Data

Latent and Sensible Heat Flux for Australia by Scaling Flux Tower Data 2000 to 2023

Terrestrial Ecosystem Research Network
Van Niel, Thomas G
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/c2ee40f0-4bd1-49a6-8880-31c9054a3675&rft.title=Latent and Sensible Heat Flux for Australia by Scaling Flux Tower Data 2000 to 2023&rft.identifier=http://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/c2ee40f0-4bd1-49a6-8880-31c9054a3675&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=This dataset comprises spatially and temporally dynamic estimates of the monthly latent heat flux (λE) and sensible heat flux (H) for all of Australia. The available energy (A, being net radiation [Rn] less the gound heat flux [G]) can be obtained by adding the λE and H datasets provided. Energy variables have been provided as hydrological equivalent units of depth, normalised to daily rates (mm/d). TERN OzFlux Surface Energy Balance (SEB) data were used to scale MODIS-based covariates of surface temperature less air temperature (Ts – Ta) and Rn using a Spatial and Temporal General Linear Model (ST-GLM) to third order. The ST-GLM SEB model was implemented across all of Australia on a 0.005° spatial grid (~ 500 m) on a monthly timestep from March 2000 through June 2023. Coefficients of the model were determined from the OzFlux network of eddy covariance flux tower data. Three flux tower sites were used to independently validate the accuracy of the model, being Calperum, SA, Howard Springs, NT, and Tumbarumba, NSW. The mean absolute difference (MAD) for λE, H and A was estimated as: 0.37, 0.39 and 0.34 mm/d, respectively. The relative errors determined by the MAD percentage (MADP) for λE, H, and A were estimated to be: 16%, 26%, and 9%, respectively. This dataset represents a new pathway for operational regional- to global-scale estimation of dynamic SEB variables.Progress Code: completedMaintenance and Update Frequency: notPlanned&rft.creator=Van Niel, Thomas G &rft.date=2024&rft.edition=1&rft.coverage=Australia.&rft.coverage=northlimit=-10; southlimit=-44; westlimit=112; eastLimit=154; projection=EPSG:4326&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_rights=TERN services are provided on an as-is and as available basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure. <br />Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting&rft_rights=Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.&rft_subject=environment&rft_subject=LATENT HEAT FLUX&rft_subject=SENSIBLE HEAT FLUX&rft_subject=NET RADIATION&rft_subject=EARTH SCIENCE&rft_subject=ATMOSPHERE&rft_subject=ATMOSPHERIC RADIATION&rft_subject=WATER MANAGEMENT&rft_subject=HUMAN DIMENSIONS&rft_subject=ENVIRONMENTAL GOVERNANCE/MANAGEMENT&rft_subject=Surface water hydrology&rft_subject=Agricultural hydrology&rft_subject=Agroforestry&rft_subject=AGRICULTURAL AND VETERINARY SCIENCES&rft_subject=FORESTRY SCIENCES&rft_subject=Agricultural Land Management&rft_subject=AGRICULTURE, LAND AND FARM MANAGEMENT&rft_subject=Forestry Fire Management&rft_subject=Earth Observation Satellite&rft_subject=air temperature (Kelvin per Kelvin)&rft_subject=Kelvin per Kelvin&rft_subject=surface temperature (Kelvin per Kelvin)&rft_subject=500 meters - < 1 km&rft_subject=Monthly - < Annual&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International Licence
http://creativecommons.org/licenses/by/4.0

TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting

Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.

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Brief description

This dataset comprises spatially and temporally dynamic estimates of the monthly latent heat flux (λE) and sensible heat flux (H) for all of Australia. The available energy (A, being net radiation [Rn] less the gound heat flux [G]) can be obtained by adding the λE and H datasets provided. Energy variables have been provided as hydrological equivalent units of depth, normalised to daily rates (mm/d). TERN OzFlux Surface Energy Balance (SEB) data were used to scale MODIS-based covariates of surface temperature less air temperature (Ts – Ta) and Rn using a Spatial and Temporal General Linear Model (ST-GLM) to third order. The ST-GLM SEB model was implemented across all of Australia on a 0.005° spatial grid (~ 500 m) on a monthly timestep from March 2000 through June 2023. Coefficients of the model were determined from the OzFlux network of eddy covariance flux tower data. Three flux tower sites were used to independently validate the accuracy of the model, being Calperum, SA, Howard Springs, NT, and Tumbarumba, NSW. The mean absolute difference (MAD) for λE, H and A was estimated as: 0.37, 0.39 and 0.34 mm/d, respectively. The relative errors determined by the MAD percentage (MADP) for λE, H, and A were estimated to be: 16%, 26%, and 9%, respectively. This dataset represents a new pathway for operational regional- to global-scale estimation of dynamic SEB variables.

Lineage

Progress Code: completed
Maintenance and Update Frequency: notPlanned

Notes

Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
Purpose
Managing and understanding water resources requires data on latent heat flux (λE) dynamics across climatic zones at scales from regions to continents in both space and time. Large-scale dynamic λE is not directly observed, however, and hence using remote sensing approaches to scale point flux tower measurements may be a solution to provide the required spatially and temporally dynamic information. The interaction between λE and the sensible heat flux (H) describes the partitioning of the available energy (A) and can provide important context for soil water dynamics and is thus a key to understanding how an ecosystem will use water and respond to heat stress. Having spatially and temporally dynamic estimates of λE and H for all of Australia means this data source can be used to model the water and energy dynamics of any ecosystem, catchment, or land management unit in Australia.

Created: 2023-06-30

Issued: 2024-10-01

Modified: 2024-10-01

Data time period: 2000-03-01 to 2023-05-30

This dataset is part of a larger collection

Click to explore relationships graph

154,-10 154,-44 112,-44 112,-10 154,-10

133,-27

text: Australia.