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opencc-by-4.0Feb 2020View details →

Country resolved combined emission and socio-economic pathways based on the RCP and SSP scenarios

Recommended citation Article citation will be added once the article is available. Content <ul> <li><a href="#use-of-the-dataset-and-full-description">Use of the dataset and full description</a></li> <li><a href="#abstract">Abstract</a></li> <li><a href="#support">Support</a></li> <li><a href="#files-included-in-the-dataset">Files included in the dataset</a></li> <li><a href="#notes">Notes</a></li> <li><a href="#data-format-description-columns">Data format description (columns)</a></li> <li><a href="#data-sources">Data sources</a></li> <li><a href="#changelog">Changelog</a></li> <li><a href="#references">References</a></li> </ul> Use of the dataset and full description Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section. The article will be referenced here as soon as it is published. Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset. When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below. Support If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de. Abstract This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data. Files included in the dataset The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows: <Source><Bunkers><Downscaling>.csv <Source> The "Source" flag indicates which input scenarios were used. <ul> <li>PMRCP: RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> <li>PMSSP: Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> </ul> <Bunkers> the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed. <Downscaling> The "Downscaling" flag indicates the downscaling technique used. <ul> <li>IE: Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.</li> <li>IC: Regional emission intensity growth rates for all countries.</li> <li>CS: Constant emission shares as a reference case independent of the socio-economic scenario.</li> </ul> All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published. Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf. Notes If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data. Data format description (columns) "source" For PMRCP files source values are <ul> <li>RCPSSP<Bunkers><Downscaling>: unharmonized downscaled RCP SSP scenarios</li> <li>PMRCP<Bunkers><Downscaling>: downscaled RCP SSP scenarios harmonized to and combined with historical data</li> <li>PMRCPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> For PMSSP files source values are <ul> <li>SSPIAM<Bunkers><Downscaling>: unharmonized downscaled SSP IAM scenarios</li> <li>PMSSP<Bunkers><Downscaling>: downscaled SSP IAM scenarios harmonized to and combined with historical data</li> <li>PMSSPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> For possible values of <Bunkers> and <Downscaling> please see section <a href="#files-included-in-the-dataset">Files included in the dataset</a> above. "scenario" For PMRCP files the scenarios have the format <RCP><SSP><group>, where <ul> <li><RCP> denotes the RCP scenario. Values are RCP3PD, RCP45, RCP6, and RCP85.</li> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><groups> denotes the SSP basic elements GDP modeling group. Values are IIASA, OECD, and PIK. Not all RCP SSP combinations exist as some SSP storylines are not compatible with all RCP emissions scenarios. For details we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.</li> </ul> For PMSSP files the scenarios have the format <SSP><forcing><model> where <ul> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><forcing> denotes the radiative forcing level of the scenario. Values are 19, 26, 34, 45, 60, 85, and BL where 19 stands for 1.9W/m2 etc. and BL stands for baseline.</li> <li><model> denotes the Integrated Assessment Model (IAM) used to generate the scenario. Values can be found below</li> </ul> Model codes in scenario names <ul> <li>AIMCGE: AIM-CGE</li> <li>IMAGE: IMAGE</li> <li>GCAM4: GCAM</li> <li>MESGB: MESSAGE-GLOBIOM</li> <li>REMMP: REMIND-MAGPIE</li> <li>WITGB: WITCH-GLOBIOM</li> </ul> "country" ISO 3166 three-letter country codes or custom codes for groups: Additional "country" codes for country groups. <ul> <li>EARTH: Aggregated emissions for all countries</li> <li>ANNEXI: Annex I Parties to the UNFCCC</li> <li>NONANNEXI: Non-Annex I Parties to the UNFCCC</li> <li>AOSIS: Alliance of Small Island States</li> <li>BASIC: BASIC countries (Brazil, South Africa, India and China)</li> <li>EU28: European Union (still including the UK)</li> <li>LDC: Least Developed Countries</li> <li>UMBRELLA: Umbrella Group</li> </ul> "category" Category descriptions. <ul> <li>IPCM0EL: Emissions: National Total excluding LULUCF</li> <li>ECO: Economical data</li> <li>DEMOGR: Demographical data</li> </ul> "entity" Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4). Gases / gas baskets and underlying global warming potentials <ul> <li>CH4: Methane (CH4)</li> <li>CO2: Carbon Dioxide (CO2)</li> <li>N2O: Nitrous Oxide (N2O)</li> <li>FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF6, NF3</li> <li>FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF6, NF3</li> <li>KYOTOGHG: Kyoto greenhouse gases (SAR)</li> <li>KYOTOGHGAR4: Kyoto greenhouse gases (AR4)</li> </ul> "unit" The following units are used: <ul> <li>Million2011GKD: Million 2011 international dollars</li> <li>ThousandPers: Thousand persons</li> <li>kt: kilotonnes</li> <li>Mt: Megatonnes</li> <li>Gg: Gigagrams</li> <li>MtCO2eq: Megatonnes of CO2 equivalents using the GWPs defined by "entity"</li> <li>GgCO2eq: Gigagrams of CO2 equivalents using the GWPs defined by "entity"</li> </ul> Remaining columns Years from 1850-2100. Data Sources The following data sources were used during the generation of this dataset: Scenario data <ul> <li>RCP scenarios <a href="https://tntcat.iiasa.ac.at/RcpDb/">website/data</a></li> <li>SSP basic elements <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li>SSP IAM scenarios <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li>SSP CMIP6 scenarios <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> </ul> Historical data <ul> <li>CDIAC <a href="http://doi.org/10.3334/CDIAC/00001_V2017">data</a></li> <li>CEDS CMIP6 data <a href="https://www.geosci-model-dev.net/11/369/2018/">paper/data</a></li> <li>EDGAR version 4.3.2: <a href="http://doi.org/10.2904/JRC_DATASET_EDGAR">data</a>, <a href="https://doi.org/10.5194/essd-2017-79">paper</a></li> <li>IMO GHG report <a href="http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf">report</a></li> <li>PRIMAP-hist v2.1 <a href="http://www.earth-syst-sci-data.net/8/571/2016/">paper</a>, <a href="https://www.pik-potsdam.de/primap-live/primap-hist/">website</a>, <a href="https://doi.org/10.5880/PIK.2019.018">data</a></li> <li>PRIMAP-hist SocioEco v2.1 <a href="https://doi.org/10.5880/PIK.2019.019">data</a></li> </ul> Changelog For future versions References For full references we refer to the pdf version of the data description available in this repository and the list of related identifiers.

opencc-by-4.0Nov 2024View details →

Projected Global Area Equipped for Irrigation Datasets during 2020-2100 under SSP scenarios

<h1>1. Background</h1> Accurately predicting the global area equipped for irrigation in the future is crucial for providing essential datasets relevant to fields such as earth system simulation, agricultural water resource management, climate change adaptation, and environmental conservation. However, the predictive datasets of the area equipped for irrigation are still lacking. To address this gap, we provide the Projected Global Area Equipped for Irrigation Datasets (PGAEID), which provide spatially explicit estimates of Area Equipped for Irrigation (AEI) from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: SSP1 (sustainable development), SSP2 (intermediate development), and SSP3 (regional rivalry), SSP4 (unequal development), and SSP5 (fossil-fueled development). <h1>2. Methodology</h1> <h3>2.1 Ensemble Machine Learning (EML) Framework</h3> <ul> <li>Algorithms: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li>Training Data: Historical national irrigation records (FAO AQUASTAT, 1961–2015).</li> <li>Validation Metrics: <ul> <li>Nash-Sutcliffe Efficiency (NSE): 0.98</li> <li>Kling-Gupta Efficiency (KGE): 0.97</li> <li>Mean Absolute Percentage Error (MAPE): 1.7%</li> </ul> </li> </ul> <h3>2.2 Spatial Downscaling</h3> <ul> <li>Baseline: FAO 2005 irrigation data combined with GMIA2005 gridded agricultural intensity maps.</li> <li>Dynamic Projection: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul> <h1>3. Dataset Overview</h1> <h3>3.1 Key Features</h3> <ul> <li>Temporal Coverage: 2020–2100 (10-year intervals).</li> <li>Spatial Resolution: 5-arcminute (≈10 km at the equator).</li> <li>Scenarios: SSP1, SSP2, SSP3, SSP4, SSP5.</li> <li>Variables: area equipped for irrigation (103 ha/year).</li> </ul> <h3>3.2 Dataset Structure</h3> The dataset is provided as a compressed archive (PGAEID_Ver3.0.rar), containing: 1.Global_Area_Equipped_for_Irrigation_GeoTiff/ <ul> <li>Subfolders: <ul> <li>SSP1</li> <li>SSP2</li> <li>SSP3</li> <li>SSP4</li> <li>SSP5</li> </ul> </li> <li>File Format: GeoTIFF (45 files total).</li> <li>Naming Convention: AEI_[SSP]_[Year].tif <ul> <li>Example: AEI_SSP1_2020.tif</li> </ul> </li> </ul> 2. National & Regional_AEI/ <ul> <li>Shapefiles: National/regional area equipped for irrigation for 26 prediction units (2020–2100).</li> <li>Excel File: Global Area Equipped for Irrigation (2020-2100).xlsx.</li> </ul> 3. Technical Annex.docx <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul> <h1>4. Applications</h1> This dataset supports: <ul> <li>Earth System Simulation: Supporting irrigation parameterization in global climate and hydrological models.</li> <li>Water Resource Management: Assisting decision-makers in sustainable irrigation planning.</li> <li>Climate Change Adaptation: Providing insights into how irrigation practices evolve under different socioeconomic pathways.</li> <li>Environmental Conservation: Assessing the impact of irrigation on regional ecosystems.</li> </ul> Note: Global aggregated totals of area equipped for irrigation derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives: <ul> <li>The country/region-based data (26 units) is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The 5-arcminute gridded data is preferable for spatially explicit modeling or subnational assessments.</li> </ul> Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.

opencc-by-4.0Jul 2023View details →

Projected Global Fertilizers Consumption Datasets during 2020-2100 under SSP scenarios

<h1>1. Background</h1> Accurate projections of future global fertilizer consumption are critical for advancing research in earth system modeling, agricultural sustainability, and fertilizer industry planning. However, existing datasets often lack long-term temporal coverage and high spatial resolution. To address this gap, we present the Projected Global Fertilizers Consumption Datasets (PGFCD), which provide spatially explicit estimates of nitrogen (N), phosphorus (P), and potassium (K) fertilizer consumption from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: SSP1 (sustainable development), SSP2 (intermediate development), and SSP3 (regional rivalry), SSP4 (unequal development), and SSP5 (fossil-fueled development).   <h1>2. Methodology</h1> <h2>2.1 Ensemble Machine Learning (EML) Framework</h2> <ul> <li>Algorithms: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li>Training Data: Historical national/regional fertilizer consumption (FAOSTAT, 1961–2015).</li> <li>Validation Metrics: <ul> <li>Nash-Sutcliffe Efficiency (NSE): 0.93</li> <li>Kling-Gupta Efficiency (KGE): 0.89</li> <li>Mean Absolute Percentage Error (MAPE): 10.97%</li> </ul> </li> </ul> <h2>2.2 Spatial Downscaling</h2> <ul> <li>Baseline: FAO 2000 fertilizer data combined with gridded nutrient application maps for major crops in 2000.</li> <li>Dynamic Projection: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul>   <h1>3. Dataset Overview</h1> <h2>3.1 Key Features</h2> <ul> <li>Temporal Coverage: 2020–2100 (10-year intervals).</li> <li>Spatial Resolution: 5-arcminute (≈10 km at the equator).</li> <li>Scenarios: SSP1, SSP2, SSP3, SSP4, SSP5. </li> <li>Variables: N, P, and K fertilizer consumption (tonnes/year).</li> </ul> <h2>3.2 Dataset Structure</h2> The dataset is provided as a compressed archive (PGFCD_Ver6.0.rar), containing: 1. Fertilization_Consumption_GeoTiff/ <ul> <li>Subfolders: <ul> <li>N_fer/: Nitrogen fertilizer projections</li> <li>P_fer/: Phosphorus fertilizer projections</li> <li>K_fer/: Potassium fertilizer projections</li> </ul> </li> <li>File Format: GeoTIFF (135 files total). <ul> <li>Naming Convention: [FertilizerType]_fer_con_[SSP]_[Year].tif <ul> <li>Example: K_fer_con_SSP1_2020.tif</li> </ul> </li> </ul> </li> </ul> 2. Country_region_based/ <ul> <li>Shapefiles: National/regional fertilizer consumption for 26 prediction units (2020–2100).</li> <li>Excel File: Global Fertilizer Consumption (2020-2100).xlsx.</li> </ul> 3. Technical Annex.docx <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul>   <h1>4. Applications</h1> This dataset supports: <ul> <li>Earth System Modeling: Improved parameterization of fertilization impacts on biogeochemical cycles.</li> <li>Agricultural Policy: Scenario-based planning for sustainable fertilizer use.</li> <li>Industry Strategy: Long-term market analysis under diverse socioeconomic pathways.</li> </ul>   <h1>Note:</h1> Global aggregated totals of fertilizer consumption derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives: <ul> <li>The country/region-based data (26 units) is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The 5-arcminute gridded data is preferable for spatially explicit modeling or subnational assessments.</li> </ul> Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context. We are profoundly indebted to Dr. Andreas Gericke at Section II 2.3 Protection of the Seas and Polar Regions, German Environment Agency, and Dr. Veronika Schlosser at Chair of Sustainability Assessment of Food and Agricultural Systems, Technical University of Munich for their diligent review and insightful feedback on the previously submitted data. Their expertise has enabled us to thoroughly correct the identified inaccuracies, strengthening the integrity of our research. We hold Dr. Andreas Gericke and Dr. Veronika Schlosser in the highest esteem and sincerely apologize for any oversights that may have marred our work.

Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - inflorescence - whole - unspecified

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Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - inflorescence - lateral view of flower

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Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - whole plant - in flower - general view

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opencc-by-4.0Jul 2022View details →

State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100)

This dataset is documented in this manuscript here- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. We project US state level income distributions using a PCA-based approach, applying a downscaled version of the approach employed by Narayan et al. (2022, in-prep). A state-level dataset had to be synthesized and projected based on existing sources. We apply a PC-based model to our derived state-level dataset, employing projected GINI’s from the SSP scenarios. We produce projected income distribution by income decile for three SSPs to year 2100. For the purpose of the projections, we developed a consistent set of tax adjusted net income deciles for all states from 2011 to 2014. This dataset was used for initialization of the projections and for validation. If/when using this dataset, please cite this paper- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta

opencc-by-4.0Dec 2004View details →

Achnatherum occidentale ssp. occidentale (Poaceae) - whole plant - in flower - general view

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opencc-by-4.0Aug 2024View details →

SSP-aligned projected European water withdrawal/consumption at 5 arcminutes

Release 0.9.1 – What is new? ·         Industrial water withdrawals were initially overestimated due to a problem with the input data, but they are now fixed. ·         Historical water withdrawal covering 1960-2020 added. Consistency between historical and projected water withdrawals is maintained. Contents and naming conventions Annual European water withdrawal: {scenario}_{sector} _year_millionm3_5min_Europe_{from_year}_{to_year}.nc Scenarios: historical, ssp1, ssp2, ssp3, ssp5 Sector: dom, ind; stand for domestic/industrial                 From/to_year: 1960/2020, 2020/2100; historical/ssp projections. Each file holds two variables: {sector}ww and {sector}wc representing gross and net (i.e. consumptive use) water withdrawal. For exmaple, for the industrial sector, there are two variables: indww and indwc. The fraction ‘1 – indwc/indww ‘ represents the share of return flows. ------------------------- The dataset provides annual water withdrawal and consumption estimates for Europe at a spatial resolution of 5 arcminutes, covering the periods 1960-2020 (historical) and 2020-2100 for four SSPs (1, 2, 3, and 5). Below, we outline the procedure used to downscale the population projections to a 5-arcminute resolution and describe the main equations applied to project water withdrawal and consumption under different SSPs. The development of the high-resolution (5 arcminute) projected water withdrawal and consumption for Europe follows the methodology outlined by Wada et al. (2011a, 2011b). This new release incorporates new projections for population, GDP per capita, and urbanization patterns from the latest SSP database (v3.0.1; available at <a href="https://data.ece.iiasa.ac.at/ssp/" target="_new">https://data.ece.iiasa.ac.at/ssp/</a>). Since this update is still in progress as of August 25th, 2024, some necessary input data are sourced from an earlier version of the SSP data (SSP 2013, see Table 1).  All data and methods used to generate the results provided in this dataset are described in the Readme - Data and Methods file. <a name="_Ref175610392"></a>Table 1: Data availability in different versions of the SSP database as of August 25th 2024. <table> <tbody> <tr> <td> Data </td> <td> SSP DatabaseVersion </td> <td> Module </td> </tr> <tr> <td> Population </td> <td> SSP v3.0.1 2024 </td> <td> Domestic </td> </tr> <tr> <td> GDP per capita </td> <td> SSP v3.0.1 2024 </td> <td> Domestic/industrial </td> </tr> <tr> <td> Energy use per capita </td> <td> SSP 2013 </td> <td> Industrial </td> </tr> <tr> <td> Electricity use per capita </td> <td> SSP 2013 </td> <td> Industrial </td> </tr> </tbody> </table>

opencc-by-4.0Sep 2023View details →

Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)

This is the outcome data from our study titled "<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.170481" target="_blank" rel="noopener">Prediction of global wheat cultivation distribution under climate change and socioeconomic development</a>" which was published in The Science of The Total Environment.  The present study represents a significant extension of our previous research on "<a href="http://dx.doi.org/10.1016/j.scitotenv.2019.06.153" target="_blank" rel="noopener">The Potential Distribution and Dynamics of Global Wheat under Multiple Climate Change Scenarios"</a>. Socioeconomic and climate change are both critical factors influencing the global distribution of crop cultivation. However, there has been limited exploration of the role of socioeconomic factors in predicting future crop cultivation distribution under climate change. We have proposed the MaxEnt-SPAM approach under the assumption that environmental conditions are the primary determinants of land suitability for cultivating wheat, while socioeconomic factors play a crucial role in influencing farmers' crop choices. In essence, the distribution of wheat cultivation is contingent upon maximizing potential revenue and ensuring suitability for wheat planting. The proposed MaxEnt-SPAM approach was utilized to estimate the distribution of wheat cultivation in three combined Representative Concentration Pathway (RCP) - Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. The methodology involved estimating wheat planting suitability under future RCP scenarios using the MaxEnt model, predicting farmers' crop choices under future SSP scenarios through Time series-Backpropagation (TS-BP) models, and ultimately estimating global wheat cultivation distribution based on the SPAM model. Validation of this approach against major known datasets on the distribution of wheat cultivation demonstrated satisfactory accuracy, with a predictive accuracy exceeding 85% and a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets. Based on the aforementioned concept and methodology, a global wheat cultivation distribution grid (0.5 degree × 0.5 degree) was projected under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The findings suggest that RCP8.5-SSP3 may offer the most favorable conditions for wheat cultivation. Additionally, socioeconomic development significantly constrains the potential distribution of wheat cultivation, with estimated areas accounting for an average of 77% of the potential distribution determined by climatic factors under the selected RCP-SSP scenarios. Socioeconomic development appears to have a positive impact on wheat cultivation in Africa. Our results illustrate the influence of socioeconomic factors on crop distribution within a market economy framework, underscoring the importance of integrating socioeconomic factors and climate change for accurate predictions of crop cultivation distribution. We contend that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable addition to existing products. This prediction data is among the few products to consider both climate change and socio-economic development, providing a more comprehensive understanding of crop cultivation distribution dynamics. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to enhance our comprehension of the dynamics and distribution of global wheat cultivation under different climate change and socio-economic development paths in the future, potentially supporting research in earth system simulation and agricultural sciences. The dataset for the Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) and the Maxent-SPAM approach code is stored in a zip package named SPAM_MaxEnt.zip, which contains two folders (code and data). code: This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information. The exampleData contains 1. h_pri.tif: prior data 2. h_res.tif: global C3 crop cultivation proportion Run the main programme: cross_entroy.m data: This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format. 1 Global distribution of the long-term wheat-cultivation area fraction: This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated. r2s1f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario r4s2f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario r8s3f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario 2 Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution: This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:{1, 2, 3}, 1 wheat cultivation existed but was predicted to be unsuitable to plant wheat; 2 presented a reduction in the wheat cultivation area compared to the land's suitability; 3 presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat. com_suit_fra126.tif: the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6 com_suit_fra245.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5 com_suit_fra385.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5 3 Differences in the proportion of long-term wheat cultivation: This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability. r2s1_f.tif: Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario r4s2_f.tif: Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario r8s3_f.tif: Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario  References: Yaojie Yue, Puying Zhang, Yanrui Shang. The Potential Distribution and Dynamic of Global Wheat under Multiple Climate Change Scenarios. Science of the Total Environment, 2019, 688: 1308-1318. Xi Guo, Puying Zhang, Yaojie Yue. Prediction of global wheat cultivation distribution under climate change and socio-economic development. Science of the Total Environment, 2024, 919: 170481. For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014). Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026. You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003. You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.002

opencc-zeroDec 2002View details →

FIGURES 1 - 2. Agrilus albogularis ssp. perisi 1 in On the new status of Agrilus perisi Cobos, 1986 (Coleoptera: Buprestidae)

FIGURES 1 - 2. Agrilus albogularis ssp. perisi 1. Habitus; scale bar = 1,0 mm. 2 Aedeagus; scale bar = 1,0 mm

opencc-by-4.0Nov 2023View details →

Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"

Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5) We use bash, CDO, and python.SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp

opencc-by-nc-sa-4.0Dec 2007View details →

Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - inflorescence - lateral view of flower

Image of Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - inflorescence - lateral view of flower

Maianthemum racemosum ssp. racemosum (Liliaceae) - inflorescence - frontal view of flower

Image of Maianthemum racemosum ssp. racemosum (Liliaceae) - inflorescence - frontal view of flower

Maianthemum racemosum ssp. racemosum (Liliaceae) - inflorescence - whole - unspecified

Image of Maianthemum racemosum ssp. racemosum (Liliaceae) - inflorescence - whole - unspecified

Cerastium fontanum ssp. vulgare (Caryophyllaceae) - stem - showing leaf bases

Image of Cerastium fontanum ssp. vulgare (Caryophyllaceae) - stem - showing leaf bases

Cerastium fontanum ssp. vulgare (Caryophyllaceae) - leaf - on upper stem

Image of Cerastium fontanum ssp. vulgare (Caryophyllaceae) - leaf - on upper stem

Cerastium fontanum ssp. vulgare (Caryophyllaceae) - inflorescence - frontal view of flower

Image of Cerastium fontanum ssp. vulgare (Caryophyllaceae) - inflorescence - frontal view of flower

Cerastium fontanum ssp. vulgare (Caryophyllaceae) - whole plant - in flower - general view

Image of Cerastium fontanum ssp. vulgare (Caryophyllaceae) - whole plant - in flower - general view

Cornus amomum ssp. obliqua (Cornaceae) - twig - unspecified

Image of Cornus amomum ssp. obliqua (Cornaceae) - twig - unspecified