This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2020 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its long-term changes (Nature, 2016) and the online Data Users Guide.

These data were generated using 4,453,989 scenes from Landsat 5, 7, and 8 acquired between 16 March 1984 and 31 December 2020. Each pixel was individually classified into water / non-water using an expert system and the results were collated into a monthly history for the entire time period and two epochs (1984-1999, 2000-2020) for change detection.

This mapping layers product consists of 1 image containing 7 bands. It maps different facets of the spatial and temporal distribution of surface water over the last 35 years. Areas where water has never been detected are masked.

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 250m water mask product utilizes the SWBD (SRTM Water Body Data) and complement it with information from 250m MODIS data to create a complete representation of global surface water. The original intent of this product is not for hydrologic modeling, rather for masking water in products.

Advances in data availability, Earth observation technologies, and geospatial sciences have transformed our ability to map Global Surface Water Extents (GSWE). However, traditional GSWE mapping has been limited to static estimates, with more recent efforts focusing on annual averages and temporal attributes like frequency and occurrence of long-term variations. We harnessed remotely sensed Sentinel-2 based near real-time Dynamic World land cover product to produce the first public, routinely available 10-meter resolution global surface water datasets. Our key contribution is an Open Science operational framework to rapidly extract the latest available Dynamic World products every 2-5 days, run geospatial analytics, and create actionable water information for educators, researchers, and stakeholders at any scale of practical interest.

This dataset has been developed by the Hydrology & Hydroinformatics Innovation Lab at the University of Texas at Arlington, United States.

The European Commission's Joint Research Centre developed this new water dataset in the framework of the Copernicus Programme. This maps the location and temporal distribution of water surfaces at the global scale over the past 32 years and provides statistics on the extent and change of those water surfaces. The dataset, produced from Landsat imagery (courtesy USGS and NASA), will support applications including water resource management, climate modelling, biodiversity conservation and food security.

Water bodies are a key element in the landscape. This layer provides a global map of large water bodies for use in landscape-scale analysis.Dataset SummaryThis layer provides access to a 250m cell-sized raster of surface water created by extracting pixels coded as water in the Global Lithological Map and the Global Landcover Map. The layer was created by Esri in 2014.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

land and atmosphere. The European Space Agency is now studying concepts for the Next Generation Sentinel-3 Topography mission (S3NGT) mission that would launch in the 2032+ time period. In order to meet the primary objectives of the S3NGT mission requirement document a complex analysis of river and lake targets is required to size the satellite mass memory and downlink system.

The Global Land Surface Water Dataset in 30m Resolution in 2010 (GlobeLand30-WTR2010 for short) was developed based on data mining methodology by integrating and analyzing the 9907 scenes of the USA Landsat TM5, ETM+ data and 2640 scenes of the China environment disaster mitigation satellite (HJ-1) data in 2010（±1）. The total area of the land surface water is 3,675,400 km2, which is 2.73% of the global land surface area. More than 40% of land surface water is located in North America. The global data were organized into 853 tiles, according to the 5° (latitude) x 6° (longitude) within the region from 60°S to 60 N, and 5° (latitude) x 12° (longitude) within the region from 60° N to 80°N (the Antarctic continent is not included). The data tiles are combined into 5 compressed data groups (Asia, Europe, North America, South America, and Africa, and Oceanic Countries), Four different data files are comprised in each of these data groups. They are: (1) land surface water data (raster data with GeoTIFF format); (2) coordinate information data (TIFF WORD format); (3) areas of selected remote sensing data (.shp format); and (4) a metadata file (XML format). In addition, the 853 data file list, including the file names, corresponding geographic coordinates and zoning codes, are listed at the file. The dataset is one of the layers of the Global Land Cover Dataset in 30m Resolution in 2010 (GlobeLand30_2010), which were donated to the United Nations by China in September 2014.

Data citation: CHEN Jun et al. : Global Land Surface Water Dataset in 30m Resolution (2010) ( GlobeLand30-WTR2010 ) ,Global Change Research Data Publishing & Repository,DOI:10.3974/geodb.2014.02.01.V1, http://www.geodoi.ac.cn/WebEn/doi.aspx?DOI=10.3974/geodb.2014.02.01.V1

Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=159

Water bodies are a key element in the landscape. This layer provides a global map of large water bodies for use inlandscape-scale analysis. Dataset Summary This layer provides access to a 250m cell-sized raster of surface water created by extracting pixels coded as water in theGlobal Lithological Mapand theGlobal Landcover Map. The layer was created by Esri in 2014.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometerson a side or an area approximately the size of Europe. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started see theLiving Atlas Discussion Group. TheEsri Insider Blogprovides an introduction to the Ecophysiographic Mapping project.

Dieser Datensatz enthält Karten der Standort- und zeitlichen Verteilung von Oberflächenwasser von 1984 bis 2021 sowie Statistiken zur Ausdehnung und Veränderung dieser Wasserflächen. Weitere Informationen finden Sie im zugehörigen Fachartikel: High-resolution mapping of global surface water and its long-term changes (Nature, 2016) und im Online-Nutzerhandbuch für Daten. Diese Daten wurden mit 4.716.475 Szenen von Landsat 5, 7 und 8 generiert, die zwischen dem 16. März 1984 und dem 31. Dezember 2021 aufgenommen wurden. Jedes Pixel wurde mithilfe eines Expertensystems einzeln in „Wasser“ und „Nicht-Wasser“ klassifiziert. Die Ergebnisse wurden in einem monatlichen Verlauf für den gesamten Zeitraum und zwei Epochen (1984–1999, 2000–2021) für die Erkennung von Veränderungen zusammengestellt. Dieses Produkt mit Kartierungsebenen besteht aus einem Bild mit 7 Bändern. Sie zeigt verschiedene Aspekte der räumlichen und zeitlichen Verteilung von Oberflächenwasser in den letzten 38 Jahren. Bereiche, in denen noch nie Wasser erkannt wurde, sind ausgeblendet.

Globally gridded dataset of electrical conductivity in surface water for the years 1992-2010, monthly observations. Data is available at the 0.5x0.5 degree gridcell level. Units are microsiemens/centimeter (uS/cm). Electrical conductivity is a common indicator for salinity in water. Data is generated using a machine learning model, as described in the report Quality Unknown: The Invisible Water Crisis (https://www.worldbank.org/en/news/feature/2019/08/20/quality-unknown). See report Appendix for more details.

Este conjunto de datos contiene mapas de la ubicación y la distribución temporal del agua superficial de 1984 a 2021 y proporciona estadísticas sobre el alcance y el cambio de esas superficies de agua. Para obtener más información, consulta el artículo de la revista asociado: High-resolution mapping of global surface water and its long-term changes (Nature, 2016) y …

Globally gridded dataset of biological oxygen demand (BOD) in surface water for the years 1992-2010, monthly observations. Data is available at the 0.5x0.5 degree gridcell level. Units are milligram per liter (mg/l). Data is generated using a machine learning model, as described in the report Quality Unknown: The Invisible Water Crisis (https://www.worldbank.org/en/news/feature/2019/08/20/quality-unknown). See report Appendix for more details.

Surface water change has been documented across the Arctic due to thawing permafrost and changes in the precipitation/evapotranspiration balance. This dataset uses Moderate Resolution Imaging Spectroradiometer (MODIS) data to track changes in surface water across the region over the past two decades. The superfine water index (SWI) is a unitless global water cover index developed specifically for MODIS data and validated in high northern latitudes. Variation in SWI can also track changes in surface water that occur at the sub-MODIS pixel scale (i.e., changes in water bodies smaller a MODIS pixel, ~500 meters (m)). This dataset (1) maps the average July SWI over pan-Arctic for each year of the MODIS record (2000-2021) and (2) maps the trends in July SWI over 2000-2012 (i.e., Sen's slope of the pixel-wise SWI vs. time). The spatial resolution of this dataset is ~500 m. The yearly SWI files are processed for the entire continuous and discontinuous permafrost zone. The trend file is processed for lake-rich regions of the Arctic (i.e., lake coverage greater than 5%), as was published in the Webb et al, 2022 paper.

Globally gridded dataset of nitrate-nitrite in surface water for the years 1992-2010, monthly observations. Data is available at the 0.5x0.5 degree gridcell level. Units are milligram per liter (mg/l). Data is generated using a machine learning model, as described in the report Quality Unknown: The Invisible Water Crisis (https://www.worldbank.org/en/news/feature/2019/08/20/quality-unknown). See report Appendix for more details.

Several layers describing density of surface water / streams projected to the Good Homolosine projection. List of layers included: hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the MERIT Hydro, hyd_river.density_gloric_p = rasterized Global River Classification (GLORIC) DB, lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's Global Surface Water, lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the Copernicus LC100 map, lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on Tootchi et al. (2019), Goode_Homolosine_domain_250m.tif = map domain prepared by Luís de Sousa, tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass, Important notes: Processing steps are described in detail here. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated. These maps were develop in connection to the OpenLandMap.org initiative. If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: hyd = theme: hydrology and water dynamics, log1p.upstream.area = variable: log(X+1)*10 of the upstream area, merit.hydro = determination method: MERIT Hydro, m = mean value, 250m = spatial resolution / block support: 250 m, b0..0cm = vertical reference: surface, 2017 = time reference: period 2017, v0.1 = version number: 0.1, {"references": ["Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418.", "Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. (2018): A multidisciplinary framework to derive global river reach classifications at high spatial resolution. Environmental Research Letters. doi: 10.1088/1748-9326/aad8e9", "Tootchi, A., Jost, A., and Ducharne, A. (2019): Multi-source global wetland maps combining surface water imagery and groundwater constraints, Earth Syst. Sci. Data, 11, 189-220, https://doi.org/10.5194/essd-11-189-2019", "Underwood, E. (2019): A more accurate global river map, Eos, 100, https://doi.org/10.1029/2019EO128033", "Yamazaki D., D. Ikeshima, J. Sosa, P.D. Bates, G.H. Allen, T.M. Pavelsky, (2019): MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets Water Resources Research, ACCEPTED, https://doi.org/10.1029/2019WR024873"]}

Brazil has the largest renewable surface water resources worldwide, at an estimated 8,647 billion cubic meters. This is roughly twice the amount of renewable surface water available in Russia, which has the second-largest figure. In contrast, many countries worldwide lack renewable surface water resources, such as Kuwait, which has the highest renewable water resources dependency ratio worldwide. Surface water is any body of water above ground, such as streams, rivers, and lakes.

Belgium Fresh Surface Water: Total Gross Abstraction data was reported at 4,221.344 Cub m mn in 2021. This records an increase from the previous number of 3,429.904 Cub m mn for 2020. Belgium Fresh Surface Water: Total Gross Abstraction data is updated yearly, averaging 5,634.532 Cub m mn from Dec 1994 (Median) to 2021, with 28 observations. The data reached an all-time high of 7,532.000 Cub m mn in 1995 and a record low of 3,429.904 Cub m mn in 2020. Belgium Fresh Surface Water: Total Gross Abstraction data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Belgium – Table BE.OECD.ESG: Environmental: Freshwater Abstractions: by Sources: OECD Member: Annual.

The Water Occurrence dataset shows where surface water occurred between 1984 and 2018 and provides information concerning overall water dynamics. This product captures both the intra and inter-annual variability and changes. The occurrence is a measurement of the water presence frequency (expressed as a percentage of the available observations over time actually identified as water). The provided occurrence accommodates for variations in data acquisition over time (i.e. temporal deepness and frequency density of the satellite observations) in order to provide a consistent characterization of the water dynamic over time.

Data associated with manuscript: Historical Trends in Snowmelt Used for Irrigation by Kinnebrew et al. 2025, Environmental Research: Food Systems (https://doi.org/10.1088/2976-601X/adacec).These data represent surface water runoff (supply) and consumption (demand) from 1985 to 2020, summarized within watershed basins (HydroBASINS level 3; https://www.hydrosheds.org/products/hydrobasins). Surface water runoff data were derived from TerraClimate (https://doi.org/10.5061/dryad.vx0k6dk2h), irrigation surface water consumption were derived from the Global Crop Water Model (https://doi.org/10.5281/zenodo.14278069), and domestic & industrial consumption were from Hoekstra et al. (2012). Please see the manuscript methods for additional information.Column names and descriptions:1. watershedNum: the HUC12 identifier number from HydroBASINS level 32. year: data year, from 1985 to 20203. month: data month, e.g., 1 (January), 2 (February), 3 (March), etc.4. CropWaterDemand_km3: surface water irrigation consumption (in km3)5. DomIndWaterDemand_km3: surface water consumption (in km3) from domestic and industrial sources6. TotalDemand_km3: combined water consumption (in km3) from irrigation, domestic, and industrial sources7. SnowRO_km3: snowmelt runoff (in km3)8. RainRO_km3: rainfall runoff (in km3)9. TotalRO_km3: combined snowmelt and rainfall runoff (in km3)10. SnowConsumed_TotDemand_km3: snowmelt runoff (in km3) that supplied total (irrigation, industrial, and domestic) water consumption11. RainConsumed_TotDemand_km3: rainfall runoff (in km3) that supplied total (irrigation, industrial, and domestic) water consumption12. AltConsumed_TotDemand_km3: alternative water sources (in km3) that supplied total (irrigation, industrial, and domestic) water consumption13. SnowConsumed_CropDemand_km3: snowmelt runoff (in km3) that supplied irrigation water consumption14. RainConsumed_CropDemand_km3: rainfall runoff (in km3) that supplied irrigation water consumption15. AltConsumed_CropDemand_km3: alternative water sources (in km3) that supplied irrigation water consumption