Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
94
datasets available to search
ShareScore release 0.7.1
Dataset results
94 results for “Eddy covariance”
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from January 2014 to December 2017 for sensor Flux1
Eddy covariance (EC) CO2 fluxes from flux sensor set "Flux1" from January 2014 to December 2017 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, and water table height from a nearby tidal creek and the marsh platform.
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from January 2014 to December 2022 for sensor Flux2
Eddy covariance (EC) CO2 fluxes from sensor set "Flux2" from January 2014 to December 2022 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, and water table height from a nearby tidal creek and the marsh platform.
Eddy covariance (EC) vertical carbon fluxes from a Georgia tidal salt marsh from 2014 to 2024
We present our methodology and data for science ready vertical carbon fluxes from a Spartina alterniflora tidal salt marsh as part of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site on Sapelo Island, Georgia, USA. Vertical carbon fluxes were measured through the eddy covariance (EC) method from 2014 to 2024. The EC flux tower was located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. The proportional influence of marsh habitats on the flux measurements were 4% tall, 38% short, and 58% medium height form Spartina alterniflora. We present the net ecosystem exchange (NEE), ecosystem respiration (ER), and gross primary production (GPP) at 30-minute fluxes (μmol CO2 m-2 s-1), daily averages (μmol CO2 m-2 s-1) and totals (g C m-2 day-1), and annual (g C m-2 year-1) quantities. We provide estimated uncertainty for each flux at each integrated timescale as 95% confidence intervals. Providing open access to 10-year carbon flux datasets can facilitate collaboration for advancing regional and global blue carbon synthesis and scale-up studies.
North Temperate Lakes LTER Processed eddy covariance time series fluxes from tower located on roof of the CFL building oriented toward Lake Mendota 2012 - current
We calculated eddy covariance based fluxes of CO2, H2O, heat, and momentum to study lake-atmosphere exchanges since 2012. These data were collected by Ankur Desai from 2012 to present using a CSAT-3 sonic anemometer and LI-7500 gas analyzer located on the roof of the CFL building. A footprint model (Kljun) was used to screen for lake only data.
Time series of carbon dioxide and methane fluxes measured with eddy covariance for Falling Creek Reservoir in southwestern Virginia, USA during 2020-2025
We measured carbon dioxide and methane flux exchange with the atmosphere at the deepest site of Falling Creek Reservoir (Vinton, Virginia, USA) every 30 minutes from 04 April 2020 to 31 December 2025. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA) as a primary drinking water source. The dataset consists of micrometeorological and flux data collected using an eddy covariance system (LiCor Biosciences, Lincoln, Nebraska, USA) and analyzed with associated Eddy Pro software (Eddy Pro Version 7.0.6), including carbon dioxide, methane, and water vapor. All analysis scripts are included for data processing and quality assurance/quality control following best practices.
Time series of carbon dioxide fluxes measured with eddy covariance for Danjiangkou Reservoir in Hubei Province, China during 2022-2024
This dataset contains half-hourly micrometeorological and eddy covariance flux measurements of carbon dioxide (CO₂) collected over the water surface of the Danjiangkou Reservoir in Hubei Province, China, from April 2022 to November 2024. The eddy covariance tower was installed at the deepest point of the reservoir, which serves as a critical water source for water supply and regional ecological functions in the middle reaches of the Yangtze River. Measurements were obtained using a LI-COR eddy covariance system (LI-COR Biosciences, Lincoln, NE, USA), and fluxes were calculated using EddyPro software (version 7.0.6). The dataset includes CO₂ and CH₄ fluxes as well as supporting micrometeorological, radiation, and water temperature measurements. All data were processed following established best practices for eddy covariance measurements, including comprehensive quality assurance and quality control procedures, which are fully documented and included with the dataset.
Eddy Covariance and Meteorological Data in the Clearcut Site at Harvard Forest 2009-2012
Clearcutting and other forest disturbances perturb carbon, water, and energy balances in significant ways, with corresponding influences on Earth’s climate system through biogeochemical and biogeophysical effects. Observations are needed to quantify the precise changes in these balances as they vary across diverse disturbances of different types, severities, and in various climate and ecosystem type settings. This dataset reports eddy covariance and micrometeorological measurements of surface-atmosphere exchanges that can be combined with related datasets from vegetation inventories and chamber-based estimates of soil respiration and leaf gas exchange to collectively quantify and understand how carbon, water, and energy fluxes changed during the first four years following forest clearing in a temperate forest environment of the northeastern US. Associated publications show rapid recovery with sustained increases in gross ecosystem productivity (GEP) over the first three growing seasons post-clearing, coincident with large and relatively stable net emission of CO2 because of overwhelmingly large ecosystem respiration. The rise in GEP was attributed to vegetation changes not environmental conditions (e.g. weather), but attribution to the expansion of leaf area versus changes in vegetation composition remains unclear. Soil respiration was estimated to contribute 44% of total ecosystem respiration during summer months and coarse woody debris accounted for another 18%. Evapotranspiration also recovered rapidly and continued to rise across years with a corresponding decrease in sensible heat flux. Gross shortwave and longwave radiative fluxes were stable across years except for strong wintertime dependence on snow covered conditions and corresponding variation in albedo. Overall, these findings underscore the highly dynamic nature of carbon and water exchanges and vegetation composition during the regrowth following a severe forest disturbance, and sheds light on both the
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the Grand Bay, Mississippi flux tower site from March 2018 to January 2019
Eddy covariance (EC) CO2 fluxes from March 2018 to January 2019 collected over a Juncus roemerianus marsh located in the Grand Bay National Estuarine Research Reserve (NERR) in Mississippi. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height within the marsh.
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from December 2018 to January 2020
Eddy covariance (EC) CO2 fluxes from December 2018 to January 2020 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height from a nearby tidal creek.
Long-term Atmospheric, Soil and Water Sensor Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia
Long-term measurements of various atmospheric, soil and water properties were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air and water temperature, relative humidity, precipitation, wind speed and direction, soil temperature, water pressure and solar radiation components (i.e. incident and reflected photosynthetically available, total, long-wave and shortwave radiation). Measurements were logged at 5 minute intervals using multiple Campbell Scientific Instruments CR3000 data loggers, and then combined into a single monotonic time series data set. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Note that some measurements were spatially replicated with multiple sensors deployed in different micro-habitats (e.g. at the tower and in a nearby marsh platform or creek). Sensors were also added to the tower at various times after the initial installation, therefore some variables do not span the entire period of record. Measurements at this site are ongoing, and the data set will be updated annually to include additional observations.
Long-term Meteorological Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia
Long-term measurements of key meteorological variables were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air temperature, relative humidity, precipitation, wind speed, wind direction, photosynthetically-available and total solar radiation. Measurements were logged at 5 minute intervals using a Campbell Scientific Instruments CR3000 data logger then re-scaled to 15 minute and daily interval data sets. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration, and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Measurements began in 2013; however, the total solar pyranometer was not installed until 2018 and minimum and maximum 5 minute air temperature were not added until 2019 so not all variables span the complete period of record. Measurements are continuing at this site and this data set will be updated annually to include additional observations.
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
ForestAge-Constrained Eddy-Covariance Gridded NEP Product
<p><strong>Description</strong></p> <p>This repository holds global spatial estimates of the Net Ecosystem Productivity of forests (NEP), circa 2010, for a grid spacing of 0.5° by 0.5º pixel size. Three different approaches were used to create the maps.</p> <ol> <li> <p><strong>Model M1 (Regional Age–NEP Relationships Per Biome)</strong>: This model scales site-level NEP observations to a global gridded field using biome-specific NEP-age curves and site-level anomalies. The random forest model (RF1) is trained on forest age, GPP, temperature, and nitrogen deposition, producing NEP anomalies that reflect site-specific deviations from biome-wide trends. Gridded predictor fields of forest age, GPP, temperature (MAT), and nitrogen deposition are used to create 0.5° by 0.5° NEP grids, with uncertainties estimated using an ensemble of 180 members. The data from Model M1 can be investigated from the ForestAge_EC_NEP_M1_v1.0.nc file.</p> </li> <li> <p><strong>Model M2 (Global Age–NEP Relationship)</strong>: This model uses a random forest algorithm (RF2) to upscale NEP observations but applies a global NEP-age relationship across all sites. It uses the same gridded predictor fields as M1—forest age, GPP, MAT, and nitrogen deposition—but the age–NEP relationship is determined globally. Uncertainty is calculated similarly to M1, using ensembles of model parameters and predictor fields. The data from Model M2 can be investigated from the ForestAge_EC_NEP_M2_v1.0.nc file.</p> </li> <li> <p><strong>Model M3 (Without Age Consideration)</strong>: This model predicts NEP solely based on GPP, MAT, and nitrogen deposition without accounting for forest age. It follows a similar approach to RF3 models from previous work and uses the same gridded predictors and uncertainty estimation methods as M1 and M2. The data from Model M3 can be investigated from the ForestAge_EC_NEP_M3_v1.0.nc file.</p> </li> </ol> <p>The variation across each model's members can assess the uncertainty in each model, which represents uncertainty caused by input variables and the k-fold cross-validation approach. </p> <p>More details about the methodologies behind the three approaches can be found in Ciais, P., Yao, Y. Besnard, S. et al. (2024) (see reference below).</p> <p><strong>Data structure</strong></p> <p>The datasets are stored in <strong>NetCDF format</strong> with a structure consistent across the different models (M1, M2, M3). Each file contains multiple variables representing components of the Net Ecosystem Production (NEP) estimates, such as the mean NEP and its quantiles. The primary variables are:</p> <ul> <li><strong>NEP_MX_mean</strong>: The mean estimate of NEP for each model (M1, M2, M3), with units of grams of carbon per square meter per year (gC m⁻² year⁻¹).</li> <li><strong>NEP_MX_quantiles</strong>: Estimates of NEP at different quantiles, providing uncertainty ranges. The quantiles represented in the data are: [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75]<br> <div> </div> </li> <li><strong>Members dimension</strong>: Each model includes a <strong>members</strong> dimension, representing several NEP estimates generated using different ensemble members. These members capture uncertainty from input variables such as GPP, temperature, nitrogen deposition, and forest age. The members dimension provides users with multiple realizations of NEP estimates, reflecting the variability these factors introduce.</li> </ul> <p>Coordinates include latitude and longitude with CRS information (EPSG:4326). Missing data values are represented by <strong>-9999</strong>.</p> <p><strong>Citation</strong></p> <p>When using the maps, please cite the dataset, including the version number and the following paper: Ciais, P., Yao, Y. Besnard, S. et al. (2024) The global carbon balance of forests based on flux towers and forest age data, <em>submitted</em>. </p> <p><strong>Version History</strong></p> <ul> <li>1.0 - Initial version, covering 2010</li> </ul>
diFUME eddy covariance dataset
<p>Net CO<sub>2</sub> flux (FC) time-series and the respective random error (RE) estimations at 30 min time step of the period 2018 - 2020 from two urban eddy covariance sites in the center of Basel. Data are produced using the EddyPro® Software v7.0.6 (LI-COR Inc.). The main processing steps include axis rotation for tilt correction using the double rotation method, linear detrending to extract turbulent fluctuations, covariance maximization for time-lag compensation between the gas analyser and the sonic anemometer and density fluctuation compensation. Spectral corrections are also applied to flux estimates for low and high frequency losses. Quality flagging is performed according to steady state and integral turbulence characteristics tests based on the 3-point flagging system of Mauder and Foken. Flux random uncertainty estimation is performed according to the method of Finkelstein and Sims (2001). The time-series are filtered according to multiple criteria to avoid problematic values.</p> <p> </p> <p>Data format: comma separated values (csv)</p> <p>Time step: 30 min</p> <p> </p> <table> <tbody> <tr> <td> <p>Station acronym</p> </td> <td> <p>Geographic location</p> </td> <td> <p>Sensor height (m a.g.l.)</p> </td> <td> <p>Sensor models (sonic anemometer, gas analyser)</p> </td> <td> <p>Sonic azimuth (<sup>o</sup>)</p> </td> <td> <p>Acquisition frequency (Hz)</p> </td> </tr> <tr> <td> <p>BKLI</p> </td> <td> <p>47.56173 °N, 7.58049 °E</p> </td> <td> <p>39</p> </td> <td> <p>HS-100 (Gill Instruments Ltd.),</p> <p>LI-7500 (LI-COR Inc.)</p> </td> <td> <p>0</p> </td> <td> <p>20</p> </td> </tr> <tr> <td> <p>BAES</p> </td> <td> <p>47.55123 °N, 7.59560 °E</p> </td> <td> <p>41</p> </td> <td> <p>CSAT3 (Campbell Scientific Inc.), LI-7500 (LI-COR Inc.)</p> </td> <td> <p>340</p> </td> <td> <p>20</p> </td> </tr> </tbody> </table> <p> </p>
GOES-R Land Surface Products at AmeriFlux and NEON Eddy Covariance Tower Locations
The terrestrial carbon cycle varies dynamically over short periods that can be difficult to observe. Geostationary (“weather”) satellites like the Geostationary Operational Environmental Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence, and its Advanced Baseline Imager (ABI) measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature, and downwelling shortwave radiation (DSR) at 318 ABI fixed grid pixels containing eddy covariance towers for years 2020-2022. We demonstrate how to best combine satellite and in-situ datasets, and show how ABI attributes useful for carbon cycle science vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.
Surface carbon, water and energy fluxes measured by eddy covariance at 3 sites within the Alaska Peatlands Experiment and Bonanza Creek Experimental Forest
These data are simultaneous and continuous measurements of carbon, water and energy fluxes of the terrestrial landscape. These fluxes are major regulatory drivers of the boreal climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. At the APEX project site, within Bonanza Creek Experimental Forest, this monitoring is repeated across a chronosequence of permafrost degradation; the Black Spruce site is an area of stable permafrost with intact black spruce forest (APEX gamma site), the Thermokarst site is an active thermokarst zone with considerable tree mortality (APEX betaSW site), the Fen site is within a stable treeless fen with deep active layer depth (APEX apexcon,low, and ele sites). The main variables being monitored are the instananeous fluxes of CO2, water vapor and surface energy (shortwave, longwave and net radiation), secondary variables included photosynthetically active radiation (PAR), air and soil temperatures, rainfall, snow depth, soil moisture content, wind direction and speed, and average atmospheric concentrations of CO2 and H2O through the year.
Inventory of Eddy Covariance Tower Data in AmeriFlux from Everglades Towers, Florida: 2004-ongoing
There are six eddy covariance towers that make continuous measurements of regional hydrology and carbon balance in the region. This long-term eddy covariance tower network includes 2008-ongoing data collection in a freshwater marl prairie (TS/Ph-1; US-Esm) and a freshwater marsh (SRS-2; US-Elm), 2016-ongoing data collection from a mangrove scrub (TS/Ph-7; US-TaS), 2004-ongoing data collection from a tall mangrove forest (SRS-6; US-Skr), 2020-ongoing data collection from a tower at the ecotone of marl prairie and mangrove scrub (SE1; US-EvM), and 2018-ongoing data collection from an aquatic Tower in Florida Bay (Bob Allen Key; US-FBE). Everglades ecosystems occur in predictable zonal patterns, and current Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) sites are arranged to capture the variation in hydrology, community composition, and productivity. The hydrology and disturbance regime in the Everglades region developed a rich diversity of communities that have variable capacities to capture and sequester carbon. At each site, open-path infrared gas analyzers (IRGA, LI-7500 and Li-7700, Li-COR Inc., Lincoln, NE) are used to measure CO2 (mg mol-1), water vapor molar density (mg mol-1), and CH4. A paired sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT) is employed to measure sonic temperature (Ts; K) and 3-dimensional wind speed (u, v and w, respectively; m s-1). Additional meteorological data is measured at the site to monitor conditions. This data package is an inventory of eddy covariance tower data and metadata available through the AmeriFlux repository.
Supplementary Dataset for "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites"
<p>These datasets are supplementary to the paper "<strong>Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites</strong>" by Chu et al. </p> <ul> <li>Dataset S1. Summary of site-specific footprint metrics <ul> <li>filename: All_site_fpt_summary.csv</li> <li>readme: All_site_fpt_summary-README.csv</li> </ul> </li> <li>Dataset S2. All monthly footprint climatology weight maps <ul> <li>filename: monthly_footprint_climatology_weight_map.zip <ul> <li>the zip folder contains individual files of all monthly footprint weight maps</li> <li>filename: <Site-ID>_<Year>_<Month>_<DAY/NIGHT>_fpt_weight.tif</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S3. All site-year footprint climatology overlapped with true-color satellite images. <ul> <li>filename: site-year_footprint_climatology_realcolor_map.zip <ul> <li>the zip folder contains individual files of footprint climatologies from all site-years</li> <li>filename: <Site-ID>_<Year>_<Spatial_Extent>_shrink_footprint_climatology.png</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S4. Site-specific results and representativeness index based on the land cover type analysis. <ul> <li>filename: All_site_land_cover_dominant_summary2.csv</li> <li>readme:All_site_land_cover_dominant_summary2-README.csv</li> </ul> </li> <li>Dataset S5. Site-specific results and representativeness index based on the EVI analysis. <ul> <li>filename: All_site_Landsat_EVI_fpt_comparison2.csv</li> <li>readme: All_site_Landsat_EVI_fpt_comparison2-README.csv</li> </ul> </li> <li>Dataset S6. All available site-month EVI and time-explicit representativeness. <ul> <li>filename: All_site_Landsat_EVI_all_cutout2.csv</li> <li>readme: All_site_Landsat_EVI_all_cutout2-README.csv</li> </ul> </li> </ul>
AusEFlux: Empirical upscaling of OzFlux eddy covariance flux tower data over Australia
<p>AusEFlux (<strong>Aus</strong>tralian <strong>E</strong>mpirical <strong>Flux</strong>es) is a high resolution (500 metre) gridded estimate of Gross Primary Productivity (GPP), Ecosystem Respiration (ER), Net Ecosystem Exchange (NEE), and Evapotranspiration over the Australian continent for the period January 2003 to Present. These datasets provide a benchmark for assessment against Land Surface Model simulations, and a means for monitoring of Australia’s terrestrial carbon cycle at an unprecedented high-resolution.</p> <p><strong>Version 2.1 </strong>of AusEFlux has just been released (as of May 2025) and was created to <strong>operationalise</strong> the research datasets published in this <a href="https://doi.org/10.5194/bg-20-4109-2023">EGU Biogeosciences publication.</a> In order to operationalise these datasets, changes to the input datasets were required to align the data sources with datasets that are regularly and reliably updated, along with general improvements. The datasets provided on Zenodo have been reprojected to 5 km resolution to facilitate easier uploading and sharing, but<strong> full resolution datasets (both v1.1 and v2.1) can be accessed freely through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal.</a></strong></p> <p><strong>Two Jupyter Notebooks</strong> have been created (one for GPP and one for NEE) that demonstrate the differences between the research datasets (v1.1) and the operational datasets (v2.1), including showing the differences in specifications and inputs. You can view/download these notebooks using the links below:</p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_GPP.ipynb">GPP comparison between versions</a></p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_NEE.ipynb">NEE comparisons between versions</a></p> <p>Each dataset contains three variables:</p> <ul> <li>"<flux>_median": represents the 'best-estimate' of a given flux, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_25th_percentile": represents the lower uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_75th_percentile": represents the upper uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> </ul> <p><span><strong>Version Guide</strong>:</span></p> <p><em>v1.0:</em> DO NOT USE THIS VERSION. There was a mistake in the modelling of ecosystem respiration, so this version of the dataset should not be used. As of version 1.1, the error has been rectified.</p> <p><em>v1.1: </em>This version of the datasets are those used to inform the EGU Publication linked above. Its time range is 2003-July 2022, and its spatial resolution is 5 km on Zenodo, but the 1 km resolution datasets can be accessed through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal</a>.</p> <p><em>v2.0: <strong>IMPORTANT NOTE:</strong> a bug in the modelling of vegetation height resulted in data artefacts in the NEE and ER fluxes over very tall mesic forests in this version. This resulted in unnaturally high ER and lower than expected NEE (less negative than would be expected). This issue has been rectified in version 2.1.</em> <strong>Version 2 datasets represent the operational version of the datasets</strong>, it includes several improvements over version 1.1. Its time-range is 2003-2024 (and will be updated annually), and its spatial resolution is 500m. A 5 km reprojected version of the dataset is included here on Zenodo, but the 500 metre datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p> <p><strong>v2.1: </strong>This version is a patch to version 2.0 to remove a bug in the modelling of vegetation height. <strong>It is recommended to use this version </strong>over v2.0. 500 metre resolution datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p>
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.