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81 results for “water and fluxes”
Summer water chemistry; sediment phosphorus fluxes and sorption capacity; sedimentation and sediment resuspension dynamics; water column thermal structure; and zooplankton, macroinvertebrate, and macrophyte communities in eight shallow lakes in northwest Iowa, USA (2018-2020)
The primary aim of this data product is to characterize change in water chemistry, sediment-water interactions, and biological communities in shallow, eutrophic lakes undergoing a fishery biomanipulation. We studied eight glacial lakes located in northwest Iowa, USA, from 2018 to 2020 during the summer season (May to September). A subset of these lakes (n = 4; Center, Five Island, North Twin, and Silver Lakes) were part of a fishery biomanipulation in which the Iowa Department of Natural Resources (IDNR) incentivized commercial harvest of common carp (Cyprinus carpio) and bigmouth buffalo (Ictiobus cyprinellus). Harvests occurred in Center and Five Island Lakes during 2018-2019 and in North Twin and Silver Lakes during 2019-2020. Between 73 and 373 kg fish biomass per ha were removed each year. The other study lakes (n = 4; Blue, South Twin, Storm, and Swan Lakes) remained unmanipulated during the study period. Over the course of the biomanipulation, we quantified a suite of physical, chemical, and biological parameters across the study lakes. High frequency aquatic sensors were used to measure water column thermal structure, dissolved oxygen concentrations, and algal pigments. Manual water chemistry sampling further quantified suspended solids, total phosphorus and nitrogen, soluble reactive phosphorus, nitrate, and water clarity. We measured flux rates of phosphorus between bottom sediments and the overlying water using ex situ sediment core incubations under both oxic and anoxic conditions. We further quantified sediment phosphorus sorption capacity using equilibrium phosphorus concentration assays. Tiered sediment traps were used to measure sedimentation rates as well as sediment resuspension in bottom waters. We also measured change in zooplankton, macroinvertebrate, and macrophyte community composition and abundance. These data will be used to better understand the mechanisms of internal phosphorus loading in shallow lakes and the ecosystem effects of fisherie
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>
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Aquatic biofilm autotrohic index, carbon dioxide flux, and environmental conditions for the APEX water table experiment 2021-2023
To better understand linkages between hydrology and ecosystem carbon flux in northern aquatic ecosystems, we evaluated the relationship between plant communities, biofilm development, and carbon dioxide (CO2) exchange following long-term changes in hydrology in an Alaskan fen. We quantified seasonal variation in biofilm composition and CO2 exchange in response to lowered and raised water-table position (relative to a control) during years with varying levels of background dissolved organic carbon (DOC). We then used nutrient-diffusing substrates to evaluate cause-effect relationships between changes in plant subsidies (i.e., leachates) and biofilm composition among water-table treatments. We found that background DOC concentration determined whether plant subsidies promoted net autotrophy or heterotrophy on nutrient diffusing substrates. In conditions where background DOC was <= 40 mg L-1, plant subsidies promoted an autotrophic biofilm. Conversely, when background DOC concentration was >= 50 mg L-1, plant subsidies promoted heterotrophy. Greater light attenuation associated with elevated levels of DOC may have overwhelmed the stimulatory effect of nutrients on autotrophic microbes by constraining photosynthesis while simultaneously allowing heterotrophs to outcompete autotrophs for available nutrients. At the ecosystem level, conditions that favored an autotrophic biofilm resulted in net CO2 uptake among all water-table treatments, whereas the site was a net source of CO2 to the atmosphere in conditions that supported greater heterotrophy. Taken together, these findings show that hydrologic history interacts with changes in dominant plant functional groups to alter biofilm composition, which has consequences for ecosystem CO2 exchange.
Concentrations, turnover rates and fluxes of polyamines in coastal waters of the South Atlantic Bight during April and October 2011
Polyamines are short-chain aliphatic compounds containing multiple amine groups. They are important components of the cytosol of eukaryotes and are present at mmol/L concentrations inside phytoplankton cells, while complex polyamines play a role in biosilica deposition. Concentrations of polyamines measured in seawater are typically in the sub nmol/L range, implying rapid and efficient uptake by osmotrophs, likely bacterioplankton. We measured turnover rates of three polyamines (putrescine, spermidine and spermine) using 3H-labeled compounds and determined their concentrations by HPLC to estimate polyamine contributions to dissolved organic matter and bacterioplankton carbon and nitrogen demand. These measurements were made on transects from the inner shelf to the Gulf Stream across the South Atlantic Bight (SAB) during April and October of 2011 and in salt marsh estuaries on the Georgia coast during August of 2011 and April of 2012. This data set includes measurements of water column variables (temperature, salinity, biogenic Silica), nutrients (nitrite, nitrate+nitrite, ammonium and dissolved inorganic nitrogen), and concentrations and turnover rates of polyamine compounds.
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.
Carbon and water fluxes in a cork oak woodland in Central Portugal
<p>The Data set contains eddy covariance measurements of carbon and water fluxes and ancillary measurements observed at a cork oak woodland (<em>Quercus suber </em>L.) in central Portugal. The climate is Mediterranean, with mild, wet winters and hot, dry summers.</p> <p>Data are available in the file data.csv (UTF-8 encoding), the description of the variables and units are available in the file meta.csv (UTF-8 encoding).</p> <p>Further description of the site, methods and data processing can be viewed in the files metadata.pdf and metadata.csv</p> <p> </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.
MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all input and output files that were used in the study reported in:</p> <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary</strong></p> <p>The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes.</p> <p>We aim to answer two questions:</p> <p>1. What is the intrinsic spatial resolution of global river dynamics?</p> <p>2. How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes?</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> <p><strong>Software</strong></p> <p>The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling.</p> <p><strong>Data Products</strong></p> <p>The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii).</p> <p><strong>Riv_coast.zip</strong> contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean.</p> <p><strong>· </strong><strong>riv_coast.zip</strong></p> <p><strong> o </strong><strong>cor:</strong> riv_coast_pfaf_ii_COR.shp</p> <p><strong> o </strong><strong>uncor: </strong>riv_coast_pfaf_ii_UNCOR.shp</p> <p><strong> </strong></p> <p><strong>Qout_rivwidth.zip </strong>contains csv files of the aggregate river discharge to the ocean (km<sup>3</sup>/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>Qout_rivwidth.zip: </strong>Qout_pfaf_ii_rivwidth.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_low.zip</strong> contains csv files of the aggregate river storage (km<sup>3</sup>) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_low.zip:</strong> V_pfaf_ii_rivwidth_low.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_nrm.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_nrm.zip: </strong>V_pfaf_ii_rivwidth_nrm.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_hig.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_hig.zip: </strong>V_pfaf_ii_rivwidth_hig.csv</p> <p><strong> </strong></p> <p><strong>Largest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins.</p> <p><strong>· </strong><strong>largest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge contributed by each basin</p> <p><strong> o </strong><strong>riv:</strong> riv_top10_nxx.shp – river reaches that drain the 10 largest basins</p> <p><strong> </strong></p> <p><strong>Smallest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m.</p> <p><strong>· </strong><strong>smallest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge to the ocean from each narrow river reach</p> <p><strong> o </strong><strong>riv: </strong>riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii</p> <p><strong> </strong></p> <p><strong>Global_summary.zip </strong>contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage.</p> <p><strong>· </strong><strong>global_summary.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth: </strong>global summary files for discharge to the ocean (km<sup>3</sup>/yr) under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_low:</strong> global summary files for total river storage (km<sup>3</sup>) for the low residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_nrm:</strong> global summary files for total river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_hig: </strong>global summary files for total river storage (km<sup>3</sup>) for the hig residence time scenario under river width sampling</p> <p><strong> o </strong><strong>cat_small_gl: </strong>cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean</p> <p><strong> </strong></p> <p><strong>Rivwidth_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations.</p> <p><strong>· </strong><strong>Rivwidth_sens.zip</strong></p> <p><strong> o </strong><strong>riv_coast</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_VIC</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_CLSM</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_NOAH</strong></p> <p><strong> o </strong><strong>global_summary_VIC</strong></p> <p><strong> o </strong><strong>global_summary_CLSM</strong></p> <p><strong> o </strong><strong>global_summary_NOAH</strong></p> <p><strong> </strong></p> <p><strong>Cor_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis.</p> <p><strong>· </strong><strong>Cor_sens.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_ENS</strong></p> <p><strong> o </strong><strong>global_summary_ENS</strong></p> <p><strong> </strong></p> <p><strong>Width_val.zip </strong>contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018).</p> <p><strong>· Width_val.zip: </strong>width_validation_pfaf_ii.csv</p> <p> </p> <p><strong>Known bugs in this dataset or the associated manuscript</strong></p> <p>No bugs have been identified at this time.</p> <p> </p> <p><strong>References</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, <em>361</em>(6402), 585-588. https://doi.org/10.1126/science.aat0636</p> <p>Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. <em>Nature Geoscience</em>, 1–7. https://doi.org/10.1038/s41561-024-01421-5</p> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. https://doi.org/10.1029/2019WR025287</p> <p>Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). <em>Bulletin of the American Meteorological Society</em>, <em>102</em>(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1</p>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </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>
Methane and carbon dioxide fluxes from vegetated and open water zones of lakes in the Peace-Athabasca Delta, Alberta, Canada, 2019
Shallow areas of lakes, known as littoral zones, emit disproportionately more methane than open water but are sometimes ignored in upscaled estimates of lake greenhouse gas emissions. Littoral zone coverage may be estimated through synthetic aperture radar (SAR) mapping of emergent aquatic vegetation, which only grows in water less than ~1.5 m deep. In an accompanying publication, we combine airborne SAR mapping with field measurements of littoral and open-water methane flux to assess the importance of littoral zones to landscape-scale methane emissions. This dataset contains the field measurements of chamber methane flux from vegetated littoral zones and open water used for the accompanying publication. Measurements come from 24 distinct sampling events of 15 lakes in the Peace-Athabasca Delta, Alberta, Canada in July through August, 2019. The dataset also includes within-lake locations, carbon dioxide measurements, simple characterizations of vegetation type, and associated limnological and meteorological measurements, when available: water and air temperature, water depth, wind speed and direction, and relative humidity.
The role of down-slope water and nutrient fluxes in the response of Arctic hill slopes to climate change, output from MBLGEMIII for typical tussock-tundra hill slope near Toolik Field Station, Alaska.
Output data sets of the MBL-GEM III model for a typical tussock-tundra hill slope. The model is described in two papers: Le Dizès, S., Kwiatkowski B.L., Rastetter E.B., Hope A., Hobbie J.E., Stow D., Daeschner S., 2003 Modelling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin (Alaska), Journal of Geophysical Research Vol. 108 No. D2 10.1029/2001JD000960. Rastetter, E.B., B. L. Kwiatkowski, S. Le Dizès, and J.E. Hobbie. 2004. The Role of Down-Slope Water and Nutrient Fluxes in the Response of Arctic Hill Slopes to Climate Change. Biogeochemistry 69:37-62.
Alaska Peatland Experiment: Biweekly flux data and environmental measurements (soil temperature, soil moisture, seasonal thaw depth, water table depth)
This dataset contains ecosystem respiration data measured biweekly at the Alaska Peatland Experiment (APEX) gradient plots. Five plots encompass the gradient all changing in dominant vegetation type. Plot labels are as follows: BS (Black Spruce), WB (Shrub), TG (Grass), EC (Sedge/forb), Control (Rich fen). Data set also includes environmental data (soil temperature, soil moisture, seasonal thaw depth, water table depth) taken at the same time as each flux.
NACLIM - Fluxes: Faroe Shetland Channel volume transport of Atlantic Water
<p><strong>Last update: 14 January 2015</strong></p> <p><strong>Data set:</strong> Faroe Shetland Channel volume transport of Atlantic Water</p> <p><strong>Description: </strong>Monthly mean volume transport of Atlantic Water</p> <p><strong>Period: </strong>December 1992 – March 2014 </p> <p><strong>Location: </strong>61° N 4° W </p> <p><strong>Instruments: </strong>CTD, moored current meters and ADCPs, altimeter </p> <p><strong>Variables:</strong> Volume transport of Atlantic water based on ADCP and altimeter observations</p> <p><strong>Source: </strong>Barbara Berx (MARLAB) </p>
Data of Water Vapor Flux-Profile Relationship in the Stable Boundary Layer over the Sea Surface
<p>data and code for paper 'Water Vapor Flux-Profile Relationship in the Stable Boundary Layer over the Sea Surface'</p>
Fig. 2 in Calcium fluxes in Hoplosternum littorale (tamoatá) exposed to different types of Amazonian waters
Fig. 2. Net Ca2+ fluxes of Hoplosternum littorale transferred from tanks of ion-poor well water to experimental flux chambers containing different Amazonian waters. The first four bars show fluxes measured over the first 2 hours, and the second set of four bars show data for the 2-4 h period after initial transfer. Data expressed as mean ± SEM. Positive values indicate net influxes and negative values net effluxes. Different letters indicate significant differences among groups in the same period of time by Kruskall-Wallis ANOVA and Mann- Whitney test (P <0.05). Asteriks (*) indicate significantly different from the same group at 2 h (P <0.05)
Fig. 1 in Calcium fluxes in Hoplosternum littorale (tamoatá) exposed to different types of Amazonian waters
Fig. 1. Net Ca2+ fluxes of Hoplosternum littorale exposed to well water as a function of time after transfer from tanks of ion-poor well water. Data expressed as mean ± SEM. Positive values indicate net influxes and negative values net effluxes. AC - after change. Asterisks (*) indicate significantly different from 2 h by Kruskall-Wallis ANOVA and Mann-Whitney test (P <0.05).
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>
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.