Geology topics
Source-linked reports with geographic coverage including Chesapeake Bay watershed.
U.S. Geological Survey (USGS) science supports the Chesapeake Bay watershed economy by linking natural resources to key economic sectors. The roles of USGS monitoring, data, and analysis inform land-use and resource-management decisions for Department of Interior leadership and other stakeholders.
Agricultural conservation practices are central to achieving Chesapeake Bay watershed restoration goals, yet long-term adoption remains challenging even when practices are technically effective and financial assistance is available. Adoption is not a single decision, but a staged and context-dependent process. Awareness and willingness to commitment, implementation, and persistence, are all important. Progress along this pathway is influenced by interacting factors such as knowledge, attitudes, beliefs, social norms, trust, perceived risk, farm characteristics, and practical constraints. Improving adoption requires outreach strategies that respond to the specific factors limiting progress at each stage rather than relying on a one-size-fits-all approach.
The lands and waters of the Chesapeake Bay watershed provide more than $100 billion in economic benefits—an amount that is expected to increase by achieving the region’s clean-water goals. Achieving those goals requires accurate and timely information about the health of the watershed’s rivers and streams. The Chesapeake Bay nontidal monitoring network (NTN), a partnership of local, state, and federal agencies, as well as other partners, was established in 2004 to provide this information. The U.S. Geological Survey analyzes data collected from NTN stations to provide monitoring-based information about the amount of nitrogen, phosphorus, and sediment entering the Chesapeake Bay through its nontidal rivers. Thus, data collected from the NTN inform watershed management by providing decision makers with information on which to base their restoration and conservation actions.
Machine learning (ML) models are often based on complex black-box architectures that are difficult to interpret. This interpretability problem can hinder the use of ML in fields like medicine, ecology, and insurance, and has boosted research in interpretable machine learning (IML). Here, we propose a novel approach for the functional decomposition of black-box predictions, which is a core concept of IML. This approach replaces the prediction function with a surrogate model consisting of simpler subfunctions, providing insights into the direction and strength of the main feature contributions and their interactions. Our method is based on a concept termed “stacked orthogonality”, which ensures that the main effects capture as much functional behavior as possible. To compute the subfunctions, we combine neural additive modeling with an efficient post-hoc orthogonalization procedure. Our method yielded plausible results in an analysis of stream biological condition in the Chesapeake Bay watershed (United States).
Freshwater streams and rivers are recognized as vital habitats within the Chesapeake Bay watershed, which has been undergoing extensive restoration efforts for more than 30 years. Resource managers need to understand stream and river condition and how these conditions are changing over time to determine whether regional long-term restoration and conservation goals are being met. The objective of this report was to document the spatial and temporal variability of conditions for seven indicators of river and stream health across the nontidal Chesapeake Bay watershed. The framework for the U.S. Geological Survey’s Nontidal Network (NTN), a network of more than 100 nutrient and suspended sediment monitoring locations, was extended to assess conditions for six additional indicators of stream health: temperature, salinity, toxic contaminants, streamflow, hydromorphology, and biological aquatic communities. For each indicator, the latest available data from multiple sources were compiled and harmonized, and key metrics were identified to describe indicator conditions across space and time. A status condition was defined for each indicator to describe overall spatial variability in recent condition, and trend analyses were used to describe changes in each indicator metric over time. The analysis revealed clear differences in spatial and temporal data coverage across the seven indicators, so individual indicator trend analyses were not constrained to a common time interval. However, a status snapshot was conducted across all indicators for the 2015–17 period to simultaneously explore spatial variability across all indicators. The status snapshot highlighted general degraded conditions across multiple indicators in large metropolitan regions, such as the Baltimore–Washington, D.C., metropolitan area. Regression analysis between indicator status metrics and major land cover for the sites suggest urbanization as a potential driver of degraded conditions for many of the indicator metrics, including total phosphorus, salinity, temperature, high-flow frequency, and metrics of habitat and biological assemblage quality. A final analysis exploring the spatial representation of each indicator network showed that some indicator monitoring networks did not cover certain settings, such as small watersheds. These results provided an initial assessment of stream health status and trends and will continue to be leveraged to describe conditions across the Chesapeake Bay watershed to help inform local and regional management decisions. These results also highlighted the need for improved coordination among monitoring organizations to support long-term multi-indicator monitoring and assessment.
This document examines the Chesapeake Bay watershed response to nutrient and sediment reduction efforts under the Clean Water Act's total maximum daily load (TMDL) regulation. As the 2025 Chesapeake Bay TMDL deadline approaches, water quality goals remain unmet, primarily because of nonpoint source pollution, the largest remaining source of nutrients and sediment, and the primary obstacle to meeting the TMDL. We focus on the factors influencing the gap between the expected effect of management to reduce nonpoint source loads reaching the Bay and empirical evidence suggesting that decades of effort have not produced the expected improvement. This gap may be caused by both insufficient scale and type of implemented water quality management practices and by an overestimation of practice effectiveness. Reasons water quality goals remain unmet include legacy nutrients and lag times masking or delaying the effects of management efforts, areas with large nutrient mass imbalances contributing disproportionate loads, and the difficulty of incentivizing behavior change in voluntary nonpoint source programs. Closing the response gap may require fundamental changes to nonpoint source programs. Apart from seeking additional funding, nonpoint source programs could develop policies to more effectively incentivize behavior change, identify and target treatment of high loading areas with appropriate management actions, and address nutrient mass imbalances.
Fish diseases in freshwater ecosystems pose significant ecological and socioeconomic challenges, yet monitoring them in wild populations is complex due to interactions between pathogens, hosts, and environmental conditions. We examine the prevalence and watershed-scale landscape drivers of external deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in 57 riverine fish species using a large dataset (577,266 individuals collected 2008–2019) from the Chesapeake Bay watershed that originated from state and federal agencies. Overall, DELT prevalence was low (1.4%), but was higher in larger, longer-lived species, including Channel Catfish ( Ictalurus punctatus ) (18.9%), Rock Bass ( Ambloplites rupestris ) (7.6%), Smallmouth Bass ( Micropterus dolomieu ) (7.3%), Brown Bullhead ( Ameiurus nebulosus ) (5.6%), and Yellow Bullhead ( Ameiurus natalis ) (5.1%), signifying their potential as regional environmental health indicators. Spatial analysis indicated warmer temperatures increased the estimated probability of DELT occurrence, whereas higher precipitation often mitigated the probability of DELT occurrence. Conservation strategies (e.g., best management practices) had mixed effectiveness in reducing DELT occurrence probability across agricultural and urban landscapes. Across the landscape, various drivers, including harvested forest, impervious land, and pesticide use, influenced DELT occurrence probability differently across species. However, uncertainty remains partly due to low prevalence and variability in sampling methods across agencies. Despite low overall prevalence, DELT occurrence is a rapid fish health indicator. Future research could emphasize species-specific responses and longitudinal studies that incorporate life stages and health indicators. Understanding these intricate, multi-scale interactions is vital for effective monitoring, conservation, and adaptive management of freshwater ecosystems.
Stream ecosystems face continuous pressures from multiple anthropogenic stressors that reshape biological communities and impact ecosystem health and services. Managers can encounter challenges in stewarding ecosystems threatened by multiple stressors, in part because most multiple stressor studies are experimental and, while valuable, offer limited management relevance in targeting these stressors on the landscape. Recent advances in causal inference coupled with large biomonitoring data sets could further understanding of observational stressor-response relationships, aiding management. In this study, we use bioassessment data in the Chesapeake Bay watershed in the mid-Atlantic region of the United States to identify how water quality and physical habitat stressors influence key benthic macroinvertebrate response metrics, considering hierarchical relationships using Bayesian networks. Results suggest water temperature and specific conductivity were prevalent stressors in a mountainous region (northern Appalachians), whereas in an agriculturally dominated region (southern Appalachians) physical habitat alterations were the predominant stressor. In mixed-land use regions (Piedmont & Coastal Plains), specific conductivity was a key stressor, but habitat heterogeneity was important for macroinvertebrate metrics. To illustrate how these stressor-response relationships can be used to guide management decisions, we applied the resist-accept-direct (RAD) framework to develop a portfolio of management options based on predicted changes in macroinvertebrate metrics in response to physical habitat and water quality stressors. For example, accepting changes in areas with co-occurring stressors may be the most feasible option, whereas directing changes through stream restoration or water quality improvements may be effective in areas with single stressor groups. By leveraging observational bioassessment data and causal inference to identify key stressor-response relationships, this research supports decision making by building a simple, strategic management portfolio.
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.
Degraded physical habitat is a common stressor affecting river ecosystems and typically addressed in the United States (US) through a regulatory focus on sediment. However, a narrow regulatory focus on sediment may overlook other aspects of physical habitat and the processes for its creation, maintenance, and degradation. In addition, there exist few “ready-to-use” regional assessments of the multiple dimensions of physical habitat to better understand continuous patterns of condition and prioritize management efforts across a large spatial scale. In this study, we use rapid habitat monitoring data to train a machine-learning ( i.e., random forest) model to predict twelve physical habitat metrics for nearly 120,000 km of nontidal rivers and streams across the Chesapeake Bay watershed, US. We capture a range of habitat conditions driven by both natural variables and anthropogenic pressures. Covariation among habitat metrics indicated two major dimensions of habitat variation: 1) coarse bed substrate and hydromorphic heterogeneity and 2) bank stability and riparian condition. The model predicted localized changes from 2001 to 2019, and the predicted areas of deterioration roughly balanced improvements across the watershed, indicating little progress towards long-term watershed management goals. To evaluate connections to regulatory and management endpoints, we compared our physical habitat predictions to paired estimates of sediment and flow alteration across the region. Sediment concentrations were greater in reaches with less bank stability and lower riparian quality; however, the relation was weak for coarse bed condition metrics, including embeddedness, which is frequently used for establishing regulatory sediment restrictions. For flow alteration, most habitat metrics had lower scores with altered flow metrics, but metrics of instream habitat heterogeneity and coarse substrate condition were most strongly affected. Increased flashy, high flows negatively affected most metrics, but coarse substrate metrics were also negatively affected by greater low flow severity. This study highlights a potential disconnect between a narrow focus on regulatory sediment targets given the multiple dimensions and responses of physical habitat. A more holistic approach to physical habitat in management interventions – one that considers hydromorphic processes, diversity and variability in microhabitats, and explicit consideration of alterations to both low and high flows – may be warranted. By providing direct estimates of multiple aspects of physical habitat, this model can help support managers in the Chesapeake Bay watershed to better understand the range of habitat conditions, identify high-quality reaches for conservation, and target potential management actions tailored to localized conditions.
Elevated conductivity (i.e., specific conductance or SC) causes osmotic stress in freshwater aquatic organisms and may increase the toxicity of some contaminants. Indices of benthic macroinvertebrate integrity have declined in urban areas across the Chesapeake Bay watershed (CBW), and more information is needed about whether these declines may be due to elevated conductivity. A predictive SC model for the CBW was developed using monitoring data from the National Water Quality Portal. Predictor variables representing SC sources were compiled for nontidal reaches across the CBW. Random forests modeling was conducted to predict SC at four time periods (1999–2001, 2004–2006, 2009–2011, and 2014–2016), which were then compared to a national data set of background SC to quantify departures from background SC. Carbonate geology, impervious cover, forest cover, and snow depth were the most important variables for predicting SC. Observations and modeled results showed snow depth amplified the effect of impervious cover on SC. Elevated SC was predicted in two-thirds of reaches in the CBW, and these elevated conditions persisted over time in many areas. These results can be used in stressor identification assessments to prioritize future monitoring and to determine where management activities could be implemented to reduce salinization.
Per- and polyfluoroalkyl substances (PFAS) have become an environmental issue worldwide. A first step to assessing potential adverse effects on fish populations is to determine if concentrations of concern are present in a region and if so, in which watersheds. Hence, plasma from adult smallmouth bass Micropterus dolomieu collected at 10 sites within 4 river systems in the mid-Atlantic region of the United States, from 2014 to 2019, was analyzed for 13 PFAS. These analyses were directed at better understanding the presence and associations with land use attributes in an important sportfish. Four substances, PFOS, PFDA, PFUnA, and PFDoA, were detected in every plasma sample, with PFOS having the highest concentrations. Sites with mean plasma concentrations of PFOS below 100 ng/ml had the lowest percentage of developed landcover in the upstream catchments. Sites with moderate plasma concentrations (mean PFOS concentrations between 220 and 240 ng/ml) had low (< 7.0) percentages of developed land use but high (> 30) percentages of agricultural land use. Sites with mean plasma concentrations of PFOS > 350 ng/ml had the highest percentage of developed land use and the highest number PFAS facilities that included military installations and airports. Four of the sites were part of a long-term monitoring project, and PFAS concentrations of samples collected in spring 2017, 2018, and 2019 were compared. Significant annual differences in plasma concentrations were noted that may relate to sources and climatic factors. Samples were also collected at two sites for tissue (plasma, whole blood, liver, gonad, muscle) distribution analyses with an expanded analyte list of 28 PFAS. Relative tissue distributions were not consistent even within one species of similar ages. Although the long-chained legacy PFAS were generally detected more frequently and at higher concentrations, emerging compounds such as 6:2 FTS and GEN X were detected in a variety of tissues.
The Chesapeake Bay Partnership is implementing conservation practices (CPs) throughout the Chesapeake Bay watershed to reduce nutrient and sediment delivery to the Bay. This study intends to provide an integrated and detailed understanding of how local streams respond to these CP-driven management efforts. Key issue: To what extent do CPs positively affect the health of local streams in the nontidal watershed (cobenefits)? Critical unknown: How do CPs change water quality and the stressors that affect stream aquatic life? Which CPs improve stream health more effectively? Critical knowledge to be delivered to stakeholders includes— the effects of CPs on local water-quality conditions, the degree to which these same CPs also provide local stream-ecosystem benefits, and a deeper understanding of local stream-ecosystems, including stressors and CPs, to guide the selection of management efforts that enhance both water quality and overall stream-ecosystem health.
Polychlorinated biphenyls (PCBs) continue to impact the environment due to historic and ongoing anthropogenic sources (for example, industrial and agricultural), despite their ban. Contaminated stormwater has been identified as a vector for PCB transport to many estuaries impaired by PCBs. Management of these regulated discharges is typically achieved by best management practices (BMPs). This review focuses on PCB reduction practices and BMPs to assist management decision making and provide information on the current state of the science. Studies have quantitatively demonstrated the efficacy of green infrastructure BMPs and gray infrastructure improvements to reduce PCB loads, and other studies have demonstrated qualitative reductions for other BMP types. This review also highlights the disconnect between PCB load reduction and PCB bioavailability when selecting a remediation strategy. Additionally, the review evaluates modeling approaches to assess PCB load reduction to inform management decisions and suggests why there are still significant barriers to implementation.
Nontarget organisms are exposed to pesticides following applications in agricultural and urban settings, potentially resulting in deleterious effects. Direct measurements of pesticides in biological tissues may aid in characterizing exposure, accumulation, and potential toxicity versus analyses in environmental media alone (e.g., water, soil, and air). Plasma represents a nonlethal sampling medium that can be used to assess recent exposures to contaminants. Herein, a method was developed to test the extraction of 210 pesticides and their transformation products in small volume plasma samples (100 μL). Plasma samples were protein precipitated with 0.5 % formic acid in acetonitrile added to the sample (ratio of 3.5:1). Pass-through solid phase extraction was used for sample matrix and lipid removal and samples were analyzed by liquid chromatography and gas chromatography with tandem mass spectrometry. Recoveries of 70.0–129.8 % were achieved for 182 pesticides and degradates across the low (25 ng mL −1 ), medium (100 ng mL −1 ), and high (250 ng mL −1 ) spike levels. Method detection levels ranged 0.4–13.0 ng mL −1 . Following development, the method was applied to smallmouth bass ( Micropterus dolomieu ) plasma samples (n = 10) collected from adults in the Chesapeake Bay watershed. Individual plasma samples resulted in four to seven analytes detected with summed concentrations ranging 16.4–95.0 ng mL −1 . Biological multiresidue pesticide methods help elucidate recent exposures of bioactive compounds to nontarget organisms.
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.
Nutrient pollution from agriculture and urban areas plus acid mine drainage (AMD) from legacy coal mines are primary causes of water-quality impairment in the Susquehanna River, which is the predominant source of freshwater and nutrients entering the Chesapeake Bay. Recent increases in the delivery of dissolved orthophosphate (PO 4 ) from the river to the bay may be linked to long-term increases in pH, decreased acidity of precipitation, and decreased acidity, iron, and aluminum loading from widespread AMD. Since the 1950s, baseline pH increased from ~6.5 to ~8 in the West Branch and “North Branch” of the Susquehanna River, which drain bituminous and anthracite coalfields of Pennsylvania. A current baseline pH of ~8 and daily maxima exceeding 9 have been documented along the lower Susquehanna River. In response to improved river quality, bioavailable PO 4 now may be released into solution from legacy sediment that has filled major impoundments in lower reaches of the river. At typical pH (5–8) of natural water, aqueous PO 4 species tend to be adsorbed by hydrous iron, aluminum, and manganese oxides that coat soil and sediment particles; however, PO 4 may be substantially desorbed at pH >8. We created a geochemical model that simulates equilibrium aqueous/solid distributions of PO 4 as pH and other solution characteristics change. Considering current conditions in the lower Susquehanna River, the model demonstrates potential for extensive release of adsorbed PO 4 at pH >8. Empirical data from laboratory experiments corroborate model results. The transfer of PO 4 into the water column may increase algae growth, which removes CO 2 and drives pH to higher values, facilitating additional PO 4 release and exacerbating the potential for harmful algal blooms. Thus, legacy sediment is a currently unquantified source of PO 4 that warrants consideration by resource managers and programs collaborating to reduce phosphorus loads to the bay and similar settings worldwide.
Accurately predicting species’ distributions is critical for the management and conservation of fish and wildlife populations. Joint Species Distribution Models (JSDMs) account for dependencies between species often ignored by traditional species distribution models. We evaluated how a JSDM approach could improve predictive strength for stream fish communities within large watersheds (the Chesapeake Bay Watershed, USA), using a cross-validation study of JSDMs fit to data from over 50 species. Our results suggest that conditional predictions from JSDMs have the potential to make large improvements in predictive accuracy for many species, particularly for more generalist species where single species models may not perform well. For some species there was no added explanatory effect from conditional information, most of which already exhibited strong marginal predictive ability. For several rare species there were significant improvements in occurrence predictions, while the results for two invasive species considered did not show the same improvements. Overall, the optimal number of species to condition upon, as well as the effects of conditioning upon an increasing number of species, varied widely among species.