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27 results for “Water demands”
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 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>
Hourly Water Demand of a Mixed District Recorded by Supplier
<p>This dataset contains time-resolved measurements of water consumption in cubic meter taken between January 2016 and March 2021 at a temporal resolution of 1 hour. Measurements of water consumption were collected at a pumping station in a regional water supply network for a mixture of residential, commercial and industrial consumers in Germany.</p> <p>The supply network consists of multiple containers, which are fed through a series of water pumps. Predicting the demand of water can help optimize the cost of water pump operation, ensure the required supply, and avoid storing superfluous amounts of water for extended time.</p> <p>This dataset was provided by Siemens AG for the purpose of a study at KIT (Karlsruhe Institute of Technology), which can be a found <a href="http://arxiv.org/abs/2405.03429" target="_blank" rel="noopener">here</a> (http://arxiv.org/abs/2405.03429).</p> <p><strong>Three variants are provided:</strong><br>1. The raw dataset<br>2. A NaN-free version where missing data has been fixed by interpolation and a large missing stretch in June 2017 has been filled with averages from previous years.<br>3. A NaN-free version with hours filled in or removed to compensate for time change and ensure all days have 24 hours.</p> <p><strong>The dataset columns are:</strong><br>Date: Date in format (M)M/(D)D/YYYY<br>Time: Hour of day in the format [start time]-[end time], with each time in the format HH:MM<br>Consumption: Water consumption in cubic meters<br>Temperature: Local temperature in centigrade<br>Rain: Local precipitation in arbitrary units<br>Sun: Local sunshine intensity in arbitrary units</p>
Dataset article "Demand-Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization"
<p>"README-SupplementaryMaterial.txt" explains the information gathered in each csv files, and including the DHW consumption profiles generated, the hourly electricity pricing for 2022 (Spain), and the experimental data utilized for the validation of the model. </p>
Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco Dataset
<p>Here you can find the official data used for the publication "Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco"</p> <p>If you want to run the model, please update line 11 in the run.jl file, to select the dataset from the scenario you want to look at. It is also recommended to change the result path, to a directory that corresponts to the current model run in order to find the results files quicker.</p> <p> </p> <p>To create a new plot a file called newPlot.jl can be found, that already take care of most data handling, only lines 136 and 141 need to be changed, in order to read in the result files of the results you want to investigate.</p>
A Refined Supply-demand Framework to Quantify variability in Ecosystem Services Related to Surface Water in support of Sustainable Development Goals
<p>The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang.</p>
Water security in an uncertain future: contrasting realities from an availability-demand perspective
<p>Here we provide the climate change data from the Coupled Model Intercomparison Project phase 6 (CMIP6) used in an assessment of future water security of two Brazilian basins: Guariroba River basin and Jaguari River basin responsible for supplying water to Campo Grande city and the São Paulo Metropolitan Region, respectively. The basins' hydrological response was simulated using the SWAT+ model considering three climate change scenarios from a CMIP6 multimodel ensemble: SSP2-4.5 (medium forcing), SSP3-7.0 (high forcing), and SSP5-8.5 (high forcing). The ensemble was constructed using seven General Circulation/Earth System models (variant ID r1i1p1f1 and further processed for bias correction using quantile delta mapping.</p> <p>The manuscript of "<em>Water security in an uncertain future: contrasting realities from an availability-demand perspective</em>" has been published in <strong>Water Resources Management</strong>, please find it <a href="https://doi.org/10.1007/s11269-022-03160-x">here</a>.</p>
Data and code for "Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"
<p>Data and code for <br>"Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"</p> <p>All R code used for the analysis is provided in the folder <em>code</em>. <br>Data and intermediate results are provided or stored in the folders <em>data </em>and <em>tmp_data</em>.<br>All results including figures will be stored in the folder <em>results</em>. </p> <p>R version: 4.3.1</p> <p>To carry out the entire analysis the code should be run in the provided order:</p> <p>1) Code to run non-metric multidimensional scaling (NMDS) for habitat groups and <br>produce Fig. 1b (habitat map and legend for Fig 1a: data/eunis_gl_habitat_ger_990m.tif,eunis_gl_habitat_ger_990m_legend.clr)<br> <br>2) Code to generate grassland vitality maps and time series from 1985 to 2021 (Fig. 3). <br>Grassland vitality maps on 30m for all grasslands in Germany provided in data/glv_1985-2021.zip.</p> <p>3) Code to model relation of grassland vitality to five drought indices (VPD, temperature, CWB, soil moisture, precipitation),<br>output are Fig. 4, Fig. S1, Tab. 1.</p> <p>4) Code for trend analysis of drought sensitivity based on 5-, 10-, and 15-year moving windows, output are Fig. 5, Fig. S2. </p> <p>5) Code to model drought sensitivity of grassland habitat groups and habitat types, output are Fig. 6 and table with sensitivity per habitat type. </p>
Raw data belonging to paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters"
<p>The Zip file contains the raw data used for drawing conclusions in the paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters" accepted for publication in Energy Informatics 2018.</p> <p>The raw data is the parameters of a sample of 50 domestic electric water heaters (DEWHs)<br> used for evaluating the algorithm in the paper. It is explained in the readme.txt.</p>
2015/16 El Niño increased water demand and pushed plants from a Mesic tropical montane grassland beyond their hydraulic safety limits
<p>In 2015/16, a strong El Niño event caused anomalously high temperatures and reduced precipitation resulting in Pantropical drought‐induced diebacks and wildfires. Although many studies have documented the El Niño impacts on tropical forests, little we know about its effects on tropical grasslands. Here, we investigated plant drought responses during and after the 2015/16 El Niño event (Jun 2016 to Aug 2017) in 12 species with contrasting drought strategies (tolerance, avoidance and escape) in a Brazilian tropical montane grassland. We tested if (1) the El Niño event induced meteorological drought anomalies, (2) the atmospheric and/or soil drought led to plant water stress and (3) plants showed signs of drought recovery. In contrast to other tropical areas, we found that the 2015/16 El Niño event did not strongly affect precipitation in our study site. However, it increased air temperature and vapour pressure deficit, thus pushing all grassland species, even the most drought‐tolerant ones, beyond their hydraulic safety margins during the dry season. Most species showed signs of drought recovery, returning to positive hydraulic margins in the wet season after the El Niño. However, the finding that all evaluated species, regardless of their drought‐response strategy, are already operating close to their hydraulic safe thresholds for stomatal closure and turgor loss suggests that this cool–humid tropical montane grassland is especially vulnerable to meteorological extremes exacerbated by the additive effects of El Niño and climate change.</p>
A unified approach to quantifying the key greenhouse gas emissions, air pollutant emissions, and water demand in concrete production
Open the record for dataset details and reuse information.
2015/16 El Niño increased water demand and pushed plants from a Mesic tropical montane grassland beyond their hydraulic safety limits
Open the record for dataset details and reuse information.
Data from: C4 savanna grasses fail to maintain assimilation in drying soil under low CO2 compared with C3 trees despite lower leaf water demand
1) C4 photosynthesis evolved when grasses migrated out of contracting forests under a declining atmospheric CO2 concentration ([CO2]a) and drying climate around 30 million years ago. C4 grasses are hypothesised to benefit from improved plant–water relations in open habitats like savannas, giving advantages over C3 plants under low [CO2]a. But experimental evidence in a low CO2 environment is limited and comparisons with C3 trees are needed to understand savanna vegetation patterns. 2) To test whether stomatal conductance (gS) and CO2 assimilation (A) are maintained in drier soil for C4 grasses than C3 trees, particularly under low [CO2]a, we investigated photosynthesis and plant–water relations of three C3 tree and three C4 grass species grown at 800, 400 or 200 ppm [CO2]a over moderate wetting–drying cycles. 3) C4 grasses had a lower soil–to–leaf water potential gradient than C3 trees, especially at 200 ppm [CO2]a, indicating reduced leaf water demand relative to supply. Yet the dependence of gS and A on predawn leaf water potential (a measure of soil water availability) was greater for the C4 grasses than trees, particularly under low [CO2]a. 4) Our findings establish that gS and A are not maintained in drier soil for C4 grasses compared with C3 trees, suggesting that this mechanism was not prevailing in the expansion of C4–dominated grasslands under low [CO2]a. This inherent susceptibility to sudden decreases in soil water availability justifies why C4 grasses have not evolved a resistant xylem allowing operation under drought, but instead shut down below a water potential threshold and rapidly recover. We point to this capacity to respond to transient water availability as a key overlooked driver of C4 grass success under low [CO2]a.
A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals
<p>This database relies on the article entitled "A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals " to be published in Earth's Future. The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang. The file named "Supplementary Data" contains the necessary Water Bulletin and Statistical Yearbook data.</p>
AWESOME Water and food demand (WATNEEDS crop model)
<p><span>In this record, we provide the results in terms of crop water requirements, calculated by the hydrological model WATNEEDS, and crop harvested area under the implemented crop replacement scenarios, that have been developed to optimize water use, crop production and agricultural value in present and future climate scenarios in the Nile Basin. These scenarios were designed, modeled and generated for the deliverable D2.3 of AWESOME WP2: <em>Dynamic and spatially distributed crop water use modeling.</em></span></p> <p><span>The analyses have been conducted at the mesoscale, with a specific focus has been reserved for Egypt, as it is particularly relevant for the AWESOME project because the demo sites of the innovative technologies are being build there. </span>In addition, we investigated present and future reallocation scenarios also for Ethiopia and Sudan, to cover the main cropland along the Nile River Basin.</p> <p><span>A baseline scenario is evaluated to quantify blue and green crop water requirements for the current climate conditions and current crop distribution (averaged for the period 2011-2016). </span>Future scenarios have been implemented using future climatic data for the average period around year 2050 (2048-2052) and year 2100 (2096-2100) under three different Representative Concentration Pathways (RCPs): RCP 4.5, RCP 2.6 and RCP 8.5.</p> <p> </p> <p><span>This record contains:</span></p> <p><span>- The Deliverable D2.3, where the optimization model and the calculation of the crop water requirements under different climatic scenarios are presented.</span></p> <p><span>- The results for each implemented scenario, in terms of harvested area and irrigation water consumption (.tif files and excel files), for the Nile River Basin and at the national level for each focus country (Ethiopia, Sudan and Egypt).</span></p> <p><span>- Description of the data (excel file and pdf file)</span></p> <p><span> </span></p>
Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City
<div> <p><span><span>Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City</span></span></p> </div> <p> </p>
Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona
<p>This dataset contains the simulation results of the combined effects of future urban growth and climate change on irrigation water use in the Phoenix Metropolitan Area, central Arizona. The simulation is conducted with the Variable Infiltration Capacity (VIC) model at 1-km, hourly resolution from 1981-2100 and aggregated to 30-yr average in this dataset. </p> <p>The 30-yr average results are compressed and organized into three files: <strong>Baseline</strong>, <strong>ICLUS2050</strong>, and <strong>ICLUS2100</strong>. The Baseline file contains results using the historical land cover map (year 2010). The <strong>ICLUS2050</strong> and <strong>ICLUS2100</strong> contain results using future land cover maps. The filename of modeling results contains the associated land cover and climate change scenario as follows: "fluxes.irri.ICLUS_<em>$YEAR</em>_<em>$LCSCE</em>.<em>$CLSCE.$GCM</em>.nc", where <em>$YEAR</em> is the year of land cover change projection (2050 or 2100), <em>$LCSCE</em> is the land cover change scenario (SSP2 or SSP5), <em>$CLSCE</em> is the climate change scenario (RCP45 or RCP85), and <em>$GCM</em> is the GCM used (eight in total) </p> <p>More details can be found on the associated paper (this record will be updated when the paper is published):</p> <p>Wang, Z., and Vivoni, E.R. 2021. Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona. <em>Journal of the American Water Resources Association (in revision)</em>.</p>
The simulation results of runoff components and estimated surface water demands for the Yarkant River basin
<p>This dataset contains two directories as below. </p> <p>1. Hydrological_Simulation</p> <p>Historical_runoff_simulation.csv : the four runoff components at monthly scale (km^3/month) aggregated from daily runoff simulation results for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP1-2.6 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP2-4.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP5-8.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "48 experiments" contains the four runoff components at monthly scale (km^3/month) simulated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p> <p>2. Water_Demand_Estimation</p> <p>Historical_water_demand_estimation.csv : the monthly water demands (km^3/month) from irrigation (including wheat, cotton, and corn) and other sectors for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP1-2.6 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP2-4.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP5-8.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "48 experiments" contains the monthly water demands (km^3/month) estimated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p>
Data Set - Seasonal variability in the global relevance of mountains to satisfy lowland water demand
<p>GENERAL INFORMATION</p> <p>The data and scripts used for the analysis of the manuscript "Seasonal variability in the global relevance of mountains to satisfy lowland water demand"</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</strong></p> <p>DATA & FILE OVERVIEW</p> <p>please have a look at readme.txt </p> <p>Don't hesitate to contact us in case of any questions (sarah.hanus@geo.uzh.ch)</p>
Data for "Food demand displaced by global refugee migration1 has unequal effects on country-level water stress"
<p>Dataset associated with the paper "Food demand displaced by global refugee migration1<br> has unequal effects on country-level water stress" to appear in Nature Communications in 2023.</p>
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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.