Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency Weekly.

Data Triage Review Frequency An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: • Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. • Where it is necessary to estimate consumption, this is calculated based on actual meter readings. • Meters of all types (smart, dumb, AMR) are included in this dataset. • The dataset is updated and published Weekly. • Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. • The dataset includes LSOAs with 2 or more meters. Any LSOAs with less than 2 meters have been excluded. • Consumption data is only included where we have the full consumption data for a year for a given meter.

Context Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.1.Ofwat guidance on water meters. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf Data Schema DATA_SOURCE: Company that provided the data YEAR: The calendar year covered by the data LSOA_CODE: LSOA or Data Zone converted code of the meter location NUMBER_OF_METERS: Number of meters within an LSOA TOTAL_CONSUMPTION: Average consumption within the LSOA TOTAL_CONSUMPTION_UNITS: Units for average consumption

CN: Water Consumption: City: Daily per Capita: Residential: Hainan data was reported at 304.646 l in 2023. This records an increase from the previous number of 287.037 l for 2022. CN: Water Consumption: City: Daily per Capita: Residential: Hainan data is updated yearly, averaging 266.620 l from Dec 1996 (Median) to 2023, with 28 observations. The data reached an all-time high of 337.850 l in 2005 and a record low of 197.606 l in 2018. CN: Water Consumption: City: Daily per Capita: Residential: Hainan data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

This dataset provides the average (annual, winter, summer) residential metered water consumption (by year) within 400 m x 400m hexagons (approximately two city blocks) provided in m3/month for the City of Edmonton.

Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December.

Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those hexagons that contain at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

This dataset provides the average (annual, winter, summer) residential metered water consumption (2016) within residential neighbourhoods provided in m3/month for the City of Edmonton. Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December. Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those residential neighbourhoods with at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

​Monthly residential water consumption grouped by zip code and customer class.

Potable water use by sector and average daily use for Canada, provinces and territories.

October 31, 2024 (Final DWR Data)

The 2018 Legislation required DWR to provide or otherwise identify data regarding the unique local conditions to support the calculation of an urban water use objective (CWC 10609. (b)(2) (C)). The urban water use objective (UWUO) is an estimate of aggregate efficient water use for the previous year based on adopted water use efficiency standards and local service area characteristics for that year.

UWUO is calculated as the sum of efficient indoor residential water use, efficient outdoor residential water use, efficient outdoor irrigation of landscape areas with dedicated irrigation meter for Commercial, Industrial, and Institutional (CII) water use, efficient water losses, and an estimated water use in accordance with variances, as appropriate. Details of urban water use objective calculations can be obtained from DWR’s Recommendations for Guidelines and Methodologies document (Recommendations for Guidelines and Methodologies for Calculating Urban Water Use Objective - https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/Performance-Measures/UWUO_GM_WUES-DWR-2021-01B_COMPLETE.pdf).

The datasets provided in the links below enable urban retail water suppliers calculate efficient outdoor water uses (both residential and CII), agricultural variances, variances for significant uses of water for dust control for horse corals, and temporary provisions for water use for existing pools (as stated in Water Boards’ draft regulation). DWR will provide technical assistance for estimating the remaining UWUO components, as needed. Data for calculating outdoor water uses include:

• Reference evapotranspiration (ETo) – ETo is evaporation plant and soil surface plus transpiration through the leaves of standardized grass surfaces over which weather stations stand. Standardization of the surfaces is required because evapotranspiration (ET) depends on combinations of several factors, making it impractical to take measurements under all sets of conditions. Plant factors, known as crop coefficients (Kc) or landscape coefficients (KL), are used to convert ETo to actual water use by specific crop/plant. The ETo data that DWR provides to urban retail water suppliers for urban water use objective calculation purposes is derived from the California Irrigation Management Information System (CIMIS) program (https://cimis.water.ca.gov/). CIMIS is a network of over 150 automated weather stations throughout the state that measure weather data that are used to estimate ETo. CIMIS also provides daily maps of ETo at 2-km grid using the Spatial CIMIS modeling approach that couples satellite data with point measurements. The ETo data provided below for each urban retail water supplier is an area weighted average value from the Spatial CIMIS ETo.

• Effective precipitation (Peff) - Peff is the portion of total precipitation which becomes available for plant growth. Peff is affected by soil type, slope, land cover type, and intensity and duration of rainfall. DWR is using a soil water balance model, known as Cal-SIMETAW, to estimate daily Peff at 4-km grid and an area weighted average value is calculated at the service area level. Cal-SIMETAW is a model that was developed by UC Davis and DWR and it is widely used to quantify agricultural, and to some extent urban, water uses for the publication of DWR’s Water Plan Update. Peff from Cal-SIMETAW is capped at 25% of total precipitation to account for potential uncertainties in its estimation. Daily Peff at each grid point is aggregated to produce weighted average annual or seasonal Peff at the service area level. The total precipitation that Cal-SIMETAW uses to estimate Peff comes from the Parameter-elevation Relationships on Independent Slopes Model (PRISM), which is a climate mapping model developed by the PRISM Climate Group at Oregon State University.

• Residential Landscape Area Measurement (LAM) – The 2018 Legislation required DWR to provide each urban retail water supplier with data regarding the area of residential irrigable lands in a manner that can reasonably be applied to the standards (CWC 10609.6.(b)). DWR delivered the LAM data to all retail water suppliers, and a tabular summary of selected data types will be provided here. The data summary that is provided in this file contains irrigable-irrigated (II), irrigable-not-irrigated (INI), and not irrigable (NI) irrigation status classes, as well as horse corral areas (HCL_area), agricultural areas (Ag_area), and pool areas (Pool_area) for all retail suppliers.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency Weekly.

Data Triage Review Frequency An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: • Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. • Where it is necessary to estimate consumption, this is calculated based on actual meter readings. • Meters of all types (smart, dumb, AMR) are included in this dataset. • The dataset is updated and published Weekly. • Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. • The dataset includes LSOAs with 2 or more meters. Any LSOAs with less than 2 meters have been excluded. • Consumption data is only included where we have the full consumption data for a year for a given meter.

Context Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.1.Ofwat guidance on water meters. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf Data Schema DATA_SOURCE: Company that provided the data YEAR: The calendar year covered by the data LSOA_CODE: LSOA or Data Zone converted code of the meter location NUMBER_OF_METERS: Number of meters within an LSOA TOTAL_CONSUMPTION: Average consumption within the LSOA TOTAL_CONSUMPTION_UNITS: Units for average consumption

China Water Consumption: City: Daily per Capita: Residential data was reported at 188.799 l in 2023. This records an increase from the previous number of 184.732 l for 2022. China Water Consumption: City: Daily per Capita: Residential data is updated yearly, averaging 178.638 l from Dec 1978 (Median) to 2023, with 46 observations. The data reached an all-time high of 220.240 l in 2000 and a record low of 120.600 l in 1978. China Water Consumption: City: Daily per Capita: Residential data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency Weekly.

Data Triage Review Frequency An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: • Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. • Where it is necessary to estimate consumption, this is calculated based on actual meter readings. • Meters of all types (smart, dumb, AMR) are included in this dataset. • The dataset is updated and published Weekly. • Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. • The dataset includes LSOAs with 2 or more meters. Any LSOAs with less than 2 meters have been excluded. • Consumption data is only included where we have the full consumption data for a year for a given meter.

Context Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.1.Ofwat guidance on water meters. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf Data Schema DATA_SOURCE: Company that provided the data YEAR: The calendar year covered by the data LSOA_CODE: LSOA or Data Zone converted code of the meter location NUMBER_OF_METERS: Number of meters within an LSOA TOTAL_CONSUMPTION: Average consumption within the LSOA TOTAL_CONSUMPTION_UNITS: Units for average consumption

This dataset provides the average (annual, winter, summer) residential metered water consumption (2017) within 400 m x 400m hexagons (approximately two city blocks) provided in m3/month for the City of Edmonton. Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December. Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those hexagons that contain at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

Note:

For 2017, there were no areas where the consumption was 60 m3/month and up - thus, the maroon colour would not appear in the legend.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

This dataset provides the average (annual, winter, summer) residential metered water consumption (2016) within 400 m x 400m hexagons (approximately two city blocks) provided in m3/month for the City of Edmonton. Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December. Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those hexagons that contain at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

Between 1990 and 2022, the daily per capita consumption of water in Germany has decreased from 147 to 125 liters. Data published by the German Association of Energy and Water Industries includes households as well as small enterprises.

Water consumption

In 2020, Colombia and the United States were the two largest per capita consumers of water in the world. The former reported an annual water consumption of approximately 1,207 cubic meters per capita. Germany ranked on the other end of spectrum of water consumption with comparatively low per capita figures of around 297 cubic meters per annum.

Increasing demand

As of 2014, the agriculture sector accounted for approximately 83 percent of worldwide water consumption. By 2040, the combined agricultural and industrial demand is expected to reach 1,550 billion cubic meters. Of those 1,550 billion cubic meters about 1,405 billion will be accounted for by the agricultural sector. In order to prevent severe droughts in water-stressed areas today and in the future, a more efficient use of water is essential.

CN: Water Consumption: City: Daily per Capita: Residential: Shaanxi data was reported at 153.623 l in 2023. This records a decrease from the previous number of 162.504 l for 2022. CN: Water Consumption: City: Daily per Capita: Residential: Shaanxi data is updated yearly, averaging 163.322 l from Dec 1996 (Median) to 2023, with 28 observations. The data reached an all-time high of 208.640 l in 1996 and a record low of 131.600 l in 2006. CN: Water Consumption: City: Daily per Capita: Residential: Shaanxi data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

This resource contains a dataset and code to analyze average day to day water use at a residential hall at Utah State University (USU). Included in this resource is a .csv file that contains high resolution time series of water flow from a residential building on the USU campus. The data was recorded every one second for approximately three weeks in 2017. The Jupyter Notebook in this resource demonstrates how to subset the Richards Hall dataset to analyze daily water use from March 18-24, 2017. It resamples the data to daily total volume and finds the daily average, then plots the average daily volume in gallons for each day of the week.

Get data on the intensity of energy and water usage and greenhouse gas (GHG) emissions as well as property use types for buildings larger than 100,000 square feet. Where possible, data is weather-normalized. Data is not cleansed.

This data set shows energy and water usage intensities and GHG emission intensities for buildings, including:

• commercial (for example, retail or office)
• multi-residential
• warehousing
• light industrial

Manufacturing, heavy industrial or agricultural buildings are not included. Data is not randomized and is reported by building owners or their agents according to Energy Star Portfolio Manager property type categories and may contain errors.

Latest data: Annual average water consumption report for Cape Town suburbs May 2016. Average (median) water consumption per suburb for single-residential properties.

Data source: Statistics Canada, Environment Accounts and Statistics Division. Includes an estimate for water use and leakages by water treatment and distribution systems. Includes an estimate for residential use of water produced by drinking water plants and for well water. The amount of municipal water use that is not residential and not assigned to industries in the Industrial Water Use Survey is distributed across the remaining industries based on expenditure data for water supplied through mains from the input-output accounts. Household water use is based on the municipal water supply from Statistics Canada's Survey of Drinking Water Treatment Plants combined with an estimate from the producers of the proportion of this water supply that serves households. In addition, the water use of households not served by the municipal supply is estimated based on average household consumption figures. The supply and use tables are built around three classification systems, namely the Input-Output Industry Classification (IOIC) for industries, the Supply and Use Product Classification (SUPC) for products (goods and services), and the Input-Output Final Demand Classification (IOFDC) for final demand categories. The Input-Output Industry Classification (IOIC) is based on the North American Industry Classification System (NAICS) and the Supply and Use Product Classification (SUPC) is based on the North American Products Classification System (NAPCS). The Input-Output Final Demand Classification (IOFDC) is based on the Classification of Individual Consumption by Purpose (COICOP) for the personal expenditure categories and the North American Industry Classification (NAICS) for the gross fixed capital formation categories. This table is published at the link 1961 level of the supply and use tables. The alphanumeric codes appearing in square brackets besides each industry title represent the Input-Output Industry Classification (IOIC) codes. The IOIC identifies both institutional sectors and industries based on the North American Industry Classification System (NAICS). The first two characters of the IOIC alphanumeric codes represent the sector. IOIC codes beginning with a BS represent business sector industries, codes beginning with an NP represent Non-Profit Institutions Serving Household (NPISH) sector industries, and codes beginning with a GS represent government sector industries. This table replaces table 38-10-0118-01. Totals may not add due to rounding. The estimate for water use does not include the use of water for hydro-electricity production. Data for 2009 are based on input-output tables, while data for 2011 onwards are based on the supply and use tables: comparisons with the 2009 data should be done with caution.

For more information on the concepts, sources and methods, please consult the Water use account (opens new window) section of the Methodological guide: Canadian System of Environmental-Economic Accounting (16-509-X (opens new window)." Data for 2009 to 2019 were revised in July 2024. For reference year 2019, data related to manufacturing, mining and thermal industries were estimated through modelling because of the unavailability of underlying data sources.

The annual amount of water billed in each ward is reported as well as the number of water accounts, both residential and commercial. N.B. One cubic metre of water (m3) is 219.97 imperial gallons. Accounts with billing data less than 6,000m3/year are deemed to be residential and are billed on a tri-annual basis (For example a variety store would probably be considered "residential" ). Accounts with billing data greater than 6,000m3 per year are billed on a monthly basis and are deemed to be "commercial". Residential readings are taken three times a year and commercial readings are taken once a month. The data will be useful in trend analysis. Some accounts are not mapped to a political ward and therefore their consumption is not included. The excluded figures are small enough not to affect an overall trend analysis. Ward - ward number Year - calendar year in yyyy format Residential Accounts - number of accounts deemed residential Annual Residential Water Usage (m3) - annual residential usage in cubic metres (m3) Average Residential Usage (m3) - computed as Annual Residential Usage / # Residential Accounts Commercial Accounts - number of accounts deemed commercial Annual Commercial Water Usage (m3) - annual commercial usage in cubic metres (m3) Average Commercial Usage (m3) - computed as Annual Commercial Usage / # Commercial Accounts Total Count - total annual residential usage Total Consumption (m3) - total annual commercial usage Average Consumption (m3) - computed as Total Consumption / Total Count