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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,
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section contains observations related to the amount and quality of water in the Delaware River Basin. Data from a subset of reservoirs in the basin include observed daily depth-resolved water temperature, water levels, diversions, and releases. Data from streams in the basin include daily flow and temperature observations. Observations were compiled from a variety of sources, including the National Water Inventory System, Water Quality Portal, EcoSHEDS stream...
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the...
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,
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,
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section provides spatial data files that describe the rivers, reservoirs, and observational data in the Delaware River Basin included in this release. One shapefile of polylines describes the 459 river reaches that define the modeling network, and another shapefile of polygons includes the three reservoirs (Pepacton, Cannonsville, and Neversink) for which data are included in this release. Additionally, a point shapefile contains locations of monitoring sites...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service,
Shapefile;
Tags: DE,
Delaware,
MD,
Maryland,
NJ,
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)...
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section includes model drivers such as gridded weather data (NOAA GEFS and GridMET), and the stream network distance matrix for the Delaware River Basin. Additionally, inputs and outputs from an uncalibrated process-based stream temperature model (PRMS-SNTemp) are included.
Maryland Department of Natural Resources, Information Technology Service and Inland Fisheries Service developed a data management system referred to as the Geographic Inland Fisheries Survey system (GIFS). The GIFS database contains all parameters typical of fisheries information including date, location, fish species, invertebrate species, habitat, water quality, and tournament details. The information is often oriented to sport fish surveys though not exclusively. MARIS is an internet-based information sharing network that allows multiple states to provide a common set of variables via a single web interface. MARIS is not a “dataset” but rather links specific content of multiple states’ datasets. MARIS does not...
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four approaches for improving groundwater process representation: 1) a custom loss function that leverages the unique patterns of air and water temperature coupling resulting from different temperature drivers, 2) inclusion of additional groundwater-relevant catchment attributes, 3) incorporation of additional process model outputs, and...
This data release and model archive provides all data, code, and modelling results used in Topp et al. (2023) to examine the influence of deep learning architecture on generalizability when predicting stream temperature in the Delaware River Basin (DRB). Briefly, we modeled stream temperature in the DRB using two spatially and temporally aware process guided deep learning models (a recurrent graph convolution network - RGCN, and a temporal convolution graph model - Graph WaveNet). The associated manuscript explores how the architectural differences between the two models influence how they learn spatial and temporal relationships, and how those learned relationships influence a model's ability to accurately predict...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: DE,
Delaware,
Hydrology,
MD,
Maryland,
This data set represents the most current depiction of the Appalachian National Scenic Trail centerline.Locational information used to create this data set were obtained from Global Positioning Systems (GPS) survey data collected between 1999-2010 using mostly Trimble GPS equipment.
Seabeach amaranth (Amaranthus pumilus) is a plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast but is now listed as threatened by the U.S. Fish and Wildlife Service. For much of the 20th century, seabeach amaranth was absent from the mid-Atlantic coast and thought to be extinct, presumably as a result of increased development and recreational pressure. One region where there has been an effort to restore the seabeach amaranth population is Assateague Island National Seashore (ASIS), a National Park Service land holding located along the coasts of Maryland and Virginia. Here, the Natural Resources staff at ASIS planted seabeach amaranth cultivars for three growing seasons from 1999...
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg/L Cl- isochlor, also known as the salt front, using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001-2020, and we compare the ML model results to results from the hydrodynamic...
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