An outline map showing the coastline, boundaries and major lakes and rivers for Canada and nearby countries. There are names for major political and geographical features.

World Water Bodies provides a detailed basemap layer for the lakes, seas, oceans, large rivers, and dry salt flats of the world.

World Water Bodies represents the open water rivers, lakes, dry salt flats, seas, and oceans of the world.For complete hydrographic coverage, use this dataset in conjunction with the World Linear Water dataset.

There are two types of boundary files: cartographic and digital. Cartographic boundary files portray the geographic areas using only the major land mass of Canada and its coastal islands. Digital boundary files portray the full extent of the geographic areas, including the coastal water area.

Water supply lakes are the primary source of water for many communities in northern and western Missouri. Therefore, accurate and up-to-date estimates of lake capacity are important for managing and predicting adequate water supply. Many of the water supply lakes in Missouri were previously surveyed by the U.S. Geological Survey in the early 2000s (Richards, 2013) and in 2013 (Huizinga, 2014); however, years of potential sedimentation may have resulted in reduced water storage capacity. Periodic bathymetric surveys are useful to update the area/capacity table and to determine changes in the bathymetric surface. In June and July 2020, the U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources and in collaboration with various cities in north- and west-central Missouri, completed bathymetric surveys of 12 lakes using a marine-based mobile mapping unit, which consists of a multibeam echosounder (MBES) and an inertial navigation system (INS) mounted on a marine survey vessel. Bathymetric data were collected as the vessel traversed longitudinal transects to provide nearly complete coverage of the lake. The MBES was electronically tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than about 2.0 meters deep (the practical limit of reasonable and safe data collection with the MBES). At some lakes, supplemental data were collected in shallow areas using an acoustic Doppler current profiler (ADCP) mounted on a remote-controlled vessel equipped with a differential global positioning system (DGPS). Bathymetric quality-assurance data also were collected at each lake to evaluate the vertical accuracy of the gridded bathymetric point data from the MBES. As part of the survey at each of these lakes, one or more reference marks or temporary bench marks were established to provide a point of known location and elevation from which the water surface could be measured or another survey could be referenced at a later date. In addition, the elevation of a primary spillway or intake was surveyed, when present. These points were surveyed using a real-time kinematic (RTK) Global Navigation Satellite System (GNSS) receiver connected to the Missouri Department of Transportation real-time network (RTN), which provided real-time survey-grade horizontal and vertical positioning, using field procedures as described in Rydlund and Densmore (2012) for a Level II real-time positioning survey. Mozingo Lake and Maryville Reservoir were surveyed in June 2020 as part of the group of lakes surveyed in 2020. However, extraordinary interest in the bathymetry at Mozingo Lake by the city of Maryville necessitated these data being released earlier than the other 2020 lakes (Huizinga and others, 2021, 2022). The MBES data can be combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table for each lake. These data also can be used to compare the current bathymetric surface with any previous bathymetric surface. Data from each of the remaining 10 lakes surveyed in 2020 are provided in ESRI Shapefile format (ESRI, 2021). Each of the lakes surveyed in 2020 except Higginsville has a child page containing the metadata and two zip files, one for the bathymetric data, and the other for the bathymetric quality-assurance data. Data from the surveys at the Upper and Lower Higginsville Reservoirs are in two zip files on a single child page, one for the bathymetric data and one for the bathymetric quality assurance data of both lakes, and a single summary metadata file. The zip files follow the format of "####2020_bathy_pts.zip" or "####2020_QA_raw.zip," where "####" is the lake name. Each of these zip files contains a shapefile with an attribute table. Attribute/column labels of each table are described in the "Entity and attribute" section of the metadata file. The various reference marks and additional points from all the lake surveys are provided in ESRI Shapefile format (ESRI, 2021) with an attribute table on the main landing page. Attribute/column labels of this table are described in the "Entity and attribute" section of the metadata file. References Cited: Environmental Systems Research Institute, 2021, ArcGIS: accessed May 20, 2021, at https://www.esri.com/en-us/arcgis/about-arcgis/overview Huizinga, R.J., 2014, Bathymetric surveys and area/capacity tables of water-supply reservoirs for the city of Cameron, Missouri, July 2013: U.S. Geological Survey Open-File Report 2014–1005, 15 p., https://doi.org/10.3133/ofr20141005. Huizinga, R.J., Oyler, L.D., and Rivers, B.C., 2022, Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in northwestern Missouri, 2019 and 2020: U.S. Geological Survey Scientific Investigations Map 3486, 12 sheets, includes 21-p. pamphlet, https://doi.org/10.3133/sim3486. Huizinga R.J., Rivers, B.C., and Oyler, L.D., 2021, Bathymetric and supporting data for various water supply lakes in northwestern Missouri, 2019 and 2020 (ver. 1.1, September 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P92M53NJ. Richards, J.M., 2013, Bathymetric surveys of selected lakes in Missouri—2000–2008: U.S. Geological Survey Open-File Report 2013–1101, 9 p. with appendix, https://pubs.usgs.gov/of/2013/1101. Rydlund, P.H., Jr., and Densmore, B.K., 2012, Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D1, 102 p. with appendixes, https://doi.org/10.3133/tm11D1.

Percent of each freshwater ecoregion’s area covered with lakes and man-made reservoirs.

We calculated the percentage of the ecoregion that is covered by lakes and reservoirs using lake and reservoir polygons from the Global Lakes and Wetlands Database (GLWD) (Lehner and Döll 2004). This database represents the best available source for lakes and wetlands on a global scale (1:1 to 1:3 million resolution). The GLWD contains shoreline polygons of the 3,067 largest lakes (surface area greater than or equal to 50 km2) and 654 largest reservoirs (storage capacity greater than or equal to 0.5 km3) worldwide, as well as shoreline polygons of approximately 250,000 smaller lakes, reservoirs, and rivers (surface area greater than or equal to 0.1 km2). For our calculations, only lake and reservoir polygons were used. It was not possible to separate natural lake polygons from reservoirs.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Lehner, B., and P. Döll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296: 1–22.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

Faecal Enterococci results and compliance

The National Hydrography Dataset (NHD) is a comprehensive set of digital spatial data that contains information about surface water features such as lakes, ponds, streams, rivers, springs and wells. Within the NHD, surface water features are combined to form reaches, which provide the framework for linking water-related data to the NHD surface waterdrainage network. These linkages enable the analysis and display of these water-related data in upstream and downstream order.

The NHD is based upon the content of USGS Digital Line Graph (DLG) hydrography data integrated with reach-related information from the EPA Reach File Version 3 (RF3). The NHD supersedes DLG and RF3 by incorporating them, not by replacing them. Users of DLG or RF3 will find the National Hydrography Dataset both familiar and greatly expanded and refined.

While initially based on 1:100,000-scale data, the NHD is designed to incorporate and encourage the development of higher resolution data required by many users.

The NHD data are distributed as tarred and compressed ARC/INFO workspaces. Each workspace contains the data for a single hydrologic cataloging unit. Cataloging units are drainage basins averaging 700 square miles (1,813 square kilometers) in area. Within a workspace, there are three ARC/INFO coverages plus several related INFO tables. There is also a folder containing the metadata text files.

The NHD data support many applications, such as: making maps; geocoding observations (i.e., the means to link data to water features); modeling the flow of water along the Nation's waterways (e.g., information about the direction of flow, when combined with other data, can help users model the transport of materials in hydrographic networks, and other applications); and cooperative data maintenance.

This data displays the locations of top rivers, streams, lakes and ponds for fishing in New York State, as determined by fisheries biologists working for the New York State Department of Environmental Conservation. These biologists recommended popular rivers, streams, lakes and ponds based on quality of fishing and public access. Data set was created based on their recommendations, and each point was placed at the center of the recommended lakes. Although every effort has been made to ensure the accuracy of information, errors may be reflected in the data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, original map scale, collection methodology, currency of data, and other conditions. To drill down to a smaller geographic area, click directly on the area of the map or click the plus sign to zoom in on the map. For more information check out http://www.dec.ny.gov/outdoor/7749.html, or go to the "About" section.

Generalized lakes and rivers in the Madison Area.

JALBTCX National Coastal Mapping Program Derived ProductsThe layers depicted in this web map were developed to serve regional geospatial data needs of USACE Districts and agency partners to discover and download products derived from USACE National Coastal Mapping Program (NCMP) high resolution, topo-bathymetric lidar and imagery. The USACE NCMP acquires high-resolution, high-accuracy topographic/bathymetric lidar elevation and imagery on a recurring basis along the sandy shorelines of the US. The program's survey footprint includes an approximately 1-mile wide swath of topography, bathymetry and imagery 500-m onshore and 1000-m offshore. The standard suite of NCMP data products include topographic/bathymetric lidar point clouds, digital surface and elevation models, shoreline vectors and both true-color and hyperspectral imagery mosaics. Value-added derivative information products may include laser reflectance images, landcover classification images, volume change metrics, and the products to help address District project requirements. USACE Headquarters initiated the NCMP in 2004. The program's update cycle follows counter-clockwise along the US West Coast, Gulf Coast, East Coast and Great Lakes approximately every 5 years. Surveys in support of USACE project-specific missions and external partners are included constituent to the current NCMP schedule and reimbursable funding. All work is coordinated with Federal mapping partners through the Interagency Working Group on Ocean and Coastal Mapping (IWGOCM) and the 3D Elevation Program (3DEP).NCMP operations are executed by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX). The JALBTCX mission is to perform operations, research and development in airborne lidar bathymetry and complementary technologies to support the coastal mapping and charting requirements of the US Army Corps of Engineers, the US Naval Meteorology and Oceanography Command and the National Oceanic and Atmospheric Administration. Survey operations are conducted worldwide using the Coastal Zone Mapping and Imaging (CZMIL) system and other industry-based coastal mapping and charting systems. CZMIL is JALBTCX's in-house survey capability that includes and Optech International, CZMIL 03-1 lidar instrument with simultaneous topographic and bathymetric capabilities. CZMIL is integrated with an Itres CASI-1500 hyperspectral imager and an 80 MP Leica RCD30 RGBN camera. CZMIL collects 10-kHz lidar data with spatially- and temporally-concurrent digital true-color and hyperspectral imagery.The Wetlands Classification Image Service layer can be accessed directly here: https://arcgis.usacegis.com/arcgis/rest/services/ERDC/ERDC_Wetlands_Classification/ImageServer

Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser. Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

This dataset comprises river centrelines, digitised from OS 1:50,000 mapping. It consists of four components: rivers; canals; surface pipes (man-made channels for transporting water such as aqueducts and leats); and miscellaneous channels (including estuary and lake centre-lines and some underground channels). This dataset is a representation of the river network in Great Britain as a set of line segments, i.e. it does not comprise a geometric network.

This water flow network dataset is a route feature class rather than a simple polyline. The geometry is generated by merging the river lines of individual geometric network datasets. This layer contains an integrated flow network that includes known flow connections through rivers, lakes and groundwater aquifers. In places where the network is depicted flowing through lakes or through underground channels, the flow channels are schematic only, and do not represent the precise location of these flow channels. The appropriate Geological Survey Ireland data sets should be consulted where underground flows or connections are known or suspected.This dataset is provided by the Environmental Protection Agency (EPA). For more information please see https://gis.epa.ie/geonetwork/srv/eng/catalog.search#/metadata/c4043e19-38ec-4120-a588-8cd01ac94a9c

1/31/08 Changes and Updates - The lakes that are split by road feature for example, but have the same name, feature type, and hydrologic flow type have been merged into one element - All of the area water features have been labeled using the USGS 24k topo maps, so even though this is a 100k hydro layer, all of the named water bodies should be included - Field names have been changed so that they can be dumped out to shapefiles without truncation. I've also added alias' so there is a meaningful name associated with each field. - All of the standard fields in the hydro layers have changed to Flow_Type (alias Flow Type), Feat_Type (alias Feature Type), Feat_Mod (alias Feature Modifier), Name (alias Primary Name), Name_2nd (alias Secondary Name). - Draw and Label fields have been added. Not all of the features in the line features are hydro features. There are river centerlines for example in the linear hydro data set that allow river areal features to be labeled along their length. The centerlines in that dataset are simply there to use for labeling rivers that are displayed as polygons. This allows the user to label a river along it's length and not horizontally like a lake would be labeled. So in this case, the Draw field would allow a river polygon to be drawn, but the Label field could be used to suppress horizontal labels from being put on the those polygons. The label field might be useful for only labeling lakes and marshes and not linear polygon features like rivers. If you were to dump this to a GDB or shapefile to customize a map, these fields could be easily modified by the user to not label smaller features for example. This isn't the best database design, but this makes it easy to label something without putting in several complicated boolean statements With the label field you could simply put Label = 'Yes' and knowing that you'd already set the rivers to 'No', you'd be done. North Dakota hydrology, polygon features, showing major rivers, ponds, and lakes

Connecticut 303(d) Impaired Waters 2020 is a subset of Connecticut 305(b) Assessed Waters 2020. If any one of the assigned designated uses is categorized as NOT Supporting, the assessed waterbody is included in this subset and is considered impaired. Additional information about Integrated Water Quality reporting is available on the CT DEEP website, Integrated Water Quality Report page.

Connecticut 305(b) Assessed Waters files includes rivers, lakes and estuaries that have been assessed in compliance with Sections 305(b) and 303(d) of the federal Clean Water Act. Section 305(b) of the Clean Water Act requires each state to monitor, assess and report on the quality of its waters relative to attainment of designated uses established by the State's water quality standards. Section 303(d) requires each State to compile a subset of that list identifying only those waters not meeting water quality standards and assign a Total Maximum Daily Load (TMDL) priority ranking to each impaired waterbody.

This assessment is based on information collected and compiled prior to 2020. It represents conditions at a particular point in time and does not represent current conditions. Depending on the type of waterbody - river, lake, or estuary - this information geographically displays attainment and non-attainment (e.g. full supporting, not supporting, not assessed) for each designated use - aquatic life, marine aquatic life, recreation use, fish consumption, shellfish harvesting, and drinking water supply.

The main waters in ancient Mesopotamia (shape files). Including the main rivers Euphrates and Tigris and their main tributaries, the wadis of the Khabur Valley and the main lakes in the area. The attribute table includes their english name, also the german names, their rank of importants to the main rivers and an ID number based on their alphabetical sequence.

Water files are provided for the mapping of inland and coastal waters, Great Lakes and the St. Lawrence River. These files were created to be used in conjunction with the boundary files.

Geologic map of the Kanayut River area, Chandler Lake Quadrangle, Alaska, Preliminary Interpretive Report 2009-7, presents 1:63,360-scale geologic mapping of the Kanayut River area in the Chandler Lake Quadrangle, North Slope, Alaska. The map area is located in the foothills of the Endicott Mountains. It is one of a series of areas selected for detailed mapping to document important structural and stratigraphic relationships along the east-central Brooks Range mountain front. The map delineates key stratigraphic and structural elements important to understanding Alaska's North Slope's oil and gas potential. The complete report, geodatabase, and ESRI fonts and style files are available from the DGGS website: http://doi.org/10.14509/19781.

The map shows the current Swissriver discharges and lake water levels compared with the monthly percentiles of the long term hourly averages (expressed in percentiles). The percentiles are calculated every month based on the "long term series" of hourly average measurements. The hourly averages from the start of measurements to date are compiled separately for each month, The percentile defines what proportion of the data is higher or lower than a specific limit. A flow at the 95% percentile means that 95% of the flows measured in the month in question are below that threshold (only 5% are above it).

Thermal use of lakes and rivers. Map types: Symbols, Choropleths. Spatial extent: Switzerland. Times: 2019, 2024. Distinction: Lakes: potential for heating, Lakes: potential for cooling, Plants