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First posted January 21, 2021 Director, Idaho Water Science CenterU.S. Geological Survey230 Collins RoadBoise, Idaho 83702-4520 Microplastics are a contaminant of increasing concern in aquatic environments. Our understanding of microplastics in freshwater environments has increased dramatically over the past decade, but we still lack information on microplastic occurrence and biological uptake in National Park Service (NPS) waters. During 2015–19, the U.S. Geological Survey and the NPS conducted a three-phase study of microplastic occurrence and biological uptake in NPS waters. This fact sheet summarizes results from Phase 3 in which microplastics were sampled at nine locations spanning various land uses on the Upper Delaware, Middle Delaware, and Lower Delaware Scenic and Recreational River and its tributaries in the northeastern United States. Water and sediment samples were collected during baseflow conditions at each location to assess microplastic occurrence, and fish and mussels were collected at a subset of locations to assess potential biological uptake of microplastics.
Austin K. Baldwin; Andrew R. Spanjer; Brett Hayhurst; Donald Hamilton. Microplastics in the Delaware River, northeastern United States. Fact Sheet 2021, 1 .
AMA StyleAustin K. Baldwin, Andrew R. Spanjer, Brett Hayhurst, Donald Hamilton. Microplastics in the Delaware River, northeastern United States. Fact Sheet. 2021; ():1.
Chicago/Turabian StyleAustin K. Baldwin; Andrew R. Spanjer; Brett Hayhurst; Donald Hamilton. 2021. "Microplastics in the Delaware River, northeastern United States." Fact Sheet , no. : 1.
The Eagle Creek watershed, a small subbasin (125 km2) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural Best Management Practices (BMPs) funded through GLRI at the field and watershed scales. The location and quantity of BMPs were obtained from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) database. A Soil and Water Assessment Tool (SWAT) model was built and calibrated for this predominantly agricultural Eagle Creek watershed, incorporating NCP BMPs and monitoring data at the watershed outlet, an edge-of-field (EOF), and tile monitoring sites. Input air temperature modifications were required to induce simulated tile flow to match monitoring data. Calibration heavily incorporated tile monitoring data to correctly proportion surface and subsurface flow, but calibration statistics were unsatisfactory at the EOF and tile monitoring sites. At the watershed outlet, satisfactory to very good calibration statistics were achieved over a 2-year calibration period, and satisfactory statistics were found in the 2-year validation period. SWAT fixes parameters controlling nutrients primarily at the watershed level; a refinement of these parameters at a smaller-scale could improve field-level calibration. Field-scale modeling results indicate that filter strips (FS) are the most effective single BMPs at reducing dissolved reactive phosphorus, and FS typically decreased sediment and nutrient yields when added to any other BMP or BMP combination. Cover crops were the most effective single, in-field practice by reducing nutrient loads over winter months. Watershed-scale results indicate BMPs can reduce sediment and nutrients, but reductions due to NCP BMPs in the Eagle Creek watershed for all water-quality constituents were less than 10%. Hypothetical scenarios simulated with increased BMP acreages indicate larger investments of the appropriate BMP or BMP combination can decrease watershed level loads.
Katherine R. Merriman; Prasad Daggupati; Raghavan Srinivasan; Chad Toussant; Amy M. Russell; Brett Hayhurst. Assessing the Impact of Site-Specific BMPs Using a Spatially Explicit, Field-Scale SWAT Model with Edge-of-Field and Tile Hydrology and Water-Quality Data in the Eagle Creek Watershed, Ohio. Water 2018, 10, 1299 .
AMA StyleKatherine R. Merriman, Prasad Daggupati, Raghavan Srinivasan, Chad Toussant, Amy M. Russell, Brett Hayhurst. Assessing the Impact of Site-Specific BMPs Using a Spatially Explicit, Field-Scale SWAT Model with Edge-of-Field and Tile Hydrology and Water-Quality Data in the Eagle Creek Watershed, Ohio. Water. 2018; 10 (10):1299.
Chicago/Turabian StyleKatherine R. Merriman; Prasad Daggupati; Raghavan Srinivasan; Chad Toussant; Amy M. Russell; Brett Hayhurst. 2018. "Assessing the Impact of Site-Specific BMPs Using a Spatially Explicit, Field-Scale SWAT Model with Edge-of-Field and Tile Hydrology and Water-Quality Data in the Eagle Creek Watershed, Ohio." Water 10, no. 10: 1299.
Subwatersheds within the Great Lakes “Priority Watersheds” were targeted by the Great Lakes Restoration Initiative (GLRI) to determine the effectiveness of the various best management practices (BMPs) from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) Database. A Soil and Water Assessment Tool (SWAT) model is created for Alger Creek, a 50 km2 tributary watershed to the Saginaw River in Michigan. Monthly calibration yielded very good Nash–Sutcliffe efficiency (NSE) ratings for flow, sediment, total phosphorus (TP), dissolved reactive phosphorus (DRP), and total nitrogen (TN) (0.90, 0.79, 0.87, 0.88, and 0.77, respectively), and satisfactory NSE rating for nitrate (0.51). Two-year validation results in at least satisfactory NSE ratings for flow, sediment, TP, DRP, and TN (0.83, 0.54, 0.73, 0.53, and 0.60, respectively), and unsatisfactory NSE rating for nitrate (0.28). The model estimates the effect of BMPs at the field and watershed scales. At the field-scale, the most effective single practice at reducing sediment, TP, and DRP is no-tillage followed by cover crops (CC); CC are the most effective single practice at reducing nitrate. The most effective BMP combinations include filter strips, which can have a sizable effect on reducing sediment and phosphorus loads. At the watershed scale, model results indicate current NCP BMPs result in minimal sediment and nutrient reductions (<10%).
Katherine Merriman; Amy Russell; Cynthia Rachol; Prasad Daggupati; Raghavan Srinivasan; Brett Hayhurst; Todd Stuntebeck. Calibration of a Field-Scale Soil and Water Assessment Tool (SWAT) Model with Field Placement of Best Management Practices in Alger Creek, Michigan. Sustainability 2018, 10, 851 .
AMA StyleKatherine Merriman, Amy Russell, Cynthia Rachol, Prasad Daggupati, Raghavan Srinivasan, Brett Hayhurst, Todd Stuntebeck. Calibration of a Field-Scale Soil and Water Assessment Tool (SWAT) Model with Field Placement of Best Management Practices in Alger Creek, Michigan. Sustainability. 2018; 10 (3):851.
Chicago/Turabian StyleKatherine Merriman; Amy Russell; Cynthia Rachol; Prasad Daggupati; Raghavan Srinivasan; Brett Hayhurst; Todd Stuntebeck. 2018. "Calibration of a Field-Scale Soil and Water Assessment Tool (SWAT) Model with Field Placement of Best Management Practices in Alger Creek, Michigan." Sustainability 10, no. 3: 851.
Brett A. Hayhurst; Benjamin Fisher; James E. Reddy. Streamflow and estimated loads of phosphorus and dissolved and suspended solids from selected tributaries to Lake Ontario, New York, water years 2012–14. Scientific Investigations Report 2016, 1 -33.
AMA StyleBrett A. Hayhurst, Benjamin Fisher, James E. Reddy. Streamflow and estimated loads of phosphorus and dissolved and suspended solids from selected tributaries to Lake Ontario, New York, water years 2012–14. Scientific Investigations Report. 2016; ():1-33.
Chicago/Turabian StyleBrett A. Hayhurst; Benjamin Fisher; James E. Reddy. 2016. "Streamflow and estimated loads of phosphorus and dissolved and suspended solids from selected tributaries to Lake Ontario, New York, water years 2012–14." Scientific Investigations Report , no. : 1-33.
Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010–12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010–11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality—using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.
Donna S. Francy; Amie Brady; Rebecca B. Carvin; Steven R. Corsi; Lori M. Fuller; John H. Harrison; Brett A. Hayhurst; Jeremiah Lant; Meredith Nevers; Paul J. Terrio; Tammy M. Zimmerman. Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches. Scientific Investigations Report 2013, 1 .
AMA StyleDonna S. Francy, Amie Brady, Rebecca B. Carvin, Steven R. Corsi, Lori M. Fuller, John H. Harrison, Brett A. Hayhurst, Jeremiah Lant, Meredith Nevers, Paul J. Terrio, Tammy M. Zimmerman. Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches. Scientific Investigations Report. 2013; ():1.
Chicago/Turabian StyleDonna S. Francy; Amie Brady; Rebecca B. Carvin; Steven R. Corsi; Lori M. Fuller; John H. Harrison; Brett A. Hayhurst; Jeremiah Lant; Meredith Nevers; Paul J. Terrio; Tammy M. Zimmerman. 2013. "Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches." Scientific Investigations Report , no. : 1.
Brett A. Hayhurst; William F. Coon; David A.V. Eckhardt. Water resources of Monroe County, New York, water years 2003-08: Streamflow, constituent loads, and trends in water quality. Scientific Investigations Report 2010, 1 .
AMA StyleBrett A. Hayhurst, William F. Coon, David A.V. Eckhardt. Water resources of Monroe County, New York, water years 2003-08: Streamflow, constituent loads, and trends in water quality. Scientific Investigations Report. 2010; ():1.
Chicago/Turabian StyleBrett A. Hayhurst; William F. Coon; David A.V. Eckhardt. 2010. "Water resources of Monroe County, New York, water years 2003-08: Streamflow, constituent loads, and trends in water quality." Scientific Investigations Report , no. : 1.
William F. Coon; Brett A. Hayhurst; William M. Kappel; David A.V. Eckhardt; Carolyn O. Szabo. Water-Quality Characterization of Surface Water in the Onondaga Lake Basin, Onondaga County, New York, 2005-08. Scientific Investigations Report 2009, 1 .
AMA StyleWilliam F. Coon, Brett A. Hayhurst, William M. Kappel, David A.V. Eckhardt, Carolyn O. Szabo. Water-Quality Characterization of Surface Water in the Onondaga Lake Basin, Onondaga County, New York, 2005-08. Scientific Investigations Report. 2009; ():1.
Chicago/Turabian StyleWilliam F. Coon; Brett A. Hayhurst; William M. Kappel; David A.V. Eckhardt; Carolyn O. Szabo. 2009. "Water-Quality Characterization of Surface Water in the Onondaga Lake Basin, Onondaga County, New York, 2005-08." Scientific Investigations Report , no. : 1.