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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017-2018. Previously published historical shorelines for South Carolina (Kratzmann and others, 2017)...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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This section of the data release supports the data used in models for the associated publication. The U.S. Geological Survey and the University of Wisconsin – Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at Mainstem Plum Creek (USGS site ID: 04084911) and West Plum Creek (USGS site ID: 04084927) in northeastern Wisconsin. Monitoring data from 2010–2020 at Mainstem Plum and 2013–2020 at West Plum were used to detect changes in hydrologic and water-quality responses during runoff events. Runoff events were defined by hydrographers and used to compute event loads and event flow-weighted mean concentrations of...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017-2018. Previously published historical shorelines for South Carolina (Kratzmann and others, 2017)...
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This data release includes two digital orthomosaic images produced from uncrewed aerial system (UAS) imagery surveys conducted on August 14, 2019, and July 8, 2022 at an edge-of-field site north of Medina River Natural Area near San Antonio, Texas. These images were compiled from sets of aerial imagery included in this data release. Orthomosaic images can be used for visual reference but do not contain elevation data.
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This data release provides computed rainfall (rain total, duration, intensity, erosivity and antecedent rainfall) and flow (flow volume, flow-weighted mean concentrations, total loads, and total yields) metrics from monitored precipitation, discharge, and water quality (nutrients and sediment concentrations) data collected at U.S. Geological Survey edge-of-field (EOF) monitoring sites located in five Great Lakes States (Wisconsin, Michigan, Ohio, Indiana, and New York). EOF monitoring sites are installed at the edge of agricultural fields, either on the field surface or using subsurface tiles, where runoff can be intercepted and channeled through monitoring equipment before it enters the natural stream system. These...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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An extreme flood in 2016 caused widespread culvert blockages and road failures across northern Wisconsin, including extensive damage along steep tributaries and ravines in the Marengo River watershed. Along with the flooding, there were fluvial erosion hazards (FEH) associated with a large amount of erosion in headwater areas. Of special concern were FEHs associated with gullying, loss of wetland storage, and valley-side mass wasting. In 2020, a pilot study was begun to map and classify ephemeral and perennial streams and wetlands in terms of their susceptibility to fluvial erosion hazards. This study combines rapid geomorphic field assessments of river corridor erosion and coupled sediment and debris delivery with...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017-2018. Previously published historical shorelines for South Carolina (Kratzmann and others, 2017)...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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This section of the data release supports an archive of the models used in the associated publication. The U.S. Geological Survey and the University of Wisconsin – Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at Mainstem Plum Creek and West Plum Creek in northeastern Wisconsin. Monitoring data from 2010–2020 at Mainstem Plum and 2013–2020 at West Plum were used to detect changes in hydrologic and water-quality responses during runoff events. Runoff events were defined by hydrographers and used to compute event loads and event flow-weighted mean concentrations of total phosphorus and total suspended solids –...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected in 2010...
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In 2004, about 90 migrating elk drowned after attempting to cross thin ice on the Mores Creek arm of Lucky Peak Lake upstream of the Highway 21 bridge. To better understand the depths over a range of reservoir pool elevations in the Mores Creek Arm, the U.S. Geological Survey, in cooperation with the Lucky Peak Power Plant Project, conducted high-resolution multibeam echosounder (MBES) bathymetric surveys on the Mores Creek arm on Lucky Peak Lake. The MBES data will assist reservoir managers and wildlife biologists with regulating reservoir water surface elevations (WSE) to support successful big game migration across Mores Creek on Lucky Peak Lake. Data collection provided nearly 100 percent coverage of bed elevations...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...


map background search result map search result map Nutrient and sediment concentrations, loads, yields, and rainfall characteristics at USGS surface and subsurface-tile edge-of-field agricultural monitoring sites in Great Lakes States (ver. 2.1, September 2023) Mores Creek Arm Bathymetric Survey, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Data Release: Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020 Model Archive: Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020 Orthomosaic images obtained by using uncrewed aerial systems from an erosion prone area north of Medina River Natural Area near San Antonio, Texas, August 14, 2019, and July 8, 2022 Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Virginia generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 VA Bias_Feature – Feature class containing Virginia proxy-datum bias information to be used in the Digital Shoreline Analysis System. Fluvial Erosion Hazard Geospatial Network from the Marengo River Watershed, Ashland County, Wisconsin Short-term shoreline change rate transects for the South Carolina coastal region using the Digital Shoreline Analysis System version 5.1 Long-term shoreline change rate transects for the South Carolina coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Intersects for the coastal region of South Carolina generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long and short-term shoreline intersect points for the western coast of North Carolina (NCwest), calculated using the Digital Shoreline Analysis System version 5.1 Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the central coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral) Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Long and short-term shoreline change rate transects for the southern North Carolina coastal region (NCsouth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Long and short-term shoreline intersect points for the southern coast of North Carolina (NCsouth), calculated using the Digital Shoreline Analysis System version 5.1 Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the northern coast of North Carolina from the Virginia border to Cape Hatteras (NCnorth) Long and short-term shoreline change rate transects for the northern North Carolina coastal region (NCnorth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Long and short-term shoreline intersect points for the northern coast of North Carolina (NCnorth), calculated using the Digital Shoreline Analysis System version 5.1 Orthomosaic images obtained by using uncrewed aerial systems from an erosion prone area north of Medina River Natural Area near San Antonio, Texas, August 14, 2019, and July 8, 2022 Mores Creek Arm Bathymetric Survey, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Data Release: Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020 Model Archive: Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020 Long and short-term shoreline intersect points for the western coast of North Carolina (NCwest), calculated using the Digital Shoreline Analysis System version 5.1 Fluvial Erosion Hazard Geospatial Network from the Marengo River Watershed, Ashland County, Wisconsin Long and short-term shoreline intersect points for the northern coast of North Carolina (NCnorth), calculated using the Digital Shoreline Analysis System version 5.1 Long and short-term shoreline change rate transects for the northern North Carolina coastal region (NCnorth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the northern coast of North Carolina from the Virginia border to Cape Hatteras (NCnorth) Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the central coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral) Intersects for coastal region of Virginia generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 VA Bias_Feature – Feature class containing Virginia proxy-datum bias information to be used in the Digital Shoreline Analysis System. Long and short-term shoreline intersect points for the southern coast of North Carolina (NCsouth), calculated using the Digital Shoreline Analysis System version 5.1 Long and short-term shoreline change rate transects for the southern North Carolina coastal region (NCsouth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Short-term shoreline change rate transects for the South Carolina coastal region using the Digital Shoreline Analysis System version 5.1 Intersects for the coastal region of South Carolina generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term shoreline change rate transects for the South Carolina coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Nutrient and sediment concentrations, loads, yields, and rainfall characteristics at USGS surface and subsurface-tile edge-of-field agricultural monitoring sites in Great Lakes States (ver. 2.1, September 2023)