Geology topics

Gary W. Shenk

Publications and source records attributed to Gary W. Shenk.

At least 19 recordsLinked to original sources

USGS2025

METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed

Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes ( i.e. , agreement between the expected and monitored trends) across the Chesapeake Bay watershed.

Chesapeake Bay watershed↗

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

Increasing phosphorus loss despite widespread concentration decline in US rivers

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

conterminous United States↗

USGS2024

Integrating monitoring and modeling information to develop an indicator of watershed progress toward nutrient reduction goals

Eutrophication has been a major environmental issue in many coastal and inland ecosystems, which is primarily attributed to excessive anthropogenic inputs of nutrients. Restoration efforts have therefore focused on the reduction of watershed nutrient loads, including in the Chesapeake Bay (USA). To facilitate watershed management, watershed models are often developed and used to assess the expected impact of scenarios of past and future management policies and practices and the impact of watershed conditions. However, the level of load reductions estimated using monitoring data often does not match with model predictions, which may cast doubt on the effectiveness of the restoration efforts, the reliability of the model, and the prospect of achieving pre-established reduction goals. To better reconcile such inconsistencies between expectation (i.e., modeling estimates) and reality (i.e., monitoring information), a watershed-wide indicator was developed for the Chesapeake Bay watershed to explicitly quantify the progress toward nutrient reduction goals in the context of the Chesapeake Bay Total Maximum Daily Load (TMDL). Results of the indicator show that since 1995 long-term progress has been made toward the TMDL planning targets for both nitrogen and phosphorus. Specifically, management practices that are implemented and realized (in monitoring data) have been increasing over time, whereas management practices that need to be implemented in the future to meet the goals have been decreasing. In addition, the progress of nutrient reduction toward meeting the goals has varied with source sectors and watershed locations: i.e., point source management has been fully or nearly fully implemented, whereas nonpoint source management has been implemented by 50%-70%. In summary, this indicator, which is largely based on monitoring data, can provide at least four benefits: (1) evaluating the validity of the modeled estimates of nutrient reductions by comparing them to monitoring information; (2) placing the monitored riverine trends into a management context; (3) comparing progress between different nutrient source sectors and watershed locations; and (4) facilitating communication of the progress to the Chesapeake Bay Program Partnership and the public. Although we focus on the indicator development and interpretation for the Chesapeake Bay watershed, the framework can be transferred to watersheds within and beyond this watershed, where similar modeling and monitoring information exists, to gauge expectations on the trajectory and pace of the progress toward meeting restoration goals.

Chesapeake Bay watershed↗

USGS2024

Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model

The Chesapeake Bay Program’s Watershed Model (CBWM) has been used as an accounting tool for the Chesapeake Bay Total Maximum Daily Load (TMDL). However, some of the fundamental parameters that underpin the watershed model may not represent local watershed characteristics at all scales. Significant investments have been made by state and local governments, and other local stakeholders, who are interested in validating loads and progress in implementing measures to achieve the pollutant reductions called for in the TMDL through local monitoring data. For the purposes of this STAC workshop, local monitoring is considered any relevant data collected by a local, regional, state, or federal organization that has not been used previously in the development, calibration, or validation of the CBWM. Some of these local monitoring efforts have been collecting data over the past 5-10 years, with some datasets extending back over more than two decades. However, the data and the CBWM are often not directly comparable due to differences in temporal and spatial scales or because the water quality parameters being monitored are not those estimated by the model. Therefore, a Scientific and Technical Advisory Committee (STAC) workshop was convened to bring together Chesapeake Bay Program (CBP) modelers, local and state government stakeholders, and scientists who are monitoring and analyzing local water quality data to recommend ways in which local monitoring data can be used to inform the CBWM, identify gaps between modeled and monitored data, and validate model predictions at the local scale. The workshop, “Using Local Monitoring Results to Inform the Chesapeake Bay Program’s Watershed Model”, was held in March 2023 to provide insight on the scope of local water quality monitoring efforts within and outside of the Bay watershed that could be used to inform the CBWM. Scientists and managers developed recommendations that could be used by modelers for either calibration or knowledge generation to inform the Phase 7 version of the CBWM currently under development for a 2027 decision by the CBP, recommendations for how local monitoring efforts could be designed or altered to better inform the CBWM, and recommendations for how monitored trends could be used in management. The preliminary presentations for the workshop provided essential background information on the CBWM and data used to parameterize it. This information was the foundation for discussions on existing data gaps, the importance of current local monitoring networks, and best practices for developing future monitoring networks. More information on this STAC-funded effort including workshop presentation slides and recordings can be accessed on the workshop webpage. Confidence in the loading estimates of the CBWM is critical because of its role as the accounting mechanism for measuring progress toward the Bay TMDL’s nutrient and sediment reduction goals. Those who are being asked or required to pay for these reductions, from state and local government managers to farmers, property owners and developers, must have confidence in the scientific validity of the CBWM’s loading estimates or trust in the restoration effort will dissipate. Toward that end, several local entities have invested in extensive urban, suburban, and agricultural monitoring programs to characterize nutrient and sediment loading (among other water quality parameters) at a relatively fine scale (from a few acres to 5 square miles). Monitoring networks outside of the Bay watershed were also included as their relevance and similarities to Bay watershed landscapes, hydrology, and climate conditions can help build the body of knowledge necessary for better parameterization of the CBWM. Local monitoring results could be analyzed for loads and trends for calibration of Phase 7, comparison against trends, informing the structure and parameterization of the model, and potentially in policy evaluation. The effectiveness of management practices at the small watershed scale is a primary question of watershed managers that could be addressed by local monitoring, but to do so study design and statistical techniques may need to be altered if these datasets are intended to inform parameterization of the Bay modeling tools. The partnership would benefit from the redesign of some existing monitoring programs so that they are hypothesis-driven, with fully described inputs, outputs, and practices. New statistical tools could be applied to evaluate the relative importance of various drivers affecting water quality and influenced by hydrogeologic setting and watershed condition.

Chesapeake Bay watershed↗

USGS2023

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↗

USGS2023

Impacts and uncertainties of climate-induced changes in watershed inputs on estuarine hypoxia

Multiple climate-driven stressors, including warming and increased nutrient delivery, are exacerbating hypoxia in coastal marine environments. Within coastal watersheds, environmental managers are particularly interested in climate impacts on terrestrial processes, which may undermine the efficacy of management actions designed to reduce eutrophication and consequent low-oxygen conditions in receiving coastal waters. However, substantial uncertainty accompanies the application of Earth system model (ESM) projections to a regional modeling framework when quantifying future changes to estuarine hypoxia due to climate change. In this study, two downscaling methods are applied to multiple ESMs and used to force two independent watershed models for Chesapeake Bay, a large coastal-plain estuary of the eastern United States. The projected watershed changes are then used to force a coupled 3-D hydrodynamic–biogeochemical estuarine model to project climate impacts on hypoxia, with particular emphasis on projection uncertainties. Results indicate that all three factors (ESM, downscaling method, and watershed model) are found to contribute substantially to the uncertainty associated with future hypoxia, with the choice of ESM being the largest contributor. Overall, in the absence of management actions, there is a high likelihood that climate change impacts on the watershed will expand low-oxygen conditions by 2050 relative to a 1990s baseline period; however, the projected increase in hypoxia is quite small (4 %) because only climate-induced changes in watershed inputs are considered and not those on the estuary itself. Results also demonstrate that the attainment of established nutrient reduction targets will reduce annual hypoxia by about 50 % compared to the 1990s. Given these estimates, it is virtually certain that fully implemented management actions reducing excess nutrient loadings will outweigh hypoxia increases driven by climate-induced changes in terrestrial runoff.

Maryland, Virginia↗

USGS2023

Recognizing political influences in participatory social-ecological systems modeling

Stakeholder participation in social-ecological systems (SES) modeling is increasingly considered a desirable way to elicit diverse sources of knowledge about SES behavior and to promote inclusive decision-making in SES. Understanding how participatory modeling processes function in the context of long-term adaptive management of SES may allow for better design of participatory processes to achieve the intended outcomes of inclusionary knowledge, representativeness, and social learning, while avoiding unintended outcomes. Long-term adaptive management contexts often include political influences -- attempts to shift or preserve power structures and authority, and efforts to represent the political and economic interests of stakeholders -- in the computer models that are used to shape policy making and implementation. In this research, we examine a period that included a major transition in the watershed model used for management of the Chesapeake Bay in the United States. The Chesapeake Bay watershed model has been in development since the 1980s, and is considered by many to be an exemplary case of participatory modeling. We use documentary analysis and interviews with participants involved in the model application and development transition to reveal a variety of ways in which participatory modeling may be subject to different kinds of political influences, some of which resulted in unintended outcomes, including: perceptions of difficulty updating the model in substantive ways, “gaming” of the model/participatory process by stakeholders, and increasing resistance against considering uncertainty in the system not captured by the model. This research suggests unintended or negative outcomes may be associated with both participatory decision-making and stakeholder learning even though they are so often touted as the benefits of participatory modeling. We end with a hypothesis that further development of a theory of computer model governance to bridge model impact and broader theories of environmental governance at the science-policy interface may result in improved SES modeling outcomes.

Maryland, Virginia↗

USGS2022

Major point and nonpoint sources of nutrient pollution to surface water have declined throughout the Chesapeake Bay watershed

Understanding drivers of water quality in local watersheds is the first step for implementing targeted restoration practices. Nutrient inventories can inform water quality management decisions by identifying shifts in nitrogen (N) and phosphorus (P) balances over space and time while also keeping track of the likely urban and agricultural point and nonpoint sources of pollution. The Chesapeake Bay Program's Chesapeake Assessment Scenario Tool (CAST) provides N and P balance data for counties throughout the Chesapeake Bay watershed, and these data were leveraged to create a detailed nutrient inventory for all the counties in the watershed from 1985–2019. This study focuses on three primary watershed nutrient balance components—agricultural surplus, atmospheric deposition, and point source loads—which are thought to be the leading anthropogenic drivers of nutrient loading trends across the watershed. All inputs, outputs, and derived metrics (n=53) like agricultural surplus and nutrient use efficiency, were subjected to short- and long-term trend analyses to discern how sources of pollution to surface water have changed over time. Across the watershed from 1985–2019, downward trends in atmospheric deposition were ubiquitous. Though there are varying effects, long-term declines in agricultural surplus were observed, likely because nutrients are being managed more efficiently. Multiple counties' point source loads declined, primarily associated with upgrades at major cities that discharge treated wastewater directly to tidal waters. Despite all of these positive developments, recent increases in agricultural surpluses from 2009–2019 highlight that water quality gains may soon be reversed in many agricultural areas of the basin. Besides tracking progress and jurisdictional influence on pollution sources, the nutrient inventory can be used for retrospective water quality analysis to highlight drivers of past improvement/degradation of water quality trends and for decision makers to develop and track their near- and long-term watershed restoration strategies.

Chesapeake Bay watershed↗

USGS2022

Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling

Seasonal hypoxia is a characteristic feature of the Chesapeake Bay due to anthropogenic nutrient input from agriculture and urbanization throughout the watershed. Although coordinated management efforts since 1985 have reduced nutrient inputs to the Bay, oxygen concentrations at depth in the summer still frequently fail to meet water quality standards that have been set to protect critical estuarine living resources. To quantify the impact of watershed nitrogen reductions on Bay hypoxia during a recent period including both average discharge and extremely wet years (2016–2019), this study employed both statistical and three-dimensional (3-D) numerical modeling analyses. Numerical model results suggest that if the nitrogen reductions since 1985 had not occurred, annual hypoxic volumes (O 2 < 3 mg L −1 ) would have been ~50–120% greater during the average discharge years of 2016–2017 and ~20–50% greater during the wet years of 2018–2019. The effect was even greater for O 2 < 1 mg L −1 , where annual volumes would have been ~80–280% greater in 2016–2017 and ~30–100% greater in 2018–2019. These results were supported by statistical analysis of empirical data, though the magnitude of improvement due to nitrogen reductions was greater in the numerical modeling results than in the statistical analysis. This discrepancy is largely accounted for by warming in the Bay that has exacerbated hypoxia and offset roughly 6–34% of the improvement from nitrogen reductions. Although these results may reassure policymakers and stakeholders that their efforts to reduce hypoxia have improved ecosystem health in the Bay, they also indicate that greater reductions are needed to counteract the ever-increasing impacts of climate change.

Chesapeake Bay↗

USGS2022

Mechanisms controlling climate warming impact on the occurrence of hypoxia in Chesapeake Bay

A Climate change represents an increasing stressor on estuarine and coastal ecosystems. A series of simulations were run using the Integrated Compartment Water Quality Model to determine the magnitude of various mechanisms controlling the effect of climate warming on dissolved oxygen (DO) in the Chesapeake Bay. The results suggested that the average hypoxic volume in the summer would increase by 9% (410 Mm 3 ) from 1995 to 2025 as air temperature increases by 1.06°C and water temperature by 0.9°C. The change in DO solubility contributes 55% of the total climate warming effect, biological rates 33%, and stratification 11%. The Rappahannock Shoal, a hydraulic control point, plays a major role in determining the effect of climate warming on DO in the Bay. Due to the abrupt change in bathymetry, the convergence between seaward-moving freshwater and landward-moving saltwater causes downwelling and enhanced vertical mixing which introduces surface water of higher temperature to the deep channel and accelerates organic matter remineralization and oxygen consumption in deep waters. Surface water DO concentrations will decrease under climate warming conditions due to lower DO solubility, reducing DO flux to the deep channel and contributing to hypoxia development. These findings provide critical information for future management decision making regarding the effects of climate warming on DO in Chesapeake Bay and other estuaries.

Maryland, Virginia↗

USGS2021

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↗

USGS2021

Quantifying the response of nitrogen speciation to hydrology in the Chesapeake Bay Watershed using a multilevel modeling approach

Excessive nitrogen (N) inputs to coastal waters can lead to severe eutrophication and different chemical forms of N exhibit varying levels of effectiveness in fueling primary production. Efforts to mitigate N fluxes from coastal watersheds are often guided by models that predict changes in N loads as a function of changes in land use, management practices, and climate. However, relatively little is known on the impacts of such changes on the relative fractions of different N forms. We leveraged a long-term dataset of N loads from over 100 river stations to investigate how the fraction, that is, the ratio of to total N ( /TN), changes as a function of spatio-temporal changes in TN loads in the Chesapeake Bay watershed. We built a hierarchical model that separates the response of to changes in TN load occurring at different scales: Across river stations, where differences in TN loads are largely driven by spatial differences in anthropogenic inputs, and within stations, where inter-annual variability in hydrology is a key driver of changes in TN loads. Results suggest that while increases in TN loads resulting from changes in anthropogenic inputs lead to an increase in the fraction, a decrease in the fraction may occur when increases in TN loads are driven by increased streamflow. These results are especially relevant in watersheds that may experience changes in N loads due to both management decisions and climate-driven changes in hydrology.

Chesapeake Bay watershed↗

USGS2021

Advancing estuarine ecological forecasts: Seasonal hypoxia in Chesapeake Bay

Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.

Chesapeake Bay↗

USGS2021

Atmospheric nitrogen deposition in the Chesapeake Bay watershed: A history of change

The Chesapeake Bay watershed has been the focus of pioneering studies of the role of atmospheric nitrogen (N) deposition as a nutrient source and driver of estuarine trophic status. Here, we review the history and evolution of scientific investigations of the role of atmospheric N deposition, examine trends from wet and dry deposition networks, and present century-long (1950–2050) atmospheric N deposition estimates. Early investigations demonstrated the importance of atmospheric deposition as an N source to the Bay, providing 25%–40% among all major N sources. These early studies led to the unprecedented inclusion of targeted decreases in atmospheric N deposition as part of the multi-stakeholder effort to reduce N loads to the Bay. Emissions of nitrogen oxides (NO x ) and deposition of wet nitrate, oxidized dry N, and dry ammonium (NH 4 + ) sharply and synchronously declined by 60%–73% during 1995–2019. These decreases largely resulted from implementation of Title IV of the 1990 Clean Air Act Amendments, which began in 1995. Wet NH 4 + deposition shows no significant trend during this period. The century-long atmospheric N deposition estimates indicate an increase in total atmospheric N deposition in the Chesapeake watershed from 1950 to a peak of ~15 kg N/ha/yr in 1979, trailed by a slight decline of <10% through the mid-1990s, and followed by a sharp decline of about 40% thereafter through 2019. An additional 21% decline in atmospheric N deposition is projected from 2015 to 2050. A comparison of the Potomac River and James River watersheds indicates higher atmospheric N deposition in the Potomac, likely resulting from greater emissions from higher proportions of agricultural and urban land in this basin. Atmospheric N deposition rose from 30% among all N sources to the Chesapeake Bay watershed in 1950 to a peak of 40% in 1973, and a decline to 28% by 2015. These data highlight the important role of atmospheric N deposition in the Chesapeake Bay watershed and present a potential opportunity for decreases in deposition to contribute to further reducing N loads and improving the trophic status of tidal waters.

New York, New Jersey, Delaware, Maryland, Pennsylv↗

USGS2021

The Chesapeake Bay program modeling system: Overview and recommendations for future development

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

Chesapeake Bay estuary and watershed↗

USGS2021

Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.

Environmental Modelling and Software↗

USGS2021

Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program's (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States' largest estuary . The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for ~200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework's current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.

Chesapeake Bay watershed↗