Geology topics

Research about Chesapeake Bay watershed

Source-linked reports with geographic coverage including Chesapeake Bay watershed.

At least 73 records · Page 4Linked to original sources

USGS2020

Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu

Ecological risk assessments play an important role in environmental management and decision-making. Although empirical measurements of the effects of habitat changes and chemical exposure are often made at molecular and individual levels, environmental decision-making often requires the quantification of management-relevant, population-level outcomes. In this study, we generalized a modeling framework to evaluate population-level ecological risk of environmental stress and bioactive chemicals. The modeling framework includes (1) a biological model module that incorporates complex and interacting biological and ecological processes, and environmental stochasticity, (2) an effect module that links the impacts of environmental changes and chemical exposure to individual characteristics, and (3) a population module that makes decisions on the choice of population-level properties to best capture the effects and thus to track in the model based on the target species and the research and management interest. This framework is a 3-module procedure that provides an alternative way for researchers to organize, present and communicate the risk assessment modeling studies. To demonstrate this framework, we used a socioeconomically important riverine fish species, smallmouth bass Micropterus dolomieu , as the model species. We developed an individual-based model as the biological model module. We evaluated the impacts of changing water temperature and flow regimes, and the impacts of exposure to estrogenic endocrine disrupting compounds (EEDC) on smallmouth bass populations in the Chesapeake Bay Watershed, USA. Warm summer water temperatures and year-round high flows had the most severe impacts on the smallmouth bass population. An increase in exposure level to EEDC, both year-round and in summer months, substantially reduced population size, spawner and recruit abundance, and the proportion of quality-length individuals. Acute exposure to EEDC was more detrimental to the population than chronic exposure. Acute exposure during spawning season had the most severe impacts. This modeling framework can be extended to other species, environmental factors and chemicals, and can be used to inform management and conservation decisions.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2020

Estimation of nonlinear water-quality trends in high-frequency monitoring data

Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.

Chesapeake Bay watershed↗

USGS2020

Hyperpigmented melanistic skin lesions of smallmouth bass Micropterus dolomieu from the Chesapeake Bay watershed

Hyperpigmented melanistic skin lesions (HPMLs) of smallmouth bass Micropterus dolomieu are observed in the Potomac and Susquehanna rivers, Chesapeake Bay watershed, USA. Routine, nonlethal population surveys were conducted at 8 sites on the mainstem Susquehanna River and 9 on the Juniata River, a tributary of the Susquehanna River, between 2012 and 2018, and the prevalence of HPMLs was documented. A total of 4078 smallmouth bass were collected from the mainstem Susquehanna River and 6478 from the Juniata River. Lesions were primarily seen in bass greater than 200 mm, and prevalence in the Susquehanna River (8%) was higher (p < 0.001) than in the Juniata River (2%). As part of ongoing fish health monitoring projects, smallmouth bass were collected at additional sites, primarily tributaries of the Susquehanna (n = 758) and Potomac (n = 545) rivers between 2013 and 2018. Prevalence in the Susquehanna River (13%) was higher (p < 0.001) than the Potomac (3%). Microscopically, HPMLs were characterized by an increased number of melanocytes in the epidermis or within the dermis and epidermis. RNAseq analyses of normal and melanistic skin identified 3 unique sequences in HPMLs. Two were unidentified and the third was a viral helicase (E1). Transcript abundance in 16 normal skin samples and 16 HPMLs showed upregulation of genes associated with melanogenesis and cell proliferation in HPMLs. The E1 transcript was detected in 12 of the 16 melanistic areas but in no samples from normal skin. Further research will be necessary to identify the putative new virus and determine its role in melanocyte proliferation.

Delaware, Maryland, Pennsylvania, Virginia, West V↗

USGS2019

Estimation bias in water-quality constituent concentrations and fluxes: A synthesis for Chesapeake Bay rivers and streams

Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO 4 ), and nitrate-plus-nitrite (NO x ). For both concentration and flux, estimation bias follows the general order of SS > TP > PO 4 > TN ≈ NO x . Median TN and NO x bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO 4 , and SS each showed a median positive bias across sites of <18% for flux and <7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.

Chesapeake Bay watershed↗

USGS2019

Controls of the spatial variability of denitrification potential in nontidal floodplains of the Chesapeake Bay watershed, USA

Identifying floodplains with high rates of denitrification will help prioritize restoration projects for the removal of nitrogen. Currently, relationships of denitrification with hydrogeomorphic, physiographic, and climate ( i.e. , largescale) characteristics of floodplains are relatively unknown, even though these characteristics have datasets ( e.g. , geographic mapping tools) that are publicly available (or soon-to-become) that could be used to understand denitrification variability. Thus, we investigated control of denitrification by these largescale characteristics in eighteen nontidal floodplains of the Chesapeake Bay watershed ( i.e. , at regional scale, >100 km, scale), using measurements or compiled data at the scales of the stream reach and respective catchment; floodplain soil and herbaceous vegetation ( i.e. , local) characteristics were additionally investigated. Soil denitrification potentials were measured in May, July, and August using complementary acetylene-based techniques under an anoxic environment. Linear largescale predictors of denitrification potential measurements included stream nitrogen and phosphorus concentrations (+), channel width-to-depth ratio (+), floodplain sedimentation (+), forested (−) and urban (+) catchment land cover, and seasonal air temperature (−). Three predictors, catchment forested land cover (strongly related to agricultural land cover), catchment urban land cover, and floodplain sedimentation were related to the most number of denitrification potential measurements. Soil structure, soil nutrient concentrations, and herbaceous vegetation characteristics that were seasonally measured (with a few exceptions) were linear predictors of denitrification potentials in May and August, with nitrogen and carbon characteristics the most consistent (positive) predictors across measurements. Nutrient amendment assays further supported the importance of nitrogen and carbon controls. Using the local characteristics as statistical mediators in path analysis, greater non-forested catchment land cover indirectly increased denitrification through greater floodplain soil nitrate, total phosphorus, and herbaceous aboveground biomass. Additionally, greater floodplain sedimentation indirectly increased denitrification through greater soil pH, total phosphorus, and potential carbon mineralization. Due to the consistency of relationships across denitrification potential measurements along with path modeling results, hotspots of floodplain denitrification should be found in urban and agricultural catchments where river-floodplain hydrologic connectivity promotes sedimentation. Largescale predictors explained 43–57% of the variation in denitrification potentials and should be useful for prediction in floodplains. Siting restoration projects in watersheds for maximum nitrate removal using publicly available largescale datasets is both feasible and effective.

Chesapeake Bay watershed↗

USGS2019

Point sources and agricultural practices control spatial-temporal patterns of orthophosphate in tributaries to Chesapeake Bay

Orthophosphate (PO 4 ) is the most bioavailable form of phosphorus (P). Excess PO 4 may cause harmful algal blooms in aquatic ecosystems. A major restoration effort is underway for Chesapeake Bay (CB) to reduce P, nitrogen, and sediment loading to CB. Although PO 4 cycling and delivery to streams has been characterized in small-scale studies, regional drivers of PO 4 patterns remain poorly understood because most water quality trend assessment focus on total P. Moreover, these trend assessments are usually at an annual timestep. To address this research gap, we analyzed PO 4 patterns over a 9-year period at 53 monitoring stations across the CB watershed to: 1) characterize the role of PO 4 in total P fluxes and trends; 2) describe spatial and temporal patterns of PO 4 concentrations across seasons and streamflow ; and 3) explore factors explaining these patterns. Agricultural watersheds exported the most total P compared with watersheds under different land uses (e.g., urban or forest), with PO 4 comprising up to 50% of those exports. Although PO 4 exports are declining at many sites, some agricultural regions are experiencing increasing trends at a rate sufficient to drive total P trends. Regression modeling results suggest that point source load reductions are likely responsible for decreasing PO 4 concentrations observed at many sites. Watersheds with more Conservation Reserve Program enrollment had lower summer PO 4 concentrations, highlighting the effectiveness of this practice. Manure inputs strongly predicted PO 4 concentrations at high flows across all seasons. Both manure applications and conservation tillage were correlated with changes in PO 4 concentrations at high flow, suggesting these activities could contribute to increasing PO 4 concentrations. This study highlights the effectiveness of point source control for reducing PO 4 exports and underscores the need for management strategies to target sources, practices, and landscape factors determining PO 4 loss from soils where manure inputs remain high.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2018

Exploring drivers of regional water-quality change using differential spatially referenced regression – A pilot study in the Chesapeake Bay watershed

An understanding of riverine water-quality dynamics in regional mixed-land use watersheds is the foundation for advances in landscape biogeochemistry and informed land management. A differential implementation of the statistical/process-based model SPAtially Referenced Regressions on Watershed attributes (SPARROW; Smith et al., https://doi.org/10.1029/97wr02171 ) is proposed to empirically relate a regional pattern of changes in flow-normalized constituent flux, over a multiyear period, to contemporaneous changes in spatially referenced explanatory variables. In a pilot application, the differential model, called Spatiotemporal Watershed Accumulation of Net effects (SWAN), is used to explore factors influencing changes in flow-normalized flux of total nitrogen over the period 1990–2010 at 43 sites in the nontidal Chesapeake Bay watershed. A seven-parameter model explains 80% of the transformed variability in independently estimated flux changes, indicating that storage effects having characteristic time scales greater than 20 years had a small influence, relative to changes in inputs, on regional water-quality response. Results suggest that 1990–2010 changes in total-nitrogen flux are largely the outcome of increased nonpoint-source pollution associated with urban and suburban development, modulated to the point of negation by terrestrial losses stemming from widespread increases in air temperature and precipitation. The loss mechanism is qualitatively consistent with denitrification; however, increases in aboveground biomass, agricultural nitrogen exports, or hydrologic flushing are also plausible contributors. Although qualified by a small sample size and constraints on explanatory data availability, the pilot suggests that SWAN is a promising approach for broadening scientific understanding of factors driving regional water-quality change and for supporting evidence-based land-management decisions.

Chesapeake Bay watershed↗

USGS2018

Estimates of nitrate loads and yields from groundwater to streams in the Chesapeake Bay watershed based on land use and geology

The water quality of the Chesapeake Bay may be adversely affected by dissolved nitrate carried in groundwater discharge to streams. To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.

Chesapeake Bay watershed↗

USGS2018

Long-term nutrient reductions lead to the unprecedented recovery of a temperate coastal region

Human actions, including nutrient pollution, are causing the widespread degradation of coastal habitats, and efforts to restore these valuable ecosystems have been largely unsuccessful or of limited scope. We provide an example of successful restoration linking effective management of nutrients to the successful recovery of submersed aquatic vegetation along thousands of kilometers of coastline in Chesapeake Bay, United States. We also show that biodiversity conservation can be an effective path toward recovery of coastal systems. Our study validates 30 years of environmental policy and provides a road map for future ecological restoration.

Chesapeake Bay watershed↗

USGS2018

Watershed export of fine sediment, organic carbon, and chlorophyll-a to Chesapeake Bay: Spatial and temporal patterns in 1984–2016

Chesapeake Bay has long experienced nutrient enrichment and water clarity deterioration. This study provides new quantification of loads and yields for sediment (fine and coarse grained), organic carbon (total, dissolved, and particulate), and chlorophyll-a from the monitored nontidal Chesapeake Bay watershed (MNTCBW), all of which are expected to drive estuarine water clarity. We conducted an integrated analysis of nine major tributaries to the Bay to understand spatial and temporal export patterns over the last thirty years (1984–2016). In terms of spatial pattern, export of these constituents from the MNTCBW was strongly dominated (~ 90%) by the three largest tributaries (i.e., Susquehanna, Potomac, and James). Among the nine tributaries, the ranking of constituent export generally follows the order of their watershed sizes, with other factors such as land use and reservoir playing important roles in some exceptions. In terms of partitioning, suspended sediment (SS) export was dominated by fine-grained sediment (SS fine ) in all nine tributaries; overall, ~ 90% of the MNTCBW SS is SS fine . Total organic carbon (TOC) export was dominated by dissolved organic carbon (DOC) in all tributaries except Potomac River; overall, ~ 60% of the MNTCBW TOC is DOC. A comparison with literature shows that the MNTCBW SS and TOC yields were ~ 80% and ~ 60% of the respective medians of worldwide watersheds. In terms of temporal pattern, flow-normalized yields from the MNTCBW show overall increases in SS (both long-term [1984–2016] and short-term [2004–2016]), SS fine (long-term and short-term), TOC (long-term), and chlorophyll-a (short-term). The rises in SS, SS fine , and TOC were largely driven by Susquehanna River where Conowingo Reservoir's trapping efficiency has greatly diminished in the last twenty years. Overall, these new results on the status and trends of sediment, organic carbon, and chlorophyll-a provide the foundation for building potential linkages between riverine inputs and estuarine water clarity patterns.

Chesapeake Bay watershed↗

USGS2018

Chesapeake Bay's water quality condition has been recovering: Insights from a multimetric indicator assessment of thirty years of tidal monitoring data

To protect the aquatic living resources of Chesapeake Bay, the Chesapeake Bay Program partnership has developed guidance for state water quality standards, which include ambient water quality criteria to protect designated uses (DUs), and associated assessment procedures for dissolved oxygen (DO), water clarity/underwater bay grasses, and chlorophyll-a. For measuring progress toward meeting the respective states' water quality standards, a multimetric attainment indicator approach was developed to estimate combined standards attainment. We applied this approach to three decades of monitoring data of DO, water clarity/underwater bay grasses, and chlorophyll-a data on annually updated moving 3-year periods to track the progress in all 92 management segments of tidal waters in Chesapeake Bay. In 2014–2016, 40% of tidal water segment-DU-criterion combinations in the Bay (n = 291) are estimated to meet thresholds for attainment of their water quality criteria. This index score marks the best 3-year status in the entire record. Since 1985–1987, the indicator has followed a nonlinear trajectory, consistent with impacts from extreme weather events and subsequent recoveries. Over the period of record (1985–2016), the indicator exhibited a positive and statistically significant trend ( p < 0.05), indicating that the Bay has been recovering since 1985. Patterns of attainment of individual DUs are variable, but improvements in open water DO, deep channel DO, and water clarity/submerged aquatic vegetation have combined to drive the improvement in the Baywide indicator in 2014–2016 relative to its long-term median. Finally, the improvement in estimated Baywide attainment was statistically linked to the decline of total nitrogen, indicating responsiveness of attainment status to the reduction of nutrient load through various management actions since at least the 1980s.

Chesapeake Bay watershed↗

USGS2018

Predicting biological conditions for small headwater streams in the Chesapeake Bay watershed

A primary goal for Chesapeake Bay watershed restoration is to improve stream health and function in 10% of stream miles by 2025. Predictive spatial modeling of stream conditions, when accurate, is one method to fill gaps in monitoring coverage and estimate baseline conditions for restoration goals. Predictive modeling can also monitor progress as additional data become available. We developed a random forests model to predict biological condition of small streams (<200 km 2 in drainage) in the Chesapeake Bay watershed. Biological condition was measured with the Chesapeake Bay Basin-wide Index of Biotic Integrity (Chessie BIBI), a stream macroinvertebrate index. Our goal was to predict biological condition in all unsurveyed small streams present in a 1:24,000 scale catchment layer as a 2004–2008 baseline. We reclassified the 5-category Chessie BIBI ratings into two categories, poor and fair/good, to align with management goals of the Chesapeake Bay Program. The model included 12 geospatial predictor variables including measures on spatial location, bioregion, land cover, soil, precipitation, and number of dams in local catchments. We trained the model with a random 75% subset of Chessie BIBI data ( n = 1449), and used the remaining 25% of Chessie BIBI data ( n = 484) as test data. The model performed well, correctly predicting 72% of samples in training data and 73% of samples in test data, but model accuracy varied among bioregions. We performed uncertainty analyses by adding bands of either ±0.05 or ±0.10 BIBI units to the cutoff between poor and fair/good. These uncertainty analyses resulted in 14.5% (±0.05 band) and 24.8% (±0.10 band) of samples in test data being classified as in uncertain condition. For 95,877 small stream reaches in the Chesapeake Bay watershed, the model predicted 64% in fair/good condition, the ±0.05 uncertainty analyses predicted 57% in fair/good condition, and the ±0.10 uncertainty analysis predicted 50% in fair/good condition. These reported values have different implications for the number of improved stream miles required to meet the goal of improving 10%. Incorporating uncertainty provides an assessment of model strength as well as confidence in predictions. We, therefore, suggest increased reporting of uncertainty in studies that spatially predict stream conditions.

Chesapeake Bay watershed↗

USGS2017

U.S. Geological Survey Science—Improving the value of the Chesapeake Bay watershed

Introduction Congress directed the Federal Government to work with States to restore the Nation’s largest estuary. Chesapeake Bay restoration provides important economic and ecological benefits: 18 million people live and work in the Bay watershed and enjoy its benefits. 3,600 types of fish, wildlife, and plants underpin the economic value of the Bay ecosystem. Poor water quality and habitat loss threaten restoration and negatively impact the economy. 10 Goals to meet by 2025 through the Chesapeake Bay Program, a voluntary partnership.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2016

Quantifying watershed-scale groundwater loading and in-stream fate of nitrate using high-frequency water quality data

We describe a new approach that couples hydrograph separation with high-frequency nitrate data to quantify time-variable groundwater and runoff loading of nitrate to streams, and the net in-stream fate of nitrate at the watershed-scale. The approach was applied at three sites spanning gradients in watershed size and land use in the Chesapeake Bay watershed. Results indicate that 58-73% of the annual nitrate load to the streams was groundwater-discharged nitrate. Average annual first order nitrate loss rate constants (k) were similar to those reported in both modelling and in-stream process-based studies, and were greater at the small streams (0.06 and 0.22 d -1 ) than at the large river (0.05 d -1 ), but 11% of the annual loads were retained/lost in the small streams, compared with 23% in the large river. Larger streambed area to water volume ratios in small streams result in greater loss rates, but shorter residence times in small streams result in a smaller fraction of nitrate loads being removed than in larger streams. A seasonal evaluation of k values suggests that nitrate was retained/lost at varying rates during the growing season. Consistent with previous studies, streamflow and nitrate concentration were inversely related to k. This new approach for interpreting high-frequency nitrate data and the associated findings furthers our ability to understand, predict, and mitigate nitrate impacts on streams and receiving waters by providing insights into temporal nitrate dynamics that would be difficult to obtain using traditional field-based studies.

Delaware, Pennsylvania, Maryland, New York, Virgin↗

USGS2016

Application of a Weighted Regression Model for Reporting Nutrient and Sediment Concentrations, Fluxes, and Trends in Concentration and Flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, Results Through Water Year 2012

In the Chesapeake Bay watershed, estimated fluxes of nutrients and sediment from the bay’s nontidal tributaries into the estuary are the foundation of decision making to meet reductions prescribed by the Chesapeake Bay Total Maximum Daily Load (TMDL) and are often the basis for refining scientific understanding of the watershed-scale processes that influence the delivery of these constituents to the bay. Two regression-based flux and trend estimation models, ESTIMATOR and Weighted Regressions on Time, Discharge, and Season (WRTDS), were compared using data from 80 watersheds in the Chesapeake Bay Nontidal Water-Quality Monitoring Network (CBNTN). The watersheds range in size from 62 to 70,189 square kilometers and record lengths range from 6 to 28 years. ESTIMATOR is a constant-parameter model that estimates trends only in concentration; WRTDS uses variable parameters estimated with weighted regression, and estimates trends in both concentration and flux. WRTDS had greater explanatory power than ESTIMATOR, with the greatest degree of improvement evident for records longer than 25 years (30 stations; improvement in median model R 2 = 0.06 for total nitrogen, 0.08 for total phosphorus, and 0.05 for sediment) and the least degree of improvement for records of less than 10 years, for which the two models performed nearly equally. Flux bias statistics were comparable or lower (more favorable) for WRTDS for any record length; for 30 stations with records longer than 25 years, the greatest degree of improvement was evident for sediment (decrease of 0.17 in median statistic) and total phosphorus (decrease of 0.05). The overall between-station pattern in concentration trend direction and magnitude for all constituents was roughly similar for both models. A detailed case study revealed that trends in concentration estimated by WRTDS can operationally be viewed as a less-constrained equivalent to trends in concentration estimated by ESTIMATOR. Estimates of annual mean flow-adjusted (ESTIMATOR) and flow-normalized (WRTDS) concentration for years initially constituting the end of a water-quality record showed a similar degree of variability as data for additional years were incrementally added and the initial estimates “aged.” On the basis of the results of this broad comparison of the two models, the U.S. Geological Survey is adopting WRTDS as the primary model for estimating constituent fluxes and trends throughout the CBNTN. Nutrient and sediment flux and trend estimates, based on WRTDS, are summarized narratively and tabulated in appendixes for all stations for which fluxes or trends were reported through water year 2012. WRTDS also was used to explore the sensitivity of flux and trend estimates to three data-quality issues common in many large-scale monitoring networks and evident in some of the CBNTN records. The potential effects of inconsistency in annual sampling effort and inconsistency in storm sampling effort were explored by way of a subsampling experiment using eight of the most densely sampled long-term (1985–2012) stations in the CBNTN as baseline datasets. From each dataset, a set of 10 “design guideline” subsamples was selected, consisting of 12 monthly samples and 8 targeted storm samples per year. The selection was conducted in a manner that preserved the overall intensity of storm sampling in the baseline data. These 10 subsamples were further manipulated to create “heterogeneous” subsamples by removing storm samples prior to 2003. The maximum relative difference between flow-normalized flux estimated in a single year from any of the 10 design guideline subsamples and values estimated in the corresponding year from baseline data was smallest for dissolved inorganic nitrogen (median of 8 stations = 6 percent of baseline estimate), but more appreciable for total phosphorus and sediment (medians of 22 and 32 percent, respectively). The maximum relative difference between flow-normalized flux estimated from from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data was more pronounced, with medians for 8 stations of 15, 30, and 53 percent of the corresponding baseline estimates for dissolved inorganic nitrogen, total phosphorus, and sediment, respectively. The worst-case maximum relative differences between flow-normalize flux estimated in a single year from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data were 25 percent for dissolved inorganic nitrogen, 37 percent for total phosphorus, and 250 percent for sediment. The results for the heterogeneous subsamples indicate that changes in storm sampling frequency can result in appreciable distortion of estimated trends in flow-normalized flux, especially for total phosphorus and sediment. Trend lines estimated from heterogeneous subsamples tended to converge with the trend lines estimated from baseline data after 2003. In contrast, 2003–12 trends based on subsamples truncated by discarding all data prior to the induced heterogeneity in 2003 showed appreciable biases and differences in slope, relative to the corresponding 2003–12 segment of the trend computed from the design guideline subsamples. Overall, the results indicate that for particulate constituents, load and trend estimates computed using long-term records recently converted to CBNTN design guideline sampling protocols will be most reliable if the trend is computed using the entire record, but reported only for the period that design guideline sampling protocols were followed. Inconsistencies related to changing laboratory methods were also examined via two manipulative experiments. In the first experiment, increasing and decreasing “stair-step” patterns of changes in censoring level, overall representing a factor-of-five change in the laboratory reporting limit, were artificially imposed on a 27-year record with no censoring and a period-of-record concentration trend of –68.4 percent. Trends estimated on the basis of the manipulated records were broadly similar to the original trend (–63.6 percent for decreasing censoring levels and –70.3 percent for increasing censoring levels), lending a degree of confidence that the survival regression routines upon which WRTDS is based are generally robust to data censoring. The second experiment considered an abrupt disappearance of low-concentration observations of total phosphorus, associated with a laboratory method change and not reflected through censoring, near the middle of a 28-year record. By process of elimination, an upward shift in the estimated flow-normalize concentration trend line around the same time was identified as a likely artifact resulting from the laboratory method change, although a contemporaneous change in watershed processes cannot be ruled out. Decisions as to how to treat records with potential sampling protocol or laboratory methods-related artifacts should be made on a case-by-case basis, and trend results should be appropriately qualified.

Maryland, New York, Pennsylvania, Virginia↗

USGS2016

Phosphorus removal from aquaculture effluents at the Northeast Fishery Center in Lamar, Pennsylvania using iron oxide sorption media

Three different iron oxide-based sorption media samples were tested for removal of phosphorus (P) from fish hatchery effluents using fixed bed processing. Two of the media samples were derived from residuals produced by the treatment of acid mine drainage, which were then compared to granular ferric hydroxide (GFH), a commercially available sorption medium. All of the media types removed from 50 to 70% of the P from the incoming aquaculture wastewater over 70–175 days of operation without regeneration. In some of the sorption trials, the GFH media showed superior adsorption in the earlier stages of the trial, but the GFH appeared to reach saturation more quickly, so that media performance was similar – at about 60% removal of P – over a longer time period of 175 days. Media regeneration tests were also conducted for both the commercial and mine drainage media, and demonstrated longer term performance, with overall P removal of 50–55%, over 223 days of total operation, with the advantages of phosphorus recycle and media reuse.

Pennsylvania↗

USGS2015

Predicting redox conditions in groundwater at a regional scale

Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O 2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.

Maryland, New York, Pennsylvania, Virginia↗

USGS2015

Coastal and wetland ecosystems of the Chesapeake Bay watershed: Applying palynology to understand impacts of changing climate, sea level, and land use

The mid-Atlantic region and Chesapeake Bay watershed have been influenced by fluctuations in climate and sea level since the Cretaceous, and human alteration of the landscape began ~12,000 years ago, with greatest impacts since colonial times. Efforts to devise sustainable management strategies that maximize ecosystem services are integrating data from a range of scientific disciplines to understand how ecosystems and habitats respond to different climatic and environmental stressors. Palynology has played an important role in improving understanding of the impact of changing climate, sea level, and land use on local and regional vegetation. Additionally, palynological analyses have provided biostratigraphic control for surficial mapping efforts and documented agricultural activities of both Native American populations and European colonists. This field trip focuses on sites where palynological analyses have supported efforts to understand the impacts of changing climate and land use on the Chesapeake Bay ecosystem.

Chesapeake Bay watershed↗