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1,141 results for “primary_productivity”
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Net Primary Production (NPP) in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes net primary production (NPP) data for dominant tree species in the Caribou-Poker Creeks Research Watershed. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Water column primary production per day integrated over the euphotic zone from CCE LTER process cruises in the California Current System, 2006 - 2021 (ongoing).
Derived dataset of vertically integrated primary production (uptake rate of carbon) of particulate organic carbon (POC) using 14C uptake measurements from in situ incubations, filtering methods and measured in mg/m²/day.
Primary production estimates from 14C uptake (in situ), determined by the incorporation of inorganic carbon into particulate organic carbon (POC) due to photosynthesis at selected light levels from CCE LTER process cruises in the California Current System, 2006 - 2021 (ongoing).
Primary productivity samples of seawater are taken each day shortly before noon on the CTD rosette up-cast during the CCE Process crusies (since 2006, ongoing). Light penetration below the surface is estimated from the Secchi disk depth. Niskin bottles from depths with ambient light intensities corresponding to light levels simulated by on-deck incubators are identified and sampled. Primary production is estimated from 14C uptake using this simulated in situ technique (followed by filtering) by which the assimilation of dissolved inorganic carbon by phytoplankton yields a measure (in µg/L/day) of the rate of photosynthetic primary production (particulate organic carbon, POC) at selected light levels in the euphotic zone within the CCE study area.
Measurements from CalCOFI cruises in the California Current System, including log of station information, weather, sea conditions as well as physical, chemical and biological measurements including including temperature, salinity, oxygen, density, sigma theta, phosphate, silicate, nitrite, nitrate, ammonia, chlorophyll a, integrated chlorophyll a, primary productivity, and integrated primary production. 1949 - January 2020
Since 1949, hydrographic and biological data of the California Current System have been collected on quarterly CalCOFI cruises. The 59+ year hydrographic time-series includes weather, temperature, salinity, oxygen and phosphate observations. In 1961, nutrient analysis expanded to include silicate, nitrate and nitrite; in 1973, chlorophyll was added; in 1984, C14 primary productivity incubations were added. These data are being provided here in collaboration with CalCOFI-SIO in order to provide an additional queriable interface to the data. The data are updated on a regular basis from the CalCOFI hydrographic database.
N and P fertilization experiment net primary productivity data for South of saddle, 1991 - 1997.
A nutrient amendment experiment was initiated in 1990 in 2 alpine plant communities, dry and wet meadow, to determine whether N and/or P limit primary production of these communities, the plants' functional response to increased nutrients, and the community structure and composition responses to changes in nutrient availability.
Aboveground net primary productivity, species composition and species richness data for NutNet site, 2007 - 2017
In 2007, a NutNet (http://www.nutnet.umn.edu/) site was established in a dry meadow site east of T-van on Niwot Ridge to assess multiple resource limitation on alpine grassland productivity and species composition. Nutrient treatments were added every other year starting in 2008. Treatments consisted of eight levels of nutrient addition (control, N, P, micro, N+P, N+micro, P+micro, and N+P+micro), replicated across four blocks. Untreated controls and all-nutrient treatments were replicated twice within each block for a total of 40 experiment plots. Starting in 2016, Potassium (K) was added as potash (K2SO4) to plots treated with micronutrients. Baseline data on above-ground biomass was sampled in 2007, prior to fertilizer application. Biomass clipping was repeated in 2013 and species composition measured in 2013 and 2017.
Pinon Juniper Net Primary Production Quadrat Data from the Sevilleta National Wildlife Refuge, New Mexico: 1999-2001
This three-year study at the Sevilleta LTER was designed to monitor net primary production (NPP) across two distinct ecosystems: pinon/juniper woodland (P) and juniper savannah woodland (J). Net primary production (NPP) is a fundamental ecological variable that measures rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses of the community to a wide range of ecological processes. While measures of both below- and above-ground biomass are important in estimating NPP, this study focused on estimating above-ground biomass production (ANPP).To measure ANPP (i.e., the change in plant biomass, represented by stems, flowers, fruit and foliage, over time), the vegetation variables in this dataset, including species composition and the cover and height of individuals, were sampled twice yearly (spring and fall) at permanent 1m x 1m plots. The data from these plots was used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV157, "Net Primary Productivity (NPP) Weight Data." In addition, volumetric measurements were obtained from permanent plots to build regressions correlating biomass and volume.Spring measurements were taken in April or May when shrubs and spring annuals reached peak biomass. Fall measurements were taken in either September or October when summer annuals reached peak biomass but prior to killing frosts. Winter measurements were taken in February before the onset of spring growth.
Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment
<p>This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.</p>
Latitudinal gradient, MEND experiment, and BioGen experiment relating species richness and net primary productivity (NPP)
<p>Aboveground net primary productivity and species richness.</p>
Data from: Opposing responses of temporal stability of aboveground and belowground net primary productivity to water and nitrogen enrichment in a temperate grassland
<p><span>Changes in water and nitrogen availability, as important elements of global environmental change, are known to affect the temporal stability of aboveground net primary productivity (ANPP). However, evidences for their effects on the temporal stability of belowground net primary productivity (BNPP), and whether such effects are consistent between belowground and aboveground, are rather scarce. Here, we investigated the responses of temporal stability of both ANPP and BNPP to water and nitrogen addition based on a 9-year manipulative experiment in a temperate grassland in northern China. The results showed that the temporal stability of ANPP increased with water addition but decreased with nitrogen addition. By contrast, the temporal stability of BNPP decreased with water addition but increased with nitrogen enrichment. The temporal stability of ANPP was mainly determined by the soil moisture and inorganic nitrogen, which modulated species asynchrony, as well as by the stability of dominant species. On the other hand, the temporal stability of BNPP was mainly driven by the soil moisture and inorganic nitrogen that modulated ANPP of grasses, and by the direct effect of soil water availability. Our study provides the first evidence on the opposite responses of aboveground and belowground grassland temporal stability to increased water and nitrogen availability, highlighting the importance of considering both aboveground and belowground components of ecosystems for a more comprehensive understanding of their dynamics.</span></p>
Code and data for 'Human modification of land cover alters net primary productivity, species richness and their relationship' manuscript
<p>The data and scripts in this database are analyses for a research paper in Global Ecology and Biogeography in 2023: Human modification of land cover alters net primary productivity, species richness and their relationship. Please refer to the README file and the paper for details about the usage of the data and methodology.</p>
Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)
<p>Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 2
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: grazing land characterized by open wooded lands (GL-owl)</p>
Dataset for 'Global declines in net primary production underestimated by climate models'
<p>The dataset provided here is to be used in conjuction with the JuPyTer notebook provided here: https://github.com/tjryankeogh/global_npp_trends/tree/main</p> <p> </p> <p>Download the file and uncompress in a root directory where there is a folder 'FIGURES'. When running the notebook make sure to change this root directory when importing packages.</p> <p> </p>
Global estimates of marine gross primary production based on machine‐learning upscaling of field observations
<p>4 variables (excluding dimension variables):</p> <p>double GPP_LD_MLD_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from the<br> dataset determined by the light-dark bottle incubation using Random Forest<br> algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_LD_ZEU_RF[Lon,Lat,Month] </p> <p> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the light-dark bottle incubation using Random<br> Forest algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_Triple_MLD_RF[Lon,Lat,Month] <br> units: mmol mmol O2 m-2 d-1<br> fillvalue: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm<br> coordinates: [Longitude, Latitude Month]<br> <br> double GPP_Triple_ZEU_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm</p> <p>3 dimensions:</p> <p> Lon Size:181<br> units: degree_north<br> long_name: Longitude</p> <p> Lat Size:91<br> units: degree_east<br> long_name: Latitude</p> <p> Month Size:13<br> units: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Annuual_mean<br> long_name: Month</p> <p><br> Author: Yibin Huang & Nicolas Cassar<br> Correspond: nicolas.cassar@duke.edu<br> <br> Request_for_citation: If you use these data in publications or presentations, please cite: Huang,<br> Y., Nicholson, D., Huang, B., & Cassar, N. (2021). Global estimates of<br> marine gross primary production based on machine‐learning upscaling of<br> field observations. Global Biogeochemical Cycles, 35, e2020GB006718.<br> https://doi.org/10.1029/2020GB006718<br> <br> Creation date: Dec/6th/2021</p>
Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production. Reproducible workflow
<p>Dataset for manuscript entitled "Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production" accepted for publication in New Phytologist. The dataset was obtained for the site Majadas del Tietar, Spain, between June/2018 and August/2018. It consists of eddy covariance data, sun-induced fluorescence data and active fluorescence data. </p>
Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018)
<p><strong>Aim:</strong> Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. However, global-scale and long-term (>30 years) studies on the cumulative<strong> </strong>effect of drought are still lacking. Thus, combining a new satellite based gross primary productivity (GPP) and multi-timescale Standardized Precipitation Evapotranspiration Index datasets, we investigated the cumulative effect of drought on global vegetation GPP.</p> <p><strong>Location: </strong>Global.</p> <p><strong>Time period: </strong>1982 – 2018 (37 years).</p> <p><strong>Major taxa studied: </strong>Forests and grasslands.</p> <p><strong>Method: </strong>Based on correlation analysis framework, we investigated the cumulative effect duration of drought on global vegetation GPP. Meanwhile, the variability of this cumulative effect across different elevation gradients and climatic zones was analyzed using variance analysis.</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>
Dataset for "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements"
<p>The final dataset used in manuscript "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements" by Kohonen et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as GPP estimates derived from COS and CO2 flux measurements as described in the manuscript from January 2013 to December 2017. Raw data are available upon request from the author.</p>
Standardized Dataset of the Ecosystem's Water Use Efficiency, Gross Primary Productivity and the Evapotranspiration Deficit Index for 1982–2017 over the Middle East
<p>This data aimed to investigate the spatial-temporal variability of Standardized Actual Evapotranspiration (sAET), Gross Primary Productivity (sGPP) and Water Use Efficiency (WUE) anomalies series, and the Standardized Evapotranspiration Deficit Index (SEDI). The Middle East (ME), was selected as a case study to monitoring drought events as one of the major natural disasters for the ecosystem. To this end, the yearly gross primary production of GLASS, GIMMS, FloxCom, and VPM datasets for the study area spanning 1982–2017 was used to develop the sGPPR data. On the other hand, the Global Land Evaporation Amsterdam Model (GLEAM-version (v3.3a)), which estimated the several components of terrestrial evaporation (annual actual and potential evaporation (AET, PET)) was used for the same period this aimed to detect the variability of the SEDI.<br> This version of the yearly GLASS-sGPPR dataset (1982–2017) is available for the ME at 0.05° spatial resolution, as the original data of the GPP-GLASS products, While, sGPPR dataset of GIMMS, FloxCom, and VPM are also at annual temporal resolution, and at 0.5 degree spatial resolution spanning 1982–2016 for GIMMS, FloxCom, and 2000-2016 for VPM (Excel wrokbook .xlsx). The SEDI data are also available at 0.25 degree spatial resolution for 1980–2018 ( Raster files (TIFF)). For more details about Standardization of the GPP and evapotranspiration deficit data see: <strong>Alsafadi, K., Al-Ansari, N., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021). An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. <em>Environmental Research Letters</em>. <a href="http://dx.doi.org/10.1088/1748-9326/ac4765">10.1088/1748-9326/ac4765</a></strong></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.