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97 results for “energy flux”
Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem
<p>This dataset contains a subset of measurements collected at the experimental site Majadas de Tietar. We collected ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean Savanna using the eddy covariance technique and a series of meteorological sensors for the time period December 2015 - February 2018. The dataset is used for the development of a series of R packages including 'bigleaf' (Knauer et al., 2018).</p> <p>The experimental site is collected in Majadas de Tietar (Casals et al., 2009) located in western Spain (39°56′25″N 5°46′29″W). The ecosystem is a typical “Iberic Dehesa”, which is characterized by an herbaceous stratum of native pasture and sparse trees, for the majority (~98%) Quercus ilex. The tree density is about 20–25 trees/ha, the fractional cover of trees is about 20%, mean DBH of 46 cm, and a canopy height of about 8 m. (El-Madany et al., 2018). The herbaceous layer is composed of native annual species of the three main functional plant forms (grasses, forbs and legumes), whose fractional cover varies seasonally and is characterized by important inter-annual variations in the seasonal dynamics related to the onset of the dry period.</p> <p>Fluxes were measured with the eddy covariance technique with two different systems, one at ecosystem scale to characterize the fluxes of the whole ecosystem (15.5 m above ground), and one at 1.65 m above ground in an open space to measure the fluxes of the well-established understory grass layer.</p> <p>The description of the set-up, equipment and processing used to calculate ecosystem scale fluxes are described in El-Madany et al., (2018), while for the understory tower can be found in Perez-Priego et al., (2017).</p> <p>The dataset is composed of two files: 'ESLMa_MainTower', which is the ecosystem eddy covariance system, and 'ESLMa_SubCanopy', which is the understory eddy covariance system. The dataset contains half-hourly, processed eddy covariance of the ecosystem and understory tower, as well as the main biometeorological data used in the big-leaf package (net radiation, soil heat fluxes, horizontal wind velocity, atmospheric pressure, precipitation, air temperature). All the processing was conducted with EddyPro software (version 5.2.0, LI-COR Biosciences Inc., Lincoln, NE, USA) and the ustar filtering, gap-filling and partitioning with the R package REddyProc (Wutzler et al., 2018). The variables and the units are described in the Readme.txt file released with the dataset.</p> <p><strong>References</strong></p> <p>Casals, P. et al., 2009. Soil CO2 efflux and extractable organic carbon fractions under simulated precipitation events in a Mediterranean Dehesa. Soil Biol. Biochem. 41, 1915–1922. <a href="https://doi.org/10.1016/j.soilbio.2009.06.015">https://doi.org/10.1016/j.soilbio.2009.06.015</a>.</p> <p>El-Madany, T.S.,et al., 2018. Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem 21. <a href="https://doi.org/10.1016/j.agrformet.2018.07.010">https://doi.org/10.1016/j.agrformet.2018.07.010</a></p> <p>Knauer, J., et al., 2018. bigleaf - An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLOS ONE, doi:10.1371/journal.pone.0201114</p> <p>Perez-Priego O, et al., 2017. Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem. Agricultural and Forest Meteorology. 236: 87-99. doi: 10.1016/j.agrformet.2017.01.009.</p> <p>Wutzler, T., et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences Discuss., p. 1-39.</p> <p> </p>
Data in: Reduced predation and energy flux in soil food webs by introduced tree species
<p>The introduction of non-native tree species has become a global concern and may disruptnative communities and related ecosystem functions. Soil food webs regulate organic matter decomposition and nutrient cycling in forests with their feeding activities, butevaluating consequences of tree species introduction on soil invertebrates is challengingdue to the complex trophic structure and wide range in body size of soil invertebrates. Here, we employed an energetic food web approach, and estimated the energy flux in soil food webs using a four-node model including soil meso- and macrofauna decomposers and predators. We examined pure and mixed stands of native European beech (<em>Fagus sylvatica</em>), introduced Douglas fir (<em>Pseudotsuga menziesii</em>) and native range-expanding Norway spruce (<em>Picea abies</em>) across site conditions. Compared to native forests, introduced tree species reduced total mass of macrofauna predators by 92% at sandy sites but not that of decomposers, suggesting trophic downgrading in soil food webs by Douglas fir. The energy flux in mixed forests was intermediate between respective monocultures, suggesting that tree mixtures mitigate potential negative impacts of introduced tree species on food web functioning. Across size classes, soil macrofauna responded more sensitively to changes in environmental conditions than soil mesofauna. Despite the lower total mass, the energy flux through mesofauna outweighed that through macrofauna when consideringenergy loss to predators, highlighting the importance of mesofauna for decomposition processes in forest soil food webs. Additionally, total energy flux positively correlated with species richness, pointing to the significance of soil biodiversity for trophic functionality. Overall, the study emphasizes the critical role of tree species composition, site conditionsand soil biodiversity in driving energy flux through soil food webs and maintaining forest ecosystem functions.</p>
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.
Evaluating F10.7 and F30 Radio Fluxes as Long-Term Solar Proxies of Energy Deposition in the Thermosphere
<p><span>We use model simulations and observations to examine how well the F10.7 and F30 solar radio fluxes represent solar forcing in the thermosphere during the last 60 years of weakening solar activity. We found that increased saturation of F10.7 during the last two extended solar minima leads to an overestimation of solar energy deposition, which manifests as a change in the linear relation between thermospheric parameters and F10.7. On the other hand, the linear relation between thermospheric parameters and F30 remains nearly the same throughout the whole studied period because of a recently found relative increase of F30 with respect to F10.7. Therefore, F30 is a more consistent proxy than F10.7 during the last 60 years. We note that continued evaluation is needed to see how well F10.7 and F30 will serve as solar proxies in the future when solar activity may start increasing toward the next grand maximum.</span></p>
The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf
<p>This dataset contains the measured hourly mean microclimate and surface energy flux data from a field experiment. The experiment consisted of three treatments: unirrigated artificial turf, unirrigated natural turf, and irrigated natural turf (4 mm/day, 13:00-13:23 local time). The experiment was conducted from 2024-01-28 to 2024-03-18 in Burnley, Melbourne, Australia.<br><br>For each treatment, the measured hourly mean data included albedo, soil moisture content, air temperature, vapour pressure of water, wind speed, black globe temperature, mean radiant temperature, universal theraml climate index, wet-bulb globe temperature, soil temperature, turf surface temperature, incoming and outgoing longwave and shortwave radiant fluxes, sensible heat flux, latent heat flux, and ground heat flux. <br><br>Turf surface temperature, and incoming and outgoing longwave and shortwave radiant fluxes were measured at 1.5 m above ground surface.<br>Air temperature and vapour pressure of water were measured at 0.6 and 1.1 m above ground surface.<br>Wind speed, black globe temperature, mean radiant temperature, universal thermal climate index, and wet-bulb globe temperature were measured at 1.1 m above ground surface.<br>Soil moisture content, soil temperature and ground heat flux were measured at 0.1 m below ground surface.<br>Sensible heat flux and latent heat flux were calculated using the Bowen ratio-energy balance method.<br><br>Additionally, the hourly mean background weather conditions (air temperature and cloud amount) from the nearest public climate station in the study period were included in 'ReferenceClimateStation.csv'. Hourly total rainfall data measured at the study site was also included. <br><br>The aims of this study was to:<br>1. Compare the microclimate and human heat stress among the three treatments.<br>2. Assess and compare the human skin burn risks of the three treaments from their turf surface temperatures.<br>3. Analyse the surface energy fluxes of the three treatments to identify the mechanisms by which artificial turf develops any microclimate, human heat stress and turf surface temperature differences.<br><br>This study was published in:</p> <p><span>Cheung, P. K., & Livesley, S. J. (2025). The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf. <em>Building and Environment</em>, 112679. https://doi.org/10.1016/j.buildenv.2025.112679<br></span><br>Contact person: Dr Paul Cheung (cheung.p@unimelb.edu.au)</p>
UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2018
<div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2018</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The UFLUX project includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div>
Mass and energy fluxes from the US-Jo2 AmeriFlux eddy covariance tower in Tromble Weir experimental watershed at the Jornada Basin LTER site, 2010-ongoing
This data package contains metadata for, and links to, 30-minute mass and energy flux data collected at an eddy covariance tower in the Tromble Weir Watershed area of the Jornada Basin in southern New Mexico, USA. These data are used to quantify the water and energy balances in a small experimental watershed, including observational studies to calculate groundwater recharge as a water balance residual, to build relations between soil moisture state and ET flux, and to quantify land-atmosphere interactions and improve our understanding of the eddy covariance method. Additionally, they have been used in modeling studies as a validation of model performance. The .csv files included in this package list and describe the data entities and variables present in data files archived at the AmeriFlux data repository (site US-Jo2; http://ameriflux.lbl.gov/sites/siteinfo/US-Jo2). The data at AmeriFlux include the 30-minute mass and energy fluxes calculated from 20 Hz data collected at the Tromble Weir Watershed tower. Instrument descriptions and detailed procedures are found in the references listed in the Methods section of this EDI package. This is an ongoing dataset that will be updated annually.
Carbon and energy Eddy-covariance fluxes dataset collected at La Guette peatland (23 ha, Loiret, France)
<p>Fluxes and energy data measured by Eddy-covariance on La Guette peatland (ec1). Measurements start on 20-01-2017 and are regularly updated with new data. Data include carbon dioxide fluxes (CO2, µmol/m²/s), methane fluxes (CH4, µmol/m²/s), sensible heat fluxes (H, W/m²), latent heat fluxes (LE, W/m²) and evapotranspiration (ETR, mm/h).</p> <p>Zip file contain :</p> <ul> <li>metadata file (TOUR_en.json) which describe stations, sensors, variables and process</li> <li>csv file contain time series data for all variables by station</li> </ul> <p>Additional information on the measurement can be found in this website : <a href="https://data-snot.cnrs.fr/data-access/">https://data-snot.cnrs.fr/data-access/</a></p> <p>We also recommend to contact sno-tourbieres to talk about data acquisition and use : <a href="mailto:contact.sno-tourbieres@cnrs-orleans.fr">contact.sno-tourbieres@cnrs-orleans.fr</a></p>
Carbon and energy Eddy-covariance fluxes dataset collected at Frasne peatland (192ha, Jura Mountains, France)
<p>luxes and energy data measured by Eddy-covariance at Frasne peatland (ec1). Measurements start on 20-07-2018 and are regularly updated with new data. Data include carbon dioxide fluxes (CO2, µmol/m²/s), methane fluxes (CH4, µmol/m²/s), sensible heat fluxes (H, W/m²), latent heat fluxes (LE, W/m²) and evapotranspiration (ETR, mm/h).</p> <p>Zip file contain :</p> <ul> <li>metadata file (TOUR_en.json) which describe stations, sensors, variables and process</li> <li>csv file contain time series data for all variables by station</li> </ul> <p>Additional information on the measurement can be found in this website : <a href="https://data-snot.cnrs.fr/data-access/">https://data-snot.cnrs.fr/data-access/</a></p> <p>We also recommend to contact sno-tourbieres to talk about data acquisition and use : <a href="mailto:contact.sno-tourbieres@cnrs-orleans.fr">contact.sno-tourbieres@cnrs-orleans.fr</a></p>
Mean Energies and Energy Fluxes from THEMIS ASIs on 2010-02-16 from 9-10 UT
<p>This dataset includes the precipitated mean energies and energy fluxes determined using the THEMIS all-sky-imagers on February 16, 2010, from 9-10 UT. Versions that assumed a Gaussian distribution and those that assumed a Maxwellian distribution of the precipitated populations are both included. The methods to create the dataset are described in the following publication, for which this data was used:</p> <p>Gabrielse C, Nishimura T, Chen M, Hecht JH, Kaeppler SR, Gillies DM, Reimer AS, Lyons LR, Deng Y, Donovan E and Evans J (2021) Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky- Imagers With Focus on Mesoscales. Front. Phys. 9:744298. doi: 10.3389/fphy.2021.744298 </p> <p><a href="https://www.frontiersin.org/articles/10.3389/fphy.2021.744298/full">https://www.frontiersin.org/articles/10.3389/fphy.2021.744298/full</a></p> <p>The data are stored in TPLOT variables, which can be accessed using the SPEDAS software routines that can be downloaded here: http://themis.igpp.ucla.edu/software.shtml </p> <p>The TPLOT variables store the data in a structure that has the following format:</p> <p>DOUBLE Array[1200]<br> X_IND LONG Array[1]<br> Y FLOAT Array[1200, 400, 400]<br> Y_IND LONG Array[1]<br> GLAT FLOAT Array[400, 400]<br> GLAT_IND LONG Array[1]<br> GLON FLOAT Array[400, 400]<br> GLON_IND LONG Array[1]</p> <p>The Y attribute contains the data (either mean energy or energy flux) in a 1200 x 400 x 400 array. The 1200 points represent time, which are recorded in the X attribute as seconds since January 1, 1970. The 400 x 400 array represent the 400 x 400 locations where data was recorded. Those locations are stored as geographic latitude and longitudes in the GLAT and GLON attributes. </p> <p> </p>
Dataset for article "Energy flux paths in lakes and reservoirs" by Guseva et al., 2021
<p>The dataset includes the measured parameters that have been analyzed in the manuscript “Energy flux paths in lakes and reservoirs” by Guseva S., Casper P., Sachs T., Spank U. and Lorke A. The manuscript has been submitted to <em>Water</em>.</p>
Modeling the recent drought and thinning impacts on energy, water and carbon fluxes in a boreal forest
<p>This dataset includes the data used for model calibration and validation, as well as the simulation files with accepted runs, which are available for the readers to re-generate the results of this work. The *.bin files are the data for driving the model and for calibration and validation. They are specifically in the format for the CoupModel. Therefore, to check the data the CoupModel software needs to be installed. </p> <p>Additionally, we provide the software for CoupModel, which the readers could install on local computers to check the simulations. For detailed instructions on how to run CoupModel, please visit the CoupModel website www.coupmodel.com.</p>
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
<p><span>Tropical rainforests around the world are rapidly being converted into cash-crop agricultural systems. The associated massive losses of plant and animal species lead to changes in arthropod food webs and the energy fluxes therein. These changes are poorly understood, in particular in the extremely biodiverse canopies of tropical ecosystems. Using canopy fogging followed by stable isotope and energy flux analyses, we show that land-use conversion from rainforest to rubber and oil palm plantations not only causes a drastic reduction in energy fluxes of up to 75% but also shifts fluxes among trophic groups. While rainforests featured high levels of both herbivory and algae-microbiology, and a balanced ratio of herbivory to predation, relative fluxes were shifted towards predation in rubber and towards herbivory in oil palm plantations, indicating profound shifts in ecosystem functioning. Our results highlight that the ongoing loss of animal biodiversity and biomass in tropical canopies degrades animal-driven functions and restructures canopy food webs.</span></p>
NEMARCO project: Dataset for the publication "Solidification Path, Strengthening Mechanisms and Hardness of Ni-Cr-Si-Fe-B Self-Fluxing Alloys Obtained by Laser-Directed Energy Deposition (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based Ni-Cr-Si-B self-fluxing alloys are excellent candidates to replace Cobalt-based alloys in aeronautical components. In this work, metal additive manufacturing by directed energy deposition using a laser beam (DED-LB, also known as LMD) and gas-atomized powders as a material feedstock is presented as a potential manufacturing route for the complex processing of these alloys. This research deals with the advanced material characterization of these alloys obtained by LMD and the study and understanding of their solidification paths and strengthening mechanisms.</p>
Iberian Summer Surface Temperature and Fluxes for Energy Balance
<p>This dataset holds selected postprocessed files for surface temperature and fluxes involved in the surface energy balance.</p><p>Four WRF experiments nested in ERA-Interim were prepared. The first one (N) was configured as in standard numerical downscaling experiments using the Noah LSM. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 hours. The third and the fourth ones (S and C) are similar to N and D but use a diffusive soil scheme instead of NOAH LSM. The experiments covered the period 2010-2014 after a year of spin-up (2019). </p><p>The following 3-hourly files are included:</p><ul><li>Tsoil: soil temperature for the first 2 top levels of the surface.</li><li>T2: 2 metre temperature. </li><li>Latent: Latent heat flux.</li><li>Sensible: Sensible heat flux.</li><li>NetSW: net short-wave radiation flux at the surface.</li><li>NetLW: net long-wave radiation flux at the surface.</li><li>GRDFLX: ground flux toward lower layers of the soil.</li></ul><p>The <i>N, D, C or S </i>characters in the file names indicate whether the files come from the WRF N, D, C or S experiments. The files include a table including the 3-hourly data for each grid point over the Iberian Peninsula: year | month | day | hour | V1 | ... | V2058 </p><p>The 2058 grid points included in each file are listed in the same order as in the file WRFmask_points_withoutUrban_withLandType.dat </p><p> </p><p> </p>
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
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Carbon, energy, and water flux data from annual and perennial agroecosystems
Open the record for dataset details and reuse information.
Surface carbon, water and energy fluxes measured by eddy covariance at 3 sites within the Alaska Peatlands Experiment and Bonanza Creek Experimental Forest 2013-2016
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. Our site naming scheme is as follows: 1) gamma = Black Spruce site = YF_2472, 2) betaSW = Thermokarst site= BC_5166, 3) (apexcon+apexele+apexlow) = Fen site = BC_FEN
Community Land Model version 4.5 (CLM4.5) simulations of water, energy, and carbon fluxes for Saddle vegetation communities, 2008 - 2013
Single point simulations of CLM4.5 that include (1) forcing data that were input to the model and subsequent (2) model output for simulations that approximate conditions in fellfield, dry meadow, moist meadow, wet meadow, and snowbed vegetation communities. Forcing data were generated with observed atmospheric conditions from Tvan, Saddle precipitation, and incoming shortwave radiation measured from the AmeriFlux tower site (US-NR1) from 2008-2013. Wintertime precipitation inputs were modified to approximate average snow depth for each vegetation community observed across the Saddle grid. Land models, like CLM, provide a cohesive framework to investigate biogeophysical and biogeochemical effects of environmental change on ecosystem processes. We used CLM4.5 to investigate if a global-scale model can represent local-scale patterns of water, energy, and carbon fluxes in a heterogeneous mountain environment. Specifically, we were interested in generating testable projections of potential ecosystem responses to climate change. Model output includes half-hourly data on fluxes of energy, water, and carbon, as well as vegetation carbon stocks and edaphic conditions. We also conducted sensitivity analyses to look at ecosystem responses to modifications intended to extend growing season length by decreasing snow albedo and warming air temperatures (black sand and M-A warm, respectively). Information on the variables, units, and data are included as attributed in the network Common Data Form (NetCDF) files for this dataset. For users unfamiliar with using NetCDF files, we have included R scripts that write (forcing data) and read (model output) .nc files include in this data archive. More information about NetCDF files is available at http://www.unidata.ucar.edu/software/netcdf/docs/index.html.
Carbon, water and energy fluxes at the subtropical forest in Kaziranga National Park in India
<p>This dataset contains measured and modeled records of gross primary productivity (GPP), sensible heat flux and latent heat flux from 2016 to 2018 at the Kaziranga National Park, India. The measured fluxes are obtained from eddy covariance technique at the flux tower established by the Indian Institute of Tropical Meteorology (IITM) Pune as part of the MetFlux India network funded by the Ministry of Earth Sciences (MoES), the Government of India, whereas the Integrated Science Assessment Model (ISAM) at the Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Illinois, USA is used to simulate these fluxes. Additionally, the leaf area index (LAI) and meteorological measurements used as the model input are also included in this dataset. </p>
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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.