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708 results for “Global dataset”
Copernicus Digital Elevation Model (DEM) for Europe at 100 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 100 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 100 m, bilinear resampling was performed in GRASS GIS (using <code>r.proj</code> and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> ETRS89-extended / LAEA Europe (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 100 m</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.proj; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Global Lagrangian dataset of Marine litter
<p><strong>Global Lagrangian dataset of Marine litter</strong></p> <p>This dataset regroups 12 yearly files (<em>global-marine-litter-[2010–2021].nc</em>) combining monthly releases of 32,300 particles initially distributed across the globe following global Mismanaged Plastic Waste (MPW) inputs. The particles are advected with OceanParcels (<a href="https://doi.org/10.5194/gmd-12-3571-2019">Delandmeter, P and E van Sebille, 2019</a>) using ocean surface velocity, a wind drag coefficient of 1%, and a small random walk component with a uniform horizontal turbulent diffusion coefficient of K<sub>h</sub> = 1m<sup>2</sup>s<sup>-1</sup> representing unresolved turbulent motions in the ocean (see <a href="https://doi.org/10.3389/fmars.2021.667591">Chassignet et al. 2021</a> for more details).</p> <p><strong>Global oceanic current and atmospheric wind</strong></p> <p>Ocean surface velocities are obtained from GOFS3.1, a global ocean reanalysis based on the HYbrid Coordinate Ocean Model (HYCOM) and the Navy Coupled Ocean Data Assimilation (NCODA; <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.667591/full#B7">Chassignet et al., 2009</a>; <a href="https://www.jstor.org/stable/24862187?seq=1#metadata_info_tab_contents">Metzger et al., 2014</a>). NCODA uses a three-dimensional (3D) variational scheme and assimilates satellite and altimeter observations as well as in-situ temperature and salinity measurements from moored buoys, Expendable Bathythermographs (XBTs), Argo floats (<a href="https://link.springer.com/chapter/10.1007/978-3-642-35088-7_13">Cummings and Smedstad, 2013</a>). Surface information is projected downward into the water column using Improved Synthetic Ocean Profiles (<a href="https://apps.dtic.mil/sti/citations/ADA585251">Helber et al., 2013</a>). The horizontal resolution and the temporal frequency for the GOF3.1 outputs are 1/12° (8 km at the equator, 6 km at mid-latitudes) and 3-hourly, respectively. Details on the validation of the ocean circulation model are available in <a href="https://apps.dtic.mil/sti/citations/AD1034517">Metzger et al. (2017)</a>.</p> <p>Wind velocities are obtained from JRA55, the Japanese 55-year atmospheric reanalysis. The JRA55, which spans from 1958 to the present, is the longest third-generation reanalysis that uses the full observing system and a 4D advanced data assimilation variational scheme. The horizontal resolution of JRA55 is about 55 km and the temporal frequency is 3-hourly (see <a href="https://www.sciencedirect.com/science/article/pii/S146350031830235X?via%3Dihub">Tsujino et al. (2018)</a> for more details).</p> <p><strong>Marine Litter Sources</strong></p> <p>The marine litter sources are obtained by combining MPW direct inputs from coastal regions, which are defined as areas within 50 km of the coastline (<a href="https://doi.org/10.1057/s41599-018-0212-7">Lebreton and Andrady 2019</a>), and indirect inputs from inland regions via rivers (<a href="https://doi.org/10.1038/ncomms15611">Lebreton et al. 2017</a>). </p> <p><strong>File Format</strong></p> <p>The locations (<em>lon</em>, <em>lat</em>), the corresponding weight (<em>tons</em>), and the source (<em>1</em>: land, <em>0</em>: river) associated with the 32,300 particles are described in the file <em>initial-location-global.csv</em>. The particle trajectories are regrouped into yearly files (<em>marine-litter-[2010–2021].nc</em>) which contain 12 monthly releases, resulting in a total of 387,600 trajectories per file. More precisely, in each of the yearly files, the first 32,300 lines contain the trajectories of particles released on January 1st, then lines 32,301–64,600 contain the trajectories of particles released on February 1st, and so on. The trajectories are recorded daily and are advected from their release until 2021-12-31, resulting in longer time series for earlier years of the dataset. </p> <p><strong>References</strong></p> <p>Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J., Halliwell, G. R., et al. (2009). U.S. GODAE: global ocean prediction with the hybrid coordinate ocean model (HYCOM). Oceanography 22, 64–75. doi: <a href="https://doi.org/10.5670/oceanog.2009.39">10.5670/oceanog.2009.39</a></p> <p>Chassignet, E. P., Xu, X., and Zavala-Romero, O. (2021). Tracking Marine Litter With a Global Ocean Model: Where Does It Go? Where Does It Come From?. <em>Frontiers in Marine Science</em>, <em>8</em>, 414, doi: <a href="https://doi.org/10.3389/fmars.2021.667591">10.3389/fmars.2021.667591</a></p> <p>Cummings, J. A., and Smedstad, O. M. (2013). “Chapter 13: variational data assimilation for the global ocean”, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, eds S. Park and L. Xu (Berlin: Springer), 303–343. doi: <a href="https://doi.org/10.1007/978-3-642-35088-7_13">10.1007/978-3-642-35088-7_13</a></p> <p>Delandmeter, P., and van Sebille, E. (2019). The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Geosci. Model Dev. 12, 3571–3584. doi: <a href="https://doi.org/10.5194/gmd-12-3571-2019">10.5194/gmd-12-3571-2019</a></p> <p>Helber, R. W., Townsend, T. L., Barron, C. N., Dastugue, J. M., and Carnes, M. R. (2013). Validation Test Report for the Improved Synthetic Ocean Profile (ISOP) System, Part I: Synthetic Profile Methods and Algorithm. NRL Memo. Report, NRL/MR/7320—13-9364 Hancock, MS: Stennis Space Center.</p> <p>Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., et al. (2014). US Navy operational global ocean and Arctic ice prediction systems. Oceanography 27, 32–43, doi: <a href="https://doi.org/10.5670/oceanog.2014.66">10.5670/oceanog.2014.66</a>.</p> <p>Metzger, E., Helber, R. W., Hogan, P. J., Posey, P. G., Thoppil, P. G., Townsend, T. L., et al. (2017). Global Ocean Forecast System 3.1 validation test. Technical Report. NRL/MR/7320–17-9722. Hancock, MS: Stennis Space Center, 61.</p> <p>Lebreton, L., and Andrady, A. (2019). Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 5:6, doi: <a href="https://doi.org/10.1057/s41599-018-0212-7">10.1057/s41599-018-0212-7</a>.</p> <p>Lebreton, L., van der Zwet, J., Damsteeg, J. W., Slat, B., Andrady, A., and Reisser, J. (2017). River plastic emissions to the world’s oceans. Nat. Commun. 8:15611, doi: <a href="https://doi.org/10.1038/ncomms15611">10.1038/ncomms15611</a>.</p> <p>Tsujino H., S. Urakawa, H. Nakano, R.J. Small, W.M. Kim, S.G. Yeager, G. Danabasoglu, T. Suzuki, J.L. Bamber, M. Bentsen, C. Böning, A. Bozec, E.P. Chassignet, E. Curchitser, F. Boeira Dias, P.J. Durack, S.M. Griffies, Y. Harada, M. Ilicak, S.A. Josey, C. Kobayashi, S. Kobayashi, Y. Komuro, W.G. Large, J. Le Sommer, S.J. Marsland, S. Masina, M. Scheinert, H. Tomita, M. Valdivieso, and D. Yamazaki, 2018. JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do).<em> Ocean Modelling</em>, <strong>130</strong>, 79-139, doi: <a href="https://doi.org/10.1016/j.ocemod.2018.07.002">10.1016/j.ocemod.2018.07.002</a>.</p>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (CMG)
<p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset is available at .... After reprojection and aggregation, the global Climate Modeling Grid (CMG) albedo climatology datasets at 0.05° and 0.5° are available here. All of the published datasets include historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached in the CMG files. The International Geosphere-Biosphere Programme (IGBP) and PFT classification results of MCD12Q1 since 2001 were reprojected and aggregated to 0.05° and 0.5° by find mode in each aggregation group. In order to check the heterogeneity of the land cover climatology, the percentage of the dominant type in each aggregation group was also calculated.</p>
Global Surface Temperature Changes over Land Dataset
<p>Annual averages of global surface temperature changes for land only based on Berkeley Earth monthly dataset above the 1951-1980 baseline. The dataset is from 1750 in °C, 3 decimal places.</p>
Global Surface Temperature Changes Datasets Converted to 1850-1900 Baseline
<p>Global warming datasets converted to the uniform baseline. NASA, NOAA and Berkeley Earth datasets of global surface temperature changes in the period 1850-2021 for land+ocean, 1750-2021 for land only and 1880-2021 for ocean only, converted to the 1850-1900 baseline.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>covid</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>counterfactual</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> Data for years 2020 to 2026 are stored in the corresponding repositories:</p> <ul> <li><em>covid</em>: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li><em>counterfactual: </em><a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>From 2020 to 2026, the dataset includes two diverging scenarios. The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as <em>hist</em>.</p> <p>The <em>Projections</em> folder includes the generated tables for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> The <em>Sources </em>folder contains the data records from the IFS and WEO databases. The <em>Method data</em> contains the data files used to generate the tables with the SPIN method and the following Python scripts:</p> <ul> <li><em>SPIN_covid19_MRIO_files_preparation.py</em> generates the data files from the source data.</li> <li><em>SPIN_covid19_RMRIO runs.py</em> is the command to run the SPIN method and generate the dataset.</li> <li><em>figures.py</em> is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>counterfactual</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>covid</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
Data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'
<p>This is the data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'. Both algorithm and dataset described in this publication can be found in this folder.</p> <p>Please cite 'Global dataset of thermohaline staircases obtained from Argo floats and Ice-Tethered Profilers' when using this data set (doi: 10.5194/essd-2020-197).</p> <p>The newest/most updated version of the code can be found on GitHub: https://github.com/cvanderboog/Staircase-detection-algorithm.</p> <p> </p> <p> </p> <p> </p>
Compilation of hydrological datasets at the global scale
<p>Hydrological variables (evapotranspiration, precipitation, runoff, and terrestrial water storage) are distributed through several datasets that do not have the same characteristics (spatial and temporal coverage, spatial and temporal resolution). This dataset aims at providing a coherent gathering of all datasets available as of January 2021, covering at least the period from 2003 to 2014, and from 50°S to 50°N.</p> <p>This file contains:</p> <ul> <li>14 datasets for evapotranspiration (ERA5-Land, FLUXCOM, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GLEAM, JRA55, MERRA2, MOD16, and SEBBop)</li> <li>11 datasets for precipitation (CPC, CRU, ERA5-Land, PGF, GPCC, GPCP, GPM, JRA55, MERRA2, MSWEP, and TRMM)</li> <li>11 datasets for runoff (ERA5-Land, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GRUN, JRA55, and MERRA2)</li> <li>2 datasets for terrestrial water storage (GRACE CSR mascons, and GRACE JPL mascons)</li> </ul> <p>Each dataset is given as the original version (on a regular grid) and also as a post-treated file averaged for 189 river basins (whose borders are also provided).</p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
GSR - Global Sea Routes, dataset
<p>GSR - Global Sea Routes, P. I. Guido Abbattista, 2021, http://gsr.nodegoat.net/ (CC BY-NC-ND 4.0). Dataset 1.0.0, 25 August 2022.</p>
A dataset of global variations in directional solar radiation exposure for ocular research using the libRadtran radiative transfer model
<p>Directional solar photon flux density has particular relevance to eye disease research (keratitis, cataract formation, macula degeneration) because ocular components (cornea, lens, retina) experience different exposures dependent on global location, structural geometry of the eye and human behaviour (Sliney, 1997). The human macula has a field of view of ~17<strong>°</strong>, or 0.06901537 sr (Strasburger, Rentschler & Jüttner, 2011) and its cone of exposure can be modelled at a range of global locations using a radiation transfer model to estimate different directions of irradiation. This dataset provides examples of spectral radiance within the macula field of vision, calculated with the radiative transfer model libRadtran v2.0.3 (Mayer & Kylling, 2005). Three data sets are provided at different latitudes without correction for spectral ocular transmission. Unless otherwise specified, all simulations were parametrized according to local meteorological condition (altitude, pressure, temperature) and atmospheric conditions on the simulated day (aerosol optical density, water column, O<sub>3</sub> and NO<sub>2</sub> concentrations). The model was parametrized for a subject looking northward toward the ground (-15<strong>°</strong> from horizon), at a height of 170 cm above the ground.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p><em>Simulation 1: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>8 cardinal directions (every 45<strong>° </strong>from North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 2: </em>This data set reports the spectral radiance from 250 - 2,500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>1 cardinal direction (North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 3: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>1 latitude (61.0: Southern Finland).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>9 cardinal directions (every 40<strong>° </strong>from North).</li> <li>3 bidirectional reflectance distribution functions for the ground (forest, urban, snow).</li> <li>2 tilt angles for the eye direction (0<strong>° </strong> from horizon or -15<strong>°</strong> from horizon, toward the ground).</li> </ul> <p> </p>
Dataset Global Warming Forecast using Acceleration Factors
<p>The dataset includes results of Global Warming forecast using four methods.</p> <p>The methods include a parabolic trendline of the last 61 years of global warming and cumulated CO2 emissions.</p> <p>Two other methods apply the velocity and the acceleration of global warming and cumulative CO2 emissions.</p> <p>The relation between the global surface temperature change and the change in the cumulative CO2 emissions was determined in previous publications as 0.000745°C/GtCO2.</p> <p>The average result from all four methods for the business as usual CO2 mitigation scenario is 4.4°C (4.1°C -5.0°C).</p> <p>According to this forecast, the global temperature change will reach 1.5°C in 2031 (9 years from now) and 2.0°C in 2047 (25 years from now).</p>
Dataset for "AGU Publications updates authorship policy to foster better equity and transparency in global research collaboration"
<p>Dataset supporting "AGU Publications updates authorship policy to foster better equity and transparency in global research collaboration." </p> <p>This file provides summary data for new submissions for "Global Biogeochemical Cycles" (GBC) and "Journal of Geophysical Research: Biogeosciences" (JGR: Biogeo) from 2012 through 2023 including International Collaboration Status (whether more than one country was represented on the author list), Research4Life Author Status (whether any author was from a country on the Research4Life eligibility list [https://www.research4life.org/access/eligibility/]), and Research4Life Abstract Status (whether the submission abstract referenced a country on the Research4Life eligibility list). </p> <p>Summary data for all AGU journals combined are provided for years 2012 and 2023, including whether more than one country was represented by the author list and whether any author was from a country on the Research4Life eligibility list. </p> <p>Summary data are presented in compliance with AGU's Privacy Policy, https://www.agu.org/Privacy-Policy</p>
A new merged dataset of global ocean chlorophyll-a concentration for better trend detection
<p>Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. </p>
Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.
<h2>Description (V2 - Latest):</h2> <p>To enable easy integration in the workflows, we have provided the main datasets in the following formats:</p> <p> </p> <ul> <li><strong><em>Vector dataset:</em><code> Folder - Vector</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>Geopackage (.gpkg)</em></code> file <strong>(</strong><strong><em>Results_Vis.gpkg</em></strong><strong>)</strong> with polygon geometries at 1/8-degree spatial resolution in an <strong>EPSG:4326 </strong>coordinate system. The <em>attribute table</em> of this file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with <em>Y</em><strong> </strong>representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em> and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <ul> <li><strong><em>Raster datasets:</em></strong><strong> <code> Folder - Raster</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>geotiff (.tif)</em></code> files with <strong>LZW</strong> compression in an <strong>EPSG:4326</strong> coordinate system. The assessed gross rooftop area datasets are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5 </em>for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with<strong> </strong><em>Y</em> representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units.</li> </ul> <p> </p> <ul> <li><strong><em>Numerical dataset:</em></strong> <strong><code> Folder - Numerical</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>parquet (.parquet)</em></code> file <strong><em>(Results.parquet).</em></strong> This file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em> narratives with <em>Y </em>representing the assessment year having values as<strong> </strong><em>20, 30, 40, and 50</em><strong> </strong>for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a <em>CF</em> column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <p>In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <strong><code> Folder - Models</code></strong></p> <ul> <li><strong><em>M2_Model.json:</em></strong><strong> </strong>This file contains the frozen parameters of the M2 model in <code><em>.json</em></code> format generated from <code>XGBoost version 2.0.3</code></li> <li><strong><em>SSP_drivers.parquet:</em><em> </em></strong>This file contains the driver data used for generating the main dataset in our study</li> <li><strong><em>FN_MAP.parquet:</em></strong><strong> </strong>This file contains the boundary information for each fishnet grid tile in a Well Known Text <em>(WKT)</em> format.</li> <li><strong><em>Prediction.ipynb:</em></strong><strong> </strong>This file provides a python notebook interface to generate inferencing from <em><code>M2_Model.json</code> </em>using <code><em>SSP_drivers.parquet</em></code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code><em>FN_MAP.parquet</em></code><code> </code>file.</li> <li><strong><em>environment.yaml:</em></strong><strong> </strong>This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul> <p> </p> <h2><strong>Version history:</strong></h2> <p><strong>This version corresponds to the revised journal submission (Round 1). <em>The version will be updated upon the completion of the review of the main manuscript.</em></strong></p> <ul> <li><em>This version <strong>V2</strong> is supersedes <strong>V1</strong> to correspond with round 1 of review.</em></li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " <em>Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 </em>", submitted to <em>Scientific Reports</em> Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul> <p> </p> <h2>Changelog:</h2> <p>The following files from version <strong>V1</strong> of this dataset are now <strong><em>archived</em></strong> based on the reviews (Round 1).</p> <ol> <li> <blockquote><em><strong>1_Geospatial_Dataset_V1.gpkg</strong></em></blockquote> </li> <li> <blockquote><em><strong>2_Countrylevel_gross_rooftop_area_V1.parquet</strong></em></blockquote> </li> <li> <blockquote><em><strong>3_Analytics_Scripts_V1.ipynb</strong></em></blockquote> </li> </ol>
Global River Topology (GRIT) vector datasets
<p>The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).</p> <p>A map of GRIT segments labelled with OSM river names is available here: <a href="https://michelwortmann.com/research/gritv05-segments-river-names/" target="_blank" rel="noopener">Map with names</a></p> <p><strong>Report bugs and feedback</strong></p> <p>Your feedback and bug reports are welcome here: <a href="https://forms.gle/JrT58QStNKBHPJAH6" target="_blank" rel="noopener">GRIT bug report form</a></p> <p>The feedback may be used to improve and validate GRIT in future versions.</p> <p><strong>Regions</strong></p> <p>Vector files are provided in 7 regions with the following codes:</p> <ul> <li>AF - Africa</li> <li>AS - Asia (excl. Siberia)</li> <li>EU - Europe</li> <li>NA - North America</li> <li>SA - South America</li> <li>SI - Siberia</li> <li>SP - South Pacific/Australia</li> </ul> <p>The domain polygons (GRITv06_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where and the domain attribute).</p> <p>Vector files are provided both in the original equal-area Equal Earth Greenwich projection (EPSG:8857) as well as in geographic WGS84 coordinates (EPSG:4326).</p> <p><strong>Change log</strong></p> <ul> <li>v0.6 - 2024-05-30 <ul> <li>Rivers/streams outside of the GRWL mask forced by all OSM water lines (not only those with waterway=river/canal)</li> <li>Some manual directions in the Irrawaddy delta and fixed erronous sink in the Volga delta</li> </ul> </li> <li>v0.5 - 2024-02-14 <ul> <li>Cyclicity and discontinuities resolved through improved algorithms, bug fixes, more sophisticated cycle solving algorithms and some manually forced directions. Only insignificant cycles (non-sinks, less than 50) were removed.</li> <li>Added segment and reach attributes</li> <li>Computational domain fixes</li> <li>Segments include OSM river names</li> <li>Asia domain split into Siberia and rest of Asia</li> <li>Vector files available in EPSG:8857 and EPSG:4326</li> </ul> </li> <li>v0.4 - 2023-03-11<br> <ul> <li>First globally complete dataset published</li> </ul> </li> </ul> <p><strong>Network segments</strong></p> <p>Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.</p> <p><em><strong>Attribute description of lines layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river segment ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_node_id</td> <td>integer</td> <td>global segment node ID at upstream end of line</td> </tr> <tr> <td>downstream_node_id</td> <td>integer</td> <td>global segment node ID at downstream end of line</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs connecting at upstream end of line</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs connecting at downstream end of line</td> </tr> <tr> <td>direction_algorithm</td> <td>float</td> <td>code of RivGraph method used to set the direction of line</td> </tr> <tr> <td>width_adjusted</td> <td>float</td> <td>median river width in m without accounting for width of segments connecting upstream/downstream</td> </tr> <tr> <td>length_adjusted</td> <td>float</td> <td>segment length in m without accounting for width of segments connecting upstream/downstream in m</td> </tr> <tr> <td>is_mainstem</td> <td>integer</td> <td>1 if widest segment of bifurcated flow or no bifurcation upstream, otherwise 0</td> </tr> <tr> <td>strahler_order</td> <td>integer</td> <td>Strahler order of segment, can be used to route in topological order</td> </tr> <tr> <td>length</td> <td>float</td> <td>segment length in m</td> </tr> <tr> <td>azimuth</td> <td>float</td> <td>direction of line connecting upstream-downstream nodes in degrees from North</td> </tr> <tr> <td>sinuousity</td> <td>float</td> <td>ratio of Euclidean distance between upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line</td> </tr> <tr> <td>drainage_area_in</td> <td>float</td> <td>drainage area at beginning of segment, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_out</td> <td>float</td> <td>drainage area at end of segment, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_mainstem_in</td> <td>float</td> <td>drainage area at beginning of segment, following the mainstem, in km2</td> </tr> <tr> <td>drainage_area_mainstem_out</td> <td>float</td> <td>drainage area at end of segment, following the mainstem, in km2</td> </tr> <tr> <td>bifurcation_balance_out</td> <td>float</td> <td>(drainage_area_out - drainage_area_mainstem_out) / max(drainage_area_out, drainage_area_mainstem_out), dimensionless ratio</td> </tr> <tr> <td>grwl_overlap</td> <td>float</td> <td>fraction of the segment overlapping with the GRWL river mask</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>dominant GRWL value of segment</td> </tr> <tr> <td>name</td> <td>text</td> <td>river name from Openstreetmap where available, English preferred</td> </tr> <tr> <td>name_local</td> <td>text</td> <td>river name from Openstreetmap where available, local name</td> </tr> <tr> <td>n_bifurcations_upstream</td> <td>integer</td> <td>number of bifurcations upstream of segment</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group ID, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Attribute description of nodes layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river node ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs flowing into node</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs flowing out of node</td> </tr> <tr> <td>node_type</td> <td>text</td> <td>description of node, one of bifurcation, confluence, inlet, coastal_outlet, sink_outlet, grwl_change</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>GRWL code at node</td> </tr> <tr> <td>grwl_transition</td> <td>text</td> <td>GRWL codes of change at grwl_change nodes</td> </tr> <tr> <td>cycle</td> <td>integer</td> <td>>0 if segment is part of an unresolved cycle, 0 otherwise</td> </tr> <tr> <td>continuity_violated</td> <td>integer</td> <td>1 if flow continuity is violated, otherwise 0</td> </tr> <tr> <td>drainage_area</td> <td>float</td> <td>drainage area, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_mainstem</td> <td>float</td> <td>drainage area, following the mainstem, in km2</td> </tr> <tr> <td>n_bifurcations_upstream</td> <td>integer</td> <td>number of bifurcations upstream of node</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Network reaches</strong></p> <p>Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.</p> <p><em><strong>Attribute description of lines layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>segment_id</td> <td>integer</td> <td>global segment ID of reach</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river reach ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_node_id</td> <td>integer</td> <td>global reach node ID at upstream end of line</td> </tr> <tr> <td>downstream_node_id</td> <td>integer</td> <td>global reach node ID at downstream end of line</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs connecting at upstream end of line</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs connecting at downstream end of line</td> </tr> <tr> <td>grwl_overlap</td> <td>float</td> <td>fraction of the reach overlapping with the GRWL river mask</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>dominant GRWL value of node</td> </tr> <tr> <td>grwl_width_median</td> <td>float</td> <td>median width of the GRWL river mask, meters</td> </tr> <tr> <td>grwl_width_std</td> <td>float</td> <td>standard deviation of width of the GRWL river mask, meters</td> </tr> <tr> <td>length</td> <td>float</td> <td>length of reach in meters</td> </tr> <tr> <td>sinuousity</td> <td>float</td> <td>ratio of eucledian distance betwen upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line</td> </tr> <tr> <td>azimuth</td> <td>float</td> <td>direction of line connecting upstream-downstream nodes in degrees from North</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p><em><strong>Attribute description of nodes layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>segment_node_id</td> <td>integer</td> <td>global ID of segment node at segment intersections, otherwise blank</td> </tr> <tr> <td>n_segments</td> <td>integer</td> <td>number of segments attached to node</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river reach node ID, same as FID</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs flowing into node</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs flowing out of node</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Catchments</strong></p> <p>Catchment outlines for entire river basins (network components, including coastal drainage areas). Catchments for segments (aka. subbasins) and reaches are also available on request.</p> <p><em><strong>Attribute description</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global catchment ID, same as global_id of segment/reach ID if is_coastal == 0 for respective catchments or the catchment_id for component_catchments, same as FID</td> </tr> <tr> <td>area</td> <td>float</td> <td>catchment area in km2</td> </tr> <tr> <td>is_coastal</td> <td>integer</td> <td>1 for coastal drainage areas, 0 otherwise</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Raster </strong></p> <p>Upstream drainage area and other raster-based products are also available upon request.</p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
ScienceDex guides
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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.