The spatially distributed industrial water use dataset is created using a Random forest regression model at 0.50 resolution. The file contains the input and output datasets, explained in each folder in the 'README.txt' file. It also includes the Python codes created while preparing the datasets.

About:

The dataset constitutes the first reconstructed global water use data product at sub-annual and sub-national/gridded resolution that is derived from different models and data sources; it was generated by spatially and temporally downscaling country-scale estimates of sectoral water withdrawals from FAO AQUASTAT (and state-scale estimates of USGS for the US). In addition, the industrial sector was disaggregated into manufacturing, mining and cooling of thermal power plants by using historical estimates from GCAM. Downscaling was performed using the output of various models and new modeling approaches, which includes the spatial and temporal downscaling methodologies for water withdrawal in previous studies (Wada et al., 2011; Voisin et al., 2013; Hejazi et al., 2014). For the consumptive water use, irrigation water consumption is reconstructed based on estimates by 4 GHMs and consumptive water use efficiency (the proportion of water consumption to water withdrawal), which is calculated based on simulation of Flörke et al (2013) and USGS estimates, is used to generated global consumptive water use for the remaining sector. Therefore, a global monthly gridded (0.5 degree) sectoral water use dataset for the period 1971–2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing, was reconstructed. The detailed descriptions for this dataset are presented in Huang et al. (in review).

The Global Water Quality database and information system GEMStat is hosted, operated, and maintained by the International Centre for Water Resources and Global Change (ICWRGC) in Koblenz, Germany, within the framework of the GEMS/Water Programme of the United Nations Environment Programme (UNEP), and in cooperation with the Federal Institute of Hydrology. GEMStat hosts water quality data of ground and surface waters providing a global overview of the condition of water bodies and the trends at global, regional and local levels.

This dataset contains a summary of the global data availability for 38 monitored water quality constituents, as described and used in: Jones et al 2024 Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ad6919This includes information on the location (e.g. site_id, latitude, longitude, country_name), the database of origin (database), water quality constituent information (e.g. group, sub-group) and the number of daily measurements in the period 1980-2021.Additionally, the spatial and temporal distribution of water quality data per constituent are provided as Figures.

Water consumption is indispensable data in the trends and changes of key factors such as the water resource restoration process and water pressure judgment. Due to difficulties in obtaining and varying statistical dimensions, as the spatial scale continues to expand, the least reliable and most inconsistent water consumption also becomes apparent. As a result, the contradiction between the demand for data refinement and the slow development is increasingly expanding. With the innovation of research methods, the transformation from regionalization to rasterization has accelerated, but it has also caused difficulty in unifying conclusions. For this type of complex data, continuous "convergence" research can lead to more reliable results for practical applications. To this end, based on existing sub-national water withdrawal, this study takes into account the idea of the trapezoid model and the development trend of socio-economic indicators, spatially quantifies the utilization coefficient of agricultural water consumption, and corrects and calculates the utilization coefficient of industrial/municipal water consumption. This study not only provides reliable insights into water consumption trends and key shifts in different sectors, but also provides strong support for the boundary constraints of sub-national data. Furthermore, by considering the changing relationship between the development rate and the averageness, the restriction situation of different sectors at the sub-national level was analyzed. Among them, industrial water consumption played a very significant role in achieving the goal of reaching the peak.

Overview

Here we provide outputs of the global simulation of water footprints of crops with a process-based gridded crop model ACEA (see model description in the references). The model is based on FAO’s AquaCrop and covers 175 widely-grown crops in the 1990–2019 period at a 5 arcminute resolution (10 x 10 km). We partition water footprints into green (water from precipitation) and blue (from irrigation or capillary rise) and differentiate between rainfed and irrigated production systems. The outputs cover several variables, including unit water footprints (expressed in m³ t^-1 yr^-1), water footprints of crop production (m³ yr^-1), and crop water use (mm yr^-1). For more information on methods, input data, validation, and uncertainties, please refer to the corresponding data descriptor paper published in Nature Scientific Data: doi.org/10.1038/s41597-024-03051-3

This dataset includes global gridded datasets (NetCDF4 files) and summary datasheets for national and global values (CSV files). For more information on each provided file, please refer to readme.pdf

Citation

To cite the dataset, please refer to doi.org/10.1038/s41597-024-03051-3

Version History

2025-05-16 (v3): new files added to provide global gridded datasets for all individual crops & years:

> unit_wf_175_crops_annual_1990_2019.zip – unit water footprints per crop in 1990–2019 for all combinations of production systems (rainfed, irrigated, total) with water types (green, blue, total)

> cwu_175_crops_annual_1990_2019.zip – same but for crop water use

> harvested_area_175_crops_annual_1990_2019.zip – rainfed, irrigated, and total harvested areas per crop in 1990–2019

> production_175_crops_annual_1990_2019.zip – same but for crop production

> classification_of_175_crops.csv – crop classification for user convenience, based on FAOSTAT

2024-02-28 (v2): new files added to provide representative data for the current state of water footprints:

> global_wf_175_crops_average_2010_2019.csv – global average values per crop over 2010–2019

> national_wf_175_crops_average_2010_2019.csv – same but for individual countries

2023-08-16 (v1): dataset uploaded for the first time (as described in the data descriptor paper)

Version 2 (under review). In the meantime use version 1.1 available at https://zenodo.org/records/12702055

There are multiple well-recognized and peer-reviewed global datasets that can be used to assess water availability and water pollution. Each of these datasets are based on different inputs, modeling approaches, assumptions, and limitations. Therefore, in SBTN Step 1: Assess and Step 2: Interpret & Prioritize, companies are required to consult different global datasets for a robust and comprehensive State of Nature (SoN) assessment for water availability and water pollution.

To streamline this process, WWF, the World Resources Institute (WRI), and SBTN worked together to develop two ready-to-use unified layers of SoN – one for water availability and one for water pollution – in line with the Technical Guidance for Steps 1: Assess and Step 2: Interpret & Prioritize (July 2024). The main outputs contain the maximum values of Water Availability and of Water Pollution as well as the individual indicators' values. This information is available at different spatial resolutions, thus in two data formats: 1) a shapefile with values at HydroBasins (Pfafstetter level 6); and 2) an excel file with values at sub-national divisions (Adm1) and national divisions (Adm0). These datasets and complete documentation are publicly available for download below.

Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).

Dataset properties:

• spatial resolution: 10 km (global-scale)
• temporal resolution: monthly time-step
• period: 1980 - 2019
• units: m3/month

Output datasets:

The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.

A global dataset of steam-electric power generating units with location, technical information, performance characteristics and associated environmental stressors (GHG emissions, freshwater consumption, thermal emissions to freshwater) as well as stressor intensities (per GJ el. produced).

Series Name: Water Use Efficiency (United States dollars per cubic meter)Series Code: ER_H2O_WUEYSTRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 6.4.1: Change in water-use efficiency over timeTarget 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcityGoal 6: Ensure availability and sustainable management of water and sanitation for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

A major problem related to large-scale water quality modeling has been the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing models. In addition to the observation data itself, insufficient or poor quality metadata has also discouraged researchers to integrate the already available datasets. Therefore, improving both the availability and quality of open water quality data woould increase the potential to implement predictive modeling on a global scale. We aim to address the aforementioned issues by presenting the new Global River Water Quality Archive (GRQA) by integrating data from five existing global and regional sources: Canadian Environmental Sustainability Indicators program (CESI), Global Freshwater Quality Database (GEMStat), GLObal RIver Chemistry database (GLORICH), European Environment Agency (Waterbase) and USGS Water Quality Portal (WQP). The resulting dataset covering the timeframe 1898 - 2020 contains a total of over 17 million observations for 42 different forms of some of the most important water quality parameters, focusing on nutrients, carbon, oxygen and sediments. Supplementary metadata and statistics are provided with the observation time series to improve the usability of the dataset.

Last update: 2022-03-11

GRQA_v1.2 contains three updated files compared to GRQA_v1.1:

• NH4N_GRQA.csv
• NO2N_GRQA.csv
• NO3N_GRQA.csv

The files were updated, because the assumed conversion constants used for the corresponding GLORICH observations were found to be incorrect. The corresponding files in GRQA_figures.zip and GRQA_meta.zip are yet to be updated, but will be in GRQA_v1.3.

The explanation for the updated conversion constants is given in this notebook:
https://nbviewer.org/github/LandscapeGeoinformatics/GRQA_src/blob/main/testing/glorich_conversion_test.ipynb

An overview of all the files in the dataset can be found in README_v1.2.txt.

Statistical overview of all 42 parameters is given in the data catalog file GRQA_data_catalog.pdf.

For more information about the development of this dataset look for Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, 2021.

This dataset comprises data from the GEMStat database that are available under an open data license (CC BY 4.0 or equivalent). It is made available on the Zenodo repository. GEMStat provides access to freshwater quality data. The data are voluntarily provided by countries and organizations worldwide within the framework of the GEMS/Water Programme of the United Nations Environment Programme (UNEP). The dataset includes more than 20 Million measurement from over 13,000 stations and covering more than 600 different parameters and spans the time period from 1906 to 2023. This represents over 70% of all GEMStat data, further data is only available under more restricted data licenses. GEMStat is operated by the GEMS/Water programme of the United Nations Environment Programme (UNEP) and hosted at the International Centre for Water Resources and Global Change (ICWRGC) and the German Federal Institute of Hydrology (BfG). The data in GEMStat is provided by National Hydrological Services of UN member states.

Much of the world’s data are stored, managed, and distributed by data centers. Data centers re-quire a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center’s water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint..

Free and up-to-date information on climate and water anywhere in the world, measured by satellites and on the ground

Notes for the 13.10.2019 update- The months of January 2019 to July 2019 were added to the ERA5 reconstructions- The robustness of the ERA5 reconstruction was improved for a few Greenland and Antarctica mascons by better handling a special case occuring when air temperature is always lower than 0°C during the calibration period.- The updated ERA5 time series might differ from the previous version (especially individual ensemble members). With the exception of the special case mentioned above, these differences are not significant.List of all filesReadme file 00_readme.txtMonthly grids - ensemble means 01_monthly_grids_ensemble_means_allmodels.zipMonthly grids - ensembles, model 1 to 6 02_monthly_grids_ensemble_JPL_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_JPL_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_JPL_ERA5_1979_201907.zip 02_monthly_grids_ensemble_GSFC_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_GSFC_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_GSFC_ERA5_1979_201907.zipDaily grids - ensemble means, model 1 to 6 03_daily_grids_ensemble_means_JPL_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_JPL_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_JPL_ERA5_1979_201907.zip 03_daily_grids_ensemble_means_GSFC_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_GSFC_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_GSFC_ERA5_1979_201907.zipGlobal averages - daily and monthly time series 04_global_averages_allmodels.zipContent of readmeGRACE TWS Reconstruction (GRACE_REC_v03)The dataset contains reconstructed time series of daily and monthly anomalies of terrestrial water storage (TWS) based on two different GRACE solutions and three different meteorological forcing datasets. There is a total of 6 different models:JPL_MSWEP - trained with GRACE JPL mascons, forced with MSWEP forcing (1979-2016)JPL_GSWP3 - trained with GRACE JPL mascons, forced with GSWP3 forcing (1901-2014)JPL_ERA5 - trained with GRACE JPL mascons, forced with ERA5 forcing (1979-present)GSFC_MSWEP - trained with GRACE GSFC mascons, forced with MSWEP forcing (1979-2016)GSFC_GSWP3 - trained with GRACE GSFC mascons, forced with GSWP3 forcing (1901-2014)GSFC_ERA5 - trained with GRACE GSFC mascons, forced with ERA5 forcing (1979-present)The reconstruction aims at reproducing the sub-decadal climate-driven variability observed in the GRACE data. Seasonal cycle and human impacts on TWS are not reconstructed. A GRACE-based seasonal cycle is provided for convenience. Long-term signals (trends over a period >15 years) are removed during the model calibration procedure but are still present in the final dataset and mainly represent precipitation-driven trends. The interpretation of the reconstructed long-term trends should be done with the awareness that there can be some uncertainty in the reconstructed trends.For most applications, uncertainty ranges can be derived from the 100 ensemble members available for each model.The grids are stored in NetCDFv4 files in units of mm (kg m^-2). Although the data is provided on a 0.5 degrees grid, the effective spatial resolution should be considered to be 3 degrees, similar to the original resolution of the GRACE datasets. This might need to be taken into account when comparing this dataset against other sources.The global means are stored as csv files in units of Gt of water. To convert back to mm of water, use the land area values given in the reference paper below.When using this dataset, please cite:Humphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.Vincent Humphrey, October 2019California Institute of TechnologyYour feedback is always welcome:vincent.humphrey[-a-t-]caltech.edu (vincent.humphrey[-a-t-]bluewin.ch) Abstract

The amount of water stored on continents is an important constraint for water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment mission (GRACE) have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near real time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g. snow, soil moisture, etc.) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis and in-situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5°, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in 6 reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modelling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series and the interpretation of the data gap between the GRACE and the GRACE Follow-On mission.Check reference for additional details and caveats.ReferenceHumphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.

Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.

Chile Public Water Supply data was reported at 1,849.000 Cub m mn in 2022. This records an increase from the previous number of 1,818.000 Cub m mn for 2021. Chile Public Water Supply data is updated yearly, averaging 1,584.030 Cub m mn from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 1,849.000 Cub m mn in 2022 and a record low of 1,396.300 Cub m mn in 2000. Chile Public Water Supply data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Chile – Table CL.OECD.ESG: Environmental: Water Made Available for Use: OECD Member: Annual.

A global salinity database, compiled from electrical conductivity (EC) monitoring data of both surface water (rivers, lakes/reservoirs) and groundwater locations over the period 1980–2019.

River basins or hydrologic units are often the spatial unit used for aggregating and analyzing components of the water cycle such as precipitation, runoff, riverine discharge, etc. The hydroSHEDS dataset, derived from the Shuttle Radar Topography Mission, are the most commonly used global hydrologic unit for these analyses. But when planning water use or gaps, political boundaries need to be considered. Water provinces (Straatsma et al 2020) provide a much more realistic hydrologic unit for such purposes.Esri’s World Administration Divisions (2011) defines 3,300 subnational units. Areas less than 150,000 sq km were aggregated into 1,099 regions. The water provinces were then calculated by overlaying these regions with the major basins from hydroSHEDS. After sliver polygons were removed, the result was 1,604 unique units based on river basins but constrained by political boundaries. These water provinces provide a suitable unit for longterm water use planning, especially at local scales.A more detailed description can be accessed here.

Data DescriptionWater Quality Parameters: Ammonia, BOD, DO, Orthophosphate, pH, Temperature, Nitrogen, Nitrate.Countries/Regions: United States, Canada, Ireland, England, China.Years Covered: 1940-2023.Data Records: 2.82 million.Definition of ColumnsCountry: Name of the water-body region.Area: Name of the area in the region.Waterbody Type: Type of the water-body source.Date: Date of the sample collection (dd-mm-yyyy).Ammonia (mg/l): Ammonia concentration.Biochemical Oxygen Demand (BOD) (mg/l): Oxygen demand measurement.Dissolved Oxygen (DO) (mg/l): Concentration of dissolved oxygen.Orthophosphate (mg/l): Orthophosphate concentration.pH (pH units): pH level of water.Temperature (°C): Temperature in Celsius.Nitrogen (mg/l): Total nitrogen concentration.Nitrate (mg/l): Nitrate concentration.CCME_Values: Calculated water quality index values using the CCME WQI model.CCME_WQI: Water Quality Index classification based on CCME_Values.Data Directory Description:Category 1: DatasetCombined Data: This folder contains two CSV files: Combined_dataset.csv and Summary.xlsx. The Combined_dataset.csv file includes all eight water quality parameter readings across five countries, with additional data for initial preprocessing steps like missing value handling, outlier detection, and other operations. It also contains the CCME Water Quality Index calculation for empirical analysis and ML-based research. The Summary.xlsx provides a brief description of the datasets, including data distributions (e.g., maximum, minimum, mean, standard deviation).Combined_dataset.csvSummary.xlsxCountry-wise Data: This folder contains separate country-based datasets in CSV files. Each file includes the eight water quality parameters for regional analysis. The Summary_country.xlsx file presents country-wise dataset descriptions with data distributions (e.g., maximum, minimum, mean, standard deviation).England_dataset.csvCanada_dataset.csvUSA_dataset.csvIreland_dataset.csvChina_dataset.csvSummary_country.xlsxCategory 2: CodeData processing and harmonization code (e.g., Language Conversion, Date Conversion, Parameter Naming and Unit Conversion, Missing Value Handling, WQI Measurement and Classification).Data_Processing_Harmonnization.ipynbThe code used for Technical Validation (e.g., assessing the Data Distribution, Outlier Detection, Water Quality Trend Analysis, and Vrifying the Application of the Dataset for the ML Models).Technical_Validation.ipynbCategory 3: Data Collection SourcesThis category includes links to the selected dataset sources, which were used to create the dataset and are provided for further reconstruction or data formation. It contains links to various data collection sources.DataCollectionSources.xlsxOriginal Paper Title: A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted ResearchAbstractAssessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.Note: Cite this repository and the original paper when using this dataset.