The baseline water stress (BWS) layer, developed as part of WRI's Aqueduct Water Risk Atlas, measures the ratio of total water withdrawals relative to the annual available renewable surface water supplies. BWS serves as a good proxy for water-related challenges more broadly, given that areas of higher water stress will likely be subject to higher depletion of surface and groundwater resources and more competition amongst users, as well as the associated impacts on water quality and other ecosystem services. Watersheds with high baseline water stress may warrant greater need to take appropriate action to respond to watershed risks. A long time series of supply (1950–2010) was used to reduce the effect of multi-year climate cycles and ignore complexities of short-term water storage (e.g., dams, floodplains) for which global operational data are nonexistent. Baseline water stress thus measures chronic stress rather than drought stress. Watersheds with less than 0.012 m/m2 /year of withdrawal and 0.03 m/m2 /year of available blue water were masked as “arid and low water use” since watersheds with low values were more prone to error in the estimates of baseline water stress. Additionally, although current use in such catchments is low, any new withdrawals could easily push them into higher stress categories. For more information on this indicator and its development as part of the Aqueduct Water Risk Atlas, please visit: www.wri.org/aqueduct.

This map provides a representation of levels of water scarcity by major hydrological basin, expressed in terms of the ratio between irrigation water that is consumed by plants through evapotranspiration and renewable fresh water resources. Contrarily to previous water scarcity maps, this map uses consumptive use of water rather than water withdrawal. Renewable fresh water resources as well as net irrigation water requirements in the river basin are calculated through a water balance model, with information regarding climate, soils and irrigated agriculture as input data. The legend distinguishes three classes: • Water scarcity in river basins where evapotranspiration due to irrigation is less than 10% of the total renewable water resources is classified as low; • Water scarcity in river basins where evapotranspiration due to irrigation is in between 10% and 20% of the total renewable water resources is classified as moderate; • Water scarcity in river basins where evapotranspiration due to irrigation is more than 20% of the total renewable water resources is classified as high.

Ecosystem water scarcity solutions and secondary themes, categories and example cases in each category.

A pre-configured, multi-layer web map for viewing all Summer Water Deficit (Jul-Sep) scenarios. (To launch the map from the Climate Change Open Data site, select "View Metadata" under the "About" heading, then look for the button labeled "Open in Map Viewer" to the upper right.) The map layers depict historical, and projected changes in, summer water deficit (Jul-Sep). Geographic units: HUC10. Map layer data include historical (1970-1999) values plus two projections each for two future time periods, 2050s (2040-2069) and 2080s (2070-2099), based on lower and higher greenhouse gas emission scenarios, RCP 4.5 and RCP 8.5. Data classes and symbology by Robert Norheim, Climate Impacts Group, based on the CMIP5 projections used in the IPCC 2013 report. Data source: Mote et al. 2015.

Water supply-based strategies, categories, and example cases in each category.

This map shows to which extent rainfed and irrigated agricultural systems as identified on SOLAW Map 1.3: "Major agricultural systems" suffer from land and / or water scarcity. Land scarcity in rainfed agriculture was assessed by comparing the rural population density, (obtained from GRUMP 2000, adjusted for UN data, excluding the urban areas indicated on the GRUMP dataset) with the suitability for rainfed crops as mapped for the Global Agro-ecological Zones 2000. Since land that is very suitable for rainfed agriculture can sustain more people than land that is not suitable, it was assumed that each suitability class has its own carrying capacity regarding population. On the map, land is considered scarce if the population density is higher that the highest quintile in the density distribution for each suitability class. Land scarce areas in climates with an Aridity Index lower than 0.5 (where the Aridity Index is defined as Yearly Precipitation divided by Yearly Reference Evapotranspiration) are considered both land and water scarce. Water scarcity in irrigated areas was assessed by combining the Map 1.2: Global distribution of physical water scarcity with the Global Map of Irrigation Areas. The areas equipped for irrigation are considered water scarce if already more than 10% of the renewable water resources in the river basin is consumed by irrigated crops.

Per California Water Code Section 10609.80 (a), DWR has released an update to the indicators analyzed for the rural communities water shortage vulnerability analysis and a new interactive tool to explore the data. This page remains to archive the original dataset, but for more current information, please see the following pages: - https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool - https://data.cnra.ca.gov/dataset/water-shortage-vulnerability-technical-methods - https://data.cnra.ca.gov/dataset/i07-water-shortage-vulnerability-sections - https://data.cnra.ca.gov/dataset/i07-water-shortage-social-vulnerability-blockgroup This dataset is made publicly available pursuant to California Water Code Section 10609.42 which directs the California Department of Water Resources to identify small water suppliers and rural communities that may be at risk of drought and water shortage vulnerability and propose to the Governor and Legislature recommendations and information in support of improving the drought preparedness of small water suppliers and rural communities. As of March 2021, two datasets are offered here for download. The background information, results synthesis, methods and all reports submitted to the legislature are available here: https://water.ca.gov/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/County-Drought-Planning Two online interactive dashboards are available here to explore the datasets and findings. https://dwr.maps.arcgis.com/apps/MapSeries/index.html?appid=3353b370f7844f468ca16b8316fa3c7b The following datasets are offered here for download and for those who want to explore the data in tabular format. (1) Small Water Suppliers: In total, 2,419 small water suppliers were examined for their relative risk of drought and water shortage. Of these, 2,244 are community water systems. The remaining 175 systems analyzed are small non-community non-transient water systems that serve schools for which there is available spatial information. This dataset contains the final risk score and individual risk factors for each supplier examined. Spatial boundaries of water suppliers' service areas were used to calculate the extent and severity of each suppliers' exposure to projected climate changes (temperature, wildfire, and sea level rise) and to current environmental conditions and events. The boundaries used to represent service areas are available for download from the California Drinking Water System Area Boundaries, located on the California State Geoportal, which is available online for download at https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc (2) Rural Communities: In total 4,987 communities, represented by US Census Block Groups, were analyzed for their relative risk of drought and water shortage. Communities with a record of one or more domestic well installed within the past 50 years are included in the analysis. Each community examined received a numeric risk score, which is derived from a set of indicators developed from a stakeholder process. Indicators used to estimate risk represented three key components: (1) the exposure of suppliers and communities to hazardous conditions and events, (2) the physical and social vulnerability of communities to the exposure, and (3) recent history of shortage and drought impacts. The unit of analysis for the rural communities, also referred to as "self-supplied communities" is U.S. Census Block Groups (ACS 2012-2016 Tiger Shapefile). The Census Block Groups do not necessarily represent socially-defined communities, but they do cover areas where population resides. Using this spatial unit for this analysis allows us to access demographic information that is otherwise not available in small geographic units.

Monthly and annual evapotranspiration (ET) maps of Berlin, Germany at a 10-m resolution are provided. This dataset is related to the manuscript "City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning" (under review).

The monthly and annual ET sums are provided as rasters (.tif files). The monthly ET sums are given in the files named "ETmonthly_2019_(month).tif" The annual ET sum for 2019 is named "ETannual_2019.tif". The coordinate reference system (CRS) is "+proj=longlat +datum=WGS84 +no_defs."

For access to daily ET maps in 2019, please contact Stenka Vulova (stenka.vulova@tu-berlin.de).

The abstract of the manuscript is given for background information on the dataset:

"The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change."

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

The map is compiled for the SOLAW Report: "Sources of water for agriculture". Data are available from AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations. Perhaps the most widespread indicator of water scarcity at country level that can be found in literature is per capita availability of average renewable water resources, using threshold values of 500, 1 000 and 1 700 m3/person per year (Falkenmark and Widstrand, 1992; UN-Water, 2006b). Under this system countries or regions are considered to be facing absolute water scarcity if water availability is < 500 m3 per capita per year, chronic water shortage if water availability is between 500 and 1 000 m3, regular water stress between 1 000 and 1 700 m3, and occasional stress or local stress can occur also at levels above 1 700 m3. This relatively simple approach to measuring water scarcity was primarily based on estimates of the number of people who can reasonably live with a certain unit of water resources (Falkenmark, 1984). This indicator is widely used because it can be easily calculated for every country in the world and for every year, based on long-term average annual water resources data (FAO, 2010a) and available population data (UN, 2009).

About this AppThe Drought Aware app provides information about areas in the U.S. affected by drought across different time intervals and over multiple drought intensities. The app shows summaries about the affected population and the potential impacts to crops, agricultural labor, rivers, and reservoirs.Use this AppDisplay drought maps for different weeks by clicking on the time-series chart (top bar) or by scrolling through time using the sector chart (top-left). Hover on each drought intensity level in the sector chart to highlight the areas on the map and display the area percentage. Click on the map to display a panel with summary information for the selected area. The panel includes three categories (1) population, (2) water, and (3) agriculture. App CategoriesThe Drought Aware app summarizes information in three categories:Population: displays the estimated people and households affected by drought at each intensity level, describes some of the vulnerable populations, and lists the related drought risk indexes. The data is available at County and State levels. Water: depicts the major local rivers, the average inter-annual river flow, and the relevant local reservoirs. The data is available at the Subregion Hydrologic Units (HUC4)Agriculture: shows the potential economic impact by major crop, the affected labor, and the agricultural exposure to droughts. The data is available at County and State levels. Drought Definitions Abnormally Dry (D0) Going into drought there is short-term dryness slowing planting, growth of crops or pastures. Coming out of drought there are some lingering water deficits; pastures or crops not fully recovered. Moderate Drought (D1) Some damage to crops and pastures. Streams, reservoirs, or wells low, some water shortages developing or imminent. Voluntary water-use restrictions requested. Severe Drought (D2) Crop or pasture losses likely. Water shortages are common. Water restrictions imposed. Extreme Drought (D3) Major crop/pasture losses. Widespread water shortages or restrictions. Exceptional Drought (D4) Exceptional and widespread crop/pasture losses. Shortages of water in reservoirs, streams, and wells create water emergencies.Data SourcesThe data layers used in this app can be found in ArcGIS Living Atlas of the World:U.S. Drought Monitor American Community Survey (ACS)USDA Census of AgricultureFEMA National Risk IndexNational Water Model (NWM)National Hydrography Dataset (NHD)National Inventory of Dams (NID)National Boundary Dataset (WBD)UpdateThe data behind the app is updated every week once a U.S. Drought Monitor map is released. The update process is automated using a live feed routine. This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.RevisionsOct 16, 2024: Official release of the Drought Aware app.

Law, policy, planning, and market-based strategies, categories, and example cases in each category.

Bahrain has one of the highest water stress levels in the world. Based on an index that reflects how much water is extracted in relation to the available renewable water supplies, Bahrain was graded five on a scale from zero to five, where five shows the highest level of water stress. Other countries with the highest scores were Cyprus, Kuwait, Lebanon, Oman, and Qatar.

This Web Feature Layer contains data that will help you determine access to safe water at a regional scale with a global extent. The data for this map was compiled in 2018 and at this time some regional water access information was unknown.Access to safe water is a good example of global inequality.

Global Inequality: where something is not fairly shared out to everyone.

In many areas of the world, we take it for granted that the tap will always provide safe and clean water for drinking, cooking and for washing with. However, more than one billion people worldwide have no choice but to use potentially harmful sources of water for bathing, cooking and even drinking. Every day this has the result of causing the death of more than 6,000 children.

In the developing world more than one billion people have inadequate access to water.

It has been estimated that 12% of the world’s population uses 85% of its water.Student workbook associated with this WebMap

Population and other GDP data. Visit https://dataone.org/datasets/sha256%3A08ad7f13f5409fe83e0c1c354c79ca31cc8289e432176003857d7d215f0da177 for complete metadata about this dataset.

Blue Raster worked with the World Resources Institute (WRI) to build the Agricultural Exposure to Water Stress interactive map which highlights the intersection between 20 commodity crops, from coffee to cocoa to oranges, with different levels of baseline water stress.WRI describes water stress as “the ratio of total water withdrawals to the available renewable supply in an area. In highly water-stressed regions, 40 percent or more of the supply is used annually. When that ratio gets up to 80 percent, it’s considered extremely stressed.”

This historical version from 2020 of the national dataset contains the category 1 nature areas from the water shortage displacement series. This map helped redistribute scarce water when water shortages occurred. The Water Act specifies how the scarce water should then be used. In a series of displacement, certain nature areas are given high priority to prevent irreparable damage from drought in these areas. Examples of damage are the settling of peat, salinization or the death of vegetation.

This map is part of SDGs Today. Please see sdgstoday.orgThe Falkenmark Water Stress Index is a widely used metric to characterize water stress based on annual renewable water supply per capita. Using this metric regions are considered to be facing absolute water stress if renewable water resources are <500 m3 per person, water stress if renewable water resources are between 500 and 1,000 m3 per person, and water scarcity if renewable water resources are between 1,000 and 1,700 m3 per person. Renewable water resources above 1,700 m3 per person are considered not stressed. This metric identifies the number of people living with each level of water stress globally, and by country during the most recent 12-month period. The metric will be smaller during relatively wet years and larger during relatively dry years. There are more sophisticated measures of water stress that compare the demand for water to the annual renewable supply of water. However, data challenges and computational complexity make it difficult to update this category of metric on a monthly basis with short lag times. Learn more about ISciences’ methodological framework here. Contact Daniel P. Baston (dbaston@isciences.com), Thomas M. Parris (parris@isciences.com) for more information.

Case list results from selecting “Grains and Forage–Forage (excl. alfalfa)” under Crop or Ecosystem in the filter tool on WATA.

The State Water Resources Control Board (SWRCB) Division of Water Rights staff have developed an interactive tool that graphically displays water availability for water rights in the lower Russian River Watershed. Water rights are symbolized by circles, the colors of which correspond to shortage determinations. Water supply information was obtained from the Drought Water Rights Allocation Tool (DWRAT), a model developed by the SWRCB Division of Water Rights. Water demand was obtained from the SWRCB's eWRIMS database. Shortage determinations were made by comparing supply to demand.Additional information about the SWRCB's Russian River drought response can be found at https://www.waterboards.ca.gov/drought/russian_river/For feedback about the map design, please email DWR@waterboards.ca.gov or call (916) 341-5300. For feedback about the underlying data or shortage determinations, please email RussianRiverDrought@waterboards.ca.gov or call (916) 341-5318.