Remote Sensing Data
- MODIS
- PhenoCam
- GeoNEX
- Publications
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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.
Includes all pixels that have acceptable quality
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-Twt:
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-Twt, 38.1087, -121.6531.
Click a square in the grid at left to display its data below.
Coordinates for selected GeoNEX Pixel
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
No NDVI data have been received for site US-Twt.
NDVI (unitless)
Resolution: 0.01° x 0.01° & 10 minutes
Coordinates for pixel:
NIRv Near-Infrared Reflectance of vegetation
No NIRv data have been received for site US-Twt.
NIRv (unitless)
Resolution: 0.01° x 0.01° & 10 minutes
Coordinates for pixel:
DSR: Surface downward shortwave radiation
No DSR data have been received for site US-Twt.
Shortwave Radiation (W/m2)
Resolution: 0.01° x 0.01° & Hourly
Coordinates for pixel:
LST: Land Surface Temperature
No LST data have been received for site US-Twt.
LST (K)
Resolution: 0.02° x 0.02° & Hourly
Coordinates for pixel:
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-Twt: Twitchell Island
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Search Publications for this Site
Publications Found: 14
| 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 |
| 2019 | Hemes, K. S., Chamberlain, S. D., Eichelmann, E., Anthony, T., Valach, A., Kasak, K., Szutu, D., Verfaillie, J., Silver, W. L., Baldocchi, D. D. (2019) Assessing The Carbon And Climate Benefit Of Restoring Degraded Agricultural Peat Soils To Managed Wetlands, Agricultural And Forest Meteorology, 268, 202-214. https://doi.org/10.1016/j.agrformet.2019.01.017 |
| 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 | Kim, Y., Johnson, M. S., Knox, S. H., Black, T. A., Dalmagro, H. J., Kang, M., Kim, J., Baldocchi, D. (2019) Gap‐Filling Approaches For Eddy Covariance Methane Fluxes: A Comparison Of Three Machine Learning Algorithms And A Traditional Method With Principal Component Analysis, Global Change Biology, . https://doi.org/10.1111/gcb.14845 |
| 2018 | Eichelmann, E., Hemes, K. S., Knox, S. H., Oikawa, P. Y., Chamberlain, S. D., Sturtevant, C., Verfaillie, J., Baldocchi, D. D. (2018) The Effect Of Land Cover Type And Structure On Evapotranspiration From Agricultural And Wetland Sites In The Sacramento–San Joaquin River Delta, California, Agricultural And Forest Meteorology, 256-257, 179-195. https://doi.org/10.1016/j.agrformet.2018.03.007 |
| 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 | Chamberlain, S. D., Verfaillie, J., Eichelmann, E., Hemes, K. S., Baldocchi, D. D. (2017) Evaluation Of Density Corrections To Methane Fluxes Measured By Open-Path Eddy Covariance Over Contrasting Landscapes, Boundary-Layer Meteorology, . https://doi.org/10.1007/s10546-017-0275-9 |
| 2016 | Knox, S. H., J. H. Matthes, C. Sturtevant, P. Y. Oikawa, J. Verfaillie, and D. Baldocchi. (2016) Biophysical controls on interannual variability in ecosystem-scale CO2 and CH4 exchange in a California rice paddy., Journal of Geophysical Research-Biogeosciences, 121, 978-1001. https://doi.org/10.1002/2015JG003247 |
| 2016 | Baldocchi, D., S. Knox, I. Dronova, J. Verfaillie, P. Oikawa, C. Sturtevant, J. H. Matthes, and M. Detto. (2016) The impact of expanding flooded land area on the annual evaporation of rice. Agricultural and Forest Meteorology, Agricultural and Forest Meteorology, 223, 181-193. https://doi.org/http://dx.doi.org/10.1016/j.agrformet.2016.04.001 |
| 2014 | Knox, S. H.,, Sturtevant, C., Matthes, J.H., Koteen, L., Verfaillie,J., Baldocchi. D. (2014) Agricultural peatland restoration: effects of land-use change on greenhouse gas (CO2 and CH4) fluxes in the Sacramento-San Joaquin Delta, Global Change Biology, 21, 750-765. https://doi.org/10.1111/gcb.12745 |
| 2012 | Hatala, J. A., Detto, M., Baldocchi, D. D. (2012) Gross Ecosystem Photosynthesis Causes A Diurnal Pattern In Methane Emission From Rice, Geophysical Research Letters, 39(6), n/a-n/a. https://doi.org/10.1029/2012gl051303 |
| 2012 | Hatala, J. A., Detto, M., Sonnentag, O., Deverel, S. J., Verfaillie, J., Baldocchi, D. D. (2012) Greenhouse Gas (CO2, CH4, H2O) Fluxes From Drained And Flooded Agricultural Peatlands In The Sacramento-San Joaquin Delta, Agriculture, Ecosystems & Environment, 150, 1-18. https://doi.org/10.1016/j.agee.2012.01.009 |
US-Twt: Twitchell Island
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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.
- Download BADM for this site*
- View Site General Info for this site (Overview tab)*
- Use Online Editor to update Site General Info or DOI Authorship
- Update information about submitted data (Variable Information tool)
- More BADM resources
* 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-Twt: Twitchell Island
- Overview
- Windroses
- Data Citation
- Data Use Log
- Image Gallery
- Remote Sensing Data
- MODIS
- PhenoCam
- GeoNEX
- Publications
- BADM
Wind Roses
Click an image below to enlarge it, or use the navigation panel.
Wind Speed (m/s)
Navigation
Annual Average
About Ameriflux Wind Roses
Wind Rose Explanation
A wind rose gives a succinct view of how wind speed and direction are typically distributed at a particular location. Presented in a circular format, a wind rose shows the frequency and intensity of winds blowing from particular directions. The length of each “spoke” around the circle indicates the amount of time (frequency) that the wind blows from a particular direction. Colors along the spokes indicate categories of wind speed (intensity). Each concentric circle represents a different frequency, emanating from zero at the center to increasing frequencies at the outer circles
Utility
This information can be useful to gain insight into regions surrounding a flux tower that contribute to the measured fluxes, and how those regions change in dependence of the time of day and season. The wind roses presented here are for four periods of the year, and in 16 cardinal directions. Graphics are available for all sites in the AmeriFlux network based on reported wind measurements at each site.
Data from each site can be downloaded by clicking the ‘download’ button.
Hover the cursor over a wind rose to obtain directions, speeds and intensities.
Note that wind roses are not equivalent to flux footprints. Specifically, the term flux footprint describes an upwind area “seen” by the instruments measuring vertical turbulent fluxes, such that heat, water, gas and momentum transport generated in this area is registered by the instruments. Wind roses, on the other hand, identify only the direction and speed of wind.
Where do these data come from?
The wind roses are based on observed hourly data from the sites registered with the AmeriFlux Network.
Parameters for AmeriFlux Wind Roses
To use wind roses for a single AmeriFlux site, the following parameters may be most useful:
- Wind Speed Scale: Per Site
- Wind Direction Scale (%): Per Site
To compare wind roses from more than one single AmeriFlux site, the following parameters may be most useful:
- Wind Speed Scale: Non-Linear
- Wind Direction Scale (%): AmeriFlux
Mar - Jun; 6am - 6pm
Mar - Jun; 6pm - 6am
Jun - Sep; 6am - 6pm
Jun - Sep; 6pm - 6am
Sep - Dec; 6am - 6pm
Sep - Dec; 6pm - 6am
Dec - Mar; 6am - 6pm
Dec - Mar; 6pm - 6am