The National Waterway Network is a comprehensive network database of the nation's navigable waterways. The data set covers the 48 contiguous states plus the District of Columbia, Hawaii, Alaska, Puerto Rico and water links between. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters.

© The National Waterway Network was created on behalf of the Office of the Assistant Secretary for Research and Technology/Bureau of Transportation Statistics, the U.S. Army Corps of Engineers, the U.S. Bureau of Census, and the U.S. Coast Guard by Vanderbilt University and Oak Ridge National Laboratory. Additional agencies with input into network development include Volpe National Transportation Systems Center, Maritime Administration, Military Traffic Management Command, Tennessee Valley Authority, U.S. Environmental Protection Agency, and the Federal Railroad Administration. This layer is sourced from maps.bts.dot.gov.

The National Waterway Network (NTAD 2015) is a comprehensive network database of the nation's navigable waterways. The data set covers the 48 contiguous states plus the District of Columbia, Hawaii, Alaska, Puerto Rico and water links between. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters.

© The National Waterway Network was created on behalf of the Bureau of Transportation Statistics, the U.S. Army Corps of Engineers, the U.S. Bureau of Census, and the U.S. Coast Guard by Vanderbilt University and Oak Ridge National Laboratory. Additional agencies with input into network development include Volpe National Transportation Systems Center, Maritime Administration, Military Traffic Management Command, Tennessee Valley Authority, U.S.Environmental Protection Agency, and the Federal Railroad Administration.

This North American Environmental Atlas data are standardized geospatial data sets at 1:10,000,000 scale. A variety of basic data layers (e.g. roads, railroads, populated places, political boundaries, hydrography, bathymetry, sea ice and glaciers) have been integrated so that their relative positions are correct. This collection of data sets forms a base with which other North American thematic data may be integrated. Any data outside of Canada, Mexico, and the United States of America included in the North American Environmental Atlas data sets is strictly to complete the context of the data.The North American Environmental Atlas – Lakes and Rivers dataset displays the coastline, linear hydrographic features (major rivers, streams, and canals), and area hydrographic features (major lakes and reservoirs) of North America at a reference spatial scale of 1:1,000,000.This map offers a seamless integration of hydrographic features derived from cartographic products generated by Natural Resources Canada (NRCan), United States Geological Survey (USGS), National Institute of Statistics and Geography, (Instituto Nacional de Estadística y Geografía-Inegi), National Water Commission (Comisión Nacional del Agua-Conagua).This current version of the North America Lakes and Rivers dataset supersedes the version published by the Commission for Environmental Cooperation in 2011.Files Download

The Navigation Data Center had several objectives in developing the U.S. Waterway Data. These objectives support the concept of a National Spatial Data Provide public access to national waterway data. Foster interagency and intra-agency cooperation through data sharing. Provide a mechanism to integrate waterway data (U.S. Army Corps of Engineers Port/Facility and U.S. Coast Guard Accident Data, for example) Provide a basis for intermodal analysis. Assist standardization of waterway entity definitions (Ports/Facilities, Locks, etc.). Provide public access to the National Waterway Network, which can be used as a basemap to support graphical overlays and analysis with other spatial data (waterway and modal network/facility databases, for example). Provide reliable data to support future waterway and intermodal applications. Source of Data The data included in these files are based upon the Annual Summary of Lock Statistics published by the U.S. Army Corps of Engineers/CEIWR, Navigation Data Center. The data are collected at each Corps owned and/or operated Lock by Corps personnel and towing industry vessel operators. This data was collected from the US Army Corps of Engineers and distributed on the National Transportation Atlas Database (NTAD).

© The U.S. Army Corps of Engineers/CEIWR, Navigation Data Center This layer is sourced from maps.bts.dot.gov.

Monthly summary statistics are based on data from the Lock Performance Monitoring System (LPMS). The LPMS was developed to collect a 100% sample of data on the locks that are owned and/or operated by the US Army Corps of Engineers. Each record contains data summarized monthly by lock chamber, and direction (upbound and number and types of vessels and lockages (recreation, commercial, tows, other), cuts, hardware operations, delay and processing times, number of tows and all vessels delayed, total tons, commodity tonnages, and number of barges. The data are by waterway and by calendar year. The waterway files contain 5 years of data for one waterway. The calendar year files contain 1 year of data for all waterways.

The Navigation Data Center had several objectives in developing the U.S. Waterway Data. These objectives support the concept of a National Spatial Data Provide public access to national waterway data. Foster interagency and intra-agency cooperation through data sharing. Provide a mechanism to integrate waterway data (U.S. Army Corps of Engineers Port/Facility and U.S. Coast Guard Accident Data, for example) Provide a basis for intermodal analysis. Assist standardization of waterway entity definitions (Ports/Facilities, Locks, etc.). Provide public access to the National Waterway Network, which can be used as a basemap to support graphical overlays and analysis with other spatial data (waterway and modal network/facility databases, for example). Provide reliable data to support future waterway and intermodal applications. Source of Data The data included in these files are based upon the Annual Summary of Lock Statistics published by the U.S. Army Corps of Engineers/CEIWR, Navigation Data Center. The data are collected at each Corps owned and/or operated Lock by Corps personnel and towing industry vessel operators. This data was collected from the US Army Corps of Engineers and distributed on the National Transportation Atlas Database (NTAD).

© The U.S. Army Corps of Engineers/CEIWR, Navigation Data Center

The National Waterway Network (NWN) is a geographic database of navigable waterways and channels in and around the United States, for analytical studies of navigation performance, for compiling commodity flow statistics, and for mapping purposes. The NWN is comprised of a link database and a node database. Links are line strings, which consist of beginning and end points (nodes) with intermediate vertices (shape points). Links represent either actual shipping lanes (i.e., channels, Intracoastal Waterways, sea lanes, rivers) or serve as representative paths in open water (where no defined shipping paths exist). Nodes may represent physical entities such as river confluence's, ports/facilities, and intermodal terminals, USACE nodes, or may be inserted for analytical purposes (i.e., to facilitate routing).

This dataset represents the Navigable Waterways data as of October 24, 2018, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The National Waterway Network is a comprehensive network database of the nation's navigable waterways. The data set covers the 48 contiguous states plus the District of Columbia, Hawaii, Alaska, Puerto Rico and water links between. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters. These data could be used for analytical studies of waterway performance, for compiling commodity flow statistics, and for mapping purposes.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Use this map to explore spatial data in the American River watershed. To add additional data: (1) click Modify Map; (2) then click Add; (3) then click Search for Layers; and (4) in the Sierra Nevada Conservancy search box, type American River Watershed to find extra data.

Note: This content was created by OpenStreetMap, not the City of Rochester. You can find more about them here.This feature layer provides access to OpenStreetMap (OSM) waterways data for North America, which is updated every 1-2 minutes with the latest edits. In the context of this map, the term "waterway" describes rivers, streams, and ditches with a flow of water from one place to another. These features are identified with a waterway tag. There are hundreds of different tag values for waterway used in the OSM database. In this map, unique symbols are used for several of the most popular waterway types, while lesser used types are grouped in an "other" category.The map is zoomed in to the Rochester area, but users can use the minus (-) sign to zoom out. If you would like to see a specific location, you can enter it into the search bar at the top right section of the map interface.

This feature layer provides access to OpenStreetMap (OSM) waterways data for South America, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM line (way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes waterway features defined as a query against the hosted feature layer (i.e. waterway is not blank).In OSM, a waterway describes rivers, streams and ditches with a flow of water from one place to another. These features are identified with a waterway tag. There are hundreds of different tag values for waterway used in the OSM database. In this feature layer, unique symbols are used for several of the most popular waterway types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. City level or 1:80k scale) to see the waterway features display. You can click on a feature to get the name of the waterway (if available). The name of the waterway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this waterway layer displaying just one or two waterway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. waterway is dam), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. streams and rivers) that are ready to use, but not for every type of waterway.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the American River near Fair Oaks, CA, acquired in October 2020. Field measurements of water depth available through another data release (Legleiter, C.J., and Harrison, L.R., 2022, Field measurements of water depth from the American River near Fair Oaks, CA, October 19-21, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P92PNWE5) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: American_mean-spec.tif, American_mean-depth.tif, American_NN-depth.tif, and American-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

The physical location of Locks and Dams maintained by the US Army Corp of Engineers along Navigable Waterways (Mississippi River) in the State of Minnesota.

Navigable water, Minnesota represents Navigable waterway centerlines for all navigable waterways within the state of Minnesota. It originated as an arc coverage with the U. S. Army Corps of Engineers. Then MnDOT extracted the arcs that lay within the state boundary. A description of the Navigable water layer is included in Section 5 of this document - Entity and Attribute Overview.

Check other metadata records in this package for more information on Locks, Dams, and navigable water.


Links to ESRI Feature Services:

Locks And Dams in Minnesota: Locks And Dams

Navigable Waterways in Minnesota: Navigable Waterways

Four digital water-surface profile maps for a 14-mile reach of the Mississippi River near Prairie Island in Welch, Minnesota from the confluence of the St. Croix River at Prescott, Wisconsin to upstream of the United States Army Corps of Engineers (USACE) Lock and Dam No. 3 in Welch, Minnesota, were created by the U.S. Geological Survey (USGS) in cooperation with the Prairie Island Indian Community. The water-surface profile maps depict estimates of the areal extent and depth of inundation corresponding to selected water levels (stages) at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). Current conditions for estimating near-real-time areas of water inundation by use of USGS streamgage information may be obtained on the internet at http://waterdata.usgs.gov/. Water-surface profiles were computed for the stream reach using HEC-GeoRAS software by means of a one-dimensional step-backwater HEC-RAS hydraulic model using the steady-state flow computation option. The hydraulic model used in this study was previously created by the USACE . The original hydraulic model previously created extended beyond the 14-mile reach used in this study. After obtaining the hydraulic model from USACE, the HEC-RAS model was calibrated by using the most current stage-discharge relations at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The hydraulic model was then used to determine four water-surface profiles for flood stages referenced to 37.00, 39.00, 40.00, and 41.00-feet of stage at the USGS streamgage on the Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The simulated water-surface profiles were then combined with a digital elevation model (DEM, derived from light detection and ranging (LiDAR) in Geographic Information System (GIS) data having a 0.35-foot vertical and 1.97-foot root mean square error horizontal resolution) in order to delineate the area inundated at each stage. The calibrated hydraulic model used to produce digital water-surface profile maps near Prairie Island, as part of the associated report, is documented in the U.S. Geological Survey Scientific Investigations Report 2021-5018 (https://doi.org/10.3133/ sir20215018). The data provided in this data release contains three zip files: 1) MissRiverPI_DepthGrids.zip, 2) MissRiverPI_InundationLayers.zip, and 3) ModelArchive.zip. The MissRiverPI_DepthGrids.zip and MissRiverPI_InundationLayers.zip files contain model output water-surface profile maps as shapefiles (.shp) and Keyhole Markup Language files (.kmz) that can be opened using Esri GIS systems (.shp files) or Google Earth (.kmz files), while the ModelArchive.zip contains model inputs, outputs, and calibration data used in creating the water-surface profiles maps.

This feature layer provides access to OpenStreetMap (OSM) waterways data for North America, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM line (way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes waterway features defined as a query against the hosted feature layer (i.e. waterway is not blank).In OSM, a waterway describes rivers, streams and ditches with a flow of water from one place to another. These features are identified with a waterway tag. There are hundreds of different tag values for waterway used in the OSM database. In this feature layer, unique symbols are used for several of the most popular waterway types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. City level or 1:80k scale) to see the waterway features display. You can click on a feature to get the name of the waterway (if available). The name of the waterway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this waterway layer displaying just one or two waterway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. waterway is dam), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. streams and rivers) that are ready to use, but not for every type of waterway.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

These two data-sets contain potentially navigable rivers for small and medium-sized boats in South-America depending on the topography, rainfall and potential evapotranspiration. Hence, it is an approximation of the location of navigable rivers, not an actual map of hidroways. Navigability is defined by the extent of a river which in this case (1) for small boats accounts to ~5-15 meters minimum extent and (2) for medium-sized boats ~30-40m meters minimum extent. The model data was parametrized and validated with land-cover data from high-resolution satellite images. Please see the full description of how the data-sets was created in the attached PDF File.

These data provide an accurate high-resolution shoreline compiled from imagery of Intracoastal Waterway, North Palm Beach to Jupiter Island, FL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution sche...

Rivers and streams of the U.S. This data was derived from data provided by the National Weather Service and the National Operational Hydrologic Remote Sensing Center (NOHRSC). The primary purpose of this dataset is to provide a common, conformant streams coverage for NWS products and processes, including IHABBS and usages as map backgrounds for river forecasts for a variety of National Weather Service applications. This data is a subset containing only major rivers.The data and related materials are made available through Esri (http://www.esri.com) and are intended for educational purposes only (see Access and Use Constraints section).

The Navigable Waterways dataset is as of October 24, 2018, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The National Waterway Network is a comprehensive network database of the nation's navigable waterways. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters. These data could be used for analytical studies of waterway performance, for compiling commodity flow statistics, and for mapping purposes. Metadata

The State of Louisiana experienced widespread flooding during the extreme rainfall events of March and August 2016. The City of Central, Louisiana, which lies above the confluence of the Amite and Comite Rivers, is bordered on the East and West respectively by these rivers. The city had extensive damage from both events, in particular the August 2016 flood in which the river basins received up to 30 inches of documented rainfall. Many streamgages in the area recorded peak of record flood levels from the event. The US Geological Survey (USGS) in cooperation with the City of Central, created a digital flood inundation map library to depict estimated areal extents and depth of flooding along 14.5 and 20.2 mile reach lengths of the Amite and Comite Rivers. The maps were created using a 2-dimensional flow model calibrated to the March and August 2016 events as well as to the current stage-discharge ratings at USGS streamgaging stations 07377300 Amite River at Magnolia, Louisiana and 07378000 Comite River near Comite, Louisiana. The maps range from flood stage to the peak of record stage at the gaging stations. Annual peak flow data was analyzed to determine multiple flooding scenario possibilities between the two gages. This data release provides the ArcGIS files and metadata for these maps. In addition, the maps will be hosted by the USGS on an interactive web mapper accessible to the cooperator and the public at: https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program Use of the maps aids city officials and emergency managers in pre-planning for a flood event in areas such as road and bridge closures, staging of man power and materials, and estimation of affected population. The maps also aid the public in foreseeing their flood risk potential and helps them in their decision making regarding life and property.

The Dams dataset is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). This map layer portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands. The map layer was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 79,777 dams in the U.S. Army Corps of Engineers National Inventory of Dams. This is a replacement for the April 1994 map layer.

These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the National Atlas of the United States in the use of these data. Supplemental_Information: In the online National Atlas of the United States Map Maker, at scales smaller than 1:4,850,000 the data is thinned for display purposes. For scales between 1: 4,850,000 and 1:22,000,000, dams are only shown if they have a height of 500 feet or more, or a normal storage capacity of 50,000 acre-feet or more, or a maximum storage capacity of 250,000 acre-feet or more (1280 dams). At scales smaller than 1:22,000,000, dams are only shown if they have a height of 5000 feet or more, or a normal storage capacity of 500,000 acre-feet or more, or a maximum storage capacity of 2,500,000 acre-feet or more (290 dams).

This vector line dataset represents the river center line that are eligible, eligible and suitable, and eligible and not suitable for designation as a National Wild and Scenic River within the contiguous United States, Alaska, Hawaii, and Puerto Rico.The data was designed for mapping and analysis. This should be used as a companion to the LSRS data.