Abstract: This file contains Hydrologic Unit (HU) polygon boundaries for the United States, Puerto Rico, and the U.S. Virgin Islands. The data is a seamless National representation of HU boundaries from 2 to 14 digits compiled from U.S. Geological Survey (USGS) National Hydrography Dataset (NHD) and U.S. Department of Agriculture (USDA) National Resources Conservation Service (NRCS) Watershed Boundary Dataset (WBD) sources. Purpose: This data is intended primarily for geographic display and analysis of regional and national data, and can also be used for illustration purposes at intermediate or small scales (1:250,000 to 1:2,000,000). See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

------ 1. Introductory information ------Title: Mapping 10-m harvested area in the major winter wheat-producing regions of China from 2018 to 2022Format: TIFNaming convention: " ChinaWheatMap10_P_2018.tif" means winter wheat planted area map of 2018, and “ChinaWheatMap10_H_2018.tif" means winter wheat harvested area map of 2018.Authors: Jinkang Hu, Bing Zhang, Dailiang Peng, Jianxi Huang, Wenjuan Zhang, Bin Zhao, Enhui Cheng, Zihang Lou, Shengwei Liu, Songlin Yang, Yunlong Tan, and Yulong LvKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of SciencesCorresponding author: Bing ZhangContact Information: hujinkang21@mails.ucas.ac.cn (JK.H.); zhangbing@aircas.ac.cn (B.Z.)------ 2. Data specific information ------The dataset contains winter wheat maps of harvested area and planted area with 10m spatial resolution for five years (2018-2022) over eight provinces. In the datasets, the values equal to one means winter wheat.Software: ArcGIS, QGIS or ENVI are needed to read the dataset.

Abstract: This file contains Hydrologic Unit (HU) polygon boundaries for the United States, Puerto Rico, and the U.S. Virgin Islands. The data is a seamless National representation of HU boundaries from 2 to 14 digits compiled from U.S. Geological Survey (USGS) National Hydrography Dataset (NHD) and U.S. Department of Agriculture (USDA) National Resources Conservation Service (NRCS) Watershed Boundary Dataset (WBD) sources. Purpose: This data is intended primarily for geographic display and analysis of regional and national data, and can also be used for illustration purposes at intermediate or small scales (1:250,000 to 1:2,000,000). See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

The percentage of voxels between -790 HU to -950 HU showed the best correlation with RV/TLC (r = 0.52, p0.05).Spearman´s correlation coefficients (r) of the threshold-based method in expiration for varying threshold ranges.

Abstract: This file contains Hydrologic Unit (HU) polygon boundaries for the United States, Puerto Rico, and the U.S. Virgin Islands. The data is a seamless National representation of HU boundaries from 2 to 14 digits compiled from U.S. Geological Survey (USGS) National Hydrography Dataset (NHD) and U.S. Department of Agriculture (USDA) National Resources Conservation Service (NRCS) Watershed Boundary Dataset (WBD) sources. Purpose: This data is intended primarily for geographic display and analysis of regional and national data, and can also be used for illustration purposes at intermediate or small scales (1:250,000 to 1:2,000,000). See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

not relevant (data are not collected but inferred, spatially exhaustive maps)

aratio of residual volume to total lung capacitybstandard deviationDemographic data for study patients.

Abstract: This file contains Hydrologic Unit (HU) polygon boundaries for the United States, Puerto Rico, and the U.S. Virgin Islands. The data is a seamless National representation of HU boundaries from 2 to 14 digits compiled from U.S. Geological Survey (USGS) National Hydrography Dataset (NHD) and U.S. Department of Agriculture (USDA) National Resources Conservation Service (NRCS) Watershed Boundary Dataset (WBD) sources. Purpose: This data is intended primarily for geographic display and analysis of regional and national data, and can also be used for illustration purposes at intermediate or small scales (1:250,000 to 1:2,000,000).

Hcropland30:A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model

Please note this dataset is undergoing peer review

Version: 1.0

Authors: Qiong Hu a, 1, Zhiwen Cai b, 1, Liangzhi You c, d, Steffen Fritz e, Xinyu Zhang c, He Yin f, Haodong Weic, Jingya Yang g, Zexuan Li a, Qiangyi Yu g, Hao Wu a, Baodong Xu b *, Wenbin Wu g, *

a Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

b College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

c Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China

d International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA

e Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria

f Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA

g State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Introduction

We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.

Dataset

1. Hcropland30: A hybrid 30-m global cropland map in 2020

****Data format: GeoTiff

****Spatial resolution: 30 m

****Projection: EPSG: 4326 (WGS84)

****Values: 1 denotes cropland and 0 denotes non-cropland

The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.

1. 1°×1° Grids: This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

Id: The grid’s ID number.

area: The area of the grid.

mode: Indicates the representative sample grid.

climate: The climate type the grid belongs to.

dem: Average DEM value of the grid.

ndvi_s1 to ndvi_s4: Average NDVI values for four seasons within the grid.

esa, esri, fcs30, fromglc, glad, globeland30: Proportion of cropland pixels of different publicly available cropland products.

inconsistent: Proportion of inconsistent pixels within the grid according to different public cropland products.

hcropland30: Proportion of cropland pixels of our Hcropland30 dataset.

1. Samples: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

type: 1 denotes cropland sample and 0 denotes non-cropland sample.

Citation

If you use this dataset, please cite the following paper:

Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.

License

The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Disclaimer

This dataset is provided as-is, without any warranty, express or implied. The dataset author is not

responsible for any errors or omissions in the data, or for any consequences arising from the use

of the data.

Contact

If you have any questions or feedback regarding the dataset, please contact the dataset author

Qiong Hu (huqiong@ccnu.edu.cn)

The User Guide V2.4 of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: L2HNet. A more detailed description of the data can be found in the paper. More related work can be found at my homepage.

Click to check all the data versions and download the data (点击查看/下载所有数据版本)

NOTE: If you have any data needs, questions, or technical issues, contact us at ashelee@whu.edu.cn (Zhuohong Li, 李卓鸿).

The land-cover mapping method with Python code is open-access at Code link. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (Paper link).

我们的最新制图算法被计算机视觉顶会CVPR2024接收(Paper link),代码开源在:Code link,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。

Citation format of the paper:
Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023.

Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, ISPRS Journal of Photogrammetry and Remote Sensing. 192, pp.244-267, 2022.

BibTex format of the paper:

@article{li2023sinolc,
 title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data},
 author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan},
 journal={Earth System Science Data},
 volume={15},
 number={11},
 pages={4749--4780},
 year={2023},
 publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

@article{li2022breaking,
 title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels},
 author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei},
 journal={ISPRS Journal of Photogrammetry and Remote Sensing},
 volume={192},
 pages={244--267},
 year={2022},
 publisher={Elsevier}
}

Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils.

For more details / to cite this dataset please use:

Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.

Custom code used for digital soil mapping and validation is available on GitHub:

https://github.com/GaborSzatmari/HU-SOC-mapping

Description of the files:

The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (https://epsg.io/23700). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps.

SOC property maps

Unit

Year

Filename

SOC content map

[g ∙ kg-1]

1992

SOCc_0_30cm_1992_pred.tif

SOC content, lower limit of the 90% PI

[g ∙ kg-1]

1992

SOCc_0_30cm_1992_q05.tif

SOC content, upper limit of the 90% PI

[g ∙ kg-1]

1992

SOCc_0_30cm_1992_q95.tif

SOC density map

[kg ∙ m-3]

1992

SOCd_0_30cm_1992_pred.tif

SOC density, lower limit of the 90% PI

[kg ∙ m-3]

1992

SOCd_0_30cm_1992_q05.tif

SOC density, upper limit of the 90% PI

[kg ∙ m-3]

1992

SOCd_0_30cm_1992_q95.tif

SOC stock map

[tons ∙ ha-1]

1992

SOCs_0_30cm_1992_pred.tif

SOC stock, lower limit of the 90% PI

[tons ∙ ha-1]

1992

SOCs_0_30cm_1992_q05.tif

SOC stock, upper limit of the 90% PI

[tons ∙ ha-1]

1992

SOCs_0_30cm_1992_q95.tif

SOC content map

[g ∙ kg-1]

2000

SOCc_0_30cm_2000_pred.tif

SOC content, lower limit of the 90% PI

[g ∙ kg-1]

2000

SOCc_0_30cm_2000_q05.tif

SOC content, upper limit of the 90% PI

[g ∙ kg-1]

2000

SOCc_0_30cm_2000_q95.tif

SOC density map

[kg ∙ m-3]

2000

SOCd_0_30cm_2000_pred.tif

SOC density, lower limit of the 90% PI

[kg ∙ m-3]

2000

SOCd_0_30cm_2000_q05.tif

SOC density, upper limit of the 90% PI

[kg ∙ m-3]

2000

SOCd_0_30cm_2000_q95.tif

SOC stock map

[tons ∙ ha-1]

2000

SOCs_0_30cm_2000_pred.tif

SOC stock, lower limit of the 90% PI

[tons ∙ ha-1]

2000

SOCs_0_30cm_2000_q05.tif

SOC stock, upper limit of the 90% PI

[tons ∙ ha-1]

2000

SOCs_0_30cm_2000_q95.tif

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Imagery used to delineate vegetation polygons included aerial photography as well as LIDAR data. The aerial image was collected in January 2007 by EarthData International for the Manatee County Government. This is true color imagery with a 0.31 m pixel size and a verified horizontal accuracy of 2.3 m at the 95% confidence interval. The LIDAR data was collected in 2003 as part of the Windstorm Simulation Modeling Project under a contract between the International Hurricane Research Center at Florida International University and Manatee County. LIDAR data consisted of digital elevation models (DEMs) for both bare earth and first return with a 1.5 m spatial resolution

Spearman rank correlation´s coefficients between the quantitative CT AT measures and RV/TLC.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Hydrologic unit boundaries define the aerial extent of surface water drainage to a point. Hydrologic units through four levels were created in the 1970's and have been used extensively throughout the United States. During that time the U.S. Geological Survey (USGS) developed a hierarchical hydrologic unit code (HUC) for Hydrologic unit boundaries define the aerial extent of surface water drainage to a point. Hydrologic units through four levels were created in the 1970's and have been used extensively throughout the United States. During that time the U.S. Geological Survey (USGS) developed a hierarchical hydrologic unit code (HUC) for the United States. This system divides the country into 21 Regions - 222 Subregions - 352 Accounting Units - and 2 - 149 Cataloging Units based on surface hydrologic features. The smallest USGS unit (8-digit HU) is approximately 448 - 000 acres. During the late 1970's the Natural Resources Conservation Service (NRCS) - formerly the Soil Conservation Service - initiated a national program to further subdivide HU's into smaller watersheds for water resources planning. A 3-digit extension was added to the 8-digit identification. By the early 1980's this 11-digit HU mapping was completed for most of the U.S. These data demonstrate Maryland's watersheds as represented by the federal Hydrologic Unit Code denoted by an 11-digit numerical code. The watersheds were defined using contours on U.S. Geological Survey (USGS) 7.5 minute quadrangle map sheets. The state has been divided into 138 watersheds that are identified by Maryland - using an 8-digit numerical code. These watersheds are equivalent to the HUC 11-digit codes. The watershed file is generally considered to be map accurate at a scale of 1:24 - 000. A new 12-digit watershed file is available from DNR which provides more detailed line work for over 1 - 100 watersheds which is approximately equivalent to the HUC 14-digit code. Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Hydrology/MD_Watersheds/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The USGS National Hydrography Dataset (NHD) service from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. The NHD from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on the NHD, go to http://nhd.usgs.gov/index.html. The Watershed Boundary Dataset (WBD) is a companion dataset to the NHD. It defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Upper Mississippi River, will be composed of multiple smaller drainage areas, such as the Wisconsin River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to http://nhd.usgs.gov/wbd.html. The National Map hydrography data is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain NHD and WBD data in either Esri File or Personal Geodatabase, or Shapefile formats.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Orthophotos acquired by the U.S. Army Corps of Engineers in 2006-2007 were determined to be the most current and detailed imagery available. The imagery used in this project was a small extract from a collection of natural color GeoTIFF orthophotos that covers the islands of Puerto Rico, Culebra, Vieques, St. Thomas, St. John, and St. Croix. An orthophoto is remotely-sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. The source imagery was obtained from November 2006 through March 2007 and used to produce orthophotos with a one foot ground sample distance (GSD). Imagery was acquired at 0.9 foot GSD resolution. Flight height maintained during mission was 8,650 feet AGL. The imagery was captured at 12-bit radiometric resolution and converted to 8-bit radiometric resolution during post processing. The imagery was captured with 30% side lap between all adjacent flight lines. The imagery was obtained and processed by all digital means beginning with data acquisition using an ADS40 digital sensor. The orthophotos are available in GeoTIFF format. The original projected coordinate system was State Plane Puerto Rico / US Virgin Islands (Zone 5200), NAD 83, GRS 80, Units Meters.

The national soil hydrologic groups map of Hungary aggregates the information available from the 100 m resolution 3D soil hydraulic maps of Hungary. The map was derived using k-means clustering combined with expert-based rules. Source data included the national 3D soil hydraulic maps (HU-SoilHydroGrids), maps of basic soil properties (DOSoReMI.hu), maps of salt affected soils, and the Hungarian Detailed Soil Hydrophysical Database (MARTHA).

The derived 68 groups are defined by distinct hydraulic properties, including van Genuchten parameters - describing water retention - and saturated hydraulic conductivity. This national map simplifies the representation of complex soil properties to support computationally intensive national-scale models and land management planning.

The dataset has a resolution of 100 m and provides full coverage of Hungary. The data is stored in soil_hydrologic_groups.tif, a GeoTIFF file using the Hungarian Unified National Projection System (EOV/HD72 – EPSG:23700; see https://epsg.io/23700).

A supplementary CSV file (map_code_cluster_VG_KS.csv) provides definitions of the map codes, including the cluster numbers and their corresponding soil hydraulic parameters.

Column descriptions:

• map_code: map codes
• map_code_depth: map code with depth interval (e.g., 1_0_5 refers to the 0–5 cm depth of map code 1)
• cluster_number: cluster number of the soil hydrologic groups
• thr_VG, ths_VG, alp_VG, n_VG: van Genuchten parameters
• thr (cm³/cm³),
• ths (cm³/cm³),
• alp (1/cm),
• n (–)
• KS: saturated hydraulic conductivity (cm/day)

The thr parameter was set to 0. The m parameter is equal to 1 - 1/n.

The statistics_based_clustering.R file contains the code for the k-means analysis. The code for the expert-based rules is stored in the expert_based_clustering.R file.

The Watershed Boundary Dataset (WBD) from The National Map (TNM) defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Upper Mississippi River, will be composed of multiple smaller drainage areas, such as the Wisconsin River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to http://nhd.usgs.gov/wbd.html. The USGS National Hydrography Dataset (NHD) service is a companion dataset to the WBD. The NHD is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. For more information on the NHD, go to http://nhd.usgs.gov/index.html. Hydrography data from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. Hydrography data is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain WBD and NHD data in either Esri File or Personal Geodatabase, or Shapefile formats.

Abstract: This file contains Hydrologic Unit (HU) polygon boundaries for the United States, Puerto Rico, and the U.S. Virgin Islands. The data is a seamless National representation of HU boundaries from 2 to 14 digits compiled from U.S. Geological Survey (USGS) National Hydrography Dataset (NHD) and U.S. Department of Agriculture (USDA) National Resources Conservation Service (NRCS) Watershed Boundary Dataset (WBD) sources. Purpose: This data is intended primarily for geographic display and analysis of regional and national data, and can also be used for illustration purposes at intermediate or small scales (1:250,000 to 1:2,000,000). See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.