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2,057 results for “river basins”
Historical and future irrigation water demand for the STARS4Water river basins
<p>Dataset contains data on historical and future irrigation water demand for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average summer net irrigation requirement [mm/year] for each combination GCM model (5 models)/time window (2 windows) was calculated within the boundaries of the project river basin hubs. The difference between the future and historical period was also calculated for each GCM. In addition, ensemble mean values for both horizons and ensemble mean differences were calculated. This dataset was prepared based on the data available in the "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" repository (Busschaert et al., 2022, DOI: 10.5281/zendo.6760976).</p>
Historical and future land use and land cover data for the STARS4Water river basins
<p>Dataset contains data on historical and future land use and land cover for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average area fraction of five general land use classes (crop, forest, grass, urban and other) within the project river basins was calculated at five-year intervals starting in 2016 and ending in 2051. This dataset was prepared based on the data available in the "LUCAS LUC future land use and land cover change dataset for Europe (Version 1.1)" repository (Hoffmann et al., 2022, DOI: 10.26050/WDCC/LUC_future_EU_v1.1).</p>
Bird surveys in French Broad River Basin, North Carolina, 2014
This dataset includes counts of birds from surveys conducted in the French Broad River Basin in western North Carolina, USA. This basin is in the Southern Appalachian Mountains. Data were collected to examine the spatial and seasonal supply of biodiversity-based cultural ecosystem services (CES), in this case, nature study through birdwatching. The data includes bird species observed at 69 sites on public and private lands during the period 2014-04-01 to 2014-08-08. Bird species were categorized with respect to migration status, level of conservation concern (both based on literature), and relative abundance in the study region (based on eBird data). Environmental data for 56 sites are provided: elevation, early season precipitation, mean summer temperature, land cover diversity, tree cover, vegetation structural diversity, vegetation annual productivity, and building density at local and landscape scales. Graves et al. (2019, doi:10.1007/s13280-018-1068-1) used these data to analyze seasonal shifts in birdwatching supply and how those shifts impacted public access to projected birdwatching hotspots. Landscape patterns of CES supply differed substantially among five CES indicators (total bird species richness, and richness of migratory, infrequent, synanthrope, and resident species). For example, total species richness hotspots seldom overlapped with hotspots of migratory or infrequent species. Public access to CES hotspots varied seasonally. This study suggests that simple, static biodiversity metrics may overlook spatial dynamics important to CES users.
Historical and future water demand for households and industry for the STARS4Water river basins
<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> <p>The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050.</p> <p>The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file:<br>1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip for years 2000-2020<br>2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5)<br><br>The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in <em>Towards a global high </em><em>resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe</em>, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others. </p>
Surface water and flooding dynamics data set based on seasonally continuous Landsat data (1986-2011) in a dryland river basin
<p>Animations of the data are available here: <a href="https://doi.org/10.5281/zenodo.2438110">https://doi.org/10.5281/zenodo.2438110</a></p> <p>If you are using this data set, please cite the following publication:</p> <p>Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466 </p> <p>The data represent statistically validated surface water and flooding extent dynamics derived from seasonally continous Landsat TM/ETM+ data and random forest models, and summarised to the maximum extent of surface water per season between 1986-2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157</p> <p>URL: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p> <p>Data are provided in GeoTIFF format per season per year. File naming convention is as follows:<br> yy_inund_freq_season_SamplingMethod. For example, "99_inund_freq_winter_max" will represent inundation frequency for winter 1999 resampled using a maximum resampling method. </p> <p>Inundation frequency represents the number of times a pixel has been flagged as flooded out of the times that pixel had valid observations * 100. Valid observation exclude no data values and clouds. The valid range of inundation frequency is 0-100 [%], with 255 indicating no data values. Data type is eight bit unsigned integer (uint8). </p> <p>The data were resampled to 120m resolution to reduce file size. The resampling methods used include max (e.g. selects the max value of all non-NODATA contributing 30m pixels) and mean (median and min can be provided upon request). If you are unsure which resampling to use, you may want to start with the mean. </p>
Wildflower survey data from French Broad River Basin, North Carolina, 2014
This dataset includes measures of the abundance of blooming wildflowers from field surveys conducted in the French Broad River Basin in western North Carolina, USA. This basin is in the Southern Appalachian Mountains. Data were collected to examine the spatial and seasonal supply of biodiversity-based cultural ecosystem services (CES), in this case, nature study by viewing wildflowers. The data includes blooming species observed at 69 sites on public and private lands during the period 2014-04-01 to 2014-08-08. Flower species were characterized as charismatic if represented in tourism websites. Environmental data for 56 sites are provided: elevation, early season precipitation, mean summer temperature, land cover diversity, tree cover, vegetation structural diversity, vegetation annual productivity, and building density at local and landscape scales. Graves et al. (2017, doi: 10.1007/s10980-016-0452-0) used these data to analyze seasonal shifts in supply of floral resources and how those shifts impacted public access to projected resource hotspots. Relationships between landscape gradients, biodiversity, and ecosystem service supply varied seasonally, and the analysis identified CES hotspots otherwise obscured by simple proxies. Landscape models of biodiversity-based cultural ecosystem services should include seasonal dynamics of biotic communities to avoid under- or over-emphasizing the importance of specific locations in ecosystem service assessments.
RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins
<p>This database contains Inputs and intermediate files of the RAPID model pre-processor (RRR), and also outputs from the RRR (<em>i.e.</em>, Inputs for RAPID); which were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. If you use this RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins in your work, please cite: <em>Sikder et al.</em>, [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>.</p> <p>The database contains;</p> <ul> <li>Global River basin and Network Shapefiles: HydroSHEDS.tar.gz</li> <li>Extracted Basin Shapefile: MIGBM_basin.tar.gz</li> <li>Extracted River Network Shapefiles: MIGBM_<strong><em>res</em></strong>_ntwk.tar.gz (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Catchment Files: rapid_catchment_as_<strong><em>riv</em></strong>_res.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Connectivity Files: rapid_connect_<strong><em>res</em></strong>_MIGBM.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Coordinate Files: coords_<strong><em>res</em></strong>_MIGBM.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Base Parameter Files: <strong><em>p</em></strong>fac_<strong><em>res</em></strong>_MIGBM_1km_hour.csv (Note: <strong><em>p</em></strong> = k or x; <strong><em>res</em></strong> = fine or coarse)</li> <li>Sort Files: sort_<strong><em>res</em></strong>_MIGBM_topo.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Sorted Basin Files: riv_bas_id_<strong><em>res</em></strong>_MIGBM_topo.csv (Note: <strong><em>res</em></strong> = fine or coarse)</li> <li>Coupling Files: rapid_coupling.tar.gz</li> <li>Parameter Files: rapid_param.tar.gz</li> <li>Volume Files: m3_riv_<strong><em>res</em></strong>_MIGBM_20000101_20091231_<strong><em>prj</em></strong>_<strong><em>LSMsr</em></strong>_<strong><em>tr</em></strong>_utc.nc (Note: <strong><em>res</em></strong> = fine or coarse; <strong><em>prj</em></strong> = GLDAS or GLDAS.2.0 or GLDAS.2.1 or ECMWF; <strong><em>LSM</em></strong> = CLM, MOS, NOAH, VIC, ERAint; <strong><em>sr</em></strong> = 10 or 025; <strong><em>tr</em></strong> = 3H or D)</li> </ul> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a></p> <p>ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a></p> <p> </p> <p>References:</p> <p>Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a></p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
RivFISH - An European database on fish species presence across river basins
<p>The RivFISH database aggregates the available data on freshwater-dependent fish presence in Europe, validated at the river basin level and considering taxonomical synonyms for species names, thus allowing for a maximization of data usage and robustness. This database also promotes interoperability with other datasets, including the IUCN Red List of Threatened Species, FishBase and the Catchment Characterisation and Modelling (CCM2) – River and Catchment Database v2.1. It is, as far as the authors know, the most up-to-date and comprehensive database on the presence of freshwater-dependent fish species for European river basins. The structure of the database is also prepared to deal with future alterations in species taxonomy, as well as new records of species occurrence in river basins.</p>
Raw and processed hydro-meteorological variables of Jucar river basin for feature selection
<p>The dataset Processed data – input WQEISS.csv was employed for the input variable selection step in Zaniolo et al., 2018. It includes monthly values of 28 hydro-meteorological variables and indexes of Jucar river basin, Spain, for the period 1986-2000, namely:</p> <ul> <li>2 temporal features: day and month of the year;</li> <li>12 inputs to the Jucar State Index: average monthly storage and groundwater levels, average three months river runoff, and cumulated areal precipitation over 12 months;</li> <li>8 additional observed variables in the basin: three months average outflows from, and inflows to, the main reservoirs, and mean monthly areal temperatures;</li> <li>6 traditional drought indicators: Standardized Precipitation Index (SPI) and Standardized Precipitation and Evaporation Index (SPEI). SPI and SPEI indicators are computed on mean monthly data over the entire basin for 3, 6, and 12 months time aggregations.</li> </ul> <p>The last column of the dataset reports the target variable, i.e., the monthly nominal shortage of water conveyed to the irrigation districts simulated via AQUATOOL model. For further details on the dataset please consult Zaniolo et al., 2018, or the dedicated website <a href="http://www.nrm.deib.polimi.it/?page_id=2438">http://www.nrm.deib.polimi.it/?page_id=2438</a></p> <p>The unprocessed data used to compute indices and temporal cumulations in Processed data – input WQEISS.csv are reported in table Raw Data.csv. Public observations of rainfall, streamflows and storage levels come from the SAIH (Hydrological Automatic Information System) of the CHJ (Jucar Hydrological Confederation). Users can directly download data for the last 12 months on the dedicated webpage <a href="http://saih.chj.es/chj/saih/?f">http://saih.chj.es/chj/saih/?f</a> while previous data records are provided for free by CHJ upon request. Observations from piezometers are downloadable from the Piezometric Network Information section section of the CHJ <a href="https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx">https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx</a>.</p>
Seasonal streamflow hindcast data for the Upper Segura and Upper Tagus river basins
<p>The datasets provided here have been produced as part of the IMPREX project for work package 11, task 2. The aim was to evaluate the performance of climate model-based seasonal hydrological forecasting system that is used to predict drought indices of the Segura River Basin and the Tajo-Segura Water transfer System. These indices rely on forecasted discharge values and describe the expected status of the water availability in both systems. Also, for the Tagus basin, the forecasts can be used to determine the amount of water transferred to the Segura river basin. The key involved stakeholder is the River Basin Authority of the Segura Basin that uses these drought indices for drought mitigation actions. This task developed and evaluated a forecasting system for these drought indices.</p> <p>The dataset - <strong>Pseudo_Obs_SPHY_Spain02_Output.csv</strong> include streamflow simulations for the Entrepenas and Buendia discharge stations located in the Upper Tagus basin, Spain in addition to the Fuensanta and Cenajo discharge stations located in the Segura river basin, Spain. Hydrological outputs are produced using The Spatial Processes in Hydrology (SPHY) model (Terink et al., 2015) forced by the Spain02 V4 meteorological dataset (Herrera et al., 2016) for the period 1979-2010. Please note however that there are known issues with the Spain02 dataset for the years 2007-2010 that affect this dataset.</p> <p>The dataset <strong>- SPHY_ECMWF_S5_Output.csv </strong>includes streamflow simulations for the same locations using the SPHY model albeit forced with the ECMWF SEAS5 hindcast data for the period 1981-2010. The column “LD” represents lead month where the first month of hindcasts is signaled as 1, second as 2 and third as 3. The “Ens” column refers to the individual ensemble members as 25 in total are used for this setup.</p>
Yangtze River Basin Water Reservoir (YWR) dataset
<p>The Yangtze River Basin Water Reservoir (YWR) dataset, developed by multi-source satellite remote sensing data, provides monthly time series data for 443 reservoirs (with a total storage capacity of 276.51 km³) in the Yangtze River Basin (YRB) from 1990 to 2023, including area and storage data for all 443 reservoirs and water level data for 175 reservoirs. This dataset is associated with the study: Wang et al., "Advanced monitoring of reservoirs in the Yangtze River Basin from 1990 to 2023 using multi-source satellite remote sensing", Journal of Remote Sensing, under review, 2025.</p>
Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)
<p>The animations provided here are part of the following publication:<br> Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466</p> <p>Please refer to the above mentioned publication for a description of the data and interpretation of the patterns.</p> <p>The animations are based on statistically validated surface water and flooding extent dynamics data derived from seasonally continous Landsat TM/ETM+ and random forest models from 1986 to 2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157 and available here: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p>
Dataset for Hydropower Expansion in Eco-Sensitive River Basins under Global Energy-Economic Change
<p>The data presented in this repository can be fed into the codes provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a> to reproduce the results of the following paper:</p> <p> </p> <p>Chowdhury, A.F.M.K., Wild, T., Zhang, Y. <em>et al.</em> Hydropower expansion in eco-sensitive river basins under global energy-economic change. <em>Nat Sustain</em> <strong>7</strong>, 213–222 (2024). <a href="https://doi.org/10.1038/s41893-023-01260-z">https://doi.org/10.1038/s41893-023-01260-z</a></p> <p> </p> <p><strong>Summary</strong></p> <p>In this study, we investigate how rapid economic growth and transition to low-carbon energy may impact hydropower development, with potential countervailing effects of increasingly cost-competitive variable renewable energy (VRE). We explore the effects of these forces on hydropower expansion in the world's 20 most eco-sensitive river basins, that have substantial untapped hydropower potential and ecological richness. Our investigation is based on the Global Change Analysis Model (GCAM), an integrated model of global energy-water-economy dynamics. The GCAM outputs and other data provided in this repository, in combination with the Jupyter Notebooks provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a>, can be used to conduct our key analysis, and reproduce the relevant results.</p>
Effects of experience and context on phototaxic behaviors of larval stream salamanders in the Upper Little Tennessee River basin
Desmognathus quadramaculatus larvae were captured from 2 locations in the Upper Little Tennessee River basin. Naïve individuals with respect to high-light environments were collected within the fully forested Ball Creek watershed at the Coweeta Hydrological Laboratory in Macon County, North Carolina. This watershed is a control basin that has been undisturbed since 1927. Individuals with experience in high-light environments, defined as habituated individuals, were collected from a first-order stream with less than 10% canopy cover located in Rabun County, Georgia. Salamanders were captured opportunistically using dipnets and cover object searches. Upon capture, salamanders were held individually in a cooler during transport where they were placed in containers with a paper towel cover object, native stream water, and kept in a temperature controlled room (15.5 C) with an indirect, natural photoperiod. Individual behaviors were tested within 48 hours of capture, and individuals were released at their capture location within one week.
Coweeta Synoptic Data from 49 sampling sites in the Upper Little Tennessee River Basin from 2009 to 2010 (mesoscale habitat data)
This data was generated as part of synoptic sampling conducted at the Coweeta LTER between June 2009 and May 2010. 49 wadeable streams with low levels of development were sampled throughout the Upper Little Tennessee River Basin in the Southern Appalachians. Effects of riparian vegetative conditions on a suite of channel morphological variables were investigated: active channel width, variability of width within a reach, large wood frequency, mesoscale habitat distributions, median particle size, and percent fines. Stream mesoscale habitat areas for each 150 m stream reach were recorded in this particular dataset. At each site, a uniform 150 meter section of stream was surveyed. Observers kept a running tally of the areas associated with various mesoscale habitat units including, cascades, riffles, pools, alcoves, pocket water, runs, glides, and obstructions.
Monthly SPEI dataset at 3-, 6-, and 12-month time scales in the Amazon River Basin
<p>SPEI datasets derived from the Global SPEI database webpage and developed by Vicente-Serrano et al. (2010). This product is disseminated by the Spanish National Research Council (CSIC) for the period from January 1901 to December 2018 (118 years, version 2.6). The SPEI datasets at 3-, 6-, and 12-month time scales are gridded over a 0.50-degree grid on the Amazon River Basin. A total of 1416 GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format, one per month, are provided.</p>
Water availability and temperature scenarios for water-dependent power plants in the Danube river basin and the Iberian Peninsula
<p>The dataset is composed by 12 files reporting the water availability and temperature scenarios for 167 water-dependent power plants in the Danube river basin and the Iberian Peninsula.</p> <p>The dataset is split into multiple files by region (Danube river basin (Danube) or Iberian Peninsula (IP)), variable (discharge or river temperature) and scenario (baseline, RCP26 or RCP85) considered.</p> <p>The title of each file is composed by the variable reported (discharge or river temperature) and the scenario considered (baseline: 1951-2004, RCP26: 2006-2100, RCP85: 2006-2100). The first row is used to report the fields considered: the first three columns report the day, the month and the year. The remaining columns report the name of the power plant considered in each region (57 for the Daube river basin and 110 for the Iberian Peninsula). In each row day, month, year and streamflow or river temperature values are reported for every water-dependent power plant examined in the study.</p> <p>Temperature is reported as daily average temperature in degrees Celsius (°C) while water availability is reported as daily average streamflow in cubic meters per second (m^3/s).</p> <p>For a description on how these files were obtained, please refer to <a href="https://doi.org/10.2777/135510">https://doi.org/10.2777/135510</a>.</p>
Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin
<p>Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added. </p> <p>A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution.</p> <p>Coding for the SDG indicator 15.3.1, trajectory, state, and performance.</p> <p>-32768 is ‘No data’</p> <p>-1 is ‘Degraded’</p> <p>0 is ‘Stable’’</p> <p>1 is ‘Improvement’</p> <p>Coding for the drought severity.</p> <p>From 0 (minimum drought severity) to 1 (maximum drought severity).</p>
River flows and nutrient discharges to the Atlantic ocean basin
<p>This dataset includes i) River flows and ii) nutrient discharges to the Atlantic ocean basin:</p> <p><strong>River flow</strong> data are a subset of the WaterGAP 2.2d model (Monthly data on a 0.5° x 0.5° grid between 90°N and 60°S. 1901–2016) (Müller Schmied et al. 2020)</p> <p>The original watergap2.2d data is provided in netcdf format. Our postprocessing includes the selection of the coastal cells and the extraction of the monthly values in these cells. The resulting dataset is provided as a shapefile (Global_YearMonthly_River_flow_watermap22_coast.shp), including Date, latitude and longitude of the coastal cell’s centroids and the discharge values (m3s-1).</p> <p>Watergap2.2d outputs offers a very good spatio-temporal extent and resolution, and according to the Müller Schmied et al. 2020, the validation results for streamflow (or discharges values) are reasonably satisfactory, although there is some spatial variability in the performance results. Moreover, the recently published “Global Freshwater Fluxes into the World's Oceans (GRDC, 2021)” product and paper, uses the yearly outcomes of this model, which has also supported our selection.</p> <p><strong>River nutrient discharges</strong> are a subset of observations from the “Global River Water Quality Archive”. (<a href="https://essd.copernicus.org/preprints/essd-2021-51/">Virro et al, 2021</a>), that among the publicly available and downloadable datasets, gathers the highest number of observations as includes data from different international and national databases.</p> <p>The GRQA data is provided as csv files (one file for each nutrient). These files have been processed to subset only observations at stations near the coastline. To do this a intersection between station locations and a buffer of 0.2 degrees around the coastline has been made. For each nutrient, a shapefile with the subsetted observations is provided.</p> <p>All shapefiles provided are accompanied by a “.qmd” file that includes metadata information in QGIS 3 format.</p>
Crop-specific salinity and irrigation data for river sub-basin water scarcity analyses in the US and AU
<p>This dataset contains observed monthly and annual salinity (EC) data for surface water (river) respectively groundwater, spatially averaged over sub-basins within the Central Valley, CA and the Murray Darling basin, AU, used for salinity-inclusive water scarcity assessments. The data also includes crop-specific irrigated area, irrigation withdrawals and salinity thresholds and other parameters specified, as well as example codes for analyses related to the manuscript: Thorslund et al., <em>Salinity impacts on irrigation water-scarcity in food bowl regions of the US and Australia.</em></p>
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.