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134 results for “global estimates”
Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment
<p>Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO<sub>2</sub> sink - an ocean model subsampling experiment, Philosophical Transactions A</p> <p>Surface ocean partial pressure of CO<sub>2 </sub>(pCO<sub>2</sub>) and air-sea CO<sub>2</sub> flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019).</p> <p>Also, all FESOM-REcoM output fields that were used in the reconstructions are provided.</p> <p>We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo).</p> <p> </p>
ECCO Iter22 Global Ocean State Estimate - 1 January 2004 to 30 April 2005
<p>Time series of global ocean temperature, salinity, and sound speed derived from the “Estimating the Circulation and Climate of the Ocean" (ECCO) program Iter22 state estimates. The sound speed fields were computed for simulation of acoustic propagation over basin scales or longer in a realistic oceanic environment. These estimates were computed in 2010 by the JPL-MIT-SIO ECCO program. <br>Original link: http://ecco2.jpl.nasa.gov/data9/cube/iter22/lat_lon/quart_80S_80N/THETA/ , now defunct.</p> <p>The solution is mesoscale permitting. The solution was obtained on a cube sphere grid between 80S and 80N with 18-km horizontal grid spacing and 50 vertical levels (Menemenlis et al., 2005, NASA supercomputer improves prospects for ocean <br>climate research, Eos Trans., AGU 86, 89, 95–96.). State estimates were averaged over a 3-day interval. File 003 is averaged over 2004/1/1 -- 2004/1/3. Three-day-mean temperature and salinity profiles from the iter22 solution were provided on 1/4 degree <br>grid for the period 1 January 2004 to 30 April 2005. There are 162 snapshots at 3-day intervals. </p> <p>Depths were decimated to the standard 33 depths of the World Ocean Atlas to 5500 m. YearDay 1 is 1 January 1992. The number of the filename indicates the yearday in 2004. In situ temperature was computed from model potential temperature. Sound speed was computed using the Del Grosso sound speed equation. Original model profiles descended only to the model sea floor. Temperature, salinity and sound speed were filled in on a uniform grid using nearest neighbor to 5500 m depth. Values on a regular grid make life easier. Product documented in Dushaw and Menemenlis, 2014, Antipodal acoustic thermometry: 1960, 2004, Deep Sea Research Part I: Oceanographic Research Papers, 86, 1–20, https://doi.org/10.1016/j.dsr.824 2013.12.008.</p> <p>Each snapshot is stored as a netcdf 4 file. Latitude, Longitude, Depth, and YearDay variables given separately in sspgrid.nc . <br>N.B.: Values in the files are stored as 32-bit or 16-bit integers to save disk space:</p> <p>Sound Speed: saved as "round( (c-1000)*1000 )", so to get actual c: c=1000. + double(c)/1000. <br>Sound speed is stored to 3 decimal places as a 32-bit integer.</p> <p>Temperature: saved as "round( (T-10)*1000 )", so to get actual T: T=10. + double(T)/1000. <br>Temperature is stored to 3 decimal places as a 16-bit integer. Note that abyssal temperature can sometimes be negative.</p> <p>Salinity: saved as "round( (S-10)*1000 )", so to get actual S: S=10. + double(S)/1000. <br>Salinity is stored to 3 decimal places as a 16-bit integer.</p> <p>Data directory also has two matlab routines: get_section.m and dist.m. get_section.m shows how to load the files, compute the physical variable from the stored value, and compute a section of ssp, T, or S. dist.m is a utility for computing geodesics; it relies on R. Pawlowitz's m_map package which can be downloaded freely from his University of Vancouver web page.</p> <p>$ md5sum *tgz <br>53ca3621f422b89c599c92fbab71d2fc S_ecco_iter22.tgz (2.99 GB)<br>a9e9109b6dae7355bf2fa0926d436d9a ssp_ecco_iter22.tgz (6.09 GB)<br>0cb8d1c2cdee4c7377353dff722ece34 T_ecco_iter22.tgz (4.33 GB)</p>
Global groundwater recharge estimates
<p>Groundwater recharge estimates have been presented in Berghuijs et al. (2022). The estimates exclude regions with mean temperatures below −2°C and regions with aridity below 1. The estimates consist of total groundwater recharge (units mm per year) and the fraction of precipitation becoming groundwater recharge (dimensionless).</p> <p> </p> <p> </p>
Towards Parameter Estimation in Global Hydrological Models
<p>The provided elementary effects are used in the publication J. Kupzig, R. Reinecke, F. Pianosi, M.Flörke and T. Wagener: Towards Parameter Estimation in Global Hydrological Models (submitted to Environmental Research Letters in Feb 2023).</p> <p>In a large sample study, the Morris Method (Morris 1991) application produces the provided elementary effects using a new lightweight version of the global hydrological model WaterGAP3: WaterGAPLite.</p> <ul> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/elementary_effects.zip?versionId=e980e961-2334-41db-900a-637b2dcec119">elementary_effects.zip </a>: elementary effects for all 50 trajectories and all basins (each trajectory is the result of 18 model runs; used bounds of parameters can be found in the Supplement of the manuscript)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/results_overview.xlsx?versionId=6f38c60f-9084-4376-907d-579414285506">results_overview.xlsx</a>: parameter ranks for each basin and different evaluation criteria based on the elementary effects.</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_Sample.csv">MC_Sample.csv</a>: normalized parameter samples of the additional Monte-Carlo Simulation (used bounds of parameters are the same as for the Morris method)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_NSE.csv">MC_NSE.csv</a>: resulting NSE values of the Monte-Carlo simulation</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/better_performing_basins.csv">better_performing_basins.csv</a>: list of basins (using GRDC no.) where minimal NSE is greater than -1 within all Monte-Carlo runs</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib.csv">standard_calib.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using the standard calibration for WaterGAP3 (fit to mean discharge)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib_mod.csv">standard_calib_mod.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using a modified version of the standard calibration for WaterGAP3 (maximizing the NSE)<br> </li> </ul>
Global vegetation productivity from 1981 to 2018 estimated from remote sensing data
<p>The MUltiscale Satellite remotE Sensing (MUSES) global vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 1981 to 2018. GPP and NPP were estimated with a light use efficiency (LUE) model and MUSES leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The detail information of the MUSES 5-km global GPP and NPP products are as below:</p> <p>Name: MUSES 5-km global GPP and NPP products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.05°</p> <p>Temporal resolution: 8 days</p> <p>Projection: geographic latitude/longitude</p> <p>Data format: Tiff</p> <p>Data type: integer (16bit)</p> <p>Upper left coordinates: -180°E, 90°N</p> <p>Scale factor: 100</p> <p>Unit: gCm<sup>-2</sup>d<sup>-1</sup></p> <p> </p> <p><span>Citation (Please cite these papers when these data are used)</span></p> <p><span>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L.,</span><span> </span><span>New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. </span><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</span></p> <p><span>2. Wang, M.J.; Sun, R.; </span><span>Zhu, A.R.;</span><span> Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency</span><span> </span><span>and Gross Primary Productivity Using Three</span><span> </span><span>Different Approaches. <span>Remote Sensing</span>. <span>2020</span>, 12, 1003.</span></p> <p><span>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. <span>Remote sensing. </span><span>2018, </span>10, 327.</span></p>
Map data of historical global estimates of soil respiration
<p>The map data of global soil respiration converted to NetCDF format.</p><p>All open access available estimates were collated.</p><p>Shoji Hashimoto, Akihiko Ito, Kazuya Nishina (2023) "Divergent data-driven estimates of global soil respiration". Communications Earth & Environment, 4 Article number: 460</p><p><a href="https://doi.org/10.1038/s43247-023-01136-2 ">https://doi.org/10.1038/s43247-023-01136-2</a> </p><p>Refer to Table 1 for the study ID and data source or the attributions of the NetCDF file. </p>
Global rooting zone water storage capacity and rooting depth estimates
<p>Global rooting zone water storage capacity (<em>S</em><sub>CWDX80</sub>, mm) and rooting depth (<em>z</em><sub>CWDX80</sub>, mm) estimates from Stocker et al., (2023). </p> <p>Additional global maps for rooting zone water storage capacity and rooting depth are provided and may be used as vegetation model forcing. These are created using the code from <code>whc_forcing_map.Rmd</code> , available <a href="https://github.com/geco-bern/mct/blob/master/whc_forcing_map.Rmd">here</a> (Zenodo entry: https://doi.org/10.5281/zenodo.7429129). The following steps were taken for creating these maps:</p> <ol> <li>The relationship between vegetation height and rooting depth was fitted using quantile regression (lower 10%) and data from Tumber-Davila et al. (2023). This yields a lower-bound rooting depth.</li> <li>A global map of vegetation height (Simard et al., 2011) was used for predicting the lower-bound rooting depth distribution globally.</li> <li>The lower-bound rooting depth was converted into a lower-bound root zone water storage capacity following methods as described in Stocker et al. (2023).</li> <li>The maximum of the lower-bound rooting depth and the inferred rooting depth (<em>z</em><sub>CWDX80</sub>) from Stocker et al., (2023) was determined for each grid cell. This is what's in the file <code>zroot_cwdx80_forcing.nc</code>. Anaologusly for <code>cwdx80_forcing.nc</code>.</li> </ol> <p>Please cite published paper:</p> <div> <div>Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 1–7, <a href="https://doi.org/10.1038/s41561-023-01125-2">https://doi.org/10.1038/s41561-023-01125-2</a>, 2023.</div> </div> <p> </p>
Estimates of Global Coastal Losses Under Multiple Sea Level Rise Scenarios
<p>Results from the Python Coastal Impacts and Adaptation Model (pyCIAM), the inputs and source code necessary to replicate these outputs, and the results presented in Depsky et al. 2023.</p> <p>All zipped Zarr stores can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>pyCIAM_outputs.zarr.zip</strong>: Outputs of the pyCIAM model, using the <a href="https://doi.org/10.5281/zenodo.6449230">SLIIDERS</a> dataset to define socioeconomic and extreme sea level characteristics of coastal regions and the 17th, 50th, and 83rd quantiles of local sea level rise as projected by various modeling frameworks (<a href="https://doi.org/10.5281/zenodo.593357">LocalizeSL</a> and <a href="https://doi.org/10.5281/zenodo.6419953">FACTS</a>) and for multiple emissions scenarios and ice sheet models.</li> <li><strong>pyCIAM_outputs_{case}.nc:</strong> A NetCDF version of <code>pyCIAM_outputs</code>, in which the netcdf files are divided up by adaptation "case" to reduce file size.</li> <li><strong>diaz2016_outputs.zarr.zip</strong>: A replication of the results from <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a> - the model upon which pyCIAM was built, using an identical configuration to that of the original model.</li> <li><strong>suboptimal_capital_by_movefactor.zarr.zip</strong>: An analysis of the observed present-day allocation of capital compared to a "rational" allocation, as a function of the magnitude of non-market costs of relocation assumed in the model. See Depsky et al. 2023 for further details.</li> </ul> <p><em>Inputs</em></p> <ul> <li><strong>ar5-msl-rel-2005-quantiles.zarr.zip</strong>: Quantiles of projected local sea level rise as projected from the LocalizeSL model, using a variety of temperature scenarios and ice sheet models developed in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014EF000239">Kopp 2014</a>, <a href="https://www.pnas.org/doi/pdf/10.1073/pnas.1817205116">Bamber 2019</a>, <a href="https://www.geo.umass.edu/climate/papers2/Deconto_Nature_2021.pdf">DeConto 2021</a>, <a href="https://www.ipcc.ch/srocc/">IPCC SROCC</a>. The results contained in <em>pyCIAM_outputs.zarr.zip</em> cover a broader (and newer) range of SLR projections from a more recent projection framework (FACTS); however, these data are more easily obtained from the appropriate Zenodo records and thus are not hosted in this one.</li> <li><strong>diaz2016_inputs_raw.zarr.zip</strong>: The coastal inputs used in <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a>, obtained from <a href="https://github.com/delavane/CIAM">GitHub</a> and formatted for use in the Python-based pyCIAM. These are based on the <a href="http://diva.globalclimateforum.org">Dynamic Integrated Vulnerability Assessment (DIVA)</a> dataset.</li> <li><strong>surge-lookup-seg(_adm).zarr.zip</strong>: Pre-computed lookup tables estimating average annual losses from extreme sea levels due to mortality and capital stock damage. This is an intermediate output of pyCIAM and is not necessary to replicate the model results. However, it is more time consuming to produce than the rest of the model and is provided for users who may wish to start from the pre-computed dataset. Two versions are provided - the first contains estimates for each unique intersection of ~50km coastal segment and state/province-level administrative unit (admin-1). This is derived from the characteristics in SLIIDERS. The second is simply estimated on a version of SLIIDERS collapsed over administrative units to vary only over coastal segments. Both are used in the process of running pyCIAM.</li> <li><strong>ypk_2000_2100.zarr.zip</strong>: An intermediate output in creating SLIIDERS that contains country-level projections of GDP, capital stock, and population, based on the Shared Socioeconomic Pathways (SSPs). This is only used in normalizing costs estimated in pyCIAM by country and global GDP to report in Depsky et al. 2023. It is not used in the execution of pyCIAM but is provided to replicate results reported in the manuscript.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>pyCIAM.zip:</strong> Contains the python-CIAM package as well as a notebook-based workflow to replicate the results presented in Depsky et al. 2023. It also contains two master shell scripts (run_example.sh and run_full_replication.sh) to assist in executing a small sample of the pyCIAM model or in fully executing the workflow of Depsky et al. 2023, respectively. This code is consistent with release 1.2.0 in the <a href="https://github.com/ClimateImpactLab/pyCIAM">pyCIAM GitHub repository</a> and is available as version 1.2.0 of the python-CIAM package on PyPI.</li> </ul> <p> </p> <p><strong>Version history:</strong></p> <p><em><strong>1.2</strong></em></p> <ul> <li>Point `data-acquisition.ipynb` to updated Zenodo deposit that fixes the dtype of `subsets` variable in `diaz2016_inputs_raw.zarr.zip` to be bool rather than int8</li> <li>Variable name bugfix in `data-acquisition.ipynb`</li> <li>Add netcdf versions of SLIIDERS and the pyCIAM results to `upload-zenodo.ipynb`</li> <li>Update results in Zenodo record to use SLIIDERS v1.2</li> <li> </li> </ul> <p><em><strong>1.1.1</strong></em></p> <ul> <li>Bugfix to inputs/diaz2016_inputs_raw.zarr.zip to make the `subsets` variable bool instead of int8.</li> </ul> <p><em><strong>1.1.0</strong></em></p> <ul> <li>Version associated with publication of Depsky et al., 2023</li> </ul>
Estimating the Global Distribution of Field Size using Crowdsourcing
<p>There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.</p> <p> </p> <p>The dataset (global field sizes.zip) contains:<br> - map of dominant field sizes (dominant_field_size_categories.tif) and description of legend items (legend_items.txt)<br> - table with all submissions by the participant (those who completed more than 10 classifications) and table description<br> - table with quality score of all the participants and table description<br> - table with estimated dominant field sizes at each location and table description</p>
Estimating global ammonia (NH3) emissions based on IASI observations from 2008 to 2018
<p>The dataset ia produced based on the Infrared Atmospheric Sounding Interferometer (IASI) observations in Luo et al.,(2022), by updating the prior ammonia (NH3) emission fluxes with the ratio between biases in simulated NH3 concentrations and effective NH3 lifetimes against the loss of the NHx family (NHx ≡ NH3 + NH4+). We then include sulfur dioxide (SO2) column to correct the NH3 emission trends over India and China, where SO2 emissions have changed rapidly in recent years. Finally, we quantify the uncertainty of NH3 emission by a series of perturbation and sensitivity experiments. The GEOS-Chem simulation driven by top-down estimates has lower bias with the IASI observations than prior emissions, demonstrating the consistency of our estimates with observations.</p>
GloRESatE - Global Rainfall Erosivity from Reanalysis and Satellite Estimates
<p>Rainfall erosivity measures the impact of rainfall kinetic energy and intensity or its potential to cause soil erosion. The sparsely available gauge rainfall dataset limits reliable rainfall erosivity assessment globally. GloRESatE is a state-of-the-art global rainfall erosivity dataset with a high spatial resolution of 0.1° × 0.1°. It integrates satellite data (CMORPH, IMERG Final Run), reanalysis data (ERA5-Land), and observations from 6,170 gauge stations worldwide. Created using advanced Gaussian Process Regression, this dataset provides accurate and reliable rainfall erosivity information. It serves as a vital resource for hydrological research, aiding studies in soil erosion, water resource management, and climate change impact assessments on a global scale.</p> <p> </p> <p>Das, S., Jain, M.K., Gupta, V., McGehee, R.P., Yin, S., de Mello, C.R., Azari, M., Borrelli, P. and Panagos, P., 2024. GloRESatE: A dataset for global rainfall erosivity derived from multi-source data. <em>Scientific Data</em>, <strong>11</strong>:926. https://doi.org/10.1038/s41597-024-03756-5</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>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
Output of global termite CH4 emission estimation (unit corrected: g CH4/m2/yr)
<p>NetCDF file: grid map of annual emissions from 1901 to 2021</p>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
First estimation of global trends in nocturnal power emissions reveals acceleration of light pollution
<p>The power emitted by different countries at night is based on DMSP and VIIRS data. Inclued also, some extra data from Spain, Portugal, Italy, UK and Greece.</p>
A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data
<p>These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript. </p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Global agricultural land use scenarios for estimating the potential of forest regeneration for climate mitigation to 2050
<p>The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use.</p> <p>The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions.</p> <p>The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)".</p> <p>Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information.</p> <p>This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.</p>
Comparison results from 'A global comparison of integrated water vapour estimates from WMO radiosondes, AERONET sun photometers and GPS for the 17 year period from 1997 to 2013' by Wilson, Hansen, Bingley and Milton
<p>This dataset contains the raw comparison results from the paper <em>A global comparison of integrated water vapour estimates from WMO radiosondes, AERONET sun photometers and GPS for the 17 year period from 1997 to 2013</em> by Wilson, Hansen, Bingley and Milton.</p> <p>The three files contain comparisons between:</p> <p>a) AERONET and Radiosonde (Validation_AERONET-Radiosonde.csv)</p> <p>b) AERONET and GPS (Validation_AERONET.csv)</p> <p>c) Radiosonde and GPS (Validation_Radiosonde.csv)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.