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83 results for “GPP”
MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference 2000-2017
<p>MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference in GPP for the period 2000-2017. Changes in GPP could be used to estimate land degradation or similar. Derived using <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD17A2H">the data.table package and quantile function in R</a>. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod17a2h_v006. Antartica is not included.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>gpp = variable: gross primary productivity in kg C m<sup>2</sup>,</li> <li>mod17a2h.oct = determination method: MOD17A2H product, GPP values for October,</li> <li>d = median value / difference = difference between periods / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>500m = spatial resolution / block support: 500 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
GPP at FLUXNET Tier 1 sites from P-model
<p>Gross primary production, simulated by the P-model for each FLUXNET 2015 Tier 1 site. The model was driven by site-specific meteorological forcing and MODIS FPAR, extracted for the pixel corresponding to the site location.</p> <p>The CSV files contain simulated GPP values from different model setups conducted with the P-model and used for the publication Stocker et al. <em>Geosci. Mod. Dev. </em>(in review). One file is given for each temporal aggregation level (daily, 8-daily, annual, spatial [= mean annual value by site], and mean seasonal cycle [= mean per day-of-year]. Each file contains output from all model setups presented in Stocker et al. (2019), as given by column <em>setup</em>.</p> <p>The data differs slightly for each file:</p> <p><strong>Daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_daily.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>date: </em>YYYY-MM-DD), date_start (in _8daily, YYYY-MM-DD specifying the first day of the respective 8-day period), year (in _annual, YYYY), doy (in __meanseason, specifying the day-of-year),</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>8-daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_8daily.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>date_start</em> : YYYY-MM-DD specifying the first day of the respective 8-day period</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>Annual</strong> gpp_pmodel_fluxnet2015_stocker19gmd_annual.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>year: </em>YYYY</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>Spatial</strong> gpp_pmodel_fluxnet2015_stocker19gmd_spatial.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p><strong>Mean seasonal cycle</strong> gpp_pmodel_fluxnet2015_stocker19gmd_meanseason.csv:</p> <ul> <li><em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015.</li> <li><em>doy: </em>day-of-year</li> <li><em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup></li> <li><em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.</li> </ul> <p> </p>
CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization
<p>Overview:<br>----------<br>CEDAR-GPP is a global Gross Primary Productivity (GPP) data product, including monthly GPP estimates at 0.05º spatial resolution. These datasets were generated via upscaling eddy covariance measurements with machine learning and satellite data. CEDAR-GPP uniquely incorporated the direct CO2 fertilization effect (CFE) using both data-driven and theoretical approaches. GPP estimates were produced from ten different model setups that vary by temporal span, direct CFE incorporation method, and GPP partitioning approaches. CEDAR stands for ups<strong>C</strong>aling <strong>E</strong>cosystem <strong>D</strong>ynamics with <strong>AR</strong>tificial intelligence.</p> <p>CEDAR-GPP consists of GPP estimates from ten model setups, differing by temporal range, methods for quantifying CO2 fertilization effects, and the partitioning methods used to derive GPP from eddy covariance measurements. Users are encouraged to refer to the user manual for a structured approach to selecting the most appropriate dataset.</p> <p> </p> <p>Authors:<br>----------<br>Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, Trevor Keenan</p> <p> </p> <p>File Structure:<br>----------<br>Each zip file contains GPP data from a CEDAR model setup.</p> <p> </p> <p>File Naming Convention:<br>----------<br>All netCDF files follow this naming convention:<br>CEDAR-GPP_<version>_<model-setup>_<YYYYMM>.nc</p> <p>Where:<br><model-setup> comprises of <temporal_span>_<CFE_option>_<GPP_partitioning><br><temporal_span>: ST denotes short-term (2001 to 2020); LT denotes long-term (1982 to 2020)<br><CFE_option>: 'Baseline' indicates no direct CO2 fertilization effect, 'CFE-ML' represents direct CO2 fertilization incorporated by ML, 'CFE-Hybrid' implies direct CO2 fertilization incorporated by theory<br><GPP_partitioning>: 'NT' for night-time GPP partitioning method, 'DT' for day-time GPP partitioning method</p> <p><br>NetCDF characteristics:<br>----------<br>- Spatial Resolution: 0.05 degree<br>- Temporal Resolution: Monthly<br>- Temporal Coverage: Short-term (ST): 2001-2020; Long-term (LT): 1982 - 2020<br>- Image Dimension: Rows: 3600, Columns: 7200<br>- Units: gCm^-2day^-1<br>- Fill Value: -9999<br>- Multiply By Scale Factor: 0.01<br>- Data Type: uint16<br>- File Size: Approximately 99 MB per file</p> <p><br>Data variables:<br>----------<br>- GPP_mean: monthly gross primary productivity (gCm^-2day^-1), mean from 30 model ensemble<br>- GPP_std: standard deviation of 30 model ensemble</p> <p><br>Support Contact:<br>----------<br>For any queries related to this dataset, please contact:</p> <p>Name: Yanghui Kang<br>Email: kangyanghui@gmail.com</p> <p> </p> <p> </p>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
<p><strong>Data from article Stocker et al. (in review) *Nature Geosci.*</strong></p> <p>The datasets provided here include:</p> <ul> <li>Site-level GPP model results from the P-model (Wang et al., 2017)</li> <li>Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)</li> </ul> <p>This data may be used to partly reproduce results presented in Stocker et al. (2019) <em>Nature Geosci</em>. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.</p> <p>For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="http://doi.org/10.5281/zenodo.1423328">Zenodo</a>, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on <a href="http://rpubs.com/stineb/si_soilm_global2">RPubs</a>.</p> <p>The present datasets are prepared by script `prepare_data_openaccess.R ` on <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="https://zenodo.org/record/1286966#.W6TFipMzbUI">Zenodo</a>.</p> <p><strong>Data description</strong></p> <p><em>Site-level data</em></p> <p>Data is provided as CSV files:</p> <ul> <li>`gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)</li> <li>`gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)</li> <li>`gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))</li> <li>`gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.</li> </ul> <p>Each column is a variable with the following name and units (not all variables are available in all files):</p> <ul> <li>`site_id`: FLUXNET site ID </li> <li>`date`: Date of measurement, units: YYYY-MM-DD</li> <li>`gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)</li> <li>`aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.</li> <li>`flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.</li> <li>`beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.</li> </ul> <p><em>Global P-model simulation outputs</em></p> <p>GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.</p> <ul> <li>`s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.</li> <li>`s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)</li> </ul> <p>Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. </p> <p><strong>References</strong></p> <p>Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).<br> Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).<br> Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).<br> Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).<br> Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).<br> </p>
Global gross primary production (GPP) product generated by data fusion based on random forest
<p>Improving the ability of gross primary production (GPP) estimates to capture extreme climate perturbations and reduce the uncertainty of GPP response processes to extreme climate is a new challenge. Based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. DFRF-GPP is highly reliable and can be used as a valuable data source for various applications, especially in high-temperature and drought-related studies.</p>
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
Data and code: Evaluation of the General Practice Pharmacist (GPP) intervention to optimise prescribing in Irish primary care: a non‐randomised pilot study
<p>This is a dataset and Stata analytical code relating to prescribing issues identified in the GPP pilot feasibility study. The paper reporting this study has been published as follows: </p> <p>Cardwell K, Smith SM, Clyne B on behalf of the General Practice Pharmacist (GPP) Study Group, et al. Evaluation of the General Practice Pharmacist (GPP) intervention to optimise prescribing in Irish primary care: a non-randomised pilot study. BMJ Open 2020;10:e035087. doi: 10.1136/bmjopen-2019-035087</p> <p>The abstract of the study is included below:</p> <p><strong>Objective:</strong> Limited evidence suggests integration of pharmacists into the general practice team could improve medicines management for patients, particularly those with multimorbidity and polypharmacy. This study aimed to develop and assess the feasibility of an intervention involving pharmacists, working within general practices, to optimise prescribing in Ireland.</p> <p><strong>Design:</strong> Non-randomised pilot study</p> <p><strong>Setting:</strong> Primary care in Ireland</p> <p><strong>Participants:</strong> Four general practices, purposively sampled and recruited to reflect a range of practice sizes and demographic profiles.</p> <p><strong>Intervention:</strong> A pharmacist joined the practice team for six months (10 hours/week) and undertook medication reviews (face-to-face or chart-based) for adult patients, provided prescribing advice, supported clinical audits, and facilitated practice-based education.</p> <p><strong>Outcome measures:</strong> Anonymised practice-level medication (e.g. medication changes) and cost data were collected. Patient-Reported Outcome Measure (PROM) data were collected on a subset of older adults (aged ≥65 years) with polypharmacy using patient questionnaires, before and six weeks after medication review by the pharmacist.</p> <p><strong>Results:</strong> Across four practices, 787 patients were identified as having 1,521 prescribing issues by the pharmacists. Issues relating to potentially inappropriate or high-risk prescribing were addressed most often by the prescriber (51.8%), compared to cost-related issues (7.5%). Medication changes made during the study equated to approximately €57,000 in cost savings assuming they persisted for 12 months. Ninety-six patients aged ≥65 years with polypharmacy were recruited from the four practices for PROM data collection and 64 (66.7%) were followed up. There were no changes in patients’ treatment burden or attitudes to deprescribing following medication review, and there were conflicting changes in patients' self-reported quality of life.</p> <p><strong>Conclusions:</strong> This non-randomised pilot study demonstrated that an intervention involving pharmacists, working within general practices is feasible to implement and has potential to improve prescribing quality. This study provides rationale to conduct a randomised controlled trial to evaluate the clinical and cost-effectiveness of this intervention.</p>
Observed and modelled GPP at 61 eddy covariance sites (2007-2018)
<p>In the frame of the ECOPROPHET project, data was collected from in situ observations, remote-sensing sources and models for 61 sites. The objective of this project was to improve our understanding of ecosystem productivity and the role of vegetation phenology as a key determinant of ecosystem carbon, water and energy balances. More info and publications can be found at http://ecoprophet.meteo.be<br> This dataset contains timeseries of observed GPP, modelled GPP (using 15 models), along with key hydrometeorological variables (shortwave radiation, air temperature, vapor pressure deficit and soil moisture).<br> The timeseries are at daily resolution, covering the period 2007-2018 (depending on the data availability per site).<br> The dataset is partly extracted from:</p> <ul> <li>FLUXNET2015 dataset (Pastorello et al., 2020) and the ICOS ’2018 drought initiative’ dataset (Drought 2018 Team<br> and ICOS Ecosystem Thematic Centre, 2019). Original dataset available at <a href="https://fluxnet.org/">https://fluxnet.org/</a> and <a href="https://www.icos-cp.eu/data-products/">https://www.icos-cp.eu/data-products/</a> YVR0-4898</li> <li>ERA5 product (Hersbach et al., 2020); Original dataset available at <a href="https://cds.climate.copernicus.eu/">https://cds.climate.copernicus.eu/</a></li> <li>MODIS: Original dataset available at <a href="https://modis.gsfc.nasa.gov/data/">https://modis.gsfc.nasa.gov/data/</a></li> <li>SPOT Vegetation/PROBA V: Original dataset available at <a href="https://land.copernicus.eu/global/">https://land.copernicus.eu/global/</a></li> <li>Downscaled GOME2 SIF product by Duveiller et al. (2020). Original dataset available at <a href="https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1">https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1</a></li> <li>FluxCom product ensemble (Jung et al., 2020). Original dataset available at <a href="http://fluxcom.org/">http://fluxcom.org/</a></li> </ul> <p><strong>File descriptions</strong>:<br> README.pdf : description<br> EcopropheciesMeta.txt : metadata<br> EcopropheciesDataset.txt : data</p> <p>More details and analysis of the data can be found in:<br> De Pue, J., Wieneke, Bastos, A., S., Barrios, J.M., Liu, L., Ciais, P., Arboleda, A., Hamdi, R., Maleki, M., Maignan, F., Gellens-Meulenberghs, F., Janssens, I., and Balzarolo, M., Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers 2023, Agricultural and Forest Meteorology (submitted)</p>
Monthly GPP datasets for 2000-2017 in China from the big-leaf TEC model and the two-leaf DTEC model
<p>The monthly GPP datasets were calculated from MODIS LAI/FPAR products (MOD15A2, Version 6) and meteorological data for 2000-2017 in China at a resolution of 0.05 degree ×0.05 degree. The TwoLeaf-DTEC-GPP-5km data was derived from the Two-leaf-based DTEC GPP model. The BigLeaf-TEC-GPP-5km data was calculated from the Big-leaf-based TEC GPP model. The BigLeaf-TEC-GPP-5km-2000-2017.zip includes 19 separate GPP data files and each represents one year data including 12 monthly data.</p>
eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
<p>Gross Primary Productivity (GPP) represents the cumulative amount of carbon dioxide (CO<sub>2</sub>) assimilated by green plants through photosynthesis at specific time intervals and spatial scales. It is the main component of the carbon exchange between the terrestrial biosphere and the atmosphere, and has a major influence on global climate and terrestrial ecosystem functioning. Over the last two decades, the continuous and reliable collection of global land surface variables by EOS-MODIS, and the parallel development of the eddy-covariance flux tower network (FLUXNET) have enabled the integration of MODIS observations with tower measurements for the calibration and validation of remote sensing models to obtain global GPP estimates. Despite the significant progress and success to date, current remote sensing GPP models based on the light use efficiency (LUE) concept share several limitations, including the difficulty in accurately predicting LUE variability and the associated use of land cover maps and look-up tables for biome specific maximum LUE, further down-regulated by coarse resolution interpolated meteorological data, which introduce significant uncertainties in the predicted GPP. To address the above limitations, here we applied a simple yet ecologically sound remote sensing GPP model based on the ecosystem light use efficiency (eLUE) concept, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global 5 km, 16-d GPP product (eLUE-GPP) from February 2000 to March 2024. Cross-validation with 202 flux tower sites (1494 site/year) showed favorable accuracy of eLUE-GPP (hereafter GPP<sub>eLUE</sub>) (<em>R</em><sup>2</sup> = 0.71, RMSE = 2.11 g C m<sup>-2</sup> d<sup>-1</sup>). The uncertainty associated with GPP<sub>eLUE</sub> is comparatively lower than that of the other global GPP datasets (MOD17, FluxSat, VPM, among others). We have also calculated the uncertainty analytically for each GPP estimate based on the law of error propagation, which allows quantification of the error budget in applications such as Earth system model benchmarking and atmospheric inversion. Our estimate of global total annual GPP, averaged over the period 2001-2023, was 138.46±13.92 Pg C yr<sup>-1</sup>. Furthermore, we found a significant increasing trend in global total annual GPP at a rate of 0.28±0.05 Pg C yr<sup>-1</sup> (<em>p</em> < 0.001) from 2001 to 2023, particularly in eastern Asia, northern India, Europe, eastern North America, and central South America. We expect that the eLUE-GPP product will enable a more accurate diagnostic analysis of the global carbon budget and thus contribute to climate change research.</p>
FluxFormer GPP Dataset
<div> <div>FluxFormer is a monthly upscaled GPP (unit: gC m-2 d-1) from 2001 to 2020 at 0.1-degree. This dataset is generated through the application of MVTS Transformer and ESA-CCI PFT data v2.0.8, utilizing ground truth data collected from 206 FLUXNET 2015 stations</div> </div> <p> </p> <p>Authors: Anh Phan, Hiromichi Fukui (Chubu University)</p> <p> </p> <p>For any inquiries related to this dataset, please contact:</p> <p>Name: Anh Phan</p> <p>Email: anhphancu@gmail.com</p>
Estimating global GPP from the plant functional type perspective using a machine learning approach
<p><span>The long-term monitoring of gross primary production (GPP) is crucial to the assessment of the carbon cycle of terrestrial ecosystems. In this study, a well-known machine learning model (Random Forest, RF) is established to reconstruct the global GPP dataset named ECGC_GPP. The model distinguished nine functional plant types, including C3 and C4 crops, using eddy fluxes, meteorological variables, and leaf area index as training data of the RF model. Based on ERA5_Land and the corrected GEOV2 data, the global monthly GPP dataset at a 0.05-degree resolution from 1999 to 2019 was estimated. The results showed that the RF model could explain 74.81% of the monthly variation of GPP in the testing dataset, of which the average contribution of Leaf Area Index (LAI) reached 41.73%. The average annual and standard deviation of GPP during 1999–2019 were 117.14 ± 1.51 Pg C yr<sup>-1</sup>, with an upward trend of 0.21 Pg C yr<sup>-2</sup> (<em>p</em> < 0.01). By using the plant functional type classification, the underestimation of cropland is improved. Therefore, ECGC_GPP provides reasonable global spatial patterns and long-term trends of annual GPP.</span></p>
WOMBAT v2.0 estimates of global GPP, respiration, and air-sea fluxes
These files contain estimates of global CO2 fluxes, split into GPP, respiration, and air-sea components produced by the WOMBAT v2.0 flux-inversion system (see https://arxiv.org/abs/2210.10479). These fluxes are further decomposed into trend and seasonality. The file WOMBAT_v2_CO2_gridded_climatology_samples.nc4 contains the estimated spatial fields for each beta in the paper that define the trend/seasonality of the fluxes. The bottom-up estimates are provided for each beta, as well as samples from the posterior distribution. A second file, WOMBAT_v2_CO2_gridded_flux_samples.nc4, contains bottom-up estimates and posterior samples for the flux component fields. These are split into components and the parts of the decomposition, so for example the linear component of GPP is in gpp_linear_bottom_up/gpp_linear_posterior for the bottom-up/posterior. The different fields can be summed to get meaningful quantities, such as the NEE (sum of all GPP and respiration parts), or the net flux (sum of all parts). The final file, samples-LNLGIS.rds, contains samples from the posterior distribution of the model parameters. This file format may be read using the readRDS function in the R programming language.
Study to Evaluate the Efficacy and Safety of Imsidolimab (ANB019) in the Treatment of Subjects With GPP
ClinicalTrials.gov study NCT05352893. IPD Sharing: NO. Countries: 15. Publications: 0.
Estimating global GPP from the plant functional type perspective using a machine learning approach
Open the record for dataset details and reuse information.
Satellite-derived trait data slightly improves tropical forest biomass, NPP, and GPP predictions
Open the record for dataset details and reuse information.
eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
Open the record for dataset details and reuse information.
Simulations disentangling temperature vs. VPD effects on tropical forest GPP
Open the record for dataset details and reuse information.
Effects of anthropogenic activity on global terrestrial gross primary production (GPP)
<p>This data set contains the 100-member ensembles of monthly gross primary production (GPP) estimated using the biosphere model BEAMS and d4PDF data for historical and non-warming climates in 1951-2010/2011, 100-member ensembles of yearly GPP of the historical sensitivity experiment for 7 input variables in 1951-2010, yearly GPP of the extended CO<sub>2</sub> sensitivity experiment using four RCP scenarios in 1951-2300. Data is 0.5625-degree (640×320) 4-byte binary (.raw). Undefined value is -9999.</p> <p>For more details, please check the ReadmeGPP.pdf</p> <p>If you questions, please contact Irina Melnikova (irina.melnikova.russia@gmail.com)</p>
Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize
Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based Vegetation Photosynthesis Model (VPM), and a process-based Soil Canopy Observation of Photochemistry and Energy flux (SCOPE) model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.
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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.