Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
486
datasets available to search
ShareScore release 0.9.0
Dataset results
486 results for “water use”
Pelagic, epilimnetic production estimates in Sparkling, Trout (Wisconsin), Acton (Ohio), and Castle (California) Lakes (USA) calculated using 14C and free-water O2 metabolism methods, 2007 - 2017
Concurrent daily estimates of pelagic, eplilimnetic production (mmol C m3 d) generated from 14C incubations and diel changes in high frequency dissolved oxygen data (free-water). Original data derived from the North Temperate Lakes Long Term Ecological Research program (Sparkling [2007-2013], Trout [2007-2012] Lakes), Castle Lake Research Station (Castle Lake [2014-2017]), and Center for Aquatic and Watershed Sciences (Acton Lake [2010-2014]). 14C production estimates were generated as part of each research programs core data collection. Free-water production estimates generated using high frequency sensor data provided by research programs and Phillips (2020) time-varying, Bayesian metabolism model.
Dataset of "MoO3-xNiMoO4 nanorods synthetized using NiO nanoparticles for hydrogen evolution in anion exchange membrane water electrolysis"
<p>Novel method of Mo-Ni catalyst for hydrogen evolution reaction in anion exchange membrane water electrolysis was used. Complete physico-chemical and electrochemical characterization was done. Prepared material showed enhanced performance when compared to the similar Ni based materials. Physico-chemical characterization showed, that final material is formed by NiMoO4 nanorods coverd on the surface by the layer of the MoO3-x.</p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
Input Dataset for Estimating Continuous Soil Water Retention Curves Using Physics-Informed Neural Networks
<p>This dataset was used as input to a physics-informed neural network (PINN) model developed to estimate continuous soil water retention curves (SWRCs). It includes basic soil properties such as particle-size distribution (sand, silt, clay), organic carbon content (OC), bulk density (BD), and measurements of soil water retention at various matric potentials. These inputs allow the model to learn the relationship between soil properties and water retention, via both data and embedded physical constraints. This data set consists of 4,200 Danish soil samples with measurements spanning the wet and dry ends of the SWRC. </p>
Large Ensemble Dataset for Discovering Global Peak Water Limit of Future Groundwater Withdrawals Using 900 GCAM Runs
<h2><strong>Global Groundwater Withdrawals Peak Over the 21st Century </strong></h2> <p>The large ensemble dataset contains groundwater related model outputs from 900 scenarios modeled using <a href="http://jgcri.github.io/gcam-doc/toc.html">Global Change Analysis Model (GCAM)</a>. The scenario ensemble members include five Shared Socioeconomic Pathways (SSPs), four Representative Concentration Pathways (RCPs), five global climate model outputs, three groundwater depletion limits, two surface water storage expansion regimes, and two historical groundwater depletion trends.</p> <h3><strong>Journal Article</strong></h3> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a> </p> <h3><strong>Data Repository </strong></h3> <p>This <em><strong>data</strong></em> repository is to be used in combination with the <em><strong>main</strong></em> <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> containing all scripts and files for reproducing the experiment as well as the analysis and post-processing of the model outputs.</p> <p>Scripts and smaller files are provided in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz">GitHub meta-repository</a> whereas larger files are provided in this data repository. Please complete the repository by placing the files as described hereunder. Please find the GitHub meta-repository here: <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">https://github.com/JGCRI/niazi-etal_2024_nature-sustainability</a></p> <p>Descriptions of files:</p> <ol> <li><em><strong>gcam-5.7z</strong></em> contains the GCAM version used to simulate 900 scenarios of plausible futures. The model folder contains all necessary input files to reproduce the simulations. <ul> <li>The model is to be used in combination with the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> to setup batch runs on cluster.</li> <li>Please navigate to <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model">model/</a> folder for other scenario-specific and model setup folders and files. <em><strong>gcam-5</strong></em> is to be extracted in the same directory (./<em>model/gcam-5/</em>). </li> <li>For the first-time users of GCAM, please follow guidance on <a href="http://jgcri.github.io/gcam-doc/toc.html">GCAM wiki</a> to setup GCAM or for background knowledge. </li> </ul> </li> <li><em><strong>crop_yeild.7z</strong></em>: This file contains inputs related to climate impacts on crop yields. This is to be downloaded and extracted in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/combined_impacts">model/combined_impacts/</a> folder. </li> <li><em><strong>outputs-all.7z: </strong></em>Key model outputs queried and collated from 900 GCAM runs are explained hereunder. The files could be downloaded individually (.csv files) or all at once in .7z format (<a href="../api/files/80b237d3-b22f-499f-8b8e-76c3846720a0/outputs-all.7z">outputs-all.7z</a>). These files are to be placed in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/outputs">model/outputs</a> folder of the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a>. <ul> <li><em><strong>ag_prod_all_GW_scenarios.csv</strong></em> - Agricultural production across all scenario for 2050 and 2100 (tonnes)</li> <li><em><strong>prices_water_withdrawal_all.csv</strong> - </em>Water prices across all scenarios and years ($/km<sup>3</sup>)</li> <li><em><strong>global_irrigated_prod_by_crop.csv</strong></em> - All irrigated agricultural production for each crop across and scenarios all years (tonnes)</li> <li><em><strong>surface_water_production_all.csv</strong></em> - Runoff across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>groundwater_production_FINAL.csv</strong></em> - Groundwater withdrawals across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>water_withdrawals_desal_all.csv</strong></em> - Water withdrawals from desalination plants across all scenarios and years (km<sup>3</sup>)</li> </ul> </li> </ol> <h3><strong>Short introduction to the study</strong></h3> <p>Using 900 GCAM runs, this study finds that global groundwater withdrawals are expected to peak around mid-century, followed by a decline through 21st century, exposing about half of the population living in one-third of basins to groundwater stress, with cost and availability of surface water storage being the most significant driver of future groundwater withdrawals. This first-ever robust, quantitative confirmation of the peak-and-decline pattern for groundwater, previously only known for fossil fuels and minerals, raises concerns for basins heavily dependent on groundwater.</p> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a></p> <h3><strong>Contact </strong></h3> <p>Please reach out to Hassan Niazi at <a href="mailto:hassan.niazi@pnnl.gov">hassan.niazi@pnnl.gov</a> for any questions. </p>
Photosynthetic pigments of water column samples analyzed using High Performance Liquid Chromatography (HPLC), sampled during the Palmer LTER field seasons at Palmer Station, Antarctica, 1991 – 2023.
Phytoplankton pigment sampling was led by Prezelin from the 1991-1992 season through the 1993-1994 season, and then by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll’s (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods. Data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, Palmer 2016-2017, Palmer 2019-2020, Palmer 2020-2021, and Palmer 2023-2024 seasons because those samples have not been analyzed yet.
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
Data for "How Nitrogen and Phosphorus Availability Change Water Use Efficiency in a Mediterranean Savanna Ecosystem"
<p>These are flux and meteorological data for the measurement sites ES-LMa (CT; control treatment), ES-LM1 (NT; nitrogen treatment), and ES-LM2 (nitrogen + phosphorus treatment) for the period from 2014-03-20 to 2020-02-01.</p> <p>These data were used for the manuscript:</p> <p>El-Madany, et al. (2021) "How Nitrogen and Phosphorus Availability Change Water Use Efficiency in a Mediterranean Savanna Ecosystem" submitted to Journal of Geophysical Research - Biogeoscience.</p>
FESOM2.1 model data used in the paper "Atlantic Water warming increases melt below Northeast Greenland's last floating ice tongue"
<p><span>This data set includes the minimal data necessary to reproduce the findings of Wekerle et al., in revision. Output of model simulations with the global ocean sea ice model FESOM2.1 is provided. In particular, the data set includes:</span></p> <p><span>a) long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (</span>Wekerle2024_FESOM2_ltm_REF.nc<span>)</span></p> <p><span>b) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from the reference experiment REF for the years 1970-2021 (</span>Wekerle2024_FESOM2_annual_avg_REF.nc<span>)</span></p> <p><span>c) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from experiment CLIM for the years 2000-2021 (</span>Wekerle2024_FESOM2_annual_avg_CLIM.nc<span>)</span></p> <p><span>d) daily mean basal melt rates of experiments with varying subglacial discharge averaged over the years 2010-2014 (</span>Wekerle2024_FESOM2_daily_avg_EXP_subglacial_discharge.nc<span>)</span></p> <p><span>e) daily mean basal melt rates of experiments with varying drag coefficients for the year 2000 (</span>Wekerle2024_FESOM2_daily_EXP_basal_drag.nc<span>)</span></p> <p><span>Each netcdf file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas).</span></p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
Datasets of titrations of mineral water hardness monitored using SERS
<p>These datasets contains the spectral information used in a publication regarding the implementation of complexometric titrations of water hardness monitored using Surface Enhanced Raman Spectroscopy (SERS).</p> <p>The data consists of two csv files with semi-column separators.</p> <p><em>« MD150 to 155_Master_df_638_corrected_mod_v02.csv »</em> and <em>« MD150 to 155_Master_df_785_corrected_mod_v02.csv »</em> correspond to spectral data acquired under 638 and 785 nm irradiation respectively.</p> <p>The data are organised as row vectors.</p> <p>Each dataset has 432 rows corresponding to 3 water samples (Evian, Volvic, Contrex), 3 replicate titration series + 1 blank titration per batch of NPs, 2 batches of NPs and 18 titration steps per titration series: .</p> <p>The row vectors consist of a first sub-vector of spectral descriptors (identity of spectra, identity and composition of measurement samples and conditions of spectral acquisition), followed by a sub-vector of baseline-substracted spectral intensities.</p> <p>In both datasets, the first 14 columns consists of the spectral descriptors.</p> <p>In the <em>« MD150 to 155_Master_df_638_corrected_mod_v02.csv » </em>dataset, the spectra section consists of 1010 columns which names are the values of the Raman shifts at which the intensities have been recorded.</p> <p>In the <em>« MD150 to 155_Master_df_785_corrected_mod_v02.csv »</em> dataset, the spectra section consists of 1746 columns which names are the values of the Raman shifts at which the intensities have been recorded.</p>
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Panel data used in the paper ""The shadow price of irrigation water in major groundwater depleting countries"
<p>The panel data are used in an econometric analysis estimating Cobb-Douglas production functions that are subsequently used to calculate the shadow price (current marginal value) or irrigation water in 11 major groundwater depleting countries.</p>
Simulated submerged aquatic vegetation spectral signatures under different water quality conditions using Hydrolight
<p>Reflectance spectra were simulated using the Hydrolight radiative transfer model (Sequoia Scientific, Bellevue, WA) for four different submerged macrophyte species under a range of water quality conditions at two different depths. We used the four-component case-2 model with spectral reflectance of four submerged species, Egeria densa, Ceratophyllum demersum, Cabomba caroliniana, and Stukenia pectinata. These four reflectance spectra were calculated from the median of 10 measurements of the canopies of the representative species placed in clear tap-water made with a handheld ASD FieldSpec Pro spectrometer. Total suspended solids concentration was varied from 1 to 40 g·m<sup>−3</sup>, chlorophyll-a concentration was varied from 0.5 to 50 mg·m<sup>−3</sup>, and colored dissolved organic matter (CDOM) was varied from 0.25 to 3.5 m<sup>−1</sup>. A total of 4,742 spectra were simulated for all four species and a mud substrate at two different depths, 1 m and 5 m, and for optically deep water.</p>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
Simulated water models with Apoferritin for use in cryo-EM image simulations with amorphous ice
<p>This dataset contains the atomic coordinates of several water models produced using the NAMD molecular dynamics software. The contents of each file is listed below.</p> <ul> <li><strong><em>water_81_coords.pdb</em></strong> - water only in a cubic box with side length 81A</li> <li><strong><em>water_243_coords.pdb</em></strong> - water only in a cubic box with side length 243A</li> <li><strong><em>water_486_coords.pdb</em></strong> - water only in a cubic box with side length 486A</li> <li><strong>water_567_coords.pdb</strong> - water only in a cubic box with side length 567A</li> <li><strong><em>water_645_coords.pdb</em></strong> - water only in a cubic box with side length 645A</li> <li><strong><em>water_735_coords.pdb</em></strong> - water only in a cubic box with side length 735A</li> <li><strong><em>apo_water_723_coords.pdb</em></strong> - water and apoferritin in a cubic box with side length 723A where apoferritin atoms are constrained</li> </ul>
The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files
<p>This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository:</p> <p>model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows:</p> <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DC<em>k</em><sub>max</sub>)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (L<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (IL<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (H<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IH<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IIL<em>k</em><sub>max</sub>) for experiment 2</li> <li>ko_dc: model output with the results of the best <em>k</em><sub>max</sub> and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best <em>k</em><sub>max</sub> and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best <em>k</em><sub>max</sub> and the vulnerable configuration of the PVC used in experiment 3</li> </ul> <p>The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).</p>
Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery
<p>Netcdf files containing a set of correctional factors for GOES-16 and GOES-17 cloud liquid-water path (LWP). The correctional factors for both satellites are fractional corrections of microwave imager LWP divided by GOES-16/17 LWP at a given solar zenith, GOES sensor zenith, relative azimuth (solar - sensor zenith), and low-cloud fraction.</p> <p>Uncorrected GOES-16/17 LWP is derived from GOES retrieved cloud-optical thickness and cloud-top effective radius, and it is multiplied by the corresponding correctional factor (i.e. the bins which the uncorrected values are in).</p>
Simulated discharge statistics in Central and Southwestern Europe considering water use under 2K global warming
<p>We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.</p> <p> </p> <p><strong>Discharge statistics</strong> based on daily output from CWatM within 1981-2010:</p> <ul> <li><strong>Q1</strong> - 1st percentile</li> <li><strong>Q5</strong> - 5th percentile</li> <li><strong>Q10</strong> - 10th percentile</li> <li><strong>Qavg</strong> - average discharge</li> <li><strong>Q90</strong> - 90th percentile</li> <li><strong>Q95</strong> - 95th percentile</li> <li><strong>Q99</strong> - 99th percentile</li> </ul> <p><strong>Files:</strong></p> <ul> <li><strong>Qxx_reference</strong>: CWatM considering historical water use forced by RACMO-ERA5</li> <li><strong>Qxx_PGW:</strong> CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.</li> <li><strong>Qxx_PGW_adjusted_demand:</strong> We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.</li> </ul> <p>An upcoming publication will be made available and linked to this research very soon.</p> <p> </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.