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38 results for “Water supply”
UWSCatCH: Urban Water Supply Catchment Contributions and Hydrological Statistics for large cities of the conterminous United States.
<p>UWSCatCH extends and enhances the Urban Water Blueprint (McDonald et al., 2014) for a selection of 116 cities (population > 150,000) and their associated surface water supply catchments in the conterminous United States. The two major enhancements to the Urban Water Blueprint are: [1] estimates of the relative contributions of each surface water catchment to each city's average water supply (as well as updated estimates of any contributions from groundwater); [2] NHDplusV2 reach codes for each water supply intake stream location and associated average flow estimates (regulated and unregulated) (local upstream USGS gage IDs are also provided). UWSCatCH also features a raster file with spatially distributed (1/24° grid) runoff (average of 1980 - 2012 reanalysis simulation) which is masked to watershed polygons (included as a shapefile) to explore spatial distribution of average runoff generation affecting each city. UWSCatCH is designed for use in the R package "gamut" (https://github.com/IMMM-SFA/gamut), and may be applied in a variety of regional and national scale research studies concerning drinking water supply to major US cities.</p>
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Alberta Municipal Water Supply Overview
<p>This dataset contains information on the water source for municipalities in Alberta, Canada. The data was collected between September 2018 and September 2019.<br> </p>
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet.
Turner et al., 2021, urban water supply contributions and GAMUT output data
<p>Output from gamut (Geospatial Analytics for Multisectoral Urban Teleconnections) model supporting Turner et al. (2021) - https://www.nature.com/articles/s41467-021-27509-9</p> <p> </p> <p> </p>
Vanishing glaciers: a cause of sea-level rise and a threat to water supply
<p><span>The video discusses the contribution of glaciers to sea-level rise and their importance for humans. Due to their proximity to 0°C temperature, glaciers respond much faster to global warming than ice sheets, making their mass loss a significant contributor to sea-level rise during the 20th century and beyond. To determine the health state of glaciers and their contribution to sea-level rise, glaciologists calculate their mass budget, which has been largely negative for several decades now, indicating that glaciers are losing mass year after year, causing them to retreat. The video emphasizes the need for immediate reductions of greenhouse gas emissions to preserve these crucial and vulnerable water resources and natural heritage.</span></p>
Fig. 3 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 3. Histological slides of partially spawned ovary of Astyanax aff. fasciatus of Passaúna and Piraquara reservoirs, HE staining. Post ovulatory follicles (arrow). Scale bar = 80 µm.
Fig. 4 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 4. Monthly distribution of the total (K) and somatic (K') condition factors of Astyanax aff. fasciatus in Iraí (a), Passaúna (b), and Piraquara (c) reservoirs. K = continuous line, K' = dotted line.
Fig. 2 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 2. Maturation curve and monthly distribution of the percentage frequency of the stages of ovarian development of Astyanax aff. fasciatus females in the Iraí (a), Passaúna (b), and Piraquara (c) reservoirs. The numbers indicated above the bars refer to absolute frequency and the letters indicated gonadal development: B = maturation, C = mature, SD = partially spawned, and D = spawned.
Fig. 5 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 5. Distribution of the percentage frequency in length classes of Astyanax aff. fasciatus individuals of Iraí, Passaúna and Piraquara reservoirs.
Fig. 1 in Reproductive tactics used by the Lambari Astyanax aff. fasciatus in three water supply reservoirs in the same geographic region of the upper Iguaçu River
Fig. 1. Location of Iraí (A), Passaúna (B), and Piraquara (C) reservoirs in the region of the upper Iguaçu River basin, Paraná State, Brazil.
Water Supply Tipping Point Data and Code
<p>This publication provides code, WaterSupplyTippingPoints.R, to calculate tipping points in water supply by using a time series of deficit data. The code uses data files that are produced from an agent-based model that simulates water supply dynamics. The code uses deficit data to calculate a water supply performance metric, Sustainability Index, and implements a decision tree change point detection method detects change points. The code implements a tipping point rule that is applied to select a change point as a tipping point. Data files of deficits that are used as input are described and published at https://zenodo.org/record/3971213.</p> <p>The code can be executed for four management scenarios and 16 shifting climate scenarios. Management Strategy 1 is no management. Management Strategy 2 is retrofitting. Management Strategy 3 is retrofitting with reactive drought restrictions. Management Strategy 4 is retrofitting with proactive drought restrictions. The 16 shifting climate scenarios correspond to shifting factors equal to 0.25 - 1.0, increasing in 0.05 increments. For each of the 64 modeling scenarios (16 climate settings X 4 management strategies), 100 simulations were run to capture stochasticity.</p> <p>The output from the code are provided in a set of .csv files, attached here. Data files (TippingPoints-mgmt1.csv, TippingPoints-mgmt2.csv, TippingPoints-mgmt3.csv, TippingPoints-mgmt4.csv) list the 100 tipping points that were identified for each shifting factor and each management strategy. Columns (V1, V2, ... V100) are 100 unique and randomized simulations for each modeling scenario. Rows correspond to shifting factors, where Row 1 is shifting factor 0.25, and Row 16 is shifting factor 1.00.</p>
Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)
<p><strong>1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong></p> <p>CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f,</p> <p>CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f,</p> <p>CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude:</strong></p> <p>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"):</strong></p> <p>Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt</p> <p>GRDC_number_WaterGAP_ArcID_220_stations.txt</p> <p><strong>4) SDHIs for four GRDC gauging stations:</strong></p> <p>time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt</p> <p><strong>5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p> </p>
Water supply improvement and isolation of native microalgae study at Primar Hatchery
<p>Revise the estuarine water collection and treatment structure to improve the water quality of the Primar hatchery of <i>Crassostrea gasar </i>and produce isolated native microalgae feed for larvae of <i>C. gasar</i></p><p> </p><p>Based on previous projects and literature, a system to improve the catchment structure and treatment of estuarine water used at Primar's hatchery was developed. Further, samples of native microalgae were collected at a location close to the Primar hatchery. The microalgae were isolated and experimental production, subsequent tests, evaluations and adjustments were performed before pilot production of native microalgae for feeding of <i>C. gasar</i> was done. </p><p>The data was collected during 2020 and 2023 at Primar hatchery, Brazil. </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>
Modelling outputs of the AWESOME water supply model of the Nile River Basin
<p>This folder contains the output of the water supply model as trajectories for the simulated storage levels and water release for the Grand Ethiopian Renaissance Dam, Merowe Dam, and High Aswan Dam. The trajectories are simulated under both historical climatic and water demand scenarios, as well as for future climatic and demographic scenarios (RCP4.5 + SSP2).</p> <p>h_GERD_ : Grand Ethiopian Renaissance Dam water level</p> <p>h_MER_ : Merowe Dam water level</p> <p>h_HAD_ : High Aswan Dam water level</p> <p>r_GERD_ : Water release from Grand Ethiopian Renaissance Dam</p> <p>r_HAD_ : Water release from High Aswan Dam</p> <p>BestHP_hist: Best performance for hydropower production at the basin level under historical conditions of water availability and demand </p> <p>BestEgypt_hist: Best performance for irrigation deficit in Egypt under historical conditions of water availability and demand </p> <p>BestSudan_hist: Best performance for irrigation deficit in Sudan under historical conditions of water availability and demand</p> <p>LowReduction_hist: Low water reduction solution under historical conditions of water availability and demand </p> <p>MediumReduction_hist: Best performance for irrigation deficit in Egypt under historical conditions of water availability and demand </p> <p>LowReduction_2050: Low water reduction solution under climatic (RCP4.5) and demographic projection (SSP2) in 2050 </p> <p>MediumReduction_2050: Best performance for irrigation deficit in Egypt under climatic (RCP4.5) and demographic projection (SSP2) in 2050 </p>
Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials
<p><strong>This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use"</strong></p> <ol> <li> <p><strong>Input Data</strong>: This file includes the model input data.</p> </li> <li> <p><strong>Payoff Matrix Solution</strong>: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution).</p> </li> <li><strong>Compromise solution</strong>: This file contains all decision variables of the compromise solution.</li> <li> <p><strong>Pareto Curve</strong>: This file presents the Pareto curve solution. </p> </li> <li><strong>Pareto Curve Solution</strong>: This file contains all decision variables in each of the Pareto curve solutions. </li> </ol>
Water Pump Dataset for a Water Supply System
<p>This dataset contains sensor data from a water supply system in a university from July 2023 - 2024. It includes Water Level Sensors, Vibration Sensors, Pressure Sensors, and Power sensors. The dataset also records three different types of system event/faults that have occurred within that time period. More details are given in README.md.</p> <p> </p>
Input Data for the Simulation of Water Supply System of Athens
<p>Input Data for the Simulation of Water Supply System of Athens:</p> <p>Stochastic time-series of water demands, Stochastic time-series of rainfall, evaporation runoff, Stochastic time-series of electricity price, Characteristics of the system, Operational Rules of the system</p>
Data to D3.3 - Institutional arrangements of water supply, distribution and uses within and across case studies -identifying the barriers and drivers in place
<p>The transition towards a water circular economy (WCE) in the sense of water reuse may generate new water loops with additional challenges to water governance such as new and different types or qualities of water, new associated risks, new responsibilities,and new sets of actors involved. These challenges demand for clear-defined responsibilities, well-engaging of the various stakeholders, strong cooperation, and coordination among actors to prevent risks. Institutional arrangements, build the formal, and informal, design schemes to ensure adequate procedures and standards to better catalyse such transition. This deliverable analyses the institutional arrangements associated to the water management on the demosites, to assess their preparedness for the implementation of the new water loops as those foreseen with use of Project Ô new technologies. Using the Institutional Analysis and Development Framework, the analysis showed that the more water uses and users a new water loop involves the more critical the institutional arrangements become. Nevertheless, the institutional arrangements of the demosites do not always configure severe barriers. This is the case of Omis (Croatia) where the water reuse loop is located inside a textile company, and Eilat (Israel) where the water reuse loop is located inside amariculture centre. On the contrary, on Almendralejo (Spain) and Lecce (Italy), there are different players involved in the management ofthe water loop,andinstitutional arrangements appear more critical. Among the different institutional design principles, the clearly defined boundaries of responsibilities among stakeholders, the congruence between appropriation and provision rules and local conditions, raise more concerns on the case studies and call for the attention of future research on water circular economy. Fairly share of cost, benefits, and risks, dedicated graduated sanctions, conflict prevention and resolution mechanisms also deserve particular attention given the limited awareness of stakeholders of their features and influence for a successful implementation of demosites. Trust building, stakeholder empowerment and investment on transparency mechanisms related to water monitoring and sharing of communities and environmental benefits and costs of water reuse may be powerful approaches to implement water circular economy.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.