Geology topics

Douglas L. Moyer

Publications and source records attributed to Douglas L. Moyer.

13 recordsLinked to original sources

USGS2025

Using monitoring and partnerships to provide management-relevant information about Chesapeake Bay rivers

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.

Chesapeake Bay watershed↗

USGS2025

Tracking status and trends in seven key indicators of river and stream condition in the Chesapeake Bay watershed

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.

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

Progress in reducing nutrient and sediment loads to Chesapeake Bay: Three decades of monitoring data and implications for restoring complex ecosystems

For over three decades, Chesapeake Bay (USA) has been the focal point of a coordinated restoration strategy implemented through a partnership of governmental and nongovernmental entities, which has been a classical model for coastal restoration worldwide. This synthesis aims to provide resource managers and estuarine scientists with a clearer perspective of the magnitude of changes in water quality within the Bay watershed, including nitrogen (N), phosphorus (P), and sediment for the River Input Monitoring (RIM) watershed and the unmonitored below-RIM watershed. The flow-normalized N load from the RIM watershed has declined in the period of 1985–2017, but P and sediment loads have lacked progress. Reductions of riverine N are largely driven by reductions of point sources and atmospheric deposition. Future reductions will require significant progress in managing agricultural nonpoint sources. The below-RIM watershed, which comprises a disproportionately high fraction of inputs to the Bay, has shown long-term declines in major sources, including point sources (N and P), atmospheric deposition (N), manure (N and P) and fertilizer (P), based on a combination of monitoring and modeling assessments. To date, the Bay cleanup efforts have achieved some progress toward reducing nutrients from the watershed, which have resulted in improving water quality in the estuary. However, further reductions are critical to achieve the Chesapeake Bay Total Maximum Daily Load goals, and emerging challenges due to Conowingo Reservoir, legacy nutrients, climate change, and population growth should be considered. Continued monitoring, modeling, and assessment are critically important for informing the restoration of this complex ecosystem.

Chesapeake Bay watershed↗

USGS2023

Tracking status and trends in seven key indicators of stream health in the Chesapeake Bay watershed

“The Bay Connects us, the Bay reflects us” writes Tom Horton in the book “Turning the Tide—Saving the Chesapeake Bay”. The Chesapeake Bay watershed contains the largest estuary in the United States. The watershed stretches north to Cooperstown, New York, south to Lynchburg and Virginia Beach, Virginia, west to Pendleton County, West Virginia, and east to Seaford, Delaware, and Scranton, Pennsylvania. The watershed is more than 64,000 square miles that contain 150 major rivers and streams, hereafter referred to collectively as streams, that total more than 100,000 miles in length. The watershed contains thousands of smaller creeks and tributaries, large numbers of plants and animals, and, in 2020, more than 18.4 million people. As changes occur in population, land use, and climate within the watershed, so too do the diversity and health of the Bay's ecosystems.

Chesapeake Bay watershed↗

USGS2021

Nutrient trends and drivers in the Chesapeake Bay Watershed

The Chesapeake Bay Program maintains an extensive nontidal monitoring network, measuring nitrogen and phosphorus (nutrients) at more than 100 locations on rivers and streams in the watershed. Data from these locations are used by United States Geological Survey to assess the ecosystem’s response to nutrient-reduction efforts. This fact sheet summarizes recent trends in nitrogen and phosphorus in nontidal tributaries and identifies some of the complex factors that affect local water quality, and ultimately, the Chesapeake Bay.

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

USGS2021

An approach for decomposing river water-quality trends into different flow classes

A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN 2Q , which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS (“Weighted Regressions on Time, Discharge, and Season”) method. The FN 2Q approach provides a daily time series of low-flow and high-flow FN flux estimates that represent the lower and upper half of daily riverflow observations that occurred on each calendar day across the period of record. These daily estimates can be summarized into any time period of interest (e.g., monthly, seasonal, or annual) for quantifying trends. The proposed approach is illustrated with an application to a record of total nitrogen concentration (632 samples) collected between 1985 and 2018 from the South Fork Shenandoah River at Front Royal, Virginia (USA). Results show that the overall FN flux of total nitrogen has declined in the period of 1985–2018, which is mainly attributable to FN flux decline in the low-flow class. Furthermore, the decline in the low-flow class was highly correlated with wastewater effluent loads, indicating that the upgrades of treatment technology at wastewater treatment facilities have likely led to water-quality improvement under low-flow conditions. The high-flow FN flux showed a spike around 2007, which was likely caused by increased delivery of particulate nitrogen associated with sediment transport. The case study demonstrates the utility of the FN 2Q approach toward not only characterizing the changes in river water quality but also guiding the direction of additional analysis for capturing the underlying drivers. The FN 2Q approach (and the published code) can easily be applied to widely available river monitoring records to quantify water-quality trends under different flow conditions to enhance understanding of river water-quality dynamics.

Virginia↗

USGS2020

Sediment dynamics and implications for management: State of the science from long‐term research in the Chesapeake Bay watershed, USA

This review aims to synthesize the current knowledge of sediment dynamics using insights from long‐term research conducted in the watershed draining to the Chesapeake Bay, the largest estuary in the U.S., to inform management actions to restore the estuary and its watershed. The sediment dynamics of the Chesapeake are typical of many impaired watersheds and estuaries around the world, and this synthesis is intended to be relevant and transferable to other sediment‐impaired systems. The watershed's sediment sources, transport, delivery, and impacts are discussed with implications for effectively implementing best management practices (BMPs) to mitigate sediment issues. This synthesis revealed three key issues to consider when planning actions to reduce sediment loading: Scale, time, and land use. Geology and historical land use generated a template that current land use and climate, in addition to management, are acting upon to control sediment delivery. Important sediment sources in the Chesapeake include the Piedmont physiographic region, urban, and agricultural land use, and streambank erosion of headwater streams, whereas floodplain trapping is important along larger streams and rivers. Implementation of BMPs is widespread and is predicted to lead to decreased sediment loading; however, reworking of legacy sediment stored in stream valleys, with potentially long residence times in storage, can delay and complicate detection of the effects of BMPs on sediment loads. In conclusion, the improved understanding of sediment sources, storage areas, and transport lag times reviewed here can help target choices of BMP types and locations to better manage sediment problems—for both local streams and receiving waters.

New York, Pennsylvania, Maryland, Delaware, 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↗

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↗

USGS2017

Riverine discharges to Chesapeake Bay: Analysis of long-term (1927–2014) records and implications for future flows in the Chesapeake Bay basin

The Chesapeake Bay (CB) basin is under a total maximum daily load (TMDL) mandate to reduce nitrogen, phosphorus, and sediment loads to the bay. Identifying shifts in the hydro-climatic regime may help explain observed trends in water quality. To identify potential shifts, hydrologic data (1927–2014) for 27 watersheds in the CB basin were analyzed to determine the relationships among long-term precipitation and stream discharge trends. The amount, frequency, and intensity of precipitation increased from 1910 to 1996 in the eastern U.S., with the observed increases greater in the northeastern U.S. than the southeastern U.S. The CB watershed spans the north-to-south gradient in precipitation increases, and hydrologic differences have been observed in watersheds north relative to watersheds south of the Pennsylvania—Maryland (PA-MD) border. Time series of monthly mean precipitation data specific to each of 27 watersheds were derived from the Precipitation-elevation Regression on Independent Slopes Model (PRISM) dataset, and monthly mean stream-discharge data were obtained from U.S. Geological Survey streamgage records. All annual precipitation trend slopes in the 18 watersheds north of the PA-MD border were greater than or equal to those of the nine south of that border. The magnitude of the trend slopes for 1927–2014 in both precipitation and discharge decreased in a north-to-south pattern. Distributions of the monthly precipitation and discharge datasets were assembled into percentiles for each year for each watershed. Multivariate correlation of precipitation and discharge within percentiles among the groups of northern and southern watersheds indicated only weak associations. Regional-scale average behaviors of trends in the distribution of precipitation and discharge annual percentiles differed between the northern and southern watersheds. In general, the linkage between precipitation and discharge was weak, with the linkage weaker in the northern watersheds compared to those in the south. On the basis of simple linear regression, 26 of the 27 watersheds are projected to have higher annual mean discharge in 2025, the target date for implementation of the TMDL for the CB basin.

Chesapeake Bay Basin↗

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

Decadal-scale export of nitrogen, phosphorus, and sediment from the Susquehanna River basin, USA: Analysis and synthesis of temporal and spatial patterns

The export of nitrogen (N), phosphorus (P), and suspended sediment (SS) is a long-standing management concern for the Chesapeake Bay watershed, USA. Here we present a comprehensive evaluation of nutrient and sediment loads over the last three decades at multiple locations in the Susquehanna River basin (SRB), Chesapeake's largest tributary watershed. Sediment and nutrient riverine loadings, including both dissolved and particulate fractions, have generally declined at all sites upstream of Conowingo Dam (non-tidal SRB outlet). Period-of-record declines in riverine yield are generally smaller than those in source input, suggesting the possibility of legacy contributions. Consistent with other watershed studies, these results reinforce the importance of considering lag time between the implementation of management actions and achievement of river quality improvement. Whereas flow-normalized loadings for particulate species have increased recently below Conowingo Reservoir, those for upstream sites have declined, thus substantiating conclusions from prior studies about decreased reservoir trapping efficiency. In regard to streamflow effects, statistically significant log-linear relationships between annual streamflow and annual constituent load suggest the dominance of hydrological control on the inter-annual variability of constituent export. Concentration-discharge relationships revealed general chemostasis and mobilization effects for dissolved and particulate species, respectively, both suggesting transport-limitation conditions. In addition to affecting annual export rates, streamflow has also modulated the relative importance of dissolved and particulate fractions, as reflected by its negative correlations with dissolved P/total P, dissolved N/total N, particulate P/SS, and total N/total P ratios. For land-use effects, period-of-record median annual yields of N, P, and SS all correlate positively with the area fraction of non-forested land but negatively with that of forested land under all hydrological conditions. Overall, this work has informed understanding with respect to four major factors affecting constituent export ( i.e. , source input, reservoir modulation, streamflow, and land use) and demonstrated the value of long-term river monitoring.

Maryland, New York, Pennsylvania↗