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23 results for “water scarcity”
Data to 'The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0'
<p>This dataset includes the AWARE2.0 characterization factors as documented in the article "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0" (<a href="https://www.doi.org/10.1111/jiec.70023" target="_blank" rel="noopener">DOI: 10.1111/jiec.70023</a>). When using the dataset in your own work, please cite the article and provide reference to this zenodo repository.</p> <p>For importing the country-level characterization factors into LCA software, please see the AWARE2.0 implementations (openLCA, SimaPro, brightway2) in IMPACT World+, version 2.1: <a title="IMPACT World+ version 2.1" href="https://doi.org/10.5281/zenodo.14041258">https://doi.org/10.5281/zenodo.14041258</a></p> <h3>Content</h3> <p><strong>- native resolution (monthly, watershed scale):</strong></p> <ul> <li><strong>AWARE20_Native_CFs_geospatial.gpkg</strong>: Geospatial file containing the AWARE2.0 basins as polygons with associated monthly and annual CFs</li> <li><strong>AWARE20_Native_CFs_geospatial.kmz</strong>: Version of <em>AWARE20_Native_CFs_geospatial.gpkg </em>for GoogleEarth</li> <li><strong>AWARE20_Native_CFs.xlsx</strong>: AWARE2.0 CFs on basin level (monthly and annual) and associated water consumption used for weighting</li> <li><strong>AWARE20_Intermediate_Variables.xlsx</strong>: Intermediate Variables from the calculation of the AWARE2.0 CFs, such as the longterm average natural and actual water availability, the AMDs, the EFRs, etc.</li> <li><strong>figures_AWARE_AWARE20_comparison_all_basins.zip</strong>: Figures comparing CFs, AMDs, Natural and Actual Availability, EWRs, and EFR coefficients between AWARE and AWARE2.0, for each of the 8149 basins individually. Consult these figures for a visual impression of how and why CFs might have changed between AWARE and AWARE2.0.</li> </ul> <p><strong>- spatiotemporal aggregations:</strong></p> <ul> <li><strong>AWARE20_Countries_and_Regions.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of GLAM and ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10) </a></li> <li><strong>AWARE20_Subnational_Resolution.xlsx</strong>: AWARE2.0 CFs aggregated to subnational resolution, using the GADM dataset version 4.1 (<a href="https://gadm.org/old_versions.html" target="_blank" rel="noopener">https://gadm.org/old_versions.html</a>)</li> <li><strong>AWARE20_Crop_Specific.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10)</a>, using crop-specific irrigation water consumption for 27 crop classes as spatiotemporal weights. See readme sheet in Excel file for more information.</li> </ul> <p> </p> <h3><strong>Changes:</strong></h3> <ul> <li>v1.0.1: <ul> <li>addition of crop-specific spatiotemporal aggregations (AWARE20_Crop_Specific.xlsx)</li> </ul> </li> <li>v1.0.0 (corresponding to published article): <ul> <li>update of readme sheets with appropriate references to corresponding article</li> <li>update of reference "Müller Schmied et al. (2024)"</li> <li>added file: AWARE20_Subnational_Resolution.xlsx</li> </ul> </li> <li> v0.0.3: <ul> <li>use bug-fixed WaterGAP2.2e data from Sept 2023</li> <li>added country and subnational aggregations</li> <li>changed "NoData" to "NotDefined" in the tables</li> <li>added gridcell pHWC to intermediate variables</li> <li>corrected table of water consumption data without post-processing in "Intermediate_Variables"</li> </ul> </li> </ul> <p> </p> <h3><strong>Caveats:</strong></h3> <ul> <li>Spatial CF aggregations for treaties: <ul> <li>Due to the creation date of the data set, the <strong>BRICS aggregations </strong>in<strong> </strong><em>AWARE20_Countries_and_Regions.xlsx</em> do not include the states that joined after 2023. In <em>AWARE20_Crop_Specific.xlsx</em>, the 10-member BRICS is labeled BRICS+.</li> </ul> </li> </ul>
Supplementary dataset for "Potential of Wastewater Reuse to Alleviate Water Scarcity under Future Warming Scenarios"
<p>The folder contains water gap data relative to the paper:<br>Kahn, M., Sangiorgio, M., and Rosa, L. (2025) Potential of wastewater reuse to alleviate water scarcity under future warming scenarios. Environmental Research Letters, 20, 034012<br>https://doi.org/10.1088/1748-9326/adb31d</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)</p> <ul> <li>baseline (2001-2010) <ul> <li>Water_gap_baseline_no_wastewater_reuse: Water gap under baseline climate scenario with no wastewater reuse.</li> <li>Water_gap_baseline_treated_wastewater_reuse: Water gap under baseline climate scenario with treated wastewater reuse.</li> <li>Water_gap_baseline_full_wastewater_reuse: Water gap under baseline climate scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>1.5°C warming <ul> <li>Water_gap_15_no_wastewater_reuse: Water gap under 1.5°C warming scenario with no wastewater reuse.</li> <li>Water_gap_15_treated_wastewater_reuse: Water gap under 1.5°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_15_full_wastewater_reuse: Water gap under 1.5°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>3°C warming (5 models + average) <ul> <li>Water_gap_3_no_wastewater_reuse: Water gap under 3°C warming scenario with no wastewater reuse.</li> <li>Water_gap_3_treated_wastewater_reuse: Water gap under 3°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_3_full_wastewater_reuse: Water gap under 3°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <p>Aggregated data (.xlsx)</p> <ul> <li>country_level_water_gaps.xlsx: Water gap aggregated by country for all the considered scenarios.</li> <li>city_water_gaps.xlsx: 0.5° pixels with populations greater than 5,000,000 and non-zero water gaps corresponding to urban center.</li> <li>seasonal_variations.xlsx: Monthly water gaps of the 5 most water scarce countries.</li> </ul> <p><br>Note: the global water gap obtained by summing all the countries is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country.</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>
Supplementary dataset for "Global agricultural economic water scarcity"
<p>This repository contains supporting data for: "<strong>Global agricultural economic water scarcity"</strong></p> <p>Cite: Rosa, L., Chiarelli, D.D., Rulli, M.C., Dell’Angelo J., and D’Odorico, P. Global agricultural economic water scarcity. Science Advances. 2020<br> Email: lorenzo_rosa@berkeley.edu</p> <p>The dataset contains the number of months (#months) croplands are facing green water scarcity (GWS), blue water scarcity (BWS), and economic water scarcity (EWS). Where "0" indicates that the pixel does not face water scarcity, "1" indicates that the pixel is facing water scarcity for 1 month, "12" indicates that the pixel is facing water scarcity for 12 months. Files are uploaded in arcmap and netcdf formats. </p> <p> </p>
Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions
<p>The datasets contain original data from the article titled" Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions "</p>
Dataset for paper "Solutions to global agricultural green water scarcity under climate change"
<p>These are datasets used to generate figures of paper "Solutions to global agricultural green water scarcity under climate change".</p> <p>(1) fig1_GWS_Baseline, fig1_GWS_1.5C and fig1_GWS_1.5C are NetCDF files reporting agricultural green water scarcity (GWS) under Baseline climate conditions (1996-2005 period), 1.5°C and 3°C warmer climates, respectively. </p> <p>(2) fig2_num_of_month_Baseline, fig2_num_of_month_1.5C and fig2_num_of_month_3C are NetCDF files reporting the number of months that each grid cell faces GWS (with threshold 0.2) under Baseline climate conditions (1996-2005 period), 1.5°C and 3°C warming climates, respectively. fig2_data_Baseline, fig2_data_1.5C and fig2_data_3C are csv files reporting the countries with the highest exposure to GWS and number of months under Baseline, 1.5 °C and 3 °C warmer climates, respectively. </p> <p>(3) fig3_data_01, fig3_data_02 and fig3_data_03 are csv files reporting the area of rain-fed croplands facing agricultural GWS in each month under GWS thresholds 0.1, 0.2 and 0.3, respectively. </p> <p>(4) fig4_data is the csv file reporting the number of people impacted by crop production loss induced by GWS under Baseline, 1.5 °C and 3 °C warmer climates with GWS thresholds of 0.1, 0.2 and 0.3.</p> <p>(5) fig5_Baseline, fig5_1.5C and fig5_3C are csv files reporting the reduction of area facing GWS and increased people fed due to green water management solutions with different evapotranspiration reduction and infiltration increase levels, under Baseline, 1.5 °C and 3 °C warmer climates, respectively.</p> <p>(6) fig6_data_area and fig6_data_population are csv files reporting reduced rain-fed croplands facing GWS and additional people fed from decreased GWS, respectively, with evapotranspiration reduction and infiltration increase levels as 0.2.</p> <p>(7) irrigation_fraction is the NetCDF file reporting the percent of irrigated cropland in each grid cell. We use it as a mask to exclude croplands with larger than 5% irrigation. It is calculated based on "Mehta, P. <em>et al.</em> Majority of 21st century global irrigation expansion has been in water stressed regions. (2022)."</p>
Unprecedented failure of the Northeastern Indian Monsoon and recent water scarcity
<p>Dataset used in the publication on "<strong>Unprecedented failure of the Northeastern Indian Monsoon and recent water scarcity" submitted in GRL.</strong></p>
Figure 3 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 3. Flow diagram for calculation of WF in Van province.
Figure 2 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 2. Monthly average air temperature, total and effective precipitation values in Van.
Figure 8 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 8. Distribution of WF , and WF by years in Van province.
Figure 1 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 1. The study area.
Figure 4 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 4. Distribution of WF in Van province.
Figure 9 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 9. Distribution of WF in Van province when feed crops total are included in WF .
Water scarcity and water footprint estimates
<p>Intra-state water conflicts have increased substantially over the past few decades particularly in developing regions, and partly attributed to water scarcity. However, empirical studies linking water scarcity and violent conflicts are sparse, while existing quantitative studies have used mostly climate variables (precipitation and temperature) to understand this link. Most studies that have used climate variables concluded that they were not strong predictors of water conflicts. The aim of this study was to identify water scarcity hotspots and to understand the links between water scarcity and violent conflicts across the Sahel and Lake Chad Basin (LCB) over the period 2000-2021. To achieve this, we combine outputs from a global hydrological model and demographic data to develop six water scarcity metrics. The developed metrics show varying levels of water scarcity across the study region. The Falkenmark index across all capital cities (Ouagadougou-Burkina Faso, Ndjamena-Chad, Bamako-Mali, and Niamey-Niger), and Maradi-Niger and Jigawa & Kano states in Nigeria was less than 100 m<sup>3</sup>/capital/year, indicating acute water scarcity in those areas. Findings further indicated that green water scarcity (GWS) and the Falkenmark index were closely linked with water conflicts compared to the other metrics. Our findings suggest that water conflicts cannot be explained by hydroclimatic factors alone without incorporating other socioeconomic variables like demographic information. Results from this study may be used by stakeholders to tackle endemic water scarcity and to predict and mitigate water conflicts in the region.</p>
Supplementary Information "Key drivers and pressures of global water scarcity hotspots"
<p>Supplementary information of "Key drivers and pressures of global water scarcity hotspots".</p> <ul> <li>Supplementary table A - SCOPUS search strings</li> <li>Supplementary table B - DPSIR definitions</li> <li>Supplementary table C - Data resources</li> <li>Supplementary figures D - Lineplots of historical data analysis</li> <li>Supplementary table E - DPSIR case study results</li> <li>Supplementary figures F - Circular barplots DPSIR analysis per hotspot</li> </ul>
Dataset for "Sensitivity of subregional distribution of socioeconomic conditions to the global assessment of water scarcity"
<p>This dataset contains the data related to the final analysis for "Sensitivity of subregional distribution of socioeconomic conditions to the global assessment of water scarcity".</p>
Assessment of Ecological Water Scarcity in China(2016-2019)
<p>BWR and water consumption data(BWF) from 2016 to 2019 were collected from Provincial water resource bulletin (Ministry of Water Resources of China 2017, 2018, 2019, 2020). GWF was calculated by pollutant loads of each province from China’s Environmental Statistical Yearbooks (National Bureau of Statistics of China 2017, 2018, 2019, 2020), and pollutant included total nitrogen (TN), total phosphorus (TP), ammonia-nitrogen (NH<sub>3</sub>-N), and chemical oxygen demand (COD).</p>
Data for global agricultural water scarcity assessment incorporating blue and green water availability under future climate change
<p>This dataset is for the publication Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change by Liu et al., 2022 (Earth's Future, doi: <a href="http://doi.org/10.1029/2021EF002567">10.1029/2021EF002567</a>).</p> <p>Three observation-based global meteorological datasets, namely PGMFD v.2, GSWP3, and WFDEI, were used to calculate ETc over the baseline period. The bias-corrected climate projections of four GCMs (namely GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) provided by the ISIMIP phase 2b (ISIMIP2b) were used to calculate the ETc over the future period.</p> <p> </p> <p>Liu, X., Liu, W., Tang, Q., Liu, B., Wada, Y., & Yang, H. (2022). Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change. Earth's Future, 10, e2021EF002567. <a href="https://doi.org/10.1029/2021EF002567">https://doi.org/10.1029/2021EF002567</a></p>
The dataset and results for the article "More people might face severe water scarcity this century than previously estimated"
<p>The dataset presents the values of each parameter and the parameters of different SSP scenarios. In addition, the results provide information about predicted values.</p>
Figure 7 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 7. Comparison WF and WF green of WF crop of the Van province with the Upper Tigris River Basin (Muratoglu, 2019), the worldwide blue average (Mekonnen and Hoekstra, 2011b) and the Turkish average (Mekonnen and Hoekstra, 2011a).
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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.
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DANDI Archive for NWB datasets
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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.