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Defining a data management strategy for USGS Chesapeake Bay studies

The mission of U.S. Geological Survey’s (USGS) Chesapeake Bay studies is to provide integrated science for improved understanding and management of the Chesapeake Bay ecosystem. Collective USGS efforts in the Chesapeake Bay watershed began in the 1980s, and by the mid-1990s the USGS adopted the watershed as one of its national place-based study areas. Great focus and effort by the USGS have been directed toward Chesapeake Bay studies for almost three decades. The USGS plays a key role in using “ecosystem-based adaptive management, which will provide science to improve the efficiency and accountability of Chesapeake Bay Program activities” (Phillips, 2011). Each year USGS Chesapeake Bay studies produce published research, monitoring data, and models addressing aspects of bay restoration such as, but not limited to, fish health, water quality, land-cover change, and habitat loss. The USGS is responsible for collaborating and sharing this information with other Federal agencies and partners as described under the President’s Executive Order 13508—Strategy for Protecting and Restoring the Chesapeake Bay Watershed signed by President Obama in 2009. Historically, the USGS Chesapeake Bay studies have relied on national USGS databases to store only major nationally available sources of data such as streamflow and water-quality data collected through local monitoring programs and projects, leaving a multitude of other important project data out of the data management process. This practice has led to inefficient methods of finding Chesapeake Bay studies data and underutilization of data resources. Data management by definition is “the business functions that develop and execute plans, policies, practices and projects that acquire, control, protect, deliver and enhance the value of data and information.” (Mosley, 2008a). In other words, data management is a way to preserve, integrate, and share data to address the needs of the Chesapeake Bay studies to better manage data resources, work more efficiently with partners, and facilitate holistic watershed science. It is now the goal of the USGS Chesapeake Bay studies to implement an enhanced and all-encompassing approach to data management. This report discusses preliminary efforts to implement a physical data management system for program data that is not replicated nationally through other USGS databases.

Chesapeake Bay

Aquatic invasive species in the Chesapeake Bay drainage—Research-based needs and priorities of U.S. Geological Survey partners and collaborators

Executive Summary The U.S. Geological Survey (USGS) is revising the Chesapeake Bay-based science plan to align it with recent U.S. Department of Interior and USGS science priorities that include, as stated in the plan, providing “an integrated understanding of the factors affecting fish habitat, fish health, and landscape conditions” in Chesapeake Bay and its watershed. A report of partner agencies’ needs and priorities related to aquatic invasive species (AIS) science was identified as an informational gap; a report would help to further development of the science program related to aquatic animal health and habitat. This objective was addressed through review of pertinent documentation and conversations with representatives of State, Federal, and regional agencies with vested interests in AIS management in Chesapeake Bay and the Chesapeake Bay drainage area, and this document was produced to summarize the related findings. All agencies and organizations (13) reported that AIS are of general concern, with most stakeholder groups reporting AIS-related issues to be of high priority, including invasive fishes and invertebrates, invasive plants, and microbes including aquatic animal pathogens. Invasive fishes are of great concern to all partner agencies. Channa argus (northern snakehead) and Ictalurus furcatus (blue catfish) are high priority and represent the two most named AIS of concern for these agencies. Nine of 10 stakeholder groups listed northern snakehead as a high priority species, and 6 listed blue catfish as a high priority species as well. Pylodictis olivaris (flathead catfish), invasive crayfish species , and dressenid mussels were also prioritized by multiple partner groups, each receiving specific mention by at least 3 of the 10 stakeholder groups in discussions or documents. Invasive carp, such as Hypophthalmichthys molatrix (silver carp), also received mention by multiple agencies (3 of the 10 stakeholder groups) because these fish represent priority AIS in nearby watersheds and a threat for introduction and dissemination within the Chesapeake Bay watershed from these neighboring regions. Invasive plants are among priority species, and Hydrilla verticillata (hydrilla) topped the list. Hydrilla was reported as a priority species by 5 of the stakeholder groups queried. Trapa natans and T. bispinosa (water chestnut), Phragmites australis (common reed), and Lythrum salicaria (purple loosestrife) were also among the aquatic invasive plants that were prioritized by multiple partner agencies. Multiple stakeholder groups (5 of the 10 groups) also considered Didymosphenia geminata (didymo) and various aquatic animal pathogens among their priority AIS for management considerations. Science needs that were recurrently indicated by stakeholders to support management of invasive species include Technology to enhance biosurveillance capability, such as reliable environmental DNA based detection methodology; Risk assessment modeling to forewarn of and prioritize AIS-related threats; Increased information and intervention methods related to vectors and pathways of AIS introductions; Increased information about the biology and life history of AIS, including information related to trophic interactions, health and disease, and distribution and abundance; and Potential applications of mitigation strategies, including genetically based biocontrol mechanisms. Potential next steps to address the science needs include Development of biosurveillance and risk assessment tools for identification of AIS in proactive management; Development of proactive management techniques to prevent AIS introductions through recognized vectors and pathways; Development of interagency biosurveillance programs to best utilize personnel, funds, and other resources among interested agencies and organizations; Investigations to address life history, consequences, and movement/dissemination of top priority invasive species in the region; Investigations to determine the potential for novel mitigation technologies, such as the application of synthetic biological (genetic) control methods; and Investigations with focus on emerging and high priority AIS in the region, including fishes (blue catfish, flathead catfish, northern snakehead), invertebrates (invasive crayfish and mollusks) and plants (hydrilla, water chestnut, phragmites).

Virginia, Maryland, Delaware, West Virginia, Penns

Chesapeake Bay: A case study in resiliency and restoration

Chesapeake Bay (“mother of waters” or the “great shellfish Bay” in Algonquin), is the largest estuary in the United States and arguably the best studied estuary in the world. Chesapeake Bay is immense, with the main stem stretching 200 nautical miles (315 km) from the mouth of the Susquehanna River to its terminus at the Atlantic Ocean and an overall watershed encompassing 64,000 mi2 (165,000 km2). The mainstem, tributaries, and Bay islands form thousands of miles of coastline (Figure 1). Because of its prominence in estuarine science and ecosystem restoration, developing a working knowledge of Chesapeake Bay science and restoration is important. Hopefully, this overview will whet the appetite to learn more from information available both in the scientific literature and on the Chesapeake Bay Program website www.chesapeakebay.net

Chesapeake Bay watershed

Knowledge gaps, uncertainties, and opportunities regarding the response of the Chesapeake Bay estuary to restoration efforts

As part of the Chesapeake Bay Program's (CBP's) Science and Technical Advisory Committee (STAC) initiative "Achieving Water Quality Goals in the Chesapeake Bay: An Evaluation of System Response", an Estuary Working Group was formed to generate an assessment of scientific knowledge gaps, uncertainties, and recent ecosystem changes to consider in light of CBP's impending goal of full implementation of management measures (Total Maximum Daily Load [TMDL] agreements and associated necessary stakeholder actions) that are being finalized and designed to achieve living resource-based nutrient targets by 2025. This document summarizes aspects of the knowledge gaps, uncertainties, and associated needs and opportunities relating to our understanding of how the Chesapeake Bay estuary has responded to previously implemented management plans, and what features of the ecosystem may slow or enhance its response to future management actions in the face of expected continuations of climatic change.

Chesapeake Bay estuary

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

Water quality impacts of climate change, land use, and population growth in the Chesapeake Bay watershed

The 2010 Chesapeake Bay Total Maximum Daily Load was established for the water quality and ecological restoration of the Chesapeake Bay. In 2017, the latest science, data, and modeling tools were used to develop revised Watershed Implementation Plans (WIPs). In this article, we examine the vulnerability of the Chesapeake Bay watershed to the combined pressures of climate change and growth in population, agricultural intensity, and economic activity for the 60-year period 1995–2055. The results will be used to revise WIPs, as needed, to account for expected increases in loads. Assessing changes relative to 1995 for the years 2025, 2035, 2045, and 2055, mean annual precipitation increases of 3.11%, 4.21%, 5.34%, and 6.91%, respectively, air temperature increases of 1.12, 1.45, 1.84, and 2.12°C, respectively, and potential evapotranspiration increases of 3.36%, 4.43%, 5.54%, and 6.35%, respectively, are projected. Population in the watershed is expected to grow by 3.5 million between 2025 and 2055. Watershed model results show incremental increases in streamflow (2.3%–6.2%), nitrogen (2.6%–10.8%), phosphorus (4.5%–26.7%), and sediment (3.8%–18.8%) loads to the tidal Bay due to climate change. Growth in population, agricultural intensity, development, and economic activity resulted in relatively smaller increases in loads compared to climate change.

Chesapeake Bay watershed

U.S. Geological Survey Chesapeake Bay Studies: Scientific Solutions for a Healthy Bay and Watershed

The U.S. Geological Survey (USGS), the science agency for the Department of Interior (DOI), has the critical role of providing objective science to document and understand ecosystem change in the Chesapeake Bay and its watershed. The human population in the Bay watershed , which grew from 8.1 million in 1950 to almost 16 million in 2000, has resulted in degraded water quality, loss of habitat, and declines in fish and bird populations. USGS scientists are leaders in understanding cause and effect of human activities and natural changes on water quality and the health of the ecosystem. The USGS interacts with resource managers and policy makers to use the science to adapt approaches for implementation, and assess effectiveness of, management actions for ecosystem conservation, restoration, and sustainability. Since the mid-1980s, the USGS has been an active partner in the Chesapeake Bay Program (CBP) , a multi-agency partnership led by the U.S. Environmental Protection Agency, working to achieve the restoration goals set forth in the Chesapeake 2000 agreement . This agreement established over 100 restoration commitments to be addressed by 2010. In 2005, which was the mid-point of the agreement, there was growing concern at all levels of government and by the public that ecological conditions in the Bay and its watershed had not significantly improved. The slow rate of improvement, coupled with the projected impact of human-population increase in the Bay watershed (fig. 1) , implied that many desired ecological conditions will not be achieved by 2010. To address these challenges, the USGS wrote a new science plan for 2006-2011 , and is synthesizing key findings to highlight the accomplishments from science activities for 2000-2005.

Delaware, Maryland, New York, Pennsylvania, Virgin

A Landscape Indicator Approach to the Identification and Articulation of the Ecological Consequences of Land Cover Change in the Chesapeake Bay Watershed, 1970-2000

The advancement of geographic science in the area of land surface status and trends and land cover change is at the core of the current geographic scientific research of the U.S. Geological Survey (USGS) (McMahon and others, 2005). Perhaps the least developed or articulated aspects of USGS land change science have been the identification and analysis of the ecological consequences of land cover change. Changes in land use and land cover significantly affect the ability of ecosystems to provide essential ecological goods and services, which, in turn, affect the economic, public health, and social benefits that these ecosystems provide. One of the great scientific challenges for geographic science is to understand and calibrate the effects of land use and land cover change and the complex interaction between human and biotic systems at a variety of natural, geographic, and political scales. Understanding the dynamics of land surface change requires an increased understanding of the complex nature of human-environmental systems and will require a suite of scientific tools that include traditional geographic data and analysis methods, such as remote sensing and geographic information systems (GIS), as well as innovative approaches to understanding the dynamics of complex systems. One such approach that has gained much recent scientific attention is the landscape indicator, or landscape assessment, approach, which has been developed with the emergence of the science of landscape ecology.

Fact Sheet

The Distribution of Submersed Aquatic Vegetation in the Fresh and Oligohaline Tidal Potomac River, 2004

Introduction Submersed aquatic vegetation (SAV) is a critical component of the Potomac River ecosystem. Though SAV provides important habitat for fauna and stabilizes bottom sediment, very dense beds may restrict recreational and commercial navigation. Exotic species of SAV are managed by the Metropolitan Washington Council of Governments Potomac Aquatic Plant Management Program (PAPMP). Selected beds of exotic SAV species that limit navigation are harvested mechanically. The program began in 1986 when approximately 40 acres of plants were harvested from 18 sites (Metropolitan Washington Council of Governments 1987). Monitoring efforts are an effective means of quantifying the distribution and abundance of the exotic species, Hydrilla verticillata (hydrilla) and other SAV species. These annual surveys provide a basis for identifying large-scale changes throughout the ecosystem and allow managers to evaluate the effectiveness of resource management policies based on a reliable scientific foundation. The U.S. Geological Survey (USGS) has monitored the distribution and composition of SAV beds in the fresh and oligohaline (salinity 0.5 to 5) tidal Potomac River since 1978 using transect sampling (1978 to 1981, 1985 to 1987, and 2002) and shoreline surveys (1983 to 2004). Shoreline survey data from the tidal Potomac River are incorporated into the Virginia Institute of Marine Science (VIMS) annual report on SAV distribution in Chesapeake Bay. The VIMS report and methods are available at http://www.vims.edu/bio/sav. Additional publications concerning SAV distribution in the Potomac River can be found at http://water.usgs.gov/nrp/proj.bib/sav/wethome.htm.

Open-File Report

The Distribution of Submersed Aquatic Vegetation in the Fresh and Oligohaline Tidal Potomac River, 2005

Submersed aquatic vegetation (SAV) is a critical component of the Potomac River ecosystem. Though SAV provides important habitat for fauna and stabilizes bottom sediment, very dense beds may restrict recreational and commercial navigation. Exotic species of SAV are managed by the Metropolitan Washington Council of Governments Potomac Aquatic Plant Management Program (PAPMP). Selected beds of primarily exotic SAV species that limit navigation are harvested mechanically. The program began in 1986 when approximately 40 acres of plants were harvested from 18 sites (Metropolitan Washington Council of Governments 1987). Monitoring efforts are an effective means of quantifying the distribution and abundance of the exotic species, Hydrilla verticillata (hydrilla) and other SAV species. These annual surveys provide a basis for identifying large-scale changes and trends throughout the ecosystem and allow managers to evaluate the effectiveness of resource management policies based on a reliable scientific foundation (Rybicki and Landwehr, 2007). The U.S. Geological Survey (USGS) has monitored the distribution and composition of SAV beds in the fresh and oligohaline (salinity 0.5 to 5) tidal Potomac River since 1978 using transect sampling (1978 to 1981, 1985 to 1987, and 2002) and shoreline surveys (1983 to 2005). The Government of the District of Columbia has monitored the portion of the Potomac and Anacostia Rivers within Washington DC since 1998 (Rottman, 1999; Ryan, 2005, 2006). The species of SAV observed in beds in the tidal Potomac River are incorporated into the Virginia Institute of Marine Science (VIMS) annual report on SAV distribution in Chesapeake Bay. The VIMS report and methods are available at http://www.vims.edu/bio/sav (Orth et al., 2006). Additional publications concerning SAV distribution in the Potomac River can be found at http://water.usgs.gov/nrp/proj.bib/sav/wethome.htm.

Open-File Report

Simulated changes in salinity in the York and Chickahominy Rivers from projected sea-level rise in Chesapeake Bay

As a result of climate change and variability, sea level is rising throughout the world, but the rate along the east coast of the United States is higher than the global mean rate. The U.S. Geological Survey, in cooperation with the City of Newport News, Virginia, conducted a study to evaluate the effects of possible future sea-level rise on the salinity front in two tributaries to Chesapeake Bay, the York River, and the Chickahominy/James River estuaries. Numerical modeling was used to represent sea-level rise and the resulting hydrologic effects. Estuarine models for the two tributaries were developed and model simulations were made by use of the Three-Dimensional Hydrodynamic-Eutrophication Model (HEM-3D), developed by the Virginia Institute of Marine Science. HEM-3D was used to simulate tides, tidal currents, and salinity for Chesapeake Bay, the York River and the Chickahominy/James River. The three sea-level rise scenarios that were evaluated showed an increase of 30, 50, and 100 centimeters (cm). Model results for both estuaries indicated that high freshwater river flow was effective in pushing the salinity back toward Chesapeake Bay. Model results indicated that increases in mean salinity will greatly alter the existing water-quality gradients between brackish water and freshwater. This will be particularly important for the freshwater part of the Chickahominy River, where a drinking-water-supply intake for the City of Newport News is located. Significant changes in the salinity gradients for the York River and Chickahominy/James River estuaries were predicted for the three sea-level rise scenarios. When a 50-cm sea-level rise scenario on the York River during a typical year (2005) was used, the model simulation showed a salinity of 15 parts per thousand (ppt) at river kilometer (km) 39. During a dry year (2002), the same salinity (15 ppt) was simulated at river km 45, which means that saltwater was shown to migrate 6 km farther upstream during a dry year than a typical year. The same was true of the Chickahominy River for a 50-cm sea-level rise scenario but to a greater extent; a salinity of 4 ppt was simulated at river km 13 during a typical year and at river km 28 during a dry year, indicating that saltwater migrated 15 km farther upstream during a dry year. Near a drinking-water intake on the Chickahominy River, for a dry year, salinity is predicted to more than double for all three sea-level rise scenarios, relative to a typical year. During a typical year at this location, salinity is predicted to increase to 0.006, 0.07, and more than 2 ppt for the 30-, 50-, and 100-cm rise scenarios, respectively.

Virginia

Nitrogen in the Chesapeake Bay watershed—A century of change, 1950–2050

Foreword Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems depends on the availability of sound water-resources data and information to develop effective, science-based policies. Effective management of water resources also brings more certainty and efficiency to important economic sectors. Taken together, these actions lead to immediate and long-term economic, social, and environmental benefits that will make a difference to the lives of the almost 400 million people projected to live in the United States by 2050. The Chesapeake Bay is the largest and most productive estuary in the United States and is a vital environmental and economic resource. Approximately half of the water volume of the Chesapeake Bay originates from streams and rivers that drain the 64,243 mi 2 Chesapeake Bay watershed. The Bay and its tributaries have been degraded by excessive nutrients, such as nitrogen, from contributing watersheds. Inputs of nitrogen to the Bay lead to increased algal growth, decreased dissolved oxygen, and declining fisheries. In 2000, the Chesapeake Bay was listed as impaired under the Clean Water Act and Total Maximum Daily Loads (TMDLs) for nutrients and sediment have been established to assist with management actions aimed at nutrient reductions. Effective nutrient management requires an understanding of past, present, and future nutrient sources, fate, and transport in the watershed. The Chesapeake Bay community has been a pioneer in science, management, and regulation to improve water quality. Factors like climate, hydrology, source inputs, and management controls play a vital role in determining the delivery and magnitude of nitrogen inputs to the Bay. Science in the form of monitoring data, predictive tools, and interpretive reports can help inform decisions to better balance the use and control of nitrogen in coastal areas. The findings in this report can contribute to effective management of the Bay and its watershed by providing a synthesis of the understanding of how human activities and environmental change in the watershed in the past, present, and future will influence the export of nitrogen to the Bay. We hope this publication will provide you with insights and information to meet your water resource needs and will foster increased civilian awareness and involvement in the protection and restoration of our Nation’s waters. The information in this report is intended primarily for those interested or involved in resource management and protection, conservation, regulation, and policymaking at the regional and national levels.

Chesapeake Bay watershed

Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin

As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use. Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.

Maryland

Predicting the next storm surge flood

The Virginia Institute of Marine Science (VIMS), National Weather Services (NWS) Sterling and Wakefield, Weather Forecast Offices (WFO), and the Chesapeake Bay Observing System (CBOS) jointly developed a prototype system of a regional capability to address national problem. The system was developed to integrate high-resolution atmospheric and hydrodynamic and storm surge models, evaluate the ability of the prototype to predict land inundation in the Washington, D.C., and provide flooding results to Emergency Managers (EM) using portive. The system is a potential tool for NWS WFOs to provide support to the EMs, first in the Chesapeake Bay region and then in other coastal regions by applying similar approaches in other coastal and Great Lakes regions. The Chesapeake Inundation Prediction System (CIPS) also is building on the initial prototype to predict the combined effects of storm surge and tidal and river flow inundation in the Chesapeake Bay and its tributaries.

Sea Technology

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

Response to L. Land comment on Bricker, Rice, Bricker (2014) From Headwaters to Coast: Influence of human activities on water quality of the Potomac River Estuary. Aquatic Geochemistry 20: 291-324

Overabundance of nutrients is considered one of the top challenges to most populated coastal water bodies, including Chesapeake Bay (Executive Order 13508). As scientists, one of our responsibilities is to contribute to the discussion and evaluation of management actions that have the potential to decrease pollution with concomitant improvement of water quality. Results of our science should inform the legislative process for public policy. Bricker et al. (2014) consider the use of shellfish (i.e., oysters) to remediate nutrient pollution in the Potomac River Estuary (PRE), a tributary to Chesapeake Bay. This concept, termed bioextraction, has generated interest around the globe and has shown promise as a nutrient-removal method in other estuaries and with other shellfish species (Lindahl et al. 2005; Ferreira et al. 2009; Shumway 2011; Silva et al. 2011; Carmichael et al. 2012; Pollack et al. 2013; Rose et al. 2014).

Aquatic Geochemistry

Long-term changes in sediment and nutrient delivery from Conowingo Dam to Chesapeake Bay: Effects of reservoir sedimentation

Reduction of suspended sediment (SS), total phosphorus (TP), and total nitrogen is an important focus for Chesapeake Bay watershed management. The Susquehanna River, the bay’s largest tributary, has drawn attention because SS loads from behind Conowingo Dam (near the river’s mouth) have been rising dramatically. To better understand these changes, we evaluated histories of concentration and loading (1986–2013) using data from sites above and below Conowingo Reservoir. First, observed concentration-discharge relationships show that SS and TP concentrations at the reservoir inlet have declined under most discharges in recent decades, but without corresponding declines at the outlet, implying recently diminished reservoir trapping. Second, best estimates of mass balance suggest decreasing net deposition of SS and TP in recent decades over a wide range of discharges, with cumulative mass generally dominated by the 75∼99.5th percentile of daily Conowingo discharges. Finally, stationary models that better accommodate effects of riverflow variability also support the conclusion of diminished trapping of SS and TP under a range of discharges that includes those well below the literature-reported scour threshold. Overall, these findings suggest that decreased net deposition of SS and TP has occurred at subscour levels of discharge, which has significant implications for the Chesapeake Bay ecosystem.

Maryland

Quantifying groundwater’s role in delaying improvements to Chesapeake Bay water quality

A study has been undertaken to determine the time required for the effects of nitrogen-reducing best management practices (BMPs) implemented at the land surface to reach the Chesapeake Bay via groundwater transport to streams. To accomplish this, a nitrogen mass-balance regression (NMBR) model was developed and applied to seven watersheds on the Delmarva Peninsula. The model included the distribution of groundwater return times obtained from a regional groundwater-flow (GWF) model, the history of nitrogen application at the land surface over the last century, and parameters that account for denitrification. The model was (1) able to reproduce nitrate concentrations in streams and wells over time, including a recent decline in the rate at which concentrations have been increasing, and (2) used to forecast future nitrogen delivery from the Delmarva Peninsula to the Bay given different scenarios of nitrogen load reduction to the water table. The relatively deep porous aquifers of the Delmarva yield longer groundwater return times than those reported earlier for western parts of the Bay watershed. Accordingly, several decades will be required to see the full effects of current and future BMPs. The magnitude of this time lag is critical information for Chesapeake Bay watershed managers and stakeholders.

Delaware, Maryland