U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2014–5073

Streamflow, Water Quality, and Aquatic 

Macroinvertebrates of Selected Streams in 

Fairfax County, Virginia, 2007–12
Cover. (Front) Map showing watersheds of Fairfax County, Virginia, and monitoring stations with associated 

watersheds included in the analyses presented herein.

(Background photograph, front and back cover) Dead Run near Mclean, Virginia, during stormflow conditions. 

Photograph by James McCulla.

(Back cover, left rear photograph) Frog Branch near Chantilly, Virginia, during stormflow conditions. 

Photograph by James McCulla.

(Back cover, right rear photograph) Big Rocky Run near Chantilly, Virginia, during stormflow conditions. 

Photograph by James McCulla.
Streamflow, Water Quality, and Aquatic 

Macroinvertebrates of Selected Streams in 

Fairfax County, Virginia, 2007–12

By John D. Jastram

Prepared in cooperation with Fairfax County, Virginia

Scientific Investigations Report 2014–5073

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

Jastram, J.D., 2014, Streamflow, water quality, and aquatic macroinvertebrates of selected streams in Fairfax County, 

Virginia, 2007–12: U.S. Geological Survey Scientific Investigations Report 2014-5073, 68 p., http://dx.doi.org/10.3133/

sir20145073

ISSN 2328-0328 (online)

ISSN 2328-031X (print)

ISBN 978-1-4113-3788-6
iii

Acknowledgments

Ecologists with the Fairfax County Department of Public Works and Environmental Services, 

primarily Shannon Curtis and Joe Sanchirico, have contributed greatly to this effort through field 

work and program responsibilities. Their dedication to the success of this effort has been vital in 

progress towards achievement of program objectives. Staff at the Fairfax County Environmental 

Services Laboratory performed the nutrient analyses for the large number of samples collected. 

Their efforts to accommodate unpredictable sample collection schedules are greatly appreciated.

This study was designed by Ken Hyer and Doug Moyer of the U.S. Geological Survey (USGS), 

who continue to contribute to the success of the effort by sharing expertise in water-resources 

monitoring and data analysis. Numerous USGS hydrologic technicians have spent substantial 

amounts of time in the field maintaining instrumentation and collecting data, and their efforts 

are greatly appreciated.

v

Contents

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

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

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

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

Methods of Investigation..............................................................................................................................3

Study Design..........................................................................................................................................5

Selection of Watersheds and Monitoring Locations .............................................................5

Description of Watersheds and Streams Monitored.............................................................5

Data Collection, Sampling, and Laboratory Analysis......................................................................5

Formulation of Suspended-Sediment and Nutrient Load Models ................................................8

Formulation of Suspended-Sediment and Nutrient Concentration Models ...............................9

Hydrologic Conditions...................................................................................................................................9

Precipitation...........................................................................................................................................9

Streamflow...........................................................................................................................................11

Peak Streamflow Frequency....................................................................................................11

Streamflow Variability...............................................................................................................13

Hydrologic Conditions Represented by Water-Quality Samples ................................................13

Temporal and Spatial Patterns in Water Quality ....................................................................................14

Patterns in Basic Water-Quality Parameters.................................................................................14

pH..................................................................................................................................................14

Specific Conductance...............................................................................................................18

Dissolved Oxygen.......................................................................................................................18

Turbidity .......................................................................................................................................18

Patterns in Nitrogen ...........................................................................................................................20

Patterns in Phosphorus .....................................................................................................................28

Suspended-Sediment and Nutrient Loads and Yields...........................................................................29

Suspended-Sediment and Nutrient Load Models.........................................................................29

Annual Suspended-Sediment and Nutrient Loads and Yields ....................................................34

Regional Comparison of Yields .........................................................................................................39

Suspended-Sediment and Nutrient Concentration Models .................................................................43

Benthic Macroinvertebrate Data and Stream Health ...........................................................................46

Changes to the Monitoring Program ........................................................................................................48

Future Directions..........................................................................................................................................48

Summary........................................................................................................................................................49

References Cited..........................................................................................................................................53

Appendix 1.....................................................................................................................................................57
vi

Figures

1. Map showing monitoring stations and watersheds ...............................................................4

2. Pie charts showing percentages of land-use types within 14 monitored 

watersheds in Fairfax County .....................................................................................................7

3. Bar graphs showing annual and monthly precipitation data from 

Dulles International Airport and Ronald Reagan Washington National Airport 

for water years 2008 through 2012 with mean annual and monthly values from 

the period 1981–2010..................................................................................................................10

4. Graphs showing cumulative streamflow yield per day of water year, by 

water year, and spline smooths of cumulative streamflow yield per day 

for water years 2008–12, by station .........................................................................................12

5. Bar graph showing flashiness, computed as percentage of time stage 

increases by 0.5 feet per hour or greater, of 14 monitored streams in Fairfax 

County, shaded by percentage of watershed area covered by road surfaces................13

6. Graphs showing distribution of streamflow and monthly samples only, 

and monthly plus storm samples, at the four intensive monitoring stations ....................15

7. Timeseries plots and monthly boxplots showing pH results from monthly 

sampling at 14 monitoring stations in Fairfax County ...........................................................16

8. Contour plots showing continuous pH data from the four intensive 

monitoring stations in Fairfax County with inset detailing a 5-day period of 

diurnal pH cycles ........................................................................................................................17

9. Timeseries plots and monthly boxplots showing specific conductance results 

from monthly sampling at 14 monitoring stations in Fairfax County...................................19

10. Boxplots showing specific conductance results, with percent road coverage 

in watershed, by monitoring station, from monthly sampling at 14 monitoring 

stations in Fairfax County ..........................................................................................................20

11. Contour plots showing continuous specific conductance data from the 

four intensive monitoring stations in Fairfax County ............................................................21

12. Timeseries plots and monthly boxplots showing dissolved oxygen results 

from monthly sampling at 14 monitoring stations in Fairfax County, with 

reference line for Virginia dissolved oxygen criterion of 4 milligrams per liter...............22

13. Contour plots showing continuous dissolved oxygen data from the four 

intensive monitoring stations in Fairfax County.....................................................................23

14. Timeseries plots and monthly boxplots showing turbidity results from monthly

sampling at 14 monitoring stations in Fairfax County ...........................................................24

15. Timeseries plots showing continuous turbidity and streamflow data from 

the four intensive monitoring stations in Fairfax County for water year 2011 ..................25

16. Timeseries plots and monthly boxplots showing total nitrogen results from 

monthly sampling at 14 monitoring stations in Fairfax County............................................26

17. Timeseries plots and monthly boxplots showing nitrate plus nitrite results 

from monthly sampling at 14 monitoring stations in Fairfax County...................................27

18. Boxplots showing total nitrogen results, with percent road coverage in 

watershed, by monitoring station, from monthly sampling at 14 monitoring 

stations in Fairfax County ..........................................................................................................28

19. Timeseries plots and monthly boxplots showing total phosphorus results from

monthly sampling at 14 monitoring stations in Fairfax County............................................30

20. Timeseries plots and monthly boxplots showing total dissolved phosphorus 

results from monthly sampling at 14 monitoring stations in Fairfax County .....................31

21. Timeseries plots and monthly boxplots showing orthophosphate results from

monthly sampling at 14 monitoring stations in Fairfax County............................................32
vii

22. Timeseries plots and monthly boxplots showing total particulate phosphorus

results from monthly sampling at 14 monitoring stations in Fairfax County .....................33

23. Graphs showing measured suspended-sediment concentration against 

computed suspended-sediment concentration for the four intensive 

monitoring stations.....................................................................................................................34

24. Graphs showing measured total dissolved nitrogen, total nitrogen, and 

total particulate nitrogen concentrations against computed concentrations .................36

25. Graphs showing measured total dissolved phosphorus, total phosphorus, 

and total particulate phosphorus concentrations against computed 

concentrations ............................................................................................................................37

26. Graphs showing suspended-sediment loads and yields for water years 2008

through 2012 for the four intensive monitoring stations.......................................................38

27. Graphs showing annual suspended-sediment load against annual peak 

streamflow and annual total streamflow, with linear regression lines, for the 

four intensive monitoring stations for water years 2008 through 2012..............................38

28. Graphs showing annual loads of total dissolved nitrogen, total nitrogen, and 

total particulate nitrogen for water years 2008 through 2012 at the four intensive

monitoring stations.....................................................................................................................39

29. Graphs showing annual total dissolved nitrogen, total nitrogen, and total

particulate nitrogen load against annual peak streamflow and annual total

streamflow, with linear regression lines, for the four intensive monitoring 

stations for water years 2008 through 2012............................................................................40

30. Graphs showing annual yields of total dissolved nitrogen, total nitrogen, and 

total particulate nitrogen for water years 2008 through 2012 at the four intensive

monitoring stations.....................................................................................................................41

31. Graphs showing annual loads of total dissolved phosphorus, total phosphorus, 

and total particulate phosphorus for water years 2008 through 2012 at the four 

intensive monitoring stations....................................................................................................42

32. Graphs showing annual yields of total dissolved phosphorus, total phosphorus, 

and total particulate phosphorus for water years 2008 through 2012 at the four 

intensive monitoring stations....................................................................................................43

33. Graphs showing annual loads of total dissolved phosphorus, total phosphorus,

and total particulate phosphorus against annual peak streamflow and total annual

streamflow, with linear regression lines, at the four intensive monitoring stations

for water years 2008 through 2012...........................................................................................44

34. Boxplots showing suspended-sediment yields, as total suspended solids and

suspended-sediment concentration, from four monitoring networks—Atlanta,

Fairfax, Occoquan, and the Chesapeake Bay River Input Monitoring...............................45

35. Boxplots showing total nitrogen yields from five monitoring networks—

Atlanta, Fairfax, North Carolina, Occoquan, and the Chesapeake Bay River 

Input Monitoring .........................................................................................................................45

36. Boxplots showing total phosphorus yields from five monitoring networks—

Atlanta, Fairfax, North Carolina, Occoquan, and the Chesapeake Bay River 

Input Monitoring .........................................................................................................................46

37. Boxplots showing benthic macroinvertebrate sampling results summarized as 

the Fairfax County Index of biotic integrity and the Virginia stream condition 

index, in order of decreasing percent road coverage from left to right............................49

38. Timeseries plots showing benthic macroinvertebrate sampling results 

summarized as the Fairfax County index of biotic integrity and the Virginia 

stream condition index ..............................................................................................................50

39. Map showing monitoring stations and watersheds added to the monitoring

network in 2012 ...........................................................................................................................51
viii

Tables

1. Monitoring station identifiers, names, abbreviations, types, watershed areas, 

and percentage of watershed in geologic terranes ...............................................................6

2. Nutrient constituents, abbreviations, and analytical methods for analyses

performed by Fairfax County Environmental Services Laboratory.......................................8

3. Number of observations, regression coefficients, and R2 for suspended 

sediment load models ................................................................................................................29

4. Number of observations, regression coefficients, and R2 for nitrogen load 

models ..........................................................................................................................................35

5. Number of observations, regression coefficients, and R2 for phosphorus load

models ..........................................................................................................................................35

6. Number of observations, regression coefficients, and R2 for suspended 

sediment concentration models...............................................................................................47

7. Number of observations, regression coefficients, and R2 for total nitrogen

concentration models ................................................................................................................47

8. Number of observations, regression coefficients, and R2 for total phosphorus

concentration models ................................................................................................................48

9. Station information for six stations added to the monitoring network in 2012.................50

1–1. Counts of benthic macroinvertebrates for each annual sample, by station 

 identifier .......................................................................................................................................57
ix

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

Area

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

ton, short (2,000 lb) 0.9072 megagram (Mg) 

ton per year (ton/yr) 0.9072 megagram per year (Mg/yr)

ton per year (ton/yr) 0.9072 metric ton per year

Precipitation rate

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

Yield

ton per square mile (ton/mi2) 0.3503 metric ton per square kilometer 

pound per square mile (lb/mi2) 0.1751 kilogram per square kilometer (kg/km2)

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

°F=(1.8×°C)+32

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

°C=(°F-32)/1.8

Horizontal coordinate information is referenced North American Datum of 1983 (NAD 83).

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 in milligrams per liter (mg/L).
Abbreviations

AEP annual exceedance probability

AMLE adjusted maximum likelihood estimator

BM benthic macroinvertebrates

BMP best management practices

DCA Ronald Reagan Washington National Airport

DO dissolved oxygen

FCSWPD Fairfax County Stormwater Planning Division

FNU formazin nephelometric units

IAD Dulles International Airport

IBI index of biotic integrity

NRTWQ National Real Time Water Quality

NWIS National Water Information System

SCI stream condition index

SSC suspended-sediment concentration

USGS U.S. Geological Survey

WY water year

x
Streamflow, Water Quality, and Aquatic 

Macroinvertebrates of Selected Streams in Fairfax 

County, Virginia, 2007–12

By John D. Jastram

Abstract

Efforts to mitigate the effects of urbanization on 

streams rely on best management practices (BMPs) that 

are implemented with the intent of reducing and retaining 

stormwater runoff. A cooperative monitoring effort between 

the U.S. Geological Survey and Fairfax County, Virginia was 

initiated in 2007 to assess the condition of county streams and 

document watershed-scale responses to the implementation of 

BMPs. Assessment of the data collected during the first 5 years 

of this monitoring program focused on characterizing the 

hydrologic and ecological condition of 14 monitored streams. 

Hydrologic, chemical, and macroinvertebrate community 

conditions in the streams monitored were found to be 

consistent, overall, with conditions commonly observed 

in urban streams. Hydrologically, the monitored streams 

were found to be flashy, with flashiness positively related to 

road cover in the watershed. Typical pH values of streams 

throughout the network centered around neutrality (pH = 7) 

with strong daily fluctuations apparent in the continuous data. 

Patterns in specific conductance were largely representative of 

anthropogenic disturbances—watersheds having the greatest 

percentage of open space and estate residential land-use had 

the lowest typical specific conductance values, and specific 

conductance variability was less than what is observed in 

watersheds that are more intensively developed. In watersheds 

having greater road coverage, and more development in 

general, increases in specific conductance over several orders 

of magnitude were observed during winter months as a result 

of the application of de-icing salts on impervious surfaces. 

Dissolved oxygen conditions were typically within the range 

required to support healthy biological communities, although 

occasional departures during summer months at some sites fell 

below the impairment threshold for streams in Virginia. 

Nitrogen (N) and phosphorus (P), concentration patterns 

were largely consistent across the network, with few exceptions. Nitrogen concentrations in monthly samples were generally low and dominated by nitrate. Exceptions to the generally 

low N concentrations occurred at Captain Hickory Run, 

which had a median total N concentration of approximately 

4.9 milligrams per liter (mg/L), compared to the network-wide 

median of approximately 1.7 mg/L, and at Popes Head Creek 

Tributary, where total N concentrations spiked to 6–8 mg/L 

during low-flow periods in August or September of each year. 

Phosphorus concentrations in monthly samples were generally 

low and dominated by the dissolved fraction. Two monitoring 

stations in the network, Flatlick Branch and Frog Branch, are 

notable for having median total P concentrations that were, on 

average, approximately three times greater than the median 

total P concentration of 0.02 mg/L observed at the other 

12 stations in the network. 

Suspended-sediment and nutrient loads and yields 

were similar to those of urbanized watersheds in other 

studies, although the yields from these urbanized basins 

were greater than, or within, the upper quartile of yields 

observed throughout the Chesapeake Bay watershed. Annual 

suspended-sediment loads ranged from 289–10,275 tons, 

with a median of 1,311 tons, and corresponding yields 

ranged from 107–2,827 tons per square mile (ton/mi2), with 

a median of 277 ton/mi2.Annual total N loads ranged from 

8,014–36,413 pounds, with a median of 21,314 pounds, and 

corresponding yields ranged from 3,361–8,360 pounds per 

square mile (lb/mi2), with a median of 6,200 lb/mi2. Annual 

total P loads ranged from 380–6,558 pounds, with a median 

of 1,874 pounds, and corresponding yields ranged from 

140–1,562 lb/mi2, with a median of 543 lb/mi2.

Benthic macroinvertebrate community metrics indicated 

that streams throughout Fairfax County are generally of poor 

health. One station, Castle Creek, was an exception with 

results indicating relatively high quality aquatic health.

Six additional monitoring stations were added to the 

network in 2012 to improve spatial coverage throughout 

Fairfax County. Monitoring activities are expected to 

continue at all 20 stations for the foreseeable future as BMP 

implementation is conducted.

Introduction

In the United States, immigration and the migration 

of populations from rural areas to cities and their 

surrounding suburbs accelerated during the 20th Century 

(Kim, 2000), and has continued into the 21st Century 

(U.S. Census Bureau, 2013a). In Virginia, the percentage 
2 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

of the population residing in urban areas has risen from 

approximately 35 percent in 1940 to over 75 percent 

in 2010 (U.S. Census Bureau, 2012). Such urbanization results 

in extensive landscape changes as residential, commercial, and 

industrial structures and associated utilities are constructed 

to support municipal populations. These landscape changes 

greatly affect natural systems and biotic communities in 

and around the affected areas. In particular, the degradation 

of streams and associated ecosystems has consistently 

been observed in urbanized areas (Paul and Meyer, 2001; 

Coles and others, 2012).

Common effects of urbanization observed in affected 

watersheds include altered hydrology, increased nutrient and 

contaminant transport, decreased channel stability, reduced 

biotic richness, and increased dominance of perturbationtolerant organisms. Collectively, these effects have been termed 

the “urban stream syndrome” (Feminella and Walsh, 2005; 

Meyer and others, 2005; Walsh and others, 2009). Many of 

these effects are caused by increased hydraulic efficiency, or 

more rapid movement of water from the landscape to receiving 

streams. The increased efficiency alters stream hydrology, 

which in turn impairs associated ecological systems.

The construction of roads, parking lots, and rooftops, as 

well as stormwater control structures designed to quickly drain 

water from the landscape, contributes to increased hydraulic 

efficiency. Rapid movement of water from the landscape to 

stream increases the frequency and magnitude of stormflows 

and (or) floods (Poff and others, 1997; U.S. Environmental 

Protection Agency, 1997; McMahon and others, 2003), 

causing stream-channel instability, increased contaminant 

transport, and degradation of biotic communities. Infiltration 

is reduced as water is quickly shunted from the landscape 

to the stream, and consequently, base flows are decreased as 

the quantity of groundwater in the contributing water table 

is reduced (Burton and Pitt, 2002). Decreased base-flow 

levels result in habitat loss and increased vulnerability to 

rapid thermal changes that further exacerbate the effects of 

urbanization on aquatic organisms (LeBlanc and others, 1997).

Recent efforts to mitigate the effects of urbanization have 

relied on the implementation of best management practices 

(BMPs) to manage urban stormwater and protect or restore 

the receiving water bodies. These BMPs include a wide range 

of structural and nonstructural practices aimed at reducing 

and (or) slowing the transport of stormwater and associated 

contaminants. Structural BMPs are features installed on the 

landscape that physically reduce the volume of stormwater 

runoff generated or retain stormwater for gradual release 

to the receiving water body and (or) infiltration into the 

groundwater system, such as stormwater detention ponds and 

retrofits of existing detention ponds with enhanced wetlands, 

bioswales, rain gardens, infiltration galleries, tree box planters, 

green roofs, permeable pavement, and stream and floodplain 

restorations. Nonstructural BMPs include restoration of 

riparian stream buffers, stream cleanup events, and outreach 

and education activities focused on both educating the public 

about the consequences of various activities and suggesting 

changes that can be made to those activities to reduce impacts 

on their local environment.

From 2007 to 2010, over $70 million was spent to protect 

and restore water quality in developed areas throughout the 

Chesapeake Bay watershed (Chesapeake Bay Program, 2013). 

Unfortunately, most watershed-scale efforts to document 

the effect of BMP implementation have not successfully 

demonstrated improvements (Meals and others, 2010; Corsi 

and others, 2013) resulting from such investments, despite 

widespread documentation of individual BMP efficiencies. 

The lack of documented improvements associated with 

BMP implementation has been attributed to several factors, 

including insufficient implementation intensity, improper 

selection of implemented BMPs, uncooperative weather and 

hydrologic conditions during study periods, misunderstood 

pollution sources and transport processes, and improper study 

design (Meals and others, 2010).

Fairfax County, Virginia, has experienced rapid 

population growth since the mid-20th Century (U.S. Census 

Bureau, 2013c), and consequently, most streams in Fairfax 

County show symptoms of urban stream syndrome. Within 

recent decades, Fairfax County has invested substantial 

resources into the assessment and protection of watersheds and 

planning for improved management of stormwater (Fairfax 

County Stormwater Planning Division, 2013). Specifically, 

watershed planning efforts in Fairfax County have focused on 

the goals of (1) improving and maintaining watershed functions, including those associated with water quality, habitat, 

and hydrology; (2) protecting human health, safety, and 

property by reducing stormwater impacts; and (3) involving 

stakeholders in the protection, maintenance, and restoration of 

watersheds (Fairfax County, 2013). Extensive implementation 

of BMPs is planned throughout Fairfax County watersheds to 

achieve the goals of the watershed plans.

In 2007, the U.S. Geological Survey (USGS) partnered 

with Fairfax County to initiate a long-term water-resourcesmonitoring study to evaluate watershed-scale effects of 

BMP implementation. This study is specifically designed 

to address the shortcomings just presented, which have led 

to inconclusive results about the effectiveness of BMPs at 

the watershed scale in other studies. During the initial years 

(2007–12) of this study, little implementation was achieved 

as a result of diminished tax revenues during the economic 

downturn that occurred at that time. A substantial amount 

of data were collected, however, that thoroughly document 

pre-implementation conditions in the streams monitored. This 

initial “baseline” characterization is the focus of the analyses 

presented herein.

Purpose and Scope

The purpose of this report is to summarize streamflow, 

water quality, and benthic macroinvertebrate community 

characteristics in Fairfax County, Virginia during 2007–12. To 

this end, the report also presents the rationale, study design, 

and initial data analysis performed for the cooperative waterresources monitoring network operated by USGS and Fairfax 
Methods of Investigation 3

County Stormwater Planning Division (FCSWPD) in Fairfax 

County Virginia. The initial 5 years of data are described and 

analyzed to provide general characterizations of hydrology, 

water quality, and stream health of the 14 monitored 

watersheds, spatial and temporal patterns in water quality 

and stream health across the 14-station network, and loads 

and yields of nutrients and sediment at four intensive 

monitoring stations. 

Description of Study Area

Fairfax County is a 395 square mile (mi2) jurisdiction 

in northern Virginia (fig. 1). The northern and southeastern 

boundaries of Fairfax County are defined by the Potomac 

River, which also defines the local boundary for the Commonwealth of Virginia. The western and southern boundaries are 

shared with Loudoun County and Prince William County, 

respectively, and portions of the eastern boundary are shared 

with Arlington County and the City of Alexandria.

As a result of its proximity to Washington, D.C., Fairfax 

County has become the most populous jurisdiction in Virginia, 

with a population of just over 1 million in 2010 (U.S. Census 

Bureau, 2013b). Much of the growth in population in Fairfax 

County has occurred since the 1940s, with the population 

increasing at an average rate of 68 percent per decade from 

1940 to 2010 (U.S. Census Bureau, 2013c). Accordingly, 

land use in Fairfax County is dominated by residential uses, 

with approximately half of the residences in those areas being 

single-family detached homes, one-quarter townhomes, and 

one-quarter multi-family dwellings (Fairfax County, 2011).

Climate—Fairfax County has a humid temperate 

climate with an average annual precipitation ranging from 

approximately 39 to 44 inches per year (in/yr) (Southeast 

Regional Climate Center, 2012). This precipitation is generally well distributed throughout the year, with low-pressure 

fronts creating rainfall during cool winter months and brief 

thunderstorms creating rainfall during warm summer months. 

The average annual temperature ranges from 55 °F to 58 °F, 

with average January minimums of 23 °F to 28 °F and average 

July maximums of 83 °F to 88 °F (Southeast Regional Climate 

Center, 2012).

Physical Setting—Geologic terranes are defined by 

the type of rock underlying an area, whereas physiographic 

provinces are defined by landforms at the surface; however, 

landforms in Virginia are strongly influenced by subsurface 

geology, and therefore geologic terranes and physiographic 

provinces are often coincident. Fairfax County is underlain 

by three major geologic terranes: the Atlantic Coastal Plain, 

the Piedmont, and the Triassic Lowland (Froelich and 

Zenone, 1985a). These geologic terranes occur in association 

with the Coastal Plain physiographic province, Piedmont 

physiographic province, and Mesozoic Basins physiographic 

subprovince, respectively (fig. 1). The physical characteristics 

of these terranes and provinces strongly influence surface 

water and groundwater flow patterns, as well as water-quality 

conditions and transport processes.

The majority of Fairfax County lies within the Piedmont 

terrane, with only the western and eastern extents of the 

county present in the other terranes (fig. 1). The Piedmont 

is underlain by highly weathered metamorphic crystalline 

rock, which has been chemically weathered at the surface to 

form saprolite (Froelich and Zenone, 1985a). The thickness 

and permeability of this saprolite vary substantially, from 

thin and nearly impervious to nearly 200 feet (ft) thick and 

well-drained, depending upon the parent material from which 

it was formed (Froelich and Zenone, 1985a). The generally 

thick overburden of the Piedmont on rolling uplands results 

in palmate drainage basins having wide alluvial floodplains 

and narrow, bedrock-controlled stream gorges (Froelich and 

Zenone, 1985a).

The bedrock of the Triassic Lowland terrane, which 

occurs along the western boundary of Fairfax County, consists 

of sedimentary rocks, such as shale and siltstone with local 

intrusions of igneous diabase (Froelich and Zenone, 1985b). 

Soils in this province are generally shallow and poorly 

drained, with topography that is generally of low relief 

(Froelich and Zenone, 1985a), resulting in dendritic stream 

networks occurring in very broad and shallow stream valleys 

(Froelich and Langer, 1983).

The eastern extent of Fairfax County is in the Atlantic 

Coastal Plain terrane, which is underlain by sedimentary 

deposits of sand and clay, capped by sheets of gravel (Froelich 

and Zenone, 1985a). The uplands of this province are relatively flat and low-lying, resulting in streams that flow at low 

velocities through broad, flat-bottomed valleys (Froelich and 

Langer, 1983).

Methods of Investigation

This study was designed to provide information to better 

understand the current condition of streams in Fairfax County 

and to track changes in stream conditions as BMPs are implemented. Additionally, the data collection and analysis approaches 

were designed to overcome the following issues, which typically 

challenge the ability to statistically quantify changes in water 

quality attributed to the implementation of BMPs:

1. The collection of numerous individual samples needed each 

year to provide the requisite data from which to calculate 

trends in water-quality typically comes at a substantial cost, 

which often limits post-implementation monitoring.

2. Confounding environmental factors, such as streamflow, 

rainfall distributions, and seasonality, often cause extensive variability in measured water-quality constituents that 

complicates attempts to quantify improvements attributed 

to BMP implementation.

3. Lag times between BMP implementation and watershed 

response may be long, and monitoring programs are often 

too short in duration to capture the signal of response 

(Meals and others, 2010).
4 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

01646305

01645762

01645704

01656903

01653717

01652860

01652789

01657394

01645940

01645745

0165694286

0165690673

01657322

01645844

Occoquan River

Potomac River

revi Rca mot oP

DCA

IAD

77°30'W 77°20'W 77°10'W

39°N

38°50'N

38°40'N

Castle Creek

FAIRFAX COUNTY

City of 

Fairfax

City of 

Alexandria

WASHINGTON, D.C.

ARLINGTON 

COUNTY

City of 

Manassas

PRINCE WILLIAM COUNTY

LOUDOUN COUNTY

MARYLAND

City of 

Falls Church

Physiography from Fenneman and 

Johnson (1946), 1:7,000,000

Base from U.S. Geological Survey 

digital line graph, 1:2,000,000, 1987

0 2.5 5 Miles

0 4 8 Kilometers

80°W 79°W 78°W 77°W 76°W 75°W

40°N

39°N

38°N

37°N

Location of 

study area

VIRGINIA

WEST

VIRGINIA

PENNSYLVANIA

MARYLAND

NORTH CAROLINA

NJ

DE

Atlantic Ocean

Chesapeake Bay

EXPLANATION

IAD

01657394

01646305

Coastal Plain

Physiographic province

Piedmont

Trend monitoring station and number

NOAA weather station and identifier

Intensive monitoring station and number

National Oceanic and Atmospheric 

 Administration

NOAA

Trend monitoring watershed

Intensive monitoring watershed

Mesozoic Basins (subprovince)

Occoquan River 

watershed boundary

Chesapeake Bay

watershed boundary

Figure 1. Monitoring stations and watersheds.
Methods of Investigation 5

Study Design

The challenges just described were addressed through the 

design of a long-term monitoring program that uses a tiered 

monitoring intensity across a network of numerous monitoring 

locations. This network includes 4 intensive water-resources 

monitoring stations and 10 low-intensity monitoring stations 

(trend-monitoring stations; fig. 1). These two tiers were 

designed specifically to address different objectives of the 

overall effort, and to do so within budgetary constraints.

The intensive monitoring stations were instrumented to 

provide continuous measurements of streamflow and waterquality parameters, and to provide those data online in near 

real-time. Automated sampling equipment was operated at 

the stations to collect discrete samples for laboratory analysis 

during periods of stormflow. In addition, the stations were 

visited monthly to collect additional discrete water-quality 

samples that represent a random distribution of hydrologic 

conditions. In contrast, the trend-monitoring stations were 

only visited monthly to collect discrete water-quality samples 

during random hydrologic conditions, and periodically to 

measure streamflow.

Selection of Watersheds and Monitoring 

Locations

Proper design of the monitoring network required careful 

selection of monitoring locations to accurately represent 

watersheds in Fairfax County. The monitoring station selection 

process relied on initial statistical categorization of candidate 

watersheds with posterior refinement using local knowledge of 

watersheds and planned implementation activities.

Candidate watersheds were those approximately 6 mi2 or 

smaller in area that were included in the first phase of Fairfax 

County’s watershed planning process, which encompassed 

approximately 50 percent of the land area in Fairfax County; 

52 watersheds met both criteria. Watersheds were characterized in terms of land use, average condition of the benthic 

macroinvertebrate community, percent imperviousness, 

presence of existing BMPs, and average watershed slope.

Cluster analysis (Shaw, 2003) was performed on 

the dataset of watershed characteristics to categorize the 

52 candidate watersheds for selection. At a coarse scale, this 

cluster analysis grouped the watersheds into three classes: 

low-density urban land-uses, high/medium-density urban 

land-uses, and industrial land-uses, where density of urbanization was primarily defined by residential density. At a finer 

scale, these coarse classes were separated into 6 clusters of 

high/medium-density urban land-uses, 5 clusters of lowdensity urban land-uses, and 1 cluster of industrial land-use. 

The industrial cluster included two watersheds, both of which 

drained the grounds of Dulles International Airport. These two 

watersheds were removed from consideration because of the 

anomalous conditions presented by the airport facilities and 

because stormwater management on airport grounds is not 

within the jurisdiction of Fairfax County.

Final watershed selection was performed through 

review of the cluster analysis results with County staff who 

had on-the-ground knowledge of the candidate watersheds. 

Final study watersheds were selected based on planned BMP 

implementation within the watersheds and known limitations 

or advantages of working in particular watersheds. Monitoring 

station locations within each selected watershed were 

determined during field inspections of the selected watersheds.

Description of Watersheds and Streams 

Monitored

The monitoring station selection process resulted in the 

selection of 14 locations for monitoring stations—4 intensive 

monitoring locations and 10 trend monitoring locations 

(table 1). The 14 monitoring stations are distributed 

throughout Fairfax County and are representative of the 

physiographic provinces within Fairfax County (fig. 1).

The 14 monitoring stations are located along streams 

draining a range of land-cover types representative of the 

land-cover distribution throughout Fairfax County, as determined from Fairfax County Stormwater Planning Division 

(2007). Monitored watersheds considered to have the least 

urban impact were those with the greatest proportions of open 

space and estate and low-density residential, such as Castle 

Creek, Little Difficult Run, and S.F. Little Difficult Run 

(fig. 2). Watersheds considered to have the greatest degree of 

urban impact were those having the greatest proportions of 

roads, industrial and commercial land-uses, and high-density 

residential uses, such as Old Courthouse Spring Branch, Big 

Rocky Run, and Indian Run (fig. 2). 

The watersheds monitored are representative of the 

distribution of physiographic provinces and geologic terranes 

of Fairfax County. The majority of watersheds are representative of the Piedmont terrane, with 10 watersheds having 

greater than 60 percent of their watershed area overlying the 

Piedmont—6 of which are 100 percent Piedmont (table 1). 

Two watersheds are entirely or nearly entirely within the 

Triassic Lowlands terrane, and one watershed is entirely 

within the Coastal Plain terrane (table 1). 

Data Collection, Sampling, and Laboratory 

Analysis

All data-collection activities performed in support of 

the cooperative monitoring program were conducted in 

accordance with prescribed methods. Generally, these were 

established USGS methods intended to ensure the accuracy 

of the collected data and to promote national consistency in 

hydrologic data. Data-collection responsibilities were shared 

between USGS and FCSWPD staff.

Streamgaging activities, which include discrete 

measurements of streamflow, operation of continuous and 

noncontinuous streamgaging stations, and streamflow data 

management, were conducted by USGS staff in accordance 
6 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

with standards defined by the USGS Office of Surface Water. 

Procedures used to collect, process, and store streamflow data are 

defined in various USGS Techniques of Water-Resources Investigations reports (U.S. Geological Survey, variously dated b).

Non-vented internally logging pressure sensors, HOBO 

U–20 (Onset Computer Corp.) water-level data loggers, 

were installed at all trend monitoring stations during 

water year 2009 as a cost-effective approach to generate 

timeseries stage data at these stations with less intensive data 

collection. The sensors were configured to record absolute 

pressure (atmospheric pressure plus water-column pressure) 

at 15-minute intervals. The absolute pressure data were 

converted to water level using atmospheric pressure data 

collected at two monitoring stations within the network and 

manually measured water level at the time of logger servicing. 

The 0.015 ft accuracy of these sensors does not meet the 

USGS Office of Surface Water accuracy requirement of 0.01 ft 

for stage sensors at streamgaging stations (Sauer, 2002); 

however, the accuracy is sufficient for the objectives of this 

component of the data-collection effort, and is consistent with 

the accuracy of data collected by numerous other recent USGS 

studies with similar objectives (Gregory and Calhoun, 2006; 

Moring, 2006; Richards and others, 2006; Sprague and 

others, 2006; Waite and others, 2006). 

Operation of the continuous water-quality monitors 

and management of the continuous water-quality data were 

conducted by USGS staff in accordance with Wagner and 

others (2006) and U.S. Geological Survey (variously dated a). 

In general, the instruments are serviced monthly to clean and 

re-calibrate the sensors as needed, and data were collected 

during these visits to support the correction of timeseries data 

in the event of calibration drift or fouling.

Collection of water samples for laboratory analyses of 

nutrients and suspended sediments was conducted by USGS 

and FCSWPD staff in accordance with U.S. Geological 

Survey (variously dated a). The small size and shallow depths 

Table 1. Monitoring station identifiers, names, abbreviations, types, watershed areas, and percentage of watershed in 

geologic terranes.

[mi2, square mile; –, no value]

Station 

identifier Station name Short name Station type

Watershed 

area 

(mi2)

Percent

Coastal 

Plain

Percent 

Piedmont

Percent 

Triassic 

Lowland

01646305 Dead Run at Whann Avenue near 

Mclean, VA DEAD Intensive 2.05 – 100 –

01645704 Difficult Run above Fox Lake near 

Fairfax, VA DIFF Intensive 5.49 – 100 –

01656903 Flatlick Branch above Frog Branch at 

Chantilly, VA FLAT Intensive 4.20 – 1 99

01645762 South Fork Little Difficult Run above 

mouth near Vienna, VA SF LIL Intensive 2.71 – 96 4

0165694286 Big Rocky Run at Stringfellow Rd 

near Chantilly, VA BRR Trend 3.40 – 63 37

01645940 Captain Hickory Run at Route 681 

near Great Falls, VA CAPT HICK Trend 1.38 – 100 –

01657394 Castle Creek at Newman Road at 

Clifton, VA CASTLE Trend 2.21 – 100 –

0165690673 Frog Branch above Flatlick Branch at 

Chantilly, VA FROG Trend 0.99 – – 100

01652789 Indian Run at Bren Mar Drive at 

Alexandria, VA INDIAN Trend 2.45 14 86 –

01645745 Little Difficult Run near Vienna, VA LIL DIFF Trend 2.99 – 97 3

01645844 Old Courthouse Spring Branch near 

Vienna, VA OCSB Trend 1.45 – 100 –

01657322 Popes Head Creek tributary near 

Fairfax Station, VA PHCT Trend 0.95 – 100 –

01653717 Paul Spring Br above North Branch 

near Gum Springs, VA PSB Trend 1.89 100 – –

01652860 Turkeycock Run at Edsall Road at 

Alexandria, VA TRKYCK Trend 2.59 57 43 –
Methods of Investigation 7

of the streams generally precluded the use of width- and 

depth-integrating sampling procedures typically employed 

on larger streams; therefore, most routine sampling was 

accomplished using a grab (dip) sampling approach. Occasionally, high flow conditions were encountered during manual 

sampling visits. The flashy nature of these urban streams 

presents a danger to sampling personnel should they attempt to 

enter the stream to collect a sample using width- and depthintegrating procedures during these times. In addition, there 

are generally no suitable stream crossings (such as bridges or 

culverts) from which to sample from; therefore, grab samples 

were collected during these conditions as well. 

Scheduled monthly sampling was conducted on 

predetermined dates each month, regardless of hydrologic 

conditions on the specified sampling day. The intent of this 

scheduled sampling approach was to randomly represent the 

range of hydrologic conditions occurring during the study 

period. All 14 monitoring stations were sampled on the same 

day to limit variability in conditions across the stations for a 

given monthly sample.

Targeted storm sampling was accomplished at the four 

intensive monitoring stations using refrigerated automatic 

samplers. These samplers, located inside the monitoring 

station shelter, consist of a peristaltic pump, control unit, 

and refrigerated sample-storage container. The samplers are 

capable of collecting 12 discrete samples, with each sample 

filling two 1-liter polyethylene bottles—one bottle is used 

for nutrient analyses and the second is used for sediment 

analyses. Samples were pumped from a single point in the 

stream channel through a 3/8-in.-diameter vinyl tube. Triggering of the autosampler was accomplished by programming 

an algorithm into the monitoring station datalogger that was 

designed to ensure stormflow conditions were occurring and 

to properly distribute the sampling throughout the stormflow 

event. The algorithm includes thresholds for turbidity, stream 

stage, rate-of-change in stage, and time elapsed since last 

sample collection. The turbidity and time elapsed since last 

sample collection thresholds are constant across monitoring 

stations—50 formazin nephelometric units (FNU) and 

30 minutes, respectively. Stream stage and rate-of-change in 

stage are site-specific thresholds, determined through observation of stormflow hydrographs. Sampling data, measured 

every 15 minutes, were transmitted via satellite and uploaded 

hourly to the USGS National Water Information System Web 

interface (NWISWeb; http://waterdata.usgs.gov/va/nwis) for 

evaluation by USGS staff. This information was then relayed 

to FCSWPD staff, who retrieved the samples for delivery to 

the appropriate laboratories for analysis.

Nutrient analyses of the water samples were conducted 

by the Fairfax County Environmental Services Laboratory, as 

approved through the USGS Laboratory Evaluation Program. 

Raw (unpreserved) samples were delivered to the laboratory, where laboratory staff promptly filtered and preserved 

aliquots as required for analyses. Samples were analyzed for 

total nitrogen and total phosphorus, as well as dissolved and 

particulate fractions of the totals, in accordance with specified 

methods (table 2). 

Figure 2. Percentages of land-use types within 14 monitored watersheds in Fairfax County. Station names 

defined in table 1.

Land use

EXPLANATION

Open space

Estate residential

Low-density residential

Medium-density residential

High-density residential

Low-intensity commercial

High-intensity commercial

Industrial

Roads

Outside jurisdiction

Unknown

BRR CAPTHICK CASTLE DEAD DIFF

PHCT PSB SF LIL TRKYCK

FLAT FROG INDIAN LILDIFF OCSB
8 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Sediment analyses of the water samples were conducted 

by the USGS Eastern Region Sediment Laboratory in Louisville, Kentucky. All samples were analyzed for suspendedsediment concentration (Guy, 1969; Shreve and Downs, 2005). 

Storm-event samples were additionally analyzed for sand-fine 

split—the percentage of material finer than 0.0625 millimeter 

(mm; Guy, 1969; Shreve and Downs, 2005).

Benthic macroinvertebrate samples were collected 

annually in the spring by FCSWPD staff, with occasional 

sampling assistance from USGS staff, in conjunction with the 

annual sampling performed for the Fairfax County Biological 

Stream Monitoring Program (Fairfax County Stormwater 

Planning Division, 2013). Samples of benthic macroinvertebrates were collected, stream-habitat metrics were evaluated, 

and basic water-quality parameters were measured according 

to defined procedures (Fairfax County Stormwater Planning 

Division, 2006). Benthic macroinvertebrate samples were 

preserved and retained for later sorting and identification by a 

FCSWPD staff entomologist using the laboratory procedures 

described by the Fairfax County Stormwater Planning Division (2006). Benthic macroinvertebrate sampling results are 

summarized using the Fairfax County index of biotic integrity 

(IBI; Fairfax County Stormwater Planning Division, 1999) 

and the Virginia stream condition index (SCI; Burton and 

Gerritsen, 2003). Both metrics are calculated from measures of 

the benthic macroinvertebrate community and provide scores 

on a 0–100 (poor to excellent) scale as a relative measure of 

stream health.

Data collection began in the fall of 2007 with the 

installation of the four intensive monitoring stations 

(table 1, fig. 1). Continuous datasets for streamflow and 

water-quality parameters are available from the beginning of 

the 2008 water year (WY; October 1, 2007), with the exception of all parameters at Dead Run and dissolved oxygen (DO) 

at all monitoring stations. Data collection at Dead Run was 

delayed until November 2007 for water-quality parameters and 

January 2008 for stage and streamflow. DO was later added 

to all monitoring stations as additional research was initiated 

at the stations. All continuous data are available online from 

NWISWeb at http://waterdata.usgs.gov/va/nwis.

Collection of monthly water-quality samples across the 

14-station network began in April 2008 and has resulted in 

the collection of 756 water-quality samples through the end 

of WY 2012. Storm sample collection at the four intensive 

monitoring stations also began in April 2008, although initially 

these samples were analyzed only for suspended sediment. At 

differing times late in WY 2008, electricity was supplied to 

the monitoring stations, and nutrient sampling was initiated 

as the sample refrigerator on the autosampler was powered to 

preserve the samples. Through the end of WY 2012, a total of 

753 storm event samples were collected and analyzed at the 

four intensive monitoring stations. All water-quality sampling 

results are available online from NWISWeb.

Annual benthic macroinvertebrate sampling was initiated 

in the spring of 2008 and has continued through the spring 

of 2012, resulting in the collection of five benthic macroinvertebrate samples at each of the 14 monitoring stations. 

Benthic macroinvertebrate sampling results are provided 

in appendix 1.

Formulation of Suspended-Sediment and 

Nutrient Load Models

Suspended-sediment and nutrient load models were 

calibrated using instantaneous loads computed from the 

discrete monthly and storm sample data as dependent variables, and continuous streamflow and water-quality parameters, including water temperature, specific conductance, pH, 

and turbidity, as potential explanatory variables. Dependent 

and explanatory variables were transformed using a natural 

logarithm (ln) transformation to normalize their distribution as required for linear regression analysis (Helsel and 

Hirsch, 2002). Model selection was conducted independently 

for each monitoring station-constituent combination by evaluating all possible models and selecting the model with the 

lowest Mallows’ Cp value (Helsel and Hirsch, 2002) using the 

software package Load Estimator (S–LOADEST; Lorenz and 

others, 2011). Selecting a model with the smallest Mallows’ 

Cp value provides a compromise between explaining the 

most variance possible in the response through incorporation

Table 2. Nutrient constituents, abbreviations, and analytical 

methods for analyses performed by Fairfax County Environmental 

Services Laboratory.

[USEPA, U.S. Environmental Protection Agency (U.S. Environmental 

Protection Agency, 2013); SM, Standard Methods (American Public Health 

Association, 1976)]

Constituent Abbreviation Method

Nitrogen

Total nitrogen TN USEPA351.2 + USEPA 353.2

Total dissolved 

nitrogen TDN USEPA 351.2 + USEPA 353.3

Total particulate 

nitrogen TPN USEPA 351.2 - USEPA 353.2

Total Kjeldahl 

nitrogen TKN USEPA 351.2

Dissolved Kjeldahl 

nitrogen DTKN USEPA 351.2

Nitrate + nitrite NOx USEPA 353.2

Ammonia NH3 USEPA 350.1

Phosphorus

Total phosphorus TP SM425C & 425E

Total dissolved 

phosphorus TDP SM 425C & 425E

Total particulate 

phosphorus TPP SM 425C & 425E

Orthophosphorus OP USEPA 365.2 
Hydrologic Conditions 9

of all relevant regressors and minimizing the variance of the 

estimates by minimizing the number of regressors (Helsel and 

Hirsch, 2002). For constituents having censored concentration 

values, the adjusted maximum likelihood estimator (AMLE) 

was used to properly fit the model to the censored data 

(Runkel and others, 2004).

As described by Runkel and others (2004), the general 

form of the load estimation models is

( ) 0

1

ln

NV

j j

j

L a aX == +∑ , (1)

where

ln (L) is the natural logarithm of instantaneous load,

0 a is the intercept,

NV is the number of explanatory variables,

j a is a model coefficient, and X j is an explanatory variable.

Once an appropriate model was selected, annual 

(water year), monthly, daily, and instantaneous loads were 

computed in S–LOADEST using the unit value (15-minute) 

data as the input file. These loads were then divided by the 

watershed area of the monitoring station to determine yields.

Formulation of Suspended-Sediment and 

Nutrient Concentration Models

Methods for regression models using streamflow and 

water-quality variables (surrogates) to estimate sediment 

and nutrient loads were presented in the previous section. 

However, although S–LOADEST generates estimation models 

with load as the dependent variable (Lorenz and others, 2011), 

concentration estimation models may also be of interest 

in some applications. Load- and concentration-estimation 

models are inherently related, as load is simply the product 

of concentration and streamflow, and earlier versions of 

LOADEST programmed in FORTRAN provide both forms of 

the estimation model (Runkel and others, 2004).

Concentration-estimation models are required for use 

in the USGS National Real Time Water Quality (NRTWQ; 

http://nrtwq.usgs.gov) Web interface. NRTWQ couples 

such models with real-time data to display estimates of 

constituent concentration and load, in near real-time, when 

such models are available. Additionally, numerous efforts 

throughout Virginia (Jastram and others, 2009, 2010) and 

the Nation (Lewis, 1996; Christensen and others, 2000; Lee 

and others, 2008; Rasmussen and others, 2008; Baldwin and 

others, 2012; Chanat and others, 2013; Miller and others, 2013) 

utilize similar models, making regional or larger scale evaluations possible when models are available in comparable forms.

Regression models to estimate suspended-sediment and 

nutrient concentrations were developed with JMP 10 software 

(SAS Institute, Cary, N.C.) using procedures similar to 

those used to develop load estimation models. In addition to 

determining the best-possible model according to Mallows’ 

Cp, models using a single explanatory variable, turbidity, 

and models using two explanatory variables, turbidity and 

streamflow, were developed. These commonly used model 

forms were developed regardless of their explanatory power to 

provide a basis for comparison to other locations.

Hydrologic Conditions

Hydrologic conditions, particularly the occurrence of 

droughts and floods, are primarily affected by precipitation 

patterns. These conditions are major drivers of the processes 

that control the transport of nutrients and sediments and the 

health of aquatic communities. Stormwater management 

actions seek to mitigate the effects of landscape development 

on hydrologic conditions in receiving streams, and streamflow 

statistics are expected to reflect the effectiveness of management actions in mitigating these effects. 

Precipitation

Annual and monthly precipitation data (National Oceanic 

and Atmospheric Administration, 2013) used to characterize 

precipitation in the study area were obtained from National 

Weather Service meteorological stations at Dulles International Airport (IAD), located in western Fairfax County, and 

Ronald Reagan Washington National Airport (DCA), located 

in Arlington County near the eastern border of Fairfax County. 

The magnitude and temporal distribution of precipitation 

during the five water years (2008–12) was variable, as shown 

by the rainfall data from the two meteorological monitoring 

stations (fig. 3A–B).

Water year 2012 had the least total precipitation of 

the 5 years evaluated at both monitoring stations (fig. 3A). 

Precipitation was below the long-term (1981–2010) average 

in WYs 2009, 2011, and 2012 at the IAD monitoring station, 

and in WYs 2009 and 2012 at the DCA monitoring station 

(fig. 3A). Although WY 2009 was drier than average, 43 and 

38 percent of the annual total precipitation fell in the months 

of May and June at IAD and DCA, respectively (fig. 3B). 

Precipitation in WY 2012, however, was much more evenly 

distributed throughout the year (fig. 3B). Water years 2008 and 

2010 had the greatest total precipitation of the 5 years evaluated, with totals 11 percent to 29 percent greater than average 

(fig. 3B). Approximately 20 percent of the annual total rainfall 

at each monitoring station fell within the month of May during 

WY 2008, whereas the precipitation was more evenly distributed throughout the year in WY 2010 (fig. 3B). 

As indicated above, some months were notable for 

high rainfall amounts, and these high precipitation periods 

have pronounced effects on hydrologic and water-quality 

conditions. Total precipitation in May 2008 was 9.4 and 
10 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12 Annual precipitation, in inches 

Water year

Monthly precipitation, in inches

2008 2009 2010 2011 2012

Observed precipitation

Mean, 1981-2010

A

B

2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

EXPLANATION

Dulles (IAD) Reagan (DCA)

0

10

20

30

40

50

60

Dulles (IAD) Reagan (DCA)

0

2

4

6

8

10

12

0

2

4

6

8

10

0

2

4

6

8

10

0

2

4

6

8

10

0

2

4

6

8

10

Figure 3. A, Annual and B, monthly precipitation data from Dulles International Airport and 

Ronald Reagan Washington National Airport for water years 2008 through 2012 with mean 

annual and monthly values from the period 1981–2010. Data from National Weather Service. 
Hydrologic Conditions 11

10.7 inches (in.) at IAD and DCA, respectively (fig. 3B), 

resulting from numerous rainfall events throughout the month. 

The remnants of Hurricane Hanna brought heavy rains to 

the study area, accounting for most of the 7.2 and 6.4 in. of 

precipitation that fell in September 2008 at IAD and DCA, 

respectively (fig. 3B). Total precipitation in May 2009 was 

10.3 and 8.1 in. at IAD and DCA, respectively (fig. 3B), 

resulting from numerous rainfall events throughout the month. 

In August 2011, Hurricane Irene and multiple severe weather 

systems delivered a total of 8.9 in. of rain at DCA, while areas 

further west received much less, as indicated by the 3.7 in. that 

fell at IAD (fig. 3B). Tropical Storm Lee brought heavy rain to 

the area in September 2011, accounting for most of the 7.9 and 

8.8 in. that fell at IAD and DCA, respectively (fig. 3B).

Streamflow

Streamflow conditions at the four intensive monitoring 

stations were summarized by computing the cumulative 

streamflow yield, or volume of streamflow generated per 

square mile of watershed area, on a daily time step, for 

each water year (fig. 4A). This representation of streamflow 

allows for comparison of streamflow conditions at a given 

site across years, clearly depicting dry and wet periods, and 

for comparisons across stations to evaluate potential differences in streamflow generation across watersheds. Further, 

the rate of cumulative streamflow yield throughout the year 

is a major driver of the magnitude of nutrient and sediment 

loads and yields because discrete, major streamflow events 

can transport substantially more sediment and associated 

nutrients than numerous smaller events having a similar total 

streamflow yield.

The first half of water year 2008 was the driest (lowest 

cumulative streamflow yield) of the 5 years evaluated, 

although high flows during the second half of the water 

year resulted in 2008 having the highest cumulative annual 

streamflow yield of the 5 years evaluated at Difficult Run and 

S.F. Little Difficult Run, and the second highest at Flatlick 

Branch (fig. 4A). High-flow events in May, resulting from 

numerous thunderstorms, quickly increased the cumulative 

flow yield for the year into the wettest conditions observed 

over the period, followed by substantial increases in cumulative flow from Tropical Storm Hanna in September (fig. 4A). 

It is important to note that streamflow data were not collected 

at Dead Run prior to January 10, 2008, resulting in incomplete 

representation of WY 2008 for Dead Run in figure 4.

Water year 2009 also began dry, and although 

above-average precipitation in May and June (fig. 3B) led 

to increases in cumulative streamflow yield, the year overall 

ended among the driest of the 5-year period (fig. 4A) as a result 

of the below-average annual precipitation for the year (fig. 3A).

Consistent with the above-average precipitation 

distributed throughout WY 2010 (fig. 3B), cumulative 

streamflow yield was consistently high throughout the year, 

with the total annual streamflow yield ranking the highest 

of the 5 years at Flatlick and second highest at Dead Run 

and S.F. Little Difficult Run (fig. 4A). The high cumulative

streamflow yields observed in WY 2010 are unique among the 

5 years because of the consistently wet conditions throughout 

the year, whereas other years with high cumulative streamflow 

conditions generally resulted from discrete high-flow events or 

relatively short periods of elevated flows.

Cumulative streamflow yields varied considerably among 

stations during much of WY 2011, with streamflow yields at 

Dead Run and Difficult Run among the highest of the 5 years 

during most of the WY and streamflow yields at Flatlick 

Branch and S.F. Little Difficult Run yields were among the 

lowest of the 5 years during most of the WY (fig. 4A). In 

September 2011 the remnants of Tropical Storm Lee delivered 

substantial precipitation throughout the study area, resulting in 

the greatest peak streamflows observed during the study period. 

Above-average precipitation in early WY 2012 (October 

and December; fig. 3B) resulted in some of the highest streamflow yields observed during the initial few months of the WY. 

Below-average precipitation throughout the remainder of the 

remainder of the WY (fig. 3B), however, resulted in WY 2012 

being among the driest (lowest streamflow yield) of the 

5 years for much of the WY (fig. 4A).

Comparison of streamflow yields across the four stations 

was achieved by fitting a smoothing spline (Eubank, 1988) 

to the 5 years of cumulative streamflow yields for each site 

(fig. 4B). For the 5 years evaluated, Flatlick Branch had the 

greatest streamflow yield throughout the WY and Difficult 

Run had the second greatest yield (fig. 4B). Streamflow yields 

at Dead Run and S.F. Little Difficult Run were very similar 

and the lowest of the four stations (fig. 4B). Numerous factors, 

including physical setting and land use, can affect streamflow 

yields, although the particular factors affecting differences 

among the monitored streams are not evaluated herein. 

Peak Streamflow Frequency

Streamflow annual exceedance probability (AEP) 

estimates, which may also be expressed as recurrence 

intervals, are useful for determining how common or rare 

a given stormflow event is expected to be. Calculation of 

these statistics requires 10 years or more of streamflow 

data (Maidment, 1993). AEP estimates can be made, 

however, for ungaged watersheds, or watersheds lacking 

sufficient data to compute the statistics, by adjusting the 

exceedance probabilities from nearby sites to account for 

differences in watershed area (Hannum, 1976; Bisese, 1994; 

Glatfelter, 1984). Austin and others (2011) computed AEPs 

for nearby sites that could be used to compute such estimates 

for the four intensive monitoring stations. The nearby sites 

for which AEPs were computed were only operated during 

the 1950s, 1960s, and 1970s (Samuel Austin, U.S. Geological 

Survey, written commun., 2013), and therefore the AEPs for 

these sites are not representative of current land use in the 

watersheds. Consequently, reliable estimates of AEPs for the 

four intensive monitoring stations could not be computed with 

the available data.
12 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

A

B

0

10

20

30

40

50

60

DEAD

Day of water year

Day of water year

Cumulative streamflow yield, in million cubic feet per square mile

Cumulative streamflow yield, in million cubic feet

per square mile

0

10

20

30

40

50

60

70

DIFF

FLAT

0

0 60 120 180 240 300

0 60 120 180 240 300 360

0 60 120 180 240 300 360

10

20

30

40

50

60

70

SF LIL

Water year

Site

2008

2009

2010

2011

2012

EXPLANATION

DEAD

DIFF

FLAT

SF LIL

EXPLANATION

Figure 4. A, Cumulative streamflow yield per day of water year, by water 

year, and B, spline smooths of cumulative streamflow yield per day for 

water years 2008–12, by station. Station names defined in table 1.
Hydrologic Conditions 13

Streamflow Variability

In addition to precipitation regimes, watershed 

characteristics greatly influence hydrologic conditions in 

streams. In particular, urban development, through the 

associated increases in impervious cover, can alter streamflow 

generation processes and consequently change the frequency, 

duration, and magnitude of extreme flow events (Paul and 

Meyer, 2001). Flashiness, or the rate of change in streamflow, 

is a commonly used measure of the rapidity of flow changes 

in response to precipitation. Streamflow characteristics such 

as flashiness are typically computed from timeseries records 

of streamflow, although McMahon and others (2003) demonstrated that the use of stage (or water level) data provides 

comparable measures of flashiness. Study limitations, such as 

duration and budget, often limit the amount of streamflow data 

collection. Within such limitations, the use of stage data alone 

to compute streamflow characteristics provides a valuable 

alternative approach to characterize streams.

Stream flashiness was determined for all 14 stations in 

the monitoring network by using 15-minute-interval stage data 

from the continuous streamgages at the 4 intensive monitoring 

stations and from the pressure sensors at the 10 trend-monitoring stations for water years 2010–12. Based on the methods 

and results of McMahon and others (2003), flashiness was 

computed as the number occurrences of stage measurements 

in which the hourly increase in stage was 0.5 ft or greater 

(PERIODR5). The PERIODR5 value was then divided by the 

total number of measurements in the analysis period to express 

flashiness as the percentage of time that PERIODR5 occurs. 

Flashiness values generally follow the expected pattern 

of increased flashiness with increased urbanization, as 

represented by road coverage in the watershed computed from 

Fairfax County Stormwater Planning Division (2007; fig. 5). 

Flashiness was much greater at two stations, Dead Run and 

Old Courthouse Spring Branch, than the others and both 

stations were among those having the greatest percentage 

of watershed area covered by road surfaces in the network 

(fig. 2). Stations that were the least flashy, Castle Creek, South 

Fork Little Difficult Run, and Captain Hickory Branch, were 

among those having the greatest percentage of estate residential and open spaces, and therefore the least impervious cover. 

Hydrologic Conditions Represented by WaterQuality Samples

To accurately evaluate water-quality conditions, 

particularly to compute loads and temporal trends, the 

samples collected need to represent the range of hydrologic 

conditions observed over the period of study. This monitoring 

program utilizes a combination of sampling strategies to 

achieve the required hydrologic representation—scheduled 

monthly sampling and targeted stormflow sampling. The 

entire 14-station monitoring network was sampled monthly 

on a prescheduled date, regardless of the conditions that 

day, to generate the requisite data for trend analysis. This 

“scheduled random” sampling approach is commonly used 

to ensure an appropriate frequency of sampling events, 

and by random chance will usually incorporate the range 

Figure 5. Flashiness, computed as percentage of time stage increases by 0.5 feet per hour or greater, of 14 monitored 

streams in Fairfax County, shaded by percentage of watershed area covered by road surfaces. Station names defined 

in table 1.

DEAD

OCSB

BRR

DIFF

PSB

FLAT

INDIAN

TRKYCK

FROG

LILDIFF

PHCT

CAPTHICK

SF LIL

CASTLE

Percent road coverage in watershed

25

20

15

10

5

0

EXPLANATION

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Percentage of times stage increase equals 

or exceeds 0.5 foot per hour

Station
14 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

of hydrologic conditions over a period of multiple years. 

Stormflow conditions were targeted and sampled at the four 

intensive monitoring stations to generate the requisite data 

for load computation—loads were not computed at the trend 

monitoring stations.

Evaluation of the sample coverage over the range of 

hydrologic conditions at each of the four intensive monitoring 

stations showed that the scheduled random sampling approach 

had effectively represented about 95 percent of the range of 

streamflow conditions (fig. 6A). Because all stations were 

sampled on the same day each month, it is reasonable to 

assume that the coverage at the 10 trend monitoring stations 

was similar—continuous streamflow data were not generated 

at the trend monitoring stations and therefore this cannot 

be directly determined. Although the monthly samples 

represented the majority of observed flow conditions, it 

is desirable to have more complete coverage of high-flow 

events to ensure that trend analyses adequately represent the 

full range of streamflow conditions for the period of study. 

The scheduled sampling scheme used is generally expected 

to achieve such coverage through random chance; however 

high-flow conditions in the small, flashy, streams monitored 

in this network do not persist for sufficient duration for this 

approach to be completely effective at representing the full 

range of streamflow.

Targeted stormflow sampling, in addition to the monthly 

sampling, was used at the four intensive monitoring stations 

to collect the requisite data for nutrient and sediment load 

computation. Evaluation of the monthly and targeted stormsampling coverage over the range of streamflow conditions 

showed that the samples collected represented the entire 

range of observed flow conditions during the study, including 

periods of highest streamflow when transport of suspended 

sediment and associated constituents is greatest (fig. 6B).

Temporal and Spatial Patterns in Water 

Quality

A primary long-term objective of this monitoring 

program is to evaluate change over time. With just 5 years of 

data, however, temporal trends were not evaluated because 

longer data sets are needed to evaluate temporal trends with 

statistical rigor. Although temporal trends were not statistically 

evaluated, there was sufficient data to evaluate temporal 

patterns and seasonality in the data. Additionally, sufficient 

data were available to evaluate spatial patterns in water-quality 

conditions, and to evaluate potential factors responsible for 

those patterns.

Patterns in Basic Water-Quality Parameters

Basic water-quality parameters—water temperature, 

pH, specific conductance, turbidity, and DO—are measured 

instream concurrently with each monthly water-quality 

sample, and continuously at each of the four intensive 

monitoring stations. Analyses of monthly and continuous data 

were performed to evaluate temporal and spatial patterns, and 

to determine potential drivers of any patterns that emerged.

pH

pH, the inverse logarithm of the hydrogen-ion 

concentration, is a measure of the acidity or basicity of a 

solution. The pH of pure water is 7, or neutral, although 

natural waters unaffected by pollution typically range between 

6.5 and 8.5 (Hem, 1985). Factors affecting pH in stream 

water include geology, biological processes, anthropogenic 

disturbances, and precipitation. 

Median pH at individual monitoring stations ranged 

from 6.9 to 7.5 and, on average, had a range of about 1.3 pH 

units. Monitoring stations having the highest median pH were 

Flatlick Branch (7.5), Frog Branch (7.5), and Big Rocky Run 

(7.4)—each of which drain watersheds within the Triassic 

Lowlands terrane. Paul Spring Branch, a Coastal Plain stream, 

had the lowest median pH at 6.9. All other monitoring stations 

had median pH values from 7.0 to 7.2. The water-quality 

standard for pH in Virginia rivers is 6.0 to 9.0 (Commonwealth 

of Virginia, 1997); data from the monthly sampling events 

indicate that none of the 14 streams monitored violated this 

standard. Visual inspection of the pH data collected during the 

monthly sampling events did not reveal any apparent change 

over time, including seasonal fluctuations (fig. 7).

In addition to being measured in association with monthly 

sample collections, pH is measured continuously (15-minute 

intervals) at the four intensive monitoring stations. Evaluation of these continuous data records reveals patterns that 

were not captured in the discrete monthly samples. Diurnal 

(daily) cycles are apparent in the pH data, particularly in the 

early spring, and especially at Flatlick Branch (fig. 8). These 

cycles, during which pH peaks in the late afternoon or early 

evening, are the result of the uptake of carbon dioxide via 

photosynthesis by aquatic organisms (Hem, 1985)—a process 

driven by available light and therefore peaking late in the day. 

This process is most apparent early in the spring in these small 

streams as the forest canopy has not yet shaded the streams and 

maximum light levels are available to fuel photosynthesis. The 

peaks and daily range are greatest, 9.5 and 2.2 units, respectively, at Flatlick Branch, where greater concentrations of the 

limiting nutrient phosphorus are available (discussed later) than 

at other sites to support biological activity.

pH is strongly affected by precipitation and the resultant 

runoff from heavy precipitation events, and such runoff is 

responsible for the observed periodic sharp decreases in 

pH (fig. 8). The typical pH of rainwater in Virginia ranges 

from 4.9 to 5.1 units (National Atmospheric Deposition 

Program, 2013) as a result of atmospheric pollution to the 

west. During excessive rainfall events, there is little opportunity for buffering of acidic runoff as it flows across the 

landscape and enters streams; therefore, pH rapidly decreases 

when streamflow becomes dominated by runoff.
Temporal and Spatial Patterns in Water Quality 15

Figure 6. Distribution of streamflow and A, monthly samples only, and B, monthly 

plus storm samples, at the four intensive monitoring stations. Station names defined 

in table 1.

A

BPercentage of values equal to or less than Percentage of values equal to or less than

Streamflow, in cubic feet per second

Streamflow, in cubic feet per second

DEAD DIFF

FLAT SF LIL

DEAD DIFF

FLAT SF LIL

0.001

0.01

0.1

1

10

100

1,000

10,000

0.001

0.01

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1

10

100

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0

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80

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40

20

0

100

80

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0

100

80

60

40

20

0

Cumulative distribution

frequency

Monthly sample

EXPLANATION

Cumulative distribution

frequency

Monthly sample

Storm sample

EXPLANATION
16 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12 Highest value within 1.5 (IQR) of 75th percentile 75th percentile Median 25th percentile

Lowest value within 1.5 (IQR)  of 25th percentile

Interquartile

Range (IQR)

pH, in standard units

9.59.08.58.07.57.06.56.09.59.08.58.07.57.06.56.09.59.08.58.07.57.06.56.09.59.08.58.07.57.06.56.09.59.08.58.07.57.06.56.09.59.08.58.07.57.06.56.09.59.08.58.07.57.06.5

CHANTILLY, VA CHANTILLY, VA FALLS, VA FALLS, VA

CHANTILLY, VA CHANTILLY, VA

CHANTILLY, VA CHANTILLY, VA

VA VA

SPRINGS, VA SPRINGS, VA

VA VA

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Jan.

Feb.

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July

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01/2008

01/2009

01/2010

01/2011

01/2012

6.0

EXPLANATION

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Brach near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Figure 7. pH results from monthly sampling at 14 monitoring stations in Fairfax County.
Temporal and Spatial Patterns in Water Quality 17

pH, in standard units

5.60 to 6.00

6.01 to 6.25

6.26 to 6.50

6.51 to 6.75

6.76 to 7.00

7.01 to 7.25

7.26 to 7.50

7.51 to 7.75

7.76 to 8.00

8.01 to 8.25

8.26 to 8.50

8.51 to 9.00

pH, in standard units

EXPLANATION

10/200702/200806/200810/200802/200906/200910/200902/201006/201010/201002/201106/201110/201102/201206/201210/2012

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

Dead Run

10/200702/200806/200810/200802/200906/200910/200902/201006/201010/201002/201106/201110/201102/201206/201210/2012

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

Flatlick Branch

10/200702/200806/200810/200802/200906/200910/200902/201006/201010/201002/201106/201110/201102/201206/201210/2012

04/01/201204/02/201204/03/201204/04/201204/05/2012

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

9.0

9.5

8.5

8.0

7.5

7.0

South Fork Little Difficult Run

11/200703/200807/200811/200803/200907/200911/200903/201007/201011/201003/201107/201111/201103/201207/2012

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

Difficult Run

Figure 8. Continuous pH data from the four intensive monitoring stations in Fairfax County 

with inset detailing a 5-day period of diurnal pH cycles.
18 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Specific Conductance

Specific conductance is a measure of the electrical 

conductivity of a solution at a specified temperature, typically 

25 °C, reported in microsiemens per centimeter (μS/cm; Hem, 

1985). The specific conductance of pure water is very low, 

approximately 0.05 μS/cm, although such water never occurs 

naturally (Hem, 1985). Charged ionic species in solution 

impart conductivity, with specific conductance increasing with 

ion concentration, and therefore specific conductance provides 

a measure of the total ion concentration in water (Hem, 1985). 

Stream water specific conductance varies greatly, from about 

50 μS/cm or less in areas underlain by geology resistant to 

weathering and with little anthropogenic influence, to 50,000 

μS/cm or more in areas having readily weathered geology and 

(or) substantial anthropogenic activity (Hem, 1985). Specific 

conductance generally increases with urbanization (Paul and 

Meyer, 2001) and, therefore, serves as an indicator of urban 

impacts in areas where other factors, such as geology and 

precipitation, are relatively constant.

Specific conductance varied substantially between 

stations, and especially months, across the monitoring 

network (fig. 9). The interstation variability is most evident in 

figure 10; the 4 stations with the least amount of road coverage 

(used as an indicator of urban intensity) have markedly lower 

specific conductance values with less variability than the other 

10 monitoring stations. Additionally, the maximum specific 

conductance values at these monitoring stations are much 

lower than the maximums observed at other stations, where 

maximum values are often at least an order of magnitude 

greater than the median (fig. 10). 

Specific conductance was greatest during the winter 

months at all monitoring stations, with maximums usually 

occurring in January (fig. 9). Thereafter, specific conductance 

typically declined over several months, with values stabilizing 

until the following winter. These seasonal spikes in specific 

conductance are the result of widespread application of 

de-icing salts on roads, parking lots, sidewalks, driveways, 

and other surfaces, when snowfall or other frozen precipitation 

occurs. As the de-icing salts contribute to the melting of the 

frozen precipitation, the resultant runoff becomes highly 

concentrated with the salts, and therefore has a very high 

specific conductance. During the initial melting process, 

instream specific conductance spikes rapidly to values 

far exceeding those captured in the monthly samples, as 

evidenced by comparison of the monthly sampling data to 

continuous specific conductance data (fig. 11). 

Sanford and others (2012) evaluated the relation between 

specific conductance and chloride (Cl-) throughout rivers in 

Virginia, and specifically during a runoff event following 

the application of road salt in the Difficult Run watershed. 

They found that where surface salts dominated the stream 

chemistry during this runoff event, the ratio of Cl- to specific 

conductance was 0.33 for samples having a specific conductance greater than 1,000 μS/cm, compared to the typical ratio 

of 0.03. Cl- standards for the support of freshwater aquatic 

life in Virginia dictate that the average Cl- concentration 

shall not exceed 860 milligrams per liter (mg/L) during any 

1-hour period or 230 mg/L over any 4-day period more than 

once in a 3-year period (Commonwealth of Virginia, 1997). 

Application of the 0.33 Cl- to specific conductance ratio 

determined by Sanford and others (2012) to the monthly and 

continuous data indicates that this water-quality standard is 

probably violated in many of the streams monitored. The 

rapid increase in specific conductance as snow and ice melts 

likely produces a shock to the biological communities that 

inhabit these streams, as chloride concentrations during these 

events probably exceed impairment thresholds for freshwater 

aquatic life.

Dissolved Oxygen

The introduction of DO to stream water occurs through 

physical and biological processes, primarily aeration through 

the entrainment of air bubbles as the water surface is disturbed 

and through photosynthesis (U.S. Geological Survey, variously dated a). DO is vitally important to organisms whose 

survival depends on respiration—a process that, in addition to 

consumption of organic materials and oxidation of inorganics, 

is a primary sink of DO in streams (Hem, 1985). Because of 

the dependence on DO by aquatic organisms, it is used as 

an indicator of stream health, and Virginia has established a 

minimum criterion of 4.0 mg/L (Commonwealth of Virginia, 

1997) for freshwater streams.

DO concentrations across the 14-station network are 

generally within a healthy range (>4 mg/L), with a few 

exceptions occurring at two stations during the summer 

months (fig. 12). The temperature-dependent nature of DO is 

responsible for the seasonality in the monthly data (fig. 12) 

and continuous data (fig. 13). Specifically, DO equilibrium 

is greatest at low temperatures, resulting in maximum 

concentrations during winter months. 

Turbidity

Turbidity is a measure of the optical properties of a 

volume of water, and therefore is directly related to the 

quantity and size distribution of particles, such as sediment, 

suspended in the water column (Davies-Colley and Smith, 

2001). Turbidity measurement is not standardized—measures 

are highly dependent on sensor configuration, and measurements from different sensor configurations are not directly 

comparable (Anderson, 2005). Turbidity measurements for 

this study were made using a YSI, Inc. Model 6136 Turbidity 

Sensor. This turbidity sensor uses near infrared wavelengths 

with 90-degree detector geometry and is calibrated using 

formazin-based standards; therefore, data are expressed in 

FNUs (Anderson, 2005). 

Turbidity results from monthly sampling across the 

14-station network are generally low, with 75 percent of 

results below about 6 FNU (fig. 14). These low values result 

from the monthly samples largely representing base-flow 
Temporal and Spatial Patterns in Water Quality 19

4,000

2,000

400

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100

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10

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100

10

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

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July

Aug.

Sept.

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01/2008

01/2009

01/2010

01/2011

01/2012

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Specific conductance, in microsiemens per centimeter

Highest value within 1.5 (IQR)  of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR)  of 25th percentile

Interquartile

Range (IQR)

EXPLANATION

Figure 9. Specific conductance results from monthly sampling at 14 monitoring stations in Fairfax County.
20 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

conditions and consequently lack of energy to entrain 

sediment particles and thereby increase turbidity during such 

flow conditions. Occasionally, low-level stormflows were 

encountered and higher turbidity values recorded, although 

the maximum turbidity recorded during the monthly sampling 

events was only 78 FNU. In contrast to the monthly sampling 

results, the continuous turbidity data are highly variable 

across the range of possible turbidity values. Such variability 

impedes graphical visualization of long periods of data; 

therefore, only WY 2011 is plotted as an example (fig. 15).

Patterns in Nitrogen

Nitrogen (N) is an essential nutrient for plants and 

animals that is widely found in the atmosphere, hydrosphere, 

and biosphere (Hem, 1985). Although agricultural land use is 

generally associated with greater N concentrations in streams 

than other land uses, urbanization has been shown to increase 

N to similarly high concentrations, particularly in areas 

with faulty or failing wastewater infrastructure or excessive 

landscape fertilization (Paul and Meyer, 2001). 

Patterns in N concentration were evaluated using the 

monthly sampling results from the entire 14-station network. 

Median total N (mg/L as N) by monitoring station ranged 

from 0.88 to 4.88 mg/L, with a median for all monitoring 

stations of 1.66 mg/L. Measured concentrations of total N 

were higher at one station, Captain Hickory Run, than at other 

stations in the network (fig. 16). Occasional high concentrations, greater than or equal to those measured at Captain 

Hickory Run, were measured at Popes Head Creek tributary 

(fig. 16). These elevated concentrations at Popes Head Creek 

tributary have been captured in a single sample per year, 

collected in either August or September and coincident with 

the lowest streamflow of the year. With the exception of 

these two stations, total N concentrations generally show 

only slight departure from median values, and there is little 

evidence of seasonal patterns (fig. 16). 

The total N concentrations measured in the monthly 

samples, which typically reflect base-flow conditions, are 

largely composed of nitrate, as evidenced by the similar 

concentrations and patterns observed between total N data and 

the nitrate plus nitrite data (figs 16 and 17). Total dissolved N 

data mirror the nitrate data more closely than the total N data 

(not presented), and because these samples largely represent 

base-flow conditions, during which there is not sufficient 

energy to entrain particulates, total particulate N concentrations 

are frequently nondetectable (data not presented).

20

30

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CASTLE

LIL DIFF

CAPT HICK

SF LIL

TRKYCK

FROG

FLAT

PHCT

DIFF

DEAD

PSB

INDIAN

OCSB

BRR

Station

Specific conductance, in microsiemens per centimeter

Percentage of watershed covered by roads

Percent road coverage in watershed

EXPLANATION

Outlier

Outlier

Highest value within 1.5 (IQR) 

 of 75th percentile

Lowest value within 1.5 (IQR) 

 of 25th percentile

75th percentile

Median

Interquartile

25th percentile

Range (IQR)

Figure 10. Specific conductance results, with percent road coverage in 

watershed, by monitoring station, from monthly sampling at 14 monitoring stations 

in Fairfax County.
Temporal and Spatial Patterns in Water Quality 21

Specific conductance, in 

microsiemens per

centimeter at 25°C

EXPLANATION

10/200704/200810/200804/200910/200904/201010/201004/201110/201104/201210/2012

10/200704/200810/200804/200910/200904/201010/201004/201110/201104/2012

10/2012

10/200704/200810/200804/200910/200904/201010/201004/2011

10/2011

04/201210/2012

10/200704/2008

10/2008

04/200910/2009

04/201010/2010

04/201110/2011

04/201210/2012

23 to 100

101 to 200

201 to 300

301 to 400

401 to 500

501 to 750

751 to 1,000

1,001 to 2,500

2,501 to 5,000

5,001 to 7,500

7,501 to 13,400

Dead Run

Flatlick Branch

South Fork Little Difficult Run

Difficult Run

12:00 a.m.

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12:00 a.m.

Figure 11. Continuous specific conductance data from the four intensive monitoring stations 

in Fairfax County.
22 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12 16141210

86420

16141210

86420

16141210

86420

16141210

86420

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86420

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86420

01/2008

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Jan.

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Jan.

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Apr.

May

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July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Dissolved oxygen concentration, in milligrams per liter

Highest value within 1.5 (IQR)  of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR)  of 25th percentile

Interquartile

Range (IQR)

EXPLANATION

Figure 12. Dissolved oxygen results from monthly sampling at 14 monitoring stations in Fairfax County, with (dashed) reference line for Virginia dissolved oxygen criterion of 4 milligrams per liter.
Temporal and Spatial Patterns in Water Quality 23

Dissolved oxygen concentration, 

in milligrams per liter

3.50 to 4.00

4.01 to 6.00

6.01 to 8.00

8.01 to 10.00

10.01 to 12.00

12.01 to 14.00

14.01 to 16.00

16.01 to 18.00

18.01 to 18.40

EXPLANATION

10/200704/200810/200804/200910/200904/201010/201004/201110/201104/201210/2012

10/200704/200810/200804/200910/200904/201010/201004/201110/201104/2012

10/2012

10/200704/200810/200804/200910/200904/201010/201004/2011

10/2011

04/201210/2012

11/200705/200811/200805/200911/200905/201011/2010

05/201111/201105/2012

Data not collected during this period

Data not collected during this period

Data not collected during this period

Data not collected

during this period

Dead Run

Flatlick Branch

South Fork Little Difficult Run

Difficult Run

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

12:00 a.m.

8:00 p.m.

4:00 p.m.

12:00 p.m.

8:00 a.m.

4:00 a.m.

12:00 a.m.

Figure 13. Continuous dissolved oxygen data from the four intensive monitoring stations in 

Fairfax County.
24 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Turbidity, in formazin nephelometric units

0

80604020

0

80604020

0

80604020

0

80604020

0

80604020

0

80604020

0

80604020

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia South Fork Little Difficult Run above mouth near Vienna, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

Highest value within 1.5 (IQR)  of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR)  of 25th percentile

Interquartile

Range (IQR)

EXPLANATION

Figure 14. Turbidity results from monthly sampling at 14 monitoring stations in Fairfax County.
Temporal and Spatial Patterns in Water Quality 25

Visual inspection of the N data revealed no readily 

apparent changes over time, either gradual or as a step 

function, that would indicate potential trends in N in any of 

the watersheds. Slight changes may be occurring, however, 

that will require a longer period of record and formal trend 

tests to detect. 

In general, watersheds having the least road coverage 

(lower urban intensity) tend to have greater N concentrations 

than those with greater urban intensity (fig. 18). Additionally, 

evaluation of the overall distribution of total N results by 

monitoring station further exemplifies the high concentrations 

observed at Captain Hickory Run and Popes Head Creek 

tributary relative to other stations (fig. 18). 

Captain Hickory Run is dominated by estate residential 

(32 percent of watershed) and low-intensity residential 

(47 percent of watershed area) land uses (fig. 2). Hypotheses 

regarding the likely source of consistently elevated N in this 

watershed include (1) fertilizer applied to large residential 

lawns, (2) human waste from failing or inefficient septic 

systems (this watershed is not served by municipal wastewater 

utilities), or (3) groundwater inputs of legacy N from previous 

agricultural activity. Investigations into the likely sources of N 

are ongoing (2013) through separate USGS studies in the larger 

Difficult Run watershed, including Captain Hickory Run.

The Popes Head Creek tributary watershed is also 

dominated by estate residential (26 percent) and low-density 

Streamflow

Turbidity

EXPLANATION

Turbidity, in formazin nephelometric units

0.1

1

10

100

1,000

0.1

1

10

100

1,000

0.1

1

10

100

1,000

0.1

1

10

100

1,000

0.001

0.01

0.1

1

10

100

1,000

10,000 10,000

0.001

0.01

0.1

1

10

100

1,000

0.001

0.01

0.1

1

10

100

1,000

0.001

0.01

0.1

1

10

100

1,000

Streamflow, in cubic feet per second

DEAD DIFF FLAT SF LIL

10/201011/201012/201001/201102/201103/201104/201105/201106/201107/201108/2011

09/201110/2011

Figure 15. Continuous turbidity and streamflow data from the four intensive monitoring stations in Fairfax 

County for water year 2011. Station names defined in table 1.
26 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Highest value within 1.5 (IQR)  of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR)  of 25th percentile

Measured

Interquartile

Range (IQR)

EXPLANATION

Total nitrogen, in milligrams per liter as N

86420

10

86420

10

86420

10

86420

10

86420

10

86420

10

86420

10

VA VA VA VA

CHANTILLY, VA CHANTILLY, VA

CHANTILLY, VA CHANTILLY, VA

SPRINGS, VA SPRINGS, VA

VA VA

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Estimated

Figure 16. Total nitrogen results from monthly sampling at 14 monitoring stations in Fairfax County.
Temporal and Spatial Patterns in Water Quality 27

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Highest value within 1.5 (IQR)  of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR)  of 25th percentile

Measured

Interquartile

Range (IQR)

EXPLANATION

Nitrate plus nitrite, in milligrams per liter as N 8

6420864208642086420864208642086420

VA VA VA VA

CHANTILLY, VA CHANTILLY, VA

CHANTILLY, VA CHANTILLY, VA

SPRINGS, VA SPRINGS, VA

VA VA

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Estimated

Figure 17.

Nitrate plus nitrite results from monthly sampling at 14 monitoring 

stations in Fairfax County.
28 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

residential (38 percent) land uses, and is not served by 

municipal wastewater facilities. The occurrence of high N 

concentrations at this site is limited to the months of August 

and September (figs. 16 and 17), when streamflows are typically at or near the annual minimum. Hypotheses of N sources 

are similar to those for Captain Hickory Run (fertilizer, 

human waste, and animal waste), and N is probably entering 

the stream continually from a groundwater source that is 

substantially diluted by all but the lowest streamflows. Efforts 

to better clarify the source in this watershed are planned, but 

no results are currently available.

Patterns in Phosphorus

Phosphorus (P) is a primary nutrient required for biological 

metabolism, although its relative scarcity in typical freshwater 

environments commonly makes it the limiting nutrient controlling biological activity (Wetzel, 2001; Denver and others, 2010). 

Natural sources of P include the wastes and remains of plants 

and animals and weathering of P-bearing minerals in soils and 

rocks (Litke, 1999; Wetzel, 2001; Filippelli, 2008). In urban 

environments, anthropogenic sources that commonly increase 

P concentrations in streams include wastewater and fertilizer 

(Paul and Meyer, 2001; Denver and others, 2010). P readily 

combines with other elements to form various phosphate 

minerals or sorbs strongly to the surface of other minerals, and 

therefore dissolved P concentrations are typically minimal, with 

the majority of P transported in association with suspended 

sediments (Hem, 1985; Litke, 1999; Fillipelli, 2008; Denver 

and others, 2010). Dissolved P, however, may be transported 

from groundwater to surface water, where ecological effects 

of the biologically available P can be pronounced (Kang and 

others, 2005; Denver and others, 2010).

Patterns in total P were evaluated in a manner similar to 

those for N. The evaluation of phosphorus was limited by the 

censoring of some P species, particularly orthophosphorus. 

Phosphorus concentrations are generally low, particularly 

during base-flow conditions, and laboratory methods capable 

of measuring small amounts of P are required to quantify these 

low levels. Statistical procedures have been developed to 

analyze datasets with concentrations below laboratory detection levels, commonly referred to as nondetects (Helsel, 2005, 

and references therein). These procedures typically have 

thresholds for the maximum proportion of the dataset that are 

nondetects, and the quality of the statistical analyses degrade 

as these thresholds are approached or exceeded. Because of 

the high proportion of nondetects for some P analytes at some 

stations, the monthly boxplots were not generated for all 

P analytes at all monitoring stations.

Percent road coverage in watershed

Outlier

Outlier

CASTLE

LIL DIFF

CAPT HICK

SF LIL

TRKYCK

FROG

FLAT

PHCT

DIFF

DEAD

PSB

INDIAN

OCSB

BRR

Station

EXPLANATION

Total nitrogen concentration, in milligrams per liter as N

Percentage of watershed covered by roads

0

1

2

3

4

5

6

7

8

9

0

10

20

30

40

50

60

70

80

90

100

Highest value within 1.5 (IQR) 

 of 75th percentile

Lowest value within 1.5 (IQR) 

 of 25th percentile

75th percentile

Median

Interquartile

25th percentile

Range (IQR)

Figure 18. Total nitrogen results, with percent road coverage in watershed, by 

monitoring station, from monthly sampling at 14 monitoring stations in Fairfax County.
Suspended-Sediment and Nutrient Loads and Yields 29

Median total P (mg/L as P) by monitoring station ranges 

from 0.01 to 0.09 mg/L. Two stations, Flatlick Branch and 

Frog Branch, have concentrations greater than most other 

monitoring stations (fig. 19). Most total P measurements in the 

network reveal concentrations between the laboratory detection limits and reporting limits, and therefore are estimated (E).

The total P concentrations measured in monthly 

samples typically collected during base-flow conditions are 

largely composed of dissolved P, as indicated by the similar 

concentrations and patterns observed between total P data and 

dissolved P data (figs. 19 and 20). Likewise, total dissolved 

P is largely composed of orthophosphate (fig. 21), although 

the higher laboratory detections limits for orthophosphate 

relative to total dissolved P hinder this evaluation. As was the 

case with N, there is not typically sufficient energy to entrain 

particulates under base-flow conditions, and therefore total 

particulate P concentrations are typically very low (fig. 22).

Seasonal patterns in total P are evident at monitoring 

stations where P concentrations are greatest—Flatlick Branch, 

Frog Branch, and Paul Spring Branch (fig. 19). It is likely that 

similar seasonal patterns occur at stations where concentrations are lower, but these patterns are not discernible because 

of the highly censored data. Phosphorus concentrations tend to 

be greatest during the late summer months and lowest during 

winter months.

The two stations associated with the greatest 

P concentrations, Flatlick Branch and Frog Branch, drain 

a landscape formed over Late Triassic shale and siltstone 

bedrock. These rocks formed from particulate matter (including 

plant and animal remains) deposited in extensive shallow lakes 

and are consequently P rich (Lee and Froelich, 1989). The 

shallow surficial soils formed by the weathering of these rocks 

are richer in P than soils elsewhere in Fairfax County. Terziotti 

and others (2010) mapped mean bed sediment P concentrations 

of 400 to 700 parts per million in these watersheds, whereas 

the remainder of the monitored watersheds are in areas 

having mean bed sediment P concentrations of 400 parts per 

million or less. Natural background sources of P have been 

shown to contribute to instream P concentrations (Denver 

and others, 2010), and the elevated P in the Flatlick Branch 

and Frog Branch watersheds can probably be attributed, in 

part, to natural sources. Anthropogenic P sources may also be 

present, however, and further work is needed to determine the 

relative contributions of natural and anthropogenic sources. 

Nonetheless, consideration of natural background sources is 

integral to understanding the probable effects of management 

activities on P.

Suspended-Sediment and Nutrient 

Loads and Yields

Suspended-sediment and nutrient loads and yields 

were computed for the four intensive monitoring stations 

(Dead Run, Difficult Run, South Fork Little Difficult Run, 

and Flatlick Branch). Loads and yields were computed 

for WY 2009–12 at Dead Run, which was not operational 

until spring 2008, and for WY 2008–12 at the three other 

monitoring stations.

Suspended-Sediment and Nutrient Load Models

All suspended-sediment load estimation models 

developed include the natural logarithm of streamflow and 

the natural logarithm of turbidity, and all models except the 

one for Dead Run include water temperature (table 3). The 

significance of streamflow and turbidity in the estimation 

model reflects the strong influence streamflow has on sediment 

transport and the strong influence sediment concentration has 

on turbidity, as described earlier. The significance of water 

temperature is attributed to seasonal effects on sediment availability, as has been found in other rivers in Virginia (Jastram 

and others, 2009, 2010). Model results were re-expressed as 

concentrations to permit direct comparison with measured 

concentrations – this comparison indicated that all models 

provide a good fit, particularly at high concentrations, which 

affect computed total loads the most (fig. 23).

The selected estimation models for N loads—total N, 

dissolved N, and particulate N—were monitoring-station- and 

constituent-specific. These models included various combinations of explanatory variables, including natural logarithm of 

streamflow, natural logarithm of streamflow squared, natural 

logarithm of turbidity, water temperature, natural logarithm 

of specific conductance, and pH (table 4). The significance of 

Table 3. Number of observations, regression coefficients, and R2 for suspended sediment load models. 

[Station names defined in table 1. Dependent variable is natural logarithm of suspended sediment load. Ln, natural logarithm; Q, streamflow; 

R2, coefficient of determination; n/a, not applicable—variable not included in selected model]

Station Number of 

observations Intercept Ln Q Ln turbidity

Water 

temperature R2

DEAD 242 –1.832 1.418 0.643 n/a 96.4

DIFF 205 –1.493 1.316 0.829 0.015 97.1

FLAT 242 –2.390 1.310 0.894 0.015 97.1

SF LIL 227 –2.189 1.468 0.762 0.027 97.6
30 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Figure 19. Total phosphorus results from monthly sampling at 14 monitoring stations in Fairfax County.

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Total phosphorus concentration, in milligrams per liter

Highest value within 1.5 (IQR) of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR) of 25th percentileMeasuredEstimatedNot detected

Interquartile

Range (IQR)

EXPLANATION
Suspended-Sediment and Nutrient Loads and Yields 31 Figure 20. Total dissolved phosphorus results from monthly sampling at 14 monitoring stations in Fairfax County.

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Highest value within 1.5 (IQR) of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR) of 25th percentileMeasuredEstimatedNot detected

Interquartile

Range (IQR)

EXPLANATION

Total dissolved phosphorus, in milligrams per liter

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

0

0.250.200.150.100.05

CHANTILLY, VA CHANTILLY, VA FALLS, VA FALLS, VA

CHANTILLY, VA CHANTILLY, VA

CHANTILLY, VA CHANTILLY, VA

VA VA

SPRINGS, VA SPRINGS, VA

VA VA

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Captain Hickory Run at Rt 681 near Great Falls, Virginia Big Rocky Run at Stringfellow Road near Chantilly, Virginia
32 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Figure 21. Orthophosphate results from monthly sampling at 14 monitoring stations in Fairfax County.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Orthophosphate, in milligrams per liter as P

0

0.240.200.160.120.080.04

0

0.240.200.160.120.080.04

0

0.240.200.160.120.080.04

0

0.240.200.160.120.080.04

0

0.240.200.160.120.080.04

0

0.240.200.160.120.080.04

0

0.240.200.160.120.080.04

CHANTILLY, VA CHANTILLY, VA FALLS, VA FALLS, VA

CHANTILLY, VA CHANTILLY, VA

CHANTILLY, VA CHANTILLY, VA

VA VA

SPRINGS, VA SPRINGS, VA

VA VA

Boxplot not providedBoxplot not providedBoxplot not providedBoxplot not providedBoxplot not providedBoxplot not provided

Boxplot not providedBoxplot not providedBoxplot not providedBoxplot not providedBoxplot not provided

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Branch near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia

Highest value within 1.5 (IQR) of 75th percentile75th percentileMedian25th percentileLowest value within 1.5 (IQR) of 25th percentileMeasuredEstimatedNot detected

Interquartile

Range (IQR)

EXPLANATION
Suspended-Sediment and Nutrient Loads and Yields 33 Figure 22. Total particulate phosphorus results from monthly sampling at 14 monitoring stations in Fairfax County. Highest value within 1.5 (IQR) of 75th percentile 75th percentile Median 25th percentile Lowest value within 1.5 (IQR) of 25th percentile Measured Estimated Not detected

Interquartile

Range (IQR)

EXPLANATION

0

0.120.100.080.060.040.02

0

0.120.100.080.060.040.02

0

0.120.100.080.060.040.02

0

0.120.100.080.060.040.02

0

0.120.100.080.060.040.02

0

0.120.100.080.060.040.02

0

0.120.100.080.060.040.02

Total particulate phosphorus concentration, in milligrams per liter

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

01/2008

01/2009

01/2010

01/2011

01/2012

Captain Hickory Run at Rt 681 near Great Falls, Virginia

Dead Run at Whann Avenue near McLean, Virginia

Flatlick Branch above Frog Branch at Chantilly, Virginia

Indian Run at Bren Mar Drive at Alexandria, Virginia

Old Courthouse Spring Branch near Vienna, Virginia

Popes Head Creek Trib near Fairfax Station, Virginia

Turkeycock Run at Edsall Road at Alexandria, Virginia

Big Rocky Run at Stringfellow Road near Chantilly, Virginia

Castle Creek at Newman Road at Clifton, Virginia

Difficult Run above Fox Lake near Fairfax, Virginia

Frog Branch above Flatlick Branch at Chantilly, Virginia

Little Difficult Run near Vienna, Virginia

Paul Spring Branch above North Brach near Gum Springs, Virginia

South Fork Little Difficult Run above mouth near Vienna, Virginia
34 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

these explanatory variables is attributed to various causative 

and associative relations with N transport. The selected 

models generally exhibited good fit, with little bias and few 

outliers in the relation between measured and computed 

concentrations (fig. 24).

The selected estimation models for P loads—total P, 

dissolved P, and particulate P—were monitoring station- and 

constituent-specific. Similar to the N models, the P models 

include various combinations of explanatory variables, 

including natural logarithm of streamflow, natural logarithm 

of streamflow squared, natural logarithm of turbidity, water 

temperature, natural logarithm of specific conductance, and 

pH (table 5). The significance of these explanatory variables 

is attributed to various causative and associative relations with 

P transport. The selected models generally exhibited good 

fit, with little bias and few outliers in the relation between 

measured and computed concentrations (fig. 25).

Annual Suspended-Sediment and Nutrient 

Loads and Yields

Substantial variability in annual suspended-sediment 

loads can be seen across monitoring stations and water years 

(fig. 26). Differences across monitoring stations may be 

attributed to varying watershed sizes, landscape characteristics 

within those watersheds, and the localized effects of some 

rainfall events. Evaluation of yields (load per unit area) is 

more appropriate for comparisons among monitoring stations 

because the effect of watershed area is removed. Although 

interannual variability is still present in the yield values, a 

clearer picture of the relative contribution of sediment from 

each watershed may be gained (fig. 26). On average, South 

Fork Little Difficult Run and Difficult Run yield about as 

much as twice the mass of sediment (1,074 and 843 tons per 

square mile (ton/mi2),respectively) as Flatlick Branch and 

Dead Run (562 and 461 ton/mi2,respectively).

Between-year differences are largely attributed to the 

hydrologic regime in a given year, particularly the effects 

of storms with high rainfall. For example, most monitoring 

stations have greater loads in WY 2008 and WY 2011, the 

years in which Tropical Storms Hanna and Lee, respectively, 

delivered record rainfall to portions of Fairfax County. The 

influence of these single, large events is demonstrated by the 

greater strength of the monitoring-station-specific relation 

between annual suspended-sediment-concentration (SSC) load 

and annual peak streamflow, compared to the relation between 

annual SSC load and total annual streamflow (fig. 27). Here, 

annual peak streamflow refers to the maximum instantaneous 

streamflow recorded during each water year and total annual 

streamflow refers to the total volume of water conveyed in a 

given water year.

Nitrogen loads generally exhibit less interannual 

variability than sediment loads, with the exception of the 

particulate N loads (fig. 28). Dissolved N composes 60 to 

85 percent of the total annual N load at the four monitoring 

stations, and the annual dissolved load is not largely affected 

by peak-flow events (fig. 29A, C, E), yet is strongly related to 

the total volume of water conveyed (fig. 29B, D, F), because 

much of the N is transported during non-stormflow periods 

that compose the majority of the annual flow. Conversely, 

particulate N loads exhibit variability very similar to that 

of sediment loads (figs. 26 and 28), because particulate N 

is liberated and transported via erosional processes, and the 

energy of stormflows is required to entrain particulate forms of 

N. Because of these transport processes, the relations between 

annual peak flow and particulate N are strongest (fig. 29E), 

whereas the relations between total streamflow and particulate 

N are weakest (fig. 29F).

Across-monitoring-station differences in N loads are 

apparent, with Difficult Run transporting the greatest N loads, 

followed by Flatlick Branch, SF Little Difficult Run, and Dead 

Run (fig. 28). This pattern is less apparent, however, when 

DEAD DIFF FLAT SF LIL

0.1

1

10

100

1,000

10,0000.1

1

10

100

1,000

10,0000.1

1

10

100

1,000

10,0000.1

1

10

100

1,000

10,000

Computed suspended-sediment concentration,

in milligrams per liter

Measured suspended-sediment concentration, in milligrams per liter

10,000

1,000

100

10

1

0

EXPLANATION

1:1 line

Figure 23. Measured suspended-sediment concentration against computed suspended-sediment concentration for the 

four intensive monitoring stations. Station names defined in table 1.
Suspended-Sediment and Nutrient Loads and Yields 35

Table 4. Number of observations, regression coefficients, and R2 for nitrogen load models. 

[Station names defined in table 1. Dependent variable is natural logarithm of constituent load. Ln, natural logarithm; Q, streamflow; SC, specific conductance; 

R2, coefficient of determination; n/a, not applicable—variable not included in selected model]

Station Number of 

observations Intercept Ln Q Ln Q2 Ln turbidity

Water 

temperature Ln SC pH R2

Total nitrogen

DEAD 206 4.285 1.109 n/a –0.046 0.013 0.323 –0.172 97.7

DIFF 187 3.855 0.982 n/a 0.145 n/a 0.188 n/a 97.0

FLAT 201 1.698 1.166 n/a n/a 0.015 0.327 0.174 97.8

SF LIL 199 2.779 1.138 n/a n/a 0.010 1.868 n/a 97.5

Total particulate nitrogen

DEAD 206 0.892 1.219 n/a 0.271 0.015 0.216 n/a 95.5

DIFF 187 -1.530 0.914 0.031 0.606 0.014 n/a 0.432 94.9

FLAT 201 2.049 1.170 n/a 0.289 0.024 n/a n/a 93.8

SF LIL 199 1.392 1.145 n/a 0.464 n/a n/a n/a 94.5

Total dissolved nitrogen

DEAD 206 3.140 1.032 –0.017 –0.130 0.015 0.293 n/a 97.8

DIFF 187 7.156 0.942 –0.026 n/a –0.013 0.109 –0.357 97.1

FLAT 201 3.142 1.083 n/a –0.071 n/a 0.311 n/a 96.7

SF LIL 199 3.502 1.020 –0.017 –0.073 n/a 0.300 n/a 97.7

Table 5. Number of observations, regression coefficients, and R2 for phosphorus load models. 

[Dependent variable is natural logarithm of constituent load. Ln, natural logarithm; Q, streamflow; SC, specific conductance; R2, coefficient of determination; 

n/a, not applicable—variable not included in selected model]

Station Number of 

observations Intercept Ln Q Ln Q2 Ln turbidity

Water 

temperature Ln SC pH R2

Total phosphorus

DEAD 206 0.107 1.121 n/a n/a 0.025 n/a n/a 97.9

DIFF 187 2.280 0.997 n/a 0.645 n/a –0.402 n/a 97.8

FLAT 201 0.690 1.012 0.032 0.364 0.036 n/a n/a 96.8

SF LIL 199 –1.431 1.154 n/a 0.623 0.437 n/a n/a 97.5

Total particulate phosphorus

DEAD 206 –0.401 1.180 n/a 0.500 0.011 n/a n/a 97.1

DIFF 187 1.268 0.975 n/a 0.718 n/a –0.302 n/a 97.3

FLAT 201 –0.477 1.148 n/a 0.548 0.024 n/a n/a 96.1

SF LIL 199 –1.686 1.194 n/a 0.667 0.033 n/a n/a 97.2

Total dissolved phosphorus

DEAD 206 2.086 0.959 n/a 0.213 0.017 –0.451 n/a 98.0

DIFF 187 3.181 1.046 n/a 0.142 n/a –0.578 n/a 94.1

FLAT 201 0.780 0.901 0.017 0.083 0.048 –0.312 0.202 96.8

SF LIL 199 3.814 0.828 –0.016 0.344 n/a –1.080 n/a 94.8
36 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Figure 24. Measured A, total dissolved nitrogen, B, total nitrogen, and C, total particulate nitrogen 

concentrations against computed concentrations. Station names defined in table 1.

1:1 line

Estimated concentration

Measured concentration

EXPLANATION

A

C

B

5

4

3

2

1

0

0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5

0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5

12

10

8

6

4

2

0

10

8

6

4

2

0

DEAD DIFF FLAT SF LIL

DEAD DIFF FLAT SF LIL

DEAD DIFF FLAT SF LIL

Estimated concentration, in milligrams 

per liter as N

Estimated concentration, in milligrams 

per liter as N

Estimated concentration, in milligrams 

per liter as N

Measured concentration, in milligrams per liter as N

Measured concentration, in milligrams per liter as N

Measured concentration, in milligrams per liter as N

0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4
Suspended-Sediment and Nutrient Loads and Yields 37

DEAD DIFF FLAT SF LIL

DEAD DIFF FLAT SF LIL

DEAD DIFF FLAT SF LIL

1:1 line

Estimated concentration

Measured concentration

EXPLANATION

0.5

0.4

0.3

0.2

0.1

0

3.0

3.5

2.5

2.0

1.5

0.5

1.0

0

3.0

3.5

2.5

2.0

1.5

0.5

1.0

0

0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20

0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20

0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20 0 0.05 0.10 0.15 0.20

A

C

BEstimated concentration, in milligrams 

per liter as P

Estimated concentration, in milligrams 

per liter as P

Estimated concentration, in milligrams 

per liter as P

Measured concentration, in milligrams per liter as P

Measured concentration, in milligrams per liter as P

Measured concentration, in milligrams per liter as P

Figure 25. A, Total dissolved phosphorus, B, total phosphorus, and C, total particulate phosphorus concentrations 

against computed concentrations. Station names defined in table 1.
38 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12 Annual suspended-sediment load, in tons Annual suspended-sediment yield, in tons per square mile 

2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Water year

0

5,000

10,000

15,000

20,000

0

1,000

2,000

3,000

4,000

5,000

CSS DEAD DIFF FLAT SF LIL

2,287

MAX

277

MED

107

MIN

10,275

MAX

1,311

MED 289

MIN

95% confidence interval

Load

95% confidence interval

Yield

EXPLANATION

Annual peak streamflow, in cubic feet per second 

0

0 1,000 2,000 3,000 4,000 5,000 6,000 0 500 1,000 1,500 2,000 2,500 3,000

2,000

4,000

6,000

8,000

10,000

12,000

R2 (DEAD): 0.77

R2 (DIFF): 0.96

R2 (FLAT): 0.95

R2 (SF LIL): 0.97

R2 (DEAD): 0.98

R2 (DIFF): 0.74

R2 (FLAT): 0.07

R2 (SF LIL): 0.24

Annual total streamflow, in million gallons

Annual suspended sediment load, in tons

EXPLANATION

DEAD

DIFF

Site

FLAT

SF LIL

Figure 26. Suspended-sediment loads and yields for water years 2008 through 2012 for the four intensive 

monitoring stations. Station names defined in table 1. [CSS, combined summary statistics; min, minimum; 

med, median; max, maximum]

Figure 27. Annual suspended-sediment load against annual peak streamflow and annual total streamflow, 

with linear regression lines, for the four intensive monitoring stations for water years 2008 through 2012. 

Station names defined in table 1. R2 is coefficient of determination.
Suspended-Sediment and Nutrient Loads and Yields 39

watershed area is accounted for, as indicated by the annual N 

yields (fig. 30). On average, total N yields across the monitoring stations are comparable, with average annual yields 

that range from approximately 5,350 to 6,650 pounds per 

square mile (lb/mi2) and an overall average of approximately 

6,200 lb/mi2 (fig. 30).

Annual loads and yields of total P and total particulate 

P show inter-monitoring station and annual variability 

comparable to the variability in suspended-sediment loads 

and yields (figs. 26, 31 and 32). Total P loads are largely 

composed of sediment-associated P transported during 

stormflow, resulting in the strong relations between total P and 

annual peak streamflow (fig. 33C), and therefore patterns in 

P loads mimic patterns in sediment loads. Although dissolved 

P contributes a relatively small amount of P to the total load,

and little interannual variability is evident in dissolved P, there 

is inter-monitoring station variability in the dissolved loads. 

Dissolved P loads in Flatlick Branch are greater than the other 

monitoring stations (fig. 32), probably as a result of elevated 

concentrations during base-flow conditions (fig. 20). As 

described previously, the Flatlick Branch watershed is located 

in the Triassic Lowland, a terrane underlain by sedimentary 

deposits, some of which contain P, and this natural source of P 

probably contributes to the P concentrations in Flatlick Branch. 

Regional Comparison of Yields

Monitoring of nutrient and sediment loads and yields in 

small, urbanized, watersheds is relatively rare (for example, 

Chesapeake Bay Program, 2009), and therefore opportunities Total dissolved nitrogen Total nitrogen Total particulate nitrogen

Annual load, in pounds

36,413

MAX

21,314

MED

8,014

MIN

25,675

MAX

14,503

MED

5,877

MIN

19,614

MAX

5,749

MED

1,743

MIN

40,000

50,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

DEAD DIFF FLAT SF LIL

Water year

CSS

EXPLANATION

95% confidence interval

Load

Figure 28. Annual loads of total dissolved nitrogen, total nitrogen, and total particulate nitrogen for water years 2008 

through 2012 at the four intensive monitoring stations. Station names defined in table 1. [CSS, combined summary 

statistics; min, minimum; med, median; max, maximum]
40 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

for comparison with other urban areas are limited. Two 

studies from the eastern United States that provide such data 

for comparison with the yields measured in Fairfax County 

were completed in Gwinnett County, Georgia (Landers and 

others, 2007), in the Atlanta metropolitan area, and Central 

and Eastern North Carolina (Harden and others, 2013). In 

addition to comparisons with other urbanized areas in the 

eastern United States, yields are compared with those of 

nearby watersheds draining into the Occoquan River (Dougherty and others, 2006) and numerous other watersheds in the 

Chesapeake Bay watershed (U.S. Geological Survey, 2013). 

Although the watersheds included in the Occoquan and 

Chesapeake Bay yields are much larger and incorporate 

many different land uses, this comparison provides an 

important perspective on how urban watersheds, which are 

under-represented in Chesapeake Bay monitoring activities 

(Chesapeake Bay Program, 2009), compare with other 

watersheds draining into Chesapeake Bay.

A comparison of sediment yields from Fairfax County 

watersheds to those from urban watersheds in the Atlanta 

metropolitan area demonstrates that the Fairfax County yields 

are generally comparable to those from another urban area 

(fig. 34). Comparison of sediment yields from Fairfax County 

watersheds with those from the larger Occoquan watersheds 

nearby, however, show that a much greater amount of sediment is being exported from the small watersheds of Fairfax 

County than these larger watersheds that drain predominantly 

forested areas. The yields from the Atlanta area and the 

0

10,000

20,000

30,000

40,000

0

10,000

20,000

30,000

10,000

20,000

30,000

0

0 1,000 2,000 3,000 4,000 5,000 6,000 0 50 100 150 200 250 300 350

Annual peak streamflow, in cubic feet per second 

R2 (DEAD): 0.63

R2 (DIFF): 0.10

R2 (FLAT): 0.00

R2 (SF LIL): 0.00

R2 (DEAD): 0.73

R2 (DIFF): 0.60

R2 (FLAT): 0.04

R2 (SF LIL): 0.18

R2 (DEAD): 1.00

R2 (DIFF): 0.97

R2 (FLAT): 0.96

R2 (SF LIL): 0.99

R2 (DEAD): 0.73

R2 (DIFF): 0.88

R2 (FLAT): 0.75

R2 (SF LIL): 0.78

R2 (DEAD): 1.00

R2 (DIFF): 0.76

R2 (FLAT): 0.29

R2 (SF LIL): 0.40

R2 (DEAD): 0.98

R2 (DIFF): 0.65

R2 (FLAT): 0.89

R2 (SF LIL): 0.68

Annual total streamflow, in million gallons

Annual load, in pounds

Total dissolved nitrogen Total nitrogen Total particulate nitrogen

A

F

C

E

D

B

EXPLANATION

Site

DEAD FLAT

DIFF SF LIL

Figure 29. Annual (A, B) total dissolved nitrogen, (C, D) total nitrogen, and (E, F) total 

particulate nitrogen load against annual peak streamflow and annual total streamflow, 

with linear regression lines, for the four intensive monitoring stations for water years 2008 

through 2012. Station names defined in table 1. R2 is coefficient of determination.
Suspended-Sediment and Nutrient Loads and Yields 41

Occoquan watersheds are probably greater than what is 

represented in figure 34, however, because the yields shown in 

the figure are for total suspended solids, a measure of sediment 

concentration that has been demonstrated to underrepresent 

actual concentrations as a result of the method used for 

analysis (Gray and others, 2000).

A comparison of sediment yields for Fairfax County 

watersheds to those from other watersheds in the Chesapeake 

Bay watershed reveals that, on average, the Fairfax watersheds 

deliver greater masses of sediment per unit of watershed area 

than most monitored Chesapeake Bay watersheds (fig. 34). 

This result is probably a result of multiple factors related to 

watershed characteristics that are diminished over the larger 

areas of the Chesapeake Bay monitoring watersheds. For 

example, the Fairfax County watersheds are higher-gradient 

headwater systems that, regardless of land-use, produce higher 

sediment yields than other watersheds as a result of the energy 

available for entrainment of sediments. The larger watersheds 

represented in the Chesapeake Bay dataset encompass a range 

of topographic gradients and the effect of the headwater areas 

is averaged over larger areas, thereby reducing the overall 

yield of these watersheds. Additionally, because of the location 

and size of the watersheds in Fairfax County, these watersheds 

more exclusively represent anthropogenically disturbed areas, 

where the Chesapeake Bay watersheds incorporate larger 

proportions of undisturbed areas along with the disturbed 

areas, thereby reducing the effect of disturbance.

Total N yields from the Fairfax County watersheds 

were comparable to yields from the Atlanta area, although 

yields from both areas were greater than those determined for 

Figure 30. Annual yields of total dissolved nitrogen, total nitrogen, and total particulate nitrogen for water years 2008 

through 2012 at the four intensive monitoring stations. Station names defined in table 1. [CSS, combined summary statistics, 

min, minimum; med, median; max, maximum] 

Annual nitrogen yield, in pounds per square mile

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

9,000

9,000

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Total dissolved nitrogen Total nitrogen Total particulate nitrogen

2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Water year

CSS DEAD DIFF FLAT SF LIL

8,360

MAX

6,200

MED

3,361

MIN

6,460

MAX

4,252

MED

2,642

MIN

3,573

MAX

1,635

MED

643

MIN

95% confidence interval

Yield

EXPLANATION
42 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

developed watersheds in North Carolina and the Occoquan 

watersheds (fig. 35). Total N yields in the Chesapeake Bay 

watershed span a much greater range than the yields observed 

in the three studies representing developed watersheds, with 

the yields from Fairfax County ranging between the 50th and 

75th percentiles of yields from the Chesapeake Bay watershed. 

Although the Fairfax County yields are greater than those 

for over 50 percent of the Chesapeake Bay watersheds, over 

25 percent of Chesapeake Bay watersheds yield greater masses 

of N, with the highest yield being over three times greater than 

the highest average yield in Fairfax County (fig. 35). 

Total P yields from Fairfax County watersheds, like 

the N yields, were comparable to yields from the Atlanta 

area, although typically greater than those determined for 

watersheds in North Carolina and the Occoquan watershed 

(fig. 36). As with Total N, Total P yields in the Chesapeake 

Bay watershed span a much greater range than the yields 

observed in three studies representing developed watersheds, 

with the yields from Fairfax County ranging between the 

75th and 90th percentiles of yields from the Chesapeake Bay 

watershed. Although the Fairfax County yields are within the 

top quartile of yields from Chesapeake Bay watersheds, the 

highest yield in the Chesapeake Bay watersheds is over two 

times greater than the highest average yield in Fairfax County. 

The elevated P yields from the Fairfax County watersheds 

match expectations, given the elevated sediment yields and 

the fact that the majority of P transported is typically bound to 

sediment particles (Hem, 1985). 

Figure 31. Annual loads of total dissolved phosphorus, total phosphorus, and total particulate phosphorus for water 

years 2008 through 2012 at the four intensive monitoring stations. Station names defined in table 1. [CSS, combined 

summary statistics, min, minimum; med, median; max, maximum]

Total dissolved phosphorus Total phosphorus Total particulate phosphorus

2,000

4,000

6,000

8,000

10,000

0

2,000

4,000

6,000

8,000

0

2,000

4,000

6,000

8,000

0

Annual load, in pounds

2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Water year

CSS DEAD DIFF FLAT SF LIL

6,558

MAX

1,874

MED

380

MIN

1,001

MAX

260

MED

60

MIN

4,501

MAX

1,355

MED 330

MIN

95% confidence interval

Load

EXPLANATION
Suspended-Sediment and Nutrient Concentration Models 43

Suspended-Sediment and Nutrient 

Concentration Models

Suspended-sediment and nutrient concentration models 

were generated to support potential future applications 

and analyses, such as inclusion in the NRTWQ system and 

comparison with concentration models developed for other 

locations. Although these models are not directly used in 

analysis herein, the models presented are valid for use in the 

aforementioned applications and analyses. 

 For most cases, little additional explanatory power 

(increase in adjusted R2) was achieved through the addition of 

streamflow to the univariate model using turbidity as the sole 

explanatory variable (table 6). In all cases, except for Dead 

Run, the best possible model included turbidity, streamflow, 

and water temperature as explanatory variables (table 6), 

although the variability explained by these models was similar 

to that of the bivariate turbidity and streamflow models. 

Concentration models for nutrients were developed 

for total N and P concentrations (tables 7 and 8). The high 

number of censored values for dissolved nutrients, particularly 

dissolved P, precluded modeling of those constituents. The 

regression models developed to estimate total N concentrations are much less effective than the models developed to 

estimate suspended sediment and total P concentrations. 

The turbidity-only model for total N at Dead Run was not 

statistically significant (α = 0.05), and the variability explained 

0

500

1,000

1,500

2,000

3,000

3,000

3,000

0

500

1,000

1,500

2,000

0

500

1,000

1,500

2,000

Annual phosphorus yield, in pounds per square mile

Total dissolved phosphorus Total phosphorus Total particulate phosphorus

2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Water year

CSS DEAD DIFF FLAT SF LIL

1,562

MAX

543

MED

140

MIN

238

MAX

77

MED

22

MIN

1,177

MAX

356

MED

122

MIN

95% confidence interval

Yield

EXPLANATION

Figure 32. Annual yields of total dissolved phosphorus, total phosphorus, and total particulate phosphorus for water 

years 2008 through 2012 at the four intensive monitoring stations. Station names defined in table 1. R2 is coefficient of 

determination. [CSS, combined summary statistics, min, minimum; med, median; max, maximum]
44 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

by the models at the other three monitoring stations was 

low (adjusted R2 = 0.15–0.30; table 7). The models having 

turbidity and streamflow as explanatory variables were not 

statistically valid because of the lack of statistical significance 

(α = 0.05) of one of the explanatory variables in each model. 

The explanatory variables included in the best possible models 

were unique to each monitoring station, using some combination of streamflow, turbidity, water temperature, specific 

conductance, and pH (table 8). Although these multivariate 

models were the best of all potential models, they only 

explained 23 percent to 37 percent of the variability in total N. 

The inability to more effectively model total N concentration 

is probably a result of the majority of N occurring in dissolved 

form, and the lack of correlation between patterns in dissolved 

N transport and measured water-quality parameters.

The turbidity-only model effectively described total 

P concentrations, and little or no improvement was gained 

through the addition of streamflow (table 8). The explanatory 

variables included in the best possible models were unique to 

each monitoring station, using some combination of streamflow, 

turbidity, water temperature, and specific conductance (table 8). 

Slight improvements in explanatory power of the models were 

made through the addition of these explanatory variables when 

compared to the univariate and bivariate models.

6,000

7,000

5,000

4,000

3,000

2,000

1,000

0

6,000

5,000

4,000

3,000

2,000

1,000

0

6,000

5,000

4,000

3,000

2,000

1,000

0

0 1,000 2,000 3,000 4,000 5,000 6,000 0 50 100 150 200 250 300 350

Annual peak streamflow, in cubic feet per second 

R2 (DEAD): 0.76

R2 (DIFF): 0.88

R2 (FLAT): 0.29

R2 (SF LIL): 0.60

R2 (DEAD): 0.74

R2 (DIFF): 0.91

R2 (FLAT): 0.89

R2 (SF LIL): 0.86

R2 (DEAD): 0.99

R2 (DIFF): 0.72

R2 (FLAT): 0.15

R2 (SF LIL): 0.33

R2 (DEAD): 0.75

R2 (DIFF): 0.90

R2 (FLAT): 0.82

R2 (SF LIL): 0.88

R2 (DEAD): 0.99

R2 (DIFF): 0.74

R2 (FLAT): 0.20

R2 (SF LIL): 0.32

R2 (DEAD): 0.99

R2 (DIFF): 0.76

R2 (FLAT): 0.64

R2 (SF LIL): 0.42

Annual total streamflow, in million cubic feet

Annual load, in pounds

Total dissolved phosphorus Total phosphorus Total particulate phosphorus

A B

C D

E F

EXPLANATION

DEAD

DIFF

FLAT

SF LIL

Site

Figure 33. Annual loads of (A, B) total dissolved phosphorus, (C, D) total phosphorus, and 

(E, F) total particulate phosphorus against annual peak streamflow and total annual 

streamflow, with linear regression lines, at the four intensive monitoring stations for water 

years 2008 through 2012. Station names defined in table 1. R2 is coefficient of determination.
Suspended-Sediment and Nutrient Concentration Models 45

Figure 34. Suspended-sediment yields, 

as total suspended solids and suspendedsediment concentration, from four 

monitoring networks—Atlanta, Fairfax, 

Occoquan, and the Chesapeake Bay River 

Input Monitoring.

Mean yield, in pounds per square mile

Fairfax Atlanta North Carolina Occoquan Chesapeake Bay

0

EXPLANATION

Outlier

5,000

10,000

15,000

20,000

25,000

Highest value within

1.5 (IQR) of 75th

percentile

Lowest value within

1.5 (IQR) of 25th

percentile

75th percentile

Median

Interquartile

25th percentile

Range (IQR)

Number of sites

(4) (6) (5) (4)

(4)

(65)

Figure 35. Total nitrogen yields 

from five monitoring networks—

Atlanta, Fairfax, North Carolina, 

Occoquan, and the Chesapeake 

Bay River Input Monitoring.

Mean yield, in tons per square mile

0

500

1,000

1,500

2,000

2,500

Fairfax Atlanta Occoquan Chesapeake Bay

(4) (6) (4) (40)

EXPLANATION

Suspended sediment

concentration

Total suspended solids

Number of sites

Outlier

Highest value within

1.5 (IQR) of 75th

percentile

Lowest value within

1.5 (IQR) of 25th

percentile

75th percentile

Median

Interquartile

25th percentile

Range (IQR)

(40)

(29)
46 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Benthic Macroinvertebrate Data and 

Stream Health

Benthic macroinvertebrates (BM) are invertebrate 

organisms that inhabit the stream channel bottom and other 

stable instream structures (such as vegetation, roots, and 

fallen trees) for some portion of their lifecycle (Hauer and 

Resh, 1996). These organisms are ubiquitous in stream 

environments, and some can even inhabit severely polluted 

streams (Hauer and Resh, 1996). Because these organisms 

are residents of the aquatic environment, measurements of 

BM communities provide a measure of stream health that is 

integrated over time, as opposed to the snapshot of conditions 

acquired through chemical sampling and physical measurements (Resh and others, 1996). This time-integrated representation of conditions, along with the ubiquity and varied 

tolerance to perturbation, has led to widespread use of BM 

community metrics as measures of stream health (Resh and 

others, 1996; U.S. Environmental Protection Agency, 2002; 

Blocksom and Winters, 2006; Cuffney and others, 2010).

The IBI scores are all higher than the corresponding SCI 

scores (fig. 37) because the Fairfax County IBI metric was 

developed using reference sites in Fairfax County where areas 

of minimal anthropogenic impact are scarce, and the SCI 

metric was developed using reference sites throughout Virginia 

and, therefore, includes many pristine locations. Collectively, 

the results for both metrics show a general decrease in stream 

health with increasing urban intensity (fig. 37). 

Evaluation of the IBI and SCI scores reveals that the 

health of the streams monitored is generally poor. A single 

value criterion of 61 has been suggested to demonstrate 

biological impairment using the SCI score (Burton and 

Gerritsen, 2003). All SCI scores at the 14 stations over 5 years 

were equal to (1 result) or less than (69 results) this criterion 

(fig. 37). A descriptive scale of very poor (0–20), poor 

(21–40), fair (41–60), good (61–80), and excellent (81–100) 

was used for the Fairfax County IBI scores; using this scale, 

84 percent of the scores obtained denote fair, poor, or very 

poor conditions, all of which indicate biological impairment. 

Castle Creek, which drains the least intensively developed 

watershed in the monitoring network (fig. 2), is the only 

stream in the monitoring network supporting a balanced and 

highly diverse macroinvertebrate community, resulting in IBI 

scores classified as excellent.

Evaluation of temporal patterns in the BM metrics 

indicates some interannual variability at individual monitoring 

stations (fig. 38). The small number of samples per monitoring 

station (n = 5) precludes statistical analysis of temporal 

trends, and elucidation of any temporal trends through visual 

observation of the data is complicated by interannual variability.

Figure 36. Total phosphorus 

yields from five monitoring 

networks—Atlanta, Fairfax, 

North Carolina, Occoquan, 

and the Chesapeake Bay 

River Input Monitoring.

Mean yield, in pounds per square mile

Fairfax Atlanta North Carolina Occoquan Chesapeake Bay

EXPLANATION

0

500

1,000

1,500

2,000

2,500

(4) (6) (5) (4)

Outlier

Highest value within

1.5 (IQR) of 75th

percentile

Lowest value within

1.5 (IQR) of 25th

percentile

75th percentile

Median

Interquartile

25th percentile

Range (IQR)

(4) Number of sites

(65)
Benthic Macroinvertebrate Data and Stream Health 47

Table 6. Number of observations, regression coefficients, and R2 for suspended sediment concentration models. 

[Dependent variable is natural logarithm of suspended sediment concentration. Ln, natural logarithm; Q, streamflow; R2, coefficient of determination; 

n/a, not applicable—variable not included in selected model]

Station Number of 

observations Intercept Ln Q Ln turbidity

Water 

temperature

Adjusted 

R2

Best possible model

DEAD 233 0.889 0.426 0.636 n/a 0.88

DIFF 205 0.214 0.319 0.823 0.016 0.91

FLAT 242 –0.468 0.310 0.891 0.016 0.91

SF LIL 212 –0.001 0.471 0.756 0.029 0.93

Turbidity and streamflow model

DEAD 233 0.889 0.426 0.636 n/a 0.88

DIFF 205 0.445 0.274 0.867 n/a 0.91

FLAT 242 –0.257 0.300 0.911 n/a 0.91

SF LIL 212 0.321 0.438 0.804 n/a 0.93

Turbidity only model

DEAD 233 0.690 n/a 0.981 n/a 0.84

DIFF 205 0.651 n/a 1.071 n/a 0.90

FLAT 242 –0.217 n/a 1.179 n/a 0.89

SF LIL 212 0.023 n/a 1.129 n/a 0.90

Table 7. Number of observations, regression coefficients, and R2 for total nitrogen concentration models. 

[Dependent variable is natural logarithm of total nitrogen concentration. Ln, natural logarithm; Q, streamflow; SC, specific conductance; R2, coefficient of 

determination; n/a, not applicable—variable not included in selected model]

Station Number of 

observations Intercept Ln Q Ln turbidity

Water 

temperature Ln SC pH Adjusted R2

Best possible model

DEAD 206 0.092 0.109 –0.046 0.013 0.323 –0.172 0.23

DIFF 187 –0.986 n/a 0.132 n/a 0.193 n/a 0.37

FLAT 201 –3.207 0.166 n/a 0.015 0.327 0.174 0.30

SF LIL 199 –1.141 0.138 n/a 0.011 0.432 n/a 0.27

Turbidity and streamflow model

DEAD

Models are not statistically valid.

DIFF

FLAT

SF LIL

Turbidity only model

DEAD 206 n/a Model not statistically significant

DIFF 187 0.162 n/a 0.117 n/a n/a n/a 0.30

FLAT 201 0.335 n/a 0.081 n/a n/a n/a 0.17

SF LIL 199 0.843 n/a 0.068 n/a n/a n/a 0.15
48 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Changes to the Monitoring Program

The 14-station network described herein has successfully 

provided abundant information about surface-water resources 

in Fairfax County since its inception in 2007. However, 

monitoring station selection for this network was performed 

using only watersheds included in the first of two phases of 

watershed planning within Fairfax County and, therefore, 

only represents about 50 percent the spatial extent of Fairfax 

County. Additionally, although some consideration of 

planned BMP implementation intensity was included in the 

monitoring-station selection process, the selected monitoring 

stations represent a range of implementation intensities. 

In 2012, the decision was made to expand the monitoring 

network to include watersheds from the remaining 50 percent 

of Fairfax County, included in the second phase of watershed 

planning, and to focus that expansion on small watersheds 

(less than 1 mi2) with high rates of planned implementation; 

that is, watersheds where responses to implementation 

are expected to be greatest. The approach and objectives 

for implementing these additional monitoring stations 

are identical to those for the network used in the original 

monitoring program.

Using monitoring station-selection procedures similar to 

those presented for the original network on a list of potential 

watersheds selected for high rates of planned implementation, an 

additional 6 stations were chosen for expansion of the network. 

Of these 6 stations, 1 was established as an intensive monitoring 

station and 5 were established as trend monitoring stations 

(table 9; fig. 39). Data collection at the 6 new stations was

initiated in October 2012, and given the short period of record 

for these sites, no analysis of their data is presented herein. 

Along with the addition of the new monitoring stations at 

the beginning of WY 2013, a change was made to the monthly 

sampling program in an attempt to improve the representation 

of hydrologic conditions by the monthly dataset. As described 

previously, the monthly sampling approach has not fully represented the range of hydrologic conditions, and consequently 

is largely composed of base-flow samples (fig. 6). Targeted 

wet-weather samples were added to the monthly sampling 

program to address this issue. Four months are randomly 

selected each water year for wet-weather sampling. During 

these 4 months, the scheduled sampling date is replaced with a 

targeted wet-weather sampling event to improve the coverage 

of high-flow conditions.

Future Directions

The monitoring activities described herein are expected 

to continue largely unchanged for the foreseeable future to 

continue to record water-quality, streamflow, and aquatic 

macroinvertebrate conditions of streams throughout Fairfax 

County. Implementation of management efforts is ongoing, and 

has increased during the latter portion of the monitoring period 

discussed. It is expected that implementation of management 

activities will continue at current rates or greater. Future efforts 

will focus on recording pertinent measures of the implementation activities to support the analysis of watershed-scale 

responses resulting from these management activities.

Table 8. Number of observations, regression coefficients, and R2 for total phosphorus concentration models. 

[Dependent variable is natural logarithm of total phosphorus concentration. Ln, natural logarithm; Q, streamflow; SC, specific conductance; R2, coefficient of 

determination; n/a, not applicable—variable not included in selected model]

Station Number of 

observations Intercept Ln Q Ln turbidity

Water 

temperature Ln SC Adjusted R2

Best possible model

DEAD 206 -4.113 0.121 0.435 0.025 n/a 0.87

DIFF 187 -2.495 n/a 0.598 n/a -0.377 0.86

FLAT 201 -3.693 n/a 0.372 0.040 n/a 0.73

SF LIL 199 -5.251 0.205 0.541 0.036 n/a 0.87

Turbidity and streamflow model

DEAD 206 -3.804 0.091 0.470 n/a n/a 0.85

DIFF 187 -4.813 0.107 0.547 n/a n/a 0.84

FLAT 201 -3.142 -0.021 0.412 n/a n/a 0.65

SF LIL 199 -4.851 0.150 0.605 n/a n/a 0.85

Turbidity only model

DEAD 206 -3.847 n/a 0.546 n/a n/a 0.85

DIFF 187 -4.735 n/a 0.627 n/a n/a 0.83

FLAT 201 -3.147 n/a 0.394 n/a n/a 0.65

SF LIL 199 -4.937 n/a 0.708 n/a n/a 0.85
Summary 49

Summary

Fairfax County, in northern Virginia, has experienced 

great population growth and urbanization since the middle 

of the 20th Century. As a result of the changing landscape to 

accommodate this growth, streams in Fairfax County have 

become impaired because they are frequently inundated with 

excessive volumes of stormwater runoff. Efforts to mitigate 

these detrimental effects through the implementation of best 

management practices (BMPs) are underway, and a study of 

the watershed-scale response to BMP implementation has been 

initiated. Results from the first five water years (2008–12) of 

this study show that the impacts of urbanization are apparent 

in water-quality, hydrologic, and biological metrics determined 

at all 14 stations of the monitoring network implemented for 

the study.

Spatial and temporal patterns in basic water-quality 

parameters measured monthly at all 14 stations and continuously at the 4 intensive monitoring stations reflect seasonal 

and other natural patterns and processes, as well as some 

patterns indicative of anthropogenic activities. Typical pH 

values of streams throughout the network centered around 

neutrality (pH = 7), particularly in streams draining watersheds within the Piedmont physiographic province. Streams 

draining the Triassic Lowland had higher typical pH values, 

and streams draining the Coastal Plain tended to have lower 

pH values. Strong daily fluctuations in pH are apparent in the 

continuous pH data, particularly at Flatlick Branch, where 

elevated phosphorus (P) concentrations fuel photosynthetic 

organisms. Additionally, pH decreases periodically in intensively monitored streams in response to rainfall events. 

Spatial and temporal patterns in specific conductance 

were largely representative of anthropogenic disturbances. 

Watersheds having the greatest percentage of open space 

and estate residential land-use had the lowest typical specific 

conductance values, and specific conductance variability 

was less than what is observed in watersheds that are more 

intensively developed. Application of de-icing salts on 

impervious surfaces during the winter months is apparent in 

the specific conductance data. In watersheds having greater 

road coverage, and more development in general, increases 

in specific conductance over several orders of magnitude 

are present in the monthly sampling data and the continuous 

data. In these watersheds, when frozen precipitation occurs, 

elevated specific conductance is observed through the winter 

months, gradually deceasing through the spring and summer. 

The rapid increase in specific conductance as snow and ice 

melts likely produces a shock to the biological communities 

Figure 37. Benthic macroinvertebrate sampling 

results summarized as the Fairfax County Index of biotic 

integrity (IBI) and the Virginia stream condition index 

(SCI), in order of decreasing percent road coverage 

from left to right. Station names defined in table 1.

Index score

CASTLE LIL DIFF CAPT

HICK SF LIL TRKYCK FROG FLAT PHCT DIFF DEAD PSB INDIAN OCSB BRR

0

20

40

60

80

100

EXPLANATION

Stream condition index

Index of biotic integrity

Highest value within

1.5 (IQR) of 75th

percentile

Lowest value within

1.5 (IQR) of 25th

percentile

75th percentile

Median

Interquartile

25th percentile

Range (IQR)
50 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Figure 38. Benthic macroinvertebrate sampling results summarized as the Fairfax County index of biotic 

integrity (IBI) and the Virginia stream condition index (SCI). Station names defined in table 1.

Table 9. Station information for six stations added to the monitoring network in 2012.

[mi2, square mile]

Station 

identifier Station name Type Watershed area (mi2)

01654500 Long Branch at Route 620 near Annandale, VA Intensive 3.72

01655305 Rabbit Branch tributary above Lake Royal near Burke, VA Trend 0.57

0164425950 Horsepen Run above Horsepen Run tributary near Herndon, VA Trend 1.19

01644343 Sugarland Run tributary below Crayton Road near Herndon, VA Trend 0.64

01653844 Dogue Creek tributary at Woodley Drive at Mount Vernon, VA Trend 0.43

01657100 Willow Springs Branch at Highway 29 near Centreville, VA Trend 0.96

01657100 Willow Springs Branch at Highway 29 near Centreville, VA Trend 0.96

CASTLE

Water year

20082009

2010

20112012

Index score

CAPT HICK

20082009

201020112012

LIL DIFF

20082009

201020112012

SF LIL PHCT DIFF

DEAD PSB TRKYCK

INDIAN FLAT OCSB

FROG BRR

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

EXPLANATION

Stream condition index

Index of biotic integrity
Summary 51

Occoquan River

Potomac River

Potomac River

#

01644343

0164425950

01657100

01655305

01654500

01653844

77°30'W 77°20'W 77°10'W

39°N

38°50'N

38°40'N

01655305

01654500

City of 

Fairfax

City of 

Alexandria

WASHINGTON, D.C.

ARLINGTON 

COUNTY

City of 

Manassas

PRINCE WILLIAM COUNTY

LOUDOUN COUNTY

MARYLAND

80°W 79°W 78°W 77°W 76°W 75°W

40°N

39°N

38°N

37°N

Location of 

study area

VIRGINIA

WEST

VIRGINIA

PENNSYLVANIA

MARYLAND

NORTH CAROLINA

NJ

DE

Atlantic Ocean

Chesapeake Bay

City of 

Falls Church

Physiography from Fenneman and 

Johnson (1946), 1:7,000,000

Base from U.S. Geological Survey 

digital line graph, 1:2,000,000, 1987

Chesapeake Bay

watershed boundary

EXPLANATION

Coastal Plain

Physiographic province

Piedmont

Trend monitoring station and number

Intensive monitoring station and number

Trend monitoring watershed

Intensive monitoring watershed

Mesozoic Basins (subprovince)

0 2.5 5 Miles

0 4 8 Kilometers

FAIRFAX COUNTY

Figure 39. Monitoring stations and watersheds added to the monitoring network in 2012.
52 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

that inhabit these streams, as chloride concentrations during 

these events probably exceed impairment thresholds for 

freshwater aquatic life.

Patterns in dissolved oxygen (DO) concentration are 

largely indicative of expected seasonal patterns driven by 

temperature dependent solubility relations and biological 

activities. DO concentrations were generally much greater 

in the colder months when solubility is greatest, and concentrations were lowest during summer months when water 

temperatures are maximized and solubility is minimized. 

In general, DO conditions were typically within the range 

required to support healthy biological communities, although 

occasional departures during summer months fell below the 

impairment threshold for streams in Virginia. These impaired 

conditions occurred most frequently at Paul Spring Branch, a 

Coastal Plain stream that typically has minimal flow during 

the summer.

Evaluation of patterns in nitrogen (N) concentrations 

revealed two monitoring locations, Captain Hickory Run and 

Popes Head Creek tributary, as having anomalous conditions 

not fully explained by the available data, whereas the other 

stations were typically characterized by low N concentrations 

with low variability. Captain Hickory Run had a median 

total N concentration of approximately 4.9 mg/L, compared 

to the network-wide median of approximately 1.7 mg/L. 

The consistently elevated N concentrations in Captain 

Hickory Run, which has a watershed primarily composed of 

estate residential land-use, are hypothesized to result from 

(1) faulty septic systems, (2) excessive fertilizer application 

on residential landscapes, or (3) a legacy source of N in the 

local groundwater. Additional USGS studies are currently 

focused on determining the source of N in this watershed. 

Popes Head Creek Tributary also had an anomalous pattern of 

N concentration, although different from the pattern observed 

in Captain Hickory Run. In this stream, N concentrations 

spiked to approximately three times their typical value during 

low-flow periods in August or September of each year. This 

isolated annual spike in concentration is probably the result of 

groundwater input that is always present, but diluted in all but 

the lowest streamflows. Additional work is needed to clarify 

the source of this N.

Phosphorus concentrations in monthly samples were 

generally low and dominated by the dissolved fraction. Two 

monitoring stations in the network, Flatlick Branch and Frog 

Branch, are notable for having median total P concentrations 

that were, on average, approximately three times greater 

than the median total P concentration observed at the other 

12 stations in the network. These two watersheds are unique in 

the network because of their location in the Triassic Lowland 

terrane. This terrane is underlain by sedimentary rocks formed 

by the deposition of particulate materials, including remains 

of plants and animals, resulting in P-rich formations. This 

naturally occurring P source is probably responsible for much 

of the elevated P concentrations observed in these streams, 

and highlights the importance of understanding the effects of 

natural setting in making decisions about management needs.

Annual loads and yields of suspended sediment and 

nutrients were computed at the four intensive monitoring 

stations using continuous water-quality data as explanatory 

variables. Yields were compared to other study areas to 

provide context for the relative contributions to downstream 

loading from the four small urban watersheds that were 

intensively monitored in this study.

Suspended sediment loads and yields were highly 

variable across water years and across monitoring stations. 

Water years (WY) 2008 and 2011 generally produced the 

greatest suspended-sediment loads as a result of tropical 

storm systems that produced large runoff events. Annual 

peak streamflow, or single highest flow event of the year, was 

strongly related to suspended-sediment load, whereas the total 

streamflow for the year was less strongly related to annual 

suspended-sediment load. Suspended-sediment yields were 

generally greatest at Difficult Run and South Fork Little Difficult Run, which were about twice the yields observed at Dead 

Run and Flatlick Branch, on average. Overall, suspendedsediment yields from the intensively monitored small urban 

watersheds were comparable to yields from similar urban 

watersheds in Atlanta, Georgia, although the yields were much 

greater than yields observed from other, larger watersheds 

throughout the Chesapeake Bay watershed.

Nitrogen loads and yields are generally less variable 

across water years than suspended-sediment loads and yields 

because N transport is dominated by dissolved forms of N 

that are less influenced by peak streamflow events. Nitrogen 

loads are more strongly related to total annual streamflow, 

however, with years of greatest total flow transporting the 

greatest amounts of total N. Nitrogen loads were greatest from 

Difficult Run and Flatlick Branch, followed by South Fork 

Little Difficult Run, and Dead Run, although these differences 

in load are largely attributed to the size of the contributing 

watershed. When adjusted for watershed area, it is apparent 

that these watersheds yielded comparable amounts of N, 

approximately 6,200 pounds per square mile, on average. 

Total N yields from the stations monitored in Fairfax County 

were similar to the N yields observed in urban streams in 

Atlanta, Ga., although they were greater than N yields from 

urban and suburban streams in North Carolina and the nearby 

Occoquan River watershed. Yields of N from throughout the 

Chesapeake Bay watershed span a much wider range than 

the yields observed in Fairfax County, with Fairfax County 

yields ranging within the 50th to 75th percentiles of yields 

throughout the Chesapeake Bay watershed.

Patterns in P loads and yields are very similar to those 

in suspended-sediment yields as a result of the transport 

processes involved; much of the P is bound to sediment particles and therefore transported in association with suspended 

sediments. As with suspended sediment, much greater P loads 

were observed in 2008 and 2011 than in other years as a result 

of tropical storms inundating the area with large volumes of 

precipitation. Dissolved P accounts for only a small fraction 

of the total P transported, and the dissolved fraction load is 

more closely related to the total annual streamflow, whereas 
References Cited 53

total P and the particulate fraction loads are more closely 

related to the annual peak streamflow. Phosphorus yields, and 

the dissolved P yield in particular, were greatest at Flatlick 

Branch as a result of the P-rich parent material in the Triassic 

physiographic province. Total P yields from the streams in 

Fairfax County were comparable to the yields observed in 

urban Atlanta, Ga., although they were greater than the yields 

observed in the developed watersheds of North Carolina and 

the Occoquan River watershed. The Fairfax County P yields 

were among the highest (75th to 90th percentile) of the yields 

observed throughout the Chesapeake Bay watershed.

Evaluation of the health of the aquatic communities in the 

streams monitored was assessed through annual benthic macroinvertebrate sampling. Benthic macroinvertebrate communities 

were summarized using the Fairfax County index of biotic 

integrity (IBI) and the Virginia stream condition index (SCI). 

The IBI metric results indicate that the aquatic communities 

in the majority of streams monitored are in poor condition, 

with just one stream, Castle Creek, having results indicating 

relatively high quality aquatic health. The SCI metric results, 

which are based on condition observed throughout Virginia, 

indicate that all but one of the samples collected were below 

the threshold indicating an impaired condition.

Although most measures indicate that conditions in 

streams throughout Fairfax County are generally poor, the 

conditions are consistent with the current understanding of the 

effects of urbanization on stream health. Efforts to mitigate the 

detrimental impacts of urbanization are being implemented 

throughout Fairfax County, and this monitoring program 

uniquely positions Fairfax County to be able to directly assess 

the effectiveness of these implementation activities.

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Appendix 1

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01644343

Annelida Oligochaeta

No samples collected

2008–10

98 21

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx 2 –

Arthropoda Insecta Coleoptera Dryopidae Helichus 1 –

Arthropoda Insecta Coleoptera Elmidae Oulimnius 1 –

Arthropoda Insecta Coleoptera Elmidae Stenelmis 2 –

Arthropoda Insecta Diptera Chironomidae 108 190

Arthropoda Insecta Diptera Empididae Chelifera 1 –

Arthropoda Insecta Diptera Simuliidae Simulium – 4

Arthropoda Insecta Diptera Tipulidae Tipula – 2

Arthropoda Insecta Plecoptera Leuctridae Leuctra – 1

Arthropoda Insecta Plecoptera Nemouridae Amphinemura 1 1

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 6 –

Arthropoda Insecta Trichoptera Philopotamidae Dolophilodes – 1

Mollusca Gastropoda Limnophila Lymnaeidae 1 –

01645704

Annelida Oligochaeta – – 44 1 –

Annelida Oligochaeta Oligochaeta – 4 – – –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 10 – 9

Arthropoda Insecta Coleoptera Elmidae Dubiraphia – – 1 – 1

Arthropoda Insecta Coleoptera Elmidae Macronychus – – 1 – 1

Arthropoda Insecta Coleoptera Elmidae Microcylloepus – 3 15 – –

Arthropoda Insecta Coleoptera Elmidae Optioservus – – 4 – –

Arthropoda Insecta Coleoptera Elmidae Oulimnius 2 – – – 2

Arthropoda Insecta Coleoptera Elmidae Stenelmis 2 2 30 20 33

Arthropoda Insecta Diptera Ceratopogonidae Mallochohelea – 1 – – –

Arthropoda Insecta Diptera Chironomidae 223 209 81 177 148

Arthropoda Insecta Diptera Simuliidae Simulium – 2 5 1 5

Arthropoda Insecta Diptera Tipulidae Antocha – 1 – – –

Arthropoda Insecta Diptera Tipulidae Tipula – – 1 – –

Arthropoda Insecta Odonata Aeshnidae Boyeria 1 – – – –

Arthropoda Insecta Odonata Calopterygidae Calopteryx 1 – 1 – 1

Arthropoda Insecta Odonata Coenagrionidae – – 1 – 1

Arthropoda Insecta Odonata Coenagrionidae Argia – – 1 – –

Arthropoda Insecta Odonata Libellulidae – – – – 1

Arthropoda Insecta Trichoptera Hydropsychidae – – 1 – –

Appendix 1 57
58 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01645704—Continued

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 3 13 5 5 3

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 2 2 4 1 1

Arthropoda Insecta Trichoptera Limnephilidae Ironoquia – – 1 – –

Arthropoda Insecta Trichoptera Philopotamidae Chimarra – – 1 – –

Arthropoda Insecta Trichoptera Philopotamidae Wormaldia – 1 – – –

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula – – – 1 –

Mollusca Bivalvia Pelecypoda Sphaeriidae Pisidium – – 1 – –

Platyhelminthes Turbellaria Tricladida Planariidae – – – 4 –

01645745

Annelida Oligochaeta – – 6 1 1

Annelida Oligochaeta Oligochaeta 3 2 – – –

Arthropoda Crustacea Amphipoda Crangonyctidae – – 1 – –

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx – – 1 – –

Arthropoda Crustacea Decapoda Cambaridae – – 1 – 1

Arthropoda Insecta Coleoptera Dryopidae Helichus 1 – – 1 1

Arthropoda Insecta Coleoptera Elmidae Ancyronyx 2 3 2 3 12

Arthropoda Insecta Coleoptera Elmidae Dubiraphia 3 6 – 4 2

Arthropoda Insecta Coleoptera Elmidae Macronychus 1 – – – 3

Arthropoda Insecta Coleoptera Elmidae Optioservus 17 4 19 – 4

Arthropoda Insecta Coleoptera Elmidae Oulimnius 2 2 – – –

Arthropoda Insecta Coleoptera Elmidae Stenelmis 1 – 2 – –

Arthropoda Insecta Diptera Ceratopogonidae Dasyhelea 1 – – – –

Arthropoda Insecta Diptera Chironomidae 149 161 140 105 150

Arthropoda Insecta Diptera Empididae Clinocera 1 – – 5 –

Arthropoda Insecta Diptera Empididae Hemerodromia 1 – – – –

Arthropoda Insecta Diptera Simuliidae Prosimulium 3 1 1 – –

Arthropoda Insecta Diptera Simuliidae Simulium – – – 1 1

Arthropoda Insecta Diptera Tipulidae Dicranota 3 1 – – 1

Arthropoda Insecta Diptera Tipulidae Tipula 2 – 1 – 1

Arthropoda Insecta Ephemeroptera Baetidae – 1 – – –

Arthropoda Insecta Ephemeroptera Ephemerellidae Eurylophella – – – 2 –

Arthropoda Insecta Ephemeroptera Heptageniidae – – – – 2

Arthropoda Insecta Ephemeroptera Heptageniidae Maccaffertium 3 6 7 28 4

Arthropoda Insecta Ephemeroptera Heptageniidae Stenonema – – – 2 –

Arthropoda Insecta Megaloptera Corydalidae Nigronia 1 – – – –

Arthropoda Insecta Odonata Calopterygidae Calopteryx – – – – 1

Arthropoda Insecta Odonata Coenagrionidae – – – – 1

Arthropoda Insecta Odonata Coenagrionidae Argia – – 1 – –

Arthropoda Insecta Odonata Gomphidae Gomphus 1 – – 1 –

Arthropoda Insecta Plecoptera Leuctridae – – – 1 –
Appendix 1 59

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01645745—Continued

Arthropoda Insecta Plecoptera Nemouridae Amphinemura – – 1 – –

Arthropoda Insecta Plecoptera Nemouridae Shipsa 1 3 – – –

Arthropoda Insecta Plecoptera Taeniopterygidae – – – 1 –

Arthropoda Insecta Plecoptera Taeniopterygidae Oemopteryx 1 – 1 – –

Arthropoda Insecta Trichoptera – – 2 – –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 1 12 6 36 15

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche – – 2 – –

Arthropoda Insecta Trichoptera Philopotamidae Chimarra 2 8 12 9 4

Arthropoda Insecta Trichoptera Uenoidae Neophylax – – – 1 –

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula – 1 – – –

Mollusca Bivalvia Pelecypoda Sphaeriidae 1 – – – –

Mollusca Gastropoda Limnophila Planorbidae – – – 1 –

01645762

Annelida Oligochaeta – – 2 13 11

Annelida Oligochaeta Oligochaeta 1 2 – – –

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx – – 2 – 3

Arthropoda Crustacea Decapoda Cambaridae – 1 1 1 –

Arthropoda Crustacea Decapoda Cambaridae Cambarus 2 – – – –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 1 2 8

Arthropoda Insecta Coleoptera Elmidae Dubiraphia 3 2 1 – 3

Arthropoda Insecta Coleoptera Elmidae Macronychus – – 1 – –

Arthropoda Insecta Coleoptera Elmidae Oulimnius 3 – – 1 1

Arthropoda Insecta Coleoptera Elmidae Stenelmis 1 1 – 1 2

Arthropoda Insecta Coleoptera Hydrophilidae Tropisternus 1 – – – –

Arthropoda Insecta Diptera Chironomidae 185 192 164 148 154

Arthropoda Insecta Diptera Empididae Chelifera – – 1 – –

Arthropoda Insecta Diptera Empididae Clinocera – – 4 1 1

Arthropoda Insecta Diptera Empididae Hemerodromia – – – – 1

Arthropoda Insecta Diptera Simuliidae Prosimulium 1 1 2 – –

Arthropoda Insecta Diptera Simuliidae Simulium – – – – 7

Arthropoda Insecta Diptera Stratiomyidae Odontomyia – – – 1 –

Arthropoda Insecta Diptera Tipulidae Antocha – – – 1 –

Arthropoda Insecta Diptera Tipulidae Dicranota – – 1 – –

Arthropoda Insecta Diptera Tipulidae Tipula 2 1 2 – 1

Arthropoda Insecta Ephemeroptera Baetidae 2 1 – – –

Arthropoda Insecta Ephemeroptera Ephemerellidae Eurylophella – – – – 1

Arthropoda Insecta Ephemeroptera Heptageniidae Maccaffertium – – – 1 –

Arthropoda Insecta Odonata Aeshnidae Boyeria 1 – – – –

Arthropoda Insecta Odonata Coenagrionidae Argia – – 3 – –

Arthropoda Insecta Odonata Coenagrionidae Enallagma – – 1 1 –
60 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01645762—Continued

Arthropoda Insecta Odonata Gomphidae Hagenius – – – 1 –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 10 5 4 17 6

Arthropoda Insecta Trichoptera Hydropsychidae Diplectrona – – – 1 –

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche – 4 3 – 1

Arthropoda Insecta Trichoptera Philopotamidae Chimarra 5 2 13 10 2

Arthropoda Insecta Trichoptera Philopotamidae Wormaldia 1 – – – 2

Arthropoda Insecta Trichoptera Uenoidae Neophylax – – – 1 –

01645844

Annelida Oligochaeta – – 12 10 29

Annelida Oligochaeta Oligochaeta 1 3 – – –

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx – – 1 – –

Arthropoda Crustacea Isopoda Asellidae Caecidotea – 1 – – –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 1 – –

Arthropoda Insecta Coleoptera Elmidae Stenelmis – 1 – – –

Arthropoda Insecta Diptera – – – 1 –

Arthropoda Insecta Diptera Chironomidae 56 147 123 130 176

Arthropoda Insecta Diptera Empididae Chelifera – – 3 – –

Arthropoda Insecta Diptera Empididae Hemerodromia – 3 2 1 –

Arthropoda Insecta Diptera Simuliidae Simulium – – 2 2 3

Arthropoda Insecta Diptera Tipulidae Antocha – 1 4 – –

Arthropoda Insecta Diptera Tipulidae Tipula 3 4 1 1 –

Arthropoda Insecta Lepidoptera – – 1 – –

Arthropoda Insecta Megaloptera Corydalidae Corydalus – – – 1 –

Arthropoda Insecta Odonata Calopterygidae Calopteryx 1 1 1 19 –

Arthropoda Insecta Odonata Coenagrionidae Argia 2 1 4 20 2

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 4 9 7 8 1

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 6 6 24 4 –

Arthropoda Insecta Trichoptera Philopotamidae Chimarra 1 10 12 3 –

Arthropoda Insecta Trichoptera Philopotamidae Wormaldia – – – – 1

Mollusca Bivalvia Pelecypoda Sphaeriidae Pisidium – – 1 – –

Mollusca Gastropoda Limnophila Physidae 1 1 2 – 1

Mollusca Gastropoda Limnophila Planorbidae Menetus – 1 – – –

01645940

Annelida Oligochaeta – – 28 31 –

Annelida Oligochaeta Oligochaeta 2 – – – –

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx 1 – – – –

Arthropoda Crustacea Decapoda Cambaridae Cambarus – 1 – – –

Arthropoda Crustacea Isopoda Asellidae Lirceus 1 – – – –

Arthropoda Insecta Coleoptera Dytiscidae – – – 1 1

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 1 – 1
Appendix 1 61

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01645940—Continued

Arthropoda Insecta Coleoptera Elmidae Dubiraphia – – – 7 7

Arthropoda Insecta Coleoptera Elmidae Macronychus – – – 1 –

Arthropoda Insecta Coleoptera Elmidae Optioservus 1 3 1 2 –

Arthropoda Insecta Coleoptera Elmidae Oulimnius 23 3 3 1 1

Arthropoda Insecta Coleoptera Elmidae Stenelmis 2 – 1 – –

Arthropoda Insecta Diptera Chironomidae 114 184 135 125 189

Arthropoda Insecta Diptera Empididae Chelifera – 2 1 – –

Arthropoda Insecta Diptera Empididae Clinocera 1 – – 1 –

Arthropoda Insecta Diptera Simuliidae Simulium – 1 2 – 8

Arthropoda Insecta Diptera Tipulidae Antocha 3 – – – –

Arthropoda Insecta Diptera Tipulidae Tipula 2 – – – –

Arthropoda Insecta Ephemeroptera Baetidae – – 1 2 1

Arthropoda Insecta Ephemeroptera Ephemerellidae – – – 1 –

Arthropoda Insecta Ephemeroptera Ephemerellidae Ephemerella – – – 1 –

Arthropoda Insecta Ephemeroptera Ephemerellidae Eurylophella – – 1 1 1

Arthropoda Insecta Ephemeroptera Ephemerellidae Serratella 3 – – – –

Arthropoda Insecta Ephemeroptera Heptageniidae Maccaffertium 4 – 2 1 1

Arthropoda Insecta Ephemeroptera Leptophlebiidae Triaenodes – 1 – – –

Arthropoda Insecta Odonata Aeshnidae – – – 1 –

Arthropoda Insecta Odonata Aeshnidae Boyeria 2 – 1 – 2

Arthropoda Insecta Odonata Calopterygidae Calopteryx 2 – – 3 2

Arthropoda Insecta Odonata Gomphidae – – – 1 –

Arthropoda Insecta Plecoptera Nemouridae Amphinemura 7 – 18 12 –

Arthropoda Insecta Plecoptera Perlidae – – 1 2 –

Arthropoda Insecta Plecoptera Perlodidae – 1 – 1 1

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 42 9 5 9 –

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche – 2 – – 1

Arthropoda Insecta Trichoptera Leptoceridae – – – – 1

Arthropoda Insecta Trichoptera Philopotamidae Chimarra 2 2 – – –

Mollusca Gastropoda Limnophila Lymnaeidae – – – 1 –

01646305

Annelida Oligochaeta – – 16 31 22

Annelida Oligochaeta Oligochaeta 25 4 – – –

Arthropoda Crustacea Isopoda Asellidae Lirceus 1 – – – –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 1 1 –

Arthropoda Insecta Diptera Chironomidae 170 220 150 155 185

Arthropoda Insecta Diptera Empididae Hemerodromia – 1 – – –

Arthropoda Insecta Diptera Simuliidae Simulium – – 6 – 2

Arthropoda Insecta Diptera Tipulidae Antocha – – 1 – 1

Arthropoda Insecta Diptera Tipulidae Brachypremna – – 1 – –
62 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01646305—Continued

Arthropoda Insecta Diptera Tipulidae Tipula 1 1 – – –

Arthropoda Insecta Ephemeroptera Baetidae – – – – 1

Arthropoda Insecta Odonata Calopterygidae Calopteryx – 1 2 – –

Arthropoda Insecta Odonata Coenagrionidae Enallagma – – 1 – –

Arthropoda Insecta Trichoptera Hydropsychidae – 1 – – –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 3 2 9 13 –

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 1 3 10 2 2

Arthropoda Insecta Trichoptera Philopotamidae Chimarra 1 – 1 3 –

Mollusca Gastropoda Limnophila Ancylidae – – 1 – –

01652789

Annelida Oligochaeta – – 35 3 1

Annelida Oligochaeta Oligochaeta 9 7 – – –

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx 2 – – – –

Arthropoda Crustacea Isopoda Asellidae Caecidotea 1 – – – –

Arthropoda Insecta Coleoptera Dytiscidae – – – – 1

Arthropoda Insecta Diptera Chironomidae 22 200 151 29 65

Arthropoda Insecta Diptera Empididae Chelifera – – 1 – –

Arthropoda Insecta Diptera Empididae Hemerodromia – 1 – – –

Arthropoda Insecta Diptera Simuliidae Simulium 1 – – – –

Arthropoda Insecta Diptera Tipulidae Antocha – 1 4 – –

Arthropoda Insecta Diptera Tipulidae Tipula 1 – 2 2 –

Arthropoda Insecta Odonata Calopterygidae Calopteryx 1 1 1 4 –

Arthropoda Insecta Odonata Coenagrionidae Argia 2 – – 2 –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 1 – 2 – 1

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 2 – 1 1 –

Mollusca Gastropoda Limnophila Lymnaeidae Pseudosuccinea – 1 – – –

Mollusca Gastropoda Limnophila Physidae – 1 1 – –

01652860

Annelida Oligochaeta – – 67 2 23

Annelida Oligochaeta Oligochaeta 5 2 – – –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 1 – –

Arthropoda Insecta Coleoptera Elmidae Stenelmis – – 1 – –

Arthropoda Insecta Diptera Chironomidae 189 208 132 36 191

Arthropoda Insecta Diptera Empididae Hemerodromia – 1 – – –

Arthropoda Insecta Diptera Simuliidae Simulium – – 1 – –

Arthropoda Insecta Diptera Tipulidae Tipula 1 – 1 2 –

Arthropoda Insecta Lepidoptera Pyralidae – – – 1 –

Arthropoda Insecta Odonata Calopterygidae Calopteryx 1 – 1 1 1

Arthropoda Insecta Odonata Coenagrionidae Argia – – 1 – –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 2 – 1 3 –

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 4 3 4 2 1

Arthropoda Insecta Trichoptera Philopotamidae Chimarra – – – 1 –
Appendix 1 63

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01653717

Annelida Oligochaeta – – 43 30 28

Annelida Oligochaeta Oligochaeta 10 7 – – –

Arthropoda Crustacea Isopoda Asellidae Caecidotea – – – 1 –

Arthropoda Insecta Diptera Chironomidae 193 205 166 180 178

Arthropoda Insecta Diptera Tipulidae Rhabdomastix – – – 1 –

Arthropoda Insecta Diptera Tipulidae Tipula – 1 1 1 1

Arthropoda Insecta Odonata Calopterygidae Calopteryx – – 1 1 –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche – – – 1 –

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula – 1 – – –

01653844

Annelida Oligochaeta

No samples collected

2008–10

82 61

Arthropoda Crustacea Amphipoda Gammaridae Gammarus 3 –

Arthropoda Crustacea Decapoda Cambaridae 2 –

Arthropoda Insecta Diptera Chironomidae 117 157

Arthropoda Insecta Odonata Coenagrionidae 1 –

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula 2 –

Mollusca Gastropoda Limnophila Planorbidae Planorbella 3 –

01654500

Annelida Oligochaeta

No samples collected

2008–10

147 12

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – 1

Arthropoda Insecta Coleoptera Elmidae Stenelmis – 1

Arthropoda Insecta Diptera Chironomidae 60 191

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 2 8

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche – 8

Mollusca Gastropoda Limnophila Physidae – 1

01655305

Annelida Oligochaeta

No samples collected

2008–10

102 33

Arthropoda Insecta Coleoptera Elmidae Ancyronyx 2 –

Arthropoda Insecta Diptera Chironomidae 92 176

Arthropoda Insecta Diptera Simuliidae Simulium 1 –

Arthropoda Insecta Diptera Tipulidae Tipula 2 –

Arthropoda Insecta Megaloptera Corydalidae Corydalus 2 –

Arthropoda Insecta Odonata Coenagrionidae Argia 1 –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche – 1

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 1 –

Mollusca Gastropoda Limnophila Physidae 1 –

01656903

Annelida Oligochaeta – – 174 15 80

Annelida Oligochaeta Oligochaeta 19 9 – – –

Arthropoda Crustacea Amphipoda – – – 1 –

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx – – – 1 –
64 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01656903—Continued

Arthropoda Crustacea Isopoda Asellidae Caecidotea – – 1 – –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 3 2 1

Arthropoda Insecta Coleoptera Elmidae Dubiraphia – – – 2 2

Arthropoda Insecta Coleoptera Elmidae Microcylloepus – – – 4 –

Arthropoda Insecta Coleoptera Elmidae Stenelmis – – – 3 4

Arthropoda Insecta Coleoptera Haliplidae Peltodytes – – – 6 –

Arthropoda Insecta Coleoptera Hydrophilidae – – – – 1

Arthropoda Insecta Diptera Chironomidae 38 184 35 144 114

Arthropoda Insecta Diptera Simuliidae Simulium – – 3 4 –

Arthropoda Insecta Diptera Tipulidae Tipula – 1 – – –

Arthropoda Insecta Ephemeroptera Ephemerellidae Ephemerella – – – – 3

Arthropoda Insecta Lepidoptera Pyralidae – – – 1 –

Arthropoda Insecta Odonata Coenagrionidae – – – – 1

Arthropoda Insecta Odonata Coenagrionidae Enallagma – 1 3 5 –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche – 2 – 8 5

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 1 3 – – –

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula 2 – – 3 3

Mollusca Bivalvia Pelecypoda Sphaeriidae – – 1 – –

Mollusca Gastropoda Limnophila Ancylidae 1 – – – –

Mollusca Gastropoda Limnophila Physidae 1 1 1 1 –

Mollusca Gastropoda Limnophila Planorbidae – 2 – – –

Mollusca Gastropoda Limnophila Planorbidae Menetus – – – 1 –

01657100

Annelida Oligochaeta

No samples collected

2008–10

74 8

Arthropoda Insecta Coleoptera Dryopidae Helichus 1 –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx 1 2

Arthropoda Insecta Coleoptera Elmidae Dubiraphia 2 4

Arthropoda Insecta Diptera Chironomidae 136 186

Arthropoda Insecta Diptera Simuliidae Simulium – 3

Arthropoda Insecta Odonata 1 –

Arthropoda Insecta Odonata Coenagrionidae 1 –

Arthropoda Insecta Odonata Coenagrionidae Argia 1 –

Arthropoda Insecta Odonata Coenagrionidae Enallagma – 5

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 2 1

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula 2 –

Mollusca Gastropoda Limnophila Ancylidae 1 –

Mollusca Gastropoda Limnophila Lymnaeidae 1 –

Mollusca Gastropoda Limnophila Physidae 3 –
Appendix 1 65

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01657322

Annelida Oligochaeta – – 24 6 67

Annelida Oligochaeta Oligochaeta 3 76 – – –

Arthropoda Insecta Coleoptera Dryopidae Helichus – – – – 1

Arthropoda Insecta Coleoptera Elmidae Stenelmis 4 1 – 1 1

Arthropoda Insecta Diptera Chironomidae 173 120 181 197 140

Arthropoda Insecta Diptera Empididae Clinocera 1 – – – –

Arthropoda Insecta Diptera Tipulidae Tipula 6 4 4 5 4

Arthropoda Insecta Odonata Calopterygidae Calopteryx – – – 1 –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche – 2 – 1 –

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche – 2 – – –

Arthropoda Insecta Trichoptera Limnephilidae Ironoquia – – – 1 –

Arthropoda Insecta Trichoptera Philopotamidae Chimarra – 1 – – –

Mollusca Bivalvia Pelecypoda Sphaeriidae 1 – – – –

Mollusca Gastropoda Limnophila Physidae 1 1 1 – –

Platyhelminthes Turbellaria Tricladida Planariidae 1 – – – –

01657394

Annelida Oligochaeta – – 2 – 1

Annelida Oligochaeta Oligochaeta – 2 – – –

Arthropoda Crustacea Amphipoda Crangonyctidae Crangonyx 1 – 6 3 –

Arthropoda Crustacea Isopoda Asellidae Caecidotea – – 1 – –

Arthropoda Crustacea Isopoda Asellidae Lirceus 1 – – – –

Arthropoda Insecta Coleoptera Dryopidae Helichus – – 3 – –

Arthropoda Insecta Coleoptera Dytiscidae – – – – 1

Arthropoda Insecta Coleoptera Elmidae – – – – 2

Arthropoda Insecta Coleoptera Elmidae Dubiraphia 2 1 3 1 –

Arthropoda Insecta Coleoptera Elmidae Microcylloepus – – 3 1 –

Arthropoda Insecta Coleoptera Elmidae Optioservus 5 – – – –

Arthropoda Insecta Coleoptera Elmidae Oulimnius 5 2 7 2 –

Arthropoda Insecta Coleoptera Elmidae Stenelmis 4 1 2 2 3

Arthropoda Insecta Coleoptera Hydrophilidae Hydrobius – – 1 – –

Arthropoda Insecta Coleoptera Hydrophilidae Tropisternus – – 2 – –

Arthropoda Insecta Coleoptera Psephenidae Ectopria 1 – – – –

Arthropoda Insecta Coleoptera Psephenidae Psephenus – – – – 1

Arthropoda Insecta Coleoptera Ptilodactylidae Anchytarsus – – 5 1 1

Arthropoda Insecta Diptera Ceratopogonidae Bezzia – – 2 1 –

Arthropoda Insecta Diptera Ceratopogonidae Dasyhelea – – 1 – –

Arthropoda Insecta Diptera Ceratopogonidae Mallochohelea – – – – 1
66 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

01657394—Continued

Arthropoda Insecta Diptera Chironomidae 56 129 90 132 121

Arthropoda Insecta Diptera Empididae Chelifera – – 1 – –

Arthropoda Insecta Diptera Empididae Clinocera 7 1 6 4 –

Arthropoda Insecta Diptera Simuliidae Prosimulium 5 – – 1 –

Arthropoda Insecta Diptera Simuliidae Simulium – 1 4 6 12

Arthropoda Insecta Diptera Tipulidae Antocha – 1 – 1 –

Arthropoda Insecta Diptera Tipulidae Tipula – – 3 5 –

Arthropoda Insecta Ephemeroptera Ameletidae Ameletus – 1 – – –

Arthropoda Insecta Ephemeroptera Baetidae Acentrella – – – – 1

Arthropoda Insecta Ephemeroptera Baetidae Centroptilum – 7 – – –

Arthropoda Insecta Ephemeroptera Caenidae Caenis – – 8 1 –

Arthropoda Insecta Ephemeroptera Ephemerellidae Ephemerella 1 – – – –

Arthropoda Insecta Ephemeroptera Ephemerellidae Eurylophella – 1 – – –

Arthropoda Insecta Ephemeroptera Heptageniidae 1 – – 2 –

Arthropoda Insecta Ephemeroptera Heptageniidae Maccaffertium – 3 4 1 –

Arthropoda Insecta Ephemeroptera Isonychiidae Isonychia – – – 1 –

Arthropoda Insecta Megaloptera Corydalidae Corydalus – – – 1 –

Arthropoda Insecta Megaloptera Corydalidae Nigronia – 1 3 – –

Arthropoda Insecta Odonata Gomphidae 1 1 – 4 –

Arthropoda Insecta Odonata Gomphidae Stylogomphus – – 2 – –

Arthropoda Insecta Plecoptera – – 2 – –

Arthropoda Insecta Plecoptera Leuctridae – 1 – – 5

Arthropoda Insecta Plecoptera Nemouridae Amphinemura 99 5 14 14 51

Arthropoda Insecta Plecoptera Perlidae Eccoptura – – 1 – –

Arthropoda Insecta Plecoptera Perlodidae Isoperla 2 – – – 2

Arthropoda Insecta Trichoptera Hydropsychidae – 2 – – –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 6 13 7 8 3

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 1 4 2 2 –

Arthropoda Insecta Trichoptera Limnephilidae – – – 1 –

Arthropoda Insecta Trichoptera Philopotamidae Chimarra – 24 11 10 1

Arthropoda Insecta Trichoptera Polycentropidae 2 – – – –

Arthropoda Insecta Trichoptera Polycentropidae Neureclipsis – 3 – – –

Arthropoda Insecta Trichoptera Polycentropidae Polycentropus – – 1 – –

Arthropoda Insecta Trichoptera Rhyacophilidae Rhyacophila – – – – 1

Arthropoda Insecta Trichoptera Uenoidae Neophylax 1 6 3 4 –

Mollusca Bivalvia Pelecypoda Sphaeriidae 1 – – – –

Mollusca Gastropoda Limnophila Physidae – – 1 – –

Mollusca Gastropoda Limnophila Planorbidae – – 1 – –
Appendix 1 67

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

0164425950

Annelida Oligochaeta

No samples collected

2008–10

119 16

Arthropoda Insecta Diptera Chironomidae 81 205

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula 1 –

0165690673

Annelida Oligochaeta – – 81 65 11

Annelida Oligochaeta Oligochaeta 22 1 – – –

Arthropoda Crustacea Amphipoda Talitridae Hyalella 1 – – 1 –

Arthropoda Crustacea Isopoda Asellidae Caecidotea – – 1 – –

Arthropoda Insecta Coleoptera Elmidae Dubiraphia – – 2 – –

Arthropoda Insecta Coleoptera Elmidae Stenelmis – 1 – 2 –

Arthropoda Insecta Diptera Ceratopogonidae Monohelea – – – 3 –

Arthropoda Insecta Diptera Chironomidae 33 194 104 119 210

Arthropoda Insecta Diptera Simuliidae Prosimulium – – – 1 –

Arthropoda Insecta Diptera Simuliidae Simulium – 1 1 3 1

Arthropoda Insecta Diptera Tipulidae Tipula – – – 1 –

Arthropoda Insecta Ephemeroptera Baetidae – – 1 – –

Arthropoda Insecta Lepidoptera Pyralidae – 1 – – –

Arthropoda Insecta Odonata Calopterygidae Calopteryx 1 – – – –

Arthropoda Insecta Odonata Coenagrionidae Argia 2 – – – –

Arthropoda Insecta Odonata Coenagrionidae Enallagma – – 1 1 –

Arthropoda Insecta Odonata Coenagrionidae Ischnura – – – 2 –

Arthropoda Insecta Trichoptera Hydropsychidae Cheumatopsyche 3 1 – 15 –

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 6 3 – 1 –

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula 7 4 1 – –

Mollusca Bivalvia Pelecypoda Sphaeriidae – – 3 – –

Mollusca Gastropoda Limnophila Ancylidae 1 1 – – –

Mollusca Gastropoda Limnophila Lymnaeidae – – 2 – –

Mollusca Gastropoda Limnophila Lymnaeidae Pseudosuccinea – 1 – – –

Mollusca Gastropoda Limnophila Physidae 1 3 9 1 1

0165694286

Annelida Oligochaeta – – 18 51 33

Annelida Oligochaeta Oligochaeta 40 2 – – –

Arthropoda Insecta Coleoptera Elmidae Ancyronyx – – 4 – 1

Arthropoda Insecta Coleoptera Elmidae Stenelmis 12 2 92 45 24

Arthropoda Insecta Diptera Chironomidae 147 208 103 120 155

Arthropoda Insecta Diptera Empididae Chelifera – – 1 – –

Arthropoda Insecta Diptera Empididae Hemerodromia 1 – – – –

Arthropoda Insecta Diptera Simuliidae Simulium – – 2 – 1

Arthropoda Insecta Odonata Calopterygidae Calopteryx – – – – 1

Arthropoda Insecta Odonata Coenagrionidae Argia 1 – – – 1
68 Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12

Table 1–1. Counts of benthic macroinvertebrates for each annual sample, by station identifier.—Continued 

[Station identifiers shown in spanner rows are defined in tables 1 and 9. –, organism not found in sample]

Phylum Class Order Family Genus 2008 2009 2010 2011 2012

0165694286—Continued

Arthropoda Insecta Trichoptera Hydropsychidae Hydropsyche 2 – 1 1 1

Mollusca Bivalvia Pelecypoda Corbiculidae Corbicula 1 – – – 3

Mollusca Gastropoda Limnophila Planorbidae – – 1 – –

Nematomorpha – – – – 1
Manuscript approved April 8, 2014

For more information about this publication, contact:

Director

USGS Virginia Water Science Center

1730 East Parham Road

Richmond, Virginia 23228

(804) 261-2600

email: dc_va@usgs.gov

or visit our website at

http://va.water.usgs.gov/

Prepared by: 

USGS Science Publishing Network

Raleigh Publishing Service Center

Edited by Mike Deacon

Illustrations by James Banton

Layout by Kimberly Swidarski
ISBN 978-1-4113-3788-6

9781411337886

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20145073

Jastram—Streamflow, Water Quality, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007–12—Scientific Investigations Report 2014–5073

Printed on recycled paper