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.

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.

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).

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)

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.

The Water Consumption Market Report is Segmented by Source of Water Procurement (Potable Water, Reclaimed / Grey Water, and More), Cooling Technology (Evaporative and Adiabatic Cooling, Liquid Immersion and Direct-To-Chip, and More), Water-Treatment Method (Filtration, Reverse Osmosis, and More), Ownership Model (Hyperscale, Wholesale Colocation, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Water withdrawals per capita in Turkmenistan amount to 2,740 cubic meters per inhabitant, according to the latest available data from 2021. This is a far higher volume than in many other countries, such as China, where per capita water withdrawals were 398.7 cubic meters as of 2021. Global water withdrawals Countries around the world withdraw huge volumes of water each year from sources such as rivers, lakes, reservoirs, and groundwater. China has some of the largest annual total water withdrawals across the globe, at 581.3 billion cubic meters per year. In comparison, Mexico withdrew almost 90 billion cubic meters of water in 2021. Water scarcity Although roughly 70 percent of Earth's surface is covered with water, less than one percent of the planet's total water resources can be classified as accessible freshwater resources. Growing populations, increased demand, and climate change are increasingly putting pressure on these precious resources. This is expected to lead to global water shortages around the world. In the United States, the megadrought in the west has seen water levels of major reservoirs that provide water to millions of people plummet to record lows. In order to prevent severe droughts in water-stressed areas today and in the future, a more efficient use of water is essential.

Brazil Water Consumption: Micromeasured data was reported at 10.500 Cub m in 2022. This records a decrease from the previous number of 10.870 Cub m for 2021. Brazil Water Consumption: Micromeasured data is updated yearly, averaging 11.300 Cub m from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 13.240 Cub m in 2012 and a record low of 10.500 Cub m in 2022. Brazil Water Consumption: Micromeasured data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB005: Operational Indicators: Water Consumption Indicators.

China Water Consumption: Agriculture data was reported at 367,240.000 Cub m mn in 2023. This records a decrease from the previous number of 378,130.000 Cub m mn for 2022. China Water Consumption: Agriculture data is updated yearly, averaging 372,311.458 Cub m mn from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 392,151.876 Cub m mn in 2013 and a record low of 343,281.297 Cub m mn in 2003. China Water Consumption: Agriculture data remains active status in CEIC and is reported by Ministry of Water Resources. The data is categorized under China Premium Database’s Land and Resources – Table CN.NLM: Water Resource.

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.

New global sub-daily meteorological forcing data are provided for use with land surface and hydrological-models. The data are derived from the ERA-40 reanalysis product via sequential interpolation to half-degree resolution, elevation correction and monthly-scale adjustments based on CRU (corrected-temperature, diurnal temperature range, cloud-cover) and GPCC (precipitation) monthly observations combined with new corrections for varying atmospheric aerosol-loading and separate precipitation gauge corrections for rainfall and snowfall. The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three of 6-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 time periods with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. The data covers land points only and excludes the Antarctica.

Assessing global freshwater resources and human water demand is of value for a number of needs but challenging. The global water use and water availability model WaterGAP is in development since 1996 and serves a range of applications and topics as such as Life Cycle Assessments, a better understanding of terrestrial water storage variations (e.g., jointly with satellite observations), water (over)use and consequently depletion of water resources, as well as model evaluation and model development. In the paper connected to this dataset, the newest model version, WaterGAP 2.2d is described by providing the water balance equations, insights to input data used and typical model applications. The most important and requested model outputs (total water storage variations, streamflow and water use) are evaluated against observation data. Standard model output is described and the reader is guided to the location where those data can be downloaded. Caveats of specific output data and an overview of model applications as well as an outlook of future model development lines are presented as well.

Water Consumption: Micromeasured: Southeast: Rio de Janeiro data was reported at 7.780 Cub m in 2022. This records a decrease from the previous number of 11.190 Cub m for 2021. Water Consumption: Micromeasured: Southeast: Rio de Janeiro data is updated yearly, averaging 16.200 Cub m from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 17.590 Cub m in 2012 and a record low of 7.780 Cub m in 2022. Water Consumption: Micromeasured: Southeast: Rio de Janeiro data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB005: Operational Indicators: Water Consumption Indicators.

The Global Environment Outlook (GEO) Data Portal was initiated in October 2000 to provide a comprehensive, reliable, and timely supply of data sets used by the United Nations Environment Programme (UNEP) and its partners in the GEO reports and other integrated environment assessments. Since then, the GEO Data Portal has matured into a unique data and information application which responds to the needs of the global environmental community for easy access to systematic and well-documented data on the environment, including the state of natural resources, as well as the societal driving forces and root causes of environmental change and degradation.

The GEO Data Portal is managed by DEWA/GRID-Geneva which is part of UNEP's global network of environmental information centres, known as the Global Resource Information Database (GRID).

The GEO Data Portal's online database holds more than 700 data sets representing over 400 unique variables. Data are provided as national, subregional, regional and global statistics or as geospatial data sets (maps), covering themes like Freshwater, Population, Forests, Emissions, Climate, Disasters, Health, and Gross Domestic Product (GDP). The data cover the time period from 1972-2002 where available. The GEO Data Portal offers users the option to: (1) draw, view and explore maps, graphs and tables online; (2) view documentation and other metadata; and/or (3) download data sets as Excel, PDF, CSV, XML or ARCINFO Shape files as appropriate.

Data from the GEO Data Portal are extracted by UNEP/DEWA/GRID-Geneva to produce another product, called the GEO-3 Data Compendium. The Compendium provides the major statistical data sets underlying the integrated analyses in the GEO reports. It is available online at [http://geocompendium.grid.unep.ch/] as well as on CD-ROM and as a printed publication.

The latest GEO report is GEO-3. This third edition complements the detailed assessment of the state of the global environment set out in GEO-2000.

The data sets held in the GEO Data Portal are derived from many different organizations and databases. These sources are listed at [http://geodata.grid.unep.ch/datasource.php]. In addition to providing readily available data sets, several institutions also have assisted in data processing. The data sources and providers are listed in the metadata that accompany each data set.

A virtual time machine that maps the location and temporal distribution of water surfaces at the global scale over the past 3.5 decades, and provides statistics on their extent and change to support better informed water-management decision-making. Data is provided on surface water occurrence, change in occurrence, surface water seasonality, surface water recurrence, transitions in surface water class (permanent or seasonal) and maximum extent over the time period of the data.

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. Canada's water use in a global context indicator reports on the amount of water removed from the environment per person per year for use in agriculture, manufacturing and in homes, and as a percentage of each country's total renewable water supply for nine countries, including Canada. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for data sources and details on how those data were collected and how the indicator was calculated. Supplemental Information Canadian Environmental Sustainability Indicators - Home page: https://www.canada.ca/environmental-indicators

It is projected that global water demand will reach ***** billion cubic meters in terms of withdrawal by 2040. In the last few decades, the growth in water demand has doubled that of population growth. Water demand growth is also likely to vary based on region and sector. Regionally, water demand growth is expected to come mostly from India, Africa, and other developing countries in Asia. The agricultural industry is one of the largest consumers of water worldwide, primarily for irrigation purposes. Trends in water use will be largely dependent on urbanization, rising living standards, demand for goods, and changes in dietary preferences. Water accessibility A vast number of people worldwide still lack access to drinking water sources, while an even larger population has no access to improved sanitation services. In India, over **** million people have no household access to a safe water source. Striving to provide safe water access to these remaining population groups would likely also increase domestic water demand as well as the energy and infrastructure that would need to be put in place to provide these basic needs.

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, and assumptions. 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. The result is a single file (shapefile) containing the maximum value both for water availability and for water pollution, as well as the datasets’ raw values (as references). This data is publicly available for download from this repository.

These unified layers will make it easier for companies to implement a robust approach, and they will lead to more aligned and comparable results between companies. A temporary App is available at https://arcg.is/0z9mOD0 to help companies assess the SoN for water availability and water pollution around their operations and supply chain locations. In the future, these layers will become available both in the WRI’s Aqueduct and in the WWF Risk Filter Suite.

For the SoN for water availability, the following datasets were considered:

• Baseline water stress (Hofste et al. 2019), data available here

• Water depletion (Brauman et al. 2016), data available here

• Blue water scarcity (Mekonnen & Hoekstra 2016), data upon request to the authors

For the SoN for water pollution, the following datasets were considered:

• Coastal Eutrophication Potential (Hofste et al. 2019), data available here

• Nitrate-Nitrite Concentration (Damania et al. 2019), data available here

• Periphyton Growth Potential (McDowell et al. 2020), data available here

In general, the same processing steps were performed for all datasets:

1. Compute the area-weighted median of each dataset at a common spatial resolution, i.e. HydroSHEDS HydroBasins Level 6 in this case.

2. Classify datasets to a common range as reclassifying raw values to 1-5 values, where 0 (zero) was used for cells or features with no data. See the documentation for more details.

3. Identify the maximum value between the classified datasets, separately, for Water Availability and for Water Pollution.

For transparency and reproducibility, the code is publicly available at https://github.com/rafaexx/sbtn-SoN-water

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/