Skip to main content
Advanced Search

Filters: Tags: Erosion (X) > Types: OGC WFS Layer (X)

378 results (15ms)   

Filters
Date Range
Extensions
Types
Contacts
Categories
Tag Types
Tag Schemes
View Results as: JSON ATOM CSV
thumbnail
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions)...
thumbnail
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated...
thumbnail
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated...
thumbnail
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions)...
thumbnail
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
thumbnail
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
thumbnail
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...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Atlantic Ocean, Barrier Island, Bayesian Network, CMHRP, Coastal Erosion, All tags...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given the magnitude of the threat posed by sea-level rise, and the urgency to better understand it, there is an increasing need to forecast sea-level rise effects on barrier islands. To address this problem, scientists in the U.S. Geological Survey (USGS) Coastal and Marine Geology program are developing Bayesian networks as a tool to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as the piping plover (Charadrius melodus)...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given the magnitude of the threat posed by sea-level rise, and the urgency to better understand it, there is an increasing need to forecast sea-level rise effects on barrier islands. To address this problem, scientists in the U.S. Geological Survey (USGS) Coastal and Marine Geology program are developing Bayesian networks as a tool to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as the piping plover (Charadrius melodus)...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
thumbnail
Sandy ocean beaches in the United States are popular tourist and recreational destinations and constitute some of the most valuable real estate in the country. The boundary between land and water along the coastline is often the location of concentrated residential and commercial development and is frequently exposed to a range of natural hazards, which include flooding, storm effects, and coastal erosion. In response, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change hazards. One component of this research effort, the National Assessment of Shoreline Change Project (http://coastal.er.usgs.gov/shoreline-change/), documents changes in shoreline position as a proxy for coastal...
thumbnail
Sandy ocean beaches in the United States are popular tourist and recreational destinations and constitute some of the most valuable real estate in the country. The boundary between land and water along the coastline is often the location of concentrated residential and commercial development and is frequently exposed to a range of natural hazards, which include flooding, storm effects, and coastal erosion. In response, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change hazards. One component of this research effort, the National Assessment of Shoreline Change Project, documents changes in shoreline position as a proxy for coastal change. Shoreline position is an easily understood...
thumbnail
In coastal areas of the United States, where water and land interface in complex and dynamic ways, it is common to find concentrated residential and commercial development. These coastal areas often contain various landholdings managed by Federal, State, and local municipal authorities for public recreation and conservation. These areas are frequently subjected to a range of natural hazards, which include flooding and coastal erosion. In response, the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data to calculate rates of shoreline change along the conterminous coast of the United States, and select coastlines of Alaska and Hawaii, as part of the Coastal Change Hazards priority...
thumbnail
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...
thumbnail
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate...
thumbnail
Borehole erosion tests were performed to quantify sediment erosion rates as a function of depth below grade and flow speed at locations along the Sacramento and American Rivers in 2019. The dataset consists of: - borehole caliper files describing the initial geometry of the borehole, and its shape after each test flow event, - where available, a digital time series describing discharge into the hole - where available, a digital time series describing water level in the water supply tank - log sheets with details such as test location, date, and personnel - digital log sheets in .pdf format include field-based and laboratory-based soil classification data, see "Drilling log file explanation.txt" for explanation of...
Transects in backwaters of Navigation Pools 4 and 8 of the Upper Mississippi River (UMR) were established in 1997 to measure sedimentation rates. Annual surveys were conducted from 1997-2002 and then some transects surveyed again in 2017-18. Changes and patterns observed were reported on in 2003 for the 1997-2002 data, and a report summarizing changes and patterns from 1997-2017 will be reported on at this time. Several variables are recorded each survey year and placed into an Excel spreadsheet. The spreadsheets are read with a SAS program to generate a SAS dataset used in SAS programs to determine rates, depth loss, and associations between depth and change through regression.
thumbnail
In 2016, the U.S. Army Corps of Engineers (USACE) started collecting high-resolution multibeam echosounder (MBES) data on Lake Koocanusa. The survey originated near the International Boundary (River Mile (RM) 271.0) and extended down the reservoir, hereinafter referred to as downstream, about 1.4 miles downstream of the Montana 37 Highway Bridge near Boulder Creek (about RM 253). USACE continued the survey in 2017, completing a reach that extended from about RM 253 downstream to near Tweed Creek (RM 244.5). In 2018, the U.S. Geological Survey (USGS) Idaho Water Science Center completed the remaining portion of the reservoir from RM 244.5 downstream to Libby Dam (RM 219.9). The MBES data collected in 2016 and 2017...
thumbnail
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...
thumbnail
Grasslands of the Sky Islands region once covered over 13 million acres in southeastern Arizona and adjacent portions of New Mexico, Sonora, and Chihuahua. Attempts to evaluate current ecological conditions suggest that approximately two thirds of these remain as intact or restorable grassland habitat. These grasslands provide watershed services such as flood control and aquifer recharge across the region, and continue to support dozens of species of concern. Prioritizing conservation interventions for these remaining grassland blocks has been challenging. Reliable data on condition and conservation value of grasslands in the region have not been systematically summarized. State and national boundaries further complicate...


map background search result map search result map Sustaining the Grassland Sea Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Alabama Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southeastern Florida (FLse) Long-term and short-term shoreline change rates for the southern coastal region of Cape Cod, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0 CoSMoS v3.1 water level projections: 1-year storm in San Luis Obispo County CoSMoS v3.1 water level projections: average conditions in Santa Barbara County CoSMoS v3.1 flood depth and duration projections: 20-year storm in San Luis Obispo County CoSMoS v3.1 wave-hazard projections: 100-year storm in San Mateo County CoSMoS v3.1 ocean-currents hazards: 20-year storm in San Mateo County points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parramore Island, VA, 2014 Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 4.0, July 2021) Lake Koocanusa Maximum and Minimum Pool Elevation Contours, Lincoln County, Montana Backwater Sedimentation in Navigation Pools 4 and 8 of the Upper Mississippi River data CoSMoS v3.1 flood depth and duration projections: 1-year storm in Monterey County Mores Creek Arm Bathymetric Survey - Depth DEM, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2008 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014 Data Release: Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020 Long-term shoreline change rates for the Southern California coastal region using the Digital Shoreline Analysis System version 5.0 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) Mores Creek Arm Bathymetric Survey - Depth DEM, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Alabama Data Release: Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020 Long-term and short-term shoreline change rates for the southern coastal region of Cape Cod, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2008 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014 CoSMoS v3.1 wave-hazard projections: 100-year storm in San Mateo County CoSMoS v3.1 ocean-currents hazards: 20-year storm in San Mateo County CoSMoS v3.1 water level projections: average conditions in Santa Barbara County Lake Koocanusa Maximum and Minimum Pool Elevation Contours, Lincoln County, Montana Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 4.0, July 2021) CoSMoS v3.1 water level projections: 1-year storm in San Luis Obispo County CoSMoS v3.1 flood depth and duration projections: 20-year storm in San Luis Obispo County CoSMoS v3.1 flood depth and duration projections: 1-year storm in Monterey County 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) Backwater Sedimentation in Navigation Pools 4 and 8 of the Upper Mississippi River data Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southeastern Florida (FLse) Long-term shoreline change rates for the Southern California coastal region using the Digital Shoreline Analysis System version 5.0 Sustaining the Grassland Sea