Prepared in cooperation with the

Charles County Department of Planning and Growth Management

Maryland Department of the Environment

Maryland Geological Survey

Summary and Interpretation of Discrete and Continuous 

Water-Quality Monitoring Data, Mattawoman Creek, 

Charles County, Maryland, 2000–11

Scientific Investigations Report 2012–5265

U.S. Department of the Interior

U.S. Geological Survey
Cover. View of Mattawoman Creek looking upstream at the U.S. Geological Survey (USGS) streamgaging station 01658000 near 

Pomfret Road, Charles County, Maryland. [Photograph taken June 2010 by David P. Brower, USGS.]
Summary and Interpretation of Discrete 

and Continuous Water-Quality Monitoring 

Data, Mattawoman Creek, Charles County, 

Maryland, 2000–11

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

David P. Brower 

Scientific Investigations Report 2012–5265

U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the

Charles County Department of Planning and Growth Management

Maryland Department of the Environment

Maryland Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

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

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Suggested citation:

Chanat, J.G., Miller, C.V., Bell, J.M., Majedi, B.F., and Brower, D.P., 2013, Summary and interpretation of discrete and 

continuous water-quality monitoring data, Mattawoman Creek, Charles County, Maryland, 2000–11: 

U.S. Geological Survey Scientific Investigations Report 2012–5265, 42 p.

ISBN 978-1-4113-3528-8
iii

Contents

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

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................4

Description of Mattawoman Creek and Watershed.......................................................................4

Description of Comparison Basins ....................................................................................................5

Choptank River near Greensboro, Maryland, U.S. Geological Survey 

Station Number 01491000..............................................................................................7

North Branch Patapsco River at Cedarhurst, Maryland, U.S. Geological 

Survey Station Number 01586000 ................................................................................7

Georges Creek at Franklin, Maryland, U.S. Geological Survey Station 

Number 01599000 ...........................................................................................................8

Northwest Branch Anacostia River near Hyattsville, Maryland, 

U.S. Geological Survey Station Number 01651000....................................................9

Potomac River at Chain Bridge, U.S. Geological Survey Station Number 

01646580...........................................................................................................................9

Methods of Study...........................................................................................................................................9

Measurements of Continuous Flow and Water Quality..................................................................9

Field Collection of Discrete Water-Quality Samples.....................................................................10

Laboratory Methods...........................................................................................................................10

Regression Models for Nutrients and Sediment ...........................................................................11

Estimates of Loads and Yields ..........................................................................................................11

Evaluation of Data Integrity........................................................................................................................12

Discrete-Sample Quality Assurance ...............................................................................................12

Constituents Computed by Difference ............................................................................................13

Lateral Mixing......................................................................................................................................13

Hydrologic Conditions.................................................................................................................................15

Continuous Water-Quality Data.................................................................................................................16

Discrete Water-Quality Samples...............................................................................................................19

Corresponding Range of Continuous Conditions...........................................................................19

Suspended-Sediment and Nutrient Concentrations.....................................................................19

Seasonal Patterns...............................................................................................................................22

Interrelations between Physical Parameters, Nutrients, and Suspended Sediment .............24

Comparisons between Watersheds—Concentration Data.........................................................26

Estimation of Continuous Concentrations of Nutrients and Suspended Sediment..........................26

Suspended Sediment .........................................................................................................................27

Total Phosphorus ................................................................................................................................28

Total Nitrogen ......................................................................................................................................29

Estimation of Nutrient and Suspended-Sediment Loads and Yields...................................................30

Comparisons between Watersheds—Load Estimates.................................................................33

Limitations of Analysis ................................................................................................................................36

Summary and Conclusions.........................................................................................................................36

Acknowledgments.......................................................................................................................................37

References Cited..........................................................................................................................................37

Appendix A1: Relations between Measured Water-Quality Parameters and 

Constituents Discussed in this Report........................................................................................41
iv

Figures

1. Map showing location of Mattawoman Creek watershed study area................................3

2. Graphs showing (A) median monthly precipitation for lower southern 

Maryland (Charles, St. Mary’s, and Calvert Counties), and (B) discharge 

at Mattawoman Creek for water years 1951–71 and 2002–10...............................................5

3. Map showing land cover in the Mattawoman Creek watershed, 2006...............................6

4. Aerial orthophotograph of the gage location showing location of main 

channel braids and the mouth of Old Womans Run relative to the location 

of the gage, continuous water-quality sonde, and sampler intake, 2007............................7

5. Graph showing example storm hydrographs for a 3-week period in 2004 

at Mattawoman Creek showing varying degrees of influence of inflow 

from Old Womans Run on the shape of the hydrograph rising limb ..................................14

6. Graphs showing examples of concurrent 15-minute data collected by 

the operational water-quality sonde at left-edge water (presumed to be 

influenced by Old Womans Run) and the temporary sonde (Sonde 2) 

deployed in 2011 in the center of the rightmost bridge culvert (presumed 

to be primarily representative of the Mattawoman Creek main channel) 

(A) specific conductance, (B) turbidity, and (C) discharge .................................................15

7. Graphs showing difference in specific conductance and turbidity between 

the operational sonde mounted at left-edge water (presumed to be 

influenced by Old Womans Run) and the temporary sonde (Sonde 2) 

deployed in 2011 in the center of the rightmost bridge culvert (presumed 

to be primarily representative of the Mattawoman Creek main channel). 

(A) specific conductance, (B) turbidity, and (C) discharge .................................................16

8. Graphs showing hydrologic conditions at Mattawoman Creek, water 

years 2002–10 (A) Annual precipitation for southern Maryland, 

(B) mean annual discharge, (C) monthly distribution of days with flow 

greater than 400 cubic feet per second (overbank at gage), and 

(D) monthly distribution of days with no measureable flow................................................17

9. Graphs showing monthly variation in 15-minute observations collected 

by the continuous water-quality sonde, Mattawoman Creek, 2003–10 

(A) discharge, (B) turbidity, (C) temperature, (D) dissolved oxygen, 

(E) pH, and (F) specific conductance ......................................................................................18

10. Graphs showing cumulative frequency distributions of 15-minute 

discharge and water-quality sonde data, showing median parameter 

values (dots) and parameter ranges (whiskers) corresponding to 

collection of discrete water-quality samples (A) discharge, (B) turbidity, 

(C) temperature, (D) dissolved oxygen, (E) pH, and (F) specific conductance.................20

11. Boxplots showing monthly variation in continuous discharge observations 

at times corresponding to collection of discrete water-quality samples, 

Mattawoman Creek, 2001–10....................................................................................................23

12. Boxplots showing monthly variation in suspended-sediment concentrations, 

discrete water-quality samples, Mattawoman Creek, 2001–10..........................................23

13. Boxplots showing monthly variation in total phosphorus concentrations, 

discrete water-quality samples, Mattawoman Creek, 2001–10..........................................23

14. Boxplots showing monthly variation in total nitrogen concentrations, 

discrete water-quality samples, Mattawoman Creek, 2001–10..........................................23

15. Graph showing monthly variation in median concentrations of major 

phosphorus species, Mattawoman Creek, 2001–10 .............................................................24
v

 16. Graph showing monthly variation in median concentrations of major 
vi

Tables

1. Station location, size, and land cover for Mattawoman Creek, four 

similarly sized comparison basins, and the Potomac River ..................................................8

2. Constituent summary statistics for discrete water-quality samples 

collected at Mattawoman Creek, October 2000 through January 2011 ............................21

3. Pearson correlation coefficients between and among physical waterquality parameters, nutrients, and suspended sediment, 

Mattawoman Creek....................................................................................................................25

4. Regression models for concentrations of suspended sediment, total 

phosphorus, and total nitrogen, Mattawoman Creek, with corresponding 

models for Northeast and Northwest Branches of the Anacostia River 

near Washington, D.C. ...............................................................................................................28

5. Comparison of annual loading estimates made during this study with 

estimates made in 2010 by the U.S. Geological Survey for Mattawoman 

Creek as a component of the Chesapeake Bay Program Nontidal Monitoring 

Network (NTN)..............................................................................................................................30

6. Regression model coefficients (p-values), and model performance 

statistics for LOADEST models, comparing models constructed using 

seven parameters with models constructed using turbidity as an 

eighth parameter ........................................................................................................................32

7. Total loading estimates and model prediction uncertainty for the period 

January 6–March 6, 2009, comparing LOADEST models constructed using 

discharge with daily data, discharge with hourly data, and discharge and 

turbidity with hourly data...........................................................................................................33

8. Annual yields of suspended sediment, total nitrogen, and total phosphorus 

for the 2008 water year (October 1, 2007–September 30, 2008) for 

Mattawoman Creek, four comparison watersheds, and the Potomac River ...................35
vii

Conversion Factors

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.004047 square kilometer (km2)

square mile (mi2) 2.590 square kilometer (km2)

Volume

ounce, fluid (fl. oz) 0.02957 liter (L) 

gallon (gal) 3.785 liter (L)

cubic inch (in3) 0.01639 liter (L)

cubic foot (ft3) 0.02832 cubic meter (m3) 

Flow rate

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

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

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

Mass

ounce, avoirdupois (oz) 28.35 gram (g) 

pound, avoirdupois (lb) 0.4536 kilogram (kg)

Concentration

milligrams per liter (mg/L) 1,000n/m* microequivalents per liter (μeq/L)

Yield

pound per year per square mile 

(lb/yr/mi2)

0.1751 kilogram per year per square 

kilometer (kg/yr/km2)

*

 n is the magnitude of the ionic charge (for example, n = +1 for Na+, n = +2 for Ca++), and m is the ionic molar 

weight.

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

 °F = (1.8 x °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 chemical constituents on suspended particulates are given in milligrams per 

kilogram (mg/kg) of dry weight of the suspended particles.

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, and ending September 30 of the following 

year.

Summary and Interpretation of Discrete and Continuous 

Water-Quality Monitoring Data, Mattawoman Creek, 

Charles County, Maryland, 2000–11

By Jeffrey G. Chanat, Cherie V. Miller, Joseph M. Bell, Brenda Feit Majedi, and David P. Brower

Abstract 

Discrete samples and continuous (15-minute interval) 

water-quality data were collected at Mattawoman Creek 

(U.S. Geological Survey station number 01658000) from 

October 2000 through January 2011, in cooperation with 

the Charles County (Maryland) Department of Planning 

and Growth Management, the Maryland Department of 

the Environment, and the Maryland Geological Survey. 

Mattawoman Creek is a fourth-order Maryland tributary to 

the tidal freshwater Potomac River; the creek’s watershed 

is experiencing development pressure due to its proximity 

to Washington, D.C. Data were analyzed for the purpose of 

describing ambient water quality, identifying potential contaminant sources, and quantifying nutrient and sediment loads 

to the tidal freshwater Mattawoman estuary. Continuous data, 

collected at 15-minute intervals, included discharge, derived 

from stage measurements made using a pressure transducer, 

as well as water temperature, pH, specific conductance, 

dissolved oxygen, and turbidity, all measured using a waterquality sonde. In addition to the continuous data, a total of 

360 discrete water-quality samples, representative of monthly 

low-flow and targeted storm conditions, were analyzed for 

suspended sediment and nutrients. Continuous observations 

gathered by a second water-quality sonde, which was temporarily deployed in 2011 for quality-control purposes, indicated 

substantial lateral water-quality gradients due to inflow from 

a nearby tributary, representing about 10 percent of the total 

gaged area upstream of the sampling location. These lateral 

gradients introduced a time-varying bias into both the continuous and discrete data, resulting in observations that were at 

some times representative of water-quality conditions in the 

main channel and at other times biased towards conditions in 

the tributary. Despite this limitation, both the continuous and 

discrete data provided insight into the watershed-scale factors 

that influence water quality in Mattawoman Creek.

Annual precipitation over the study period was representative of the long-term record for southern Maryland. The 

median value of continuously measured discharge was 25 

cubic feet per second (ft3/s), and the maximum observed value 

was 3,210 ft3/s; there were 498 days, or about 15 percent of 

the study period, when flow was zero or too low to measure. 

Continuously measured water temperature followed a seasonal 

trend characteristic of the geographic setting; the trend in 

dissolved oxygen was inverted relative to temperature, and 

reflected nearly saturated conditions year round. Relations 

between discharge and both pH and specific conductance 

indicate that stream water can be conceptualized as a mixture 

of acidic, dilute precipitation with pH-neutral groundwater 

of higher conductance. Specific conductance data showed a 

pronounced winter peak in both median and extreme measurements, indicating the influence of road salt. However, this 

influence is minor relative to that observed in the Northeast 

Branch Anacostia River (U.S. Geological Survey station number 01649500), a nearby, more heavily urbanized comparison 

basin.

The median suspended-sediment concentration in discrete 

samples was 24 milligrams per liter (mg/L), with minimum 

and maximum concentrations of 1 mg/L and 2,890 mg/L, 

respectively. Total nitrogen ranged from 0.21 mg/L to 4.09 

mg/L, with a median of 0.69 mg/L; total phosphorus ranged 

from less than 0.01 mg/L to 0.98 mg/L, with a median of 0.07 

mg/L. Total nitrogen was dominated by the dissolved organic 

fraction (49 percent based on median species concentrations); total phosphorus was predominantly particulate (70 

percent). Seasonal trends in suspended-sediment concentration indicate a supply subsidy in late winter and spring; this 

could be linked to flood-plain interaction, mobilization of 

sediment from the channel or banks, or anthropogenic input. 

Seasonal trends for both total phosphorus and total nitrogen 

generally corresponded to seasonal trends for suspended 

sediment, indicating a common underlying physical control, 

likely acting in synchrony with seasonal biological controls 

on total nutrient concentrations. Speciation of phosphorus, 

including proportional concentration of the biologically available dissolved inorganic fraction, did not vary seasonally. The 

speciation of nitrogen reflected demand for inorganic nitrogen 

and associated transformation into organic nitrogen during the 

growing season.
2 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Stepwise regression models were developed, using 

continuous data corresponding to collection times for discrete 

samples as candidate surrogates for suspended sediment, total 

phosphorus, and total nitrogen. Turbidity and discharge were 

both included in the model for suspended sediment (R2

 = 0.76, 

n = 185); only turbidity was selected as a robust predictor of 

total phosphorus and nitrogen (R2

 = 0.68 and 0.61, respectively, n = 186 for both). Loads of sediment and nutrients to 

the downstream Mattawoman estuary were computed using 

the U.S. Geological Survey computer program LOADEST. 

Load estimation included comparison of a routinely applied 

seven-parameter regression model based on time, season, and 

discharge, with an eight-parameter model that also includes 

turbidity. Adding turbidity decreased total load estimates, 

based on hourly data for a fixed 2-month period, by 21, 8, and 

3 percent for suspended sediment, total phosphorus, and total 

nitrogen, respectively, in addition to decreasing the standard 

error of prediction for all three constituents.

The seasonal pattern in specific conductance, reflecting 

road salt application, is the strongest evidence of the effect 

of upstream development on water quality at Mattawoman 

Creek. Accordingly, ongoing continuous monitoring for trends 

in specific conductance would be the most reliable means 

of detecting further degradation associated with increased 

development.

Introduction

Effective watershed management requires a comprehensive understanding of hydrology and water quality in addition 

to geography and land use within a watershed. Mattawoman 

Creek, a tributary of the tidal freshwater Potomac River, 

drains 93.8 square miles (mi2

) in the Coastal Plain of southern 

Maryland (fig. 1). Although the watershed is predominantly 

forested, it is becoming urbanized due to its proximity to the 

Washington, D.C. urban corridor. The creek is regionally recognized as one of the most pristine Coastal Plain Chesapeake 

Bay tributaries of comparable size; it provides an important 

spawning ground for anadromous fish, a thriving sport fishery, 

habitat for a wide variety of terrestrial and aquatic organisms, 

and numerous recreational opportunities (Denny, 2009). 

However, as one of the least-developed areas within comparable commuting time of Washington, D.C., the watershed 

is changing rapidly: growth rates in Charles County, where 

most of the watershed lies, were 21.6 percent between 2000 

and 2010, compared to 9.0 percent for the entire State of 

Maryland for the same period (U.S. Census Bureau, 2011). 

The creek is listed on Maryland’s 303(d) list, the mechanism 

for reporting the regulatory status of the State’s waters to 

the U.S. Environmental Protection Agency, as impaired for 

nutrients and sediment. The Charles County Department of 

Planning and Growth Management is developing a comprehensive management plan with a major focus on preservation 

of Mattawoman Creek and its watershed. Major technical 

recommendations of a 2003 study by the U.S. Army Corps of 

Engineers, commissioned by Charles County, include protecting the stream valley from development, using low-impact 

development (LID) with distributed stormwater management, 

and retrofitting existing stormwater conveyance facilities to 

the maximum extent practical (U.S. Army Corps of Engineers, 

2003).

Some of the most challenging problems in the field 

of watershed science involve quantifying the mass flux, or 

load, of sediment and nutrients leaving a watershed, as well 

as identifying both the sources of sediment and nutrients 

and the transformations that they undergo during transport. 

These sources and transformations can vary substantially, 

even between neighboring watersheds in areas of relatively 

undisturbed land use, due to factors evident only with detailed 

study (Böhlke and Denver, 1995; Feller, 2009). Sediment and 

nutrient fluxes typically increase with urbanization (Paul and 

Meyer, 2001; Walsh and others, 2005). However, the magnitude and timing of these increases, as well as the potential for 

transformation, is highly dependent on site-specific factors 

such as the presence of wastewater treatment plant discharge, 

type of wastewater treatment, design and age of retention and 

conveyance infrastructure, land use preceding urbanization, 

and watershed hydrology and lithology (Paul and Meyer, 

2001). These factors complicate the separation of land-use 

impacts from variability in natural watershed processes.

Weller and others (2003) presented evidence that watersheds in the Atlantic Coastal Plain are generally more sensitive 

to development impacts than those in the Piedmont Province 

to the west. Ator and others (2011) constructed and interpreted 

an empirical model of the factors influencing nitrogen and 

phosphorus loading throughout the Chesapeake Bay watershed. They identified the Coastal Plain in general as a source 

of excess phosphorus, and speculated that this could be the 

effect of historical agricultural application; legacy phosphorus 

associated with agriculture can be re-mobilized if agricultural 

land is subsequently re-developed (Paul and Meyer, 2001). 

Ator and others (2011) also found that both local vegetation, 

indicative of the potential for nitrogen uptake, and the available water capacity of local soil, a proxy for denitrifying conditions, influenced nitrogen loadings. They also found that the 

percentage of total nitrogen input attributable to atmospheric 

deposition for larger watersheds ranged from 10 to 31; locally 

the significance of atmospheric sources depended largely on 

the presence of other sources, especially point sources and 

agriculture. Considering the Bay watershed as a whole, Ator 

and others (2011) estimated that 27 percent of atmospheric 

nitrogen input reaches streams.

Mattawoman Creek has been the subject of a number of 

water-quality research studies. Hall and others (1993) conducted a study relating stream-water acidity to juvenile fish 

mortality in three Maryland Coastal Plain streams, including 

Mattawoman Creek. They found that the streams were only 

moderately buffered against acidic atmospheric inputs, and 

that in some cases, critical acidic conditions in these streams 

coincided with the presence of several species’ susceptible 
Introduction 3

Figure 1. Location of Mattawoman Creek watershed study area.

life stages. The Smithsonian Environmental Research Center 

(SERC) conducted a study in 1998–99 to assess current conditions, define nonpoint sources of nutrients and sediment, and 

project future conditions under different development scenarios (Jordan and others, 2000). Noe and Hupp (2007) investigated the role of the Mattawoman flood plain as a source, sink, 

or transformer of nutrients in four short-duration overbank 

flooding events. They noted that when forested flood plains 

are inundated for weeks to months, they are generally found 

to reduce inorganic nitrogen and increase organic nitrogen. 

However, they found less consistent patterns for short floods at 

Mattawoman, with substantial variability depending on season 

and flood magnitude. Gellis and others (2009) conducted 

a broad-scale study of sediment sources in the Chesapeake 

Bay watershed; Mattawoman Creek was among three small 

tributaries where they applied a “sediment fingerprinting” 

approach to associate water-column sediment with specific 

watershed sources. They found the streambanks and forest 

floor to be the likely dominant sources of suspended sediment 

at Mattawoman, but found that sediment characteristic of construction sites accounted for roughly a quarter of the total load 

in one of six storm events sampled.

The U.S. Geological Survey (USGS) has maintained 

a streamgage (USGS station number 01658000) on 

Mattawoman Creek near Pomonkey, Maryland, with support 

from the Maryland Department of the Environment (2001–02) 

and the Maryland Geological Survey (2003 to present). 

Starting in 2000, this station was used in a 2-year USGS study 

of water-quality conditions in the Potomac River watershed. 

The Charles County Department of Planning and Growth 
4 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Management has supported continued collection of nutrient 

and suspended-sediment data at the station since 2003, for the 

purpose of characterizing ambient water quality, quantifying 

nutrient and sediment loading to the downstream estuarine 

part of Mattawoman Creek, and identifying water-quality 

conditions that might be indicative of impacts associated with 

increased development in the watershed. In 2003, the USGS 

added 15-minute water-quality monitoring for temperature, 

specific conductance, pH, turbidity, and dissolved oxygen at 

this station. The USGS has collected over 10 years of data at 

this station since October 2000, and data collection is ongoing.

Purpose and Scope

The purpose of this report is to summarize and interpret 

the discrete and continuous data collected between October 

2000 and January 2011 at USGS station 01658000 on the nontidal part of Mattawoman Creek in relation to the watershed’s 

geographic setting, hydrology, and land use. Specifically, the 

report provides:

1. an analysis and interpretation of nitrogen, phosphorus, 

and suspended-sediment data with inferences related to 

watershed geography, runoff processes, and potential 

sources of water-quality impairments;

2.regression models to estimate concentrations of total 

nitrogen, total phosphorus, and suspended sediment 

using continuously measured surrogates including discharge, water temperature, pH, specific conductivity, 

dissolved oxygen, and turbidity;

3. estimates of total nitrogen, total phosphorus, and suspended sediment loading to the Mattawoman estuary; 

and 

4. a comparison of results for Mattawoman Creek with 

five other Chesapeake Bay tributaries, in terms of both 

ambient water quality and downstream loading.

Description of Mattawoman Creek and 

Watershed

The Mattawoman Creek watershed encompasses 93.8 mi2

within the Atlantic Coastal Plain Physiographic Province in 

Charles and Prince George’s Counties, Maryland (fig. 1). The 

creek valley is incised into the Waldorf Upland Plain District, 

a series of alluvial plains and fluvial-estuarine terraces that 

make up the higher elevations of southern Maryland (Reger 

and Cleaves, 2008). Predominant soils are porous silts and fine 

sandy loams of the Beltsville, Leonardtown, and Sassafras Soil 

series, atop interbedded sequences of upland gravel, sand, and 

cobbles (Hack, 1955; McCartan, 1989; Glaser, 2003; Hall and 

Mathews, 1974). The broad, flat flood plain, which represents 

the sediment-choked inland extension of the Mattawoman 

estuary, is separated from the upland by slopes typically 

exceeding 15 percent (U.S. Army Corps of Engineers, 2003). 

The creek is a fourth-order tributary of the tidal freshwater 

Potomac River, originating in forested wetlands in the vicinity 

of Brandywine, Maryland, and flowing southwest roughly 20 

miles (mi) into a 7-mi-long tidal estuary with its mouth near 

Indian Head, Maryland (fig. 1).

Regionally, southern Maryland has a humid continental 

climate characterized by large seasonal temperature differences and generally evenly distributed annual precipitation. 

The 30-year normal mean annual temperature for lower 

southern Maryland (National Climatic Data Center Climate 

Division 3; Charles, St. Mary’s, and Calvert Counties) for the 

period 1971–2000 was 55.6 degrees Fahrenheit (°F), with a 

standard deviation of 1.0 degrees; the coldest and warmest 

months are January and July, respectively, with 30-year 

normal mean temperatures of 34.0 and 76.3 degrees, respectively (National Climatic Data Center, 2002a). The 30-year 

normal mean annual rainfall for the same region and period 

was 44.75 inches (in.) with a standard deviation of 6.98 in. 

Month-to-month, normal mean precipitation varied only 

slightly more than an inch, with the driest and wettest months 

being February (3.02 in.) and March (4.31 in.), respectively 

(National Climatic Data Center, 2002b).

The discharge and water-quality monitoring station 

on Mattawoman Creek (USGS station number 01658000), 

located off Route 227 near Pomonkey, Maryland, measures 

flow from a gaged area of 54.8 mi2

 (fig. 1). Daily discharge 

data were collected between 1950 and 1972, and again beginning in January 2001, when this study began. Mean annual 

discharge for this entire period, through water year 2010, was 

58.1 cubic feet per second (ft3

/s). Despite the nearly even 

distribution of monthly precipitation over this period, seasonal 

evapotranspiration resulted in marked seasonality in mean 

monthly discharge, with median values highest in March (114 

ft3

/s) and lowest in September (2.34 ft3/s; fig. 2). Periods of 

zero flow frequently occurred during the summer; the 25th 

percentile for mean monthly flows for the months July through 

October is less than 2 ft3

/s. 

Predominant land cover in the gaged part of the watershed in 2006 was forest (47 percent), urban (28 percent), and 

forested wetland (12 percent; Fry and others, 2011) (fig. 3). 

The highest-density urban development occurs in the upper 

half of the watershed on the south side of the creek main stem, 

along the U.S. Route 301 corridor between Waldorf and St. 

Charles. The primary source of water for this developed area 

is groundwater: Charles County withdrew roughly 12 million 

gallons per day (Mgal/d) in 2005 (Charles County Department 

of Planning and Growth Management, 2006). The Waldorf 

water-supply system is the largest in the County, with a total 

of 16 wells tapping the confined Magothy and Patapsco 

aquifers. Most of the developed area above the gage is served 

by the Mattawoman Sewer Service Area (MSSA). This wastewater is treated at the Mattawoman Wastewater Treatment 

Plant, downstream of the gage, and discharged directly to the 

Potomac River; there are no permitted point wastewater discharges in the gaged area. However, it is estimated that there 
Introduction 5

Figure 2. (A) median monthly precipitation for lower southern 

Maryland (Charles, St. Mary’s, and Calvert Counties; National 

Climatic Data Center, 2002b), and (B) discharge at Mattawoman 

Creek for water years 1951–71 and 2002–10 (the water years for 

which complete monthly discharge data are available).

are over 1,000 failing septic systems in the MSSA (Charles 

County Department of Planning and Growth Management, 

2006). Also, whereas County stormwater management 

practices are consistent with the 2007 Maryland Stormwater 

Management Act, wet-weather urban runoff was considered 

the predominant point source of nutrients in the 2005 Total 

Maximum Daily Load (TMDL) (Maryland Department of the 

Environment, 2004). 

Aside from the pattern of urbanization, perhaps the most 

prominent land-cover feature in the watershed is the flood 

plain of Mattawoman Creek, classified almost entirely as 

woody wetland, and accounting for nearly all of that landcover classification (fig. 3). The bottomland hardwood forest 

on the flood plain has both Zone IV and V (drier) flood-plain 

plant communities (Wharton and others, 1982) dominated 

by Oak (Quercus spp), Swamp Maple (Acer rubrum) and 

Pawpaw (Asimina triloba), with a patchy herbaceous layer 

(Noe and Hupp, 2007). The flood plain is episodically inundated, with overbank flows in the vicinity of the streamgage 

initiating at a discharge of about 400 ft3

/s (Noe and Hupp, 

2007); for water years 2001—10, daily mean discharge 

exceeded this value an average of 11 days per year.

Mattawoman Creek in the vicinity of the gage (and for 

most of its length) flows in a low-gradient, sand-bottomed, 

intermittently braided channel, and is generally well-shaded, 

with abundant woody debris and occasional beaver dams. 

The gage is located on the downstream side of the Route 227 

(Pomfret Road) bridge. The bridge is a concrete structure with 

two vertical mid-channel supports. Both are solid, perpendicular to the road centerline, and span the entire bridge width 

under the road; along with the abutments on each end of 

the bridge, this configuration creates three side-by-side box 

culverts of equal dimension (fig. 4). Two main-channel braids 

join in a small pool just above the bridge. Old Womans Run, 

a tributary with a drainage area of 5.8 mi2

 (see fig. 1), enters 

the river-left braid about 100 feet (ft) upstream of the pool. All 

instrumentation, including the staff gage, water-level sensor, 

auto-sampler intake, and multi-sensor sonde, are mounted to 

the concrete bridge abutment downstream of the bridge at leftedge water (LEW) (fig. 4). 

Description of Comparison Basins

In order to evaluate the nutrient- and sediment-concentration data and load estimates for Mattawoman Creek, four 

other watersheds in Maryland that are all tributaries to the 

Chesapeake Bay and similar in size to Mattawoman Creek 

were identified for comparison. Selection of these watersheds 

was based on size, availability of a similar record of concentration and flow data supporting load estimates, and representativeness of a range of hydrogeomorphic and land-use settings 

(table 1). Samples for nutrients and suspended sediment were 

collected by USGS from Mattawoman Creek, the Northwest 

Branch Anacostia River, and the Choptank River, and by the 

Maryland Department of Natural Resources (MDDNR) from 

Georges Creek and the North Branch Patapsco River. In order 

to facilitate comparison of water quality in Mattawoman 

Creek with that of its downstream Bay tributary, the USGS 

station at the Potomac River at Chain Bridge was included 

in the comparison group. All of the sites listed in table 1 are 

currently sampled using a similar protocol, which minimally 

includes routine monthly sampling as well as at least eight 

storm samples per year. However, inter-agency differences 

in regulatory requirements and sampling design, as well as 

project-specific data needs, result in records that differ in 

subtle ways; for the comparison sites, the most significant differences are included in table 1. In 2006, MDDNR adjusted its 

sampling strategy in order to align itself with protocols applied 

throughout the Chesapeake Bay region. At the two MDDNR 

stations (North Branch Patapsco River and George’s Creek), 
6 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Figure 3. Land cover in the Mattawoman Creek watershed, 2006 (modified from Fry and others, 2011).

data collected prior to July 2005 consisted of fixed-interval 

monthly samples; storm sampling was added after July 2005. 

At that time, the sampling method was changed from edge-ofwater point samples to equal-width-increment (EWI) depthintegrated samples, consistent with USGS methods. Also at 

that time, the method for measuring water-borne sediment 

during storms and high monthly flows was changed from total 

suspended solids (TSS) to suspended-sediment concentration 

(SSC; W. Romano, MDDNR, oral commun., October 2012). 

The entire periods of record for both Mattawoman Creek and 

the Northwest Branch Anacostia River include a mixture of 

EWI samples collected manually and point samples drawn 

by an auto-sampler on the bank. All of these differences can 

influence site-to-site comparisons of concentration and load 

in ways that are both site- and constituent-specific, but which 

have not been researched in depth. Compilation of comparison 

data did not include an in-depth analysis of point sources; such 

sources are mentioned in the basin descriptions below only in 

cases where water-quality impacts associated with the source 

have been documented. 
Introduction 7

Figure 4. Aerial orthophotograph of the gage location showing location of main channel braids and the mouth of 

Old Womans Run relative to the location of the gage, continuous water-quality sonde, and sampler intake, 2007 

(source: U.S. Geological Survey National Geospatial Program). [Blue cross indicates location of the operational 

sonde and auto-sampler intake; red cross indicates location of the temporary sonde deployed in 2011; yellow dashed 

line indicates location where equal-width-increment (EWI) samples are collected; and black dashed lines indicate 

positions of flow-segregating bridge supports (underside of bridge)]. 

Choptank River near Greensboro, Maryland, U.S. 

Geological Survey Station Number 01491000

The streamgage at the Choptank River near Greensboro, 

Maryland (USGS station number 01491000), has been in 

operation since 1948 and intensive water-quality monitoring 

by the USGS River Input Monitoring (RIM) Program has been 

conducted since 1985. The Choptank River above Greensboro 

drains a 113-mi2

 watershed in the south-central region of the 

Delmarva Peninsula. The watershed is primarily agricultural 

and contributes less than 1 percent of the total freshwater input 

to the Chesapeake Bay estuary. The Choptank River Basin lies 

entirely within the Atlantic Coastal Plain and has relatively 

flat topography with unconsolidated sediments. Phillips and 

Bachman (1996) classified the watershed above the Choptank 

River station as “poorly drained,” however Ator and others 

(2005) classified this area as well-drained sands with overlying 

gravels with strong agricultural land cover indicative of 

excellent drainage. Land cover in the Choptank River watershed is predominantly agricultural (49 percent; table 1), but 

there is also a significant amount of wetlands (23 percent), 

indicating the presence of some areas with poor drainage as 

described by Phillips and Bachman (1996). Poorly drained 

uplands on the Delmarva often yield lower concentrations of 

nitrogen than well-drained areas, because saturated soils support reducing conditions necessary for denitrification (Phillips 

and Bachman, 1996).

North Branch Patapsco River at Cedarhurst, 

Maryland, U.S. Geological Survey Station 

Number 01586000

The North Branch Patapsco River at Cedarhurst, 

Maryland (USGS station number 01586000) has a period of 

record for discharge from 1945 to the present. The watershed 
8 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Table 1. Station location, size, and land cover for Mattawoman Creek, four similarly sized comparison basins, and the Potomac River.

[USGS, U.S. Geological Survey; mi2, square miles; EWI, equal-width increment; EW, edge water; SSC, suspended-sediment concentration; TSS, total 

suspended solids; point, samples collected by auto-sampler at EW]

Station location

USGS

station 

number 

Watershed 

size

(mi2

)

Physiographic 

province

Land cover 

(percent)1 Sampling 

frequency

Sampling 

method

Sediment 

Urban Forest Agriculture Wetlands analysis

Mattawoman Creek 

near Pomonkey, Md. 01658000 54.8 Coastal 

Plain 28 47 9 12 Monthly + storms

EWI + 

point 

(EW)

SSC

Northwest Branch 

Anacostia River near 

Hyattsville, Md.

01651000 49.4

Piedmont, 

Coastal 

Plain

68 22 7 2 Monthly 

+ storms

EWI + 

point 

(EW)

SSC

Choptank River near 

Greensboro, Md. 01491000 113 Coastal Plain 6 20 49 23 Monthly + storms EWI SSC

Georges Creek at 

Franklin, Md. 01599000 72.4 Appalachian Plateau 9 78 9 0

Monthly; 

storms 

added 

beginning 

2005

Point 

(EW); 

EWI 

beginning 

2005

TSS; 

SSC 

during 

storms 

beginning 

2005

North Branch Patapsco River at Cedarhurst, Md.

01586000 56.8 Piedmont 17 27 52 0.4

Monthly; 

storms 

added 

beginning 

2005

Point 

(EW); 

EWI 

beginning 

2005

TSS; 

SSC 

during 

storms 

beginning 

2005

Potomac River at 

Chain Bridge 01646580 4,467

Valley and 

Ridge, 

Blue Ridge, 

Piedmont

9 54 27 0.4 Monthly 

+ storms EWI SSC

1

 Fry and others (2011).

above the station is in the Piedmont Physiographic Province 

in northern Maryland. The geology in the area is fractured and 

deformed crystalline rock with relatively good drainage at the 

surface. The topography includes rolling hills with sometimes 

steep gorges and steep-sided river valleys. The watershed is 

primarily rural with a significant amount of row cropping and 

pasture.

Georges Creek at Franklin, Maryland, U.S. 

Geological Survey Station Number 01599000

Georges Creek at Franklin, Maryland (USGS station 

number 01599000) has operated as a streamflow monitoring 

station intermittently since 1905, and continuously from 1929 

to the present. The watershed is in the Allegheny High Plateau 

Region of the Appalachian Plateau Physiographic Province 

in western Maryland, with steep stratigraphy and topography, 

little or no flood plains, and high-energy, well-dissected 

streams. The watershed is predominantly forested, although 

acid mine drainage has had a significant impact, influencing 

both water quality and hydrology. The site is approximately 

2.5 mi downstream from the Allegany County Treatment Plant 

(wastewater treatment) and is affected by combined sewer 

overflows (CSOs). Georges Creek was listed on Maryland’s 

303(d) list for overall impairment by biological oxygen 

demand (BOD) and nutrients, but was de-listed for nutrients 

subsequent to TMDL development (Maryland Department 

of the Environment, 2004). Water-quality samples have been 

collected by MDDNR under the same program and at the 

same sampling frequency as the North Branch Patapsco River 

station.
Methods of Study 9

Northwest Branch Anacostia River near 

Hyattsville, Maryland, U.S. Geological Survey 

Station Number 01651000

The Northwest Branch Anacostia River near Hyattsville, 

Maryland (USGS station number 01651000) is just above 

the highest point of tidal influence, which occurs near the 

confluence with the Northeast Branch Anacostia River at 

Bladensburg, Maryland. The watershed above the gaging 

station is highly developed, due to its close proximity to 

Washington, D.C. Urbanization is the most dominant influence 

on the Anacostia River; the highest percentage of land cover 

in the Northwest Branch watershed is urban with several 

industrial point-source discharges, but no municipal sewagetreatment plants. In the headwaters, there is some agricultural 

land cover (7 percent) including row and grain crops, pasture, 

orchards, and forested areas. The physiography of the watershed is transitional between the Atlantic Coastal Plain and the 

Piedmont Plateau with some steeper gradients through the Fall 

Line (the junction of these two provinces). The combination 

of relatively high gradients through the Fall Line and stream 

channelization from development makes this a very flashy 

stream with a lot of energy during storm events. There have 

been numerous stream-restoration projects, both upstream and 

downstream of the sampling station on the Northwest Branch, 

and they have potentially influenced the amount of sediment 

and nutrients that were transported (Miller and others, 2007).

Potomac River at Chain Bridge, U.S. Geological 

Survey Station Number 01646580

The Potomac River is the second largest tributary to the 

Chesapeake Bay, contributing approximately 20 percent of 

the total flow to the Bay. The Potomac River at Chain Bridge 

(USGS station number 01646580) has been monitored by 

the RIM Program since 1985 and was included as a major 

large-watershed station in the early 1990s for the Potomac 

River study unit of the USGS National Water-Quality 

Assessment (NAWQA) Program. This station is of interest to 

the Chesapeake Bay Program because it is the most downstream nontidal station for the Potomac River Basin, although 

it is upstream of most of the densely urban influences of 

Washington, D.C. For logistical reasons and for the purposes 

of calculating sediment and nutrient loadings, flow measurements are measured 1.2 mi upstream at the Potomac River 

near Washington, D.C. Little Falls Pumping Station (USGS 

station number 01646500). The Potomac River watershed 

above Chain Bridge is a transition of topography and geomorphology from the Appalachian Plateau in the headwaters, 

through the Valley and Ridge, and Piedmont Physiographic 

Provinces. The predominant land cover in the watershed 

is mixed but unevenly distributed across the basin, with 

primarily forested areas in the headwaters, dense agriculture 

in the mid- basin, particularly in the Great Valley region, and 

transitioning into densely populated urban areas in the east. 

Major agriculture in the watershed includes crops such as corn 

and hay with some soybeans, and livestock (poultry, cattle, 

and sheep).

Methods of Study

Two types of water-quality and water-quantity data were 

collected at the sampling station on Mattawoman Creek: 

(1) 15-minute (hereinafter, “continuous”) measurements 

were made for discharge, pH, water temperature, specific 

conductance, concentration of dissolved oxygen, and turbidity; and (2) discrete water-quality samples were collected 

to determine the concentrations of nutrients and suspended 

sediment. Continuous discharge data were collected beginning in January 2001, turbidity data were collected beginning 

in October 2003, and all other continuous parameters were 

collected beginning in March 2003. Discrete samples were 

collected beginning in October 2000. All data were archived in 

the USGS National Water-Information System (NWIS) database and are available upon request from the USGS MarylandDelaware-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 for the Collection of 

Water-Quality Data (U.S. Geological Survey, variously dated) 

and Guidelines and Standard Procedures for ContinuousWater-Quality Monitors (Wagner and others, 2006). Chapters 

of the National Field Manual are updated on a continuous 

basis and are available online at http://water.usgs.gov/owq/

FieldManual/.

Measurements of Continuous Flow and Water 

Quality

The streamflow-gaging station at Mattawoman Creek was 

operated according to standard USGS streamgaging protocols 

(Rantz and others, 1982). Briefly, continuous discharge 

measurements were estimated from river stage and recorded 

using a pressure transducer. The gage height was recorded 

every 15 minutes, and a stage-discharge relation, constructed 

from individual measurements of discharge made throughout 

the range of stages over the course of the study, was applied 

to create a record of continuous discharge. Continuous-data 

collection for pH, water temperature, specific conductance, 

concentration of dissolved oxygen, and turbidity was performed using a YSI multi-parameter sonde deployed at LEW 

(fig. 4). Over the course of the study, Models 6600 and 6980 

V2 sondes were used. The sonde reported readings of each 

parameter collected at 15-minute intervals to a Campbell 

Scientific CR10X data logger, which stored the data for transmission via satellite to the USGS at 4-hour intervals. Specific 

conductance and pH data were temperature-compensated to 77 

°F (25 degrees Celsius, or °C). Continuous data were reported 
10 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

in near-real time online at http://waterdata.usgs.gov/md/nwis/

current/?type=quality.

In order to ensure the accuracy and precision of the data, 

equipment was field-inspected and calibrated at intervals ranging from 1 to 4 weeks as dictated by hydrologic conditions, 

data quality (fouling and drift), and the known functional limitations of the equipment. For cleaning and calibration of the 

water-quality sensors, multiple observations of each parameter 

were made during the entire process, and were used to determine separate corrections for both fouling and drift. Standards 

for specific conductance and pH were quality-assured by 

their manufacturers (RICCA Chemical and Fisher Scientific 

Company, respectively), and include traceable lot numbers. 

Standards for turbidity were manufactured by GFS or YSI and 

were used without dilution. Shifts and corrections, calculated 

using criteria outlined in Wagner and others (2006), were 

applied to the continuous water-quality records in the USGS 

NWIS database after each field inspection was completed. 

Discharge and discrete and continuous (beginning in 2003) 

water-quality data were published in the USGS Annual Water 

Data Report for water years (October through September) 

2001 to 2010.

Field Collection of Discrete Water-Quality 

Samples

Discrete water-quality samples were collected both manually and by an auto-sampler. Manual samples were collected 

about 30 ft downstream from the gaging station during wadeable conditions using an isokinetic sampler and EWI sampling 

techniques (U.S. Geological Survey, variously dated). To 

collect a sample representative of stream conditions at the time 

of sample collection, multiple depth-integrated subsamples 

were collected at equal intervals across the stream (approximately 5 to 10 sections, depending on discharge at the time of 

sample collection) and composited into a pre-cleaned polyethylene churn splitter. Field data for water and air temperature, 

barometric pressure, pH, specific conductance, concentration 

of dissolved oxygen, and turbidity were collected from the 

stream cross section using a handheld YSI field meter (Model 

6600 or 6920 V2) at the time of each manual sampling event.

The water-quality samples were processed on site in the 

gage house, in a clean USGS field vehicle, or at the USGS 

MD-DE-DC WSC laboratory in Baltimore, to avoid environmental contamination. Subsamples of whole water were 

withdrawn from the churn splitter into clean polyethylene 

bottles while churning at a rate of 9 in. per second. After 

all whole-water samples had been withdrawn, samples for 

dissolved analysis were collected from the churn using a 

peristaltic pump with an in-line polycarbonate capsule filter 

(0.45-micrometer or μm effective pore size). Samples for 

whole-water analysis of nutrients were preserved with 1 milliliter (mL) of sulfuric acid. Samples for nutrients were chilled 

to less than 39 °F (4 °C) and shipped on ice overnight to the 

USGS National Water Quality Laboratory (NWQL) in Denver, 

Colorado. Samples for suspended sediment were stored and 

shipped later to the USGS Sediment Laboratory in Louisville, 

Kentucky.

An ISCO refrigerated auto-sampler was installed so that 

samples could be collected more frequently during storm 

events. Samples were collected through the sampler intake 

into 2.5-liter (L) polyethylene sampler bottles. Automatic 

sample collection during a storm event was initiated by a 

pre-determined rise in stream stage above a specified value 

and proceeded at timed intervals. Three to six discrete samples 

were collected over each storm hydrograph, based on changes 

in flow and precipitation. After each targeted storm event, 

USGS personnel reviewed the hydrograph and selected a subset of samples, typically three, that best represented the rise, 

peak, and recession of the storm hydrograph. These samples 

were then processed and analyzed in the manner described 

above for manually collected samples. To compare methods 

for EWI and autosampling, several samples were collected 

simultaneously using both techniques. 

Laboratory Methods

Nutrients were analyzed at the NWQL in Denver, 

Colorado. Prior to water year 2006, samples for total and 

soluble phosphorus and Kjeldahl nitrogen were digested 

concurrently in block digestors at high temperature with a 

mercury, Hg(II), catalyst. Ammonium ions, including those 

that were originally present and those that were generated by 

the procedure, were analyzed colorimetrically by a salicylatehypochlorite Berthelot-reaction procedure using an airsegmented continuous-flow analyzer. Ammonia nitrogen was 

analyzed separately on a filtered sample by the same colorimetric procedure, and organic nitrogen was determined by 

difference; specific relations between measured water quality 

parameters and constituents computed by sum or difference 

are provided in Appendix A. Phosphate was analyzed in a 

separate aliquot from the same digestate using the ammoniummolybdate 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 then determined by 

difference. Total nitrogen was calculated as the sum of total 

Kjeldahl nitrogen and total (filtered) nitrate/nitrite-nitrogen. 

Beginning in October 2005, total nitrogen and phosphorus 

were determined using alkaline persulfate digestion (Patton 

and Kryskalla, 2003) in place of Kjeldahl digestion on both 

filtered and whole-water samples. For nitrogen, this technique 

measures both organic and inorganic species. Dissolved 

organic, dissolved inorganic, and particulate components were 

obtained by difference (Appendix A). 

Suspended-sediment concentrations and the percent 

of sediment that was finer than 63 μm were analyzed using 
Methods of Study 11

filtration and gravimetric methods at the USGS Sediment 

Laboratory in Louisville, Kentucky. All methods and qualityassurance procedures for sediment analyses are documented 

in the laboratory quality-assurance plan (Shreve and Downs, 

2005).

Regression Models for Nutrients and Sediment

The use of high-frequency in situ observations of 

parameters that are relatively easy to measure as surrogates 

for parameters that may be more ecologically relevant, but 

difficult to measure, is a well-established approach to estimating concentrations and loads, and for interpreting water quality 

in running waters (Christensen and others, 2000; Christensen, 

2001; Rasmussen and Ziegler, 2003; Rasmussen and others, 

2005; Jastram and others, 2009). For this study, linear regression relations were determined to estimate suspended sediment, total phosphorus, and total nitrogen at Mattawoman 

Creek using continuous sonde observations. Model development approaches followed the general procedures described 

for multiple linear regression in Helsel and Hirsch (1992). The 

first step in this analysis was to plot chemical concentrations 

against in-stream sensor measurements in order to visually 

identify potential relations, explore candidate transformations 

to linearize relations, and initially screen for potentially 

problematic observations. Models were then selected using 

the stepwise procedure as implemented in the SPlus Statistical 

Package, version 8.1 (TIBCO Corporation, 2008). This procedure iteratively adds and removes candidate predictors from 

the model, using Akaike’s Information Criterion (AIC) (Helsel 

and Hirsch, 1992) as a measure of the degree of improvement 

or degradation associated with adding or dropping terms. 

The “best” model selected on the basis of the AIC balances 

two opposing objectives: maximizing explanatory power and 

minimizing the number of predictors used. Models selected 

with this process were diagnostically screened for adherence 

to standard assumptions of linear regression, and to assess 

the degree to which the selection of predictors may have been 

based on one or a few influential observations. Models identified as robust were further evaluated for susceptibility to bias 

associated with any required back-transformation to linear 

space, and a bias-mitigating correction (the “smearing correction”; Duan, 1983; Helsel and Hirsch, 1992) was computed 

and applied.

Estimates of Loads and Yields

The USGS RIM Program is an ongoing effort to quantify 

loads and long-term trends in concentrations of nutrients and 

suspended material entering the tidal part of the Chesapeake 

Bay Basin from nine major tributaries. Computations for RIM 

load and trend estimates are performed by fitting a sevenparameter regression model to estimate log-transformed 

constituent concentration, using an intercept and terms 

representing log-transformed daily discharge, log-transformed 

daily discharge squared, time, time squared, and two sinusoidal terms representing season-of-year (Langland and 

others, 2006). Regression coefficients are estimated using the 

Adjusted Maximum Likelihood Estimator (AMLE), which 

substantially reduces bias in parameter estimates when the 

data contain censored observations (Cohn, 1988; Cohn and 

others, 1992; Cohn, 2005). To minimize the effects of multicollinearity, discharge and time terms are represented relative 

to their mean value (they are “centered”; Langland and others, 2006). Daily loads are then calculated as the product of 

estimated concentration and observed daily discharge, and 

summed to compute a total load, with associated confidence 

bounds, over any period of interest (for example, a water 

year). These estimates can also be normalized to basin area, to 

determine “yields” for each basin that are independent of the 

size of the watershed.

Subsequent to its initial application as the basis for Bay 

loading estimates, the seven-parameter regression model 

was published by the USGS as the FORTRAN program 

LOADEST (Runkel and others, 2004); LOADEST has 

recently been packaged for either interactive or scripted use 

as an S-Plus add-on (Lorenz and others, 2011). LOADEST 

offers considerable additional flexibility, including the ability 

to include predictor variables other than flow, and to compute 

estimates based on hourly rather than daily data.

By virtue of the length of its discharge and water-quality 

record, as well as the sampling protocol that targets both low 

flows and storm hydrographs, Mattawoman Creek is included 

in a group of about 40 smaller watersheds in the Chesapeake 

Bay Program’s Nontidal Network (NTN; Chesapeake Bay 

Program, 2004), for which USGS estimates downstream loads 

and yields using the same seven-parameter model used for the 

nine RIM sites. The gaged part of Mattawoman Creek is less 

than half the size of the smallest of the nine RIM watersheds 

(Choptank River; gaged area is 113 mi2), and the proportion 

of total loading to the Bay that originates in the watershed is 

correspondingly small. However, estimates computed using 

this modeling framework still provide a credible indication 

of loading from the upland part of the Mattawoman Creek 

watershed to the Mattawoman estuary itself. Thus, the results 

are ecologically relevant at the local scale.

In this component of the study, annual loading estimates 

for suspended sediment, total phosphorus, and total nitrogen 

for water years 2002–10 were computed using the LOADEST 

implementation of the seven-parameter model, and compared 

to estimates for the same period published by the NTN 

Program, to confirm the comparability of the models. 

LOADEST was then used to assess the effect of applying the 

seven-parameter models for these constituents at an hourly, 

as opposed to daily, time step. Finally, the potential benefit 

of including continuously measured parameters as additional 

predictor terms in the model, as suggested by the regression 

models developed for nutrients and sediment, was explored.
12 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Evaluation of Data Integrity

Quality-control measures for this study included (1) 

field maintenance of the continuous water-quality sondes and 

subsequent application of corrections for fouling and drift (discussed earlier); (2) analysis and interpretation of 78 discrete 

quality-assurance and quality-control (QA-QC) water-quality 

samples, (3) QA checks on selected constituents computed 

from water-quality parameters by difference, and (4) temporary deployment of an additional continuous sonde to assess 

the potential effects of incomplete lateral mixing, associated 

with the location of the sampling equipment downstream of 

the mouth of Old Womans Run.

Discrete-Sample Quality Assurance

QC discrete samples included blanks and replicates. 

Blanks provide an indication of the concentrations of target 

water-quality parameter(s) present in the environmental 

sample that could be attributed to field procedures, sample 

handling, or analytical methods. Of the 19 blanks processed 

during the study period, analysis of 15 of them resulted in concentrations below the laboratory reporting limit for all parameters considered; this indicates that the influence of collection, 

processing, and laboratory procedures on reported environmental concentrations is generally negligible. Analysis of a 

blank collected on October 20, 2009 indicated a suspendedsediment concentration of 12 milligrams per liter (mg/L), 

well above the reporting limit of 1 mg/L and greater than the 

25th percentile of concentrations observed for environmental 

samples. Analysis of a blank collected on December 19, 2006 

resulted in a suspended-sediment concentration of 4 mg/L, 

near the 10th percentile of concentrations observed in environmental samples, and had detectable filtered total nitrogen. 

Results for a blank collected on December 12, 2007 indicated 

a concentration of ammonia of 0.04 mg/L, twice the reporting 

limit and greater than the median of observed environmental 

concentrations. Finally, analysis of a blank collected on March 

27, 2008 resulted in a concentration of filtered total nitrogen 

of 0.28 mg/L, above the reporting limit of 0.1 mg/L but less 

than the 10th percentile of observed environmental concentrations. All four of these blanks were auto-sampler blanks; they 

were obtained by drawing pure water through the normally 

submerged end of the auto-sampler intake, using the same 

pre-collection flushing procedure used for environmental sampling. The source of the contamination may have been inadequate flushing, or material dislodged from the interior of the 

submerged part of the sampler intake. However, the unusual 

manipulation required to artificially expose the intake to obtain 

the blank is itself a potential source of contamination. Of the 

19 blanks analyzed over the course of this study, 9 were autosampler blanks, and laboratory results for 5 of these indicated 

concentrations below the reporting limits for all parameters. 

For the first three of the four problematic blanks discussed 

above, affected parameters (suspended sediment, suspended 

sediment and filtered total nitrogen, and ammonia, respectively) associated with environmental samples collected on the 

same day were excluded from further analysis. The sampler 

tubing was changed immediately after the blank was collected 

on March 27, 2008, and analysis of a second blank collected 

immediately thereafter indicated that all concentrations were 

below reporting limits; thus, the corresponding environmental 

sample, collected through the new tubing, was retained.

Replicates provide an indication of variability in the 

environmental process being sampled, or of variability introduced at a particular stage of sample collection, processing, 

and analysis. For the purposes of discussion, replicates are 

grouped herein as persulfate, cross-section, or other procedural/environmental. Persulfate replicates are split samples, 

one of which was analyzed for nutrients using Kjeldahl digestion (used in this study through water year 2005) and the other 

using alkaline persulfate digestion (beginning in water year 

2006). A total of nine such replicates were collected and analyzed, all during water year 2005. Only the first, collected on 

December 28, 2004, indicated a notable discrepancy between 

the two methods. Both filtered and unfiltered total nitrogen 

determined by the persulfate method for that sample were 

lower than values determined by the corresponding Kjeldahl 

method (Kjeldahl plus nitrate plus nitrite) by a factor of 

roughly 2.5. No explanation was evident from examination of 

field and laboratory notes/remarks.

Cross-section replicates in this study consist of paired 

observations, one of which was drawn by the auto-sampler 

at LEW, and the other collected simultaneously as an EWI 

cross-section composite. Comparison of analytical results for 

the two samples provides an indication of the degree to which 

samples drawn at LEW are representative of the entire stream 

cross section. The 10 cross-section replicates collected and 

analyzed over the course of the study indicate the potential 

for incomplete lateral mixing under low-flow conditions. 

Considering missing observations for some parameters, the 

10 cross sections yielded 88 possible pair-wise comparisons 

of parameter values. Of these, 79 comparisons, or roughly 90 

percent, showed absolute differences in parameter values less 

than the 10th percentile of that parameter’s observed concentration in all environmental samples. In three cases, absolute 

differences in parameter values exceeded the 25th percentile 

of the parameter’s observed range. In a comparison of samples 

collected on September 5, 2001 under low-flow conditions, the 

concentration of suspended sediment observed at LEW was 

14 mg/L higher than the concentration observed in the cross 

section composite (19 mg/L compared to 5 mg/L, a difference 

of 280 percent). In another low-flow comparison performed 

on July 19, 2005, suspended-sediment concentration observed 

at LEW was higher than the cross section composite value by 

34 mg/L (43 mg/L compared to 9 mg/L, a relative difference 

of 380 percent). In the same pair of samples, Kjeldahl nitrogen observed at LEW was 0.3 mg/L less than that observed 

in the composite (0.5 mg/L compared to 0.8 mg/L, a relative 
Evaluation of Data Integrity 13

difference of about 40 percent). The two samples with noteworthy discrepancies were collected under two of the three 

lowest flow conditions encountered over the 10 visits at which 

cross-section comparisons were made. This indicates that there 

is potential for incomplete lateral mixing downstream of the 

mouth of Old Womans Run, at least under steady low-flow 

conditions (fig. 4).

Other procedural/environmental replicates included 

pairs collected either concurrently from different samplers or 

sequentially from the same sampler at exactly the same location, or a single sample that is split at some point subsequent 

to sampling, and then subjected to identical handling and laboratory analysis. Depending on the specific intent of the replication method, differences in analytical results for this set of 

replicates may reflect sample-to-sample variability in the environment, or variability introduced through sample handling, 

processing, and analysis. The 18 such replicates collected and 

analyzed over the course of the study generally indicated that 

the sampling protocol accurately and precisely represented 

environmental conditions. Of all of the water-quality parameters considered, 86 percent of all possible pair-wise differences 

in concentration between these replicates were below the laboratory reporting limit for the parameter under consideration. 

Because of differences in the intent of specific replicates, and 

because water-quality parameters varied widely in their range 

of environmental concentrations, exceptions were considered 

on a case-by-case basis. Generally, exceptions deemed “practically significant” differed in absolute magnitude by more than 

the 10th percentile of the given parameter’s observed concentration in environmental samples and differed in relative 

magnitude by more than 20 percent. For suspended sediment, 

differences of this type were observed in the analysis of a split 

replicate derived from the auto-sampler collected on the rising 

limb of a storm hydrograph on January 20, 2001. Analysis 

of another auto-sampler split replicate collected near a large 

storm peak on April 14, 2002 showed practically significant 

differences in unfiltered Kjeldahl nitrogen, probably resulting from the presence of entrained particulate organic matter 

in one of the subsamples. Analysis of a split replicate derived 

from an EWI cross-sectional sample collected at low flow on 

June 1, 2005 revealed differences in both filtered ammonia and 

filtered nitrite, indicating spurious contamination during or 

after the splitting procedure. Finally, analysis of a sequential 

replicate obtained by manually re-triggering the auto-sampler 

immediately after a programmed sample on the falling limb 

of a large storm hydrograph on April 18, 2007 resulted in 

practically significant differences in both orthophosphate and 

suspended sediment. Only these 6 pair-wise discrepancies 

were present in 18 procedural/environmental replicates, each 

analyzed for 8–12 water-quality parameters. These results 

indicate that (1) variability associated with sample collection 

and handling is generally small compared to environmental 

variability, and (2) environmental sampling is repeatable and 

precise.

Constituents Computed by Difference

 In this study, particulate organic nitrogen was computed 

using the difference between unfiltered and filtered Kjeldahl 

nitrogen prior to water year 2006, and using the corresponding 

difference in values obtained by the persulfate method beginning in water year 2006 (Appendix A). Of 346 samples for 

which particulate organic nitrogen was computed, 64 results 

were less than zero, a physically unrealistic outcome. Of the 

64, 6 were computed using the Kjeldahl method, and 58 were 

computed using the persulfate method. The median particulate 

nitrogen concentration considering all observations was 0.10 

mg/L, with the distribution of the persulfate values shifted to 

the left (negatively) relative to the Kjeldahl values by 0.07 

mg/L (median values 0.06 mg/L and 0.13 mg/L, respectively, 

n = 167 and 179; Wilcoxon rank sum p is less than 0.0001). 

For context, the smallest total (particulate plus dissolved 

organic plus dissolved inorganic) nitrogen concentration 

observed in environmental samples (n = 346) was 0.22 mg/L.

In 2009, the USGS Office of Water Quality documented 

several instances of suspected under-estimation of unfiltered 

total nitrogen using the persulfate method. Subsequent 

investigation revealed that in some cases, the method can fail 

to digest and detect an environmentally relevant proportion 

of particulate nitrogen; the degree of underestimation was 

found to be related to suspended-sediment concentration, but 

the nature of the relation was site-specific (U.S. Geological 

Survey, 2009). The marked increase in negative values for 

computed particulate nitrogen at Mattawoman after the persulfate method was adopted in water year 2006 indicate that this 

analytical problem likely introduced a bias in the Mattawoman 

particulate nitrogen data. Analysis of particulate nitrogen in 

this study was limited to reporting median monthly concentrations, relative to other nitrogen species. Since particulate nitrogen was the smallest component of total nitrogen in this study 

(median overall value of 0.10 mg/L, compared to 0.35 mg/L 

and 0.26 mg/L for dissolved organic and inorganic nitrogen, 

respectively), no adjustment was attempted. The anomaly does 

indicate that total nitrogen after 2005 was slightly underestimated, which could influence conclusions regarding trends 

in loading. Beginning in September 2011, the differencing 

computation of particulate nitrogen using persulfate digestion 

at Mattawoman was replaced with a combustion method, as 

recommended by USGS RIM Program staff (J.D. Blomquist, 

U.S. Geological Survey, written commun., August 2011). 

Lateral Mixing

As discussed in the Description of Mattawoman Creek 

and Watershed section, inflow from Old Womans Run enters 

Mattawoman Creek on river left just upstream of the Route 

227 bridge. The sampling instrumentation is located just 

downstream of the bridge, also on river left (fig. 4). This 

geometry suggests a potential for lateral (cross-channel) 
14 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

gradients in water quality resulting from inflow from Old 

Womans Run, particularly in light of the flow-segregating 

box-culvert style construction of the bridge. Some degree of 

bias, at least under low steady-flow conditions, was evident 

in the analysis of the cross-section replicates discussed 

earlier. In addition, the rising limbs of storm hydrographs 

at Mattawoman frequently contain features indicative of a 

short-lived inflow that precedes the main hydrograph peak 

(fig. 5). These features range from a subtle “shoulder,” evident 

as an inflection point in the slope of the rising limb, to a 

distinct “double-peak” effect; when evident as distinct peaks, 

the features persist for time spans of approximately 6 hours. 

The delay between the transient inflows inferred from these 

features and the main hydrograph peak can range from less 

than 12 hours to longer than a day. One possible explanation 

is that these features are associated with the left braid of the 

Mattawoman main stem, inactive under low-flow conditions 

such as those shown in figure 4, becoming active as main-stem 

flow increases. However, it seems unlikely that simple initiation of flow in the left channel braid would induce features 

as prominent and persistent as the most evident cases shown 

in figure 5. The features are more consistent with a transient 

inflow from a smaller, relatively flashy nearby tributary 

superimposed on a delayed, more rounded flood wave from 

a larger watershed. Whereas flow from Old Womans Run has 

not been directly measured, the physical configuration of the 

channel system upstream of the sampling location indicates 

that tributary as the probable source (fig. 4). If this hypothesized explanation is accurate, the existence of a second, nearby 

upstream source on the same side of the creek as the sampling 

equipment could induce time-varying lateral gradients in water 

quality; this in turn could complicate the interpretation of 

discrete samples or sonde readings collected by the instrumentation deployed at LEW.

In order to investigate the potential for cross-sectional 

gradients in water quality under dynamic conditions at this 

site, a second continuous water-quality sonde was temporarily 

deployed from the bridge from March through June 2011. The 

sonde was suspended in the water column from the center 

of the box culvert on river right; essentially, it sampled river 

water striking the bridge from the river-right channel braid, 

opposite the permanent sonde (fig. 4). Both sondes were 

programmed to sample simultaneously, and the temporary 

sonde was subject to the same level of field maintenance and 

data QA-QC as the operational sonde at LEW. Both sondes 

revealed highly dynamic behavior during storms, with maximum changes in continuous parameters at LEW observed just 

before the inflection points evident on the rising limb of the 

discharge hydrograph, and maximum changes at the temporary sonde observed later, just prior to peak flow (fig. 6). 

Frequently, large changes observed at LEW were simultaneously evident in subdued form at the temporary sonde, and 

large changes observed at the temporary sonde were evident 

in subdued form at LEW; this is particularly evident in the 

turbidity time series in figure 6. The pattern is consistent with 

the conceptual model of two time-varying flow sources, each 

with its own time-varying chemistry, mixing incompletely 

above the bridge. Downstream flow on river left then has a 

water-quality signature biased towards the presumed input 

from Old Womans Run, whereas water quality on river right is 

biased towards the larger Mattawoman watershed. The dualsonde data thus provide a more complete characterization of 

the situation indicated, at least under low steady-flow conditions, by the cross-section replicates discussed previously in 

the Discrete-Sample Quality Assurance section.

This situation highlights some of the challenges associated with sampling a braided channel with multiple tributaries 

entering along its length (fig. 1). Lateral water-quality gradients associated with tributary inflows can persist downstream, 

in extreme cases, for miles (for example, Sommer and others, 

2008). Because of the time-varying nature of the lateral gradients observed at the Route 227 bridge, quantitative interpretation of the magnitude and timing of runoff components or 

pollution sources based on data gathered at LEW on the time 

scale of individual storms may be inaccurate (fig. 6); trends 

and patterns evident at longer time scales, such as months or 

seasons, may be more robust. Regardless of time scale, all 

data must be interpreted as representative of some amalgam 

of water-quality conditions both in Old Womans Run and 

Mattawoman Creek; the data do not distinguish the waterquality effects of land-use change in the greater Mattawoman 

watershed from the effects of land-use change in the Old 

Womans Run watershed. Of the 360 discrete water-quality 

samples collected, 100 were collected as EWI cross sections, 

and the remainder were collected by the auto-sampler at LEW. 

Consequently, regression analyses investigating the utility of 

continuous surrogates for discretely measured parameters such 

as sediment and nutrients, in which all discrete samples are 

paired with corresponding continuous parameters measured 

by the sonde at LEW, may show more unexplained variability 

Figure 5. Example storm hydrographs for a 3-week period in 

2004 at Mattawoman Creek showing varying degrees of influence 

of inflow from Old Womans Run on the shape of the hydrograph 

rising limb.
Hydrologic Conditions 15

Figure 6. Examples of concurrent 15-minute data collected by 

the operational water-quality sonde at left-edge water (presumed 

to be influenced by Old Womans Run) and the temporary 

sonde (Sonde 2) deployed in 2011 in the center of the rightmost 

bridge culvert (presumed to be primarily representative of the 

Mattawoman Creek main channel) (A) specific conductance, 

(B) turbidity, and (C) discharge.

than would be anticipated under well-mixed conditions. 

Finally, the full record of simultaneous sonde observations 

indicates that water sampled at LEW, presumably more influenced by inflow from Old Womans Run, is consistently more 

dilute and episodically more turbid than presumed main-stem 

flow (fig. 7). To the extent that these differences are representative of differences over the entire study period, loading 

estimates, which integrate the product of concentration and 

flow, will be biased towards the presumed inflow from Old 

Womans Run. The results that are interpreted in this report 

take into account all possible influences on the data; limitations associated with incomplete lateral mixing are discussed 

in more detail in the following sections, in association with 

results to which they are particularly relevant.

Hydrologic Conditions

Mean annual precipitation for water years 2002 (the first 

year of this study period for which complete discharge data 

were available) through 2010 was typical for lower southern 

Maryland (46.0 in., compared to the 1971–2000 regional 

normal of 44.8 in.; National Climatic Data Center, 2002b; 

fig. 8a). However, this period included both an extremely 

dry year and an extremely wet year: only 26.20 in. in 2002 

followed by 66.66 in. in 2003; below the 5th and above the 

95th percentiles, respectively, for the period 1971–2000. These 

extremes were reflected in the record of annual discharge for 

the study period, with the minimum mean annual flow of only 

9.4 ft3

/s in 2002, followed by a maximum of 118 ft3/s, greater 

than the previous year by an order of magnitude, in 2003 (fig. 

8b). The mean discharge for the nine water years during the 

study period was 68 ft3

/s , higher than the overall mean of 58 

ft3

/s for the 31 years (1951–72, 2002–10) for which complete 

data were available for Mattawoman Creek. The mean annual 

runoff ratio (the proportion of annual rainfall that is discharged 

as streamflow) for the study period was 0.35; however, this 

was also highly variable, ranging from less than 10 percent to 

over one-half (0.09 in 2002 compared to 0.52 in 2007; data not 

shown).

Grouped by month, flows at Mattawoman Creek show 

a seasonal pattern typical of the humid temperate regional 

climate, with highest median continuous flow of 51 ft3

/s 

in February and minimum of 0 ft3

/s in September (fig. 9a). 

Median continuous flows during winter and spring are 

consistent, differing by only 10 ft3

/s between the months 

of December and April; similarly, median flows during the 

low-flow months of July, August, and September differ by 

only about 3 ft3

/s. Overbank flows in the vicinity of the 

Mattawoman Creek gage, estimated by Noe and Hupp (2007) 

to start at approximately 400 ft3

/s, follow a slightly different 

pattern, occurring most frequently in the months of December 

and March (16 and 15 days, respectively, over the study 

period) and relatively rarely between the months of July and 

October (fig. 8c). Days with no measureable flow during summer and early fall are common: over the study period, daily 

flow was zero or too low to measure for 14.7 percent of the 

continuous record, most frequently in August and September 

(fig. 8d). Altogether, there were 26 no-flow episodes ranging 

in length from 1 to 72 days.

The apparent susceptibility of the watershed to year-toyear variability in meteorological conditions, as evidenced by 

variability both in annual runoff and runoff ratio, together with 

the frequency and duration of no-flow periods, may indicate 

a limited capacity for water storage and (or) transmission. 

Ator and others (2005) noted that the surficial sediments of 

Inner Coastal Plain sands and gravels are typically completely 

dissected, with little hydrologic connectivity between upland 

deposits on adjacent hills. Alternately, the patterns may be 

indicative of the effects of groundwater withdrawal. Charles 

County reported withdrawals of roughly 12 Mgal/d in 2005, 

with a major focus in the Waldorf area (Charles County 
16 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Figure 7. Difference in specific conductance and turbidity between the 

operational sonde mounted at left-edge water (presumed to be influenced by Old 

Womans Run) and the temporary sonde (Sonde 2) deployed in 2011 in the center 

of the rightmost bridge culvert (presumed to be primarily representative of the 

Mattawoman Creek main channel). (A) specific conductance, (B) turbidity, and 

(C) discharge.

Department of Planning and Growth Management, 2006). This 

amount corresponds to approximately 18 ft3

/s if withdrawn 

directly from the creek. However, the dissected surficial sediments of the watershed are separated from the deeper regional 

water-supply aquifers by low-permeability units, collectively 

150–300 ft thick in the Waldorf area, including the Calvert 

Formation, Nanjemoy Formation, and Marlboro Clay (Wilson 

and Fleck, 1990). The latter formation, a dense, silty clay, 15 

to 30 ft thick in the Waldorf area but widespread and readily 

identifiable throughout southern Maryland, has particularly 

low vertical hydraulic conductivity (10-7 to 10-6 feet per day 

or ft/d; Wilson and Fleck, 1990). Thus, although a more 

targeted investigation would be required to conclusively rule 

out the influence of groundwater withdrawal on streamflow at 

Mattawoman Creek, this explanation seems unlikely.

Continuous Water-Quality Data

Collection of continuous water-quality data for all 

parameters except turbidity began in March 2003; the turbidity sensor was deployed in October of that year. Data reported 

herein cover the period from those start dates through January, 

2011—nearly 8 years for most parameters and slightly more 

than 7 for turbidity.

Continuous turbidity observations were highly skewed: 

whereas the majority of observations ranged from about 1 

to 100 Formazin Nephelometric Units (FNU) (fig. 9b), the 

overall range covered roughly four orders of magnitude, with 

minima on the order of 0.1 FNU and maxima near 1,000 FNU. 

Median monthly values were relatively constant throughout 

the year, on the order of 4–5 FNU; both median and 95th 
Continuous Water-Quality Data 17

Figure 8. Hydrologic conditions at Mattawoman Creek, water years 2002–10 (A) Annual precipitation for southern Maryland 

(National Climatic Data Center, 2002b), (B) mean annual discharge, (C) monthly distribution of days with flow greater than 400 cubic 

feet per second (overbank at gage), and (D) monthly distribution of days with no measureable flow.

percentile values were slightly elevated between April and 

July, with medians of 7.3 and 8.3 FNU in May and June, 

respectively. The coincidence of the highest median turbidity 

values in late spring, after the high-median-flow months of 

December through April, indicates the influence of an additional source of particulates during those months, in addition 

to increased transport capacity relative to low-flow conditions 

(figs. 9a, b).

Seasonal patterns in water temperature corresponded 

to those in air temperature, with the lowest monthly median 

water temperature (32 °F) in January and highest (70.3 °F) in 

July (fig. 9c). Seasonal variability in median dissolved oxygen 

concentrations showed a very similar, but inverted pattern (fig. 

9d). Median values for all months were within about 1 mg/L 

of the theoretical solubility of oxygen at the corresponding 

median monthly temperature (data not shown). The annual 

pattern, with nearly saturated conditions year round, indicates 

that the creek is not subject to any significant external source 

of seasonal oxygen demand, such as the demand that might 

be associated with organic waste input (Wilhelm, 2009; Allen, 

1995).

Month-to-month variation in median pH was only 0.2 

units, with medians of 6.6 or 6.7 for all but 1 month (fig. 9e). 

The slight peak in median value of 6.8 in May probably 

reflects increased photosynthetic CO2

 consumption by instream autotrophs, whose activity would be expected to peak 

as the stream warms in the spring, but prior to full leaf-out.

Monthly variability in continuous specific conductance 

observations is shown in figure 9f. A common conceptual 

framework for interpreting stream-water conductivity 
18 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Figure 9. Monthly variation in 15-minute observations collected by the continuous water-quality sonde, Mattawoman Creek, 

2003–10 (A) discharge, (B) turbidity, (C) temperature, (D) dissolved oxygen, (E) pH, and (F) specific conductance.
Discrete Water-Quality Samples 19

considers streamflow to be a mixture of water from overland 

and shallow subsurface flow paths, with groundwater originating from deeper flow paths. The former is considered dilute in 

terms of ionic strength, and the latter more concentrated, by 

virtue of having had extended contact with watershed minerals 

(Matsubayashi and others, 1993). This concept is consistent 

with the pattern of episodic reduction in specific conductance 

associated with storms, followed by recovery as discharge 

decreases to pre-storm levels. This is evident, despite ambiguity associated with incomplete lateral mixing, in the trace for 

the LEW sonde in figure 6a. Assuming that streamflow in the 

lowest-flow months is sustained primarily by groundwater, 

this conceptual model predicts highest conductance values 

during the summer months. However, the opposite pattern 

is evident in the Mattawoman data (fig 9f). Moreover, the 

prevalence of high outliers in winter months indicates episodic 

enrichment. This pattern is consistent with the application of 

salt and other de-icing agents to roads and other impervious 

surfaces. Herlihy and others (1998) examined stream chemistry data relative to watershed land use in a probabilistic survey 

of 368 wadeable streams in the Mid-Atlantic region, including 

streams on the Atlantic Coastal Plain. They concluded that 

concentration of the chloride ion, primarily from road salt 

but also from other human-influenced sources, was the single 

best overall indicator of human disturbance throughout the 

region. In large quantities, de-icing agents have been implicated in raising surface-water salinity regionally to levels that 

threaten both aquatic life and suitability for human consumption (Kaushal and others, 2005). Median values of specific 

conductance at Mattawoman were highest in February (177 

microsiemens per centimeter or μS/cm) and decreased to the 

lowest values in late summer (122, 122, and 117 μS/cm in 

July, August, and September, respectively; fig. 9f). Ninetyfifth percentiles were also highest in February and lowest in 

July (418 and 136 μS/cm, respectively). Although this pattern 

is a clear indication of human influence on water quality at 

Mattawoman, it is most appropriately considered in context: 

for the same period in the heavily urbanized Northeast Branch 

of the Anacostia River watershed near Washington, D.C. 

(USGS station number 01649500, area 72.8 mi2

), median 

values of continuous specific conductance observations in 

February and July were 686 and 298 μS/cm, respectively. The 

corresponding 95th percentiles were 3,800 and 390 μS/cm 

(data not shown). Ongoing continuous monitoring of specific 

conductance, coupled with analysis of trends in monthly 

statistics, may be the best-available sentinel of the effects 

of increasing urbanization on water quality at Mattawoman 

Creek.

Discrete Water-Quality Samples

A total of 360 discrete water-quality samples were 

analyzed between October 24, 2000 and January 10, 2011. Of 

these, 260 were storm samples collected by the auto-sampler 

and 100 were cross-section integrated samples collected 

manually.

Corresponding Range of Continuous Conditions

The ranges of continuous parameter values corresponding to the times of discrete sample collection, relative to 

each continuous parameter’s overall cumulative frequency 

distribution, are summarized in figure 10. Consistent with 

the sampling program design, sampling took place across a 

wide range of discharge values, with sampling occurring at 

instantaneous values from a minimum of 0.1 ft3

/s to a maximum of 2,920 ft3

/s. The median value of discharge at the time 

of sample collection was 127 ft3

/s, somewhat higher than the 

median of all continuous discharge observations over the study 

period, which was 26 ft3

/s (fig. 10a). Samples were collected 

at turbidity values ranging from 1 to 450 FNU, with a median 

of 16 FNU (fig. 10b). For both discharge and turbidity, the 

range of the continuous parameter corresponding to times of 

discrete EWI cross-section sampling was somewhat lower 

than the range corresponding to times at which discrete automatic (LEW) samples were collected; this reflects the relative 

difficulty of collecting cross-section samples at high water. 

Water temperature at times corresponding to discrete sampling 

ranged from 32.0 to 76.3 °F, with a median value of 51.3 °F 

(fig. 10c). Dissolved oxygen values associated with discrete 

samples ranged from 5.5 to 15.8 mg/L, with a median of 10.3 

mg/L (fig. 10d). For both parameters, the range represented 

by EWI samples, from less than the 10th percentile to greater 

than the 90th percentile of the parameters’ overall distribution, 

was similar to the range represented by auto-sampling. Median 

pH associated with discrete samples was 6.4, with a minimum 

of 5.6 and a maximum of 7.2 (fig. 10e). Specific conductance 

values corresponding to discrete sampling times ranged from 

43 to 478 μS/cm, with a median of 112 μS/cm (fig. 10f). 

Comparison of the relative ranges of pH and specific conductance represented by cross-section samples with the ranges 

represented by auto-sampling revealed a pattern opposite the 

one shown for discharge and turbidity: samples collected by 

the auto-sampler tended to represent lower ranges of pH and 

specific conductance than samples collected by cross section. 

Both pH and specific conductance were negatively correlated 

with discharge (see section on Interrelations between Physical 

Parameters, Nutrients, and Suspended Sediment), so the 

observed bias was another result of the tendency of auto-sampling to represent a higher range of discharge values.

Suspended-Sediment and Nutrient 

Concentrations

Suspended-sediment concentrations ranged over three 

orders of magnitude, with minimum and maximum values 

of 1 and nearly 3,000 mg/L (2,890 mg/L; table 2). The 

median of all observations was 24 mg/L. The distribution of 
20 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Figure 10. Cumulative frequency distributions of 15-minute discharge and water-quality sonde data, showing median 

parameter values (dots) and parameter ranges (whiskers) corresponding to collection of discrete water-quality samples 

(A) discharge, (B) turbidity, (C) temperature, (D) dissolved oxygen, (E) pH, and (F) specific conductance.
Discrete Water-Quality Samples 21

Table 2. Constituent summary statistics for discrete water-quality samples collected at Mattawoman Creek, October 2000 through 

January 2011.

[The complete dataset is available online at http://waterdata.usgs.gov/nwis; mg/L, milligrams per liter; μm, micrometers; LTMDL, long-term method detection 

limit; MRL, method reporting level (MRLs routinely change over time); values separated by slash (“/”) indicate the minimum and maximum MRL applied 

over the course of this study; <, less than; --, not available]

Constituent

Nitrate

(mg/L as N)

Nitrite

(mg/L as N)

Ammonia

(mg/L as N)

Total inorganic 

nitrogen

(mg/L as N)

Dissolved organic nitrogen

(mg/L as N)

Particulate organic nitrogen

(mg/L as N)

Total organic 

nitrogen

(mg/L as N)

Source1

computed analyzed analyzed computed computed2 computed2 computed2

LTMDL -- 0.001/0.003 0.001/0.02 -- -- -- --

MRL -- 0.001/0.006 0.002/0.04 -- -- -- --

Number of observations

353 353 353 353 353 346 346

Minimum 0.01 < 0.001 < 0.015 0.01 0.06 -1.193 0.12

25th percentile 0.14 0.002 < 0.015 0.15 0.26 0.03 0.33

Median 0.23 0.002 0.020 0.26 0.35 0.10 0.44

75th percentile 0.34 0.003 0.027 0.36 0.45 0.21 0.59

Maximum 0.72 0.040 0.251 0.90 1.42 3.23 3.69

Constituent

Total nitrogen

(mg/L as N)

Dissolved 

inorganic 

phosphorus

(mg/L as P)

Particulate 

phosphorus

(mg/L as P)

Dissolved 

organic phosphorus

(mg/L as P)

Total 

phosphorus

(mg/L as P)

Suspended 

sediment

(mg/L)

Fraction of 

sediment less 

than 63 μm

(percent)

Source1

computed2 analyzed computed computed analyzed analyzed analyzed

LTMDL -- 0.003/0.01 -- -- 0.004 -- --

MRL -- 0.006/0.02 -- -- 0.008 1 --

Number of observations

346 353 346 353 347 341 120

Minimum 0.22 < 0.003 0.005 -0.0214 0.010 1 4

25th percentile 0.56 0.006 0.026 0.007 0.045 9 44

Median 0.68 0.009 0.047 0.011 0.070 24 63

75th percentile 0.92 0.013 0.081 0.015 0.106 67 77

Maximum 4.09 0.114 0.952 0.039 0.980 2,890 95

1

 Entries marked “analyzed” are parameters determined by laboratory analysis; entries marked “computed” are computed from more than one laboratory 

result; refer to text and Appendix 1 for specific formulae.

2

 Determination of dissolved and particulate nitrogen concentrations was done using Kjeldahl digestion prior to October 2005, and alkaline persulfate digestion afterwards. Differences in interpretation of analytical results require that different formulae be applied to calculate the indicated constituent; refer to text 

and Appendix 1 for specific formulae.

3

 Particulate fraction is calculated as the difference between unfiltered and filtered samples. Concentrations of the filtered sample exceeded concentrations 

of the unfiltered sample for a total of 66 observations; see text section “Evaluation of Data Integrity.” Tabulated statistics include all observations.

4

 Dissolved organic fraction is calculated as the difference between total dissolved and dissolved inorganic fraction. Concentrations of the dissolved inorganic sample exceeded concentrations of the total dissolved sample for one observation.
22 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

concentrations was highly skewed, with 75 percent of the 

observations falling below 69 mg/L. The weight fraction 

of total suspended-sediment particles below 63 microns in 

diameter provides an indication of the proportion of the total 

concentration that appears as silt-size and finer, rather than 

sand-size particles. Reporting for this parameter was constrained to samples that had sufficient overall concentrations 

to confidently determine this fraction; in general, this result 

was not available for samples with total concentrations below 

about 50 mg/L. Values for the 120 observations where this 

parameter was determined were fairly uniformly distributed, 

ranging from 4 to 95 percent, with a median of 63 percent 

(table 2).

Total phosphorus concentrations ranged from 0.010 to 

0.980 mg/L, with a median value of 0.070 mg/L (table 2). 

The median concentration for dissolved inorganic phosphorus 

(ortho-phosphorus), 0.009 mg/L, was slightly over one-tenth 

the median for total phosphorus; the minimum observation 

was less than 0.003 mg/L, and all but one observation were 

below 0.04 mg/L. The distribution of dissolved organic phosphorus was very similar to that for dissolved inorganic phosphorus, with median and maximum values of 0.011 and 0.039 

mg/L. Particulate phosphorus, computed as the difference 

between unfiltered total phosphorus and filtered total phosphorus, ranged from 0.005 to 0.952 mg/L, with a median value 

of 0.047 mg/L. Considering only median concentrations to 

characterize a “typical” sample, about 70 percent of total phosphorus was in particulate form, with the remaining 30 percent 

split approximately equally between dissolved inorganic and 

dissolved organic species.

The median observation for total nitrogen was 0.68 mg/L; 

concentrations ranged from 0.22 to 4.09 mg/L. The distribution of total nitrogen and all nitrogen species was positively 

skewed (table 2). Median values for the dissolved inorganic 

species nitrate, ammonia, and nitrite were 0.23, 0.02, and 

0.002, respectively; this reflects decreasing dominance in the 

concentration of the latter two constituents, relative to nitrate, 

by roughly one and two orders of magnitude, respectively. 

The median observation of total inorganic nitrogen, the sum of 

nitrate, ammonia, and nitrite, was 0.26 mg/L; total inorganic 

nitrogen ranged from 0.01 to 0.90 mg/L. Dissolved and 

particulate organic nitrogen were computed as the difference 

between filtered and unfiltered total nitrogen, respectively, and 

total inorganic nitrogen; the median values for these species 

were 0.35 mg/L and 0.10 mg/L, respectively. When considering only aggregated median concentrations, about half of total 

nitrogen is typically in dissolved organic form, 35 percent is 

dissolved inorganic, and the remaining 15 percent is particulate. As discussed earlier in the Evaluation of Data Integrity 

section, the figure of 0.10 mg/L for particulate nitrogen is 

likely a modest underestimate. However, assuming there was 

no large shift in true environmental concentrations after the 

persulfate method was adopted in 2005, no correction for bias 

associated with the persulfate method would influence the 

ranking of species concentrations.

Seasonal Patterns

As discussed in the Methods section, discrete sampling 

was biased by design to collect samples from flows that 

were representative of the full range of discharge conditions 

encountered at Mattawoman Creek. Partly as a result of this 

design, monthly patterns in the distribution of continuous discharge measurements made at times corresponding to discrete 

sample collection (fig. 11) only slightly resembled corresponding patterns in the continuous discharge record (fig. 9a). The 

data show a dip towards low flows beginning in June, very low 

flows in August, and a recovery ending in October; beyond 

that feature, there was little other evidence of any seasonal 

pattern. Monthly patterns in suspended-sediment concentration 

corresponded somewhat to the pattern in discharge, but with 

evidence of a more consistently increasing trend between the 

months of February and June (fig. 12). A similar trend during 

these months was evident in the record of continuous turbidity (fig. 9b). The pattern of increasing median suspendedsediment concentration throughout the spring, in the absence 

of a parallel trend in discharge (a proxy for transport capacity) 

implies an increase in supply. Potential natural sources 

include material delivered from the flood plain associated with 

increased out-of-bank flows beginning in March (fig. 8c), or 

simply within-channel or channel-bank material mobilized by 

higher flows not exceeding bankfull. Alternately, the pattern 

could reflect sediment from increased warm-weather construction activity, conveyed by wet-weather urban runoff.

Seasonal patterns in total phosphorus and total nitrogen 

concentrations were similar in overall shape to each other, and 

to the seasonal pattern in suspended-sediment concentration, 

with phosphorus showing the most sharply resolved seasonal 

pattern of all (figs. 13, 14). The strength of these patterns and 

their similarity to the pattern for sediment is noteworthy in 

several respects. Foremost is the implication of one or more 

common watershed-scale mechanisms, natural or otherwise, 

that eclipse both sampling bias and any uncertainty in representativeness associated with location of the sampling 

equipment relative to the mouth of Old Womans Run. Second, 

unlike suspended sediment, water-column concentrations of 

nutrients are determined not only by supply and transport 

capacity, but also by terrestrial and aquatic biological activity, 

which influences both total concentrations and the relative 

sizes of the nutrient species’ pools (Allen, 1995). The similarity in the seasonal pattern of concentrations of biologically 

inert (suspended sediment) and reactive (total nitrogen and 

phosphorus) constituents therefore implies either that physical controls dominate biological influences on total nutrient 

concentration, or that biological influences occur in seasonal 

synchrony with physical controls. Third, the simplest manifestation of a dominant physical control would have both 

nitrogen and phosphorus in predominantly particulate form; 

it would then be plausible that their concentrations could be 

influenced by the same physical processes that mobilize sediment. However, particulate nitrogen is the smallest component of total nitrogen; the largest component is the dissolved 
Discrete Water-Quality Samples 23

Figure 11. Monthly variation in continuous discharge 

observations at times corresponding to collection of discrete 

water-quality samples, Mattawoman Creek, 2001–10.

Figure 12. Monthly variation in suspended-sediment 

concentrations, discrete water-quality samples, Mattawoman 

Creek, 2001–10.

Figure 13. Monthly variation in total phosphorus concentrations, 

discrete water-quality samples, Mattawoman Creek, 2001–10.

Figure 14. Monthly variation in total nitrogen concentrations, 

discrete water-quality samples, Mattawoman Creek, 2001–10.
24 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

organic fraction (table 2). Thus, the similarity of the seasonal 

pattern for total nitrogen to the seasonal pattern of sediment 

and total phosphorus implies that either dissolved organic 

nitrogen is mobilized by the same physical processes that 

mobilize sediment and particulate phosphorus, or a biological 

transformation of nitrogen is acting in seasonal step with the 

physical processes underlying the observed monthly patterns 

for sediment and phosphorus. 

Speciation of phosphorus at Mattawoman Creek showed 

little seasonal variability, with monthly patterns in the proportions of dissolved inorganic, dissolved organic, and particulate 

phosphorus very similar to the monthly pattern of median 

concentrations in the aggregate data discussed earlier (fig. 15). 

The key factors influencing seasonality in the total concentration and speciation of phosphorus in forest streams include 

delivery from the terrestrial environment, transformation of 

dissolved inorganic phosphorus into particulate phosphorus 

through plant and microbial assimilation, sorption/desorption 

onto particulates, precipitation and dissolution, and channel 

transport (Withers and Jarvie, 2008). Conclusively determining the relative importance of these mechanisms in a given 

setting and season generally involves targeted direct process 

measurements. Because of the strong tendency for phosphorus 

to associate with particulate matter, the same physical mechanisms responsible for seasonal trends in suspended-sediment 

concentration probably influence phosphorus. Assimilative 

transformation of inorganic phosphorus into particulate 

organic phosphorus during the growing season could be an 

additional biological factor underlying the observed seasonal 

pattern in particulate and, by extension, total phosphorus concentration in Mattawoman Creek. If so, the relative constancy 

of the species’ proportions throughout the year could reflect 

the continuous buffering effect of concentration-dependent 

sorption-desorption processes (Allen, 1995). The distinct 

October peak in particulate phosphorus may be associated 

with input of fallen leaves.

In contrast to seasonal patterns for phosphorus, seasonal 

patterns in nitrogen speciation, particularly for dissolved 

inorganic and dissolved organic nitrogen, are clearly evident 

(fig. 16). The decreasing trend in dissolved inorganic nitrogen 

between February and August likely reflects increasing 

demand for incorporation of this biologically available form 

into living biomass as the growing season proceeds. Similarly, 

the increase in dissolved organic nitrogen, and, by extension, 

total nitrogen during the growing season is also characteristic 

of increased biological activity, reflecting animal excretion and 

cell lysis associated with decomposition of plant and animal 

tissue (Allen, 1995; Jorgensen, 2009). The pattern could also 

be indicative of delivery of dissolved organic nitrogen to the 

channel (for example from the flood plain or other upland 

sources) by the same mechanism underlying the seasonal 

pattern in suspended-sediment concentration (fig. 12). Fallen 

leaves are an important terrestrial source of carbon for forested 

streams, and the particularly low value of dissolved inorganic 

nitrogen in November may reflect a pulse in demand, resulting 

from increased heterotrophic activity associated with leaf 

decomposition (Allen, 1995).

Interrelations between Physical Parameters, 

Nutrients, and Suspended Sediment

The strength and direction of correlation relations 

between physical water-quality parameters, and nutrients and 

suspended sediment can provide insights into the first-order 

controls on water quality. Relations in this study were evaluated both visually and statistically; a summary correlation 

matrix is shown in table 3. Many variables had positively 

Figure 15. Monthly variation in median concentrations of 

major phosphorus species, Mattawoman Creek, 2001–10.

Figure 16. Monthly variation in median concentrations of 

major nitrogen species, Mattawoman Creek, 2001–10.
Discrete Water-Quality Samples 25

skewed distributions; to emphasize linear relations, these 

variables were log-transformed. Also, in order to clarify 

natural watershed relations, and based on the strong evidence 

of anthropogenic chloride enrichment from road salt (fig. 9f), 

values of specific conductance above 160 μS/cm were 

censored for purposes of correlation and regression analysis. 

This threshold was selected on the basis of (1) examination 

of a plot of continuous specific conductance compared to 

discharge, which revealed that observations above about 160 

μS/cm only occurred as distinct episodic excursions, and (2) 

a monthly histogram of the number of continuous specific 

conductance observations exceeding 160 μS/cm, which indicated the greatest frequency in January through March, and no 

instances between July and September.

The most evident features in table 3 are the strong positive correlations between total nitrogen, total phosphorus, and 

suspended sediment, in the range of 0.80–0.84. Correlations of 

this strength may indicate that the same mechanisms responsible for mobilizing and transporting sediment also are responsible for mobilizing and transporting nutrients. As discussed 

earlier, this hypothesis would be strongly supported if both 

phosphorus and nitrogen were predominantly in particulate 

form; however, particulate nitrogen constituted the smallest 

proportion of total nitrogen, on average (table 2). Particulate 

nitrogen was weakly positively correlated with both dissolved 

inorganic and dissolved organic nitrogen (r = 0.33 and 0.31, 

respectively; data not shown). Thus, even in the absence of 

a conclusive biogeochemical interpretation, the results are 

statistically plausible.

Regarding the continuous water-quality parameters, the 

strong negative correlation between temperature and dissolved 

oxygen (r = -0.83; table 3) is a consequence of the nearly 

oxygen-saturated conditions that predominate year round at 

Mattawoman (figs. 9c, d). The decrease in pH with increasing 

discharge (r = -0.68) is a consequence of the chemistry of 

precipitation and the buffering capacity of watershed minerals. 

Although significant progress has been made in reducing 

atmospheric sources of acidity, the northeastern United 

States still receives some of the most acidic precipitation of 

any area in the country (National Atmospheric Deposition 

Program, 2011). Acid-buffering capacity in the conterminous 

United States can range from under 50 microequivalents 

per liter (μeq/L) to over 400 μeq/L (Omernik and others, 

1988). Buffering capacity of water in the Mattawoman Creek 

watershed is estimated to be between 100–200 μeq/L (Hall 

and Matthews, 1974), which is considered moderate. In the 

absence of strong buffering capacity, the negative correlation 

between pH and discharge can be conceptualized approximately as a mixing process. With road-salt censoring applied 

to specific conductance, a positive relation between pH and 

specific conductance becomes evident, along with corresponding negative relations between conductance and discharge 

(r = +0.61 and -0.54, respectively; table 3). Taken together 

with the previous discussion of water-column specific conductance as the product of a mixing process, the correlations 

between both of these properties and discharge can be interpreted in terms of mixing of acidic but dilute (in terms of 

dissolved solids, reflected as low conductance) rainfall, with 

pH-neutral groundwater higher in dissolved solids.

In terms of practical utility for regression modeling, turbidity shows a strong positive correlation with total nitrogen, 

total phosphorus, and suspended sediment, with correlation 

coefficients for log-log relations in the range 0.76–0.81 (table 

3). This is a direct outcome of the tendency for suspended 

material to scatter light. It also appears that discharge may 

have some utility as a predictor for suspended sediment; 

however, turbidity is positively correlated with discharge, 

so the predictors may provide some amount of redundant 

Table 3. Pearson correlation coefficients between and among physical water-quality parameters, nutrients, and suspended 

sediment, Mattawoman Creek.

[Correlation coefficients greater in magnitude than 0.60 are shaded; ft3/s, cubic feet per second; °F, degrees Fahrenheit; FNU, Formazin Nephelometric Units; 

mg/L, milligrams per liter; μS/cm, microseimens per centimeter; N, nitrogen; P, phosphorus]

log10

(Discharge)

(ft3/s)

Temperature

(°F)

pH

log10

(Specific 

conductance)

(μS/cm)

Dissolved 

oxygen

(mg/L)

log10

(Turbidity)

(FNU)

log10

(Total N)

(mg/L)

log10

(Total P)

(mg/L)

log10

(Suspended 

sediment)

(mg/L)

log10(Discharge) 1.00 -0.19 -0.68 -0.54 0.10 0.64 0.50 0.44 0.65

Temperature 1.00 -0.02 -0.18 -0.83 0.03 0.13 0.25 -0.04

pH 1.00 0.61 0.18 -0.47 -0.30 -0.29 -0.42

log10(Specific conductance) 1.00 0.14 -0.50 -0.42 -0.53 -0.55

Dissolved oxygen 1.00 -0.09 -0.11 -0.25 -0.05

log10(Turbidity) 1.00 0.76 0.76 0.81

log10(Total N) 1.00 0.84 0.80

log10(Total P) 1.00 0.83

log10(Suspended sediment) 1.00
26 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

information. Relations between predictors including discharge 

and turbidity, and response variables such as total nitrate, total 

phosphorus, and suspended sediment, are explored in more 

detail in the section Estimation of Continuous Concentrations 

of Nutrients and Suspended Sediment.

Comparisons between Watersheds—

Concentration Data

Distributions of concentration data for Mattawoman 

Creek, the four comparison watersheds, and the Potomac 

River from 2000 through 2010 are shown in figure 17. The 

distribution of sediment and phosphorus was similar among 

watersheds, reflecting the predominantly particle-reactive 

nature of the latter. For both constituents, the highest concentrations occurred in the Northwest Branch Anacostia River. 

This is a factor of both supply (for example, construction) 

and transport capacity (for example, stream channelization) 

in this highly urban watershed (Miller and others, 2007; 

Walsh and others, 2005). Concentration ranges for the other 

five watersheds had a substantial degree of overlap (fig. 17). 

Mattawoman Creek had the second highest median suspendedsediment concentration and the third highest median total 

phosphorus concentration. This could reflect anthropogenic 

sediment and, as discussed in the Introduction, legacy phosphorus (Paul and Meyer, 2001; Ator and others, 2011). 

However, the limited record of difference in continuous turbidity between the operational sonde deployed throughout this 

study and the temporary sonde deployed in 2011 (for about 

4 months; fig. 7) shows several large storm-related pulses 

recorded by the operational sonde, sampling just downstream 

of the mouth of Old Womans Run, that were not evident in 

data from the temporary sonde, which was presumed to be 

more representative of the Mattawoman main stem. Turbidity 

is a good surrogate for nutrients and sediment at Mattawoman 

Creek (table 3). This indicates that sediment and phosphorus 

concentrations in discrete samples collected at LEW may also 

be biased high relative to concentrations in the main stem. The 

results of the sediment loading and sediment source fingerprinting study conducted nearby on Mattawoman Creek by 

Gellis and others (2009) support this contention. However, 

they employed a sampling scheme more suited to comparing loads and yields, rather than concentrations; implications 

of the results are discussed in the section on Estimation of 

Nutrient and Suspended-Sediment Loads and Yields. The 

North Branch Patapsco River had the lowest median concentrations of both sediment and phosphorus (fig. 17), an unexpected result in light of the proportion of agricultural land 

cover in the basin (52 percent; table 1).

Nitrogen concentration is generally enriched by several 

source factors including manure and fertilizers in agriculture 

and fertilizers and sewage sources in urban areas, but physical 

conditions along groundwater-flow paths also are important. 

Nitrogen concentrations in Mattawoman Creek are lower 

than those in any of the comparison watersheds (fig. 17). 

Concentrations of nitrogen are highest at the North Branch 

Patapsco River. As discussed earlier, the dominant species of 

nitrogen at Mattawoman was dissolved organic nitrogen; in 

contrast, total nitrogen in the Patapsco River is overwhelmingly inorganic, with a median total nitrogen concentration of 

4.0 mg/L and a median nitrate-plus-nitrite concentration of 3.4 

mg/L (data not shown). The differences are caused by at least 

two factors—first, heavily agricultural land use in the Patapsco 

watershed has likely elevated groundwater nitrate levels relative to the largely forested Mattawoman watershed. Second, 

wetland conditions on the Mattawoman flood plain—broad, 

flat, poorly drained and rich in organic matter—favor long 

residence times and the development of reducing conditions 

favorable to denitrification (Krantz and Powars, 2000; Ator 

and others, 2005). The highly urbanized Northwest Branch 

Anacostia River also had high total nitrogen concentrations 

(fig. 17). Dissolved organic nitrogen was the smallest component of total nitrogen at this site (about 15 percent; median 

dissolved organic and total nitrogen concentrations were 0.39 

mg/L and 2.4 mg/L, respectively); particulate and inorganic 

nitrogen were co-dominant (median concentrations were 1.2 

mg/L and 1.0 mg/L, respectively; data not shown).

Estimation of Continuous 

Concentrations of Nutrients and 

Suspended Sediment

Regression model equations and model statistics for 

suspended sediment, total phosphorus, and total nitrogen at 

Mattawoman Creek are shown in table 4; plots of observed 

and predicted values for the three models are shown in figures 

18–20. For comparison, table 4 also presents models constructed by Miller and others (2007) for the same constituents 

at the Northeast Branch Anacostia River at Riverdale, 

Maryland (USGS station number 01649500) and the 

Northwest Branch Anacostia River near Hyattsville, Maryland 

(USGS station number 01651000). In all three Mattawoman 

models, the model residuals in log-log space showed evidence 

of modest deviation from a normal (Gaussian) distribution; 

specifically, diagnostic plots revealed higher-than-expected 

incidences of both high and low outliers. In only a few cases, 

examination of field and laboratory remarks for the corresponding observations provided justification for their removal. 

Moreover, the situation was not improved by considering standard data transformations other than logarithms. The outliers 

could be indicative of natural variability in the environmental 

system, or they could be related to sampler intake location, 

as discussed in the QA section on lateral mixing. The deviation from the assumption of normally distributed residuals 

undermines (1) the validity of formal hypothesis tests for the 

significance of model parameters, and (2) the estimation of 

uncertainty, in terms of confidence and prediction intervals, of 

estimated values (Helsel and Hirsch, 1992).
Estimation of Continuous Concentrations of Nutrients and Suspended Sediment 27

Figure 17. Boxplots comparing distributions of concentration data from Mattawoman Creek with data from four comparison 

watersheds, and the Potomac River, 2000–10 (A) suspended sediment (total suspended solids for Georges Creek and Patapsco River, 

see text); (B) total phosphorus; and (C) total nitrogen.

Suspended Sediment

The model for suspended sediment at Mattawoman 

Creek included terms for both turbidity and discharge (table 

4). This was also the case for sediment models constructed 

by Miller and others (2007) using the same approach for the 

Northeast and Northwest Branches of the Anacostia River. In 

all three cases, coefficients on both predictors were positive, 

possibly indicating a common basis for their selection. One 

plausible explanation lies in the physics of sediment entrainment and transport: higher bed shear stresses associated with 

higher discharges are capable of moving relatively coarser bed 

material, which has smaller surface-area-to-weight ratio and 

therefore influences turbidity less than an equivalent weight 

of the generally finer material transported at lower discharges. 

As a result, the common positive coefficient on discharge may 

be interpreted as elevating the estimate of suspended sediment 

above what might be expected from turbidity alone at high 

discharge. The model has a degree of negative relative bias 

[-10 percent, computed as (mean of estimated values – mean 

of observed values) * 100 / (mean of observed values); table 

4]; this is also evident as a somewhat higher frequency of 

large under-predictions, relative to large over-predictions, 

in a 1:1 plot of observed and predicted values (fig. 18). An 

alternate model for suspended sediment based only on turbidity had much smaller bias, +0.4 percent, with a slightly lower 

R2

 (72 percent compared to 76 percent; alternate model results 

not shown). However, inspection of the distribution of model 
28 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Table 4. Regression models for concentrations of suspended sediment, total phosphorus, and total nitrogen, Mattawoman Creek, 

with corresponding models for Northeast and Northwest Branches of the Anacostia River near Washington, D.C. for comparison.

[USGS, U.S. Geological Survey; 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; Q, discharge in cubic feet per second; Turb, turbidity in Formazin Nephelometric Units (FNU); R2, coefficient of 

determination]

Constituent Regression equation Adjusted

R2

Actual 

relative bias1

(percent)

Degrees of 

freedom

Mattawoman Creek (USGS station number 01658000)

Suspended sediment (mg/L) log10(SS) = 0.0005 + 0.9125*log10(Turb) + 0.2152*log10(Q) 0.76 -9.9 185

Total phosphorus (mg/L as P) log10(TP) = -1.7392 + 0.5377*log10(Turb) 0.68 -4.0 186

Total nitrogen (mg/L as N) log10(TN) = -0.5177 + 0.3194*log10(Turb) 0.61 -1.4 186

Northeast Branch Anacostia River (USGS station number 01649500)2

Suspended sediment (mg/L) log10(SS) = -0.8655 + 0.4228*log10(Turb) + 0.8201*log10(Q) 0.95 -5.5 70

Total phosphorus (mg/L as P) log10(TP) = -2.0342 + 0.2798*log10(Turb) + 0.2924*log10(Q) 0.88 -3.7 61

Total nitrogen (mg/L as N) log10(TN) = 0.0419 + 0.1427*log10(Turb) 0.67 -3.8 68

Northwest Branch Anacostia River (USGS station number 01651000)2

Suspended sediment (mg/L) log10(SS) = -0.4754 + 0.6827*log10(Turb) + 0.5252*log10(Q) 0.95 -3.4 67

Total phosphorus (mg/L as P) log10(TP) = -2.1355 + 0.4591*log10(Turb) + 0.2270*log10(Q) 0.92 -3.8 58

Total nitrogen (mg/L as N) log10(TN) = 0.0755 + 0.1646*log10(Turb) 0.65 -3.8 64

1

 Bias of residuals in linear space.

2

 Miller and others, 2007.

residuals in linear space indicated that the difference in mean 

bias between the two models was highly influenced by only 

one or two outlying observations; for this reason, the apparent 

large bias was not taken as a sufficient basis for rejecting the 

model based on turbidity and discharge.

The regression model for suspended sediment at 

Mattawoman Creek had somewhat poorer explanatory 

power relative to the corresponding models identified 

for the Anacostia watersheds (adjusted R2

 values of 0.76 for 

Mattawoman compared to 0.95 for both the Northeast 

and Northwest Branch Anacostia; table 4). One possible 

explanation is the issue of sampler location relative to the 

mouth of Old Womans Run. Since discharges of highly turbid 

water associated with early peaks from Old Womans Run tend 

to occur well before peak discharge from the Mattawoman 

main stem (fig. 6), it may be that samples gathered early on a 

storm hydrograph where such input is significant may reflect 

higher turbidity values for a given discharge than might be 

observed otherwise due to the incomplete mixing. This bias 

due to sampling location would result in a generally more 

variable dataset than would have been observed if mixing 

were not an issue. However, there are other plausible reasons 

why a sediment model at Mattawoman might not perform 

as well as those identified for Anacostia. Differences in the 

underlying lithology of the watersheds, with both Anacostia 

basins lying at least in part on crystalline Piedmont rocks and 

the Mattawoman watershed lying entirely on Coastal Plain 

sediments, may also play a role. The sandy nature of the floodplain sediment and the streambed itself, along with the shifting nature of the streambed and consequent tendency of the 

streambed elevation to shift in the vicinity of the sampler, may 

contribute to a generally more variable dataset. Finally, the 

Anacostia watersheds are extremely urbanized relative to the 

Mattawoman watershed. This has undoubtedly led to changes 

in the channel structure and sediment regime, both indirectly 

through changes in the hydrologic regime (such as increased 

peak flows and consequent channel degradation), and directly 

as evidenced by ubiquitous instances of channelized reaches 

and engineered stabilization measures. It is therefore entirely 

plausible that urbanization in the Anacostia watersheds has led 

to a less variable, more predictable relation between discharge, 

turbidity, and suspended sediment than the relation that is 

evident at Mattawoman Creek.

Total Phosphorus

The stepwise model selection for total phosphorus at 

Mattawoman only identified turbidity as a robust predictor 

(table 4; fig. 19). This contrasts with the model for suspended 

sediment at Mattawoman, and the models for phosphorus at 

both Anacostia watersheds, which included both turbidity and 

discharge. Phosphorus tends to strongly sorb to particulate 

matter; hence, the identification of turbidity has a plausible 
Estimation of Continuous Concentrations of Nutrients and Suspended Sediment 29

Figure 18. Comparison of observed suspended-sediment 

concentrations at Mattawoman Creek with values predicted 

by model equation shown in table 4.

Figure 19. Comparison of observed total phosphorus 

concentrations at Mattawoman Creek with values predicted 

by model equation shown in table 4.

Figure 20. Comparison of observed total nitrogen 

concentrations at Mattawoman Creek with values predicted 

by model equation shown in table 4.

physical interpretation. However, this particle affinity, ultimately related to electrical charge on the particles’ surface, 

is a significant phenomenon only with particles of clay size 

and smaller. Thus, whereas sand-sized material mobilized by 

high discharge would contribute to measured sediment mass, 

it would not significantly increase phosphorus concentration. 

The significance of a discharge term in the models for the 

Anacostia watersheds may be related to their high degree of 

urbanization, with associated wet-weather sources such as 

street wash-off and CSOs. 

Total Nitrogen

Turbidity was selected as the only robust predictor of 

total nitrogen at Mattawoman Creek (table 4). Of the three 

constituents for which models were constructed, the nitrogen 

model had the poorest explanatory power (R2

 = 0.61, compared to 0.68 and 0.76 for phosphorus and sediment, respectively); this was also the case for both Anacostia watersheds 

(table 4). The most likely explanation for the relative unpredictability of total nitrogen is its reactivity: it can assume more 

forms and undergoes more transformations than the other two 

constituents. Notably, the difference in the explanatory power 

of the nitrogen model, relative to the sediment and phosphorus 

models, was much smaller than the corresponding difference 

for the models developed for the Anacostia watersheds (for 
30 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

example, a difference in R2 of about -7 percent relative to the 

phosphorus model for Mattawoman, compared to well over 

-20 percent for the corresponding Anacostia models). One 

hypothesis is that delivery pathways for nitrogen and phosphorus are more similar at Mattawoman than at the Anacostia 

watersheds. 

Estimation of Nutrient and SuspendedSediment Loads and Yields

On the basis of data collected since October 2000, 

estimates of mean annual load in pounds expressed as a mean 

daily flux for Mattawoman Creek computed using LOADEST 

compared closely to those published by the NTN Program 

(table 5). This confirms that the implementation of the sevenparameter regression model in LOADEST is essentially 

equivalent to the implementation used by the RIM and NTN 

Programs. Estimates were nearly identical for total nitrogen, 

differed in mean value by about 2 percent for total phosphorus, 

and differed in mean value by about 6 percent for suspended 

sediment. Small differences could be attributed to slight differences in the number of observations considered by the two 

studies, which also influence the value used to “center” time 

and discharge terms in the regression model. The pattern in 

magnitude of mean differences between NTN Program and 

LOADEST estimates (nitrogen less than phosphorus less than 

sediment) parallels the pattern in model explanatory power 

(R2

), which was 96, 91, and 86 percent, respectively, for the 

load models computed by LOADEST. This indicates that sensitivity of the two methods to small differences in data may be 

a function of the quality of the model fit. As was the case with 

the regressions relating nutrients and sediment to continuous 

surrogates, both NTN Program and LOADEST model residuals for all three constituents showed signs of deviation from 

the assumed normal distribution. Also, residuals in models for 

nitrogen and phosphorus had serial correlation coefficients just 

over 0.3 (data not shown), indicating a mild deviation from 

the assumption of independent observations. Violation of this 

assumption can introduce bias into model parameter estimates 

and undermine the validity of confidence intervals on model 

predictions (Helsel and Hirsch, 1992).

Annual LOADEST estimates of flux for suspended 

sediment, total phosphorus, and total nitrogen based on daily 

discharge data (the same estimates shown in table 5) with 

corresponding estimates based on hourly discharge data are 

shown in figures 21 a-c. Regardless of time step, year-to-year 

patterns in loading for all three constituents closely resemble 

the year-to-year pattern in mean annual discharge for the same 

period (fig. 8b). This underscores the importance of flow as a 

first-order control on loading. Its significance stems from two 

mechanisms: first, since water is the transport medium for all 

constituents, higher flows at any given concentration result in 

higher loading. Second, the concentration of all three constituents examined here is positively correlated with flow (see 

Table 5. Comparison of annual loading estimates made during this study with estimates made in 2010 by the U.S. Geological Survey 

for Mattawoman Creek as a component of the Chesapeake Bay Program Nontidal Monitoring Network (NTN).

[NTN estimates compare status and trends from 9 large and approximately 40 smaller Bay tributaries using a common data acquisition and analysis protocol 

(Langland and others, 2012). Estimates are routinely updated; current estimates are available online at http://cbrim.er.usgs.gov. Annual loads are expressed as 

an average daily flux.]

Water 

year

Estimated daily flux 

(pounds per day)

Suspended sediment Total phosphorus Total nitrogen

NTN, 2010 This study1 Percent 

difference NTN, 2010 This study1 Percent difference NTN, 2010 This study1 Percent difference

2002 1,300 1,200 8 2.9 2.6 10 33 33 0

2003 88,000 88,000 0 82 83 -1 630 630 0

2004 57,000 55,000 4 59 59 0 450 450 0

2005 55,000 52,000 5 46 45 2 360 360 0

2006 72,000 68,000 6 58 58 0 340 340 0

2007 84,000 78,000 7 56 54 4 380 380 0

2008 63,000 56,000 11 48 47 2 320 320 0

2009 22,000 19,000 14 28 26 7 200 190 5

2010 48,000 41,000 15 45 43 4 350 340 3

Mean 54,000 51,000 6 47 46 2 340 340 0

1

 Quality-control data collected in 2011 indicate that reported annual fluxes of sediment and particulate nutrients at Mattawoman Creek may be overestimates; see section Comparisons between Watersheds—Load Data for discussion.
Estimation of Nutrient and Suspended-Sediment Loads and Yields 31

Figure 21. LOADEST estimates of flux for suspended 

sediment, total phosphorus, and total nitrogen, Mattawoman 

Creek, water years 2002–10, comparing estimates based on 

daily and hourly time steps. Whiskers indicate 95-percent 

confidence intervals (A) suspended sediment, (B) total 

phosphorus, and (C) total nitrogen.

table 3). Therefore, the coefficients on the flow terms in the 

regression equations are positive, so higher flows correspond 

to higher estimated concentrations.

In any year, differences between annual load estimates 

based on daily and hourly discharge data are generally small; 

estimates based on hourly data are slightly larger than corresponding estimates based on daily data (fig. 21). The largest 

differences are evident for suspended sediment. The reduction 

in the widths of the 95-percent confidence intervals around the 

total load estimates is more significant—in the case of total 

phosphorus and suspended sediment, confidence intervals are 

generally over a factor of two narrower for estimates based on 

hourly data than daily data. This decrease in the width of the 

confidence intervals, which results simply by moving from 

daily to hourly discharge time steps, stems from LOADEST 

model assumptions related to aggregating autocorrelated load 

estimates made at daily rather than sub-daily time steps.

The established significance of turbidity as a surrogate 

for both sediment and nutrients (table 4) indicates that loading 

estimates may be improved by incorporating turbidity into the 

underlying regression equations. A set of three eight-parameter 

LOADEST models was estimated by adding log-transformed 

turbidity to the set of seven parameters used in the load models for nitrogen, phosphorus, and sediment discussed earlier. 

These models were then applied to estimate loads using discharge and turbidity at hourly time steps, with hourly turbidity 

computed as the arithmetic mean of (typically) four 15-minute 

observations. This makes it possible to compare load estimates 

from three models: a discharge-only model run at a daily time 

step, a discharge-only model run at an hourly time step, and 

an hourly model incorporating both discharge and turbidity. 

Ideally, these comparisons would be made on mean annual 

estimates, such as those shown in figure 21. However, sensor 

maintenance issues inevitably result in gaps in the continuous 

record for turbidity. The study period began October 7, 2003 

(the date the turbidity sensor was deployed) and continued 

through January 10, 2011; considering only the 15-minute 

intervals when there was measureable discharge, coverage 

of the turbidity sensor was 79.7 percent. Although many of 

these gaps were on the order of 15 minutes to several hours, 

and could arguably be filled by interpolation, between-model 

comparisons of annual loads would ultimately be qualified by 

the method used to fill in the larger gaps in order to produce an 

unbroken turbidity record. As an alternative, a 58-day period 

with a nearly unbroken quality-controlled record of both 

discharge and turbidity was identified. Two missing 15-minute 

observations were estimated to form a complete record. The 

period included several small storm events, the largest with a 

peak flow of 417 ft3

/s, as well as extended periods of low flow. 

The total load for this period, January 6 through March 6, 

2009, was estimated using each of the three models (daily discharge, hourly discharge, and hourly discharge with turbidity) 

for total nitrogen, total phosphorus, and suspended sediment.

For all three constituents, regression diagnostics 

improved somewhat in hourly models with turbidity as 

an added predictor; in particular, subjective assessment of 
32 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

residual histograms and Q-Q plots indicated significantly 

improved fit to a normal distribution, although four–six high 

outliers remained for all constituents. Assessment of the variance inflation factor (VIF) regression diagnostic indicated 

that correlation between turbidity and discharge (table 3) was 

not a significant concern. As expected, adding turbidity as a 

predictor increased model explanatory power, most notably 

in the case of suspended sediment, where model R2

 rose from 

0.85 to 0.94 (table 6). Estimated loading, expressed for all 

three constituents as an average daily flux over the 58-day 

period (table 7) and for sediment, as a total flux (fig. 22b), was 

somewhat smaller in models that included turbidity. Given that 

turbidity typically peaks on the rising limb of the storm hydrograph, the smaller estimates may result from the associated 

limitation of models based on discharge alone, which generally predict the same loading for equal discharges on both the 

rising and falling limbs of the hydrograph (fig. 22). Whereas 

the largest decreases in confidence-interval width were still 

associated with transitioning from daily to hourly time steps, 

adding turbidity as a predictor further decreased this measure 

of uncertainty by 14–19 percent relative to daily time steps 

(table 7). Unlike the reductions discussed earlier, which were 

associated with LOADEST assumptions related to estimation 

time step (fig. 21), improvements associated with adding turbidity reflect reduced regression residual variance.

Results of this component of the study were consistent 

with the findings of others who performed similar loading 

comparisons. Jastram and others (2009) found that estimated 

monthly and annual suspended-sediment loads for the James 

River at Cartersville, Virginia, tended to decrease for a model 

including discharge, turbidity, and water temperature, relative 

to the standard seven-parameter discharge model used by the 

RIM and NTN Programs; prediction uncertainty decreased as 

well. Working in several Kansas rivers, Rasmussen and others 

(2005) also found that suspended-sediment estimates based 

on turbidity alone were considerably smaller than estimates 

based on discharge alone; the authors also speculated that the 

difference was due to concentration-discharge hysteresis, as 

discussed earlier. Miller and others (2007) observed decreased 

uncertainty in estimates of suspended sediment, based on 

turbidity and discharge, in the highly urbanized Northeast 

and Northwest Branches of the Anacostia River, but found 

that the estimated load of all constituents increased relative 

to estimates produced by the standard seven-parameter model 

using only discharge. This discrepancy with the findings of 

Jastram and others (2009) and Rasmussen and others (2005) 

may be related to the extraordinarily high suspended-sediment 

yields (load per unit area) from these watersheds, which 

exceeded yields of any of the other RIM watersheds by 

roughly an order of magnitude (Langland and others, 2012).

Table 6. Regression model coefficients (p-values), and model performance statistics for LOADEST models, comparing models 

constructed using seven parameters with models constructed using turbidity as an eighth parameter.

[ln, natural logarithm; n, number of observations; Q, discharge in cubic feet per second; QC, central value of discharge in cubic feet per second; T, time in 

years; TC, central value of time in years; Turb, turbidity in Formazin Nephelometric Units (FNU); R2, coefficient of determination; --, not available; 

<, less than]

Intercept

Discharge Time Season Turbidity

n Adjusted 

R2

Residual 

ln(Q – QC) ln(Q – QC)2 (T – TC) (T – TC)2 sin(2πT) cos(2πT) ln(Turb) variance

Suspended sediment

Q 7.9549 1.6932 0.0402 -0.0504 0.0115 0.0368 -0.2423 -- 190 85 1.152

(<0.0001) (<0.0001) (0.0849) (0.1522) (0.5461) (0.7385) (0.0632) --

Q + Turb 5.7594 1.1735 0.0099 -0.0731 -0.0130 0.2248 -0.0460 0.9543 190 94 0.4894

(<0.0001) (<0.0001) (0.5075) (0.0016) (0.2943) (0.0022) (0.5909) (<0.0001)

Total phosphorus

Q 2.1673 1.2747 0.0217 -0.0419 0.0209 -0.2288 -0.2864 -- 190 90 0.3915

(<0.0001) (<0.0001) (0.1036) (0.0466) (0.0665) (0.0005) (0.0002) --

Q + Turb 0.9183 0.9742 0.0056 -0.0552 0.0059 -0.1145 -0.1686 0.5453 190 96 0.1721

(<0.0001) (<0.0001) (0.5256) (0.0001) (0.4356) (0.0089) (0.0010) (<0.0001)

Total nitrogen

Q 4.4612 1.1787 0.0039 -0.0427 0.0163 0.0074 -0.1668 -- 190 96 0.1505

(<0.0001) (<0.0001) (0.6359) (0.0012) (0.0211) (0.8527) (0.0004) --

Q + Turb 3.7440 1.0062 -0.0053 -0.0503 0.0077 0.0731 -0.0991 0.3131 190 98 0.07821

(<0.0001) (<0.0001) (0.3714) (<0.0001) (0.1315) (0.0131) (0.0038) (<0.0001)
Estimation of Nutrient and Suspended-Sediment Loads and Yields 33

One method for using a long record of single-station 

observations to support inferences about water-quality impacts 

is the identification of trends. Mattawoman Creek is a component of the Chesapeake Bay NTN, a set of stations for which 

coefficients of the seven-parameter LOADEST model are used 

to infer “flow-adjusted trends” in water quality. In the interest 

of ensuring comparability among all NTN stations, trends 

for all stations are computed, reviewed and reported by NTN 

Program staff using consistent methodology (Langland and 

others, 2012). This information is periodically updated on the 

U.S. Geological Survey’s RIM web site (http://cbrim.er.usgs.

gov/) as new data become available; flow-adjusted trends 

were not computed or analyzed independently as a part of this 

study.

Comparisons between Watersheds—Load 

Estimates

Concentration summaries for the study period were compared earlier, but in some cases where sampling frequencies 

differ, for example as shown in table 1, this comparison may 

be biased. To control for biases, annual loadings of total nitrogen, total phosphorus and suspended sediment for the 2008 

water year, representative of the 30-year normal discharge (fig. 

8b), also were compared. To facilitate comparison between 

watersheds of differing size, loads were normalized to basin 

area, and reported as yields in units of pounds per year per 

square mile (lb/yr/mi2

). Rankings of the watersheds in terms of 

yield estimates were generally consistent with rankings based 

on concentration estimates (table 8, fig. 17); in the context of 

the regression model that was used, and assuming a monotonic 

trend in the relation between constituent concentration and 

discharge in each watershed, this implies that sampling was 

representative of a broad range of flow regimes in each river. 

The Northwest Branch Anacostia River had the highest yields 

of sediment and phosphorus, and the North Branch Patapsco 

River had the lowest. Higher yields were reported for the 

Northwest Branch Anacostia River in 2005–06 (Miller and 

others, 2007), however, there were extensive stream restoration projects during that time period, which likely contributed 

to increased sediment loads. There do not appear to be any 

obvious point sources of phosphorus that cause significantly 

higher yields. The Choptank River has very low sediment 

yields due to its dendritic drainage patterns and lower energy 

in the system, with more storage of sediment in the wetland 

areas. Mattawoman Creek had the second highest yields of 

both sediment and phosphorus (380,000 and 310 lb/yr/mi2

 for 

water year 2008, respectively; table 8), possibly from a large 

supply of Coastal Plain sand that is transported in the trellised 

watershed.

Gellis and others (2009) collected 327 samples of 

suspended sediment during water year 2004 at Mattawoman 

Creek as a part of a research study on sediment loading 

and source tracking. Using the USGS program Graphical 

Constituent Loading Analysis System (GCLAS), Gellis and 

others (2009) estimated total suspended-sediment yield for 

water year 2004 at Mattawoman to be 103,000 lb/yr/mi2

. 

Applying LOADEST to data collected during this study 

Table 7. Total loading estimates and model prediction uncertainty for the period January 6–March 6, 2009, comparing LOADEST 

models constructed using discharge with daily data, discharge with hourly data, and discharge and turbidity with hourly data.

[Loads are expressed as an average daily flux; C.I., confidence interval; lb/d, pounds per day; --, not available; %, percent]

Time step and model terms

Estimated 

daily flux

(lb/d)

Lower 95% 

C.I.

(lb/d)

Upper 95% 

C.I.

(lb/d)

C.I. width

(lb/d)

C.I. width 

reduction1

(%)

Suspended sediment

Daily discharge 9,500 2,000 29,000 27,000 --

Hourly discharge 10,000 6,400 15,000 8,600 68

Hourly discharge and turbidity 7,900 6,100 10,000 3,900 86

Total phosphorus

Daily discharge 13 7.4 20 13 --

Hourly discharge 13 10 16 6.0 52

Hourly discharge and turbidity 12 10 13 3.0 76

Total nitrogen

Daily discharge 120 92 160 68 --

Hourly discharge 120 110 140 30 56

Hourly discharge and turbidity 120 110 130 20 71

1

 Relative to the estimate based on daily discharge.
34 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Figure 22. Time series of discharge, turbidity, and LOADEST estimates of suspended-sediment flux for 

a 10-day period in January 2009 comparing time series of load estimates based on a daily model with 

discharge, an hourly model with discharge, and an hourly model with discharge and turbidity 

(A) discharge and turbidity, and (B) load estimates.

results in an estimate of roughly 370,000 lb/yr/mi2 for water 

year 2004 (55,000 lb/d; table 5), which is between three 

and four times the estimate of Gellis and others (2009). The 

discrepancy likely results from several factors: the first is 

the density of samples and corresponding method for load 

estimation. Gellis and others (2009) conducted high-frequency 

sampling of most of the major storm events that occurred during water year 2004, resulting in a finely resolved characterization of the variability in suspended-sediment concentration 

over the course of each storm. In particular, they accurately 

characterized hysteretic effects; that is, the tendency for 

suspended-sediment concentration on the rising limb of a 

discharge hydrograph to be higher than that at the equivalent 

discharge on the receding limb (fig. 22). The GCLAS loadestimation program is appropriate for such a densely sampled 

short-term research study. In contrast, only 38 samples were 

collected in water year 2004 as a part of this study. LOADEST 

is best suited for characterizing inter-annual variability, detecting long-term trends, and comparing watersheds, because 

it uses sampling programs that span a long period but are 
Estimation of Nutrient and Suspended-Sediment Loads and Yields 35

comparatively sparse in any given year. Among the disadvantages of using the seven-parameter regression model in 

LOADEST is the inability to represent hysteretic effects 

(fig. 22); this likely contributes to overestimation, as evident 

in the comparison of the seven- and eight-parameter models 

presented in table 7.

Differences in sampler location also likely contribute to 

the discrepancy between water year 2004 loading estimates in 

this study and those of Gellis and others (2009). To decrease 

the potential influence of lateral gradients in water quality 

associated with time-varying inflows from Old Womans 

Run, Gellis and others located their sampler intake midchannel, roughly 300 ft downstream of the location of the 

equipment deployed at LEW for this study. As discussed in the 

Comparison between Watersheds—Concentration Data section, a limited record of differences between turbidity recorded 

by the operational sonde deployed at LEW throughout this 

study and the temporary sonde deployed in 2011 indicates that 

turbidity and, by inference, particulate constituent concentration in discrete samples collected at LEW is biased high 

relative to concentrations in the Mattawoman main stem; these 

differences tend to appear in association with storm events 

(fig. 7b). Quantitative estimation of the corresponding difference in load would require development of separate regression 

models for the LEW and temporary sonde data; this was not 

possible given the limited comparison period. Thus, separation 

of the effects of study design and sampler location on the large 

difference in sediment loading estimates between this study 

and that of Gellis and others (2009) is not possible. Because 

Gellis and others (2009) sampled at a much higher frequency 

and sampled a presumably better-mixed location (mid-channel 

about 300 ft downstream from the mouth of Old Womans 

Run), their lower estimate is probably more indicative of the 

actual sediment load from the Mattawoman watershed for 

water year 2004. The degree to which the data collected in 

this study lead to overestimates for other years, as well as 

the degree to which annual loads of nitrogen and phosphorus 

shown in tables 5 and 8 may also be overestimated, cannot be 

determined using the available data.

The North Branch Patapsco River had the highest yield 

of total nitrogen, and Mattawoman Creek had the lowest 

(table 8). Since total nitrogen, like total phosphorus, was best 

predicted by turbidity (table 4), the same potential for overestimation due to sampling bias outlined above for sediment and 

phosphorus applies here; in this case, however, the inaccuracy 

results in a more favorable comparison. As discussed earlier 

for the concentration data, the low nitrogen yield is likely 

due to the configuration of the Mattawoman flood plain as a 

zone that is highly favorable to denitrification; lower overall 

loading of nitrogen from the watershed may also play a role. 

Table 8. Annual yields of suspended sediment, total nitrogen, and total phosphorus for the 2008 water year (October 1, 2007–

September 30, 2008) for Mattawoman Creek, four comparison watersheds, and the Potomac River.

[USGS, U.S. Geological Survey; %, percent; lb/yr/mi2, pounds per year per square mile]

Predominant 

land cover1

Annual yields with 95% confidence intervals

(lb/yr/mi2

)

2

Suspended 

sediment3

Total 

nitrogen

Total 

phosphorus

Mattawoman Creek near Pomonkey, Md.4 Forest, urban, wetland 380,000 

(120,000–910,000)

2,100 

(1,800–2,500)

310 

(220–430)

Northwest Branch Anacostia River near 

Hyattsville, Md.

Urban 800,000 

(290,000–1,700,000)

4,700 

(4,000–5,400)

570 

(360–910)

Choptank River near Greensboro, Md. Agriculture, wetlands 25,000 

(17,000–34,000)

3,000 

(2,700–3,100)

160 

(140–180)

Georges Creek at Franklin, Md. Forest 160,000 

(110,000–230,000)

3,800 

(3,300–4,300)

190 

(140–250)

North Branch Patapsco River at Cedarhurst, Md. Agriculture, forest, 

urban

130,000 

(26,000–420,000)

6,900 

(6,900–7,400)

130 

(74–210)

Potomac River at Chain Bridge Forest, agriculture, 

urban

230,000 

(100,000–430,000)

3,400 

(3,200–3,700)

220 

(170–290)

1

 (Fry and others, 2011); refer to table 1 for percentage of land cover.

2

 (Langland and others, 2012); lower and upper 95-percent confidence intervals are in parentheses.

3

 Water-column sediment was measured by USGS as suspended-sediment concentration (SSC) for Mattawoman Creek, Northwest Branch Anacostia River, 

Choptank River, and Potomac River at Chain Bridge, and by the Maryland Department of Natural Resources as both total suspended solids and SSC for 

Georges Creek and North Branch Patapsco River; refer to table 1 for details.

4

 Quality-control data collected in 2011 indicate that reported annual fluxes of sediment and particulate nutrients at Mattawoman Creek may be overestimates; see section Comparisons between Watersheds—Load Data for discussion.
36 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Flood-plain denitrification may also at least partially explain 

the differences between nitrogen yields in the Choptank and 

North Branch Patapsco Rivers. These two watersheds have 

very similar percentages of land cover in agriculture, but have 

a very different occurrence of nitrogen in the streams. The 

topography in the Patapsco River is steeper and better-drained, 

whereas the Choptank River has a higher percentage of wetlands where reducing conditions can develop.

Limitations of Analysis

Some elements of the interpretation provided herein were 

limited by QA issues associated with location of the autosampler and continuous water-quality sonde at LEW immediately downstream of, and on the same side of Mattawoman 

Creek as the mouth of Old Womans Run, a tributary draining 

roughly 10 percent of the total gaged area. Lateral gradients 

in water quality resulting from this sampling configuration 

(1) limited the interpretability of both continuous and discrete 

data on the time scale of individual storm events (for example, 

figure 6 and accompanying discussion in Evaluation of Data 

Integrity—Lateral Mixing), (2) yielded data that were at times 

representative of conditions in the Mattawoman Creek main 

stem, and at other times more representative of conditions in 

Old Womans Run; this ambiguity confounds the discrimination of the water-quality effects of land-use change in the 

greater Mattawoman watershed from the effects of land-use 

change in the Old Womans Run watershed (figs. 1 and 2), (3) 

likely introduced additional scatter into empirical regression 

relations between discretely sampled constituents and continuous surrogates (for example, figures 18–20 and accompanying 

discussion in Estimation of Continuous Concentrations of 

Nutrients and Suspended Sediment), and (4) introduced 

incompletely characterized, but potentially substantial, biases 

into estimates of downstream loading (for example, figure 7, 

tables 5 and 8, and discussion in Comparisons between 

Watersheds—Load Estimates). All of these limitations could 

be remedied by relocating the gage and water-quality instrumentation to a site where more complete lateral mixing could 

be assured, or by collecting all samples using EWI techniques.

Summary and Conclusions

Discharge and water-quality data, consisting of both 

discrete samples and continuous measurements collected 

at Mattawoman Creek over a 10-year period beginning in 

October 2000 and ending in January 2011 were summarized 

and interpreted. The monitoring, conducted in cooperation 

with the Charles County (Maryland) Department of Planning 

and Growth Management, the Maryland Department of 

the Environment, and the Maryland Geological Survey, 

was undertaken with the goals of describing ambient water 

quality, identifying potential contaminant sources, and 

quantifying nutrient and sediment loads to the tidal freshwater 

Mattawoman estuary. In spite of data limitations associated 

with sampling location and resulting complications in interpretation, both the discrete and continuous data yielded a number 

of robust and meaningful insights into water-quality conditions 

at Mattawoman Creek. Of all the data collected, seasonal and 

episodic patterns in specific conductance provide the strongest 

evidence of water-quality impacts caused by human activity 

in the Mattawoman watershed. The data show that the highest 

median concentrations occurred during the winter months, 

with monthly median concentrations decreasing throughout 

the spring, summer, and fall; episodic extremes in winter 

reflected enrichment, whereas those in summer reflected dilution. All of these observations are consistent with the application of de-icing compounds to roads in winter. As of the end of 

the study period (January 2011), salinity levels at Mattawoman 

Creek show a modest impact relative to that inferred from similar records at U.S. Geological Survey monitoring locations in 

and around Washington, D.C. However, the evidence of both 

episodic and seasonal influences on specific conductance in 

stream water implies the existence of both rapid and delayed 

pathways for other development-related contaminants to reach 

the stream.

Total nitrogen concentrations and yields at Mattawoman 

Creek were the lowest of those reported among five basins of 

similar size, distributed throughout Maryland, with comparable records. This is likely due partly to the denitrification 

capacity of the flood plain; lower nitrogen loading from the 

watershed, relative to the other more heavily agricultural 

comparison watersheds, might also play a role. Concentrations 

and yields of suspended sediment and total phosphorus were 

comparatively high, exceeded only by those observed in the 

highly urbanized Northwest Branch Anacostia River; however, several lines of evidence indicate that the concentrations 

of these constituents measured at left-edge water are biased 

upward, relative to concentrations in the Mattawoman main 

stem. Any bias in measured concentration will influence flux 

estimates in the same direction. Comparison basins were 

selected on the basis of their size, the availability of data of 

comparable length, and representativeness of flow conditions. 

Between-station differences in sampling frequency and protocol may influence comparison of both concentrations and 

loads. Regional factors such as watershed lithology can significantly influence sediment and nutrient fluxes, both in terms 

of the expectation for undeveloped conditions, as well as the 

response to land-use change. Thus, while the comparisons provide a degree of regional context for the conditions observed 

at Mattawoman, rankings of concentrations and loads should 

not be taken as an indication of the degree of human influence 

relative to local pre-development conditions at Mattawoman 

Creek, compared to human influence in the other watersheds.

The data were also examined in terms of consistency with 

general expectations related to land use. Although far from 

conclusive, the Mattawoman data show several features and 

patterns that are indicative of a functioning forested stream 

ecosystem. The stream is well-oxygenated with no evidence 
References Cited 37

of excessive seasonal oxygen demand. Evidence of a small 

suspended-sediment subsidy in spring is consistent with 

increased frequency of overbank flows, promoting interaction 

with the channel banks and flood plain during those months; 

moreover, sediment source fingerprinting conducted in an 

earlier study indicates that the streambanks and forest floor are 

the predominant sources of suspended sediment. The dominant 

form of nitrogen is the dissolved organic fraction, consistent 

with a forested land cover, and in sharp contrast to the heavily 

agricultural North Branch Patapsco River, overwhelmingly 

dominated by dissolved inorganic nitrogen, or the urbanized 

Northwest Branch Anacostia River, dominated by inorganic 

and particulate nitrogen. Seasonal patterns in the speciation 

of both nitrogen and phosphorus are consistent with demand 

and transformation by aquatic and terrestrial biota during the 

growing season, and with the input of leaf litter in the fall. 

None of these patterns rule out the presence of significant 

development-related impacts on nutrient and sediment concentrations in Mattawoman Creek; however, they provide at least 

circumstantial evidence that some elements of a functioning 

stream ecosystem remain intact.

Stepwise regression models, relating concentrations 

of suspended sediment, total phosphorus, and total nitrogen 

in discrete water-quality samples to continuously measured 

surrogates, identified turbidity and, in the case of suspended 

sediment, turbidity and discharge as the strongest predictors. 

For the same response variables, incorporation of turbidity 

as an additional parameter in the LOADEST loading model, 

which is typically based on only discharge, season, and time, 

decreased model residual variance by over a factor of two. 

Estimates of total sediment and nutrient flux based on hourly 

data for a single 58-day period were consistently lower for 

LOADEST models that incorporated turbidity, relative to 

models that did not. The observed improvements in model performance, and overall lower loading estimates, are consistent 

with findings of other comparative studies that incorporated 

turbidity in water-quality load and trend analyses.

In the absence of significant change in de-icing practices 

in the watershed, long-term continuous monitoring of specific 

conductance would provide a useful and inexpensive indication of the effects of increased development on water quality. 

The conductance values reached in winter episodic extremes, 

for example the 90th percentile of continuous observations 

in February, would serve as a good indicator of the degree 

to which road salt applied in any given year is influencing 

stream chemistry; of course, such an indicator would fluctuate 

year-to-year according to the amount of winter precipitation 

and aggressiveness of de-icing measures. Year-to-year trends 

in specific conductance values during low flow, such as the 

median of continuous observations in a low-flow month such 

as July, or the median value for all continuous observations 

when flow is less than 10 cubic feet per second, would provide 

an indication of the degree that de-icing agents applied to the 

surface in winter are reaching the water table and influencing 

groundwater chemistry.

Acknowledgments

Joel Blomquist, John Jastram, and Judith Denver of 

USGS, along with Karen Wiggen of the Charles County 

Department of Planning and Growth Management, provided 

thoughtful technical review comments that greatly improved 

the manuscript. Greg Noe of USGS and Paul Bukaveckas of 

Virginia Commonwealth University provided helpful commentary on nutrient data. Mitchell Donovan of USGS assisted 

with maps, Valerie Gaine of USGS provided a helpful editorial 

review, and Timothy Auer of USGS assisted with graphics and 

publication layout.

References Cited

Allen, J.D., 1995, Stream ecology structure and function of 

running waters: Philadelphia, Pennsylvania, Chapman and 

Hall, 388 p.

Ator, S.W., Brakebill, J.W., and Blomquist, J.D., 2011, 

Sources, fate, and transport of nitrogen and phosphorus in 

the Chesapeake Bay watershed: An empirical model: U.S. 

Geological Survey Scientific Investigations Report 2011–

5167, 27 p. (also available online at http://pubs.usgs.gov/

sir/2011/5167/).

Ator, S.W., Denver, J.M., Krantz, D.E., Newell, W.L., and 

Martucci, S.K., 2005, A surficial hydrogeologic framework 

for the Mid-Atlantic Coastal Plain: U.S. Geological Survey 

Professional Paper 1680, 44 p., 4 pls. (also available online 

at http://pubs.usgs.gov/pp/2005/pp1680/). 

Böhlke, J.K., and Denver, J.M., 1995, Combined use of 

groundwater dating, chemical, and isotopic analyses to 

resolve the history and fate of nitrate contamination in two 

agricultural watersheds, Atlantic Coastal Plain, Maryland: 

Water Resources Research, v. 31, no. 9, p. 2,319–2,339, 

DOI:10.1029/95WR01584. 

Charles County Department of Planning and Growth 

Management, 2006, Comprehensive water and sewer plan: 

La Plata, Maryland, Charles County Government, 223 p.

Chesapeake Bay Program, 2004, Establishing a Chesapeake 

Bay Nontidal Watershed Water-Quality Network: 

Annapolis, Maryland, Report of the Nontidal Water Quality 

Monitoring Workgroup, Chesapeake Bay Program, 28 p.

Christensen, V.G., 2001, Characterization of surface-water 

quality based on real-time monitoring and regression 

analysis, Quivira National Wildlife Refuge, south-central 

Kansas, December 1998 through June 2001: 

U.S. Geological Survey Water-Resources Investigations 

Report 01–4248, 28 p.
38 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Christensen, V.G., Jian, X., and Ziegler, A.C., 2000, 

Regression analysis and real-time water-quality monitoring 

to estimate constituent concentrations, loads, and yields in 

the Little Arkansas River, south-central Kansas, 1995–99: 

U.S. Geological Survey Water-Resources Investigations 

Report 00–4126, 36 p.

Cohn, T.A. 1988, Adjusted maximum likelihood estimation of 

the moments of lognormal populations from type I censored 

samples: U.S. Geological Survey Open-File Report 88–350, 

34 p.

Cohn, T.A., 2005, Estimating contaminant loads in rivers: 

An application of adjusted maximum likelihood to type 1 

censored data: Water Resources Research, v. 41, W07003, 

DOI:10.1029/2004WE003833, 13 p.

Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjuk, L.D., and 

Summers, R.M., 1992, The validity of a simple statistical 

model for estimating fluvial constituent loads—An empirical study involving nutrient loads entering Chesapeake Bay: 

Water Resources Research, v. 28, no. 9, p. 2,353–2,363.

Denny, C., 2009, Maryland’s Mattawoman Creek—Protecting 

an extraordinary place: Annapolis, Maryland, Chesapeake 

Bay Foundation Save the Bay Magazine, v. 35, no. 1, 

p. 6–9.

Duan, N., 1983, Smearing estimate: A nonparametric retransformation method: Journal of the American Statistical 

Association, v. 78, no. 383, p. 605–610.

Feller, M.C., 2009, Deforestation and nutrient loading to fresh 

waters, in Likens, G.E., ed., Encyclopedia of Inland Waters: 

Boston, Massachusetts, Elsevier, p. 832–851.

Fishman, M.J., ed., 1993, Methods of analysis by the U.S. 

Geological Survey National Water Quality Laboratory—

Determination of inorganic and organic constituents in 

water and fluvial sediments: U.S. Geological Survey OpenFile Report 93–125, 217 p.

Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., 

Barnes, C., Herold, N., and Wickham, J., 2011, Completion 

of the 2006 National Land Cover Database for the conterminous United States: Photogrammatic Engineering and 

Remote Sensing, v. 77, no. 9, p. 858–864. 

Gellis, A.C., Hupp, C.R., Pavich, M.J., Landwehr, J.M., 

Banks, W.S.L., Hubbard, B.E., Langland, M.J., Ritchie, 

J.C., and Reuter, J.M., 2009, Sources, transport, and storage of sediment in the Chesapeake Bay watershed: U.S. 

Geological Survey Scientific Investigations Report 2008–

5186, 95 p. (also available online at http://pubs.usgs.gov/

sir/2008/5186/).

Glaser, J.D., 2003, Geologic map of Prince George’s County: 

Maryland Geological Survey Map, 1 sheet, scale 1:62,500.

Hack, J.T., 1955, Geology of the Brandywine area and origin 

of the upland of southern Maryland: U.S. Geological Survey 

Professional Paper 267–A, 43 p.

Hall, L.W., Jr., Fischer, S.A., Killen, W.D., Ziegenfuss, 

M.C., Anderson, R.D., and Klauda, R.J., 1993, Assessing 

the relationship between critical acidic conditions 

in Maryland coastal plain streams and predicted 

mortality of early life stages of migratory fish: Aquatic 

Toxicology, v. 26, nos. 3–4, p. 239–272, http://dx.doi.

org/10.1016/0166-445X(93)90033-W.

Hall, R.L., and Mathews, E.D., 1974, Soil survey of Charles 

County, Maryland: U.S. Department of Agriculture, Soil 

Conservation Service, 94 p. 

Helsel, D.R., and Hirsch, R.M., 1992, Statistical Methods in 

Water Research: Amsterdam, Elsevier, 529 p.

Herlihy, A.T., Stoddard, J.L., and Johnson, C.B., 1998, The 

relationship between stream chemistry and watershed land 

cover data in the Mid-Atlantic region, U.S: Water, Air, and 

Soil Pollution, v. 105, nos. 1–2, p. 377–386.

Jastram, J.D., Moyer, D.L., and Hyer, K.E., 2009, A comparison of turbidity-based and streamflow-based estimates of 

suspended-sediment concentrations in three Chesapeake 

Bay tributaries: U.S. Geological Survey Scientific 

Investigations Report 2009–5165, 37 p. (also available 

online at http://pubs.usgs.gov/sir/2009/5165/).

Jordan, T.E., Correll, D.L., and Weller, D.E., 2000, 

Mattawoman Creek watershed: Nutrient and sediment 

dynamics, Final Report, Smithsonian Environmental 

Research Center, 45 p. plus appendixes, accessed August 

15, 2012 at http://www.charlescountymd.gov/sites/default/

files/pgm/planning/mattawoman_sedimentreport.pdf.

Jorgensen, N.O.B., 2009, Organic nitrogen, in Likens, G.E., 

ed., Encyclopedia of inland waters: Boston, Massachusetts, 

Elsevier, p. 832–851.

Kaushal, S.S., Groffman, P.M., Likens, G.E., Belt, K.T., 

Stack, W.P., Kelly, V.R., Band, L.E., and Fisher, G.T., 2005, 

Increased salinization of fresh water in the northeastern 

United States: Proceedings of the National Academy of 

Sciences of the United States of America, v. 102, no. 38, 

p. 13,517–13,520.
References Cited 39

Krantz, D.E., and Powars, D.S., 2000, Hydrogeologic setting 

and potential for denitrification in ground water, Coastal 

Plain of Southern Maryland: U.S. Geological Survey WaterResources Investigations Report 00–4051, 24 p.

Langland, M.J., Blomquist, J.D., Moyer, D.L., and Hyer, K.E., 

2012, Nutrient and suspended-sediment trends and yields 

and indicator development of streamwater quality conditions at nontidal sites in the Chesapeake Bay watershed, 

1985-2010: U.S. Geological Survey Scientific Investigations 

Report 2012–5093, 26 p. (also available online at http://

pubs.er.usgs.gov/publication/sir20125093).

Langland, M.J., Raffensperger, J.P., Moyer, D.L., Landwehr, 

J.M., and Schwarz, G.E., 2006, Changes in streamflow and 

water quality in selected nontidal basins in the Chesapeake 

Bay watershed, 1985–2004: U.S. Geological Survey 

Scientific Investigations Report 2006–5178, 75 p. plus 

appendixes (on CD), (also available online at http://pubs.

usgs.gov/sir/2006/5178/).

Lorenz, D.L., and others, 2011, USGS library for S-PLUS for 

Windows—Release 4.0: U.S. Geological Survey Open-File 

Report 2011–1130 (also available online at http://water.

usgs.gov/software/S-PLUS/).

Maryland Department of the Environment, 2004, Mattawoman 

Creek Nutrient TMDL: Baltimore, Maryland, 30 p., plus 

appendixes.

Matsubayashi, U., Velasquez, G., and Takagi, F., 1993, 

Hydrograph separation and flow-analysis by specific electrical conductance of water: Journal of Hydrology, v. 152, 

nos. 1–4, p. 179–199.

McCartan, L., 1989, Geologic map of Charles County: 

Maryland Geological Survey Map, 1 sheet, scale 1:62500.

Miller, C.V., Gutiérrez-Magness, A.L., Feit Majedi, B.L., and 

Foster, G.D., 2007, Water quality in the upper Anacostia 

River, Maryland: Continuous and discrete monitoring with 

simulations to estimate concentrations and yields, 2003–05: 

U.S. Geological Survey Scientific Investigations Report 

2007–5142, 43 p. (also available online at http://pubs.usgs.

gov/sir/2007/5142/).

National Atmospheric Deposition Program (NRSP-3), 2011, 

Deposition isopleth maps for 2009, NADP Program Office, 

Champaign, Illinois, accessed November 7, 2011 at http://

nadp.sws.uiuc.edu/maps/Default.aspx.

National Climatic Data Center, 2002a, Climatography of 

the United States No. 85, divisional normals and standard 

deviations of temperature, precipitation, and heating and 

cooling degree days 1971–2000, Section 1—temperature: 

Asheville, North Carolina, 71 p.

National Climatic Data Center, 2002b, Climatography of 

the United States No. 85, divisional normals and standard 

deviations of temperature, precipitation, and heating and 

cooling degree days 1971–2000, Section 2—precipitation: 

Asheville, North Carolina, 69 p.

Noe, G.B., and Hupp, C.R., 2007, Seasonal variation in 

nutrient retention during inundation of a short-hydroperiod 

floodplain: River Research and Applications, v. 23, 

p. 1,088–1,101, DOI: 10.1002/rra.1035.

Omernik, J.M., Griffith, G.E., Irish, J.T., and Johnson, C.B., 

1988, Total alkalinity of surface waters: Corvallis, Oregon, 

U.S. Environmental Protection Agency, Environmental 

Research Laboratory, single-page map, accessed August 15, 

2012 at http://water.usgs.gov/owq/alkus.pdf.

Patton, C.J., and Kryskalla, J.R., 2003, Methods of analysis 

by the U.S. Geological Survey National Water Quality 

Laboratory—Evaluation of alkaline persulfate digestion 

as an alternative to Kjeldahl digestion for determination of 

total and dissolved nitrogen and phosphorus in water: U.S. 

Geological Survey Water-Resources Investigations Report 

03–4174, 40 p., accessed July 15, 2012 at http://nwql.usgs.

gov/Public/pubs/WRIR03-4174/WRIR03-4174.html.

Patton, C.J., and Truitt, E.P., 2000, Methods of analysis 

by the U.S. Geological Survey National Water Quality 

Laboratory—Determination of ammonium plus organic 

nitrogen by a Kjeldahl digestion method and an automated 

photometric finish that includes digest cleanup by gas diffusion: U.S. Geological Survey Open-File Report 00–170, 

31 p., accessed July 15, 2012 at http://nwql.usgs.gov/Public/

pubs/OFR00-170/OFR00-170.html.

Paul, M.J., and Meyer, J.L., 2001, Streams in the urban landscape: Annual Reviews of Ecology and Systematics, v. 32, 

p. 333–365.

Phillips, P.J., and Bachman, L.J., 1996, Hydrologic landscapes 

on the Delmarva Peninsula, Part 1—Drainage basin type 

and base-flow chemistry: Journal of the American Water 

Resources Association, v. 32, no. 4, p. 767–778.
40 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11

Rantz, S.E., and others, 1982, Measurement and computation 

of streamflow, volume 1. Measurement of stage and discharge: U.S. Geological Survey Water Supply Paper 2175, 

284 p. (also available online at http://pubs.usgs.gov/wsp/

wsp2175/#pdf.

Rasmussen, T.J., and Ziegler, A.C., 2003, Comparison and 

continuous estimates of fecal coliform and Escherichia coli

bacteria in selected Kansas streams, May 1999 through 

April 2002: U.S. Geological Survey Water-Resources 

Investigations Report 03–4056, 80 p., accessed July 15, 

2012 at http://ks.water.usgs.gov/pubs/abstracts/wrir.

abstract.03-4056.html.

Rasmussen, T.J., Ziegler, A.C., and Rasmussen, P.P., 2005, 

Estimation of constituent concentrations, densities, loads, 

and yields in lower Kansas River, northeast Kansas, using 

regression models and continuous water-quality monitoring, 

January 2000 through December 2003: U.S. Geological 

Survey Scientific Investigations Report 2005–5165, 

117 p. (also available online at http://pubs.usgs.gov/

sir/2005/5165/).

Reger, J.P., and Cleaves, E.T., 2008, Explanatory text for the 

physiographic map of Maryland: Maryland Geological 

Survey Open-File Report 08–03–1, 60 p.

Runkel, R.L., Crawford, C.G., and Cohn, T.A., 2004, Load 

estimator (LOADEST): A FORTRAN program for estimating constituent loads in streams and rivers: U.S. Geological 

Survey Techniques and Methods, book 4, chap. A5, 69 p., 

accessed July 15, 2012 at http://pubs.usgs.gov/tm/2005/

tm4A5.

Shreve, E.A., and Downs A.C., 2005, Quality-assurance plan 

for the analysis of fluvial sediment by the U.S. Geological 

Survey Kentucky Water Science Center Sediment 

Laboratory: U.S. Geological Survey Open-File Report 

2005–1230, 28 p. (also available online at http://pubs.usgs.

gov/of/2005/1230/).

Sommer, T.R., Harrell, W.C., and Swift, T.J., 2008, Extreme 

hydrologic banding in a large-river floodplain, California, 

U.S.A.: Hydrobiologia, v. 598, p. 409–415, DOI 10.1007/

s10750-007-9159-1.

TIBCO Corporation, 2008, TIBCO Spotfire S+ 8.1 for 

Windows Users® Guide: TIBCO Software Corporation, 

582 p.

U.S. Army Corps of Engineers, 2003, Mattawoman Creek 

Watershed Management Plan, Charles County, Maryland, 

40 p., plus appendixes, accessed August 15, 2012, at http://

www.charlescountymd.gov/sites/default/files/pgm/planning/

mattawoman_management_plan.pdf.

U.S. Census Bureau, 2011, State and County Quick Facts—

Charles County, Maryland, accessed November 7, 2011, at 

http://quickfacts.census.gov/qfd/states/24/24017.html.

U.S. Geological Survey, 2009, Potential bias in alkaline 

persulfate analysis of total nitrogen in whole water samples 

and recommendations for quantifying bias in whole water 

samples, Office of Water Quality Technical Memorandum 

2009.03, accessed May 26, 2011, at http://water.usgs.gov/

admin/memo/QW/qw09.03.html.

U.S. Geological Survey, variously dated, National field manual for the collection of water-quality data: U.S. Geological 

Survey Techniques of Water-Resources Investigations, book 

9, chaps. A1–A9, available online at http://water.usgs.gov/

owq/FieldManual/.

Walsh, C.J., Roy, A.H., Feminella, J.W., Cottingham, P.D., 

Groffman, P.M., and Morgan, R.P., 2005, The urban stream 

syndrome: current knowledge and the search for a cure: 

Journal of the North American Benthological Society, v. 24, 

no. 3, p. 706–723.

Wagner, R.J., Boulger, R.W., Jr., Oblinger, C.J., and Smith, 

B.E., 2006, Guidelines and standard procedures for continuous water-quality monitors: station operation, record 

computation, and data reporting: U.S. Geological Survey 

Techniques and Methods 1–D3, 96 p. (also available online 

at http://pubs.usgs.gov/tm/2006/tm1D3/).

Wharton, C.H., Kitchens, W.M., Pendleton, E.C., and Sipe, 

T.W, 1982, The ecology of bottomland hardwood swamps 

of the Southeast: A community profile: Washington, 

D.C., U.S. Fish and Wildlife Service, Biological Services 

Program, 134 p. 

Weller, D., Jordan, T., Correll, D., and Liu, Z., 2003, Effects 

of land-use change on nutrient discharges from the Patuxent 

River watershed: Estuaries, v. 26, no. 2A, p. 244–266.

Wilhelm, F.W., 2009, Pollution of aquatic ecosystems I, in

Likens, G.E., ed., Encyclopedia of inland waters: Boston, 

Massachusetts, Elsevier, p. 110–119.

Wilson, J.M., and Fleck, W.B., 1990, Geology and hydrologic 

assessment of Coastal Plain aquifers in the Waldorf Area, 

Charles County, Maryland: Maryland Geological Survey 

Report of Investigations No. 53, 138 p.

Withers, P., and Jarvie, H., 2008, Delivery and cycling 

of phosphorus in rivers: A review: Science of the Total 

Environment, v. 400, nos. 1–3, p. 379–395.
Appendix A1: Relations between Measured Water-Quality Parameters and Constituents Discussed in this Report 41

Appendix A1: Relations between Measured 

Water-Quality Parameters and Constituents 

Discussed in this Report

Appendix A1 provides a key relating water-quality parameter codes (rows in the 

table) to constituents discussed in this report (columns in the table). The concentration 

of a constituent listed in a column heading is computed by combining parameters in 

that column’s non-blank rows, using plus (“+”) or minus (“-”) signs indicated. For 

example: Nitrate (first column) = P00631 (fourth row) minus P00613 (fifth row). Waterquality parameter concentrations reported as “less than” (“<”) were treated as zero in 

summations.
42 Summary and Interpretation of Discrete and Continuous Water-Quality Monitoring Data, Mattawoman Creek, Maryland, 2000–11 Appendix A1. Key relating water-quality parameter codes to constituents discussed in this report. [mg/L, milligrams per liter; N, nitrogen; P, phosphorus] Parameter code Description Inorganic N (mg/L as N) Organic N, Kjeldahl analysis (mg/L as N) Organic N, Persulfate analysis (mg/L as N) Phosphorus (mg/L as P) Nitrate Nitrite Am- monia Total inor- ganic nitro- gen Par- ticulate organic nitro- gen Dis- solved or- ganic nitro- gen Total or- ganic nitro- gen Total nitro- gen Partic- ulate or- ganic nitro- gen Dis- solved or- ganic nitro- gen Total or- ganic nitro- gen Total nitro- gen Dis- solved or- ganic phos- phorus Partic- ulate phos- phorus Dis- solved inor- ganic phos- phorus Total phos- phorus P00623 Ammonia + organic nitrogen, water, filtered, mg/L as N

–

+

P00625 Ammonia + organic nitrogen, water, 

unfiltered, mg/L as N

+

+

+

P00608 Ammonia, water, filtered, mg/L as N

+

+

–

–

–

–

P00631 Nitrate + nitrite, water, filtered, mg/L as N

+

+

+

–

–

P00613 Nitrite, water, filtered, mg/L as N

–

+

P62854 Total nitrogen (nitrate + nitrite + ammonia 

+ organic N), water, filtered, analytically determined, mg/L

–

+

P62855 Total nitrogen (nitrate + nitrite + ammonia 

+ organic N), water, unfiltered, analytically determined, mg/L

+

+

+

P00671 Orthophosphate, water, filtered, mg/L as P

+

P00666 Phosphorus, water, filtered, mg/L as P

+

–

P00665 Phosphorus, water, unfiltered, mg/L as P

+

+
Prepared by USGS West Trenton Publishing Service Center.

Edited by Valerie M. Gaine.

Graphics and layout by Timothy W. Auer.

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
Chanat, J.G., and others—Summary and Interpretation of Water-Quality Monitoring Data, Mattawoman Creek, Charles County, Maryland, 2000–11—Scientific Investigations Report 2012–5165

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