This dataset contains netcdf files for the indices calculated in the report. Timeseries of the index (for each tridecade, year, season, or month) are provided for each grid cell and for each model.

Accuracy: Index-dependent caveats are detailed in the report.

Update Frequency: One-time upload (2020)

Obtained from: Findings obtained during the project.

Contact: Climate Change and Resiliency Unit

The data uploaded includes GPS, GLONASS and GALILEO vTEC, latitude, longitude and elevation angle on 15 January 2022. The TEC is 30-seconds sampled (2880 epochs). The data is in 3-dimensions, with one dimension for number of epochs, other one for number of satellites, and third dimension for the number of stations. There are three separaet mat files for each GNSS mission.The files with names GPSTEC_20220115.mat, GALTEC_20220115.mat, and GLOTEC_20220115.mat are the TEC data over the Japan/Taiwan - Australia/NewZealand region. The TEC data used for the India, Africa and America sectors in Figure S1 are upload with the corresponding names of the sectors/regions included in the filenames.

The GIS electron densiy is uploaded as a compress tar file GISdata.tar.gz.. This includes GIS data for 11, 12, 13, and 15 January 2022, in separate folders. Each folder contains 24 netCDF files correspodning to each UT hour. The data have 31 alttiude points (100-7000 km @20km), 73 latitude points (-90 to 90 @2.5 degree) and 72 longitude points (-180 to 175 @5 degrees).

The FORMOSAT-7/COSMIC-2 IVM data used in the figures and supporting information is uploaded as ivm_Tonga_20220115.nc in netCDF format. The file includes the variables, IVMden, IVMlat, IVMlon, and IVMUT. Here IVMden is the ion density per cm3, IVMlat, IVMlon, and IVMUT are the corresponding latitude, longitude and UT hour.

This webmap is a subset of Global Landcover 1992 - 2020 Image Layer. You can access the source data from here. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This is a gridded dataset of monthly industrial water withdrawal (IWW) for China, namely, the China industrial water withdrawal dataset (CIWW). The dataset begins in January 1965 and is ongoing (currently up to December 2020) with a temporal resolution of a month and a spatial resolution of 0.1°/0.25°. The CIWW dataset, together with its auxiliary data, will be useful for water resource management and hydrological models.Version history:V1.1_20240403Update the seasonal variability.Compared to version 1.0, we estimated the seasonality of the subsector (Electricity and Heating Power Production and Supply,) based on spatial classification and then recreated the CIWW data with the updated seasonal variability. More details are described in Hou et al. (2023). The seasonal variation in the updated version is less different from the previous one.V1.0_20230209Using notes：Updated notes about opening the data with ArcGIS and other software (Jan 13, 2025)When opening the CIWW dataset (NetCDF format) in ArcGIS, the following issues may appear, as reported by users:1) The file cannot be successfully opened in ArcGIS.2) The time dimension value could not be properly displayed (e.g., time fixed to January 1, 1965).a) For ArcGIS users, it is recommended to utilize the Multidimension Tools in the toolbox and select the Make NetCDFRaster Layer tool. During the import process:Choose iww_layer as the variable.Select time as the third dimension in addition to longitude and latitude.After importing, open the Properties of the layer and navigate to the Symbology tab. You will see 672 different bands, representing the monthly data from January 1965 to December 2020.If the dataset is directly dragged into ArcGIS, the variable cell_area will be opened by default. This variable represents the area of each grid cell at a resolution of 0.25°/0.1° within the longitude and latitude range of China. The industrial water withdrawal is provided in units of mm/month. If needed, you can convert this to m³/month by multiplying the values by the corresponding grid cell area. For detailed variable descriptions, refer to the readme.txt file.b) The CIWW dataset can be opened using QGIS. Users can select the relevant dimensions and drag the dataset directly into QGIS. The time dimension includes 672 bands, with each band representing the number of days since January 1, 1965.c) The NetCDF format CIWW dataset can be easily opened by any programing language with NetCDF capabilities, for example, the xarry package in Python, Matlab, R, and others).Authors: Chengcheng Hou (cch@mail.bnu.edu.cn), Yan Li (yanli@bnu.edu.cn).Reference: Hou, C., Li, Y., Sang, S., Zhao, X., Liu, Y., Liu, Y., and Zhao, F.: High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-66, in review, 2023.

The gbr100 dataset is a high-resolution bathymetry and Digital Elevation Model (DEM) covering the Great Barrier Reef, Coral Sea and neighbouring Queensland coastline. This DEM has a grid pixel size of 0.001-arc degrees (~100m) with a horizontal datum of WGS84 and a vertical datum of Mean Sea Level (MSL).

For the latest version of this dataset download the data from http://deepreef.org/bathymetry/65-3dgbr-bathy.html

This dataset was developed as part of the 3DGBR project.

This grid utilises the latest available multibeam, singlebeam, lidar and satellite bathymetry source datasets provided by Federal and State Government agencies, in addition to significant new multibeam data collected during research expeditions in the area.

The large increase in source bathymetry data added much detail to improving the resolution of the current Australian Bathymetry and Topography Grid (Whiteway, 2009). The gbr100 grid provides new insights into the detailed geomorphic shape and spatial relationships between adjacent seabed features.

The accompanying report contains an explanation of the various source datasets used in the development of the new grid, and how the data were treated in order to convert to a similar file format with common horizontal (WGS84) and vertical (mean sea level) datums. Descriptive statistics are presented to show the relative proportion of source data used in the new grid. The report continues with a detailed explanation of the pre-processing and gridding process methodology used to develop the grid. A description is also provided for additional spatial analysis on the new grid in order to derive associated grids and layers. The results section provides a short overview of the improvement of the new grid over the current Australian Bathymetry and Topography Grid (Whiteway, 2009). The report then presents the results of the new grid, called gbr100, and the associated derived map outputs as a series of figures. A table of metadata for the current source data accompanies this report as Appendix 1. The report is available at: http://www.deepreef.org/publications/reports/67-3dgbr-final.html

Data details and format:

gbr100 bathymetry grid: Height/Depth in metres (MSL) Formats: 19000x18000 pixel grid (32 bit float) in ESRI raster grid file, GMT/netCDF grid file, Fledermaus sd file, 100m contour ESRI shapefile, GeoTiff grid file.

Total Vertical Uncertainty: Total Vertical Uncertainty (TVU) in the bathymetry estimated from uncertainty classification of each source dataset. Formats: 19000x18000 pixel grid (32 bit float) in ESRI raster, GeoTiff.

Hillshading: Hillshading for full gbr100 and also ocean areas only. Derived from the gbr100 grid. Format: 19000x18000 pixel grid (8 bit) in GeoTiff.

Funding history:

This dataset was initially developed as part of project 2.5i.1 from the MTSRF program (2010).

Subsequent versions of the dataset were developed from other funding sources.

Version history:

• July 2010 - Version 1

• Dec 2014 - Version 3 This version incorporates dozens of new bathymetric surveys including many new navy LADS surveys and some satellite derived bathy to fill in some gaps left by LADS.

• Jan 2016 - Version 4 This version incorporates estimates of bathymetry from satellite imagery in shallow clear waters.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\ongoing\GBR_JCU_Beaman_3DGBR-bathymetry-gbr100

eAtlas Processing:

To visualize this dataset on the eAtlas the format of the data was converted from the ESRI ArcInfo grid format into a GeoTiff format. This was done by loading the data in ArcMap then exporting it as a GeoTiff image. Overview images and final compression options were then performed using GDAL tools.

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

The Prediction of Worldwide Energy Resource (POWER) Project is funded through the NASA Applied Sciences Program within the Earth Science Mission Directorate. The POWER Project supports three user communities with solar and/or meteorological data: 1) Renewable Energy (RE), 2) Sustainable Buildings (SB), and 3) Agroclimatology (AG)POWER Data Sources:The POWER project provides access to community-based Analysis Ready Data (ARD) for meteorology and solar-related parameters, specifically formulated for assessing and designing renewable energy systems.The data is available on at the source models’ native latitude and longitude global grid.Temporal levels include Hourly, Daily, Monthly, Annual, and Climatology. Download options include single point, regional, and global data.Formats include NetCDF, CSV, ASCII, geoJSON, ICASA, & EPW.Meteorological parameters are derived from:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – 3 Months Behind Near Real Time)NASA's GEOS 5.12.4 FP-IT archive (End of MERRA2 – Near Real Time)Solar parameters are derived from:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000) NASA's CERES SYN1deg (Jan. 1, 2001 – 3 Months Behind Near Real Time)NASA's FLASHFlux (3 Months Behind Near Real Time – Near Real Time)If you have any comments or questions, please do not hesitate to contact us at larc-power-project@mail.nasa.gov