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 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

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.

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.

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.

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.

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

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 ...

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.

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.

A slow-moving area of low pressure and a high amount of atmospheric moisture produced heavy rainfall across Louisiana and southwest Mississippi in August 2016. Over 31 inches of rain was reported in Watson, 30 miles northeast of Baton Rouge, over the duration of the event. The result was major flooding that occurred in the southern portions of Louisiana and included areas surrounding Baton Rouge and Lafayette along rivers such as the Amite, Comite, Tangipahoa, Tickfaw, Vermilion, and Mermentau. The U.S. Geological Survey (USGS) Lower Mississippi-Gulf Water Science Center operates many continuous, streamflow-gaging stations in the impacted area. Peak streamflows of record were measured at 10 locations, and seven other locations experienced peak streamflows ranking in the top 5 for the duration of the period of record. In August 2016, USGS personnel made fifty streamflow measurements at 21 locations on streams in Louisiana. Many of those streamflow measurements were made for the purpose of verifying the accuracy of the stage-streamflow relation at the associated gaging station. USGS personnel also recovered and documented 590 high-water marks after the storm event by noting the location and height of the water above land surface. Many of these high water marks were used to create twelve flood-inundation maps for selected communities of Louisiana that experienced flooding in August 2016. This data release provides the actual flood-depth measurements made in selected river basins of Louisiana that were used to produce the flood-inundation maps published in the companion product (Watson and others, 2017). Reference Watson, K.M., Storm, J.B., Breaker, B.K., and Rose, C.E., 2017, Characterization of peak streamflows and flood inundation of selected areas in Louisiana from the August 2016 flood: U.S. Geological Survey Scientific Investigations Report 2017–5005, 26 p., https://doi.org/10.3133/sir20175005.

This is a preview of the HISAR dataset (Hydrologic Indices of South American Rivers).

The HISAR dataset is freely available for non-commercial use. The files provided are (i) drainage line shapefile with river reaches as represented by the MGB model and 73 attributes corresponding to hydrologic indices derived from simulated time series; (ii) gauge points shapefile with 73 attributes corresponding to hydrologic indices derived from observed time series; (iii) maps with hydrologic indices, (iv) maps with information of the error of some indices and (v) the scripts used to calculate the indices. This database provides a spatial view of the variability of the river flow regime characteristics.

The line shapefile has 33,749 river reaches with an average length of 15 km and drainage area > 1000 km². The ESRI shapefile also has the attributes of drainage area (Upst_Area_ in km²), length (Ltr_Km_ in km), UC (corresponding catchment attribute from hydrological modelling), HYear_min (starting month of the hydrological year of minimum flow) and HYear_max (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’.

Some river reaches do not have all hydrologic indices calculated, due to series of streamflows that could not meet specific criteria. For instance, the baseflow recession constant was automatically calculated using at least five consecutive days of decreasing streamflow, all of which below the Q90 (streamflow value that is exceeded 90% of the time), and this condition was not found in all cases.

The gauge points shapefile has 1329 points with 73 attributes corresponding to hydrologic indices derived from observed time series. The drainage area of the gauges ranging from 1,000 to 4,703,503 km2. The ESRI shapefile also has the attributes of code, name, latitude (lat), longitude (long), drainage area (Upst_Area_ in km²), Country were the gauge point are located, HYear_min (starting month of the hydrological year of minimum flow) and HYear_max (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’.

For more information about HISAR dataset see the journal article DOI: in preparation.

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.

This data release includes images and field measurements used to map salmon spawning locations along the American River near Sacramento, California, via remote sensing; the data were collected November 5-7, 2018. The purpose of this study was to develop and test a spectrally based technique for identifying salmon spawning locations, known as redds, from various types of remotely sensed data. Traditionally, redds have been mapped by eye while walking the bank or from a boat, or by an observer in an aircraft or an interpreter visually examining aerial images. The goal of this proof-of-concept investigation was to assess the potential for more efficient, objective, and automated redd mapping from conventional true color (RGB, or red/green/blue) and hyperspectral images. This child page provides redd locations mapped in the field, with their coordinates defined by surveying the redd centers with real-time kinematic (RTK) GPS receivers. The field-mapped redds were used to train and validate various image-based approaches to mapping salmon spawning locations.

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An unmanned aerial system (UAS) was used to acquire red/green/blue (RGB) and hyperspectral image data from the American River in California November 5-7, 2018, to support research on remote sensing of rivers, specifically mapping salmon spawning locations (redds) as part of an overall salmon habitat assessment program. The RGB images were acquired uisng a DJI MAtrice 210 equipped with a Zenmuse 4S optical camera. Imagery was collected along several parallel flight lines to ensure full coverage of the study reach. Raw Zenmuse images were georeferenced using real-time kinematic (RTK) global positioning system (GPS) and inertial motion unit (IMU) data recorded onboard the UAS and individual flight strips were combined into an orthorectified mosaic using Agisoft Metashape. The resulting orthoimage had a pixel size of 0.15 m. The georeferenced RGB ortho-image had accurate image coregistration with surveyed ground control targets used as checkpoints. The hyperspectral data were collected by the Nano-Hyperspec imaging system, manufactured by Headwall Photonics, Inc. The flight was conducted by the NOAA Southwest Fisheries Science Center and colleagues from the University of California Santa Barbara who also performed the initial radiometric and geometric data processing. The data were acquired from a DJI Matrice M600 UAS and have a spatial resolution (pixel size) of 0.15 m. The data set consists of 252 spectral bands spanning the visible and near infrared wavelength range from 398 - 956 nanometers. Reflectance retrieval was performed using a calibration tarp as an in-scene white reference. The image pixel values represent reflectances and are stored as floating point 32-bit single precision numbers. The image data file has a band sequential (BSQ) interleave and is in an ENVI-compatible file format with an associated header (*.hdr) text file. The initial hyperspectral ortho-image had poor alignment with field-surveyed ground control points (GCPs) and required additional georeferencing to improve horizontal accuracy. We used ENVI software tools to perform image-to-image registration using the RGB ortho-image as the base image and the hyperspectral image as the warp image. Following this initial radiometric and geometric processing, the hyperspectral ortho-mosaic was masked to include only the water area within the river channel. The images provided in this data release is focuses on the reach of the American River immediarely below Nimbus Dam. Supporting field data from this reach were collected in coordination with the acquisition of the remotely sensed data.

The goal of developing HydroSHEDS was to generate key data layers to support regional and global watershed analyses, hydrological modeling, and freshwater conservation planning at a quality, resolution and extent that had previously been unachievable.

The boundaries of five different geographic areas -- North America, South America, Europe, Africa, and Asia -- are digitally represented in this collection of data files that can be used in the production of computer maps. Each of the five areas is encoded in three distinct files: (1) coastline, islands, and lakes, (2) rivers, and (3) international boundaries. There is an additional file for North America (Part 4: North America: Internal Boundaries) delineating state lines in the United States and provincial boundaries in Canada. The data in each of the files is hierarchically structured into subordinate geographic features and ranks, which may be used for output plotting symbol definition. The mapping scale used to encode the data ranged from 1:1 million to 1:4 million.

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

This dataset divides the North American continent in major hydrological basins and their sub-basins according to its hydrological characteristics. It was obtained by delineating drainage basin boundaries from hydrologically corrected elevation data (WWF HydroSHEDS and Hydro1K).

The dataset consists of the following information:- numerical code and name of the major basin (MAJ_BAS and MAJ_NAME); - area of the major basin in square km (MAJ_AREA); - numerical code and name of the sub-basin (SUB_BAS and SUB_NAME); - area of the sub-basin in square km (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea)

Supplemental Information:

This dataset is developed as part of a GIS-based information system on water resources for the Asian continent. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations.

Contact points:

Metadata contact: AQUASTAT FAO-UN Land and Water Division

Contact: Jippe Hoogeveen FAO-UN Land and Water Division

Data lineage:

The majority of the linework of the map was obtained by delineating drainage basin boundaries from hydrologically corrected elevation data with a resolution of 15 arc-seconds. The elevation dataset was part of a mapping product, HydroSHEDS, developed by the Conservation Science Program of World Wildlife Fund. Original input data had been obtained during NASA's Shuttle Radar Topography Mission (SRTM). Areas north of the SRTM extent, 60 degrees N, were obtained by merging with the HYDRO1k basin layer.

Online resources:

Download - Hydrological basins in North America (ESRI shapefile)

General information regarding the HydroSHEDS data product

HydroSHEDS dataset download and technical information

Rivers of North America