The World Stress Map (WSM) database is a global compilation of information on the crustal present-day stress field. It is a collaborative project between academia and industry that aims to characterize the stress pattern and to understand the stress sources. It commenced in 1986 as a project of the International Lithosphere Program under the leadership of Mary-Lou Zoback. From 1995-2008 it was a project of the Heidelberg Academy of Sciences and Humanities headed first by Karl Fuchs and then by Friedemann Wenzel. Since 2009 the WSM is maintained at the GFZ German Research Centre for Geosciences and since 2012 the WSM is a member of the ICSU World Data System. All stress information is analysed and compiled in a standardized format and quality-ranked for reliability and comparability on a global scale. The WSM database release 2016 contains 42,870 data records within the upper 40 km of the Earth’s crust. The data are provided in three formats: Excel-file (wsm2016.xlsx), comma separated fields (wsm2016.csv) and with a zipped google Earth input file (wsm2016_google.zip). Data records with reliable A-C quality are displayed in the World Stress Map (doi:10.5880/WSM.2016.002). Further detailed information on the WSM quality ranking scheme, guidelines for the various stress indicators, and software for stress map generation and the stress pattern analysis is available at www.world-stress-map.org. VERSION HISTORY:Version 1.1. (15 June 2019): updated version of the zip-compressed Google Earth .kml (wsm2016_google.zip) with a new URL of the server.

Stress maps show the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In stress maps SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org. The stress map of Great Britain and Ireland 2022 is based on the WSM database release 2016. All data records have been checked and we added a number of new data from earthquake focal mechanisms from the national earthquake catalog and borehole data. The number of data records has increased from n=377 in the WSM 2016 to n=474 in this map. Some locations and assigned quality of WSM 2016 data were corrected due to new information. The digital version of the map is a layered pdf generated with GMT (Wessel et al., 2019) using the topography of Tozer et al. (2019). We also provide on a regular 0.1° grid values of the mean SHmax orientation which have a standard deviation < 25°. The mean SHmax orientation is estimated using the tool stress2grid of Ziegler and Heidbach (2019). For this estimation we used only data records with A-C quality and applied weights according to data quality and distance to the grid points. The stress map is available at the landing page of the GFZ Data Services at http://doi.org/10.5880/WSM.GreatBritainIreland2022 where further information is provided.

The stress map of Germany shows the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In the stress map the SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. Data with E-Quality are shown without additional information as dots on the map. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org.The German version of the World Stress Map Germany is available via http://doi.org/10.5880/WSM.Germany2016.

The Stress Map of the Mediterranean and Central Europe 2016 displays 5011 A-C quality stress data records of the upper 40 km of the Earth’s crust from the WSM database release 2016 (Heidbach et al, 2016, http://doi.org/10.5880/WSM.2016.001). Focal mechanism solutions determined as being potentially unreliable (labelled as Possible Plate Boundary Events in the database) are not displayed. Further detailed information on the WSM quality ranking scheme, guidelines for the various stress indicators, and software for stress map generation and the stress pattern analysis is available at www.world-stress-map.org.

The stress map of Iceland shows the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In the stress map the SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. Data with E-Quality are shown without additional information as dots on the map. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org.

Context

In CDP competition's starter notebook, one of the KPI mentioned is shadow water price. The research paper used World Resources Institutes' data on water stress to estimate the shadow price.

Content

This is a geo file, a world map showing water stress by regions.

Acknowledgements

https://www.wri.org/resources/charts-graphs/water-stress-country

Inspiration

Shortage of water is one of the big consequences of climate change. This data reveals at regional level where the risky areas are and how severe is the problem.

Present databases are results of the article Seismo-tectonics of Greater Iberia: An updated review.

Authors:Olaiz, A., Álvarez Gómez, J.A., De Vicente, G., Muñoz-Martín, A., Cantavella, J.V., Custódio, S., Vales, D. and Heidbach, O.

Current status: under review in Solid Earth

The file focal mechanism is a compilation of the moment tensor focal mechanisms in Iberia. It contains 472 events, including 9 new focal mechanisms.

The World Stress Map database is an updated version of the last database published in 2016. The focal mechanisms have been added and categorized according to WSM quality standards.

Agricultural Stress Index System (ASIS) is a global agricultural drought monitoring system developed and operated by FAO which enables to monitor agricultural areas affected by dry spells, or severe drought in extreme cases, using satellite data. It provides a collective quick-look indicators that facilitate the early identification of cropland/grassland with a high likelihood of water stress (drought). ASIS related products (maps, zonal statistics) are processed by FAO GIEWS (Global Information and Early Warning System on Food and Agriculture) every 10 days. Pre-processed, published-ready maps, zonal statistics of ASIS are published FAO GIEWS Earth Observation website at: https://www.fao.org/giews/earthobservation/index.jsp?lang=en. All ASIS raster datasets are accessible through the FAO Hand-in-Hand Geospatial Portal, Web Map Service (WMS) and Google Earth Engine (GEE). More information, please visit ASIS Data Access page: https://www.fao.org/giews/earthobservation/access.jsp?lang=en Agricultural Stress Index System is composed of two type of indicators: seasonal indicators such as Agricultural Stress Index (ASI) to detect the severe agricultural drought, Drought Intensity to classify the severity of the drought and no-seasonal indicators, such as vegetation indicators (NDVI anomaly, VCI and VHI). The seasonal indicators are designed to allow easy identification of areas of cropped land with a high likelihood of water stress (drought). The indices are based on remote sensing data of vegetation and land surface temperature combined with information on agricultural cropping cycles derived from historical data and a global crop mask. The final maps highlight anomalous vegetation growth and potential drought in crop zones during the growing season. In ASIS, two cropping cycles (major season /minor season) and crop/grassland zones are applied. Some countries have three or four crop seasons within a crop year. For these countries, Global ASIS cannot properly capture the agricultural drought occurred between the first and the last season (e.g. for a country has four crop seasons, the drought occurred during the 2nd and 3rd season). The satellite data used in the calculation of the mean VHI and the ASI is the 10-day (dekadal) vegetation data from the METOP-AVHRR sensor at 1 km resolution (2007 and after). Data at 1 km resolution for the period 1984-2006 was derived from the NOAA-AVHRR dataset at 16 km resolution. The crop/grass mask is FAO GLC-SHARE. Pixel with at least 5% covered by the class is defined as a cropland/grassland pixel. Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed] For more information, please visit GIEWS Earth Observation website.

This open access database compiles stress magnitude information from various sources. It currently includes 568 data records in the area of Germany and adjacent regions (latitude: 47 - 55.5 N; longitude: 5.8 - 15.1 E). The data records are ranked after a newly developed quality scheme for stress magnitude data. The data are provided in two formats: Excel-file (stressmagdata_germany_2020.xlsx), comma separated fields (stressmagdata_germany_2020.csv). Additional files include a) an overview over the compiled parameters including the abbreviation keys for stress magnitude indicators and stress regimes (List_of_parameters.pdf); b) the key for the referenced data sources (Key_for_ref_labels.pdf); and c) the applied quality ranking scheme (Quality_ranking_scheme.pdf).

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.

Global Water Scarcity Map

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

The World Stress Map (WSM) is the global compilation of information on the present-day stress field in the Earth's crust. The current WSM database release 2016 (Heidbach et al., 2016) has 42,870 data records, but the data are unevenly distributed and clustered.To analyse the wave-length of the crustal stress pattern of the orientation of maximum horizontal stress Shmax, we use so-called smoothed stress maps that show the mean SHmax orientation on regular grids. The mean SHmax orientation is estimated with the Matlab® script stress2grid (Ziegler and Heidbach, 2017) which is based on the statistics of bi-polar data. The script provides two different approaches to calculate the mean SHmax orientation on regular grids.The first is using a constant search radius around the grid point and computes the mean SHmax orientation if sufficient data records are within the given fixed search radius. This can result in mean SHmax orientations with a high standard deviation of the individual mean SHmax orientation and it may hide local perturbations. Thus, the mean SHmax orientation is not necessarily reliable for a local stress field analysis.The second approach is using variable search radii and determines the search radius for which the standard deviation of the mean SHmax orientation is below a user-defined threshold. This approach delivers the mean SHmax orientations with a user-defined degree of reliability. It resolves local stress perturbations and is not available in areas with no data or conflicting information that result in a large standard deviation.The search radius starts with 1000 km and is decreased in 100 km steps down to 100 km. Mean SHmax orientation is taken and plotted here for the largest search radius when the standard deviation of the mean SHmax orientation at the individual grid points is smaller than 25°. For the estimation of the mean Shmax we selected the following data: A-C quality data without PBE flag.Furthermore, only data records located on the same tectonic plate as the grid point is used to calculate the mean SHmax orientation. Minimum number of data records within the search radius is n = 5 and data records within a distance of d ≤ 200 km to the nearest plate boundary are not used. Plate boundaries are taken from the global model PB2002 from Bird (2003).Furthermore, a distance and data quality weight is applied; the distance threshold is set to 10% of the search radius. We provide the resulting smoothed stress data for four global grids (0.2°, 0.5°, 1°, and 2° grid spacing) using two fixed search radii (250 and 500 km) and the approach with variable search radii. Details on the format of the data files with the mean SHmax orientation are provided in the 2018-002_readme file.

Explore a full description of the map.The daily updating dataset was collected by the National Oceanic and Atmospheric Administration’s (NOAA) Coral Reef Watch program. NOAA uses satellites to monitor coral reefs globally and model them to monitor and predict large coral bleaching events. This dataset shows a region's alert level. The alert level is an index of the likelihood of coral bleaching, with a scale of zero (no heat stress) to four (coral mortality likely) based on the following:Sea surface temperature—The average temperature of the surface of the ocean measured by satellites.Hotspots—How many degrees the temperature is above what the coral can tolerate safely.Temperature anomaly—How much the temperature of the ocean surface differs from the historical average measured from 1982-2010.Degree heating weeks—The amount of time the corals have experienced heat stress.CreditsNOAA, Esri, Map from National Geographic MapMaker.Terms of Use This work is licensed under the Esri Master License Agreement.View Summary | View Terms of Use

The Agricultural Stress Index (ASI) is a quick-look indicator that facilitates the early identification of cropped land with a high likelihood of water stress (drought). ASI related products (maps, spatial aggregation based on administrative unit) are processed by FAO GIEWS (Global Information and Early Warning System on Food and Agriculture) every 10 days. ASI Annual Summary is processed at the end of crop season. It depicts the percentage of arable land, within an administrative area, that has been affected by drought conditions over the entire cropping season. It differs from ASI dekadal product, which is based on conditions from the start of the season up to the current dekad. The Index is based on the integration of the Vegetation Health Index (VHI) in two dimensions that are critical in the assessment of a drought event in agriculture: temporal and spatial. The first step of the ASI calculation is a temporal averaging of the VHI, assessing the intensity and duration of dry periods occurring during the crop cycle at the pixel level; this calculation includes the use of crop coefficients, which introduces sensitivity of a crop to water stress during each phenological phase. The second step determines the spatial extent of drought events by calculating the percentage of pixels in arable areas with a VHI value below 35 percent (this value was identified as a critical threshold in assessing the extent of drought in previous research by Kogan, 1995). Each administrative area is classified according to the percentage of the affected area to facilitate the quick interpretation of results. More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]

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.”

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

This dataset is produced for the STRASA project in which breeders are developing varieties tolerant to drought. The intended use is that tolerant varieties can be sent to places with high risk of drought.

Water is essential to the progress of human societies. It is required for a healthy environment and a thriving economy. Food production, electricity generation, and manufacturing, among other things, all depend on it. However, many decision-makers lack the technical expertise to fully understand hydrological information.

In response to growing concerns from the private sector and other actors about water availability, water quality, climate change, and increasing demand, WRI applied the composite index approach as a robust communication tool to translate hydrological data into intuitive indicators of water-related risks.

This dataset updates the Aqueduct™ water risk framework, in which we combine 13 water risk indicators—including quantity, quality, and reputational risks—into a composite overall water risk score.

This database and the Aqueduct tools enable comparison of water-related risks across large geographies to identify regions or assets deserving of closer attention. Aqueduct 3.0 introduces an updated water risk framework and new and improved indicators. It also features different hydrological sub-basins. We introduce indicators based on a new hydrological model that now features (1) integrated water supply and demand, (2) surface water and groundwater modelling, (3) higher spatial resolution, and (4) a monthly time series that enables the provision of monthly scores for selected indicators.

Key elements of Aqueduct, such as overall water risk, cannot be directly measured and therefore are not validated. Aqueduct remains primarily a prioritization tool and should be augmented by local and regional deep dives.

User Guide Includes column descriptors and other metadata regarding the dataset https://github.com/wri/aqueduct30_data_download/blob/master/metadata.md

Source https://www.wri.org/resources/data-sets/aqueduct-global-maps-30-data

About Aqueduct Aqueduct’s tools map water risks such as floods, droughts, and stress, using open-source, peer-reviewed data. Beyond the tools, the Aqueduct team works one-on-one with companies, governments, and research partners to help advance best practices in water resources management and enable sustainable growth in a water-constrained world.

Over the past six years, the Aqueduct tools have reached hundreds of thousands of users across the globe and informed decision-makers in and beyond the water sector. Aqueduct data and insights have been featured in major media outlets including, the Economist, the Guardian, Bloomberg Businessweek, the New York Times and Vox’s Netflix show Explained.

This iteration of Aqueduct represents our most robust look at water risks to date, including more granular data, higher resolution, new indicators, improved tool function and access to underlying hydrological models.

Repository for USArray seismic data, geodetic data, and SAFOD data. Complimentary data includes: PBO H2O science products, global strain rate map, World Stress Map, Crustal Models, Lithoprobe, Physical Properties, COCORP, Heat Flow, Volcanic and Intrusive Rock Data, and the Database of the Geologic Map of North America.