US-Mpj: Mountainair Pinyon-Juniper Woodland
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| Tower_team: | |
| PI: | Marcy Litvak mlitvak@unm.edu - University of New Mexico |
| DataManager: | Rae DeVan raedevan@unm.edu - University of New Mexico |
| Technician: | Anthony Luketich luketich@unm.edu - University of New Mexico |
| Lat, Long: | 34.4385, -106.2377 |
| Elevation(m): | 2196 |
| Network Affiliations: | AmeriFlux, Phenocam |
| Vegetation IGBP: | WSA (Woody Savannas: Lands with herbaceous and other understory systems, and with forest canopy cover between 30-60%. The forest cover height exceeds 2 meters.) |
| Climate Koeppen: | Bsk (Steppe: warm winter) |
| Mean Annual Temp (°C): | 10.5 |
| Mean Annual Precip. (mm): | 385 |
| Flux Species Measured: | CO2, H2O |
| Years Data Collected: | 2007 - Present |
| Years Data Available: | AmeriFlux BASE 2008 - 2024 Data Citation AmeriFlux FLUXNET 2008 - 2020 Data Citation |
| Data Use Policy: | AmeriFlux CC-BY-4.0 Policy1 |
| Description: | This site is located in central New Mexico on an extensive mesa approximately 25 km south of Mountainair, NM, owned by the Heritage Land Conservancy. The ... This site is located in central New Mexico on an extensive mesa approximately 25 km south of Mountainair, NM, owned by the Heritage Land Conservancy. The dominant tree species, Pinus edulis and Juniperus monosperma make up >95% of the area’s tree cover. Total tree is cover is ~60%. The dominant herbaceous plant at the site is the C4 perennial grass Bouteloua gracilis. In July 2013, we observed pinon mortality at the site, which continued through 2015. We have used a variety of techniques to document the rate of mortality. The mortality was triggered by a combination of drought (2011-2013) and Pinon ips bark beetle outbreak. Juniper was not affected. See MoreShow Less |
| URL: | http://www.litvaklab.org/pinon-juniper-woodland.html |
| Research Topics: | Research topics and objectives include 1) To understand the coupled water and energy cycles in semiarid environments; 2) Quantify carbon, water and energy ... Research topics and objectives include 1) To understand the coupled water and energy cycles in semiarid environments; 2) Quantify carbon, water and energy fluxes in addition to inter-annual variability in these fluxes; 3) Quantify the extent to which water and carbon fluxes are controlled by soil moisture and rainfall, and the sensitivity of fluxes in this biome to changes in temperature and precipitation; 4) Quantifying the consequences of large-scale piñon pine dieoff in piñon -juniper woodlands for ecosystem processes such as carbon storage and water availability. See MoreShow Less |
| Acknowledgment: | — |
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US-Mpj: Mountainair Pinyon-Juniper Woodland
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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-Mpj 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-Mpj data are combined with data from sites that require the AmeriFlux Legacy Policy.
- AmeriFlux BASE: https://doi.org/10.17190/AMF/1246123
Citation: Marcy Litvak (2024), AmeriFlux BASE US-Mpj Mountainair Pinyon-Juniper Woodland, Ver. 25-5, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/1246123 - AmeriFlux FLUXNET: https://doi.org/10.17190/AMF/1832161
Citation: Marcy Litvak (2021), AmeriFlux FLUXNET-1F US-Mpj Mountainair Pinyon-Juniper Woodland, Ver. 3-5, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/1832161
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.
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US-Mpj: Mountainair Pinyon-Juniper Woodland
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This page displays the list of downloads of data for the site US-Mpj.
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-Mpj: Mountainair Pinyon-Juniper Woodland
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Search Images for this Site Images found : 2
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US-Mpj 2017.US.Mpj.Sitevisit.IMG_6678
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2017.US.Mpj.Sitevisit.IMG_6678
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Site ID: US-Mpj
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US-Mpj Rainbow sunset over Pinyon Juniper flux tower
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Credit: Jonathan Furst
Site ID: US-Mpj
Location: New Mexico, United States
Location: New Mexico, United States
Image Date: 2014/06/17
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US-Mpj: Mountainair Pinyon-Juniper Woodland
- 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-Mpj:
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-Mpj, 34.4385, -106.2377.
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-Mpj.
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-Mpj.
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-Mpj.
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-Mpj.
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-Mpj: Mountainair Pinyon-Juniper Woodland
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Search Publications for this Site
Publications Found: 19
| AmeriFlux Publications | Add Publication |
| Year | Publication |
|---|---|
| 2025 | Xia, Y., Sanderman, J., Watts, J. D., Machmuller, M. B., Mullen, A. L., Rivard, C., Endsley, A., Hernandez, H., Kimball, J., Ewing, S. A., Litvak, M., Duman, T., Krishnan, P., Meyers, T., Brunsell, N. A., Mohanty, B., Liu, H., Gao, Z., Chen, J., Abraha, M., Scott, R. L., Flerchinger, G. N., Clark, P. E., Stoy, P. C., Khan, A. M., Brookshire, E. N., Zhang, Q., Cook, D. R., Thienelt, T., Mitra, B., Mauritz‐Tozer, M., Tweedie, C. E., Torn, M. S., Billesbach, D. (2025) Coupling Remote Sensing With A Process Model For The Simulation Of Rangeland Carbon Dynamics, Journal Of Advances In Modeling Earth Systems, 17(3), . https://doi.org/10.1029/2024MS004342 |
| 2024 | Webb, R., Knowles, J., Fox, A., Fabricus, A., Corrie, T., Mooney, K., Gallais, J., Frimpong, N., Akurugu, C., Barron‐Gafford, G., Blanken, P., Burns, S., Frank, J., Litvak, M. (2024) Energy‐Water Asynchrony Principally Determines Water Available For Runoff From Snowmelt In Continental Montane Forests, Hydrological Processes, 38(10), . https://doi.org/10.1002/hyp.15297 |
| 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 | Senay, G. B., Schauer, M., Velpuri, N. M., Singh, R. K., Kagone, S., Friedrichs, M., Litvak, M. E., Douglas-Mankin, K. R. (2019) Long-Term (1986–2015) Crop Water Use Characterization Over The Upper Rio Grande Basin Of United States And Mexico Using Landsat-Based Evapotranspiration, Remote Sensing, 11(13), 1587. https://doi.org/doi:10.3390/rs11131587 |
| 2019 | Remy, C. C., Krofcheck, D. J., Keyser, A. R., Litvak, M. E., Collins, S. L., Hurteau, M. D. (2019) Integrating Species‐Specific Information In Models Improves Regional Projections Under Climate Change, Geophysical Research Letters, 46(12), 6554-6562. https://doi.org/10.1029/2019GL082762 |
| 2018 | Fox, A. M., Hoar, T. J., Anderson, J. L., Arellano, A. F., Smith, W. K., Litvak, M. E., MacBean, N., Schimel, D. S., Moore, D. J. (2018) Evaluation Of A Data Assimilation System For Land Surface Models Using Clm4.5, Journal Of Advances In Modeling Earth Systems, 10(10), 2471-2494. https://doi.org/10.1029/2018MS001362 |
| 2018 | Dettweiler-Robinson, E., Nuanez, M., Litvak, M. E. (2018) Biocrust Contribution To Ecosystem Carbon Fluxes Varies Along An Elevational Gradient, Ecosphere, 9(6), e02315. https://doi.org/10.1002/ecs2.2315 |
| 2018 | Smith, W. K., Biederman, J. A., Scott, R. L., Moore, D. J., He, M., Kimball, J. S., Yan, D., Hudson, A., Barnes, M. L., MacBean, N., Fox, A. M., Litvak, M. E. (2018) Chlorophyll Fluorescence Better Captures Seasonal And Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems Of Southwestern North America, Geophysical Research Letters, 45(2), 748-757. https://doi.org/10.1002/2017GL075922 |
| 2017 | Morillas, L., Pangle, R. E., Maurer, G. E., Pockman, W. T., McDowell, N., Huang, C., Krofcheck, D. J., Fox, A. M., Sinsabaugh, R. L., Rahn, T. A., Litvak, M. E. (2017) Tree Mortality Decreases Water Availability And Ecosystem Resilience To Drought In PiñOn-Juniper Woodlands In The Southwestern U.S., Journal Of Geophysical Research: Biogeosciences, 122(12), 3343-3361. https://doi.org/10.1002/2017JG004095 |
| 2017 | Montané, F., Fox, A. M., Arellano, A. F., MacBean, N., Alexander, M. R., Dye, A., Bishop, D. A., Trouet, V., Babst, F., Hessl, A. E., Pederson, N., Blanken, P. D., Bohrer, G., Gough, C. M., Litvak, M. E., Novick, K. A., Phillips, R. P., Wood, J. D., Moore, D. J. (2017) Evaluating The Effect Of Alternative Carbon Allocation Schemes In A Land SurfaceModel (Clm4.5) On Carbon Fluxes, Pools And Turnover In Temperate Forests, Geoscientific Model Development, . https://doi.org/doi:10.5194/gmd-2017-74 |
| 2017 | Biederman, J. A., Scott, R. L., Bell, T. W., Bowling, D. R., Dore, S., Garatuza-Payan, J., Kolb, T. E., Krishnan, P., Krofcheck, D. J., Litvak, M. E., Maurer, G. E., Meyers, T. P., Oechel, W. C., Papuga, S. A., Ponce-Campos, G. E., Rodriguez, J. C., Smith, W. K., Vargas, R., Watts, C. J., Yepez, E. A., Goulden, M. L. (2017) Co2 Exchange And Evapotranspiration Across Dryland Ecosystems Of Southwestern North America, Global Change Biology, 23(10), 4204-4221. https://doi.org/10.1111/gcb.13686 |
| 2017 | Brewer, W. L., Lippitt, C. L., Lippitt, C. D., Litvak, M. E. (2017) Assessing Drought-Induced Change In A PiñOn-Juniper Woodland With Landsat: A Multiple Endmember Spectral Mixture Analysis Approach, International Journal Of Remote Sensing, 38(14), 4156-4176. https://doi.org/10.1080/01431161.2017.1317940 |
| 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 | 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 | Biederman, J. A., Scott, R. L., Goulden, M. L., Vargas, R., Litvak, M. E., Kolb, T. E., Yepez, E. A., Oechel, W. C., Blanken, P. D., Bell, T. W., Garatuza-Payan, J., Maurer, G. E., Dore, S., Burns, S. P. (2016) Terrestrial Carbon Balance In A Drier World: The Effects Of Water Availability In Southwestern North America, Global Change Biology, 22(5), 1867-1879. https://doi.org/10.1111/gcb.13222 |
| 2016 | Stark, S. C., Breshears, D. D., Garcia, E. S., Law, D. J., Minor, D. M., Saleska, S. R., Swann, A. L., Villegas, J. C., Aragão, L. E., Bella, E. M., Borma, L. S., Cobb, N. S., Litvak, M. E., Magnusson, W. E., Morton, J. M., Redmond, M. D. (2016) Toward Accounting For Ecoclimate Teleconnections: Intra- And Inter-Continental Consequences Of Altered Energy Balance After Vegetation Change, Landscape Ecology, 31(1), 181-194. https://doi.org/10.1007/s10980-015-0282-5 |
| 2015 | Krofcheck, D., Eitel, J., Lippitt, C., Vierling, L., Schulthess, U., Litvak, M. (2015) Remote Sensing Based Simple Models Of Gpp In Both Disturbed And Undisturbed PiñOn-Juniper Woodlands In The Southwestern U.S., Remote Sensing, 8(1), 20. https://doi.org/10.3390/rs8010020 |
| 2014 | Krofcheck, D. J., Eitel, J. U., Vierling, L. A., Schulthess, U., Hilton, T. M., Dettweiler-Robinson, E., Pendleton, R., Litvak, M. E. (2014) Detecting Mortality Induced Structural And Functional Changes In A PiñOn-Juniper Woodland Using Landsat And Rapideye Time Series, Remote Sensing Of Environment, 151, 102-113. https://doi.org/http://dx.doi.org/10.1016/j.rse.2013.11.009 |
| 2011 | Anderson-Teixeira, K. J., Delong, J. P., Fox, A. M., Brese, D. A., Litvak, M. E. (2011) Differential Responses Of Production And Respiration To Temperature And Moisture Drive The Carbon Balance Across A Climatic Gradient In New Mexico, Global Change Biology, 17(1), 410-424. https://doi.org/10.1111/j.1365-2486.2010.02269.x |
US-Mpj: Mountainair Pinyon-Juniper Woodland
- Overview
- Windroses
- Data Citation
- Data Use Log
- Image Gallery
- Remote Sensing Data
- MODIS
- PhenoCam
- GeoNEX
- Publications
- BADM
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-Mpj: Mountainair Pinyon-Juniper Woodland
- 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