Prepared in cooperation with

Montgomery County, Maryland

Prince George's County, Maryland 

Water Quality in the Anacostia River, Maryland 

and Rock Creek, Washington, D.C.: Continuous

and Discrete Monitoring with Simulations to

Estimate Concentrations and Yields of Nutrients, 

Suspended Sediment, and Bacteria

Open-File Report 2013–1034

U.S. Department of the Interior

U.S. Geological Survey
Cover. View of water-quality monitoring station at Paint Branch near College Park, Maryland (USGS station 01649190). Photograph by 

U.S. Geological Survey.
Prepared in cooperation with 

Montgomery County, Maryland

Prince George's County, Maryland

Water Quality in the Anacostia River, Maryland and Rock 

Creek, Washington, D.C.: Continuous and Discrete 

Monitoring with Simulations to Estimate Concentrations 

and Yields of Nutrients, Suspended Sediment, and 

Bacteria

By Cherie V. Miller, Jeffrey G. Chanat, and Joseph M. Bell

Open-File Report 2013–1034

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013 

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Suggested citation:

Miller, C.V., Chanat, J.G., and Bell, J.M., 2013, Water quality in the Anacostia River, Maryland and Rock Creek, 

Washington, D.C.: Continuous and discrete monitoring with simulations to estimate concentrations and yields of 

nutrients, suspended sediment, and bacteria: U.S. Geological Survey Open-File Report 2013-1034, 37 p.

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

Abstract ......................................................................................................................................................................... 1

Introduction.................................................................................................................................................................... 1

Purpose and Scope.................................................................................................................................................... 2

Description of the Study Area .................................................................................................................................... 3

Methods......................................................................................................................................................................... 6

Measurements of Stream Discharge and Continuous Water Quality ......................................................................... 6

Collection of Discrete Water-Quality Samples............................................................................................................ 7

Laboratory Methods ................................................................................................................................................. 11

Evaluation of Data Integrity ...................................................................................................................................... 11

Multiple Linear-Regression Models.......................................................................................................................... 14

Estimation of Constituent Loads............................................................................................................................... 16

Hydrologic Conditions over the Study Period............................................................................................................... 17

Summary of Water-Quality Data.................................................................................................................................. 18

Multiple Regression Analysis....................................................................................................................................... 22

Loading Estimates ....................................................................................................................................................... 28

Summary ..................................................................................................................................................................... 33

Acknowledgments........................................................................................................................................................ 33

References Cited......................................................................................................................................................... 34

Figures

Figure 1. Map showing locations of the Anacostia River and Rock Creek watersheds and U.S. Geological 

Survey monitoring stations used in this study [Northeast Branch Anacostia River at Riverdale, Maryland 

(01649500), Northwest Branch Anacostia River near Hyattsville, Maryland (01651000), Paint Branch near College 

Park, Maryland (01649190), Rock Creek at Joyce Road, Washington, D.C. (01648010) and Rock Creek at Sherrill 

Drive, Washington, D.C. (01648000)]........................................................................................................................ 4

Figure 2. Photographs showing collection of water-quality samples using equal-width-increment (EWI) sampling: 

(A) collecting depth-integrated subsamples at equal-width intervals across the stream cross section; (B) 

compositing and collecting subsamples from a churn splitter in a clean van; and (C) collecting an integrated EWI 

sample with a DH-95 sampling device from a bridge................................................................................................. 8

Figure 3. Photographs showing (A) automatic sampler with (B) data logger. The system records physical 

parameters from (C) a datasonde deployed in the stream, uploads data via satellite to the worldwide web, and 

triggers the automatic sampler at a selected gage height to collect water-quality samples over a storm hydrograph 9

Figure 4. Graphs showing duration curves for (A) discharge, (B) water temperature, and (C) turbidity at Paint 

Branch near College Park, Maryland (USGS station 01649190) over the period of study. The ranges of flow 

conditions over which equal-width-increment (EWI) and automatic-sampler samples were collected are plotted on 

the duration curves............................................................................................................................10

Figure 5. Scatter plot comparing specific conductance to discharge at Paint Branch near College Park, Maryland 

(USGS station 01649190) during the study period showing the effects of road salt and the cutoff value of 350 

microsiemens per centimeter for model censoring. ................................................................................................. 15

Figure 6. Boxplots comparing monthly summaries of 15-minute-interval discharge data for the Northeast Branch 

Anacostia River (USGS station 01649500) for water years 2004 through 2011. ..................................................... 17

Figure 7. Boxplots comparing monthly summaries of discrete water-quality data for Paint Branch near College 

Park, Maryland (USGS station 01649190) for water years 2008 to 2011. Concentrations of (A) suspended sediment 
iv

in milligrams per liter, (B) total nitrogen in milligrams per liter, (C) total phosphorus in milligrams per liter, and (D) E. 

coli in most probable number of viable colony-forming units per 100 milliliters........................................................ 21

Figure 8. Scatter plots comparing continuous physical parameters and discrete water quality for Paint Branch 

near College Park, Maryland (USGS station 01649190), including all data from automatic samplers and equal-width 

increment (EWI) sampling, showing correlations and spread for each relation. Simple least-square fits to the data 

are drawn for each plot............................................................................................................................................ 23

Figure 9. Time-series plots comparing estimated and measured concentrations of (A,B) suspended sediment, 

(C,D) total nitrogen, (E,F) total phosphorus, and (G,H) E. coli with an overlay of discharge during two storm events 

for Paint Branch near College Park, Maryland (USGS station 01649190)............................................................... 26

Figure 10. Scatter plots comparing estimated and observed concentrations of (A) suspended sediment, (B) 

total nitrogen, (C) total phosphorus, and (D) E. coli for Paint Branch near College Park, Maryland (USGS station 

01649190), using the model equations and bias corrections provided in table 9..................................................... 27

Tables

Table 1. Percentages of land cover in the watersheds above U.S. Geological Survey monitoring stations: 

Northeast Branch Anacostia River at Riverdale, Maryland (01649500); Northwest Branch Anacostia River near 

Hyattsville, Maryland (01651000); Paint Branch near College Park, Maryland (01649190); and Rock Creek at Joyce 

Road, Washington, D.C. (01648010)......................................................................................................................... 5

Table 2. Periods of record for discharge and water-quality data that are summarized in this report for Northeast 

Branch Anacostia River at Riverdale, Maryland (01649500), Northwest Branch Anacostia River near Hyattsville, 

Maryland (01651000), Paint Branch near College Park, Maryland (01649190), Rock Creek at Joyce Road, 

Washington, D.C. (01648010, water quality only), and Rock Creek at Sherrill Drive, Washington, D.C. (01648000, 

flow only)..........................................................................................................................................5

Table 3. Summary of potential bias from all field and automatic-sampler blanks for the 90th percentile of 

samples..........................................................................................................................................12

Table 4. Summary of sampling and analytical precision determined by field replicates..................................... 14

Table 5. Top eight daily mean discharge events for the Northeast Branch Anacostia River at Riverdale, 

Maryland (USGS station 01649500) for water years 2004-2011. ............................................................................ 18

Table 6. Summaries of continuous field parameters for each of the U.S. Geological Survey stations in the 

Anacostia River and Rock Creek watersheds.......................................................................................................... 19

Table 7. Summaries of discrete water-quality data for each of the U.S. Geological Survey stations in the 

Anacostia River and Rock Creek collected over the study period............................................................................ 20

Table 8. Table of Pearson correlation coefficients for Paint Branch near College Park, Maryland (USGS station 

01649190).......................................................................................................................................24

Table 9. Summary of regression models for the Northeast (NE) Branch Anacostia River at Riverdale, Maryland 

(USGS station 01649500), the Northwest (NW) Branch Anacostia River near Hyattsville, Maryland (USGS station 

01651000), and Paint Branch near College Park, Maryland (USGS station 01649190). ......................................... 25

Table 10. Summary of annual loads for suspended sediment, total nitrogen, total phosphorus, and bacteria for 

the Anacostia River at three stations: Northeast Branch Anacostia River at Riverdale, Maryland (USGS station 

01649500); Northwest Branch Anacostia River near Hyattsville, Maryland (01651000); Paint Branch near College 

Park, Maryland (01649190); and for one combined station on Rock Creek: Rock Creek at Joyce Road, 

Washington, D.C. (01648010, for water quality) and Rock Creek at Sherrill Drive, Washington, D.C. (01648000, for 

flow)...............................................................................................................................................29

Table 11. Estimates of annual yields (loads per square area) for suspended sediment, total nitrogen, total 

phosphorus, and E. coli for the Anacostia River (USGS stations 01649190, 01649500, and 01651000) and Rock 
v 

Creek (USGS stations 01648010 (water quality) and 01648000 (flow) for data collected variously in water years 

2005 to 2011............................................................................................................................................................ 30

Table 12. Comparison of estimates of yields for the Northeast and Northwest Branches Anacostia River from 

different studies. ...................................................................................................................................................... 32

 
vi

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 33.82 ounce, fluid (fl. oz)

liter (L) 0.2642 gallon (gal)

liter (L) 61.02 cubic inch (in3) 

cubic foot (ft3) 0.02832 cubic meter (m3) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

cubic meter per second (m3/s) 70.07 acre-foot per day (acre-ft/d) 

cubic meter per second (m3/s) 22.83 million gallons per day

(Mgal/d)

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

Yield Rate

kilogram per year per square

kilometer (kg/yr/km2) 350.4 ton per year per square mile(tn/yr/mi2)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25°C).

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or micrograms per liter (μg/L).

Concentrations of E. coli bacteria are given in most probable number (MPN) of viable colony forming units per 100 milliliters 

(mL). 

Units of turbidity are given in formazin turbidity units (FNU), which are nephelometric turbidity units based on a formazin 

standard.

Water year is the 12-month period starting October 1 of the previous year, and ending September 30 of the water year.
1 

Water Quality in the Anacostia River, Maryland and Rock Creek, Washington, 

D.C.: Continuous and Discrete Monitoring with Simulations to Estimate 

Concentrations and Yields of Nutrients, Suspended Sediment, and Bacteria

By Cherie V. Miller, Jeffrey G. Chanat, and Joseph M. Bell

Abstract

Concentrations and loading estimates for nutrients, suspended sediment, and E.coli bacteria were

summarized for three water-quality monitoring stations on the Anacostia River in Maryland and one station on 

Rock Creek in Washington, D.C. Both streams are tributaries to the Potomac River in the Washington, D.C. 

metropolitan area and contribute to the Chesapeake Bay estuary. Two stations on the Anacostia River, Northeast 

Branch at Riverdale, Maryland and Northwest Branch near Hyattsville, Maryland, have been monitored for water 

quality during the study period from 2003 to 2011 and are located near the shift from nontidal to tidal conditions 

near Bladensburg, Maryland. A station on Paint Branch is nested above the station on the Northeast Branch 

Anacostia River, and has slightly less developed land cover than the Northeast and Northwest Branch stations. 

The Rock Creek station is located in Rock Creek Park, but the land cover in the watershed surrounding the park is 

urbanized. 

Stepwise log-linear regression models were developed to estimate the concentrations of suspended 

sediment, total nitrogen, total phosphorus, and E. coli bacteria from continuous field monitors. Turbidity was the 

strongest predictor variable for all water-quality parameters. For bacteria, water temperature improved the models 

enough to be included as a second predictor variable due to the strong dependence of stream metabolism on 

temperature. Coefficients of determination (R2

) for the models were highest for log concentrations of suspended 

sediment (0.9) and total phosphorus (0.8 to 0.9), followed by E. coli bacteria (0.75 to 0.8), and total nitrogen 

(0.6). 

Water-quality data provided baselines for conditions prior to accelerated implementation of multiple

stormwater controls in the watersheds. Counties are currently in the process of enhancing stormwater controls in 

both watersheds. Annual yields were estimated for suspended sediment, total nitrogen, total phosphorus, and E. 

coli bacteria using the U.S. Geological Survey model LOADEST with hourly time steps of turbidity, flow, and 

time. Yields of all four parameters were within ranges found in other urbanized watersheds in Chesapeake Bay. 

Annual yields for all four watersheds over the period of study were estimated for suspended sediment (65,500 – 

166,000 kilograms per year per square kilometer; kg/yr/km2

), total nitrogen (465 - 911 kg/yr/km2), total 

phosphorus (36 - 113 kg/yr/km2

), and E. coli bacteria (6.0 – 38 x 1012 colony forming units/yr/km2). The length of 

record was not sufficient to determine trends for any of the water-quality parameters; within confidence intervals 

of the models, results were similar to loads determined by previous studies for the Northeast and Northwest 

Branch stations of the Anacostia River. 

Introduction

The Anacostia River and Rock Creek are two heavily urbanized tributaries to the Potomac River and the 

Chesapeake Bay. The lower parts of both streams are within Washington, D.C., and the headwaters are in

Maryland. The chemical and physical stressors of urban development affect the biological integrity of these

tributaries and have led to degraded habitat for aquatic organisms as well as some threats to human health;

segments of both rivers are listed as impaired in Maryland’s 305b report and selected fish-consumption advisories 
2 

are in effect (Maryland Department of the Environment, 2012). Urban sources of chemical and bacterial 

contamination include leaking sewer infrastructure, outfalls from industry and sewage-treatment plants, landfills, 

storm runoff from lawns, gardens, and paved surfaces, and atmospheric deposition. Typically, development 

around urban streams includes modifications to stream geomorphology such as cement channels, buried segments 

encased in pipes, decreased or absent riparian buffers, and rechannelization that reduces sinuosity. Modifications 

to stream channels combined with increases in impervious surfaces in the watershed cause storm discharges to be 

flashy with higher peak discharges and less infiltration to groundwater, thus enhancing downstream fluxes of 

contaminants.

With the signing of the Chesapeake Bay Agreement in 1983, all of the states in the bay watershed 

pledged to work together to study and improve pollution problems in the bay. Signatories to this agreement 

included Maryland, Pennsylvania, Virginia, Washington, D.C., and the U.S. Environmental Protection Agency 

(USEPA), which became the Chesapeake Executive Council. In 1987, a new agreement was signed that 

quantified goals for reductions in nitrogen and phosphorus, and focused research upstream into the watersheds. In 

2000, a more comprehensive agreement (Chesapeake 2000) was signed by all of the watershed states and the 

District of Columbia, with more detailed goals for pollution reduction, habitat restoration, protection of living 

resources, and public engagement. Due to the slow pace in documented water-quality improvements, in 2010, the 

USEPA established Total Maximum Daily Loads (TMDLs) for nitrogen, phosphorus, and sediment for the entire 

bay (U.S. Environmental Protection Agency, 2010). The TMDLs for the bay include 98 impaired stream 

segments that will be addressed by Watershed Implementation Plans (WIPs) based on maximum allowable loads 

from all of the sources within the stream segment and developed by each state. Within the State of Maryland, 

local jurisdictions were provided countywide nutrient-reduction targets and these were used in the development 

of county-specific WIPs for tracking pollutant reductions toward the target goals (U.S. Environmental Protection 

Agency, 2010). 

Both the Anacostia River and Rock Creek are listed in Maryland’s 303(d) report (Maryland Department 

of the Environment, 2012) as being impaired for biological integrity, nutrients, sediment, and bacteria, as well as 

some segments specifically listed for certain organic compounds. The District of Columbia and the State of 

Maryland have developed local TMDLs as goals for limiting the amount of substances that impair these water 

bodies. The District of Columbia, the State of Maryland, and local jurisdictions are working to improve both 

tributaries with stormwater retrofits and stream-restoration programs that address sources of contaminants as well 

as reductions in runoff volume (Montgomery County Department of Environmental Protection, 2012a,b). The

Municipal Separate Storm Sewer System (MS4) permits require local jurisdictions to document current 

conditions and progress towards TMDL goals over time. Water-quality data for nutrients, suspended sediment, 

and bacteria have been collected on Rock Creek and the Anacostia River at multiple locations over the last 

several decades and more intensively over the last decade to address these needs. Data have provided baselines 

for conditions prior to accelerated implementation of multiple stormwater controls in the watersheds. 

Montgomery and Prince George’s Counties have been adding stormwater retrofits in the Anacostia River 

watershed since the late 1990s. The results in the current study help to address some of the data and modeling 

needs for tracking progress for the Maryland WIPs and towards meeting MS4 permit requirements.

Purpose and Scope

The purpose of this report is to summarize water-quality data collected during water years 2005 through 

2011 (each water year begins on October 1 of the previous calendar year and ends on the following September 

30) from the Anacostia River in Maryland and Rock Creek in Washington, D.C. There are some gaps in the 

datasets due to discontinued record. Water-quality data included concentrations of nutrients, suspended sediment, 

and E. coli bacteria, as well as continuous monitoring for physical parameters, water temperature, specific 

conductance, pH, concentration of dissolved oxygen and turbidity, at each station. Loads and yields, estimated 

from water-quality data, also are presented. Estimates of loads will be used by the Montgomery County 

Department of Environmental Protection and the State of Maryland to satisfy MS4 permits, and to track progress 

towards achieving TMDLs for impacted stream segments.
3 

The report summarizes water-quality monitoring data collected at four stations: Northeast Branch 

Anacostia River at Riverdale, Maryland (USGS station 01649500), Northwest Branch Anacostia River near

Hyattsville, Maryland (USGS station 01651000), Paint Branch near College Park, Maryland (USGS station 

01649190), and Rock Creek at Joyce Road, Washington, D.C. (USGS station 01648010). Summaries include 

discrete and continuous water-quality data, regression models that relate concentrations and instantaneous loads 

of total nitrogen (TN), total phosphorus (TP), suspended sediment (SS), and E. coli bacteria to continuous 

physical parameters measured by a multiparameter datasonde, and annual loads for TN, TP, SS, and E. coli. 

Description of the Study Area

The Anacostia River and Rock Creek are adjacent tributaries to the tidal Potomac River. The headwaters 

of Rock Creek are in Montgomery County, Maryland and the headwaters of the Anacostia River are in 

Montgomery and Prince George’s Counties, Maryland; the lower, oligohaline tidal parts of both streams are in or 

near Washington, D.C. (fig. 1). The Northeast Branch Anacostia River at Riverdale, Maryland and the Northwest 

Branch Anacostia River near Hyattsville, Maryland monitoring stations are both near the major confluence of the 

Anacostia River at Bladensburg, Maryland. This is approximately at the Fall Line, a physiographic feature in the 

Mid-Atlantic region that defines the boundary between the Piedmont and Coastal Plain Physiographic Provinces 

and generally marks a shift in slope and transition to tidal influence. Flow at these two stations has been 

monitored by USGS since 1938. Water-quality data for nutrients, organic compounds, SS, bacteria, trace metals, 

and continuous physical parameters have been collected and measured intermittently by USGS at these two 

stations since 2003. Measurements of water quality by USGS were discontinued at Northwest Branch in June 

2010, and are now collected by the Maryland Department of Natural Resources as part of their CORE and 

TRENDS network. Load estimates for nutrients, trace metals, and SS for calendar years 2004 and 2005 for the 

Northeast and Northwest Branches of the Anacostia River were published in an earlier USGS report (Miller, 

Gutiérrez-Magness, and others, 2007).

Paint Branch near College Park, Maryland (USGS station 01649190) is nested in the upper watershed of 

the Northeast Branch of the Anacostia River at the boundary between Montgomery and Prince George’s 

Counties. The Paint Branch watershed has similar but slightly less urban land cover compared to the Northeast 

Branch watershed (table 1). Flow, discrete samples for nutrients, SS, and bacteria, and continuous water quality 

have been monitored by USGS at the Paint Branch station since October 2007 and are reported herein through 

September 2011. One important influence on the Paint Branch watershed over the study period has been the 

development of an 18-mile-long major highway, the Intercounty Connector (ICC), which was begun in 2007 and 

just recently opened in 2011 (fig. 1). The ICC cuts through the forested upper watersheds of the Anacostia River 

and Rock Creek, and the Maryland State Highway Administration has invested in a fairly extensive 

environmental program to mitigate negative environmental impacts during and after construction. These include a 

Special Protection Area (SPA) in the upper watershed where development projects since the mid 1990s have 

required enhanced environmental review and additional stormwater management to protect high-quality stream 

resources. Throughout the Anacostia River watershed, other restoration projects also are planned or being 

completed (U.S. Army Corps of Engineers, 2010). 

The monitoring station Rock Creek at Joyce Road (USGS station 01648010) was established in 

December 2006 for the collection of water-quality data. Flow had been measured approximately 2 kilometers 

(km) upstream at Rock Creek at Sherrill Drive (USGS station 01648000) since 1929, and discharge 

measurements from the Sherrill Drive station were used in conjunction with water-quality data from the Joyce 

Road station for loading calculations. A small tributary and a storm outfall from 16th Street in the District of 

Columbia enter Rock Creek between these stations and have a small effect on discharge at the Joyce Road station 

that is discussed in more detail in the next section.

Historical evaluations of water quality in the Anacostia River and Rock Creek have been documented in 

other USGS studies that provide more detailed watershed descriptions (Anderson and others, 2002; Miller, 

Weyers, and others, 2006; Miller, Gutiérrez-Magness, and others, 2007; Phelan and Miller, 2010). The periods of 

record for water-quality data varied among the stations and are subject to several gaps in data (table 2).
4 

Figure 1. Locations of the Anacostia River and Rock Creek watersheds and U.S. Geological Survey monitoring 

stations used in this study [Northeast Branch Anacostia River at Riverdale, Maryland (01649500), Northwest 

Branch Anacostia River near Hyattsville, Maryland (01651000), Paint Branch near College Park, Maryland

(01649190), Rock Creek at Joyce Road, Washington, D.C. (01648010) and Rock Creek at Sherrill Drive, 

Washington, D.C. (01648000)]. 
5 

Table 1. Percentages of land cover in the watersheds above U.S. Geological Survey monitoring stations: Northeast 

Branch Anacostia River at Riverdale, Maryland (01649500); Northwest Branch Anacostia River near 

Hyattsville, Maryland (01651000); Paint Branch near College Park, Maryland (01649190); and Rock Creek at 

Joyce Road, Washington, D.C. (01648010). 

 [Data are from the National Land Cover Database (NLCD) 2006 (Fry and others, 2011). Categories are grouped so that 

urban/suburban contains all developed land cover, agriculture contains pasture/hay and row crops, forested/undeveloped land 

cover includes forests and mixed shrublands, and wetlands include woody and emergent herbaceous wetlands; USGS, U.S. 

Geological Survey; %, percent; percentages may not total 100 due to rounding and minor categories that were not included] 

USGS station 

number

Urban/

Suburban

%

Agriculture

%

Forested/

Undeveloped

%

Wetlands

%

01649190 57 7.1 33 2.1

01649500 63 5.6 26 4.8

01651000 66 7.6 24 1.5

01648010 67 7.2 23.8 1.6

Table 2. Periods of record for discharge and water-quality data that are summarized in this report for Northeast

Branch Anacostia River at Riverdale, Maryland (01649500), Northwest Branch Anacostia River near 

Hyattsville, Maryland (01651000), Paint Branch near College Park, Maryland (01649190), Rock Creek at Joyce 

Road, Washington, D.C. (01648010, water quality only), and Rock Creek at Sherrill Drive, Washington, D.C.

(01648000, flow only). 

[USGS, U.S. Geological Survey; km2

, square kilometers; --, not collected] 

USGS station 

number

Basin 

area

(km2) Discharge

Discrete

water-quality

data

Continuous

water-quality

data

01649190 34 2007 - present Jul/2007 – Sep/2011 Oct/2007 – Sep/2011 

01649500 189 1938 - present

Jul/2003 – Dec/2005

Jul/2006 – Jun/2007 

Mar/2008 – Sep/2011

Dec/2003 – Dec/2005

Jul/2006 – Nov/2007 

Mar/2008 – Sep/2011

01651000 128 1938 - present

Jul/2003 – Dec/2005

Jul/2006 – Jun/2007 

Mar/2008 – Jun/2010

Feb/2004 – Dec/2005

Sep/2006 – Nov/2007 

Feb/2008 – Jun/2010

01648010 165 -- Dec/2006 – Sep/2011 - - 

01648000 161 1929 – present - - - - 
6 

Methods

Two basic types of water-quality and -quantity data were collected at the sampling stations on the 

Northeast and Northwest Branches of the Anacostia River and at Paint Branch: (1) continuous measurements with 

a pressure transducer for gage height (converted to stream discharge), and a multiparameter datasonde for water 

temperature, specific conductance, pH, concentration of dissolved oxygen, and turbidity; and (2) discrete 

measurements for concentrations of nutrients, SS, and bacteria. On Rock Creek, only discrete samples were

collected during the period of record for this study. All data are archived in the USGS National WaterInformation System (NWIS) database and are available upon request from the USGS Maryland-Delaware-D.C. 

Water Science Center (MD-DE-DC WSC) in Baltimore, Maryland. 

Field methods described in this report are documented in the USGS National Field Manual (NFM) for the 

collection of water-quality data (U.S. Geological Survey, variously dated) and Guidelines and Standard 

Procedures for Continuous-Water-Quality Monitors (Wagner and others, 2006). Chapters of the NFM are updated 

on a continuous basis and are available online at http://water.usgs.gov/owq/FieldManual/index.html. 

Measurements of Stream Discharge and Continuous Water Quality

Discharge was estimated from river stage (gage height) that had been historically recorded at each of the 

stations using a stilling well and float recorder, but more recently by use of a pressure transducer. The gage height 

was recorded every 15 minutes, individual measurements of flow were made throughout the range of stages, and 

a stage-discharge relation was maintained to create a continuous record of flow. The gaging stations at both the 

Northeast and Northwest Branches of the Anacostia River have operated almost continuously from 1938 to the 

present. The station at Paint Branch was started in 2007. Discharge for Rock Creek was monitored upstream at 

the Sherrill Drive station and a rating curve was developed to document differences in discharge measurements 

under multiple flow conditions between the Joyce Road and Sherrill Drive stations. Rating curves between 

Sherrill Drive and Joyce Road quantified an 11-percent difference in discharge during base-flow and low-flow 

conditions (21 cubic feet per second (ft3

/s) or less). Bank-full and stormflows differed less between sites with, for 

example, a 7-percent difference observed at 32 ft3

/s, a 3.5-percent difference observed at 70 ft3/s, and less than a 

1-percent difference observed during a stormflow of 1,905 ft3

/s. Mean-daily discharges below 21 ft3/s occurred 

25 percent of the time from 2007 to the present; however, the majority of flow volume and contaminant loadings 

occurred during storm events when differences between the sites were smaller. Of the water-quality samples 

collected at Joyce Road, 9 percent were collected within the low-flow regime. Discharge at Sherrill Drive was 

used for modeling in this report, without further modification from the rating between the two stations. 

Continuous data for water temperature, specific conductance, pH, concentration of dissolved oxygen, and 

turbidity were collected using YSI models 6600 or 6920V2 multiparameter datasondes deployed in the rivers and 

configured to record each parameter every 15 minutes. Observations were transmitted to an electronic Campbell 

Scientific CR10X data logger, that uploaded data every 4 hours to satellite and then to a USGS server at the 

USGS MD-DE-DC WSC. Continuous data are reported in near-real time online at 

http://waterdata.usgs.gov/md/nwis/current/?type=quality. 

Data accuracy and precision were maintained by inspecting and calibrating equipment at intervals 

ranging from 1 to 6 weeks. Hydrologic conditions, data quality (fouling and drift), and the known functional 

limitations of the equipment dictated servicing frequency. Multiple observations of each parameter were made 

during site visits to determine separate corrections for fouling and instrument drift. Standards for specific 

conductance were prepared and quality-assured at the USGS National Water-Quality Laboratory (NWQL) in 

Denver, Colorado. Standards for pH were purchased from the Fisher Scientific Company. Formazin-based 

standards for turbidity were manufactured by GFS Chemicals (AMCO Clear) and were used without dilution. 

When calibrations exceeded acceptable criteria as defined by Wagner and others (2006), shifts and corrections 

were applied to the continuous-data records in the USGS NWIS database after each completed field inspection; 

data that did not meet quality criteria were either flagged with a rating of lower quality or deleted from the final 
7 

record (Wagner and others, 2006). Corrected (provisional data) were displayed in near-real time on the USGS 

web page of the MD-DE-DC WSC. After review, final approved data were published in the USGS Annual WaterData Report series for water years during which data were collected. USGS Annual Data Reports are available 

online at http://wdr.water.usgs.gov. 

Collection of Discrete Water-Quality Samples

Discrete samples for water quality were collected at a fixed monthly frequency, using equal-width 

increment (EWI) sampling techniques and an isokinetic sampler for vertical integration when flow velocity was 

greater than 1.5 ft3

/s or a weighted-bottle sampler for slower flows (U.S. Geological Survey, variously dated). 

Each discrete sample had associated in-stream water temperature, pH, specific conductance, dissolved oxygen, 

and turbidity recorded from a handheld YSI field meter (model 6600 or 6920V2). Multiple depth-integrated

subsamples were collected at equal intervals across the stream (approximately 10 sections) and composited into 

an acid-washed polyethylene churn splitter to collect a representative flow-weighted sample of the entire stream

(fig. 2A). Samples for bacteria were collected from the center of flow or in the automatic sampler into a sterile 

polyethylene bottle and stored chilled. The samples were processed within the housing at the monitoring station, 

in a clean USGS field van, or at the USGS MD-DE-DC WSC laboratory to avoid environmental contamination 

(fig. 2B). Subsamples of whole water for nutrients and SS were dispensed first into clean polyethylene bottles, 

while churning at a constant prescribed rate. Samples for dissolved analysis were then collected from the churn 

splitter using a peristaltic pump with an in-line polycarbonate capsule filter [0.45 μm (micrometer) effective pore 

size]. During base flow, the cross section was waded for sampling, and during storm events, EWI samples were 

collected from a bridge using a USGS DH-95 isokinetic sampler (fig. 2C) or with an automatic sampler with a 

single-point intake on the side of the stream. Samples for whole-water analysis of nutrients were preserved with 1 

milliliter (mL) of sulfuric acid. Samples for nutrients were chilled to less than 4°C (degrees Celsius) and shipped 

on ice overnight to the USGS NWQL in Denver, Colorado. Samples for SS were stored in a cool dark location 

prior to being shipped within 1 to 2 months to the USGS Sediment Laboratory in Louisville, Kentucky.
8 

(A)

(B)

(C)

Figure 2. Collection of water-quality samples using equal-width-increment (EWI) sampling: (A) collecting depthintegrated subsamples at equal-width intervals across the stream cross section; (B) compositing and collecting 

subsamples from a churn splitter in a clean van; and (C) collecting an integrated EWI sample with a DH-95 

sampling device from a bridge. (Photographs by U.S. Geological Survey) 
9 

An automatic sampler was installed at each station to facilitate sampling during storm events (fig. 3A). 

Automatic samplers initiated when pressure transducers returned a stage value that exceeded a preset threshold or 

they were manually triggered remotely. For each sample, four 1-liter bottles were collected: a pre-tared bottle that 

was capped and stored for sediment without further subsampling, two bottles for processing nutrient samples, and

a pre-sterilized bottle for bacteria. After a storm, the hydrograph was examined and three to four sets of the timediscrete samples were selected to capture the rise, peak, and falling limbs of the storm. To compare results for 

EWI and automatic sampler methods, quality-control samples were collected simultaneously using both 

techniques over a range of hydrologic conditions. 

Figure 3. (A) Automatic sampler with (B) data logger. The data logger records physical parameters from (C) a 

datasonde deployed in the stream, uploads data via satellite to the worldwide web, and triggers the automatic 

sampler at a selected gage height to collect water-quality samples over a storm hydrograph. (Photographs by 

U.S. Geological Survey)

 

(A)

(B)

(C)
10

Figure 4. Duration curves for (A) discharge, (B) water temperature, and (C) turbidity at Paint Branch near College

Park, Maryland (USGS station 01649190) over the period of study. The ranges of flow conditions over which 

equal-width-increment (EWI) and automatic-sampler samples were collected are plotted on the duration 

curves. 

Duration curves of discharge, water temperature, and turbidity measured over the study period at Paint 

Branch with an overlay of the ranges of flows where water-quality samples were collected, demonstrate that 

measurements of water quality covered full ranges of hydrologic conditions (figs. 4A-C). Duration curves for the 

other three stations are not shown here, but are similar. 
11

Laboratory Methods

For the Northeast and Northwest Branches of the Anacostia, all nutrient samples were analyzed at the 

USGS NWQL in Denver, Colorado, samples for SS were analyzed at the USGS Sediment Laboratory in 

Louisville, Kentucky, and samples for bacteria were analyzed at the MD-DE-DC WSC. For the stations on Paint 

Branch and Rock Creek, from July 2007 through February 1, 2008, samples for nutrients and E. coli were 

analyzed at the Washington Suburban Sanitary Commission (WSSC) laboratory in Laurel, Maryland. Since

February 11, 2008, all water-quality samples collected at Paint Branch and Rock Creek have been analyzed by 

USGS laboratories. 

Samples for total and soluble forms of phosphorus and Kjeldahl nitrogen were digested concurrently in 

block digestors at high temperature with a mercury (HgII) catalyst. Ammonium ions (including both those 

originally present and those generated by the procedure) were analyzed colorimetrically by a salicylatehypochlorite Berthelot-reaction procedure using an air-segmented continuous-flow analyzer. Ammonia nitrogen 

was analyzed separately by the same colorimetric procedure. Phosphorus was analyzed in a separate aliquot from 

the same digestate using the ammonium-molybdate colorimetric method and automated segmented flow 

(Fishman, 1993; Patton and Truitt, 2000). Nitrite-nitrogen was analyzed by diazotization and colorimetric 

detection in automated segmented flow. Nitrate was reduced by cadmium metal and analyzed as nitrite to give 

total nitrate/nitrite-nitrogen (Fishman, 1993). Nitrate-nitrogen was determined by difference. TN was calculated 

as the sum of total Kjeldahl nitrogen and total nitrate/nitrite nitrogen. All methods at the USGS NWQL are 

quality-assured by the National Environmental Laboratory Accreditation Program (NELAP), the USGS Branch of 

Quality Systems that conducts blind audits, and the USGS Standard Reference Sample (SRS) Program.

Concentrations of SS and the percent of sediment finer than 0.63 μm were analyzed using filtration and 

gravimetric methods at the USGS Sediment Laboratory in Louisville, Kentucky. All methods and qualityassurance procedures for sediment analyses are documented in the quality-assurance plan for the USGS Sediment 

Laboratory in Louisville, Kentucky (Shreve and Downs, 2005). 

E. coli bacteria were enumerated using the Colilert method. In essence, 100 mL of sample collected in a 

sterile bottle was combined with a prepackaged dry-enzyme media, transferred to sterile Quanti-Trays (IDEXX) 

with counting wells, and incubated for 24 or 18 hours (+/- 1 hour) at 35°C (+/- 1°). Wells that were positive for 

total coliform (nutrient indicator turned yellow) and for E. coli (nutrient indicator fluoresced under blacklight) 

were counted and statistically converted to Most Probable Number (MPN) of viable colony-forming units per 100 

mL of sample. Initially Colilert-24 was used, which has a 24-hour incubation period, but beginning in August 

2008, the method was changed from 24- to 18-hour incubation periods using the Colilert-18 media. For storm 

events, where bacterial concentrations were normally higher than those for base flow, samples were diluted to

10X or 100X with sterile water to accurately quantify the bacteria. Multiple dilutions were used to bracket the 

expected concentrations for each sample, and the lowest dilution that could be enumerated was recorded for the 

result. Colilert and traditional multi-tube fermentation or membrane filtration methods have been shown to be 

very comparable, but the Colilert method is more sensitive, particularly when concentrations of bacteria are low

(Eckner, 1998). 

Evaluation of Data Integrity

From 2008 through 2011, a total of 55 field and automatic-sampler blanks were collected among the four 

sampling stations and analyzed for nutrients, SS, and (or) bacteria. Field blanks were collected by processing 

inorganic-free blank water for nutrients and suspended sediment, and sterile water for bacteria through the entire 

collection process including the bottle sampler, churn splitter, and capsule filter. To evaluate potential bias with 

autosampling, intake lines were raised out of the river, flushed with deionized water, and then used to pass 

inorganic-free blank water through the intake system and the entire sample-processing procedures. 

Concentrations were found to be below reporting limits or not detected for most water-quality constituents. On 

occasion, measurable concentrations indicating contamination were observed but usually at a level near or below 
12

reporting thresholds. Significant contamination (when blank values were greater than 10 percent of

environmental values) was observed in a field blank for SS collected on October 14, 2009 and reported as 47 

milligrams per liter (mg/L). All other constituents in this blank sample were below their reporting limits. It was 

determined that this was an anomalous result of an improperly cleaned sediment-sample bottle, and that this 

occurrence was not typical of other samples. Additionally, approximately 55 percent of field and automaticsampler blanks had concentrations of ammonia that exceeded 10 percent of the environmental sample values. 

Topical blanks on equipment and processes did not return detections of ammonia above the 0.01 mg/L reporting 

threshold, indicating that ambient contamination at sampling locations is a probable source of low-level ammonia 

contamination. Review of all data for ammonia blanks indicates that samples are subject to background 

contamination by ammonia at levels close to the reporting limit. Any individual environmental concentrations 

within the 10-percent range of background blank values are likely to be influenced by low-level background 

contamination, but for TN, the datasets had minimal bias relative to ambient concentrations and were adequate 

for load calculations and other data applications. A summary of the results for blank samples (table 3) provides 

the potential bias for each parameter. 

Table 3. Summary of potential bias from all field and automatic-sampler blanks for the 90th percentile of samples. 

[The estimated bias for each parameter can be interpreted as, for example for total Kjeldahl nitrogen - in at least 90 percent 

of the samples the potential contamination is expected to be no greater than 0.11 milligrams per liter (mg/L) with a 96-

percent degree of confidence; MPN, most probable number of viable colony-forming units per 100 milliliters; mL, 

milliliters; %, percent]

Parameter Reporting limit

Estimate of potential 

positive bias (upper 

confidence limit)

Percentage of blanks 

below reporting level

Total Kjeldahl nitrogen 0.07 mg/L 0.11 mg/L (96%) 73%

Ammonia nitrogen 0.020 mg/L 0.025 mg/L (90%) 89%

Nitrate/nitrite nitrogen 0.020 mg/L 0.03 mg/L (98%) 80%

Nitrite nitrogen 0.0010 mg/L 0.001 mg/L (98%)* 100%

Total phosphorus 0.01 mg/L 0.01 mg/L (96%) 87%

E. coli 1 MPN / 100 mL 5 MPN / 100 mL (92%) 93%

Suspended sediment 0.05 mg/L 1 mg/L (94%) 56%

*Estimated, all values were censored below the reporting level. 

Water-quality samples for nutrients and bacteria collected from July 2007 to March 2008 were analyzed 

at the WSSC laboratory in Laurel, Maryland. Samples from the USGS SRS Program and split replicate samples 

sent to each laboratory were used to investigate variability and significant differences between laboratory results 

for nutrients from the WSSC laboratory and NWQL. For example, in the spring and fall of 2007, independent 

standard reference samples were sent to the WSSC laboratory and the NWQL for comparative analysis and lab 

performance testing. The WSSC laboratory’s percent difference from standard ranged from 0 to 74 percent with 

a significant difference from standard in one of the reported measurements of nitrite + nitrate. NWQL’s percent 

difference from standard ranged from 0 to 7.4 percent with no observed significant differences from standard. 

Significant differences from standards are defined as greater than +/- 2 standard deviations from the most 

probable value. Since March 2008, the NWQL has been used for nutrient analysis, and NWQL performance is 

monitored on a regular basis to ensure data integrity. All methods at NWQL are audited by the NELAP, the 

USGS Branch of Quality Systems that conducts blind sample audits, and the USGS SRS Program. More 

information regarding NWQL quality assurance can be found online at http://nwql.usgs.gov/Public/quality.shtml. 

When the project changed analytical laboratories for nutrients, measurements of bacteria also were also changed
13

to the USGS MD-DE-DC WSC to obtain the accuracy and reporting limits that were needed for modeling loads. 

Multiple replicate samples were collected at that time to compare data from both labs, but relative percent 

differences (RPDs) were large. Data from both the WSSC laboratory and the NWQL were used for modeling 

purposes because the model evaluates the errors and provides confidence intervals on the results that include error 

from data scatter, and because there was enough data from WSSC laboratory to compensate for the loss of 

precision. 

In August 2008, bacterial-testing methods were changed from Colilert-24 to Colilert-18 in order to 

facilitate analysis; this step reduced the incubation period from 24 to 18 hours. Fifteen sets of replicate samples 

from study sites were analyzed, and RPDs were computed to determine comparability between these two 

methods. RPDs ranged from 0 to 30 percent, with 73 percent of the samples showing less than a 15 RPD. 

Observed variability greater than 15 percent in the USGS laboratory comparisons typically occurred at levels that 

required sample dilution (1:10 or 1:100), where precision is unavoidably reduced regardless of the method. It 

was concluded by USGS personnel that a switch from Colilert-24 to Colilert-18 would not diminish data integrity 

as variability in USGS processed replicates was similar to environmental variability (approximately 10 RPD 

observed in replicate comparisons), and that datasets derived from the two methods were comparable and could 

be combined for analysis and interpretation. 

USGS has developed very detailed methodologies to collect representative samples from stream cross 

sections. These methods typically involve analysis of the cross-sectional and vertical variability in flow and 

segmentation of the cross section into EWI or equal-discharge increments (EDI) that are sampled and composited 

into a churn splitter (U.S. Geological Survey, variously dated). Conversely, automatic samplers collect point 

samples that are often not representative of the entire stream cross section, but they provide a distinct advantage 

for sampling designs as they allow collection of multiple samples over a storm at key points in the hydrographs at 

times when physically collecting a sample may not be practical or even possible. Therefore, when automatic 

samplers are used, documentation of representativeness of a true EWI or EDI measurement must be made. 

Comparisons are often complicated by rapidly changing conditions over a storm event, but nonetheless provide 

insight on concentration data generated from an automatic sampler. Replicate samples comparing point to crosssectional sampling across different hydrologic conditions and among the four sampling stations had RPDs that 

were usually less than 10 percent, but TP and SS had recurring large (greater than 10 percent) RPDs at all sites, 

which makes physical sense as suspended particles are dependent on flow velocities and may be highly variable, 

both spatially and temporally, in high-flow waters (Horowitz, 1991). Out of a total of 41 replicate pairs for the 

concentration of TP, 3 exceeded +/- 10-percent difference: Northwest Branch (33 percent on May 27, 2008), 

Northeast Branch (20 percent on July 19, 2011), and Paint Branch (17 percent on May 26, 2009). RPDs among 

replicate samples of SS at all four sites frequently exceeded 10 percent and ranged from 0 to 60 percent without 

an observable bias between the sampling methods. Multiple explanations are possible for observed differences in 

replicate comparisons of SS including: (1) intake tubing may be overwhelmed by sediment during the storm and 

not completely purge leading to the collection of an over-biased sediment concentration; (2) large sand-size 

particles are not as easily pulled up through the sampling lines and may drop out before reaching the automatic 

sampler; (3) flow conditions are highly variable during storm events, sometimes changing on the order of minutes 

and comparisons between the automatic sampler and EWI samples are difficult to sync in time; and (4) the 

system is naturally heterogeneous with random large pieces of materials that may or may not be picked up in a 

particular sample. When buried intake lines were identified as a problem, related data were deleted from the 

dataset. The loss of large particles through the intake was unavoidable and was documented by comparisons 

during high flows. One way to control for temporal variability during sampling was to use a datasonde to more 

quickly profile field parameters across the stream. An EWI sample takes approximately 30 to 45 minutes to 

collect and represents an average of the flow over that time period, while a cross section with a datasonde can be 

accomplished in approximately 10 minutes. Cross sections of specific conductance at all stations varied by less 

than 5 percent and usually by less than 1 percent. Relative standard deviations for turbidity also were usually less 

than 5 percent but were more variable than specific conductance with a few occurrences of high variability. For 

example during a base-flow measurement at Northeast Branch Anacostia River on March 3, 2011, all of the 

turbidity readings were 6.7 to 9.2 formazin nephelometric units (FNU), except for the left edge of water where 

there was a visible grey plume and the value was 39 FNU. An example of a storm event with an average 
14

discharge of 4,610 ft3/s at the same station on May 12, 2008 had cross-sectional measurements of turbidity of 

240, 201, 260, and 240 FNU, with a relative standard deviation of 8.7 percent. In spite of the limitations of point 

sampling with an automatic sampler, the data still provide very useful information, given the critical need for data 

collected during the initial first flush of a storm event in an urban stream when many contaminants are first

mobilized. 

Comparisons of cross-section to automatic-sampler replicate data at Rock Creek showed nonsystematic 

biasing of concentrations of nutrients and SS. At this station, high energy during storm events mobilized silt and 

sand-sized particles that may have made up a large fraction of the SS. Additionally, an eddy in the stream 

sometimes formed just below the bridge at the intake site, causing large grain-size particles to drop out and cover 

the automatic-sampler intake orifice during sample collection. Biasing due to siltation at the intake resulted in a 

total of seven samples being excluded from the Rock Creek record during the 4-year study period. The intake 

has recently been moved further into the stream course to avoid siltation in the future. 

Analytical precision of the collection and analytical methods was determined by pooling all pairs of field 

replicates from the four stations. Replicate pairs were collected over a range of hydrologic conditions and 

included pair sets from both EWI and automatic sampling. Replicate pairs analyzed by the WSSC lab were

included in the summaries, but not replicate comparisons between labs or between sampling techniques. For each 

replicate pair, the RPD was calculated and the averages of the RPDs for each parameter are presented in table 4.

Replication of whole-water samples (total Kjeldahl nitrogen, TN, E. coli, and SS) was more variable than for 

dissolved parameters, particularly during storm events when there are larger particles and more debris in the 

turbulent water column.

Table 4. Summary of sampling and analytical precision determined by field replicates. 

 [%, percent]

Parameter

Average relative

percent difference

Number of 

replicate pairs

Total Kjeldahl nitrogen 9.1% 9

Ammonia nitrogen 1.9% 6

Nitrate/nitrite nitrogen 2.6% 10

Nitrite nitrogen 0.0% 5

Total phosphorus 8.3% 6

E. coli 8.6% 28

Suspended sediment 13.1% 10

Data integrity for the water-quality samples collected by USGS at Northeast and Northwest Branches of 

the Anacostia River was similarly evaluated in Miller, Gutiérrez-Magness, and others (2007). Quality-control 

data are available from the USGS MD-DE-DC WSC upon request. 

Multiple Linear-Regression Models

A multiple log-linear-regression model was used to develop proxies for the estimation of concentrations 

of SS, TN, TP, and E. coli from continuous field measurements with a multiparameter datasonde. Previous 

studies have shown that turbidity, which can be measured relatively inexpensively and at frequent intervals in the 

field, is an excellent indicator of water-quality conditions and a statistically good predictor of relative

concentrations of constituents such as SS and nutrients (Christensen, 2001; Christensen and others, 2000; 

Rasmussen and others, 2005; Miller, Gutiérrez-Magness, and others, 2007; Jastram and others, 2009; Chanat and 
15

others, 2013). Following the approach for stepwise model selection described by Helsel and Hirsch (1992), the 

first step was to review summary statistics for each variable and to test transformations to improve linearity of 

relations and normality of distributions of the residuals. Models for SS, TN, TP, and E. coli were developed using 

stepwise regression in the SPlus Statistical Package (version 8.1, TIBCO Corporation, 2008), whereby variables 

were added iteratively to maximize the best fit of the model with the minimum number of predictive variables. 

Rather than basing model decisions on coefficients of determination (R2

), the SPlus algorithm determined the best 

model using Akaike’s Information Criterion (AIC, Helsel and Hirsch, 1992), which created a statistic based on 

the measure of model error with a negative correction for the number of explanatory variables. This may be more 

similar to an adjusted R2

, which takes into account the degrees of freedom and thus gives less credit to a model 

with more explanatory variables.

Transformations have the potential for negative bias when the data are transformed from log space back 

to linear space (Cohn and others, 1989; Cohn and others, 1992; Helsel and Hirsch, 1992). This is primarily an 

issue with the highest outliers, which when transformed back to linear space, dominate the calculation of 

smearing corrections. This results in all back-transformed values being slightly over estimated and becomes a 

more important issue when loads from estimated concentrations are integrated over time and bias accumulates in 

the estimates. Following the guidelines of Helsel and Hirsch (1992), once each model was selected, smearing 

corrections as defined in Duan (1983) were applied to correct for this bias. 

For the purposes of regression modeling, data for specific conductance were subsetted to remove biased

values during winter months; measurements of specific conductance are strongly and consistently skewed high at 

all sites during periods when road salt is applied. These high values represent true observations but are erroneous 

for correlation of SC to nutrients, sediment, or bacteria. The cutoff values for specific conductance at each site 

were selected by visual inspection and confirmed with correspondence to winter events. The example from Paint 

Branch (fig. 5) shows the data that were removed with a cutoff value of 350 microsiemens per centimeter 

(μS/cm). Note that these data are plotted on a log scale. For the Northeast and Northwest Branches Anacostia 

River, the censoring thresholds for SC were 500 and 570 μS/cm, respectively.

Figure 5. Scatter plot comparing specific conductance to discharge at Paint Branch near College Park, Maryland 

(USGS station 01649190) during the study period showing the effects of road salt and the cutoff value of 350

microsiemens per centimeter for model censoring. 
16

Estimation of Constituent Loads

Annual loads are the mass of a chemical or physical constituent such as nitrogen or sediment that is 

delivered to a particular point in the river over a specified period of time. They are basically the product of 

concentration (for example, in units of mg/L), stream discharge at that point (for example, in units of ft3

/s), and a 

units conversion factor: 

LT = ∫ kCiQidt ti

t0 (1) 

where

LT is the total load over the period of time T; 

Ci is the concentration at time ti; 

Qi is the stream discharge at time ti; and 

k is a units conversion factor. 

 Whereas discharge is often measured at high frequency, as in the case of the four stations in the current 

study where it was measured every 15 minutes, concentration data are usually collected less frequently and must 

be interpolated by regression to other measured parameters to complete the integration for load estimation. The 

most common predictive variables for concentrations of nutrient and sediment are discharge and time (Cohn, 

2005; Cohn and others, 1989), but because of new technology that enables measurement of other common 

physical parameters of water quality continuously, predictors such as turbidity and SC can greatly improve the 

precision of loading estimates. Continuous water-quality data were not available for Rock Creek at the time of 

this study, so the loads for Rock Creek were based solely on flow and time. The likely effect of this was lower 

precision in the Rock Creek load estimates.

Annual loads for SS, TP, TN, and E. coli were estimated for this report using the USGS program 

LOADEST for all four stations for water years 2005 through 2011, except for years where there were large gaps 

in data. LOADEST (Runkel and others, 2004) is based on an earlier log-linear regression model, ESTIMATOR 

(Cohn, 1988, 2005; Cohn and others, 1989; Cohn and others, 1992); ESTIMATOR uses a FORTRAN code with

log-transformed variables to fit a linear regression to estimate concentrations of water-quality constituents from 

mean daily values of stream discharge, discharge2

, time, time2, and sin(time) and cos(time) terms to approximate 

seasonality. A Minimum Variance Unbiased Estimator (MVUE) within the model corrects for bias created when 

data are transformed from log space back to linear space (Bradu and Mundlak, 1970). Centering variables 

(normalization of each variable to the mean) reduce or eliminate multicollinearity, or non-independence between 

explanatory variables, and the Adjusted Maximum Likelihood Estimator (AMLE) reduces bias when estimating

values from censored data. Whereas ESTIMATOR is calibrated using mean-daily flow (averaged over a 24-hour 

time step) and uses only flow and time as predictor variables, LOADEST has the advantage of being able to use 

hourly time steps and to include other variables, such as turbidity, which has been determined in this and other 

studies to be the best predictor of many water-quality variables. LOADEST also has been shown to improve error 

estimates and bias over ESTIMATOR (Miller, Gutiérrez-Magness, and others, 2007; Rasmussen and others, 

2005; Chanat and others, 2013). Missing data presents a problem for both models. For the current study,

estimated values were interpolated using a linear estimate between the two data points for gaps of less than 1 day, 

and using medians for the entire dataset for larger gaps in time. The variables used for LOADEST modeling were 

turbidity, discharge, discharge2

, time, time2, sin(time), and cos(time) at hourly time steps.

For comparison purposes, loads were normalized to basin area and reported as yields (kilograms per year 

per square kilometer or MPN per year per square kilometer). For Rock Creek, the basin area for the site at Sherrill 

Drive where flow was measured was used for yield calculations. 
17

Hydrologic Conditions over the Study Period

Rainfall in the region is generally consistent throughout the year, but seasonal flows peak in the spring 

and fall with lower average flows in late summer due to increases in evapotranspiration. Monthly summaries of 

discharge data for the Northeast Branch Anacostia River over the study period are shown in figure 6. Major storm 

events tend to occur in the spring and fall, but can occur at any time of the year. 

Hydrologic conditions over the study period were normal, with the highest mean annual discharge for 

each site occurring in 2010, but with a higher incidence of major storms and hurricanes occurring in 2011. The 

top eight daily mean discharge events observed on the Northeast Branch Anacostia River at Riverdale, Maryland

(USGS station 01649500) with the meteorological event that triggered the observed high flows during water years 

2004 to 2011 are listed in table 5. 

 

Figure 6. Boxplots comparing monthly summaries of 15-minute-interval discharge data for the Northeast Branch 

Anacostia River (USGS station 01649500) for water years 2004 through 2011. 
18

Table 5. Top eight daily mean discharge events for the Northeast Branch Anacostia River at Riverdale, Maryland

(USGS station 01649500) for water years 2004-2011. 

[Median discharge for this period of record at this station is 40.2 cubic feet per second (ft3/s); Archived radar imagery can be 

viewed at http://locust.mmm.ucar.edu/. Historical Hurricane information was obtained on October 1, 2012 at 

http://weather.unisys.com/hurricane/index.php] 

Date

Daily Mean 

Discharge (ft3/s) Event

10/8/2005 4,321 Tropical Storm Tammy

6/26/2006 4,294 Tropical Storm Ernesto

5/12/2008 4,051 Extratropical event

9/8/2011 2,968 Tropical Storm Lee

9/7/2011 2,544 Tropical Storm Lee

12/26/2009 2,530 Large snowmelt event

4/15/2007 2,438 Nor'easter

8/28/2011 2,378 Hurricane Irene

Summary of Water-Quality Data

Multiple types of water data were collected at the monitoring stations in the Anacostia River and Rock 

Creek watersheds during this study. Continuous monitoring of flow and physical parameters (table 6) were used 

in conjunction with discrete sampling for water quality during base-flow and storm events (table 7) to provide a 

summary of the environmental conditions for both rivers. All water-quality and flow data are available on the 

USGS NWIS Web site at http://waterdata.usgs.gov/nwis. 

Water quality showed strong seasonal patterns (fig. 7) that were not necessarily reflected in seasonal flow 

patterns (fig. 6). The growth of bacteria is very closely related to temperature, so as expected, numbers of E. coli

followed seasonal temperatures very closely. Concentrations of SS appeared to start to increase during the spring 

months, and had the highest ranges in concentrations during the summer. TP, and to a lesser extent, TN, followed 

similar patterns to SS, strongly indicating that particulates were the dominant transport phases for nutrients in 

these urbanized streams. This is consistent with earlier observations for the Anacostia River (Miller, GutiérrezMagness, and others, 2007). Increases in nutrients in the summer are likely related to increases in fertilizer usage 

but may also relate to increases in sediment sources, as nitrogen and particularly phosphorus are primarily 

transported in particulate phases. Possible explanations for increases in SS could be increases in development 

activities during the warmer months and (or) increases in the energy in flows during spring freshets that could 

change bank-erosion patterns, but neither of these connections were investigated in the current study. Chanat and 

others (2013) found similar patterns in Mattawoman Creek, a small Coastal Plain watershed with less 

development, but found conversely that particulate nitrogen was the smallest component of total nitrogen as 

opposed to more typically urban watersheds where particulate organic nitrogen is often the dominant component 

of TN (Miller, Gutiérrez-Magness, and others, 2007).
19

Table 6. Summaries of continuous field parameters for each of the U.S. Geological Survey stations in the Anacostia River and Rock Creek

watersheds. 

[Continuous data are summarized from data collected during the period of this study (see table 2), and are presented as the median with the minimum and 

maximum values in parentheses. Continuous water-quality data were not collected at Rock Creek over the study period; ft3

/s, cubic feet per second; °C, degrees 

Celsius; μS/cm, microsiemens per centimeter at 25 °C; mg/L, milligrams per liter, FNU, formazin nephelometric units; <, less than; --, not available]

Discharge 

(ft3/s)

Water 

Temperature

(°C) pH

Specific 

Conductance

(μS/cm)

Dissolved 

Oxygen 

(mg/L)

Turbidity

(FNU)

Paint Branch near College Park, Md. (01649190)

Median

(Range)

6.72

(0.1 – 3,452)

13.2

(0 – 28.0)

7.5

(6.7 – 8.7)

245

(15 – 13,000)

10.4

(6.4 – 16.7)

1.7

(0 – 1,330)

Northeast Branch Anacostia River at Riverdale, Md. (01649500)

Median

(Range)

40.2

(1.5 – 11,953)

14.5

(0 – 33.6)

7.4

(5.2 – 9.7)

315

(34 – 9,020)

10.2

(2.0 – 20.2)

5.8

(0 – 1140)

Northwest Branch Anacostia River near Hyattsville, Md. (01651000)

Median

(Range)

25.6

(0 – 13,933)

15.4

(0 – 31.8)

7.3

(6.5 – 9.5)

368

(57 – 14,000)

9.9

(1.6 – 19.3)

4.2

(0 – 1,130)

Rock Creek at Sherrill Drive, D.C. (01648000)

Median

(Range)

30.2

(0.44 – 2,173) -- -- -- -- --
20

Table 7. Summaries of discrete water-quality data for each of the U.S. Geological Survey stations in the Anacostia River and Rock Creek collected 

over the study period. 

[See table 2 for period of record; mg/L, milligrams per liter; MPN/100 mL, most probable number of viable colony-forming units per 100 milliliters; Range, 

minimum and maximum values; n, number of samples; total nitrogen is calculated as the sum of each sample concentration of total Kjeldahl nitrogen and 

nitrate/nitrite nitrogen; <, less than; > greater than] 

Stations

Total

Kjeldahl

nitrogen

(mg/L)

Ammonia

nitrogen

(mg/L, as N)

Nitrate/nitrite

nitrogen

(mg/L, as N)

Total 

 nitrogen 

(mg/L)

Total 

phosphorus

(mg/L)

E. coli

(MPN/100 mL)

Suspended

Sediment

(mg/L)

Paint Branch near College Park, Md. (01649190)

n 182 156 183 182 182 177 187

Median

(Range)

.79

(<.05 – 5.8)

.02

(<.01 - .322)

0.74

(.15 – 2.67)

1.6

(.62 – 6.51)

.12

(<.01 – 0.85)

3,100

(<10 – >55,000)

99

(<.5 – 4,300)

Northeast Branch Anacostia River at Riverdale, Md. (01649500)

n 179 178 178 178 179 150 211

Median

(Range)

1.3

(.18 – 5.1)

.052

(<.005 - .53)

.62

(.17 – 1.4)

1.8

(.48 – 5.7)

0.22

(.01 – 1.05)

5,100

(36 – 120,000)

170

(1 – 2,000)

Northwest Branch Anacostia River near Hyattsville, Md. (01651000)

n 129 127 127 127 129 92 149

Median

(Range)

1.6

(.16 – 5.1)

0.047

(<.006 - .50

0.73

(.17 – 2.0)

2.2

(.82 – 5.9)

0.3

(<.009 - .93)

3,050

(33 – >290,000)

160

(2 – 1,700)

Rock Creek at Joyce Road, D.C. (01648010)

n 227 151 226 223 230 171 238

Median

(Range)

1

(.24 – 6.0)

0.044

(<.01 – 1.2)

0.68

(<.03 – 3.5)

1.9

(.63 – 6.3)

0.16

(<.004 – 1.2)

3,400

(2 - >61,000)

120

(<.5 – 3,100)
21

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

0

1000

2000

3000

4000

Suspended Sediment in mg/L

11 11 21 16 15 11 17 20 18 16 13 18 

(A)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

0

2

4

6

Total Nitrogen in mg/L

11 12 20 15 14 15 16 20 18 15 7 19 

(B)

 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

0.0

0.2

0.4

0.6

0.8

Total Phosphorus in mg/L

 11 12 20 15 14 15 16 20 18 15 7 19

(C)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

0

10000

20000

30000

40000

50000

E. coli, MPN per 100 mL

 11 8 17 16 14 15 19 21 18 12 7 19 

(D)

Figure 7. Boxplots comparing monthly summaries of discrete water-quality data for Paint Branch near College Park, 

Maryland (USGS station 01649190) for water years 2008 to 2011. Concentrations of (A) suspended sediment in 

milligrams per liter, (B) total nitrogen in milligrams per liter, (C) total phosphorus in milligrams per liter, and (D) E. 

coli in most probable number of viable colony-forming units per 100 milliliters. 
22

Multiple Regression Analysis

Multiple log-linear regression models were developed to estimate concentrations of SS, TN, TP, and E. 

coli from physical parameters. The first step in this process was to evaluate all relations between parameters in 

terms of predictive power, scatter and bias from outliers, and transformations that improved the normality of the 

datasets. Transformations to base-10 logarithms provided the most linear relations, normal distributions, and the 

most constant variance except for a few outliers in each dataset. The models were calculated separately with the 

most influential outliers removed, to determine whether they had an effect on the selection of predictor variables. 

In both cases, with and without the outliers, stepwise regression selected the same model. With the outliers 

removed, the residuals were smaller with higher R2 values, but the datasets were still not normal. Therefore, 

according to the recommendations of Helsel and Hirsch (1992, table 9.1), outlier values were included in the final 

models. As an example of the modeling process, scatter plots for all of the parameters being tested on the Paint 

Branch station are shown in figure 8, and a matrix of Pearson linear correlation coefficients for these same 

relations is shown in table 8. All parameters were log-transformed, except for temperature, which varied over a 

small range of values without outliers, and pH, which is already a log-transformed parameter.

As expected, turbidity was a good predictor variable for most of the water-quality parameters, with R2 for

the log-concentrations of SS, TP, and E .coli ranging from .85 to .93. Similarly, Jastram and others (2009) did a 

very in-depth comparison of turbidity- and streamflow-based models to estimate concentrations of SS, TN, and 

TP and found that using turbidity rather than traditional models based on streamflow as the primary predictor 

variable significantly decreased the errors of the estimates.

In the current study, the correlation coefficient between log-TN and log-turbidity was lower than for the 

other three relations, and the upward-concave shape of the scatter plot indicates a non-linear relation. The 

regression equations for TN were improved for the Northwest Branch Anacostia River and Paint Branch by 

adding a squared term and then centering the variables by subtracting the mean of the log of all of the 

observations. The centering adjustment mitigated colinearity when both linear (x) and squared (x2

) terms were 

included in the model. For Northwest Branch and Paint Branch, the values of the mean log10(Turb) were 1.7607 

and 1.5013, respectively. For bacteria, a second parameter, temperature, improved the relation enough to be left 

in the regression equation for all three Anacostia River sites, which made physical sense given the temperature 

dependence of bacterial metabolism as observed by the strong seasonal patterns in numbers of bacteria (fig. 7D). 

Regression models for SS, TP, TN, and E. coli with R2 and bias corrections are presented in table 9. The 

values of R2

 are calculated on log-transformed values and variances may increase when values are transformed 

back to real space, especially for outlier concentrations.

Plots of two storms in April 2009 and September 2010 at Paint Branch show a comparison of 

concentrations of SS, TN, TP, and E. coli estimated by the regression models and measured discrete 

concentrations (fig. 9). These two storms provide some contrast of results for a spring and a fall storm and show 

the effects of different storm intensities. Scatter plots of the entire dataset demonstrate the spread and bias across 

the ranges of concentrations (fig. 10). As for similar models (Miller, Gutiérrez-Magness, and others, 2007; 

Chanat and others, 2013), estimates of SS and TP based on turbidity are the most accurate, because turbidity has a 

direct physical relation to suspended material in the water column, and phosphate partitions strongly into the 

particulate phase. The models appeared to predict peak concentrations very well, but were sometimes less 

accurate on the receding limbs of the hydrographs, where due to hysteretic effects, the models tended to overpredict concentrations. The noise in the predicted values of TN during the latter part of the September storm was 

an artifact of the model where small changes in turbidity were amplified in the log-turbidity-squared term. The 

models for E. coli were less accurate for predicting individual concentrations (figs. 9G,H). Measurements of the 

number of E. coli tend to be difficult to reproduce because of high spatial variability in the water column. Thus, 

even though coefficients of determination were in the range of 0.75 to 0.82, predictions of individual 

concentrations of bacteria are not recommended. In fact, Rasmussen and Ziegler (2003) suggested reporting 

estimated levels of bacteria as probabilities of exceeding regulated thresholds rather than concentrations because 

of high variability in individual measurements. 
23

Figure 8. Scatter plots comparing continuous physical parameters and discrete water quality for Paint Branch near College Park, Maryland (USGS 

station 01649190), including all data from automatic samplers and equal-width increment (EWI) sampling, showing correlations and spread for 

each relation. Simple least-square fits to the data are drawn for each plot. The axes for each plot correspond to the parameter in the diagonal of 

that row or column and the units for parameter are at the top or bottom of the row or column. [log, base 10 logarithm of variable; Q, discharge 

in cubic feet per second; Temp, temperature in degrees Celsius; SC, specific conductance in microsiemens per centimeter; DO, dissolved 

oxygen in milligrams per liter (mg/L); Turb, turbidity in formazin nephelometric units (FNU); TOTN, total nitrogen in mg/L; TOTP, total 

phosphorus in mg/L; SS, suspended sediment in mg/L; Ecoli, most probable number (MPN) of viable colony-forming units of colony-forming 

units per 100 milliliters] 
24

Table 8. Table of Pearson correlation coefficients for Paint Branch near College Park, Maryland (USGS station 01649190). 

[Continuous physical parameters are compared to discrete water-quality parameters. Values for parameters that were used in the regression models are in bold; 

Q, stream discharge; Temp, temperature; SC, specific conductance; DO, concentration of dissolved oxygen; Turb, turbidity; TN, concentration of total nitrogen; 

TP, concentration of total phosphorus; SS, concentration of suspended sediment]

Parameters log10(Q) Temp pH log10 (SC) DO log10 (Turb) log10(TN) log10(TP) log10 (SS) log10 (E. coli)

log10(Q) 1

Temp .16 1

pH -.47 -.30 1

log10 (SC) -.69 -.33 .63 1

DO -.21 -.97 .25 .33 1

log10 (Turb) .91 .24 -.42 -.59 -.32 1

log10 (TN) .63 .16 -.34 -.39 -.16 .62 1

log10 (TP) .83 .20 -.35 -.60 -.23 .87 .73 1

log10 (SS) .89 .24 -.35 -.59 -.30 .93 .77 .93 1

log10 (E. coli) .76 .51 -.45 -.62 -.57 .85 .56 .79 .86 1
25

Table 9. Summary of regression models for the Northeast (NE) Branch Anacostia River at Riverdale, Maryland (USGS station 01649500), the 

Northwest (NW) Branch Anacostia River near Hyattsville, Maryland (USGS station 01651000), and Paint Branch near College Park, Maryland

(USGS station 01649190). 

[mg/L, milligrams per liter; SS, concentration of suspended sediment in mg/L; TP, concentration of total phosphorus in mg/L; TN, concentration of total nitrogen

in mg/L; EC, E. coli bacteria in MPN; MPN, most probable number of viable colony-forming units per 100 milliliters (mL); MA, mean-adjusted observations;

Temp, water temperature; n, number of sample-pair observations; R2

, coefficient of determination; %, percent; log10TurbMA, the "Mean-Adjusted" log10

turbidity for each observation, defined as the log10(Turb) minus the mean of log10(Turb) over all observations. For Northwest Branch and Paint Branch, the mean 

log10(Turb) are 1.7607 and 1.5013, respectively.] 

Constituent Watershed Equation

Adjusted 

R2 n

Corrected 

bias (%)

1

Suspended

Sediment

(mg/L)

NE Branch log10(SS) = 0.1005 + 1.0618*log10(Turb) 0.93 197 10.5

NW Branch log10(SS) = 0.1851 + 1.0373*log10(Turb) 0.91 138 40.1

Paint Branch log10(SS) = 0.3167 + 0.9359*log10(Turb) 0.88 166 22.8

Total

Phosphorus

(mg/L)

NE Branch log10(TP) = -1.7717 + 0.5303*log10(Turb) 0.86 167 1.6

NW Branch log10(TP) = -1.8424 + 0.6113*log10(Turb) 0.89 118 2.1

Paint Branch log10(TP) = -1.7272 + 0.4675*log10(Turb) 0.80 166 3.5

Total

Nitrogen

(mg/L)

NE Branch log10(TN) = -0.0688 + 0.1816*log10(Turb) 0.54 166 -0.2

NW Branch log10(TN) = 0.2888 + 0.2240*log10(TurbMA) + 0.0653*log10(TurbMA)2 0.63 116 -0.2

Paint Branch log10(TN) = 0.1638 + 0.1758*log10(TurbMA) + 0.0764*log10(TurbMA)2 0.56 166 -0.7

E. coli

Bacteria

(MPN/100 mL)

NE Branch log10(EC) = 1.4938 + 0.8226*log10(Turb) + 0.0369*Temp 0.77 146 39.8

NW Branch log10(EC) = 1.7093 + 0.7808*log10(Turb) + 0.0373*Temp 0.75 89 17.1

Paint Branch log10(EC) = 1.5254 + 0.7636*log10(Turb) + 0.0052*Temp 0.82 161 62.9 1 Reported values include application of Duan's "smearing correction"; see Helsel and Hirsch, 1992, sec. 9.6.2 and 9.6.3.
26

 

 

Figure 9. Time-series plots comparing estimated and measured concentrations of (A,B) suspended sediment, 

(C,D) total nitrogen, (E,F) total phosphorus, and (G,H) E. coli with an overlay of discharge during two storm 

events for Paint Branch near College Park, Maryland (USGS station 01649190). 
27

 

Figure 10. Scatter plots comparing estimated and observed concentrations of (A) suspended sediment, (B) 

total nitrogen, (C) total phosphorus, and (D) E. coli for Paint Branch near College Park, Maryland (USGS 

station 01649190), using the model equations and bias corrections provided in table 9. [mg/L, milligrams per 

liter; mL, milliliters; MPN, most probable number of viable colony-forming units per 100 milliliters] 
28

Loading Estimates

Estimates of loads were calculated using both ESTIMATOR and LOADEST and compared to obtain 

estimates with the least bias and uncertainty. On the basis of results of multiple regression-model testing that 

determined turbidity to be the best predictive variable for all for water-quality constituents, the load estimates 

using LOADEST with turbidity as the main independent variable had smaller residual errors than those made

with ESTIMATOR; thus, only the results from the LOADEST models are presented here. Annual loads for each 

of the USGS water-quality-monitoring stations in this study are summarized in table 10. Annual loads were 

calculated for every water year where there was a full year of continuous flow and turbidity data, and discrete 

samples had been collected over base-flow and storm conditions for each parameter of interest. Averages of

annual loads for each station are presented. Load estimates from the State and County TMDL reports (Maryland 

Department of the Environment, 2007a,b, 2008a,b, 2011, 2012) and the Anacostia Watershed Restoration Plan 

(U.S. Army Corps of Engineers, 2010) have been converted to the same units used in this report and provide 

comparison and context for loads at the USGS stations. The U.S. Army Corps of Engineers (USACE) used the 

same dataset as the TMDL reports, but re-computed the Hydrological Simulation Program—Fortran (HSPF) 

models at the subwatershed scale with an added land-use layer for road cover. Differences in methodologies for 

the water-quality parameters must be considered when making comparisons among the load results shown in 

table 10. Most notably, the bacterial loads by USGS were based on E. coli and the State and USACE used 

measurements of enterococci. Both E. coli and enterococci are components of total fecal-coliform bacteria loads 

and are good indicators of safety thresholds for human health. Concentrations of each are often similar in 

magnitude, but they can vary significantly from each other as sources and hydrologic conditions differ (Jin and 

others, 2004). There also are differences in watershed areas used for the different models; USGS loads were 

calculated for subwatersheds of the Anacostia River, whereas some of the loads from TMDL reports include the 

lower tidal areas of the Anacostia River. The watershed areas used for Rock Creek load estimates are more 

similar, including most of the nontidal watershed, but loads were calculated based on different sampling 

locations. Finally, differences in sampling designs, such as representativeness of sampling over the ranges of 

hydrologic and seasonal conditions, and collection methods, all affect the summary of water-quality conditions. 

As expected due to the described differences in monitoring and modeling approaches, there is considerable 

variability among the load estimates in table 10, but most of the estimates are still within errors of the model 

outputs. 

Estimates of annual load in the current study were normalized to basin area and are presented in table 11

as “yields” (for example in kilograms per year per square mile, kg/yr/mi2

) so that the results from each site could 

be more easily compared. The 95-percent confidence intervals of the modeled load estimates are presented with 

each value, and within these confidence intervals, the loads are indistinguishable between sites. The yields for SS, 

TN, and TP have not been adjusted for flow, so comparisons between years and trends cannot be made due to the 

strong correlation between load and flow (Langland and others, 2006). In addition, detection of statistically 

significant trends requires a minimum of 10 years of data (Langland and others, 2012). 
29

Table 10. Summary of annual loads for suspended sediment, total nitrogen, total phosphorus, and bacteria for the 

Anacostia River at three stations: Northeast Branch Anacostia River at Riverdale, Maryland (USGS station 

01649500); Northwest Branch Anacostia River near Hyattsville, Maryland (01651000); Paint Branch near 

College Park, Maryland (01649190); and for one combined station on Rock Creek: Rock Creek at Joyce Road , 

Washington, D.C. (01648010, for water quality) and Rock Creek at Sherrill Drive, Washington, D.C. (01648000, 

for flow). 

[For USGS station estimates, annual loads were calculated where data were available for the full water year, and presented 

below as averages of those estimates. Results from State and County TMDL development and the Anacostia Watershed 

Restoration Plan (ARP) are presented for comparison; kg, kilograms; km, kilometers; MPN, most probable number of viable 

colony-forming units per 100 milliliters; yr, year; --, not available] 

Annual 

Load 

Estimates

Suspended 

sediment

kg/yr

Total nitrogen 

kg/yr

Total 

phosphorus

kg/yr

E. coli3

MPN/yr 

(in billions)

Enterococci3

MPN/yr 

(in billions)

Anacostia River

01649500 21,000,000 142,000 14,200 4,450,000 --

01651000 14,300,000 98,200 10,700 2,940,000 --

01649190 3,650,000 21,100 1,550 323,000 --

Maryland 

State 

Baseline1

42,600,000 430,000 47,300 -- 1,166,0002

TMDL1 6,440,000 69,800 6,980 -- 113,000

ARP 

Baseline4 14,700,000 407,000 36,600 -- --

ARP 

Goal4 2,210,000 85,500 7,310 -- --

Rock Creek

01648010 20,300,000 122,000 14,300 2,650,000

State 

Baseline5 14,100,000 -- 20,100 -- 1,440,000 

TMDL5 9,710,000 -- 7,360 -- 45,600 1

Annual Maryland State baseline loads and Total Maximum Daily Loads (TMDLs) for suspended sediment, total nitrogen, 

and total phosphorus in the Anacostia River are based on Hydrological Simulation Program-Fortran (HSPF) modeling of 

source estimates and include the entire watershed (Maryland Department of the Environment, 2007b, 2008a). 2

Baseline loads and TMDLs for enterococci in the Anacostia River are the sum of estimates for the subwatersheds of

Northeast and Northwest Branches above their confluence (Maryland Department of the Environment, 2008b). 3

Loads for bacteria are calculated for E. coli by USGS and for enterococci using State estimates. 4

Estimates of loading and reduction goals are from the Anacostia Watershed Restoration Plan (U.S. Army Corps of 

Engineers, 2010). 5

State baseline loads and TMDLs for Rock Creek were determined for the watershed above the Maryland/Washington, D.C. 

boundary (Maryland Department of the Environment, 2007a, 2011, 2012).
30

Table 11. Estimates of annual yields (loads per square area) for suspended sediment, total nitrogen, total 

phosphorus, and E. coli for the Anacostia River (USGS stations 01649190, 01649500, and 01651000) and Rock 

Creek (USGS stations 01648010 (water quality) and 01648000 (flow) for data collected variously in water years 

2005 to 2011. 

[The 95-percent confidence interval is in parentheses below each value; ft3/s, cubic feet per second; kg, kilograms; yr, year; 

km2

, square kilometers; MPN, most probable number of viable colony-forming units; --, not available due to partial years of 

missing data] 

Water 

Year

Mean Annual 

Flow

(ft3/s)

Suspended sediment

(kg/yr/km2)

Total nitrogen 

(kg/yr/km2)

Total

phosphorus

(kg/yr/km2)

E. coli

(MPN/yr/km2

in billions)

Paint Branch near College Park, Md. (01649190)

2008 10.5 65,500

(39,200-103,000)

465

(417-518)

37

(29-45)

6,000

(4,000-8,800)

2009 12.4 117,000

(60,900-204,000)

569

(518-624)

36

(29-45)

7,300

(4,600-11,000)

2010 17.1 135,000

(88800-196000)

794

(733-858)

53

(45-63)

13,000

(8.600-18,000)

2011 13.6 113,000

(52,100-214,000)

656

(591-725)

56

(43-72)

12,000

(7,400-19,000)

Northeast Branch Anacostia River at Riverdale, Md. (01649500)

2005 89.7 91,000

(75,000-109,000)

747

(696-800)

70

(63-78)

38,000

(23,000-61,000)

2006 97.4 -- -- -- --

2007 76.5 67,700

(55,200-82,300)

593

(545-645)

51

(45-58)

12,000

(8,300-16,000)

2008 93.9 -- -- -- --

2009 88.1 166,000

(134,000-204,000)

763

(711-818)

87

(78-96)

26,000

(19,000-34,000)

2010 114.3 113,000

(95,300-133,000)

854

(798-912)

81

(74-89)

19,000

(14,000-25,000)

2011 97.6 117,000

(925,00-147,000)

791

(725-861)

85

(75-97)

23,000

(15,000-33,000)

Northwest Branch Anacostia River near Hyattsville, Md. (01651000)

2005 57.1 127,000

(100,000-159,000)

900

(835-969)

101

(88-115)

30,000

(16,000-50,000)

2006 77.2 -- -- -- --

2007 49.1 66,400

(49,800-86,700)

613

(550-682)

62

(52-73)

13,000

(7,600-21,000)

2008 55.7 -- -- -- --

2009 52.9 141,000

(992,00-196,000)

788

(729-849)

89

(77-103)

26,000

(16,000-40,000)

Rock Creek at Joyce Road, D.C. (01648010, Flow measured upstream at station 01648000)

2008 57.6 136,000

(49,400-302,000)

690

(614-774)

85

(60-118)

16,600

(6,500-35,300)

2009 59.9 104,000

(49,300-195,000)

677

(605-754)

75

(57-98)

13,200

(6.980-22,800)

2010 85.4 162,000

(66,100-333,000)

911

(825-1,000)

113

(82-153)

19,400

(8,010-39,900)

2011 70.4 103,000

(34,000-242,000)

752

(661-852)

83

(55-121)

16,500

(6,273-35,700)
31

Some of the estimates of yields from this study were recalculated on a calendar-year basis (instead of the

water-year basis that was used in tables 10 and 11) to compare to results from other studies and to provide some 

context for the variations between modeling approaches and periods of record (table 12). Because the annual 

cycles vary slightly, the yield estimates in table 12 based on calendar years are not comparable to those presented

in table 11 based on water years. Langland and others (2012) have provided calendar-year loads for the 

Northwest Branch Anacostia River (data accessed on January 2, 2013 at http://cbrim.er.usgs.gov/). Differences 

between the yield estimates from the current study and those of Langland and others include: (1) data used by 

Langland and others were collected by the Maryland Department of Natural Resources for their CORE and 

TRENDS network using a center-of-flow grab sample with fixed-interval sampling and no autosampling or 

emphasis on storms; (2) under the CORE and TRENDS program, concentrations of SS were measured as total 

suspended solids (TSS), which are based on subsamples from a sampler, and have been shown to be biased low as 

opposed to whole-sample analysis (Horowitz, 1991; Gray and others, 2000); (3) Langland and others used the 

model ESTIMATOR to calculate loads based on mean daily values of flow and time, as compared to the current 

study, which used LOADEST that added turbidity and smaller time steps (hourly rather than daily); and (4) 

estimates by Langland and others were based on a longer period of record with 10 or more years of data. 

Comparisons between Langland’s estimates and those of the current study are fairly consistent for TN and TP, 

but both the patterns and magnitudes of the SS loads are quite variable and do not compare well. The degree to 

which each source of variability has affected the modeling results has not been investigated for this report. 

The second comparison study in table 12 was by Miller, Gutiérrez-Magness, and others (2007), which 

used similar sampling and modeling techniques to the current study. Previous estimates of yields in Miller, 

Gutiérrez-Magness, and others were slightly higher for SS than those in the current study, but within the bounds 

of the 95-percent confidence intervals, the yields are comparable. The estimates for TN and TP for both the 

Northeast and Northwest Branches of the Anacostia River actually compare quite well. Further, the earlier 

estimates by Miller, Gutiérrez-Magness, and others were based on only 2 years of data, which is less than optimal 

for calculations of annual loads (Yokum, 2000), so it is interesting that the results are close. 

Langland estimated trends for the Northwest Branch Anacostia River separately for the 24-year span of 

the entire dataset and for the 10-year period from 2001-10. Results for the Northwest Branch station were similar 

to the general trend of other stations in the bay watershed with improvements (decreases) or insignificant trends 

in nitrogen loads but increasing trends for loads of phosphorus and sediment (as measured by TSS). The trend for 

nitrogen was only significant for the 24-year period of record, not for data collected over the last 10 years, 

indicating that a longer period of record is needed to detect subtle shifts in loads. Interestingly, the trend in 

phosphorus was not significant for the 24-year period, but has been degrading over the last 10 years, which may 

indicate non-linear behavior and possibly a shift in the trend. Comparison of Langland’s results for Northwest 

Branch to the results of the current study is difficult given the lack of data, but qualitatively the patterns do not 

match, indicating that a longer period of data collection is necessary to make definitive, statistically significant 

conclusions about water-quality trends at these stations.
32

Table 12. Comparison of estimates of calendar-year yields for the Northeast and Northwest Branches Anacostia 

River from different studies.

[Yields were calculated for calendar years (January to December) and are in units of kilograms/year/square kilometer; ft3

/s, 

cubic feet per second; --, not available] 

Langland1 This Study Miller2

Mean 

Annual 

Flow

ft

3 Constituent Year ESTIMATOR LOADEST ESTIMATOR LOADEST ESTIMATOR /s

Northwest Branch Anacostia River near Hyattsville, Md. (01651000)

Suspended 

Sediment

2005 31,900 188,870 217,000 248,000 233,000 57.1

2006 868,000 -- 411,000 -- -- 77.2

2007 25,200 -- 48,300 -- -- 49.1

2008 53,500 -- 118,000 -- -- 55.7

2009 50,700 172,211 155,000 -- -- 52.9

Total 

Nitrogen

2005 765 900 882 1,360 1,200 57.1

2006 1,220 -- 1,285 -- -- 77.2

2007 634 -- 630 -- -- 49.1

2008 797 -- 792 -- -- 55.7

2009 758 788 773 -- -- 52.9

Total 

Phosphorus

2005 80 147 162 161 161 57.1

2006 578 -- 191 -- -- 77.2

2007 64 -- 48 -- -- 49.1

2008 108 -- 89 -- -- 55.7

2009 104 119 124 -- -- 52.9

Northeast Branch Anacostia River at Riverdale, Md. (01649500)

Suspended 

Sediment

2004 -- 178,000 118,000 224,000 131,000 104

2005 -- 132,000 174,000 176,000 219,000 105

Total 

Nitrogen

2004 -- 982 951 962 959 104

2005 -- 876 953 976 1,010 105

Total 

Phosphorus

2004 -- 115 99 97 110 104

2005 -- 91 107 109 107 105 1

Langland and others (2012)

2

Miller, Gutiérrez-Magness, and others (2007)
33 

Summary 

Water-quality data were collected intermittently from 2003 to 2011 at four monitoring stations in the 

Anacostia River and Rock Creek watersheds: Northeast Branch Anacostia River at Riverdale, Maryland (USGS 

station 01649500); Northwest Branch Anacostia River near Hyattsville, Maryland (USGS station 01651000); 

Paint Branch near College Park, Maryland (USGS station 01649190); and Rock Creek at Joyce Road, 

Washington, D.C. (USGS station 01648010). The Anacostia River and Rock Creek are both tributaries to the 

Potomac River and the Chesapeake Bay, with watersheds in the urbanized corridor in and around Washington, 

D.C. Data collected during this study include discrete and continuous water-quality data, regression models to 

estimate concentrations and instantaneous loads of suspended sediment (SS), total nitrogen (TN), total 

phosphorus (TP), and E. coli bacteria from continuous physical parameters measured by multiparameter 

datasondes, and annual yields for SS, TN, TP, and E. coli. 

Water-quality conditions in the Anacostia River were comparable within confidence intervals to those 

determined in previous studies on the Anacostia River. Concentrations of TN, TP, and SS were similar to other 

urban streams in the Chesapeake Bay watershed. There were strong seasonal patterns in the concentrations of SS, 

TN, TP, and bacteria, but it was not determined whether this was related to changes in sources in the watersheds, 

or seasonal shifts in hydrology. Concentrations in all four parameters, SS, TN, TP, and E. coli, were strongly 

correlated to flow, emphasizing the importance of storm-event sampling. Increases in numbers of bacteria during 

spring and summer months was certainly a direct result of increases in stream metabolism with higher 

temperatures, but sources of bacteria in urban watersheds can be highly variable over multiple time scales, and 

the effects of other sources of variability were not investigated in this study. Examples of sources of bacteria in 

urban streams include resuspension of residual materials in streambeds and storm drains, broken sewer lines, 

groundwater inputs from leaking sewer infrastructure, and runoff that carries pet and wildlife wastes. Data 

collected during the current study provided a baseline for conditions prior to implementation of multiple 

stormwater controls in the watersheds, and future data-collection efforts may provide indications of the efficacy 

of best management practices.

Multiple log-linear regression models were developed to estimate concentrations of nutrients, SS, and 

bacteria from continuous monitoring of physical parameters. Turbidity was the strongest predictive variable for 

all models. Coefficients of determination (R2

) were best for SS and TP models with values of 0.9, and 0.8 to 0.9, 

respectively. Models for E. coli bacteria were improved by adding temperature as a second predictive variable, 

and had R2 values of approximately 0.8, but specific comparisons of model estimated values to discrete measured 

concentrations were not good due to hysteretic effects over the course of storms and to high variability in the 

measurements. Estimates of concentrations of TN had the lowest R2 values from 0.5 to 0.6. 

Annual yields were estimated for SS, TN, TP, and E. coli bacteria using the U.S. Geological Survey 

model LOADEST with hourly time steps of turbidity, discharge, discharge2

, time, time2, sin(time), and cos(time). 

Yields of SS, TN, and TP were similar to previous estimates in the Anacostia River and were within ranges found 

in other urban watersheds across the Chesapeake Bay Watershed. Ranges of estimates of annual yields for all four 

watersheds over the period of study were: SS (65,500 – 166,000 kilograms per year per square kilometer or 

kg/yr/km2

), TN (465 - 911 kg/yr/km2), TP (36 - 113 kg/yr/km2), and E. coli bacteria (6.0 – 38 x 1012 viable 

colony-forming units per year per square kilometer). The study period was not long enough to determine trends 

for any of the constituents; however, results differed qualitatively from loads and trends determined by a previous 

study for the Northwest Branch Anacostia River station.

Acknowledgments

The authors thank the following people for their assistance and contributions to this study. Keith 

VanNess, Meo Curtis, and Steven Shofar of the Montgomery County Department of Environmental Protection 

have partnered with USGS to develop a strong monitoring network in Rock Creek and the Anacostia River. The 

cooperative relationship has supported new technologies and methods for enhanced datasets and better 
34

understanding of watershed processes. Water quality in the Northeast Branch Anacostia River has been monitored 

under a cooperative agreement with Dr. Mow-Soung Cheng of the Prince George’s County Department of 

Environmental Resources, providing a long-term dataset in an important urban stream in the Chesapeake Bay 

watershed. Excellent field support for data collection was provided by USGS hydrologic technicians Brenda 

Majedi, David Brower, Anthony Moduno, Michael Myrga, and Mitchell Donovan. The initial map of the study 

area was done by Mitchell Donovan. The authors thank Robert Shedlock, Charles Walker, Timothy Lathrop, Meo 

Curtis, and Keith VanNess for very thoughtful technical reviews that improved the quality of this report, Valerie 

Gaine and Kevin Breen for excellent editorial review, and Tim Auer for creative help with the final layout. 

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For additional information, contact:

Director, MD-DE-DC Water Science Center

U.S. Geological Survey

5522 Research Park Drive

Baltimore, MD 21228

or visit our Web site at:

http://md.water.usgs.gov
Miller, C.V., and others—Water Quality in the Anacostia River, Maryland and Rock Creek, Washington, D.C.: Continuous and Discrete Monitoring and Interpretation of Nutrients, Suspended Sediment, and Bacteria—Open-File Report 2013–1034

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