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Research about Delaware, Maryland, Virginia

Source-linked reports with geographic coverage including Delaware, Maryland, Virginia.

At least 19 recordsLinked to original sources
USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗
USGS2024

Identifying the forage base and critical forage taxa for Chesapeake waterbirds

To effectively maximize the conservation value of management plans intended to capture ecosystem-wide health, it is essential to obtain an understanding of emergent patterns in dietary dynamics spanning many species. Chesapeake Bay, USA, is a critical ecosystem used annually by a diverse assortment of waterbird species, including several of conservation concern. However, the ecosystem is threatened by many ecological pressures driven largely by the dense human population of the surrounding region. These issues necessitate proactive monitoring and management efforts to track the health of ecosystems like the Chesapeake Bay. Such monitoring efforts of population dynamics require adequate data on the connections between trophic levels to understand how changes to the forage base might influence higher trophic levels, such as these diverse avian predators. However, we have historically lacked standardized quantitative data drawing these connections at the community level, as well as the relative importance of these taxa in the diet of such predators. We collated existing quantitative data on avian dietary composition to construct a database on the diets of 58 waterbird species that make use of the Chesapeake Bay. From this database, we quantified the relative importance of forage taxa to the diet of each waterbird species. Such data can enable managers to develop a comprehensive suite of forage taxa indicators whose abundance and distributions can be monitored as a proxy for ecosystem health. It is our goal that this database be harnessed as a tool to enable conservation practitioners to prioritize indicator taxa for monitoring purposes, contributing towards conservation plans that best address the health of the ecosystem at large.

Delaware, Maryland, Virginia↗
USGS2023

Biophysical drivers of coastal treeline elevation

Sea level rise is leading to the rapid migration of marshes into coastal forests and other terrestrial ecosystems. Although complex biophysical interactions likely govern these ecosystem transitions, projections of sea level driven land conversion commonly rely on a simplified “threshold elevation” that represents the elevation of the marsh-upland boundary based on tidal datums alone. To determine the influence of biophysical drivers on threshold elevations, and their implication for land conversion, we examined almost 100,000 high-resolution marsh-forest boundary elevation points, determined independently from tidal datums, alongside hydrologic, ecologic, and geomorphic data in the Chesapeake Bay, the largest estuary in the U.S. located along the mid-Atlantic coast. We find five-fold variations in threshold elevation across the entire estuary, driven not only by tidal range, but also salinity and slope. However, more than half of the variability is unexplained by these variables, which we attribute largely to uncaptured local factors including groundwater discharge, microtopography, and anthropogenic impacts. In the Chesapeake Bay, observed threshold elevations deviate from predicted elevations used to determine sea level driven land conversion by as much as the amount of projected regional sea level rise by 2050. These results suggest that local drivers strongly mediate coastal ecosystem transitions, and that predictions based on elevation and tidal datums alone may misrepresent future land conversion.

Delaware, Maryland, Virginia↗
USGS2022

Variability in marsh migration potential determined by topographic rather than anthropogenic constraints in the Chesapeake Bay region

Sea level rise (SLR) and saltwater intrusion are driving inland shifts in coastal ecosystems. Here, we make high-resolution (1 m) predictions of land conversion under future SLR scenarios in 81 watersheds surrounding Chesapeake Bay, United States, a hotspot for accelerated SLR and saltwater intrusion. We find that 1050–3748 km 2 of marsh could be created by 2100, largely at the expense of forested wetlands. Predicted marsh migration exceeds total current tidal marsh area and is ~ 4× greater than historical observations. Anthropogenic land use in marsh migration areas is concentrated within a few watersheds and minimally impacts calculated metrics of marsh resilience. Despite regional marsh area maintenance, local ecosystem service replacement within vulnerable watersheds remains uncertain. However, our work suggests that topography rather than land use drives spatial variability in wetland vulnerability regionally, and that rural land conversion is needed to compensate for extensive areal losses on heavily developed coasts globally.

Delaware, Maryland, Virginia↗
USGS2021

Quantifying slopes as a driver of forest to marsh conversion using geospatial techniques: Application to Chesapeake Bay coastal-plain, USA

Coastal salt marshes, which provide valuable ecosystem services such as flood mitigation and carbon sequestration, are threatened by rising sea level. In response, these ecosystems migrate landward, converting available upland into salt marsh. In the coastal-plain surrounding Chesapeake Bay, United States, conversion of coastal forest to salt marsh is well-documented and may offset salt marsh loss due to sea level rise, sediment deficits, and wave erosion. Land slope at the marsh-forest boundary is an important factor determining migration likelihood, however, the standard method of using field measurements to assess slope across the marsh-forest boundary is impractical on the scale of an estuary. Therefore, we developed a general slope quantification method that uses high resolution elevation data and a repurposed shoreline analysis tool to determine slope along the marsh-forest boundary for the entire Chesapeake Bay coastal-plain and find that less than 3% of transects have a slope value less than 1%; these low slope environments offer more favorable conditions for forest to marsh conversion. Then, we combine the bay-wide slope and elevation data with inundation modeling from Hurricane Isabel to determine likelihood of coastal forest conversion to salt marsh. This method can be applied to local and estuary-scale research to support management decisions regarding which upland forested areas are more critical to preserve as available space for marsh migration.

Delaware, Maryland, Virginia↗
USGS2021

Molluscan aminostratigraphy of the US Mid-Atlantic Quaternary coastal system: Implications for onshore-offshore correlation, paleochannel and barrier island evolution, and local late Quaternary sea-level history

The Quaternary record of the US Mid-Atlantic coastal system includes onshore emergent late Pleistocene shoreline deposits, offshore inner shelf and barrier island units, and paleovalleys formed during multiple glacial stage sea-level lowstands. The geochronology of this coastal system is based on uranium series, radiocarbon, amino acid racemization (AAR), and optically stimulated luminescence (OSL) methods. We report over 600 mollusk AAR results from 93 sites between northeastern North Carolina and the central New Jersey shelf, representing samples from both onshore cores or outcrops, sub-barrier and offshore cores, and transported shells from barrier island beaches. AAR age estimates are constrained by paired 14 C analyses on specific shells and associated U-series coral ages from onshore sites. AAR data from offshore cores are interpreted in the context of detailed seismic stratigraphy. The distribution of Pleistocene-age shells on the island beaches is linked to the distribution of inner shelf or sub-barrier source units. Age mixing over a range of time-scales (~1 ka to ~100 ka) is identified by AAR results from onshore, beach, and shelf collections, often contributing insights into the processes forming individual barrier islands. The regional aminostratigraphic framework identifies a widespread late Pleistocene (Marine Isotope Stage 5) aminozone, with isolated records of middle and early Pleistocene deposition. AAR results provide age estimates for the timing of formation of the three major paleochannels that underlie the Delmarva Peninsula: Persimmon Point paleochannel ≥800 ka; Exmore paleochannel ~400–500 ka (MIS 12); and Eastville paleochannel > 125 ka (MIS 6). The results demonstrate the value of synthesizing abundant AAR chronologic data across various coastal environments, integrating multiple distinct geologic studies. The ages and elevations of the Quaternary units are important for current hypotheses about relative sea-level history and crustal dynamics in the region, which was likely influenced by the Laurentide ice sheet, the margin just ~400 km to the north.

Delaware, Maryland, Virginia↗
USGS2020

Substantially greater carbon emissions estimated based on annual land-use transition data

Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km 2 , respectively, but developed lands gained 293 km 2 (29 km 2 annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr −1 ) and agricultural expansion (0.046 Tg C·yr −1 ); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics.

Delaware, Maryland, Virginia↗
USGS2020

Seismic stratigraphic framework of the continental shelf offshore Delmarva, U.S.A.: Implications for Mid-Atlantic Bight evolution since the Pliocene

Understanding how past coastal systems have evolved is critical to predicting future coastal change. Using over 12,000 trackline kilometers of recently collected, co-located multi-channel boomer, sparker and chirp seismic reflection profile data integrated with previously collected borehole and vibracore data, we define the upper (< 115 m below mean lower low water) seismic stratigraphic framework offshore of the Delmarva Peninsula, USA. Twelve seismic units and 11 regionally extensive unconformities (U1-U11) were mapped over 5900 km 2 of North America's Mid-Atlantic continental shelf. We interpret U3, U7, U9, U11 as transgressive ravinement surfaces, while U1,2,4,5,6,8,10 are subaerial unconformities illustrating distinct periods of lower sea-level. Based on areal distribution, stratigraphic relationships and dating results (Carbon 14 and amino acid racemization estimates) from earlier vibracore and borehole studies, we interpret the infilled channels as late Neogene and Quaternary courses of the Susquehanna, Potomac, Rappahannock, York, James rivers and tributaries, and a broad flood plain. These findings indicate that the region's geologic framework is more complex than previously thought and that Pleistocene paleochannels are abundant in the Mid-Atlantic. This study synthesizes and correlates the findings of other Atlantic Margin studies and establishes a large-scale Quaternary framework that enables more detailed stratigraphic analysis in the future. Such work has implications for inner continental shelf systems tract evolution, the relationship between antecedent geology and modern coastal systems, assessments of eustacy, glacial isostatic adjustment, and other processes and forcings that play a role in passive margin evolution.

Delaware, Maryland, Virginia↗
USGS2019

Influenza A virus detected in native bivalves in waterfowl habitat of the Delmarva Peninsula, USA

We evaluated the prevalence of influenza A virus (IAV) in different species of bivalves inhabiting natural water bodies in waterfowl habitat along the Delmarva Peninsula and Chesapeake Bay in eastern Maryland. Bivalve tissue from clam and mussel specimens ( Macoma balthica , Macoma phenax , Mulinia sp., Rangia cuneata , Mya arenaria , Guekensia demissa , and an undetermined mussel species) from five collection sites was analyzed for the presence of type A influenza virus by qPCR targeting the matrix gene. Of the 300 tissue samples analyzed, 13 samples (4.3%) tested positive for presence of influenza virus A matrix gene. To our knowledge, this is the first report of detection of IAV in the tissue of any bivalve mollusk from a natural water body.

Delaware, Maryland, Virginia↗
USGS2019

Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture

Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000‐km 2 watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15‐yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change.

Delaware, Maryland, Virginia↗
USGS2019

Holocene Sea-Level Variability from Chesapeake Bay Tidal Marshes

We reconstructed the last 10,000 years of Holocene relative sea-level rise (RSLR) from sediment core records in near Chesapeake Bay, eastern U.S.A., including new marsh records from the Potomac and Rappahannock Rivers, Virginia. Results show mean RSLR rates of 2.6 mm yr-1 from 10 to 8 kilo-annum (ka) due to combined final ice-sheet melting during deglaciation and glacio-isostatic adjustment (GIA subsidence). Mean RSLR rates from ~6 ka to present were 1.4 mm yr-1 due mainly to GIA, consistent with other east coast marsh records and geophysical models. However, a progressively slower mean rate (< 1.0 mm yr-1) characterized the last 1000 years when a multi-century-long period of tidal marsh development occurred during the Medieval Climate Anomaly (MCA) and Little Ice Age (LIA) in the Chesapeake Bay region and other East Coast marshes. This decrease was most likely due to climatic and glaciological processes and, correcting for GIA, represents a fall in global mean sea level (GMSL) at near the end of Holocene Neoglacial cooling. These pre-historical climate- and GIA-driven Chesapeake Bay sea-level changes contrast sharply with those based on Chesapeake Bay tide-gauge rates (3.1-4.5 mm yr-1) (back to 1903). After subtracting the GIA subsidence component, these rates can be attributed to long-term (millennial) global factors of accelerated ocean thermal expansion (~ 1.0 mm yr-1) and mass loss from alpine glaciers and Greenland and Antarctic Ice Sheets (1.5-2.0 mm yr-1).

Delaware, Maryland, Virginia↗
USGS2019

Optimizing an inner-continental shelf geologic framework investigation through data repurposing and machine learning

The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5,400 km2 of geophysical and hydrographic data on the Atlantic continental shelf between Delaware and Virginia over the past decade and a half. Although originally acquired for different objectives, the comprehensive coverage and variety of data (bathymetry, backscatter, imagery and physical samples) presents an opportunity to merge collections and create high-resolution, broad-scale geologic maps of the seafloor. This compilation of data repurposes hydrographic data, expands the area of geologic investigation, highlights the versatility of mapping data, and creates new geologic products that would not have been independently possible. The data are classified using a variety of machine learning algorithms, including unsupervised and supervised methods. Four unique classes were targeted for classification, and source data include bathymetry, backscatter, slope, curvature, and shaded-relief. A random forest classifier used on all five source data layers was found to be the most accurate method for these data. Geomorphologic and sediment texture maps are derived from the classified acoustic data using over 200 groundtruth samples. The geologic data products can be used to identify sediment sources, inform resource management, link seafloor environments to sediment texture, improve our understanding of seafloor structure and sediment pathways, and demonstrate how ocean mapping resources can be useful beyond their original intent to maximize the footprint and scientific impact of a study.

Delaware, Maryland, Virginia↗
USGS2016

Chesapeake Bay recovery and factors affecting trends: Long-termmonitoring, indicators, and insights

Monitoring the outcome of restoration efforts is the only way to identify the status of a recovery and the most effective management strategies. In this paper, we discuss Chesapeake Bay and watershed recovery and factors influencing water quality trends. For over 30 years, the Chesapeake Bay Program Partnership’s long-term tidal and watershed water quality monitoring networks have measured physical, chemical and biological parameters throughout the bay and its surrounding watershed underpinning an adaptive management process to drive ecosystem recovery. There are many natural and anthropogenic factors operating and interacting to affect the watershed and bay water quality recovery responses to management actions. Across habitats and indicators, the bay and its watershed continue to express a diverse spatial and temporal fabric of multiscale conditions, stressors and trends that show a range of health conditions and impairment s , as well as evidence of progress and degradation. Recurrent independent reviews of the monitoring program have driven a culture of continued adaptation of the monitoring networks to reflect ever evolving management information needs. The adherence to bay and watershed-wide consistent monitoring protocols provides monitoring data supporting analyses and development of scientific syntheses that underpin indicator and model development, regulatory assessments, targeting of management actions, evaluation of management effectiveness, and directing of priorities and policies.

Delaware, Maryland, Virginia↗
USGS2014

National Oceanic and Atmospheric Administration hydrographic survey data used in a U.S. Geological Survey regional geologic framework study along the Delmarva Peninsula

The U.S. Geological Survey initiated a research effort in 2014 to define the geologic framework of the Delmarva Peninsula inner continental shelf, which included new data collection and assembly of relevant extant datasets. Between 2006 and 2011, Science Applications International Corporation, under contract to the National Oceanic and Atmospheric Administration National Ocean Service, carried out 23 hydrographic surveys covering more than 4,100 square kilometers of the continental shelf using Reson multibeam echosounders and Klein towed sidescan sonars to update nautical charts along the Delmarva Peninsula. Acoustic backscatter data from these instruments are valuable for characterizing aspects of shallow geologic framework, including seafloor geology, sediment transport pathways, and marine resources. The data cover an area that extends from the entrance of Delaware Bay, Delaware, south to Parramore Island, Virginia, in water depths of about 3 to 35 meters below mean lower low water. Data were collected along lines spaced 40 meters apart, resulting in 40 to 100 percent seafloor coverage for multibeam bathymetry. Processed bathymetric data within the Delmarva Peninsula study area are available through a National Ocean Service interactive map interface, but towed sidescan data products are limited, and multibeam backscatter data products have not been available in the past. The U.S. Geological Survey obtained raw Reson multibeam data files from Science Applications International Corporation and the National Oceanic and Atmospheric Administration for 20 hydrographic surveys and extracted backscatter data using the Fledermaus Geocoder Toolbox from Quality Positioning Service. The backscatter mosaics produced by the U.S. Geological Survey for the inner continental shelf of the Delmarva Peninsula using National Oceanic and Atmospheric Administration data increased regional geophysical surveying efficiency, collaboration among government agencies, and the area over which geologic data can be interpreted by the U.S. Geological Survey. This report describes the methods by which the backscatter data were extracted and processed and includes backscatter mosaics and interpolated bathymetric surfaces.

Delaware, Maryland, Virginia↗
USGS2006

Ground water beneath coastal bays of the Delmarva Peninsula: Ages and nutrients

To complement a large-scale geophysical investigation of occurrence and discharge of fresh water beneath Delaware, Maryland, and Virginia (Delmarva) coastal bays, we measured (1) salinity and nutrient concentrations in ground water samples from several offshore coring sites and (2) a suite of chemical and isotopic parameters, including age tracers, in ground water samples from a Delaware site. Samples were collected in a variety of Holocene and Plio-Pleistocene sediments in nearshore and offshore areas of the bays. Ground waters that were significantly fresher than overlying waters were found in plumes up to at least 15 m thick extending to more than 500 m offshore in some areas. Steep salinity and nutrient gradients occur within a few meters of the sediment surface in most locations studied. The zone of transition from deeper fresher waters to shallower brackish waters is generally thin near shore, but thickens and becomes more gradual offshore. Ground water ages at the Delaware site were mostly < 50 yr in both fresh waters and brackish waters up to 22 m below the bay bottom. Water chemistry and age data indicate that fresh water plumes beneath the estuary are active extensions of the surficial aquifer carrying nitrate from recharge areas on land, whereas brackish ground water surrounding the fresh water plumes is recharged beneath the estuary and contains ammonium and phosphate released by diagenesis of shallow estuarine sediments. Denitrification affects some of the fresh water nitrate before it mixes with brackish ground water or discharges to surface water.

Delaware, Maryland, Virginia↗
USGS2006

Studying ground water under Delmarva coastal bays using electrical resistivity

Fresh ground water is widely distributed in subsurface sediments below the coastal bays of the Delmarva Peninsula (Delaware, Maryland, and Virginia). These conditions were revealed by nearly 300 km of streamer resistivity surveys, utilizing a towed multichannel cable system. Zones of high resistivity displayed by inversion modeling were confirmed by vibradrilling investigations to correspond to fresh ground water occurrences. Fresh water lenses extended from a few hundred meters up to 2 km from shore. Along the western margins of coastal bays in areas associated with fine-grained surficial sediments, high-resistivity layers were widespread and were especially pronounced near tidal creeks. Fresh ground water layers were less common along the eastern barrier-bar margins of the bays, where sediments were typically sandy. Mid-bay areas in Chincoteague Bay, Maryland, did not show evidence of fresh water. Indian River Bay, Delaware, showed complex subsurface salinity relationships, including an area with possible hypersaline brines. The new streamer resistivity system paired with vibradrilling in these investigations provides a powerful approach to recovering information required for extension of hydrologic modeling of shallow coastal aquifer systems into offshore areas.

Delaware, Maryland, Virginia↗
USGS2006

Development of land segmentation, stream-reach network, and watersheds in support of hydrological simulation program: Fortran (HSPF) modeling, Chesapeake Bay watershed, and adjacent parts of Maryland, Delaware, and Virginia

The U.S. Geological Survey, U.S. Environmental Protection Agency Chesapeake Bay Program Office, Interstate Commission on the Potomac River Basin, Maryland Department of the Environment, Virginia Department of Conservation and Recreation, Virginia Department of Environmental Quality, and the University of Maryland Center for Environmental Science are collaborating on the Chesapeake Bay Regional Watershed Model, using Hydrological Simulation Program - FORTRAN to simulate streamflow and concentrations and loads of nutrients and sediment to Chesapeake Bay. The model will be used to provide information for resource managers. In order to establish a framework for model simulation, digital spatial datasets were created defining the discretization of the model region (including the Chesapeake Bay watershed, as well as the adjacent parts of Maryland, Delaware, and Virginia outside the watershed) into land segments, a stream-reach network, and associated watersheds. Land segmentation was based on county boundaries represented by a 1:100,000-scale digital dataset. Fifty of the 254 counties and incorporated cities in the model region were divided on the basis of physiography and topography, producing a total of 309 land segments. The stream-reach network for the Chesapeake Bay watershed part of the model region was based on the U.S. Geological Survey Chesapeake Bay SPARROW (SPAtially Referenced Regressions On Watershed attributes) model stream-reach network. Because that network was created only for the Chesapeake Bay watershed, the rest of the model region uses a 1:500,000-scale stream-reach network. Streams with mean annual streamflow of less than 100 cubic feet per second were excluded based on attributes from the dataset. Additional changes were made to enhance the data and to allow for inclusion of stream reaches with monitoring data that were not part of the original network. Thirty-meter-resolution Digital Elevation Model data were used to delineate watersheds for each stream reach. State watershed boundaries replaced the Digital Elevation Model-derived watersheds where coincident. After a number of corrections, the watersheds were coded to indicate major and minor basin, mean annual streamflow, and each watershed's unique identifier as well as that of the downstream watershed. Land segments and watersheds were intersected to create land-watershed segments for the model.

Delaware, Maryland, Virginia↗