U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2016–5093

Prepared in cooperation with the U.S. Environmental Protection Agency 

Chesapeake Bay Program

Spatial and Temporal Variation of Stream Chemistry 

Associated With Contrasting Geology and Land-Use 

Patterns in the Chesapeake Bay Watershed: Summary 

of Results from Smith Creek, Virginia; Upper Chester River, 

Maryland; Conewago Creek, Pennsylvania; and Difficult Run, 

Virginia, 2010–2013

Spatial and Temporal Variation of Stream 

Chemistry Associated With Contrasting 

Geology and Land-Use Patterns in the 

Chesapeake Bay Watershed: Summary 

of Results from Smith Creek, Virginia; 

Upper Chester River, Maryland; Conewago 

Creek, Pennsylvania; and Difficult Run, 

Virginia, 2010–2013

By Kenneth E. Hyer, Judith M. Denver, Michael J. Langland, James S. Webber, 

J.K. Böhlke, W. Dean Hively, and John W. Clune

Prepared in cooperation with the U.S. Environmental Protection Agency 

Chesapeake Bay Program

Scientific Investigations Report 2016–5093

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, Director

U.S. Geological Survey, Reston, Virginia: 2016

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Suggested citation:

Hyer, K.E., Denver, J.M., Langland, M.J., Webber, J.S., Böhlke, J.K., Hively, W.D., and Clune, J.W., 2016, Spatial 

and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the 

Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; 

Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013: U.S. Geological Survey Scientific Investigations Report 2016–5093, 211 p., http://dx.doi.org/10.3133/sir20165093.

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

ISBN 978-1-4113-4085-5
iii

Acknowledgments

Funding for this study was provided by the U.S. Geological Survey (USGS) Ecosystems Mission 

Area as part of the Priority Ecosystem Science (PES) Program. Sincere appreciation is extended 

to the many landowners in all four study watersheds who provided us with access to many of 

the study sites. Cooperation from the U.S. Department of Agriculture, Natural Resources Conservation Service, as well as the Soil and Water Conservation Service, was instrumental for better 

understanding each basin and for gaining access to study sites. Additional contributions of data 

and information were made by the Virginia Department of Environmental Quality, Pennsylvania 

Department of Environmental Protection, the Maryland Department of Natural Resources, and 

the U.S. Environmental Protection Agency. Appreciation is extended to the Stormwater Planning Division of the Fairfax County Department of Public Works and Environmental Services for 

supporting elements of water-quality monitoring in Difficult Run that were incorporated into this 

report and for providing data on the implementation of best management practices throughout 

Difficult Run. 

We are indebted to the many USGS field technicians and hydrologists who have contributed 

to the collection of field samples and other technical content throughout the report. Technical reviews by Doug Burns of the USGS New York Water Science Center and Angie Crain of 

the USGS Indiana-Kentucky Water Science Center strengthened this report and are sincerely 

appreciated. 

v

Contents

Acknowledgments........................................................................................................................................iii

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

Individual Small Watershed Summaries...........................................................................................1

Smith Creek...................................................................................................................................1

Upper Chester River....................................................................................................................2

Conewago Creek..........................................................................................................................3

Difficult Run...................................................................................................................................4

Introduction.....................................................................................................................................................4

Purpose and Scope ..............................................................................................................................6

Description of Study Watersheds ......................................................................................................6

The Smith Creek Watershed ......................................................................................................6

The Upper Chester River Watershed........................................................................................9

The Conewago Creek Watershed.............................................................................................9

The Difficult Run Watershed....................................................................................................12

Study Approach and Methods...................................................................................................................12

General Study Design.........................................................................................................................12

Data Collection....................................................................................................................................14

Collection of Discrete Water-Quality Samples .....................................................................14

Laboratory Analysis of Water-Quality Samples....................................................................14

Continuous Monitoring of Water Quality ...............................................................................15

Statistical Analysis of Surface-Water and Water-Quality Data..................................................15

Development of Surrogate Water-Quality Models and Computation of Loads...............17

Hierarchical Agglomerative Cluster Analysis.......................................................................19

Principal Components Analysis...............................................................................................20

Interpretation of Nitrate Isotopes ....................................................................................................20

Development of Land-Use-Based Nitrogen Inputs.......................................................................21

Development of Land-Use and Conservation-Practice Datasets...............................................22

Smith Creek Watershed Water-Quality Characterization .....................................................................23

Streamflow and Water-Quality Characterization at the Intensive Monitoring Site.................23

Characterization of the Physical Hydrology..........................................................................23

Characterization of Continuous Water-Quality Data ...........................................................26

Characterization of Discrete Water-Quality Data ................................................................31

Surrogate Models for the Computation of Sediment and Nutrient Loads.................................38

Model Development ..................................................................................................................38

Interpretation of the Computed Loads ...................................................................................39

Spatial Patterns in Water Quality.....................................................................................................46

Characterization of Nitrogen Sources ............................................................................................53

Implementation of Conservation Practices and Water-Quality Response ...............................59

Conceptual Watershed Model and Implications for Management Actions .............................61

Upper Chester River Watershed Water-Quality Characterization......................................................64

Streamflow and Water-Quality Characterization at the Intensive Monitoring Site.................64

Characterization of the Physical Hydrology..........................................................................64

Characterization of Continuous Water-Quality Data ..........................................................67
vi

Characterization of Discrete Water-Quality Data ................................................................72

Surrogate Models for the Computation of Sediment and Nutrient Loads.................................81

Model Development ..................................................................................................................81

Interpretation of the Computed Loads ...................................................................................84

Spatial Patterns in Water Quality.....................................................................................................88

Characterization of Nitrogen Sources ............................................................................................95

Implementation of Conservation Practices and Water-Quality Response ...............................97

Conceptual Watershed Model and Implications for Management Actions ...........................100

Conewago Creek Watershed Water-Quality Characterization .........................................................102

Streamflow and Water-Quality Characterization at the Intensive Monitoring Site...............102

Characterization of the Physical Hydrology........................................................................102

Characterization of Continuous Water-Quality Data ........................................................105

Characterization of Discrete Water-Quality Data ..............................................................109

Surrogate Models for the Computation of Sediment and Nutrient Loads...............................121

Model Development ................................................................................................................121

Interpretation of the Computed Loads ................................................................................125

Spatial Patterns in Water Quality ..................................................................................................130

Characterization of Nitrogen Sources ..........................................................................................133

Implementation of Conservation Practices and Water-Quality Response ............................136

Conceptual Watershed Model and Implications for Management Actions ..........................138

Difficult Run Watershed Water-Quality Characterization...................................................................141

Streamflow and Water-Quality Characterization at the Intensive Monitoring Site...............141

Characterization of the Physical Hydrology .......................................................................141

Characterization of Continuous Water-Quality Data ........................................................145

Characterization of Discrete Water-Quality Data ..............................................................150

Surrogate Models for the Computation of Sediment and Nutrient Loads...............................158

Model Development ................................................................................................................158

Interpretation of the Computed Loads .................................................................................158

Spatial Patterns in Water Quality...................................................................................................164

Characterization of Nitrogen Sources ..........................................................................................170

Implementation of Conservation Practices and Water-Quality Response .............................174

Conceptual Watershed Model and Implications for Management Actions ..........................175

Comparison of Water-Quality Patterns Among Study Watersheds ..................................................178

Future Directions........................................................................................................................................190

Summary and Conclusions.......................................................................................................................191

Smith Creek Watershed..........................................................................................................191

Upper Chester River Watershed ...........................................................................................192

Conewago Creek Watershed ................................................................................................192

Difficult Run Watershed..........................................................................................................193

References Cited........................................................................................................................................194

Appendix 1...................................................................................................................................................201
vii

Figures

1. Map showing physiographic provinces and the location of the four study

watersheds within the Chesapeake Bay watershed..............................................................7

2. Maps showing monitoring stations, land use, underlying geology, and

sinkhole density in the Smith Creek watershed ......................................................................8

3. Maps showing monitoring stations, land use, and underlying geology in the

Upper Chester River watershed...............................................................................................10

4. Maps showing monitoring stations, land use, and underlying geology in the

Conewago Creek watershed ....................................................................................................11

5. Maps showing monitoring stations, land use, and underlying geology in the

Difficult Run watershed .............................................................................................................13

6. Graph showing typical variability in δ15N and δ18O values for nitrate derived 

from various sources, with emphasis on conditions relevant to the Chesapeake 

Bay watershed ............................................................................................................................21

7. Map showing monitoring stations and the stream network within the Smith 

Creek watershed, Virginia.........................................................................................................24

8. Hydrograph for the Smith Creek streamgage and quantiles that represent 

historical monthly flow data with the timing of various sampling events from 

April 2010 through September 2013 .........................................................................................25

9. Contour plots showing continuous water temperature, dissolved oxygen,

and pH data with the hydrograph from the Smith Creek streamgage, 2010–2013...........27

10. Contour plots showing continuous turbidity, specific conductance, and

nitrate data with the hydrograph from the Smith Creek streamgage................................28

11. Graphs and boxplots showing water temperature, specific conductance, and

pH data from continuous monitoring and discrete storm and routine samples 

for warm (April to September) and cool (October to March) seasons, Smith Creek 

streamgage, 2010–2013..............................................................................................................29

12. Graphs and boxplots showing dissolved oxygen, turbidity, and nitrate data 

from continuous monitoring and discrete storm and routine samples for warm

(April to September) and cool (October to March) seasons, Smith Creek 

streamgage..................................................................................................................................30

13. Cluster analysis dendrogram showing discrete samples from the Smith Creek

streamgage with a heat map of constituent values and the breakpoint used to

produce three cluster groups, 2012–2013...............................................................................31

14. Graph showing the first two principal component axis scores for discrete 

samples from the Smith Creek streamgage along with time and streamflow, 

2012–2013 .....................................................................................................................................33

15. Graphs showing concentration-discharge data for discrete samples from the 

Smith Creek streamgage by warm and cool season............................................................35

16. Time-series plots showing concentrations of various constituents in discrete

samples from the Smith Creek streamgage ...........................................................................37

17. Graphs showing variability in nitrate isotopes in discrete samples from the 

Smith Creek streamgage and potential causes for variability, 2011–2013........................38

18. Diagnostic plots showing the observed and estimated values from the best

suspended-sediment surrogate model for the Smith Creek streamgage and 

the residual plot of model predictions, 2010–2013 ................................................................40

19. Diagnostic plots showing the observed and estimated concentrations of total

nitrogen from the best and alternative surrogate models for the Smith Creek

streamgage and the residual plot of model predictions, 2010–2013 ..................................41
viii

20. Diagnostic plots showing the observed and estimated concentrations of total

phosphorus from the best and alternative surrogate models for the Smith

Creek streamgage and the residual plot of model predictions, 2010–2013.......................42

21. Graph showing the suspended-sediment load and total annual streamflow 

at the Smith Creek streamgage for water years 2011 through 2013 ..................................43

22. Time-series plots showing the suspended-sediment load at the Smith Creek

streamgage for water years 2010 through 2013 along with the percentage of

load accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow in each water year..................................................43

23. Graph showing the total nitrogen load and total annual streamflow at the 

Smith Creek streamgage for the 2013 water year.................................................................44

24. Time-series plots showing the percentage of total nitrogen load accumulated

and the hydrograph during the period of study and the accumulation of load

and streamflow at the Smith Creek streamgage for water years 2012 and 2013.............44

25. Graph showing the total phosphorus load and total annual streamflow at the 

Smith Creek streamgage for water years 2011 through 2013..............................................45

26. Time-series plots showing the percentage of total phosphorus load

accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow at the Smith Creek streamgage for 

water years 2010 through 2013.................................................................................................45

27. Graphs showing streamflow inputs and drainage area for samples collected 

during each synoptic sampling event between sites 8 and 11 in the Smith 

Creek watershed.........................................................................................................................46

28. Map showing monitoring locations, total nitrogen concentrations measured 

during each synoptic sampling event, and the stream network in the Smith 

Creek watershed.........................................................................................................................48

29. Map showing monitoring locations, the percentage of the maximum total 

nitrogen load measured during each synoptic sampling event, and the stream 

network in the Smith Creek watershed...................................................................................49

30. Map showing monitoring locations, the total phosphorus concentration 

measured during each synoptic sampling event, and the stream network in 

the Smith Creek watershed.......................................................................................................50

31. Map showing monitoring locations, the maximum total phosphorus load 

measured during each synoptic sampling event, and the stream network in 

the Smith Creek watershed.......................................................................................................51

32. Cluster analysis dendrogram showing samples collected from each synoptic 

event within the Smith Creek watershed with a heat map of constituent values 

and the breakpoint used to produce four cluster groups ....................................................52

33. Map showing synoptic sampling events at monitoring locations in the Smith 

Creek watershed as grouped in a cluster analysis...............................................................54

34. Graphs showing nitrate isotope results from each synoptic sampling event 

within the Smith Creek watershed...........................................................................................55

35. Pie charts showing percentages of land-use types from Homer and others 

(2015) within the Smith Creek watershed, Rockingham County and Shenandoah 

County, Virginia ...........................................................................................................................56

36. Graphs showing nitrogen sources for Rockingham County and Shenandoah 

County, Virginia, from 1987 to 2012 and pie charts showing nitrogen sources 

in 2002 for Rockingham County and Shenandoah County, Virginia....................................57

37. Pie charts showing the percentages of input and output sources of total 

nitrogen for the Smith Creek watershed as generated by the 2002 Chesapeake 

Bay Total Nitrogen SPARROW model as described in Ator and others (2011).................57
ix

38. Time-series plot showing nitrate concentrations from the Smith Creek 

streamgage for water years 1979 through 2013 ....................................................................61

39. Graph showing nitrate loads estimated from the Chesapeake Bay watershed 

model for the model segment containing Smith Creek with and without best 

management practice implementation with input sources and the annual 

percentage difference of load attributed to implementation, 1985–2012..........................62

40. Map showing monitoring stations, select subwatershed boundaries, and the

stream network within the Upper Chester watershed, Maryland......................................65

41. Hydrograph for the Chesterville Branch streamgage and quantiles that 

represent historical monthly flow data with the timing of various sampling 

events from July 2011 through September 2013 within the Upper Chester 

River watershed..........................................................................................................................66

42. Contour plots showing continuous water temperature, dissolved oxygen,

and pH data with the hydrograph from the Chesterville Branch streamgage,

2012–2013 .....................................................................................................................................68

43. Contour plots showing continuous specific conductance, nitrate, and turbidity 

data with the hydrograph from the Chesterville Branch streamgage, 

2012–2013 .....................................................................................................................................69

44. Graphs and boxplots showing water temperature, specific conductance,

and pH data from continuous monitoring and discrete storm and routine

samples for warm (April to September) and cool (October to March) seasons,

Chesterville Branch streamgage, 2012–2013 .........................................................................70

45. Graph and boxplots showing dissolved oxygen, turbidity, and nitrate data

from continuous monitoring and discrete storm and routine samples for warm

(April to September) and cool (October to March) seasons, Chesterville Branch

streamgage, 2012–2013..............................................................................................................71

46. Graph showing the Chesterville Branch hydrograph, continuous nitrate

concentrations, and continuous specific conductance data, July 10 through 

July 16, 2013.................................................................................................................................72

47. Graphs showing concentration-discharge data for discrete samples from the

Chesterville Branch streamgage by warm and cool seasons, 2011–2013 ........................74

48. Time-series plots showing concentrations of various constituents in discrete

samples from the Chesterville Branch streamgage, 2011–2013 .........................................76

49. Graphs showing variability in nitrate isotopes in discrete samples from the

Chesterville Branch streamgage and potential causes for variability, 2011–2013 ..........77

50. Cluster analysis dendrogram showing discrete samples from the Chesterville

Branch streamgage with a heat map of constituent values and the breakpoint 

used to produce three cluster groups, 2011–2013 ................................................................79

51. Graph showing the first two principal component axis scores for discrete 

samples from the Chesterville Branch streamgage along with time and 

streamflow, 2011–2013 ...............................................................................................................80

52. Diagnostic plots showing observed and estimated values from the best and

alternative suspended-sediment surrogate models for the Chesterville Branch

streamgage and the residual plot of model predictions, 2012–2013 ..................................82

53. Diagnostic plots showing observed and estimated values from the best and

alternative total phosphorus surrogate models for the Chesterville Branch

streamgage and the residual plot of model predictions, 2012–2013 ..................................83

54. Diagnostic plots showing observed and estimated values from the best total

nitrogen surrogate model for the Chesterville Branch streamgage and the 

residual plot of model predictions, 2012–2013 .......................................................................84
x

55. Graph showing the suspended-sediment load and total annual streamflow at 

the Chesterville Branch streamgage for the 2013 water year ............................................85

56. Time-series plots showing the suspended-sediment load at the Chesterville 

Branch streamgage for water years 2012 and 2013 along with the percentage 

of load accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow in each water year..................................................85

57. Graph showing the total phosphorus load and total annual streamflow at the

Chesterville Branch streamgage for the 2013 water year...................................................86

58. Time-series plots showing the percentage of total phosphorus load

accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow at the Chesterville Branch streamgage 

for water years 2012 and 2013 ..................................................................................................86

59. Graph showing the total nitrogen load and annual total streamflow at the

Chesterville Branch streamgage for the 2013 water year...................................................87

60. Time-series plots showing the percentage of total nitrogen load accumulated

and the hydrograph during the period of study and the accumulation of load

and streamflow at the Chesterville Branch streamgage for water years 

2012 and 2013...............................................................................................................................87

61. Graphs showing streamflow and drainage area for samples collected from the

Upper Chester River watershed during each synoptic event.............................................88

62. Map showing monitoring locations, total nitrogen concentrations measured 

during each synoptic sampling event, and the stream network in the Upper 

Chester River watershed...........................................................................................................89

63. Stiff plots showing the major-ion chemistry from the December 2011 synoptic

sampling event and pie charts representing percentages of land use within the

Chesterville Branch, Foreman Branch, and Pearl Creek subwatersheds ........................89

64. Map showing monitoring locations, total nitrogen load measured during each

synoptic sampling event, and the stream network in the Upper Chester River

watershed ....................................................................................................................................90

65. Map showing monitoring locations, the total phosphorus concentration measured

during each synoptic sampling event, and the stream network in the Upper Chester

River watershed..........................................................................................................................91

66. Map showing monitoring locations, the total phosphorus load measured during

each synoptic sampling event, and the stream network in the Upper Chester River

watershed ....................................................................................................................................91

67. Cluster analysis dendrogram showing samples collected from each synoptic 

event within the Upper Chester River watershed with a heat map of constituent 

values and the breakpoint used to produce four cluster groups .......................................92

68. Map showing monitoring locations as identified in a cluster analysis for each

synoptic sampling event and the stream network of the Upper Chester River

watershed ....................................................................................................................................93

69. Graphs showing nitrate isotope results from each synoptic sampling event 

within the Upper Chester River watershed ............................................................................94

70. Pie charts showing percentages of land-use types from Homer and others 

(2015) within the Upper Chester River watershed, Queen Anne’s County, and 

Kent County, Maryland...............................................................................................................95

71. Graphs showing nitrogen sources for Kent County and Queen Anne’s County, 

Maryland, from 1987 to 2012, and pie charts showing nitrogen sources in 2002 

for Kent County and Queen Anne’s County, Maryland .........................................................96

72. Pie charts showing the percentages of input and output sources of total nitrogen 

for the Upper Chester River watershed as generated by the 2002 Chesapeake 

Bay Total Nitrogen SPARROW model......................................................................................96
xi

73. Time-series plot showing nitrate concentrations from the Chesterville Branch

streamgage between 1990 and 2013 .......................................................................................99

74. Graph showing nitrate load estimated from the Chesapeake Bay watershed 

model for the model segment containing Chesterville Branch with and without 

best management practice implementation with input sources and the annual

percentage difference of load attributed to implementation, 1985–2012..........................99

75. Map showing the stream network, monitoring stations, subwatershed 

boundaries, and sampling locations within the Conewago Creek watershed, 

Pennsylvania .............................................................................................................................103

76. Hydrographs for the Conewago Creek Bellaire and Conewago Creek Falmouth 

streamgages with the timing of various sampling events .................................................104

77. Contour plots showing continuous water temperature and specific

conductance data with the hydrograph from the Conewago Creek Bellaire

streamgage, 2012–2013............................................................................................................106

78. Contour plots showing continuous water temperature and specific

conductance data with the hydrograph from the Conewago Creek Falmouth

streamgage, 2011–2013............................................................................................................107

79. Graphs and boxplots showing water temperature and specific conductance

data from continuous monitoring and discrete storm and routine samples for

warm (April to September) and cool (October to March) seasons, Conewago 

Creek Bellaire streamgage, 2012–2013 .................................................................................108

80. Graphs and boxplots showing water temperature and specific conductance

data from continuous monitoring and discrete storm and routine samples for 

warm (April to September) and cool (October to March) seasons, Conewago 

Creek Falmouth streamgage, 2011–2013...............................................................................109

81. Cluster analysis dendrogram showing discrete samples from the Conewago 

Creek Bellaire and Falmouth streamgages with a heat map of constituent values 

and the breakpoint used to produce four cluster groups, 2011–2013..............................112

82. Graph showing the first two principal component axis scores for discrete 

samples from the Conewago Creek Bellaire and Falmouth streamgages along 

with time and streamflow, 2011–2013....................................................................................113

83. Graphs showing concentration-discharge data for discrete samples from 

the Conewago Creek Bellaire and Falmouth streamgages by warm 

(April to September) and cool (October to March) seasons .............................................117

84. Time-series plots showing concentrations of various constituents in discrete

samples from the Conewago Creek Bellaire and Falmouth streamgages......................120

85. Diagnostic plots showing the observed and estimated values from the best

suspended-sediment surrogate models for the Conewago Creek Bellaire and

Falmouth streamgages, the observed and estimated values along with

streamflow, and the residual plot of model predictions.....................................................122

86. Diagnostic plots showing the observed and estimated values from the best 

total nitrogen surrogate models for the Conewago Creek Bellaire and Falmouth 

streamgages, the observed and estimated values along with streamflow, and 

the residual plot of model predictions...................................................................................123

87. Diagnostic plots showing the observed and estimated values from the best total

phosphorus surrogate models for the Conewago Creek Bellaire and Falmouth 

streamgages, the observed and estimated values along with streamflow,

and the residual plot of model predictions...........................................................................124

88. Graphs showing the total sediment load and total annual streamflow at the

Conewago Creek Bellaire and Falmouth streamgages for water years 2012

and 2013......................................................................................................................................126
xii

89. Time-series plots showing the total sediment load at the Conewago Creek 

Bellaire streamgage for water years 2012 and 2013 along with the percentage 

of load accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow in each water year................................................126

90. Time-series plots showing the total sediment load at the Conewago Creek

Falmouth streamgage for water years 2011 through 2013 along with the

percentage of load accumulated and the hydrograph during the period of 

study and the accumulation of load and streamflow in each water year.......................127

91. Graphs showing the total phosphorus load and total annual streamflow at the

Conewago Creek Bellaire and Falmouth streamgages for the 2012 and 2013

water years................................................................................................................................128

92. Time-series plots showing the total phosphorus load at the Conewago Creek

Bellaire streamgage for water years 2012 and 2013 along with the percentage

of load accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow in each water year................................................128

93. Time-series plots showing the total phosphorus load at the Conewago Creek

Falmouth streamgage for water years 2011 through 2013 along with the

percentage of load accumulated and the hydrograph during the period of 

study and the accumulation of load and streamflow in each water year.......................129

94. Maps showing instantaneous total nitrogen, total phosphorus, and

suspended-sediment concentrations and loads at the Conewago Creek 

watershed monitoring locations, May 15, 2012 ...................................................................131

95. Pie charts showing percentages of land-use types from Homer and others 

(2015) within the Conewago Creek watershed, Dauphin County, and Lebanon 

County.........................................................................................................................................134

96. Graphs showing nitrogen sources for Dauphin County and Lebanon County 

from 1987 to 2012 and pie charts showing nitrogen sources in 2002 for Dauphin 

County and Lebanon County, Pennsylvania .........................................................................135

97. Pie charts showing the percentages of input and output sources of total nitrogen

for the Conewago Creek watershed as generated by the 2002 Chesapeake Bay

Total Nitrogen SPARROW model............................................................................................135

98. Graph showing the nitrate loads estimated from the Chesapeake Bay watershed

model for the Conewago Creek Bellaire land-river segment with and without 

best management practice (BMP) implementation with input sources and the 

annual percentage difference of load attributed to BMP implementation, 

1985–2012 ...................................................................................................................................139

99. Graph showing nitrate loads estimated from the Chesapeake Bay watershed 

model for the Conewago Creek Falmouth land-river segment with and without 

best management practice (BMP) implementation with input sources and the 

annual percentage difference of load attributed to BMP implementation, 

1985–2012 ...................................................................................................................................139

100. Map showing monitoring stations and the stream network within the Difficult 

Run watershed, Virginia ..........................................................................................................142

101. Hydrograph for the Difficult Run streamgage and quantiles that represent 

historical monthly flow data with the timing of various sampling events from 

June 2011 through September 2013 within the Difficult Run watershed.........................143

102. Graph showing the cumulative daily streamflow values from the Difficult Run

streamgage for water years 2011 through 2013 with the median, 75th quantile, 

and 25th quantile of daily streamflow values observed from water years 1935 

through 2010 ..............................................................................................................................143
xiii

103. Contour plots showing continuous water temperature, dissolved oxygen,

and specific conductance data with the hydrograph from the Difficult Run

streamgage, 2013 water year .................................................................................................146

104. Contour plots showing continuous pH, turbidity, and nitrate data with the

hydrograph from the Difficult Run streamgage, 2013 water year.....................................147

105. Graphs and boxplots showing water temperature, specific conductance,

and pH data from continuous monitoring and discrete storm and routine

samples for warm (April to September) and cool (October to March) seasons,

Difficult Run streamgage, 2012–2013.....................................................................................148

106. Graphs and boxplots showing Dissolved oxygen, turbidity, and nitrate

data from continuous monitoring and discrete storm and routine samples 

for warm (April to September) and cool (October to March) seasons, Difficult 

Run streamgage, 2012–2013....................................................................................................149

107. Cluster analysis dendrogram showing discrete samples from the Difficult Run

streamgage with a heat map of constituent values and the breakpoint used to

produce three cluster groups, 2012–2013.............................................................................150

108. Graph showing the first two principal component axis scores for discrete 

samples from the Difficult Run streamgage along with time and streamflow, 

2012–2013 ...................................................................................................................................152

109. Graphs showing concentration-discharge data for discrete samples from the

Difficult Run streamgage by warm (April to September) and cool (October to

March) seasons, 2011–2013....................................................................................................154

110. Time-series plots showing concentrations of various constituents in discrete

samples from the Difficult Run streamgage, 2011–2013.....................................................156

111. Graphs showing variability in nitrate isotopes in discrete samples from the 

Difficult Run streamgage and potential causes for variability, 2011–2013......................157

112. Diagnostic plots showing the observed and estimated values from the best

suspended-sediment surrogate model for the Difficult Run streamgage and the

residual plot of model predictions, 2011–2013 .....................................................................159

113. Diagnostic plots showing the observed and estimated values from the best total

nitrogen surrogate model for the Difficult Run streamgage and the residual plot 

of model predictions, 2011–2013 ............................................................................................159

114. Diagnostic plots showing the observed and estimated values from the best total

phosphorus surrogate model for the Difficult Run streamgage and the residual 

plot of model predictions, 2011–2013.....................................................................................160

115. Graph showing the suspended-sediment load and total annual streamflow at the

Difficult Run streamgage for the 2013 water year...............................................................161

116. Time-series plots showing the estimated percentage of suspended-sediment

load accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow at the Difficult Run streamgage for the 

2013 water year.........................................................................................................................161

117. Graph showing the total nitrogen load and total annual streamflow at the 

Difficult Run streamgage for the 2013 water year...............................................................162

118. Time-series plots showing the estimated percentage of total nitrogen load 

accumulated and the hydrograph during the study period and the accumulation 

of load and streamflow at the Difficult Run streamgage for the 2013 water year.........162

119. Graph showing the total phosphorus load and total annual streamflow at the

Difficult Run streamgage for the 2013 water year...............................................................163

120. Time-series plots showing the estimated percentage of total phosphorus load

accumulated and the hydrograph during the period of study and the

accumulation of load and streamflow at the Difficult Run streamgage for the 

2013 water year.........................................................................................................................163
xiv

121. Graphs showing streamflow and drainage area for samples collected from the

Difficult Run watershed during each synoptic event .........................................................165

122. Graph showing the ratio of streamflow to drainage area for each monitoring

location during the synoptic sampling events in the Difficult Run watershed...............165

123. Map showing monitoring locations, the percentage of the maximum total 

nitrogen load measured during each synoptic sampling event, and the 

streamflow network in the Difficult Run watershed...........................................................166

124. Map showing monitoring locations, the total nitrogen concentration measured

during each synoptic sampling event, and the streamflow network in the 

Difficult Run watershed ...........................................................................................................166

125. Map showing monitoring locations, the percentage of the maximum total

phosphorus load measured during each synoptic sampling event, and the

streamflow network in the Difficult Run watershed...........................................................167

126. Map showing monitoring locations, the total phosphorus concentration 

measured during each synoptic sampling event, and the streamflow network 

in the Difficult Run watershed ................................................................................................167

127. Cluster analysis dendrogram showing samples collected from each synoptic 

event within the Difficult Run watershed with a heat map of constituent values 

and the breakpoint used to produce four cluster groups ..................................................168

128. Map showing synoptic sampling events at monitoring locations in the Difficult 

Run watershed as grouped in a cluster analysis ................................................................169

129. Map showing the streamflow network, watershed boundaries of each 

monitoring location, and county-level tax parcels served and not served by 

sewage infrastructure in the Difficult Run watershed.......................................................171

130. Graph showing the median nitrate concentration results from synoptic sampling

events within the Difficult Run watershed and from additional U.S. Geological

Survey base-flow monitoring in Fairfax County compared to the septic tank 

density within the drainage area of each monitoring location.........................................172

131. Time-series plots showing nitrate concentrations by hydrologic event from 

Captain Hickory Run and South Fork Little Difficult Run for water years 2007 

through 2012 ..............................................................................................................................172

132. Graphs showing nitrate isotope results from each synoptic sampling event 

within the Difficult Run watershed ........................................................................................173

133. Graphs showing median nitrate isotope results from synoptic sampling events

within the Difficult Run watershed compared to the septic tank density within the

drainage area of each monitoring location with dashed lines indicating literature

values of nitrate sources.........................................................................................................173

134. Pie charts showing the percentages of input and output sources of total nitrogen

for the Difficult Run watershed as generated by the 2002 Chesapeake Bay Total

Nitrogen SPARROW model .....................................................................................................174

135. Time-series plot showing nitrate concentrations for the Difficult Run watershed 

water years 1972 through 2013...............................................................................................175

136. Graph showing the nitrate loads estimated from the Chesapeake Bay watershed

model for the Difficult Run land-river segment with and without best management

practice implementation with input sources and the annual percentage difference

of load attributed to implementation, 1985–2012 .................................................................176

137. Graphs showing continuous specific conductance data compared to normalized

streamflow quantiles measured at the Conewago Creek Bellaire and Falmouth,

Smith Creek, Chesterville Branch, and Difficult Run streamgages with colored

bands that represent the density of data points for a “warm” (April through

September) and “cool” (October through March) season................................................180
xv

138. Graphs showing continuous nitrate data compared to normalized streamflow 

quantiles measured at the Smith Creek, Chesterville Branch, and Difficult 

Run streamgages with colored bands that represent the density of data points 

for a “warm” (April through September) and “cool” (October through March) 

season ........................................................................................................................................181

139. Graphs showing continuous dissolved oxygen data compared to normalized

streamflow quantiles measured at the Smith Creek, Chesterville Branch, and

Difficult Run streamgages with colored bands that represent the density of data

points for a “warm” (April through September) and “cool” (October through

March) season ..........................................................................................................................182

140. Graphs showing the cumulative distribution of various water-quality constituents

collected from the Smith Creek, Conewago Creek Bellaire and Falmouth,

Chesterville Branch, and Difficult Run streamgages..........................................................183

141. Cluster analysis dendrogram showing discrete samples collected from the Smith

Creek, Conewago Creek Bellaire and Falmouth, Chesterville Branch, and Difficult

Run streamgages with a heat map of constituent values and the breakpoint used 

to produce five clusters...........................................................................................................186

142. Boxplots showing streamflow for the five groups identified in a cluster analysis

along with individual samples from the Smith Creek, Conewago Creek Bellaire 

and Falmouth, Upper Chester River, and Difficult Run watersheds .................................186

143. Graphs showing discrete data from the Smith Creek, Difficult Run, Chesterville

Branch, and Conewago Creek Bellaire and Falmouth streamgages representing

the ratio of nitrate to total nitrogen compared to streamflow and nitrate

compared to total nitrogen concentrations, 2010 through 2013 .......................................187

144. Graphs showing discrete data from the Smith Creek, Difficult Run, Chesterville

Branch, and Conewago Creek Bellaire and Falmouth streamgages representing

the ratio of orthophosphate to total phosphorus compared to streamflow 

and orthophosphate compared to total phosphorus concentrations, 2010 

through 2013 ..............................................................................................................................187

145. Graphs showing discrete data from the Smith Creek, Difficult Run, and 

Chesterville Branch streamgages, and the relationships between δ15N, δ18O, 

and nitrate results.....................................................................................................................188

146. Graphs showing data from the Smith Creek, Difficult Run, and Upper Chester River

watersheds from each synoptic sampling event and the relationships between

δ15N, δ18O, and nitrate results..................................................................................................188

147. Graphs showing the suspended-sediment yield and total annual streamflow

and the suspended-sediment yield and streamflow yield for the 2013 water 

year from the Smith Creek, Chesterville Branch, Conewago Creek Bellaire and 

Falmouth, and Difficult Run streamgages.............................................................................189

148. Graphs showing the total nitrogen yield and total annual streamflow and the 

total nitrogen yield and streamflow yield for the 2013 water year from the Smith

Creek, Chesterville Branch, and Difficult Run streamgages .............................................189

149. Graphs showing the total phosphorus yield and total annual streamflow and the 

total phosphorus yield and streamflow yield for the 2013 water year from the Smith 

Creek, Chesterville Branch, Conewago Creek Bellaire and Falmouth, and Difficult 

Run streamgages .....................................................................................................................190
xvi

Tables

1. Watershed names, watershed areas, and streamgage station IDs and names..............14

2. Dates of continuous water-quality monitoring and sample-collection activities............14

3. Analysis of replicate and blank discrete water-quality data ..............................................15

4. Constituents, parameter codes, analysis sources, method codes, reporting units,

and reporting limits for discrete water-quality constituents collected at the Smith

Creek and Difficult Run watersheds........................................................................................16

5. Constituents, parameter codes, analysis sources, method codes, reporting units,

and reporting limits for discrete water-quality constituents collected at the Upper

Chester River watershed...........................................................................................................17

6. Constituents, parameter codes, analysis sources, method codes, reporting units,

and reporting limits for discrete water-quality constituents collected at the

Conewago Creek watershed ....................................................................................................18

7. Continuous water-quality data collected within each watershed .....................................18

8. Streamflow partitioning for streamgage station 01632900 in the Smith Creek

watershed ....................................................................................................................................25

9. Loading of water-quality constituents collected at the Smith Creek streamgage 

on principal component axes 1 and 2......................................................................................32

10. Summary of hydrologic and seasonal correlations for discrete water-quality

constituents collected at the Smith Creek streamgage.......................................................34

11. Statistical results of the top-ranked suspended sediment, total nitrogen, and total

phosphorus concentration estimation models for the Smith Creek streamgage with

an alternative model presented for total nitrogen and total phosphorus .........................39

12. Nitrogen delivery factors and source estimates identified by the SPARROW model

for the Smith Creek watershed.................................................................................................58

13. Number of USDA-compliant conservation practices implemented in water 

years 2007 through 2013 in the Smith Creek watershed, aggregated by 

sponsoring program...................................................................................................................59

14. Implementation of USDA-compliant conservation practices within the Smith 

Creek watershed for water years 2007 through 2013 ...........................................................59

15. Implementation of state-funded conservation practices within the Smith Creek

watershed for water years 2007 through 2014.......................................................................60

16. Streamflow partitioning for streamgage station 01493112 in the Upper Chester 

River watershed..........................................................................................................................66

17. Summary of hydrologic and seasonal correlations for discrete water-quality

constituents collected at the Chesterville Branch streamgage.........................................73

18. Loading of water-quality constituents collected at the Chesterville Branch

streamgage on principal component axes 1 and 2 ...............................................................78

19. Details of the top-ranked suspended sediment, total nitrogen, and total 

phosphorus concentration estimation models for the Chesterville Branch 

streamgage with an alternative model presented for each constituent...........................81

20. Number of USDA-compliant conservation practices implemented in water years

2007 through 2013 in the Upper Chester River watershed, aggregated by

sponsoring program...................................................................................................................97

21. Implementation of USDA-compliant conservation practices within the Upper

Chester River watershed for water years 2007 through 2013 .............................................97

22. Implementation of conservation practices sponsored by state programs or adopted

on a voluntary basis within the Upper Chester River watershed for water years 

2007 through 2013 .......................................................................................................................98
xvii

23. Streamflow partitioning for streamgage stations 01573695 and 01573710 in the

Conewago Creek watershed ..................................................................................................105

24. Summary statistics for discrete water-quality constituents collected at the

Conewago Creek Bellaire streamgage with a comparison between storm and

base-flow samples ...................................................................................................................110

25. Summary statistics for discrete water-quality constituents collected at the

Conewago Creek Falmouth streamgage with a comparison between storm and

base-flow samples ...................................................................................................................111

26. Loading of water-quality constituents collected at the Conewago Creek Bellaire

and Falmouth streamgages on principal component axes 1 and 2..................................114

27. Summary of hydrologic and seasonal correlations for discrete water-quality

constituents collected at the Conewago Creek Bellaire streamgage.............................115

28. Summary of hydrologic and seasonal correlations for discrete water-quality

constituents collected at the Conewago Creek Falmouth streamgage ..........................116

29. Details of the top-ranked suspended sediment, total nitrogen, and total 

phosphorus concentration estimation models for the Conewago Creek 

Bellaire and Falmouth streamgages......................................................................................121

30. Station information, sampling time, and water-quality results from the high-flow

synoptic samples collected in the Conewago Creek watershed .....................................132

31. Number of USDA-compliant conservation practices implemented in water years

2007 through 2013 in the Conewago Creek watershed, aggregated by sponsoring

program......................................................................................................................................136

32. Implementation of USDA-compliant conservation practices within the Conewago

Creek watershed for water years 2007 through 2013 .........................................................137

33. Implementation of State-level conservation practices sponsored by the EPA

Section 319 Nonpoint Source Management Program within the Conewago 

Creek watershed.......................................................................................................................137

34. Rainfall data from two NOAA weather monitoring stations including average

precipitation for two historical time periods and total precipitation for water 

years 2011 through 2013 ..........................................................................................................144

35. Streamflow partitioning for streamgage station 01646000 in the Difficult Run

watershed ..................................................................................................................................144

36. Loading of water-quality constituents collected at the Difficult Run streamgage 

on principal component axes 1 and 2....................................................................................151

37. Summary of hydrologic and seasonal correlations for discrete water-quality

constituents collected at the Difficult Run streamgage.....................................................153

38. Details of the top-ranked suspended sediment, total nitrogen, and total 

phosphorus concentration estimation models for the Difficult Run streamgage..........158

39. Estimated annual nitrate loads contributed by septic system leachate and

residential fertilizer application at the Captain Hickory Run monitoring 

location (01645940) ...................................................................................................................174

40. The classes/types of conservation practices that were implemented in the 

Difficult Run watershed for water years 2009 through 2014..............................................175

41. Median concentrations of select discrete water-quality constituents at the Smith

Creek, Difficult Run, Chesterville Branch, and Conewago Creek streamgages ............185
xviii

Conversion Factors

Inch/Pound to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.004047 square kilometer (km2)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

gallon (gal) 3.785 liter (L) 

gallon (gal) 0.003785 cubic meter (m3) 

gallon (gal) 3.785 cubic decimeter (dm3) 

Flow rate

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

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3/s)

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

ton, short (2,000 lb) 0.9072 metric ton (t) 

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

International System of Units to Inch/Pound

Multiply By To

obtain

Length

micrometer (μm) 0.00003937 inch (in)

Area

hectare (ha) 2.471 acre

hectare (ha) 0.003861 square mile (mi2) 

Mass

kilogram (kg) 2.205 pound avoirdupois (lb)

Application rate

kilograms per hectare per year [(kg/ha)/yr] 0.8921 pounds per acre per year [(lb/acre)/yr]

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

°F = (1.8 × °C) + 32.

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

°C = (°F – 32) / 1.8.

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) or 

micrograms per liter (μg/L).
xix

Datum

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

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

Abbreviations

AMLE adjusted maximum likelihood estimation

BFI base-flow index

BMP best management practice

C-Q concentration-discharge

CEAP Conservation Effects Assessment Project

CTA Conservation Technical Assistance

DCLS Division of Consolidated Laboratory Services

DO dissolved oxygen

E. coli Escherichia coli

EPA U.S. Environmental Protection Agency

FNU formazin nephelometric units

FSA Farm Services Agency

LOADEST load estimator

MACS Maryland Agricultural Water Quality Cost-Share Program

MDE Maryland Department of the Environment

N nitrogen

NAWQA National Water-Quality Assessment

NLCD national land-cover dataset

NRCS Natural Resources Conservation Service

O oxygen

PADEP Pennsylvania Department of Environmental Protection

PCA principal components analysis

R2 coefficient of determination

SEP standard error of prediction

SPARROW SPAtially Referenced Regressions on Watershed attributes

SSC suspended-sediment concentration

TMDL Total Maximum Daily Load

TN total nitrogen

TP total phosphorus

TSC total sediment concentration

TSS total suspended sediment

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

VADCR Virginia Department of Conservation and Recreation

VADEQ Virginia Department of Environmental Quality

VIF variance inflation factor

VSMOW Vienna Standard Mean Ocean Water

WRTDS Weighted Regression on Time, Discharge, and Season 

Spatial and Temporal Variation of Stream Chemistry 

Associated With Contrasting Geology and Land-Use 

Patterns in the Chesapeake Bay Watershed: Summary of 

Results from Smith Creek, Virginia; Upper Chester River, 

Maryland; Conewago Creek, Pennsylvania; and Difficult 

Run, Virginia, 2010–2013

By Kenneth E. Hyer, Judith M. Denver, Michael J. Langland , James S. Webber, J.K. Böhlke, W. Dean Hively, 

and John W. Clune

Abstract

Despite widespread and ongoing implementation of 

conservation practices throughout the Chesapeake Bay 

watershed, water quality continues to be degraded by excess 

sediment and nutrient inputs. While the Chesapeake Bay 

Program has developed and maintains a large-scale and 

long-term monitoring network to detect improvements in 

water quality throughout the watershed, fewer resources 

have been allocated for monitoring smaller watersheds, even 

though water-quality improvements that may result from the 

implementation of conservation practices are likely to be first 

detected at smaller watershed scales.

In 2010, the U.S. Geological Survey partnered with the 

U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in 

four selected small watersheds that were targeted for increased 

implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of 

Virginia that is dominated by cattle and poultry production, 

and the Upper Chester River watershed is an agricultural 

watershed on the Eastern Shore of Maryland that is dominated 

by row-cropping activities. The Conewago Creek watershed 

is an agricultural watershed in southeastern Pennsylvania that 

is characterized by mixed agricultural activities. The fourth 

watershed, Difficult Run, is a suburban watershed in northern 

Virginia that is dominated by medium density residential 

development. The objective of this study was to investigate 

spatial and temporal variations in water chemistry and 

suspended sediment in these four relatively small watersheds 

that represent a range of land-use patterns and underlying 

geology to (1) characterize current water-quality conditions in 

these watersheds, and (2) identify the dominant sources, sinks, 

and transport processes in each watershed.

The general study design involved two components. The 

first included intensive routine water-quality monitoring at an 

existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality 

sampling) to develop a detailed understanding of the temporal 

and hydrologic variability in stream chemistry and sediment 

transport in each watershed. The second component involved 

extensive water-quality monitoring at various sites throughout 

each watershed to develop a detailed understanding of spatial 

patterns. Both components were used to improve understanding 

of sources and transport processes affecting stream chemistry, 

including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management 

practices. This report summarizes the results of monitoring that 

was performed from April 2010 through September 2013.

Individual Small Watershed Summaries

Summaries for each of the four small watersheds are 

presented below. Each watershed has a more descriptive 

and detailed section in the report, but these summaries may 

be particularly useful for some watershed managers and 

stakeholders desiring slightly less technical detail.

Smith Creek

Smith Creek is a 105.39-mi2 watershed within the 

Shenandoah Valley that drains to the North Fork Shenandoah 

River. The long-term Smith Creek base-flow index is 

72.3 percent, indicating that on average, approximately 

72 percent of Smith Creek flow was base flow, which suggests 

that Smith Creek streamflow is dominated by groundwater 

discharge rather than stormwater runoff. A series of cluster 

and principal components analyses demonstrated that the 
2 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

majority of the variability in Smith Creek water quality 

could be attributed to hydrologic and seasonal variability. 

Statistically significant positive correlations with flow were 

observed for turbidity, suspended sediments, total nitrogen, 

ammonium, orthophosphate, iron, total phosphorus, and the 

ratio of calcium to magnesium. Statistically significant inverse 

correlations with flow were observed for specific conductance, 

magnesium, δ15N of nitrate, pH, bicarbonate, calcium, and 

δ18O of nitrate. Of particular note, flow and nitrate were not 

statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed 

in continuous and discrete datasets. Statistically significant 

seasonal patterns were observed for numerous water-quality 

constituents: water temperature, turbidity, orthophosphate, 

total phosphorus, suspended-sediment concentration, and 

silica were higher during the warm season, but pH, dissolved 

oxygen, and sulfate were higher during the cool season. 

Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous 

water-quality monitors. The mean Smith Creek in-stream 

sediment load was approximately 6,900 tons per year, with 

nearly 90 percent of the sediment load over the 3-year study 

period contributed during the eight largest storm events during 

that period. The Smith Creek total phosphorus load was 

approximately 21,000 pounds of phosphorus per year, with 

the majority of the load contributed during stormflow periods, 

although a substantial phosphorus load still occurs during 

base-flow conditions. The Smith Creek total nitrogen load was 

approximately 400,000 pounds per year, with total nitrogen 

accumulation less dominated by stormflow contributions (as 

was the case for sediment and total phosphorus) and strongly 

affected by base-flow export of nitrogen from the basin.

Extensive water-quality monitoring throughout the Smith 

Creek watershed revealed how the complex geology and 

hydrology interacted to result in variable water chemistry. 

During relatively dry and low base-flow periods, much of 

the discharge in Smith Creek was contributed by a single 

dominant spring—Lacey Spring. During wetter base-flow 

periods, the flows in Smith Creek were largely generated by a 

mixture of headwater springs and forested mountain tributaries 

with very different geochemical composition. The headwater 

springs generally issued from limestone bedrock and were 

characterized as having relatively high nitrate, specific 

conductance, calcium, and magnesium, as well as relatively 

low concentrations of phosphorus, ammonium, iron, and 

manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength 

waters with low nutrient concentrations. Nitrate isotope data 

from the limestone springs generally were consistent with 

manure-derived nitrogen sources (such as cattle and poultry), 

although the possibility of other mixed sources cannot be 

excluded. Nitrate isotope data from the undeveloped, highgradient forested mountain sites were more consistent with 

nitrogen from undisturbed soils, atmospheric deposition, or 

nitrogen fixation. Regardless of the nitrogen source, oxygen 

isotope data indicate that the nitrate was largely a result of 

nitrification. Land-use data indicate that manure sources of 

nitrogen dominated watershed nitrogen inputs. Phosphorus 

sources were less well studied. The presence of a single pointsource discharge near the town of New Market contributed the 

majority of the phosphorus to Smith Creek under base-flow 

conditions, but nonpoint sources of phosphorus dominated the 

loading to Smith Creek during stormflow periods.

Implementation of conservation practices increased in 

the Smith Creek watershed during the study period, and even 

though a broad range of practice types was implemented, the 

most common practices included stream fencing (for cattle 

exclusion), the development of nutrient management plans, 

conservation crop rotation, and the planting of cover crops. 

While the implementation of these conservation practices 

is encouraging, results indicate small increases in nitrate 

concentrations at the streamgage over the last 29 years, 

concurrent with small decreases in nitrate fluxes. It will likely 

be years before the cumulative effect of these practices can be 

detected in the Smith Creek water quality, and the magnitude 

of the effect of these conservation practices detected in Smith 

Creek will depend largely on whether nutrient loading (of 

manure and commercial fertilizer) is reduced over time.

Upper Chester River

The Upper Chester River watershed includes the 

36-square-mile (mi2) watershed area around several nontidal 

tributaries that drain into the tidal Chester River. The streamgage 

is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi2

) and is the site for continuous water-quality 

monitoring for this project. The base-flow index at Chesterville 

Branch is about 72 percent and indicates that, as in most of 

the Coastal Plain, groundwater is the greatest contributor to 

streamflow. As such, more than 90 percent of the nitrogen in the 

stream is in the form of nitrate from groundwater. Continuous 

and discrete data collected at Chesterville Branch show the 

effects of streamflow and season on water quality. Significantly 

positive correlations with flow were observed for ammonium, 

dissolved and total phosphorus, sediment, and turbidity as runoff 

carried these constituents from the land surface into Chesterville 

Branch. Other constituents that increased significantly with 

flow include potassium, sulfate, iron, and manganese, which 

are likely contributed from near-stream areas and ponds with 

high organic-matter content. Total nitrogen, pH, and specific 

conductance, along with chemical constituents associated with 

groundwater inputs including nitrate, calcium, ratio of calcium 

to magnesium, silica, bicarbonate, and sodium, were negatively 

correlated with flow because concentrations of these constituents 

were diluted by runoff.

Seasonal differences in water chemistry, which are most 

likely related to increased biologic effects on the uptake and 

release of chemicals in the stream and near-stream areas, also 

were observed. Water temperature, orthophosphate, δ15N of 

nitrate, bicarbonate, sodium, and the ratio of sodium to 

chloride were higher during the warm season, and dissolved 

oxygen, total nitrogen, nitrate, magnesium, sulfate, and 

manganese were higher during the cool season.
Abstract 3

Surrogate-regression models developed by using 

continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, 

with more than 90 percent of this sediment contributed 

during two storms. The total phosphorus load in 2013 was 

about 13,000 pounds with more than 90 percent contributed 

during the same two storms as sediment. The load of total 

nitrogen, 140,000 pounds, accumulated steadily throughout 

the 2013 water year as nitrate in groundwater continuously 

discharged into the stream. The same two large storms that 

contributed 90 percent of the suspended-sediment and total 

phosphorus load only contributed about 20 percent of the 

annual total nitrogen load.

Extensive water-quality monitoring of stream base flow 

throughout the Upper Chester River watershed identified 

how differences in land use and hydrogeology affected water 

chemistry. In parts of the watershed with well-drained soil and 

thick sandy aquifer sediments, concentrations of nitrate and 

other chemicals associated with fertilizer and lime application 

increased in streams as agricultural land use increased. More 

than 90 percent of the nitrogen in streams from these areas 

was in the form of nitrate, and concentrations ranged from 

about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen 

in the two largest tributaries. Stream nitrate concentrations 

were about 1 mg/L as nitrogen where soils were more poorly 

drained, the surficial aquifer sediments were thinner, and 

forests and wetlands were more widespread than agriculture. 

Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N2

 fixation as sources of 

nitrogen, and with nitrification as the dominant nitrate-forming 

process. Nitrate reduction was indicated by elevated δ15N and 

δ18O values in some samples from streams draining watersheds 

with poorly drained soils. An analysis of land-use data and 

SPARROW modeling input data attributed almost 90 percent 

of the nitrogen sources in the Upper Chester River watershed 

to inorganic fertilizer and fixation of atmospheric nitrogen by 

legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are 

limited in this area. Total phosphorus concentrations during 

base flow ranged from below detection to about 0.2 mg/L. 

Stream phosphorus concentrations during base flow were 

generally lower than those measured during storms because 

most phosphorus transport likely occurs as phosphorus 

attached to sediment particles during runoff. Because manure 

is not widely used in this area, the major source of phosphorus 

is likely fertilizer.

The implementation of conservation practices in the Upper 

Chester River watershed increased substantially during the 

study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently 

used practices were oriented towards nutrient and sediment 

control, including cover crops, nutrient management planning, 

conservation crop rotation, conservation tillage, and irrigation 

management. The current Chesapeake Bay model for this area 

predicts that implementation of best management practices 

should result in a 13-percent decrease in overall delivery of 

nitrogen to the Upper Chester River. Because most nitrogen 

travels through the groundwater system for years to decades 

before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on 

water quality. The magnitude of the effect that may eventually 

be detected will depend on the degree to which nitrate leaching 

into the groundwater system is reduced over time. Loadings of 

phosphorus and sediment are primarily transported during large 

runoff events and are difficult to control and analyze for trends 

because of their timing and episodic nature.

Conewago Creek

Conewago Creek has two primary monitoring locations—

one near the middle of the 47-mi2 watershed and the other 

near the outlet just upstream of the Susquehanna River. The 

base-flow index was 47.3 percent for 2012–2013, indicating 

that on average, approximately 53 percent of the streamflow in 

Conewago Creek exited the watershed as surface flow, which 

suggests that the stormwater runoff was somewhat greater 

than groundwater discharge (base flow). A series of cluster and 

principal components analyses demonstrated that the majority 

of the variability in the Conewago Creek water quality could 

be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed 

at both monitoring sites for ammonium, total phosphorus, 

orthophosphate, iron, and manganese; additionally, at the 

upstream monitoring station, total nitrogen demonstrated a 

statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed 

at both sites for water temperature, specific conductance (at the 

downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed 

for several water-quality constituents. Water temperature, 

phosphorus (upstream site only), and orthophosphate were 

higher during the warm season, and nitrate and total nitrogen 

(upstream site only) were higher during the cool season.

Surrogate regression models were developed to compute 

sediment and nutrient load in Conewago Creek by using the 

continuous water-quality monitors and water-quality samples. 

Conewago Creek sediment load was approximately 9,900 tons 

in 2012 and approximately 18,900 tons in 2013, with nearly 

80 percent of the sediment load in 2013 contributed by the 

three largest storm events. Annual total nitrogen loads could 

not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording 

nitrate sensor could improve estimates of total nitrogen loads. 

During 2012 and 2013, phosphorus loads in Conewago Creek 

were approximately 50,000 pounds in each year.

Combining data from one high-flow synoptic sampling 

with the data from routine sampling revealed how the geology 

and hydrology interact to result in variable water chemistry 

throughout the Conewago Creek watershed. The areas above 

the upstream gage in the headwaters are generally underlain 

by forested non-carbonate bedrock and are characterized 

by relatively low nitrate, specific conductance, calcium, 
4 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

and magnesium, as well as relatively low concentrations of 

phosphorus, ammonium, iron, and manganese. The more 

developed, agricultural areas below the upstream site were 

generally characterized by higher ionic strength waters with 

higher nutrient and metal concentrations. An analysis of landuse data and SPAtially Referenced Regressions On Watershed 

(SPARROW) modeling data indicates that manure sources of 

nitrogen dominate the input of nitrogen to the watershed.

Implementation of conservation practices increased in 

the Conewago Creek watershed during the study period, and 

while a broad range of practice types were implemented, the 

most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream 

fencing (for animal exclusion or bank restoration). While the 

implementation of these conservation practices is encouraging, 

the cumulative effects of these practices probably will not be 

detected in Conewago Creek water quality for several years. 

The magnitude of the effects of these conservation practices 

on water quality in Conewago Creek will depend largely 

on the extent to which nutrient loading (septic, manure, and 

commercial fertilizer) and sediment-producing activities are 

reduced over time.

Difficult Run

The Difficult Run watershed is a 57.82-mi2 watershed 

that drains to the Potomac River. The long-term Difficult Run 

base-flow index (from 1936 to 2010) was 57.9, indicating that 

approximately 58 percent of streamflow exited the watershed 

as base flow and 42 percent as stormflow; however, with 

continued development and urbanization of the watershed, the 

base-flow index has decreased to 50 percent during the last 

20 years. This base-flow index was less than those of the other 

watersheds evaluated in this study, likely because the Difficult 

Run watershed largely is underlain by crystalline piedmont 

metamorphic rocks and has a greater proportion of impervious 

urban land cover. A series of cluster and principal components 

analyses indicated that most of the variability in Difficult Run 

water quality could be attributed to hydrologic variability 

and seasonality. Statistically significant positive correlations 

with flow were observed for turbidity, dissolved oxygen, 

suspended sediments, ammonium, orthophosphate, iron, and 

total phosphorus. Statistically significant inverse correlations 

with flow were observed for water temperature, pH, specific 

conductance, bicarbonate, calcium, magnesium, nitrate, δ15N 

of nitrate, and silica. Statistically significant seasonal patterns 

were observed for numerous water-quality constituents: water 

temperature, ammonium, orthophosphate, and δ15N of nitrate 

were higher during the warm season, and dissolved oxygen, 

nitrate, and manganese were higher during the cool season. 

Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment 

load was approximately 8,000 tons per year, with greater than 

95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus 

load in Difficult Run was approximately 14,000 pounds of 

phosphorus per year, with the majority of the load contributed 

during stormflow periods. The total nitrogen load in Difficult 

Run is estimated to have been approximately 140,000 pounds 

per year, with total nitrogen accumulation less dominated by 

stormflow contributions than that of phosphorus and strongly 

affected by base-flow export of nitrogen from the basin.

Extensive water-quality monitoring throughout the 

Difficult Run watershed revealed relatively uniform generation 

of flow per unit of watershed area, as well as spatial variation 

in water quality that is strongly related to land-use activities. 

Elevated nitrate concentrations were observed in a subset of 

monitoring sites that are inversely correlated with population 

density and positively correlated to the septic system density 

within each subwatershed. The majority of the elevated nitrate 

concentrations for these sites are hypothesized to be caused by 

nitrate leaching from septic systems, more so than homeowner 

fertilizer usage among these subwatersheds that have lower 

population densities than other parts of the watershed. Nitrate 

isotope data, temporal patterns in the water-quality data, 

mass-balance computations, and a separate land-use analysis 

all generally indicate that leachate from septic systems was the 

likely source of the elevated nitrate. Another group of waterquality sites have relatively low nitrogen concentrations, are 

located in areas that are served by city sewer lines, and have 

experienced stream restoration activities. A final group of sites 

drained the areas with the highest imperviousness and had 

strongly elevated specific conductance, chloride, and sodium, 

which were likely caused by a combination of road salting and 

other anthropogenic sources draining these urbanized areas 

in the watershed. A fourth group of sites represents a mixture 

of water sources and had water quality similar to that at the 

Difficult Run streamgage. Analysis of the nitrate isotope data 

generally indicates a broad range of composition indicative of 

mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run 

watershed during the study period, and while a broad range of 

practice types was implemented, the most common practices 

included stream restoration. While the implementation of these 

conservation practices is encouraging, the cumulative effect of 

these practices probably will not be detected in Difficult Run 

water quality for several years.

Introduction

Despite widespread and ongoing implementation of 

conservation practices, the Chesapeake Bay water quality 

continues to be degraded as a result of excess sediment and 

nutrient inputs (U.S. Environmental Protection Agency, 2010). 

Elevated sediment and nutrient inputs are responsible for a 

cascade of problems, including elevated algal densities, the 

burial of aquatic habitats, as well the creation of anoxic zones 

throughout the Bay itself—all of which generally result in less 

favorable conditions for aquatic life such as fish, crabs, and 

oysters (Kemp and others, 2005). To date, most reductions in 

nutrient loadings have been related to the implementation of 
Introduction 5

point-source controls, while nonpoint-source controls have 

been less effective in agricultural communities and growing 

urban areas (Weller and others, 2010).

In May of 2009, Executive Order 13508 was issued 

for the Protection and Restoration of the Chesapeake Bay 

(Obama, 2009). Executive Order 13508 recognized the Bay 

as a National Treasure and announced a new era of Federal 

leadership in the restoration of the Chesapeake Bay. The Order 

established a Federal Leadership Committee to oversee the 

activities of the various Federal agencies that are involved 

in the restoration of the Bay. The Executive Order further 

directed the involved agencies to prepare a coordinated 

strategy for the restoration and protection of the Chesapeake 

Bay (Obama, 2009), including the development of annual 

action plans and progress reports. One important element 

of the coordinated strategy for the protection and restoration of the Chesapeake Bay watershed committed the U.S. 

Geological Survey (USGS), U.S. Department of Agriculture 

(USDA), and U.S. Environmental Protection Agency (EPA) 

to initiate enhanced monitoring of water-quality conditions in small agricultural and urban watersheds to help 

document the effectiveness of conservation practices and 

restoration activities (Federal Leadership Committee for the 

Chesapeake Bay, 2010).

Insufficient progress of the Bay and its tributaries 

towards meeting basic water-quality criteria, including clarity, 

submerged aquatic vegetation, and dissolved oxygen (DO), 

resulted in the EPA developing a Total Maximum Daily Load 

(TMDL) for the entire Chesapeake Bay watershed in 2010 

(U.S. Environmental Protection Agency, 2010). A TMDL is a 

calculation of the maximum amount of pollution that a water 

body can receive and still meet water-quality standards. The 

TMDL is designed to reduce sediment and nutrient inputs 

to the Bay for improving water quality by identifying the 

necessary pollution reductions from the major nitrogen and 

phosphorus source sectors through the implementation of 

best management practices (BMPs) and other conservation 

efforts. The established TMDL calls for a 25-percent decrease 

in nitrogen loading, a 24-percent reduction in phosphorus, 

and a 20-percent reduction in sediment loadings to the 

watershed from 2010 levels (U.S. Environmental Protection 

Agency, 2010). The assigned nutrient and sediment limits are 

subdivided by jurisdiction and major river basin, highlighting 

the need for extensive implementation of conservation 

practices throughout the watershed (U.S. Environmental 

Protection Agency, undated). The TMDL calls for the 

development of phased Watershed Implementation Plans, 

with 60 percent of all conservation practices to be installed 

in Phase I by 2017, and the remainder of the implementation 

activities to be completed by 2025. Phase II and Phase III 

Watershed Implementation Plans are expected to provide 

local targeted areas for implementation at smaller scales 

(U.S. Environmental Protection Agency, 2010); consequently, 

small-scale information regarding how water quality responds 

to focused implementation activities will be of great value in 

the development of these watershed improvement plans and 

the local decisionmaking related to the implementation of new 

conservation practices.

While the Chesapeake Bay Program has developed and 

maintains a large-scale and long-term monitoring network 

to detect improvements in water quality throughout the Bay 

watershed (Langland and others, 2012), few resources have 

been dedicated toward monitoring of smaller watersheds, even 

though the monitoring of smaller watersheds offers a number 

of distinct benefits for the detection of water-quality improvements that are related to the implementation of conservation 

practices. The benefits of monitoring at smaller watershed 

scales were described by a pair of recent Chesapeake Bay 

Program summary documents (Weller and others, 2010; 

National Research Council, 2011) to include the following:

• Intensive monitoring of smaller watersheds can be 

used to isolate different basin types (for example, 

agricultural without urban), while larger watersheds tend to represent mixtures of land-use types 

and sources.

• Within these relatively isolated land-use types, the 

specific sources of sediment and nutrients (manurederived nitrogen or commercial fertilizer-derived 

nitrogen) can be studied and potentially resolved in a 

more controlled setting.

• Intensive monitoring of smaller watersheds can be used 

to understand the transport processes that are responsible for routing sediment and nutrients into streams.

• Monitoring at smaller scales provides the best mechanism 

for directly measuring the water-quality response to 

BMP implementation.

• Monitoring at smaller spatial scales can provide vastly 

improved spatial resolution within a given basin that 

can reveal stream reaches or “hot spots” with disproportionately higher or lower nutrient and sediment 

contributions.

• Monitoring at smaller watershed scales provides a 

natural complement to edge-of-field studies which are 

generally performed at the plot scale and are effective 

at determining the efficacy of individual practices, 

while small watershed studies can document the 

cumulative effect of many practices that are distributed 

over a landscape.

• Monitoring results from these smaller scales can 

be critical for setting public expectations regarding 

the timescales of change (also known as time lags) 

and the magnitude of expected change in these and 

other watersheds.

Despite the benefits of these small watershed studies, they 

are not without challenges because small watershed studies 

can be rather expensive, data may be affected by extended 

timescales of response (depending on the system), and understanding generated at the smaller scale may not be immediately 

applicable to larger scales. Even so, small watershed studies 

represent a critical component of monitoring in the Chesapeake 
6 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Bay watershed and are likely the type of monitoring that is 

best able to link the implementation of management actions to 

watershed-scale changes in water quality.

While anthropogenic activities, including intensive 

agriculture and urbanization, have long been known to 

adversely affect water quality and runoff dynamics (Paul 

and Meyer, 2001; Feminella and Walsh, 2005; Meyer and 

others, 2005; Mueller and Spahr, 2006), most studies have 

struggled to document water-quality improvements that are 

related to the effects of BMP implementation (Meals and 

others, 2010; Corsi and others, 2013). Working in small 

watersheds to understand nutrient and sediment sources and 

transport processes generally requires an interdisciplinary 

“toolbox” approach that uses various hydrologic, geochemical, 

and modeling approaches to understand the occurrence and 

transport of sediment and nutrients through these relatively 

complex small watersheds. Because of the inherent variability among watersheds, the particular response of a given 

watershed to management or development will depend heavily 

on the site-specific conditions within a given watershed, such 

as land use, watershed hydrology, lithology, the presence of 

wastewater treatment plants, and the design and age of existing 

stormwater and sewage infrastructure (Paul and Meyer, 2001). 

Detailed hydrologic analysis can be used to understand 

how urbanization and other changes to the landscape affect 

hydrologic conditions (Burton and Pitt, 2002; Walsh and 

others, 2009). A broad characterization of the major-ion water 

chemistry can be used to characterize differences between 

sites and to elucidate mixing patterns among water sources 

(Böhlke and Denver, 1995; Hyer and others, 2001; Denver 

and others, 2010; Clune and Denver, 2012). Nitrate isotopes 

(Heaton, 1986; Böhlke, 2003; Kendall and others, 2007), 

groundwater age dating (Plummer and others, 1993), and sediment fingerprinting techniques (Gellis and others, 2009) can 

be used to identify the sources of nitrogen and sediment within 

a stream system. In the study described herein, a complex suite 

of hydrologic and geochemical data were used to determine 

the sources and transport mechanisms of sediment and 

nutrients within differing watersheds.

In 2010, the USGS partnered with the EPA and USDA 

to initiate long-term water-quality monitoring in selected 

small watersheds that were targeted for increased implementation of conservation practices. The objective of this 

study was to investigate sediment and nutrient dynamics in 

four relatively small watersheds that are representative of 

a range of land-use patterns and underlying geology types 

to (1) characterize current water-quality conditions in these 

watersheds, (2) identify the dominant sources, sinks, and 

transport processes in each watershed, (3) provide guidance 

to stakeholders (USDA, the Natural Resources Conservation 

Service [NRCS], and local watershed groups) related to the 

implementation of conservation practices, and (4) document changes in water quality and relate those changes to 

the implementation of various water-quality improvement 

activities. By intensively monitoring water quality in four 

relatively common watershed types, observations and conditions encountered should be representative of regional patterns 

in the Chesapeake Bay watershed.

Purpose and Scope

This report provides an initial characterization of water 

quality in four intensively monitored small watersheds by 

presenting the water-quality data collected from 2010 through 

2013. To the extent possible, the major sources, sinks, and 

transport processes for sediment and nutrients are identified 

in each watershed. A summary of the conservation practices 

that have been implemented in each watershed is provided, 

along with ideas of how future conservation practices can be 

targeted or optimized within these watersheds to potentially 

improve efficiency. Lastly, plans for future work to achieve the 

remaining project objectives are described.

This report also represents a contribution to the objectives 

of the USGS Chesapeake Bay Science Plan (Phillips and 

Blomquist, 2015) and a contribution to several of the 

integration questions contained therein, such as

• What are the relationships among land use, water quality, 

contaminants, and the health of fish and wildlife?

• How is the water quality of rivers and estuaries 

responding to restoration actions and changing land use?

• What are some of the best opportunities for management 

actions and monitoring approaches to benefit multiple 

restoration outcomes?

Description of Study Watersheds

The intensive water-quality study is ongoing at four small 

(less than 150-square-mile [mi2]) watersheds as part of Executive Order 13508 (fig. 1). Agricultural land use is dominant 

in three of these small watersheds, which are being studied 

in partnership with USDA and NRCS. The fourth watershed 

is being studied in partnership with Fairfax County, Virginia. 

These four study watersheds provide a range of land-use and 

geologic settings for study of sediment- and nutrient-transport 

processes. These watersheds also have a range of conservation practices being implemented for the improvement of 

water quality.

The Smith Creek Watershed

Smith Creek is a 105.39-mi2 watershed within 

Shenandoah and Rockingham Counties, Virginia (fig. 2). 

The watershed is within the Shenandoah Valley and is part 

of the Valley and Ridge Physiographic Province (fig. 1). The 

Massanutten Mountain ridge transects the watershed north to 

south and is the division between the two predominant land 

uses: agriculture and forest. The predominant agricultural 

activities on the valley floor include extensive pastureland 

(41 percent of the watershed area) devoted to beef and dairy 

cattle production, poultry production, and, to a lesser extent, 

row cropping (3 percent of the watershed area). The relatively 

undeveloped eastern part of the watershed along the mountain 

slope is dominated by deciduous forests, with forests making 
Introduction 7

80°20' 79°15' 78°10' 77°05' 76°00' 74°55'

42°10'

41°05'

40°00'

38°55'

37°50'

36°45'

0 20 40 60 80 100 MILES

0 20 40 60 80 100 KILOMETERS

State lines from U.S. Department of Commerce, U.S. Census Bureau (2014)

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

North American Datum of 1983

Chesapeake Bay

Watershed

PENNSYLVANIA

VIRGINIA

MARYLAND

DELAWARE

WEST

VIRGINIA

NEW

JERSEY

NEW YORK

EXPLANATION

Watershed boundaries

Appalachian Plateau

Valley and Ridge

Blue Ridge

Piedmont

Coastal Plain

Smith Creek

Upper Chester River

Conewago Creek

Difficult Run

Chesapeake Bay

 watershed boundary

Stream

USGS streamgage

Physiographic Province

(from Fenneman and Johnson, 1946)

Figure 1. Physiographic provinces and the location of the four study watersheds within the Chesapeake Bay 

watershed.
8 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

78°50' 78°45' 78°40' 78°35'

38°45'

38°40'

38°35'

38°30'

38°25'

38°45'

38°40'

38°35'

38°30'

38°25'

78°50' 78°45' 78°40' 78°35'

0 5 10 MILES

0 5 10 KILOMETERS

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

A. Land use B. Geology

C. Sinkhole density

Sinkholes from Hubbard (1983)

Land use from Homer and others (2015) Geology from Dicken and others (2005)

EXPLANATION

A. Land use

Evergreen forest

Deciduous forest

Pasture/hay

Cultivated crops

Developed, low intensity

Developed, medium intensity

Developed, high intensity

Developed, open space

B. Geology

Dolostone

Limestone

Shale

Sandstone

C. Sinkhole density

Low High

Mainstem of Smith Creek

Stream

Sampling location

 USGS streamgage 01632900

 Synoptic

Figure 2. Monitoring stations, (A) land use, (B) underlying geology, and (C) sinkhole density in the Smith Creek watershed.
Introduction 9

up 48 percent of the watershed area. Approximately 90 percent 

of the land use within the Smith Creek watershed is represented by agriculture and forest, in relatively even proportions, 

and 6 percent is developed, primarily in the headwaters near 

Harrisonburg, Virginia.

Smith Creek is a third-order stream that flows south 

to north and is a tributary of the North Fork Shenandoah 

River. The headwaters display a typical dendritic pattern as a 

result of erosion of Middle Cambrian to Middle Ordovician 

carbonate rocks (a series of limestone and dolostone units 

including the Elbrook Dolomite, Conococheague Limestone, 

Beekmantown Group, New Market Limestone, Lincolnshire 

Limestone, Edinburg Formation, and the Stickley Run 

Member of Martinsburg Formation) that underlie the upper 

part of the watershed (fig. 2B). These carbonate rocks in the 

watershed are characterized by numerous headwater springs 

and sinkholes (fig. 2C). The lower part of the watershed is 

generally underlain by Middle to Late Ordovician Martinsburg 

Formation (primarily shale and secondarily sandstone). The 

very top of the mountains on the eastern edge of the watershed 

is underlain by the Silurian Massanutten Sandstone (Rader and 

Gathright, 2001a,b). Numerous zero- and first-order streams 

flow off the forested mountainside into Smith Creek in this 

lower section (fig. 2B).

Previous water-quality monitoring by the Virginia 

Department of Environmental Quality (VADEQ) revealed that 

sections of Smith Creek were in violation of State standards 

for bacteria and benthic macroinvertebrates. In response, a 

TMDL was established in 2004 for Escherichia coli (E. coli) 

and fecal coliform, and sediment was identified as a primary 

stressor to aquatic life (Virginia Department of Environmental 

Quality, 2014). The implementation of BMPs and waterquality monitoring increased in response to these listings, 

and the Smith Creek Partnership was formed to improve 

coordination and communication within the watershed. This 

partnership benefits from input by a number of State, local, 

and Federal agencies as well as citizen environmental groups. 

A detailed description of these TMDL listings, potential 

sources of pollutants, and various management activities is 

presented in the 2009 Smith Creek Watershed Implementation Plan (Virginia Tech Department of Biological Systems 

Engineering and University of Virginia Institute for Environmental Negotiation, 2009). The selection of Smith Creek as 

a Showcase Watershed within this study has contributed to 

the additional implementation of management practices and 

water-quality monitoring.

The Upper Chester River Watershed

The Upper Chester River watershed is a 36.54-mi2

watershed located in the Coastal Plain on the Eastern Shore 

of the Chesapeake Bay in Maryland (fig. 1). The watershed 

is generally characterized as 64 percent agricultural land 

use with predominantly row-crop agriculture (54 percent of 

the watershed area) (fig. 3A). Subtle differences in the soils 

and topography on the northern and southern sides of the 

Upper Chester River manifest themselves in the land use. On 

the northern side of the river, topography is gently rolling, 

soils are well drained, and land use is mostly agricultural. 

In contrast, the southern part of the watershed has shallower 

stream incision and a smaller area of well-drained soils, 

resulting in a lesser amount of agricultural activity and a 

greater amount of forested, undeveloped land.

The Upper Chester River watershed includes the 

watershed area around several nontidal tributaries that drain 

into the tidal Chester River. The watershed is largely underlain 

by fine-grained sands and clays of the Calvert Formation of 

Miocene age or glauconitic sands of the Paleocene age Aquia 

Formation, which are, at the surface in many incised stream 

channels (fig. 3B). On the watershed uplands, deposits of 

gravel and sand of Quaternary age overly these formations and 

form a surficial unconfined aquifer that supplies most of the 

flow to local streams. The geologic configuration of the watershed affects transport of nitrate to streams. Where streams are 

underlain by sandy deposits, nitrate can be present at relatively 

high concentrations in discharging groundwater. In contrast, 

where streams are incised through the sandy deposits into finer 

grained sediments, nitrate in groundwater is more likely to be 

removed through denitrification prior to stream discharge.

The Upper Chester River watershed in this study is part 

of the larger area of the Upper Chester River regulated by the 

State of Maryland. The Maryland part of the Upper Chester 

River was listed as impaired by nutrients in 1996 by the 

Maryland Department of the Environment (MDE). TMDLs 

for nitrogen and phosphorus were later developed by the MDE 

and approved in 2006 by the EPA (Maryland Department of 

the Environment, 2006). Since that time, numerous BMPs 

have been implemented in this area. The designation of 

this watershed as a Showcase Watershed by NRCS in 2010 

directed more resources to this area and increased implementation of practices intended to improve water quality in the 

Upper Chester River.

The Conewago Creek Watershed

Conewago Creek watershed is a 52.5-mi2 agricultural 

watershed located in southeastern Pennsylvania (fig. 4A). 

Predominant agricultural activities include cattle production 

and row cropping. Approximately 43 percent of the watershed 

is forested, and 17 percent is developed. The basin is underlain 

by several different types of rock (fig. 4B). The watershed lies 

entirely in the Piedmont Physiographic Province (specifically 

the Gettysburg-Newark Lowland Section; fig. 1) and is underlain by red sandstone, mudstone, conglomerate, and thin beds 

of limestone of Triassic age that have been intruded by diabase 

of Jurassic age. The Gettysburg-Newark Lowland Section 

consists mainly of rolling low hills and valleys developed on 

red sedimentary rock deposited in a long, narrow inland basin. 

There are also isolated higher hills (forming the eastern and 

southern watershed boundaries) that are developed on diabase 

and conglomerates. A horseshoe-shaped diabase sill extends 

through the upper and middle part of the watershed and forms 

its southern boundary (fig. 4B).

The basic drainage pattern is dendritic (fig. 4). Soils 

are usually red and often have a striking contrast to the 

green vegetation. Relief is generally on the order of 
10 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

76°00' 75°56' 75°52'

39°16'

39°12'

39°16'

39°12'

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

A. Land use

B. Geology

Geology from Dicken and others (2005)

Land use from Homer and others (2015)

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Department of Agriculture, Natural Resources Conservation Service 

 unpub. data for HUC 020600020605, 2005

North American Datum of 1983

B. Geology

A. Land use

Evergreen forest

Deciduous forest

Pasture/hay

Developed, open space

Developed, low intensity

Developed, medium intensity

Developed, high intensity

Cultivated crops

Woody wetlands

Open water

Stream

Sampling location

 USGS streamgage 01493112

 Synoptic

Lowland deposits—Gravel, sand, 

silt, and clay

Upland deposits—Cross-bedded, 

poorly sorted, medium- to 

coarse-grained white to red 

sand and gravel

Aquia Formation—Dark green 

to gray-green, argillaceous, 

highly glauconitic, well sorted 

fine- to medium-grained sand; 

locally indurated shell beds

Calvert Formation—Interbedded 

dark green to dark bluish-gray, 

fine-grained argillaceous sand 

and sandy clay 

EXPLANATION

Figure 3. Monitoring stations, (A) land use, and (B) underlying geology in the Upper Chester River watershed.
Introduction 11

40°15'

40°10'

40°05'

76°40' 76°35' 76°30'

40°15'

40°10'

40°05'

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

A. Land use

B. Geology

Geology from Dicken and others (2005)

Land use from Homer and others (2015)

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Geological Survey StreamStats program (2012b)

North American Datum of 1983

B. Geology

EXPLANATION

A. Land use

Evergreen forest

Deciduous forest

Pasture/hay

Developed, open space

Developed, low intensity

Developed, medium intensity

Developed, high intensity

Cultivated crops

Woody wetlands

Open water

Diabase

Mudstone

Arkose

Sandstone

Conglomerate

Mainstem of Conewago Creek

Stream

Sampling location

 USGS streamgage and number

 Synoptic

01573695

01573710

01573695

01573710

Figure 4. Monitoring stations, (A) land use, and (B) underlying geology in the Conewago Creek watershed.
12 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

100 to 200 feet (ft), but locally is up to 600 ft on some of the 

isolated hills. Elevations in the Conewago Creek watershed 

range from approximately 300 to 1,120 ft.

Approximately 40 percent of the entire stream length is 

listed as impaired, with sediment being primarily responsible 

for the impairment (Pennsylvania State University Environmental Resources Research Institute, 2001). Approximately 

two-thirds of the mainstem Conewago Creek is impaired. 

Three tributaries, Lynch Run and two unnamed tributaries, 

are 100 percent impaired. A fourth tributary, Hoffer Creek, is 

approximately 50 percent impaired. Siltation is the leading 

impairment and likely resulted from the migration and erosion 

of soil from higher slopes after lands were cleared of forest for 

farming activities, mainly growing of crops, in the mid-1800s 

to early 1900s. Land stewardship was minimal, which helped 

to create more slope erosion. As a result of past anthropogenic 

actions, including the building of milldams, streams in many 

areas of Piedmont, including Conewago Creek, are elevated, 

entrenched streams with steep, erodible banks. Compounding 

the problem is that the majority of the streambank sediments 

are cohesive silts and clays, and in agricultural areas, these 

soils can be enriched with nutrients.

The Difficult Run Watershed

The Difficult Run watershed is a 57.82-mi2 watershed 

in the Piedmont Physiographic Province (figs. 5 and 1). 

Difficult Run is the largest watershed within Fairfax County, 

which is part of the Washington, D.C., metropolitan area and 

is the most populous jurisdiction in Virginia (U.S. Census 

Bureau, 2013). Historically, this region was heavily used for 

row-crop agricultural activities and pastureland until intense 

urbanization began in the 1960s. Residential land use now 

accounts for more than 57 percent of watershed area, and 

much of this development is alongside the Difficult Run 

stream channel (Fairfax County Stormwater Planning Division, 2007) (fig. 5). Commercial land use makes up 9 percent 

of the watershed and is generally accounted for by three main 

urban areas—Reston to the northwest, the city of Fairfax 

to the south, and Tysons Corner to the east (Fairfax County 

Stormwater Planning Division, 2007). Scattered pockets of 

open area make up 20 percent of additional land use and are 

typically found near the stream valleys that are preserved 

by the Fairfax County Park Authority (Fairfax County 

Stormwater Planning Division, 2007).

Difficult Run is a fourth-order stream that flows northeast 

for 39 stream miles to the Potomac River. First- and secondorder tributaries join the mainstem of Difficult Run and 

contribute 106 stream miles that are predominantly pool-riffle 

flow regimes with gravel and cobble substrates (Fairfax 

County Stormwater Planning Division, 2007). The majority of 

the watershed is underlain by schist and meta-argillite of the 

Late Proterozoic to Early Cambrian Mather Gorge Formation, 

with minor contributions from numerous metamorphic and 

sedimentary rock formations (fig. 5B). Four major impoundments are located throughout the watershed: Lake Anne, Lake 

Fairfax, Lake Thoreau, and Lake Audubon.

The VADEQ and the Fairfax County Health Department 

have monitored water quality throughout the watershed for 

several years. This work revealed impairments that placed 

sections of Difficult Run on the 303(d) list for benthic aquatic 

communities and fecal coliform bacteria (Virginia Department 

of Environmental Quality, 2014). These impairments, as well 

as a detailed description of ongoing work and management 

plans are presented in the Difficult Run Watershed Management Plan that was developed by Fairfax County in 2007. The 

watershed has been the focus for understanding nutrient and 

sediment dynamics within an urban setting for many USGS 

scientists, and monitoring has continued to increase as part 

of this study (Hupp and others, 2013; Noe and others, 2013; 

Schenk and others, 2013; Jastram, 2014).

Study Approach and Methods

Described below is the study design for the small 

watershed study, including the methods used for the collection 

and analysis of samples, as well as the statistical analysis 

and interpretation. A wide range of monitoring and analysis 

tools were used throughout the study; however, slight study 

design differences were necessary for some sites for a number 

of reasons that involved site constraints, research questions, 

available funding, and water-quality constituents of interest.

General Study Design

The small watershed study design is based on two 

fundamental study components:

• Intensive monitoring at the streamgage within each 

study area to develop a detailed understanding of the 

temporal and hydrologic variability in nutrient and 

sediment transport in each watershed. These intensive 

data also provide substantial understanding of the 

nutrient and sediment transport processes operating 

within each study watershed. The Smith Creek, Upper 

Chester River, and Difficult Run watersheds each had a 

single intensively instrumented site, but the Conewago 

Creek watershed had two intensively monitored 

sites (table 1).

• Extensive monitoring at various sites throughout the 

watershed to develop a detailed understanding of the 

spatial patterns in sediment and nutrient transport 

throughout the watershed. These extensive data also 

provide an enhanced understanding of the nutrient and 

sediment sources and transport processes within each 

watershed. The extensive data collection efforts generally involved segregating each watershed into 15 to 

40 roughly evenly spaced stream and tributary reaches 

that could be sampled within 1 to 2 days to provide a 

“synoptic snapshot” of the water-quality conditions 

over a period of relatively uniform flow conditions.
Study Approach and Methods 13

77°24' 77°20' 77°16' 77°12'

39°00'

38°56'

38°52'

39°00'

38°56'

38°52'

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

A. Land use

B. Geology

Geology from Dicken and others (2005)

Land use from Homer and others (2015)

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Conglomerate

Schist

Meta-argillite

Gravel

Granodiorite

Ultramafatite

Mafic gneiss

B. Geology

EXPLANATION

A. Land use

Evergreen forest

Deciduous forest

Pasture/hay

Developed, open space

Developed, low intensity

Developed, medium intensity

Developed, high intensity

Cultivated crops

Woody wetlands

Open water

Mainstem of Difficult Run

Stream

Sampling location

 USGS streamgage 01646000

 Synoptic

Figure 5. Monitoring stations, (A) land use, and (B) underlying geology in the Difficult Run watershed.
14 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Data Collection

Standard USGS methods were used for the collection 

of water-quality data. The following section describes 

data collection efforts for the study, including discrete 

water-quality sampling and laboratory analysis procedures, 

as well as the methods used to operate the continuous waterquality monitors.

Collection of Discrete Water-Quality Samples

Collection of discrete water-quality samples was 

completed following standard USGS methods (U.S. Geological Survey, variously dated). Discrete water-quality samples 

were collected on one predetermined date each month (routine 

samples) and during eight targeted hydrologic conditions 

(storm samples, periods of elevated discharge) from each 

streamgage (table 2). Water-quality samples were collected 

by using width- and depth-integrated sampling procedures. 

Width integration was accomplished by dividing the stream 

into equally spaced intervals (usually 5 or 10). Depth integration was accomplished by using isokinetic samplers during 

appropriate conditions.

Extensive synoptic sample collection was performed 

four times at various sites within the Smith Creek, Difficult 

Run, and Upper Chester River watersheds during low-flow 

hydrologic conditions (table 2). Synoptic sampling locations 

were selected to characterize the spatial differences within 

each basin. Width- and depth-integrated sampling methods 

were followed at all but the smallest streams where a grab 

sampling approach was used. An instantaneous discharge 

measurement was paired with these discrete water-quality 

measurements. All discharge measurements were made using 

hand-held acoustic doppler velocity profilers, following the 

mid-section method described by Turnipseed and Sauer (2010) 

in accordance with USGS procedures.

Collection of field blanks and sequential replicates 

was built into all discrete-sampling activities to estimate 

contamination bias and variability in the data (table 3). Most 

field blank concentrations were below the laboratory detection 

limits, indicating that no contamination issues were caused by 

sample collection, processing, or analysis. All detectable field 

blank concentrations were insignificant compared to sampled 

values. Variability between replicate pairs generally was low, 

particularly for major ions and nutrients.

Laboratory Analysis of Water-Quality Samples

Discrete water-quality samples were analyzed by a number 

of different laboratories for a range of nutrients, cations, 

anions, trace elements, and isotopes (tables 4, 5, and 6). 

All samples were immediately processed and preserved 

Table 1. Watershed names, watershed areas, and streamgage 

station IDs and names.

[USGS, U.S. Geological Survey; mi2, square mile]

Watershed Watershed 

area (mi2)

USGS 

station 

ID

USGS 

streamgage 

name

Smith Creek 105.39 01632900 Smith Creek near New 

Market, VA

Difficult Run 57.82 01646000 Difficult Run near 

Great Falls, VA

Upper Chester River 36.54 01493112 Chesterville Branch 

near Crumpton, MD

Conewago Creek 52.53 01573695 Conewago Creek 

near Bellaire, PA

01573710 Conewago Creek 

near Falmouth, PA

Table 2. Dates of continuous water-quality monitoring and sample-collection activities.

[—, activity did not occur]

Watershed Streamgage station 

name

Monthly and 

storm sample 

collection1

Extensive synoptic sample collection2

Collection of 

continuous 

water-quality 

data1

Collection of 

continuous 

nitrate data1

Smith Creek Smith Creek near New 

Market, VA

April

2010

August

2011

December

2012

May 

2012

April 

2013

April 

2010

March 

2012

Difficult Run Difficult Run near 

Great Falls, VA

July

2011

September

2011

December

2012

April 

2012

May 

2013

October 

2012

October 

2012

Upper Chester RiverChesterville Branch 

near Crumpton, MD

July

2011

July

2011

December

2012

May 

2012

April 

2013

June 

2012

July 

2012

Conewago 

Creek

Conewago Creek near 

Bellaire, PA

May

2011

— — May 

2012

— January 

2012

—

Conewago Creek near 

Falmouth, PA

October

2011

— — May 

2012

— June 

2011

—

1

Date activity began. 

2

Date of each sample-collection event.
Study Approach and Methods 15

according to methodological specification and delivered to the 

appropriate analytical laboratories within specified holding 

times. Discrete monthly and storm samples from Smith Creek 

and Difficult Run were delivered on the same day as collection 

to the Division of Consolidated Laboratory Services (DCLS) 

in Richmond, Virginia, for analysis of nutrients and sediment. 

Discrete monthly and storm samples from the Upper Chester 

River and Conewago Creek were delivered to the USGS 

Sediment Laboratory in Louisville, Kentucky, for sediment 

analysis. All samples collected from Conewago Creek were 

analyzed by the Pennsylvania Department of Environmental 

Protection (PADEP) laboratory in Harrisburg, Pennsylvania. 

Monthly, storm, and extensive synoptic samples from Smith 

Creek, Difficult Run, and the Upper Chester River were 

analyzed for nitrogen and oxygen isotope ratios of nitrate 

by the Reston Stable Isotope Laboratory in Reston, Virginia. 

All other samples were chilled to 4 degrees Celsius (°C) 

and shipped overnight to the USGS National Water Quality 

Laboratory in Denver, Colorado.

Samples for nitrate isotope analysis were pumped 

through 0.45-micrometer (μm) capsule filters, followed 

by 0.2-μm syringe filters, and preserved with potassium 

hydroxide. All nitrate concentrations are presented in units of 

milligrams per liter as nitrogen (N). Isotopic analyses of N 

and oxygen (O) in nitrate were done at the USGS in Reston, 

Virginia, by using the bacterial reduction method with Pseudomonas aureofaciens (Sigman and others, 2001; Casciotti 

and others, 2002; Coplen and others, 2004). The data were 

calibrated by analyzing international nitrate isotopic reference 

materials and applying normalization data from Böhlke and 

others (2003). Variations in the isotope ratios of N and O are 

reported as delta values, defined as the relative difference 

between the molar (or atomic) ratio of isotopes in a sample 

and that in a standard: δiE = Rsample / Rstandard –1. For nitrogen, 

δiE is δ15N, R is n(15N)/n(14N), and the standard is atmospheric 

nitrogen gas. For oxygen, δiE is δ18O, R is n(18O)/n(16O), 

and the standard is Vienna Standard Mean Ocean Water 

(VSMOW). Delta values are reported in parts per thousand 

(per mil, or ‰); for example, δ15N = 0.006, δ15N = 6 ‰, or 

1,000 × δ15N = 6. Uncertainties (2 sigma) of normalized δ15N 

and δ18O values were approximately ± 0.5 per mil (‰) and 

± 1.0 ‰, respectively.

Continuous Monitoring of Water Quality

Continuous water-quality data were collected at each 

small watershed monitoring station (table 7). All continuous 

water-quality data were collected following USGS guidelines 

(U.S. Geological Survey, variously dated; Wagner and 

others, 2006). Modified field protocols were used for servicing 

the Conewago Creek sites because of sonde-accessibility 

issues. Fouling and calibration checks were performed 

approximately monthly during the servicing of each continuous monitor. Continuous water-quality records were refined 

by applying appropriate fouling and drift corrections following 

these inspections. Records were then reviewed and approved 

by different hydrographers or hydrologists.

Statistical Analysis of Surface-Water and 

Water-Quality Data

Only final, approved USGS data were used in statistical 

analyses. The streamflow record was analyzed by using the 

hydrograph separation software package PART to compute a 

base-flow index (BFI) for the watershed over multiple time 

periods (Rutledge, 1998). The BFI represents the ratio of the total 

base-flow volume to the total streamflow volume for a given 

period and is indicative of whether a given stream is dominated 

by groundwater discharge or direct runoff during storms.

Table 3. Analysis of replicate and blank discrete water-quality 

data.

[USGS, U.S. Geological Survey; BN, number of field blanks; BND, number 

of blanks that were above the laboratory detection limit; BMAX, maximum 

reported concentration in field blanks; RN, number of replicates; RUNC, estimated uncertainty in reported concentrations, in percent; mg/L, milligram per 

liter; μg/L, microgram per liter; —, value does not exist]

Constituent

USGS

parameter 

code

Reporting 

units

Contamination 

bias

Analytical 

uncertainty

BN BND BMAX RN RUNC

1

Calcium 00915 mg/L 14 3 0.03 16 0.79

00916 mg/L 1 0 — 3 0.90

Magnesium 00925 mg/L 14 0 — 16 0.46

00927 mg/L 1 0 — 3 0.87

Sodium 00930 mg/L 14 1 0.069 16 0.89

Potassium 00935 mg/L 14 0 — 16 1.17

Bicarbonate 00453 mg/L 1 1 4.2 15 1.75

Sulfate 00945 mg/L 15 0 — 19 0.42

Chloride 00940 mg/L 15 0 — 19 0.19

Nitrate-N 00631 mg/L 22 3 0.021 37 0.56

Total nitrogen 00600 mg/L 8 0 — 15 0.82

Ammonium 00608 mg/L 22 6 0.04 26 3.11

Orthophosphate 00671 mg/L 22 3 0 28 1.41

Total phosphorus 00665 mg/L 22 4 0.01 32 1.59

Iron 01046 μg/L 14 1 3.419 16 3.73

01045 μg/L 1 0 — 3 7.57

Manganese 01056 μg/L 14 5 0.33 16 0.92

01055 μg/L 1 0 — 3 1.63

Silica 00955 mg/L 8 1 0.05 10 0.66

Suspendedsediment 

concentration

80154 mg/L 7 — 2.13 13 14.89

Total suspended 

solids

00530 mg/L 8 0 5 15 0.00

1

Estimated uncertainty is the median of the relative standard deviation of 

reported concentrations for replicate sets in which the compound was detected 

in all replicates. The relative standard deviation is the standard deviation 

divided by the mean.
16 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 4. Constituents, parameter codes, analysis sources, method codes, reporting units, and reporting limits for discrete waterquality constituents collected at the Smith Creek and Difficult Run watersheds.

[USGS, U.S. Geological Survey; NWQL, National Water Quality Laboratory; DCLS, Division of Consolidated Laboratory Services; EPA, U.S. Environmental 

Protection Agency; NFM, National Field Manual; ICP-MS, inductively coupled plasma mass spectrometry; mg/L, milligram per liter; ASTM, American Society 

for Testing and Materials; μg/L, microgram per liter]

Constituent

USGS

parameter 

code

Analysis 

source

Method

code Matrix Reportingunits

Reporting

limit

Smith Creek and Difficult Run watersheds

Calcium 00915 NWQL USGS I-1472-87 filtered water mg/L 0.022

Magnesium 00925 NWQL USGS I-1472-87 filtered water mg/L 0.011

Sodium 00930 NWQL USGS I-1472-87 filtered water mg/L 0.06

Potassium 00935 NWQL 3120 ICP-MS filtered water mg/L 0.03

Bicarbonate1 00453 Calculated in field USGS NFM 6.6 unfiltered water mg/L —2

Alkalinity 29801 NWQL USGS I-2030-85 filtered water mg/L 4.6

Chloride 00940 NWQL USGS I-2057-90 filtered water mg/L 0.02

Sulfate 00945 NWQL USGS I-2057-90 filtered water mg/L 0.022

Nitrate-N 00631 NWQL3 NWQL RED01 filtered water mg/L 0.04

DCLS4 EPA 353.2 0.004

Ammonium 00608 NWQL3 NWQL 00048 filtered water mg/L 0.01

DCLS4 USGS I-2523-85 0.006

Total nitrogen 62855 NWQL NWQL AKP01 unfiltered water mg/L 0.02

00600 DCLS4 EPA Standard 

Method 4500-N,

Part C (20th ed.) 

0.05

Orthophosphate 00671 NWQL3 NWQL 00048 filtered water mg/L 0.004

DCLS4 EPA 365.1 0.002

Total phosphorus 00665 NWQL3 EPA 365.1 unfiltered water mg/L 0.004

DCLS4 EPA 365.4 0.01

Suspended-sediment 

concentration

80154 DCLS4 ASTM 3977-97 

Method B

unfiltered water mg/L 1

Iron 01046 NWQL USGS I-1472-87 filtered water μg/L 4

Manganese 01056 NWQL USGS I-1472-87 filtered water μg/L 0.2

Fluoride 00950 NWQL NWQL IC003 filtered water mg/L 0.01

Silica 00955 NWQL USGS I-1472-87 filtered water mg/L 0.018

Delta N-15 of nitrate, 

per mil

82690 Reston Stable Isotope 

Laboratory

USGS Techniques 

and Methods 

10-C17

filtered water per mil 0.5

Delta O-18 of nitrate, 

per mil

63041 Reston Stable Isotope 

Laboratory

USGS Techniques 

and Methods 

10-C17

filtered water per mil 0.5

1

Missing bicarbonate values were calculated from alkalinity results (U.S. Geological Survey, 2012a). 

2

 Errors associated with the calculation of bicarbonate values using the advanced speciation method are typically limited to less than 10 percent of the sample 

alkalinity (U.S. Geological Survey, 2012a).

3

Analysis source used during synoptic samples only.

4

Analysis source used during monthly and storm samples only.
Study Approach and Methods 17

In general terms, most water-quality data used in 

these analyses were collected between April 2010 and 

September 2013. Non-parametric analyses were used to describe 

statistical relationships in the data following recommendations 

by Helsel and Hirsch (2002). All statistical tests were evaluated 

at the 95-percent confidence level (ɑ=0.05). Concentrationdischarge plots were created to visualize relations to flow 

and season, with seasons classified as “warm” (April through 

September) or “cool” (October through March). Statistical relationships of discrete parameters to flow and season were defined 

using Spearman’s rho correlation matrices and Wilcoxon test 

of rank scores, respectively. Several statistical methods deserve 

special mention in addition to these aforementioned analyses.

Development of Surrogate Water-Quality Models 

and Computation of Loads

Continuous water-quality parameters were used to create 

surrogate models that estimate suspended-sediment, total 

phosphorus (TP), and total nitrogen (TN) concentrations. 

The surrogate regression models were developed following 

published USGS approaches (Helsel and Hirsch, 2002; 

Jastram and others, 2009; Rasmussen and others, 2009), so 

only a summary of the pertinent details is presented here.

Models were built and evaluated by using JMP 

version 11.0. Models were developed using best subsets 

Table 5. Constituents, parameter codes, analysis sources, method codes, reporting units, and reporting limits for discrete waterquality constituents collected at the Upper Chester River watershed.

[NWQL, National Water Quality Laboratory; EPA, U.S. Environmental Protection Agency; USGS, U.S. Geological Survey; NFM, National Field Manual; ICPMS, inductively coupled plasma mass spectrometry; mg/L, milligrams per liter; ASTM, American Society for Testing and Materials; μg/L, microgram per liter]

Constituent

USGS

parameter 

code

Analysis source Method

code Matrix Reportingunits

Reporting

limit

Upper Chester River watershed

Calcium 00915 NWQL USGS I-1472-87 filtered water mg/L 0.022

Magnesium 00925 NWQL USGS I-1472-87 filtered water mg/L 0.011

Sodium 00930 NWQL USGS I-1472-87 filtered water mg/L 0.06

Potassium 00935 NWQL 3120 ICP-MS filtered water mg/L 0.03

Bicarbonate1 00453 Calculated in field USGS NFM 6.6 unfiltered water mg/L —2

Alkalinity 29801 NWQL USGS I-2030-85 filtered water mg/L 4.6

Chloride 00940 NWQL USGS I-2057-90 filtered water mg/L 0.02

Sulfate 00945 NWQL USGS I-2057-90 filtered water mg/L 0.022

Nitrate-N 00631 NWQL NWQL RED01 filtered water mg/L 0.04

Ammonium 00608 NWQL NWQL 00048 filtered water mg/L 0.01

Total nitrogen 62855 NWQL NWQL AKP01 unfiltered water mg/L 0.02

Orthophosphate 00671 NWQL NWQL 00048 filtered water mg/L 0.004

Total phosphorus 00665 NWQL EPA 365.1 unfiltered water mg/L 0.004

Suspended-sediment 

concentration

80154 Kentucky 

Sediment 

Laboratory3

ASTM 3977-97 

Method B

unfiltered water mg/L 1

Iron 01046 NWQL USGS I-1472-87 filtered water μg/L 4

Manganese 01056 NWQL USGS I-1472-87 filtered water μg/L 0.2

Fluoride 00950 NWQL NWQL IC003 filtered water mg/L 0.01

Silica 00955 NWQL USGS I-1472-87 filtered water mg/L 0.018

Delta N-15 of nitrate, 

per mil

82690 Reston Stable 

Isotope 

Laboratory

USGS Techniques 

and Methods 

10-C17

filtered water per mil 0.5

Delta O-18 of nitrate, 

per mil

63041 Reston Stable 

Isotope 

Laboratory

USGS Techniques 

and Methods 

10-C17

filtered water per mil 0.5

1

Missing bicarbonate values were calculated from alkalinity results (U.S. Geological Survey, 2012a).

2

Errors associated with the calculation of bicarbonate values using the advanced speciation method are typically limited to less than 10 percent of the sample 

alkalinity (U.S. Geological Survey, 2012a).

3

Analysis source used during monthly and storm samples only
18 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 6. Constituents, parameter codes, analysis sources, method codes, reporting units, and reporting limits for discrete waterquality constituents collected at the Conewago Creek watershed. 

[USGS, U.S. Geological Survey; NWQL, National Water Quality Laboratory; PADEP, Pennsylvania Department of Environmental Protection; EPA, U.S. Environmental Protection Agency; mg/L, milligram per liter; CaCO3

, calcium carbonate; <, less than; mm, millimeter; μg/L, microgram per liter]

Constituent

USGS

parameter 

code

Analysis source Method

code Matrix Reportingunits

Reporting

limit

Conewago Creek watershed

Calcium 00916 PADEP EPA 200.7 Revision 4.4 unfiltered water mg/L 0.03

Magnesium 00927 PADEP EPA 200.7 Revision 4.4 unfiltered water mg/L 0.01

Hardness 00900 PADEP SM 2340A+B and EPA 

200.7 revision 4.4

unfiltered water mg/L as CaCO3 0.11

Chloride 00940 PADEP EPA 300.0 filtered water mg/L 0.5

Sulfate 00945 PADEP EPA 300.0 filtered water mg/L 1

Nitrate-N 00631 PADEP EPA 353.2 filtered water mg/L 0.04

Ammonium 00608 PADEP EPA 350.1 filtered water mg/L 0.02

Total nitrogen 00600 PADEP SM 4500N-ORG unfiltered water mg/L 0.064

Orthophosphate 00671 PADEP EPA 365.1 filtered water mg/L 0.01

Total phosphorus 00665 PADEP EPA 365.1 unfiltered water mg/L 0.01

Organic carbon 00680 PADEP SM 5310C unfiltered water mg/L 0.5

Total suspended 

solids

00530 PADEP USGS-I-3765 unfiltered water mg/L 2

Suspended sediment 

concentration1

80154 Kentucky Sediment 

Laboratory

ASTM 3977-97 Method B unfiltered water mg/L 1

Suspended sediment 

<0.0625 mm1

70331 Kentucky Sediment 

Laboratory

ASTM 3977-97 Method C unfiltered water percent 5

Suspended sediment 

<1 mm1

70335 Kentucky Sediment 

Laboratory

ASTM 3977-97 Method C unfiltered water percent 5

Iron 01045 PADEP EPA 200.7 Revision 4.4 unfiltered water μg/L 20

Manganese 01055 PADEP EPA 200.7 Revision 4.4 unfiltered water μg/L 10

Aluminum 01105 PADEP EPA 200.7 revision 4.4 unfiltered water μg/L 200

Copper 01042 PADEP EPA 200.7 revision 4.4 unfiltered water μg/L 10

Zinc 01092 PADEP EPA 200.8 revision 5.4 unfiltered water μg/L 10

1

Constituent collected during storm samples and synoptic samples.

Table 7. Continuous water-quality data collected within each watershed. 

[x= parameter collected; deg C, degrees Celsius; mg/L, milligram per liter; FNU, formazin nephelometric unit; μS/cm, microsiemens per centimeter]

Parameter USGS parameter code Method code Reporting 

units

Watershed

Smith 

Creek

Difficult 

Run

Upper 

Chester

River

Conewago 

Creek

Water temperature 00010 THM01 deg C x x x x

Specific conductance 00095 SC001 μS/cm x x x x

pH 00400 PROBE Standard units x x x

Dissolved oxygen 00300 LUMIN mg/L x x x

Turbidity 63680 TS087 FNU x x x

Nitrate-N 99137 UV012/UV0131 mg/L x x x

1

UV012 is used at Smith Creek and Difficult Run; UV013 is used at the Upper Chester River.
Study Approach and Methods 19

regression, which calculates all possible 2k regressions 

(where k is the number of explanatory variables evaluated). 

The minimization of Mallows’ Cp was used as an initial 

decision point for model selection. Mallows’ Cp is an overall 

measure of model quality that balances the goals of explaining 

the most amount of variability in the response variable, while 

minimizing standard error by reducing the number of predictor 

variables (Helsel and Hirsch, 2002).

Models with the smallest Mallows’ Cp were further 

evaluated by using the adjusted R2 statistic. This statistic is 

similar to the R2 parameter, which represents the amount of 

variability explained in a model, but is weighted based on the 

number of explanatory variables (Helsel and Hirsch, 2002). 

Models with the smallest mean squared error have the highest 

adjusted R2. This statistic compensates for the weakness that 

R2 will always increase with each additional model parameter 

(Helsel and Hirsch, 2002).

Selected models were also evaluated based on 

reasonableness. Reasonable models contain variables with 

significant terms and with coefficients that accurately represent relationships to the response variable. Variables with a 

p-value <0.05 indicate that the parameter explains a significant 

amount of variation and should be present in the model (Helsel 

and Hirsch, 2002). All selected models were forced to include 

a flow variable to function properly within Load Estimator 

(LOADEST). Because of this limitation, some models contain 

a flow parameter with an insignificant term. The sign and 

magnitude of assigned coefficients were compared to the 

relationship between each predictor and response variable. 

Conspicuous coefficients with inverse relationships often 

indicate a multicollinearity concern.

Multicollinearity occurs when at least two model 

parameters are closely related to one another (Helsel and 

Hirsch, 2002). The variance inflation factor (VIF) was used 

to test for excessive multicollinearity. The variance inflation 

factor determines the independence of each explanatory variable and should be minimized, with serious concern occurring 

at VIF>10 (Helsel and Hirsch, 2002).

Explanatory and response variables were log or 

square-root transformed, if necessary, to develop linear 

relationships and homoscedasticity in the data. These two 

characteristics are assumptions required by linear regression 

(Helsel and Hirsch, 2002). Natural logarithm transformation was used instead of log base 10 to coincide with the 

transformation choices used by LOADEST.

Nutrient and sediment loads were calculated by using the 

rloadest version 0.1.0 package in RStudio. Total mass loading 

of a constituent can be defined using the equation

0

t

L QCdt t = ∫ (1)

where

Lt is the total load,

Q is the instantaneous streamflow,

C is the concentration, and

t is the time.

Loads were generated for each water year based on 

an hourly time step (24 observations of concentration and 

discharge per day). An average hourly value was calculated 

from the 15-minute continuous dataset. After being provided a 

selected surrogate-concentration model, LOADEST calculated 

concentration by using ordinary least-squares regression 

(Runkel, and others, 2004). Instantaneous loads were then 

estimated in log space and were retransformed and summed 

over the entire time period to obtain total load (Runkel and 

others, 2004).

Loads were reported from the adjusted maximum 

likelihood estimation (AMLE) method. The AMLE method 

was selected because it effectively handles retransformation 

bias and provides a measure of uncertainty on each estimate of 

load (Runkel and others, 2004). The standard error of prediction (SEP) calculated for each load is the sum of parameter 

uncertainty and random error (Runkel, and others, 2004). 

The SEP was used to develop 95-percent confidence intervals 

around each estimate of load. Caution should be applied in the 

interpretation of these confidence intervals, however, when 

using continuous water-quality surrogates because serially 

correlated predictors could affect the computation of these 

confidence intervals.

LOADEST requires an uninterrupted record of discharge 

and model parameters used to predict concentration to function properly, so steps are required to replace missing data. 

Missing values were replaced using the fillMissing command 

within the USGSwsBase version 0.7 package in RStudio. This 

method uses interpolation to substitute values by incorporating 

information from data surrounding the missing point. During 

extended periods (typically greater than 8 hours) of missing 

data, the fillMissing command produced unrealistic results, 

and a median value was substituted for the missing points 

(Jastram and others, 2009).

Hierarchical Agglomerative Cluster Analysis

Hierarchical agglomerative cluster analysis is a 

multivariate statistical technique that is used to determine if 

samples can be combined into distinct groups based on their 

water chemistries. Cluster analyses have been previously used 

to explore patterns in surface-water-quality data (Guler and 

others, 2002; Ryberg, 2006) and were used in this study to 

describe spatial and temporal trends. A cluster analysis was 

used to identify which of the extensive synoptic water-quality 

sites were geochemically similar and to describe waterchemistry patterns at the intensive streamgage locations.

A subset of water-chemistry parameters was used in 

each cluster analysis. This specific suite of parameters was 

selected to inform the analysis as much as possible without 

providing undesirable redundancy. Inclusion of variables 

that are strongly related reduces the strength of the analysis 

(Guler, and others, 2002). For example, water temperature and 

turbidity were not included because they are mostly explained 

by DO and TP, respectively. Clustering methods require values 

for all selected parameters, so samples with missing values 

were not included in the analysis.
20 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Cluster analyses require normally distributed variables 

with equal variance, so a data transformation was applied prior 

to evaluation (Guler and others, 2002). Data were subtracted 

from the column mean and divided by the standard deviation 

to meet these assumptions.

The hierarchical agglomerative clustering method 

was run in JMP version 11.0. Hierarchical agglomerative 

clustering places no constraints on the number of groups 

formed, which allows for an optimal group structure (McCune 

and Grace, 2002). The method places each sample into its 

own cluster, and then the two closest clusters are joined 

based on similarity and a specific linkage method. Similarity 

measurements were calculated using Euclidean distance. This 

similarity matrix calculates the straight line distance between 

two points and leads to distinct group formation (Guler 

and others, 2002). Ward’s method was used as the linkage 

rule. Ward’s method joins clusters together to minimize the 

within-cluster sum of squares.

Principal Components Analysis

Principal components analyses (PCA) were used to 

identify patterns in water-chemistry data collected from 

discrete sampling efforts at the streamgage locations. PCA 

is an ordination technique that attempts to describe the 

maximum amount of variability in a dataset by generating 

axes that represent a combination of parameters (Helsel and 

Hirsch, 2002). Principal component axes can be related to 

external characteristics such as flow and season to explore 

processes influencing stream chemistry. PCA has been 

previously used to interpret water-quality patterns in the 

Chesapeake Bay watershed (Liu and others, 2000; Ator and 

others, 2004). The analysis was run in JMP version 11.0.

This ordination technique requires linear relationships 

among variables, so water-chemistry results were ranked prior 

to analysis (McCune and Grace, 2002). PCA requires values 

for all selected parameters, so samples with missing values 

were excluded from analysis.

Interpretation of Nitrate Isotopes

Patterns of nitrogen and oxygen isotopic variation in 

nitrate generally were related to between-site (spatial) and 

within-site (temporal) variations in (1) sources of nitrogen 

contributing to nitrate in groundwater recharge, (2) direct 

atmospheric nitrate contributions during runoff events, 

and (3) nitrate reduction (denitrification, assimilation) in 

groundwater or surface water. Compilations of published data 

indicate considerable variability of δ15N of nitrate derived 

from different nitrogen sources (for example, Heaton, 1986; 

Hübner, 1986; Fogg and others, 1998; Böhlke, 2003; Kendall 

and others, 2007; Xue and others, 2009). Some variability 

may be related to climatic differences. For example, δ15N 

values of plant and soil organic matter may vary in relation to 

temperature, moisture, and other factors affecting the openness 

of soil systems to net loss of 15N-depleted gases (Handley 

and others, 1999; Amundsen and others, 2003; Houlton 

and others, 2006), and those variations may be transmitted 

to nitrate formed by nitrification in soils (McMahon and 

Böhlke, 2006). Other variability may be related to differences 

in soil properties or handling of anthropogenic nitrogen 

sources causing isotopic fractionation prior to nitrification. For 

example, reduced forms of nitrogen in domestic wastewater 

commonly have bulk δ15N < 6 ‰, but nitrogen losses and 

transformation processes cause δ15N to increase during 

nitrogen passage through septic systems, such that δ15N of 

nitrate leaving the systems and recharging groundwater 

typically is > 6 ‰ (Schroeder and others, 1993; Fogg and 

others, 1998; Hinkle and others, 2008). Likewise, δ15N of 

nitrate derived from animal waste may be relatively high 

where manure is spread on the land surface and ammonia 

volatilization losses are substantial (Krietler, 1975), but not 

so high where nitrate is formed in subsurface septic system 

infiltration beds (Hinkle and others, 2007). Similar shifts 

occur between relatively low δ15N values of reduced nitrogen 

fertilizers (urea, ammonia) and slightly higher δ15N values 

of recharging nitrate beneath fields receiving those fertilizers 

(Krietler, 1975; Böhlke, 2002; Böhlke and others, 2002).

The δ18O values of nitrate also can vary depending on the 

source of the nitrate (Michalski and others, 2003; Kendall and 

others, 2007; Jackson and others, 2015). Atmospheric nitrate 

formed in part by photochemical reactions between NOx and 

ozone has much higher δ18O (typical mean value of +75 ‰) 

than biogenic nitrate formed by microbial oxidation (nitrification) of reduced nitrogen from various sources. Biogenic 

nitrate δ18O values can vary with climate and other local 

conditions, including the δ18O of water, during nitrification. 

Approximate δ18O values of biogenic nitrate commonly are 

estimated from 2/3 × δ18O[H2O] + 1/3 × δ18O[O2] (Amberger 

and Schmidt, 1987). For example, if δ18O[O2] = +24 ‰ (the 

atmospheric value) and δ18O[H2O] = −7 ‰ (for example, 

representing precipitation and soil water), this equation would 

yield δ18O[NO3

−

] = +3 ‰. This simplistic representation of 

oxygen incorporated into nitrate during nitrification has been 

proposed on the basis of laboratory experiments with isotopic 

tracers. Its general applicability in environmental systems is 

questionable because of potential variation in the parameters of 

the equation, plus potential isotopic fractionation and exchange 

during the nitrification process; nonetheless, it appears to 

be consistent with data for samples most likely to represent 

biogenic nitrate in parts of the Chesapeake Bay watershed.

Typical intervals of δ15N and δ18O values used to 

interpret the nitrate isotopic data in this report are summarized 

in figure 6. Ranges of values in figure 6 can vary and are 

presented primarily as context for site-specific discussions. 

Published δ18O values of nitrate can be difficult to compile 

because of unreported analytical biases in some studies 

(Révész and Böhlke, 2002). For example, reported δ18O values 

of atmospheric nitrate were relatively low in some early 

studies, but higher in later studies that used improved methods. 

To minimize various sources of uncertainty and variability, the 
Study Approach and Methods 21

nitrate isotopic data were interpreted with reference to selected 

previous studies done with current methods and calibrations, 

and data from within or near the Chesapeake Bay watershed 

or similar representative climate and land-use conditions were 

primarily considered (Aravena and others, 1993; Böhlke and 

Denver, 1995; McMahon and Böhlke, 1996; Plummer and 

others, 2001; Böhlke and others, 2002; Böhlke, 2003; Hinkle 

and others, 2008; Böhlke and Hannon, written commun.; 

J.K. Böhlke, 2015). Note that ranges given for δ15N of nitrate 

from different sources are not necessarily the same as the δ15N 

values of the source materials themselves because the nitrate 

values may reflect common isotope fractionation effects related 

to ammonia volatilization and other losses prior to conversion 

of source materials to nitrate. Similarly, although atmospheric 

nitrate can be an important source of nitrogen in some areas, 

the characteristically high δ18O value of atmospheric nitrate 

may not be found in groundwater and surface water if the 

atmospheric nitrate is assimilated, remineralized, and regenerated by nitrification in soils prior to recharge or runoff.

Figure 6 also shows previously published isotopic 

trajectories for nitrate undergoing progressive biologically 

mediated reduction, such as denitrification to N2 + N2O, or 

assimilation into organic N compounds. Laboratory studies 

indicate that nitrate reduction causes δ15N and δ18O of 

residual (unreacted) nitrate to increase approximately equally 

(∆δ15N/∆δ18O ≈ 1) (Granger and others, 2004, 2008). Field 

studies of groundwater and surface-water nitrate commonly 

indicate nitrate loss attributable to denitrification with apparent 

∆δ15N/∆δ18O between 1 and 2 (Böttcher and others, 1990; 

Lehmann and others, 2003; Böhlke and others, 2006; Houlton 

and others, 2006; Kendall and others, 2007). Explanations 

for reported variations in this ratio include artifacts in some 

analytical procedures (Révész and Böhlke, 2002), as well as 

variations in reaction processes such as re-oxidation of intermediate NOx species, oxygen exchange between water and 

nitrate or intermediate NOx species, and mixing of different 

sources of nitrate. Acknowledging some uncertainty about 

the processes, positively correlated variations in δ15N and 

δ18O of nitrate with ∆δ15N/∆δ18O = 1 to 2 were interpreted in 

the current study as permissive evidence for nitrate reduction 

(denitrification or assimilation) if associated with nitrate loss 

in groundwater or streams (fig. 6).

Development of Land-Use-Based 

Nitrogen Inputs

Evaluation of land-use-based nitrogen inputs to the 

study watersheds relied on previously published methods and 

publicly available data for computing the relative mass inputs 

of nitrogen from fertilizer applications, manure applications, 

atmospheric deposition, and wastewater sources. Fertilizer 

Delta O-18 of nitrate, per mil

–20

Delta N-15 of nitrate, per mil

–10

–5

0

5

10

15

20

0 20 40 60 80 100

Atmospheric

nitrate

(photochemical)

Fractionation trends

(nitrate reduction)

Biogenic

nitrate

(nitrification in

meteoric water)

Forest

soils

N2

fixation

Artificial

fertilizer

Septic

systems

Manure

spreading

Air N2

Air O2

2 1=εN/εO

Synthetic

nitrate

Figure 6. Typical variability in δ15N and δ18O values for nitrate derived from various 

sources, with emphasis on conditions relevant to the Chesapeake Bay watershed.
22 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

inputs were computed by using the approach outlined by 

Gronberg and Spahr (2012). Manure inputs were computed 

based on the approach of Ruddy and others (2006), using 

livestock counts reported in the appropriate Virginia, Maryland, 

or Pennsylvania Census of Agriculture. Atmospheric wet deposition (nitrate plus ammonium) to the study watersheds was 

computed by using the methods described in Ruddy and others 

(2006) and data from the National Atmospheric Deposition 

Program. Although this approach uses only wet deposition data, 

the wet fraction is the majority fraction in this region (Sickles 

II and Shadwick, 2007). Wastewater inputs were computed 

by using the approach of Lindsey and others (2009), with 

population data obtained from the United States Census Bureau.

Development of Land-Use and ConservationPractice Datasets

Land use was calculated from the 2011 National Land 

Cover Database (NLCD) (Homer and others, 2015). Agricultural BMP conservation implementation data were obtained 

from the USDA by using methods outlined in Hively and 

others (2013). The USGS signed Conservation Cooperator 

agreements with the USDA NRCS and the USDA Farm 

Services Agency (FSA) in 2010. This allowed the USGS to 

gain access to privacy protected conservation implementation data for farm locations throughout the Chesapeake Bay 

watershed. Aggregated totals of the implementation data 

can be released to the public as long as five or more farmers 

are enrolled in the reported practice within the aggregated 

area, in this case within the boundary of each Showcase 

Watershed. Implementation totals reported here were obtained 

from the USDA in April 2014, and the data for any practice 

with less than five customers per Showcase Watershed were 

deleted. The dataset included implemented practices for 

which the NRCS or FSA had provided cost-share dollars, 

as well as implemented practices for which the NRCS had 

provided Conservation Technical Assistance (CTA). In 

addition to the USDA data, information on non-Federal BMP 

implementation was provided by the Virginia Department of 

Conservation and Recreation, the PADEP, and the Maryland 

Department of Agriculture.
Smith Creek Watershed Water-Quality Characterization 23

Smith Creek Watershed Water-Quality 

Characterization

Lead Author: Kenneth E. Hyer

To characterize water quality within the Smith Creek 

watershed, 38 water-monitoring stations were established 

(fig. 7). Intensive monitoring activities were performed at 

site 32, the site of the existing streamgage (USGS station 

number 01632900), and extensive monitoring was performed 

at all stations identified in figure 7. The intensive site provides 

sufficient data to compute annual loads and trends of sediment 

and nutrients, and the extensive monitoring sites provide data to 

characterize the sources, sinks, and transport processes for nutrients. The extensive monitoring sites were selected to provide a 

distribution of monitoring sites along the length of Smith Creek, 

as well as to provide data from springs and tributaries.

Streamflow and Water-Quality Characterization 

at the Intensive Monitoring Site

The intensive Smith Creek monitoring location (site 32) 

is approximately 4.6 miles above the confluence with the 

North Fork of the Shenandoah River. This site was selected 

for intensive monitoring because of the presence of an existing 

streamgage, which provided a continuous record of discharge 

since 1960. Presented in this report section is an overview and 

characterization of the physical hydrology and water-chemistry 

data that were collected at this intensive monitoring site.

Characterization of the Physical Hydrology

Smith Creek hydrology during the monitoring period and 

long-term hydrology are presented in figure 8, with divisions 

used to indicate each water year (beginning October 1 and 

ending September 30). Relatively typical streamflow conditions occurred during the monitoring period, with base flows 

typically in the range of the 25th to 75th percentiles of flows 

that were observed over the long-term record of the gage. 

During periods of heavy rainfall, instantaneous discharge 

values as great as 3,000 cubic feet per second (ft3/s) were 

observed during the monitoring period, while flows as low as 

10 ft3/s were observed during extremely dry periods. Because 

the hydrologic conditions were relatively typical during the 

monitoring period, the geochemical patterns and processes 

observed should be fairly representative of long-term patterns 

in the basin.

The computed long-term BFI for Smith Creek is 

72.3 percent, indicating that approximately 72 percent of the 

Smith Creek flow exits the watershed as base flow (table 8), 

which suggests that the Smith Creek hydrology is likely 

dominated by groundwater discharge rather than stormwater 

runoff. This finding is consistent with earlier work of Sanford, 

Nelms, and others (2012), who utilized chemical separation 

techniques to demonstrate that groundwater inputs and 

base flow were the dominant sources of flow in the Smith 

Creek watershed (with potentially as much as 83 percent of 

streamflow occurring as base flow). Of particular note, the BFI 

during the 2010–2013 study period is identical to the longterm BFI of 72.3 percent, further confirming that relatively 

typical hydrologic conditions occurred during the 2010–2013 

monitoring period described in this report.
24 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Shenandoah River

North Fork

25 41

26 27

28

29

30

32

35

34

33

36

15

16

18

17 19

13 20

14 21 22

23

24

6

7

8

9

10

42

11

12

39

1

2

38

4

40

78°50' 78°45' 78°40' 78°35'

38°40'

38°35'

38°30'

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

Roads are from U.S. Census Bureau TIGER/Line shapefiles (2015) 

Elevation from U.S. Geological Survey National Elevation Dataset (2015)

North American Vertical Datum of 1988

North American Datum of 1983

VIRGINIA

DELAWARE

MARYLAND

 WEST

VIRGINIA

PENNSYLVANIA

Atlantic

Ocean

Study area

Chesapeake Bay

ROCKINGHAM COUNTY

ROCKINGHAM

COUNTY

SHENANDOAH COUNTY

PAGE COUNTY

Direction of flow

Massanutten Mountain Ridge

EXPLANATION

ElevationHigh LowMonitoring station and identifier

USGS streamgage 01632900

Spring sampling location

Draining Massanutten 

 Mountain ridge

Synoptic sampling location

 and identifier

Other

39–Smith Creek spring

42–Lacey spring

40–Big spring

19–Unnamed tributary

 to War Branch

Watershed 

Smith Creek 

 (watershed area=

 105.39 mi2

)

Dry Fork

War Branch

Smith Creek

Tributary to

 Smith Creek

35

32

40

38

New

Market

Figure 7. Monitoring stations and the stream network within the Smith Creek watershed, Virginia.
Smith Creek Watershed Water-Quality Characterization 25

Streamflow, in cubic feet per second

1

10

100

1,000

10,000

WY 2010 WY 2011 WY 2012 WY 2013

Minimum daily flow to 5th percentile

5th percentile to 10th percentile

10th percentile to 25th percentile

25th percentile to 75th percentile

75th percentile to 90th percentile

90th percentile to 95th percentile

95th percentile to maximum daily flow

Median flow

EXPLANATION

Monthly flow statistics —Historical daily

 flows from WY 1961 to WY 2013 

Streamflow, in cubic feet

 per second 

WY Water year

Sampling event

Routine sampling

Storm sampling

Synoptic sampling

MA J J A S O N D J F A J JMM A S O N D J F A J JMM A S O N D J F A J JMM A S

Figure 8. Smith Creek streamgage and quantiles that represent historical monthly flow data with the timing of various 

sampling events from April 2010 through September 2013.

Table 8. Streamflow partitioning for streamgage station 01632900 in the Smith Creek watershed.

[mi2, square mile; ft3/s, cubic foot per second; in/yr, inch per year; %, percent; data were computed using the 

software package PART (Rutledge, 1998)]

Watershed

Drainage 

area 

(mi2

)

Period of 

record

Mean 

streamflow

Mean 

base 

flow Base-flow 

index (%)

ft3

/s in/yr ft3/s in/yr

Smith Creek 93.6 1970–2013 78.79 11.43 56.94 8.26 72.3

2010–2013 69.94 10.15 50.55 7.34 72.3
26 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Characterization of Continuous Water-Quality Data

Temporal variability in the continuously monitored 

water-quality constituents is presented for the monitoring 

period as multidimensional contour plots in figures 9 and 10. 

Seasonal, diel, and hydrologic fluctuations in most of the 

continuously monitored constituents can be observed to 

varying degrees, depending on the constituent. Not surprisingly, water temperature showed some of the strongest 

seasonality fluctuations with summertime temperatures 

commonly reaching greater than 25 °C and wintertime temperatures falling below 5 °C. DO solubility varies inversely with 

water temperature; consequently, observed concentrations of 

DO are inversely related to the water temperature. The DO 

concentrations generally remained above 6 milligrams per 

liter (mg/L), indicating that DO concentrations are unlikely 

to be a stressor on the Smith Creek biota near the intensive 

monitoring site. The Smith Creek pH generally ranged 

from 7.9 standard units to 8.5 standard units, which is fairly 

typical of limestone basins in the Shenandoah Valley. The 

DO concentrations and pH levels demonstrate some fairly 

pronounced diel variations, with maximum levels occurring 

around 3:00 p.m. The in-stream turbidity concentrations were 

generally low, except during stormflow periods, when the 

turbidity values commonly exceeded 50 formazin nephelometric units (FNUs), a pattern which is typically observed 

because of re-suspension of in-stream sediment, erosion of 

stream banks, and other nonpoint-source contributions of sediment. Specific conductance values were generally about 475 to 

500 microsiemens per centimeter at 25 °C (μS/cm at 25 °C), 

with variability related to variability in the flow conditions; 

higher specific conductance levels occurred during relatively 

dry periods, and lower levels were observed during stormflow 

periods. This pattern in specific conductance is generally 

indicative of a groundwater-dominated system, whereby 

water with relatively high specific conductance at base flow is 

diluted to a lower specific conductance by runoff water from 

other sources. The continuous nitrate monitor was operated 

for a much shorter time period, but substantial variability in 

concentrations was observed that seem poorly explained by 

either season or flow. Observed nitrate concentrations ranged 

from less than 1 mg/L to greater than 3 mg/L; nitrate patterns 

are further explained in subsequent report sections to improve 

understanding of this constituent.

The hydrologic variability in the continuously monitored 

constituents was explored by using a series of concentrationdischarge plots (known as C-Q plots) that were subset 

according to warm periods (April–September) and cooler 

periods (October–March). For this assessment, the continuously monitored parameters were combined with available 

discrete water-quality samples for this same collection of 

continuously monitored constituents, and each constituent was 

further characterized seasonally by using the C-Q plot and 

seasonal boxplots to provide the distribution in parameters 

(figs. 11 and 12). Water temperature had a pronounced 

seasonality, with greater temperatures observed during the 

April–September period. Specific conductance values showed 

little seasonal variability; however, water samples had 

substantial dilution during elevated flow conditions during 

both periods. Levels of pH were slightly higher during the 

cooler months and decreased during wet weather conditions, 

regardless of season. DO concentrations were sharply different 

between warm and cool periods, and the difference was likely 

driven by water-temperature-controlled saturation dynamics. 

The DO response during wet weather conditions was variable, 

and likely was still controlled by temperature variations during 

wet-weather periods. Turbidity levels were generally strongly 

related to hydrology, with elevated turbidity values occurring 

during higher flows. Interestingly, the turbidity C-Q curve had 

greater variability during the spring and summer months than 

during the winter months; this increased variability could be 

the result of convective thunderstorms which develop more 

often in the summer, or it could be related to greater exposed 

soil and erosion during the spring months. Nitrate concentrations showed little seasonal difference, in contrast to other 

watersheds in this study but not uncommon elsewhere (Martin 

and others, 2004). Peak nitrate concentrations occurred 

at flows of about 70 to 80 ft3/s, with lower concentrations 

observed during particularly low flows and higher flows. It is 

hypothesized that dilution processes were responsible for the 

decreased concentrations during high flows and that biologic 

processing or changing groundwater inputs were responsible 

for the lower nitrate concentrations at extremely low flows, 

although contributions from lower nitrate groundwater sources 

during low flows also could be responsible for this pattern and 

cannot be ruled out.
Figure 9. Continuous (A) water temperature, (B) dissolved oxygen, and (C) pH data with the hydrograph from the Smith Creek streamgage, 2010–2013.

EXP

Water temperat

0.00 to 5.005.01 to 7.007.01 to 9.00

9.01 to 11.0

11.01 to 1313.01 to 16Streamflow

Dissolved oxyge

5.30 to 6.006.01 to 7.007.01 to 8.00

8.01 to 9.00

9.01 to 10.010.01 to 11

Streamflo

pH, in s

7.40 to 7.907.91 to 8.008.01 to 8.05

8.06 to 8.10

8.11 to 8.158.16 to 8.20Streamflow

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

ABC

101001,00010,000101001,00010,000101001,00010,000

Streamflow, in cubic feet per second

Water year

2010 2011 2012 2013

O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O
Figure 10. Continuous (A) turbidity, (B) specific conductance, and (C) nitrate data with the hydrograph from the Smith Creek streamgage.

EXPLAN

Turbidity, in formazi

0.00 to 1.001.01 to 1.501.51 to 2.00

2.01 to 2.50

2.51 to 3.003.01 to 5.00Streamflow, in 

Specific conductan

per centimeter at 2

151 to 300301 to 350351 to 400

401 to 425

426 to 450451 to 475Streamflow, in Streamflow, in 

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

Water year

2010 2011 2012 2013

Nitrate plus nitrite, in mill

0.85 to 1.001.01 to 1.251.26 to 1.501.51 to 1.751.76 to 2.002.01 to 2.25

101001,00010,000101001,00010,000101001,00010,000

Nitrate data not collected during this period

ABC

Streamflow, in cubic feet per second

O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O
Smith Creek Watershed Water-Quality Characterization 29

Figure 11. (A) Water temperature, (B) specific conductance, and (C) pH data from 

continuous monitoring and discrete storm and routine samples for warm (April to 

September) and cool (October to March) seasons, Smith Creek streamgage, 2010–2013.

–5

0

5

10

15

20

25

30

Water temperature, in degrees Celsius

0

100

200

300

400

500

600

700

Specific conductance, in microsiemens 

per centimeter at 25 degrees Celsius pH, in standard units

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

Streamflow, in cubic feet per second

1 10 100 1,000 1 10 100 1,000 4,000

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

7.0

7.2

7.4

7.6

7.8

8.0

8.2

8.4

8.6

8.8

9.0

A

B

C
30 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 12. (A) Dissolved oxygen, (B) turbidity, and (C) nitrate data from continuous 

monitoring and discrete storm and routine samples for warm (April to September) and 

cool (October to March) seasons, Smith Creek streamgage.

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

Streamflow, in cubic feet per second

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

0

0.5

1 10 100 1,000 1 10 100 1,000 4,000

1.0

1.5

2.0

2.5

3.0

3.5

0

4

8

12

16

20

Dissolved oxygen, in milligrams per liter Turbidity, in formazin nephelometric units

0.001

0.01

0.1

1

10

100

1,000

2,000

Nitrate plus nitrite,

in milligrams per liter as nitrogen

A

B

C
Smith Creek Watershed Water-Quality Characterization 31

Characterization of Discrete Water-Quality Data

A large number of water-quality samples were collected 

during the study period, and many parameters were analyzed 

in each discrete water-quality sample collected. Systematic 

patterns in the discrete water-quality samples were explored 

by using cluster analysis and PCA because these tests offer 

rigorous approaches to investigate patterns in complex datasets.

A cluster analysis was run on the geochemical results 

for the 27 discrete samples for which a complete set of 

analyses was available. Variables that were obviously strongly 

correlated to flow, such as turbidity and suspended-sediment 

concentration (SSC), or season, such as water temperature, 

were excluded from this analysis to better explore the stream 

geochemistry and more subtle patterns in the data. Three 

clear clusters developed, representing seasonal and hydrologic variability (fig. 13). All of the Group 1 samples were 

collected during the cool season (October–March), and the 

Group 2 samples were all collected during the warm season 

(April–September). The Group 3 samples included only 

samples collected during the largest storm events during the 

monitoring period, and demonstrated strongly diluted majorion concentrations, which are generally indicative of groundwater sources being diluted by lower ionic strength runoff 

water. Discrete samples collected during some smaller storm 

events were assigned membership into Group 1 or Group 2, 

Figure 13. Discrete samples from the Smith Creek streamgage with a heat map of constituent values and 

the breakpoint used to produce three cluster groups, 2012–2013.

Total nitrogen

Nitrate

Iron

Total phosphorus

Ammonium

Silica

Orthophosphate

Potassium

Sodium

Chloride

Sulfate

Magnesium

Calcium

Bicarbonate

Specific conductance

Manganese

Dissolved oxygen

pH

Group 1

Cool-season samples

Group 2

Warm-season samples

Group 3

High-flow

samples

Cluster group

Dendrogram break point

EXPLANATION

Relative constituent value

Low High
32 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

depending on the season in which they were collected, likely 

indicating that during relatively small storms, the groundwater 

contributions and effects of seasonality continue to dominate 

the in-stream geochemistry. Runoff processes only dominated 

and changed the Smith Creek water chemistry during the 

largest storm events.

A PCA was run using all 27 discrete samples with 

complete analytical results to further explore the patterns 

in these discrete data. Again, water temperature, turbidity, 

and SSC were withheld from the analysis. Two principal 

components explained 58 percent of the variability in the data 

(table 9). The first component (explaining 39.6 percent of the 

data variability) had high positive loadings for pH, specific 

conductance, calcium, magnesium, bicarbonate, and sulfate, 

with large negative loadings for TP and iron. Component 1 

appears to be a traditional flow axis; at low flow, calcium, 

magnesium, bicarbonate, sulfate, pH, and specific conductance 

are elevated, but concentrations of iron and TP are relatively 

low. At higher flows, and under runoff conditions, concentrations of iron and TP increase, but pH, specific conductance, 

and concentrations of calcium, magnesium, and bicarbonate 

decrease because of dilution. This flow effect is clearly evident 

in a plot of component 1 versus flow, which demonstrated a 

strong negative relationship (fig. 14). Constituents load less 

strongly on the second component; however, more positive 

component 2 scores are associated with higher silica and TN 

concentrations, and lower component 2 scores are driven 

by concentrations of DO and manganese. A comparison of 

component 2 scores relative to the timing of sample collection 

confirmed that this second component is a seasonal axis, with 

greater component 2 scores during the warmer seasons and 

lower component 2 scores during the cooler seasons. The 

presence of a flow and a seasonal axis in discrete data like 

these is rather typical (Ator and others, 2004) and reflects 

that hydrology and seasonality contribute significantly to the 

variability of water quality in Smith Creek.

Because the cluster analysis and the PCA highlighted the 

seasonal and hydrologic variability of the parameters in this 

system, all analytes were subsequently tested individually for 

relationships to streamflow and seasonality (table 10; fig. 15). 

Large (Spearman’s rho > 0.7) significant positive relationships with flow are indicative of those constituents that are 

transported during wet weather and runoff conditions; large 

positive relationships with flow were observed for turbidity, 

SSC, and the calcium/magnesium ratio (table 10), and weaker 

(but still statistically significant) positive relationships were 

observed for TN, ammonium, orthophosphate, iron, and TP. 

Large (Spearman’s rho < –0.7) inverse relationships with 

flow were observed for specific conductance, magnesium, 

and δ15N of nitrate, while smaller inverse correlations were 

observed for pH, bicarbonate, calcium, and δ18O of nitrate. 

These negative relationships are consistent with dilution of 

base flow by relatively lower ionic strength waters during 

storm events. Note that flow and nitrate did not have a statistically significant relationship, likely because of the relatively 

complex C-Q relationship observed in the continuous and 

discrete datasets (fig. 12). The general relationships between 

flow and various water-quality constituents are fairly typical 

(with the exception of nitrate). During storm events, sediment 

and particulate nitrogen and phosphorus concentrations 

typically increase and groundwater-derived constituents (such 

as calcium, magnesium, and bicarbonate) tend to decrease 

because the runoff from the landscape is relatively less 

concentrated in these constituents than the groundwater which 

has had considerably longer residence time and contact with 

bedrock containing these constituents. Non-significant C-Q 

relationships are provided in the appendix as supplemental 

material (fig. 1–1).

Statistically significant seasonal patterns existed for a 

number of monitored constituents (table 10). Water temperature, turbidity, orthophosphate, TP, SSC, and silica were 

higher during the warm season (fig. 15). Seasonal patterns in 

water temperature were expected and demonstrated previously 

with C-Q plots. Seasonally variable turbidity, TP, and sediment patterns were likely related to a greater availability of 

sediment for transport during the spring and summer, although 

greater algal production during warm seasons or more cattle 

wading into the streams and stirring up sediment cannot 

be excluded as contributing factors. The seasonally higher 

concentrations of orthophosphate and silica during warm 

weather periods were somewhat unexpected and the cause 

Table 9. Loading of water-quality constituents collected at the 

Smith Creek streamgage on principal component axes 1 and 2.

[%, percent; PC1, first principal components axis; PC2, second principal 

components axis; loadings with absolute value greater than 0.70 are shown in 

bold; those with absolute value less than |0.40| are omitted]

Constituent PC1 

(39.6%)1

PC2 

(18.7%)1

pH 0.80

Specific conductance 0.78 0.42

Dissolved oxygen 0.44 –0.69

Bicarbonate 0.76 0.45

Sulfate 0.73

Chloride 0.66

Calcium 0.86 0.41

Magnesium 0.73

Sodium 0.53

Potassium 0.58

Nitrate-N 0.52 0.55

Ammonium –0.47

Total nitrogen 0.63

Orthophosphate –0.63

Total phosphorus –0.82

Iron –0.83

Manganese –0.55

Silica –0.46 0.68

1

Percent of overall variance explained.
Smith Creek Watershed Water-Quality Characterization 33

unresolved, although the silica patterns were likely partially 

related to temperature-driven silica solubility, as well as other 

factors. Levels of pH, DO, and sulfate were higher during the 

cool season (figs. 11, 12, and 16). The seasonally higher DO 

concentrations during cool periods were related to temperaturedriven solubility, as previously shown (fig. 12). Seasonally 

higher pH values during the cool season would be consistent 

with increased discharge of high-pH groundwater during the 

fall and winter, while the higher cool-season sulfate concentrations are likely related to soil storage during drier summer 

periods with release during wetter winter periods (Böhlke 

and Michel, 2009). Non-significant seasonal relationships are 

provided in the appendix as supplemental material (fig. 1–2).

The variability in the discrete nitrate isotope results 

is shown in figure 17. The δ15N values generally range 

from +8 ‰ to +14 ‰, while δ18O values generally range 

from +3 ‰ to +6 ‰. Routine samples generally demonstrate a 

positive correlation between δ15N and δ18O values, consistent 

with varying amounts of biologic processing (denitrification, 

assimilation) within the Smith Creek watershed. Stormflow 

samples tend to have lower δ15N and δ18O values than routine 

samples, indicating contributions of less-altered nitrate 

components during high flows. Nitrogen isotopes are likely 

indicative of manure or sewage sources of nitrogen; these 

possible sources will be further investigated in more detail 

later in the report.

Figure 14. First two principal component axis scores for discrete samples from the Smith Creek 

streamgage along with time and streamflow, 2012–2013. 

Principal component 1*

–4

–6

–2

0

2

4

Principal component 2**

–3

–2

–1

0

1

2

3

Date Streamflow, in cubic feet per second

5

EXPLANATION

Cluster analysis group

Group 1—Cool-season sample

Group 2—Warm-season sample

Group 3—High-flow sample 

*Principal component 1 explains 

 39.6 percent of the variability in the data

**Principal component 2 explains 

 18.7 percent of the variability in the data

Sept. 10

2012

Sept.

2013

Jan.

2013

May

2013

100 1,000 2,000
34 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 10. Summary of hydrologic and seasonal correlations for discrete water-quality constituents collected at the Smith Creek 

streamgage.

[Results in bold are significant at α=0.05; n, number of observations; APR, April; SEPT, September; OCT, October; MAR, March; <, less than]

Parameter

Streamflow Season1

n rho2 p-value2 nAPR-SEPT nOCT-MAR p-value3 Season with 

higher values

Water temperature 73 –0.14 0.254 28 20 <0.001 APR-SEPT

pH 73 –0.52 <0.001 28 20 0.002 OCT-MAR

Specific conductance 73 –0.70 <0.001 28 20 0.826

Dissolved oxygen 73 0.14 0.227 28 20 <0.001 OCT-MAR

Turbidity 73 0.77 <0.001 28 20 <0.001 APR-SEPT

Sulfate 30 –0.23 0.225 12 8 0.003 OCT-MAR

Chloride 30 –0.34 0.068 12 8 0.097

Bicarbonate 30 –0.68 <0.001 12 8 0.969

Calcium 46 –0.59 <0.001 16 14 0.064

Magnesium 46 –0.87 <0.001 16 14 0.803

Sodium 46 –0.26 0.082 16 14 0.506

Potassium 46 0.13 0.405 16 14 0.071

Calcium/magnesium 46 0.78 <0.001 16 14 0.329

Sodium/chloride 30 0.35 0.055 12 8 0.375

Nitrate-N 73 –0.09 0.468 28 20 0.908

Total nitrogen 73 0.27 0.023 25 17 0.828

Ammonium 72 0.39 <0.001 28 19 0.124

Orthophosphate 73 0.25 0.030 28 20 <0.001 APR-SEPT

Total phosphorus 73 0.54 <0.001 28 20 <0.001 APR-SEPT

Delta N-15 of nitrate, 

per mil

33 –0.79 <0.001 9 13 0.083

Delta O-18 of nitrate, 

per mil

33 –0.50 0.003 9 13 0.689

Suspended sediment 

concentration

67 0.72 <0.001 25 17 <0.001 APR-SEPT

Iron 45 0.63 <0.001 16 14 0.14

Manganese 45 0.07 0.655 16 14 0.253

Silica 44 0.14 0.353 15 13 <0.001 APR-SEPT

1

Only routine samples included in the Wilcoxon test of rank scores. 

2

rho is Spearman’s rho; p-value is from Spearman’s test for correlation.

3

p-value is from the Wilcoxon test of rank scores.
Smith Creek Watershed Water-Quality Characterization 35

0

1

2

3

4

5

6

5

10

15

2

0

4

6

0.001

10 100 1,000 10 100 1,000

0.01

0.1

1

0.1

1

10

100

10,00

10,000

0

50

100

150

200

250

300

Streamflow, in cubic feet per second Streamflow, in cubic feet per second

Total nitrogen,

in milligrams per liter

Delta N-15 of nitrate,

per mil

Delta O-18 of nitrate,

per mil

Ammonium,

in milligrams per liter as nitrogen

Suspended-sediment concentration,

in milligrams per liter

Bicarbonate,

in milligrams per liter

EXPLANATION

Season

April to September

October to March

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)

Figure 15. Concentration-discharge data for discrete samples from the Smith Creek streamgage by warm and 

cool season.
36 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

0

0.05

0.10

0.15

0.20

0.01

0.1

1

10

0

20

40

60

80

100

0

5

10

15

20

25

2

3

4

5

6

7

1

10

100

1,000

10 100 1,000

Streamflow, in cubic feet per second

Orthophosphate,

in milligrams per liter as phosphorus 

Total phosphorus,

in milligrams per liter

Calcium,

in milligrams per liter

Magnesium,

in milligrams per liter

Ratio of calcium to magnesium

Iron,

in micrograms per liter

EXPLANATION

Season

April to September

October to March

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)

Silica,

in milligrams per liter as SiO

0

2

4

6

8

10

12

10 100 1,000

Streamflow, in cubic feet per second

2

Figure 15. Concentration-discharge data for discrete samples from the Smith Creek streamgage by warm and 

cool season.—Continued
Smith Creek Watershed Water-Quality Characterization 37

Figure 16. Concentrations of various constituents in discrete samples from the Smith Creek streamgage.

0

0.05

0.10

0.15

0.20

0.01

0.1

1

10

0.1

1

10

100

1,000

10,000

Orthophosphate, in milligrams

per liter as phosphorus

Total phosphorus,

in milligrams per liter

Suspended-sediment

concentration,

in milligrams per liter

0

4

8

12

16

20

24

Sulfate,

in milligrams per liter

0

2

4

6

8

10

12

Silica,

in milligrams per liter as SiO

1

10

100

1,000

0

10

20

30

40

Iron,

in micrograms per liter

Manganese,

in micrograms per liter

EXPLANATION

WY 2010 WY 2011 WY 2012 WY 2013

WY 2010 WY 2011 WY 2012 WY 2013 2

A J O J A J O J A J O J A J O

Routine sample

Storm sample

Hydrologic conditions

WY Water year

A J O J A J O J A J O J A J O
38 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Surrogate Models for the Computation of 

Sediment and Nutrient Loads

The discrete and continuous water-quality data were 

subsequently analyzed to explore the development of 

surrogate regression models to predict SSC and TN and TP 

concentrations. The development of these surrogate models 

is critical for the computation of constituent loads and an 

improved understanding of the watershed function.

Model Development

Standard USGS multiple linear regression methods 

were used to develop the best surrogate models (Helsel 

and Hirsch, 2002; Jastram and others, 2009; Rasmussen 

and others, 2009), and a detailed overview of the model 

selection process is offered in the Methods section. In addition to identifying the statistically best surrogate model, 

other commonly used model formulations were considered 

because these common formulations can provide continuity 

to other monitoring programs and could provide useful 

model inputs for other data analyses. The best models for the 

prediction of SSC and TN and TP concentrations, basic model 

diagnostics, and the other common model formulations were 

developed (table 11). For the best SSC, TN, and TP models, 

additional model diagnostics and information are presented 

in figures 18, 19, and 20. For each of the five models, a 

three-panel figure summarizes the model diagnostics using

• A plot of the observed versus the predicted concentrations,

• A plot of the observed and predicted concentrations 

relative to flow, and

• A plot of the residuals versus the predicted 

concentration.

In all five cases, the presented models are valid for the 

prediction of SSC, TP, and TN, but the models have subtle 

differences. For SSC, the best model included turbidity and 

flow terms, a classical model formulation. For prediction of 

TN concentrations, the strongest predictor always included 

the continuous nitrate data, likely because 70 to 80 percent 

of the nitrogen at the Smith Creek monitoring site occurs as 

nitrate. The best nitrate model included a flow-squared term, 

Figure 17. Variability in nitrate isotopes in discrete samples from the Smith Creek streamgage and potential 

causes for variability, 2011–2013.

Delta N-15 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

2

0

4

6

0

1

2

3

EXPLANATION

Routine sample

Storm sample

Dilution

Addition of atmospheric nitrate in runoff

Nitrate reduction (denitrification, assimilation)

Different nitrification N sources

Hydrologic conditions

Schematic changes potentially associated with

*Typical range of error, 2-sigma:

 delta N-15: 0.5 per mil,

 delta O-18: 1.0 per mil

0 5 10 15

2 4 6 8

Nitrate plus nitrite,

in milligrams per liter as nitrogen
Smith Creek Watershed Water-Quality Characterization 39

and because flow-squared models can have problems with 

load prediction (Moyer and others, 2012), also presented 

is a slightly less robust model for the prediction of TN that 

does not include a flow-squared term. For the prediction of 

TP concentrations, the best concentration model included 

flow and turbidity, as expected, but also included pH (with a 

negative coefficient), which seemed reasonable given that pH 

tended to decrease in Smith Creek during storm events and TP 

concentrations are highest during storm events. Because other 

published TP models sometimes do not commonly include 

a pH term (Jastram and others, 2009), an alternative TP 

concentration model is presented using flow and turbidity only 

as predictor variables.

Interpretation of the Computed Loads

After developing surrogate models for estimating SSC 

and concentrations of TN and TP, water-year loads of these 

constituents were computed by using the record of continuous 

flow (from the streamgage) and predicted water quality (from 

the regression equations presented above in table 11). Methods 

for the replacement of missing records are described in the 

Development of Surrogate Water-Quality Models and Computation of Loads section of the report. The USGS software 

program LOADEST was used for these computations because 

the computed load could be calculated with confidence 

intervals around the predicted load, which is necessary for 

comparisons between water years and sites.

The water-year SSC loads are presented in figure 21, 

along with the total flow for each complete water year. At 

a basic level, the total mass of sediment transported seems 

related to the total flow for a given year; however, annual 

runoff in 2012 was approximately 30 percent less than in 2011, 

but the sediment load in 2012 was approximately 60 percent 

less than in 2011, indicating that factors in addition to total 

annual flow (such as rainfall intensity, duration, and antecedent 

conditions) affect the amount of sediment transported. The 

mean annual sediment yield during 2011–2013 was 6,900 tons 

Table 11. Statistical results of the top-ranked suspended sediment, total nitrogen, and total 

phosphorus concentration estimation models for the Smith Creek streamgage with an alternative 

model presented for total nitrogen and total phosphorus.

[ln, natural logarithm; top-ranked models are listed in bold]

Explanatory 

variables

Model 

coefficient p-value

Variance 

inflation 

factor

Number of 

observations

Adjusted 

coefficient of 

determination

Mallows’ Cp

Suspended-sediment concentration

Intercept 0.886 0.537

ln(Flow) 0.135 0.332 3.42 67 0.833 5.23

ln(Turbidity) 0.817 <0.001 3.42

Total nitrogen

Intercept 0.056 <0.001

73 0.714 5.99

ln(Flow) 0.102 <0.001 1.08

ln(Flow)2 0.062 <0.001 1.33

Nitrate-N 0.418 <0.001 1.42

Intercept –0.089 0.414

ln(Flow) 0.092 <0.001 1.06 73 0.602 6.84

Nitrate-N 0.331 <0.001 1.06

Total phosphorus

Intercept 1.565 0.241

73 0.816 2.94 ln(Flow) –0.238 0.004 3.44

pH –0.651 0.018 1.76

ln(Turbidity) 0.563 <0.001 4.07

Intercept –2.756 <0.001

ln(Flow) –0.229 0.007 3.43 73 0.803 6.76

ln(Turbidity) 0.618 <0.001 3.43
40 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

1,000

10

1

100

Observed suspended sediment, in milligrams per liter

Estimated suspended sediment, in milligrams per liter

EXPLANATION

Observed concentration

Estimated concentration

1,000

10

1

100

Suspended sediment, in milligrams per liter Residual error

Streamflow, in cubic feet per second

–4

–3

–2

–1

0

1

2

3

4

1 10 100 1,000

1 10 100 1,000

1 10 100 1,000

Estimated suspended sediment, in milligrams per liter

0.2

0.2

0.2

Figure 18. Observed and estimated values from the 

best suspended-sediment surrogate model for the 

Smith Creek streamgage and the residual plot of model 

predictions, 2010–2013.

per year (ton/yr). Further insight into the sediment transport 

patterns is gained by looking at the cumulative sediment load 

for the entire monitoring period, as well as total flow and 

sediment load for each water year (fig. 22). Sediment loading 

within Smith Creek is highly episodic, with the eight largest 

observed storms generating nearly 90 percent of the estimated 

sediment load. In fact, three events during the spring of 2011 

contributed nearly 50 percent of the Smith Creek sediment 

load, while the single largest storm contributed 30 percent 

of the sediment load during this 3.5-year period. Although 

relatively similar streamflows occurred during 2011 and 

2013 (fig. 8), substantially more sediment was transported 

in 2011, likely because of two large events in 2011, compared 

to numerous small events in 2013. Little sediment moved 

during 2012 because there were fewer large storm events. 

Sediment yields will be presented in the Comparison of WaterQuality Patterns Among Study Watersheds section to promote 

comparisons between sites.

 Annual TN loading for Smith Creek in the 2013 water 

year was approximately 400,000 pounds (lb), which can be 

considered typical given that 2013 had annual runoff totals 

that were close to the long-term mean at this site (fig. 23). 

Interestingly, the TN load accumulation (fig. 24) seems to 

occur more gradually and is less driven by episodic inputs 

than the SSC accumulation (fig. 22). This more gradual 

accumulation rate is likely related to the relatively elevated 

Smith Creek BFI and sustained groundwater contributions 

of nitrogen that support relatively steady nitrogen loading 

that is only occasionally augmented by significant stormflow 

events. Additional years of monitoring will be needed before 

comparing TN accumulation rates between water years.

The water-year TP loads are presented in figure 25, along 

with the total flow for each complete water year. Given the 

typically strong association between sediment and phosphorus, 

the annual loading patterns for TP (fig. 26) look very similar 

to the loading plots for suspended-sediment load (fig. 22), 

with the greatest loading occurring during the wettest year. 

The mean annual total phosphorus yield from 2011 to 2013 

was 21,390 pounds, which can be considered typical given 

that 2011–2013 had relatively typical hydrology. The eight 

largest storm events contributed the bulk of the sediment 

load; however, similar to the nitrogen accumulation plot, 

base-flow periods contribute materially to the annual loading. 

For example, nearly 10 percent of the total load was already 

delivered to Smith Creek prior to the three large storm events 

in the spring of 2011. In fact, during the relatively drier 2012 

water year, approximately half of the TP was transported 

by Smith Creek during relatively low-flow periods. In 

short, the TP loadings are driven by storm events and 

base-flow contributions.
Smith Creek Watershed Water-Quality Characterization 41

Figure 19. Observed and estimated concentrations of total nitrogen from the (A) best and (B) alternative surrogate 

models for the Smith Creek streamgage and the residual plot of model predictions, 2010–2013.

2.0

3.0

5.0

4.0

1.0

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

5.5

4.5

3.5

2.5

1.5

Observed total nitrogen, in milligrams per liter

Estimated total nitrogen, in milligrams per liter Estimated total nitrogen, in milligrams per liter

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

EXPLANATION

Observed concentration

Estimated concentration

EXPLANATION

Observed concentration

Estimated concentration

2.0

3.0

5.0

4.0

1.0

5.5

4.5

3.5

2.5

1.5

Total nitrogen, in milligrams per liter

–0.4

0.4

0

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

0.6

0.2

–0.2

–0.6

Residual error

1.5 2.0 2.5 3.0 3.5 4.0

Estimated total nitrogen, in milligrams per liter Estimated total nitrogen, in milligrams per liter

Streamflow, in cubic feet per second Streamflow, in cubic feet per second

5 10 100 1,000 2,000 5 10 100 1,000 2,000

A B
42 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 20. Observed and estimated concentrations of total phosphorus from the (A) best and (B) alternative surrogate 

models for the Smith Creek streamgage and the residual plot of model predictions, 2010–2013.

1

0.01

2

0.1

Observed total phosphorus, in milligrams per liter

Estimated total phosphorus, in milligrams per liter Estimated total phosphorus, in milligrams per liter

EXPLANATION

Observed concentration

Estimated concentration

Streamflow, in cubic feet per second Streamflow, in cubic feet per second

–1.0

1.0

0

1.5

0.5

–0.5

–1.5

Residual error

Estimated total phosphorus, in milligrams per liter Estimated total phosphorus, in milligrams per liter

1

0.01

2

0.1

Total phosphorus, in milligrams per liter

EXPLANATION

Observed concentration

Estimated concentration

A B

5 10 100 1,000 2,000 5 10 100 1,000 2,000

0.01 0.1 1 2 0.01 0.1 1 2

0.01 0.1 1 2 0.01 0.1 1 2
Smith Creek Watershed Water-Quality Characterization 43

0

5,000

10,000

15,000

20,000

0

500

1,000

1,500

2,000

2,500

2011 2012 2013

Annual suspended-sediment load, in tons

Annual total streamflow, in millions of cubic feet

Water year

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

Figure 21. Suspended-sediment load and total annual streamflow at the Smith Creek 

streamgage for water years 2011 through 2013.

0

10

20

30

40

50

60

70

80

90

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

0

500

1,000

1,500

2,000

2,500

3,000

0

500

1,000

1,500

2,500

2,000

100 3,500

Streamflow, 

in cubic feet per second

Total annual streamflow, 

in millions of cubic feet

Percent of total suspended-sediment 

load estimated during the study period

Annual suspended-sediment load, in tons

EXPLANATION

Suspended-sediment 

load

Streamflow 

A

B

O N J F M A M J J A S O N D

Water year 2010 Water year 2011 Water year 2012 Water year 2013

D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S

Figure 22. Suspended-sediment load at the Smith Creek streamgage for water 

years 2010 through 2013 along with (A) the percentage of load accumulated and the 

hydrograph during the period of study and (B) the accumulation of load and streamflow 

in each water year.
44 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

0

100,000

200,000

300,000

400,000

500,000

0

500

1,000

1,500

2,000

2,500

2013

Water year

Annual total nitrogen load, in pounds

Annual total streamflow, in millions of cubic feet

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

Figure 23. Total nitrogen load and total annual 

streamflow at the Smith Creek streamgage for the 2013 

water year

A

B

Water year 2012 Water year 2013

O N D J F M A M J J A S O N D J F M A M J J A S

0

10

20

30

40

50

60

70

80

90

0

50,000

100,000

150,000

200,000

250,000

300,000

400,000

350,000

0

500

1,000

1,500

2,000

2,500

3,000

0

500

1,000

1,500

2,500

2,000

100 3,500

Streamflow, in 

cubic feet per second

Total annual streamflow, 

in millions of cubic feet

Annual total nitrogen load, in pounds

EXPLANATION

Total nitrogen load

Streamflow 

Percent of total nitrogen load 

estimated during the study period

Figure 24. (A) The percentage of total nitrogen load accumulated and the hydrograph 

during the period of study and (B) the accumulation of load and streamflow at the Smith 

Creek streamgage for water years 2012 and 2013.
Smith Creek Watershed Water-Quality Characterization 45

Water year

0

10,000

20,000

30,000

40,000

0

500

1,000

1,500

2,000

2,500

2011 2012 2013

Annual total phosphorus load, in pounds

Annual total streamflow, in millions of cubic feet

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

Figure 25. Total phosphorus load and total annual streamflow at the Smith Creek 

streamgage for water years 2011 through 2013.

0

500

1,000

1,500

2,000

2,500

3,000

0

500

1,000

1,500

2,500

2,000

3,500

0

10

20

30

40

50

60

70

80

90

100

EXPLANATION

Total phosphorus load

Streamflow 

O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S

0

5,000

10,000

15,000

20,000

30,000

25,000

Percent of total phosphorus load

estimated during the study period

Annual total phosphorus load, 

in pounds

Streamflow, 

in cubic feet per second

Total annual streamflow, 

in millions of cubic feet

A

B

Water year 2010 Water year 2011 Water year 2012 Water year 2013

Figure 26. (A) The percentage of total phosphorus load accumulated and the 

hydrograph during the period of study and (B) the accumulation of load and streamflow 

at the Smith Creek streamgage for water years 2010 through 2013.
46 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Spatial Patterns in Water Quality

A series of synoptic sampling events was performed 

to provide an understanding of the spatial patterns in water 

quality, as well as information related to sources, sinks, and 

transport processes for nutrients. For synoptic water-quality 

sampling, 38 sites were selected throughout the Smith Creek 

watershed; sites were selected to isolate various tributaries, 

major springs, and stream reaches. During a given synoptic 

sampling event, all sites were sampled during no more than 

a 36-hour period of stable base flow. By sampling all sites 

under steady flow conditions, a consistent understanding of the 

chemical and hydrologic spatial variability within the watershed can be developed. Four synoptic sampling events were 

performed within the Smith Creek watershed over a range of 

base-flow conditions (low base flow through high base flow), 

which resulted in slightly different hydrologic conditions 

being characterized by each synoptic event (fig. 27). During 

the relatively low-flow synoptic (August 2011), the flows in 

Smith Creek were largely generated within the upper part 

of the basin (by a drainage area approximately 43 mi2), with 

the lower part of the watershed serving essentially as an 

aqueduct that conveyed the flows from the headwaters out 

of the basin, likely because the zero- and first-order streams 

on the eastern side of the watershed were not flowing under 

such dry conditions. Conversely, during the high base-flow 

synoptic sampling event (December 2011), the flow in Smith 

Creek increased in the downstream direction, likely because 

all of the zero- and first-order streams on the eastern side of 

the watershed were flowing. During the intermediate flow 

conditions, it is likely that only some of the eastern watershed 

boundary tributaries were flowing, or that they were flowing at 

low levels.

A few synoptic-monitoring stations deserve special 

mention because their measured discharge relative to their 

topographically derived drainage area is unusual. The 

synoptic site located at the mouth of Dry Fork (site 9) 

generally has very low discharge, typically less than 1 ft3/s, 

even though it drains a basin that is slightly larger than 

20 mi2 (fig. 27). The prevalence of sinkholes within this 

subwatershed (fig. 2C) is hypothesized to result in substantial 

connectivity to groundwater and loss of streamflow and 

precipitation to groundwater recharge. Conversely, the Lacey 

Spring synoptic site (originally sampled as site 10 during the 

first three synoptic events and later approximately 1,000 feet 

upstream at site 42 during the fourth synoptic event) has a 

relatively small topographically derived drainage area, but 

a much larger than expected discharge value that commonly 

exceeds 10 ft3/s (fig. 27). This relatively large discharge from 

Lacey Spring commonly represents almost half of the flow to 

Smith Creek in this headwater area, indicating that the waterquality composition of Lacey Spring will strongly affect that 

of Smith Creek.

August 2011

0

10

20

30

40

50

60

70

December 2011

May 2012

0

10

20

30

40

50

60

0 20 40 60 80 100 120 0 20 40 60 80 100 120

70

May 2013

EXPLANATION

Synoptic sampling location 

Streamflow inputs between 

 site 8 and site 11 

Dry Fork (site 9) 

Lacey spring (site 10) 

Site 8 and site 11 

Other

From Dry Fork (site 9) 

From Lacey spring (site 10) 

Unaccounted for by synoptic

 sampling locations

Smith Creek streamgage 

 (site 32)

Drainage area, in square miles

Streamflow, in cubic feet per second

Figure 27. Streamflow inputs and drainage area for samples collected during each 

synoptic sampling event between sites 8 and 11 in the Smith Creek watershed.
Smith Creek Watershed Water-Quality Characterization 47

Little evidence of direct groundwater discharge into the 

Smith Creek stream channel was observed during synoptic 

sampling, with the exception of the reach between Smith 

Creek stations 8 and 11, which consistently demonstrated an 

excess of flow at station 11 that cannot be accounted for by 

the flow at station 8 and the contributions from Dry Fork and 

Lacey Spring. Given that site 11 is near the bedrock contact 

between the upper carbonate section of the watershed and the 

lower shale portion of the watershed, groundwater is likely 

being forced into Smith Creek within this reach because of 

contrasting permeability along this bedrock contact. The 

amount of unmeasured flow entering Smith Creek between 

stations 8 and 11 seemed to vary with hydrologic conditions, 

with greater groundwater inputs during wetter synoptic 

sampling events. These unmeasured flow contributions were 

assumed to be direct discharges to Smith Creek, rather than an 

unmonitored spring or tributary, because subsequent in-stream 

temperature monitoring confirmed extensive discharge of 

relatively cool groundwater directly into the bottom of Smith 

Creek at many points in the vicinity of Lacey Spring (Kurt J. 

McCoy, U.S. Geological Survey, oral commun., 2014).

Strong variability in nitrogen concentrations and loads 

were observed throughout the Smith Creek Watershed during 

the synoptic sampling; however, consistent spatial patterns 

were observed between monitoring sites for all four synoptic 

events (figs. 28 and 29). TN concentrations were consistently 

low (<0.5 mg/L) for sites that drained the forested tributaries 

on the eastern edge of the watershed. Conversely, concentrations were commonly in the 5–8-mg/L range for the various 

springs in the headwaters of the watershed. In general, and for 

all four synoptic events, TN concentrations were highest in 

the headwaters of the basin and decreased in the downstream 

direction to values of 2–3 mg/L at the streamgage (fig. 28).

Although elevated TN concentrations occurred at 

the spring sites, measured discharge strongly affected the 

observed nitrogen loadings in the watershed. Several of the 

springs with elevated nitrogen concentrations had small 

discharge values, which resulted in relatively low loadings, 

whereas the discharge at Lacey Spring (sites 10 and 42), 

combined with moderate TN concentrations, resulted in 

dominant nitrogen loadings to Smith Creek (fig. 29). During 

the driest synoptic sampling event (August 2011), maximum 

stream loads occurred in the central part of the watershed 

(downstream of Lacey Spring and in the area of the confluence 

with War Branch); nitrogen loads decreased downstream of 

this location, likely because of in-stream processes such as 

uptake and denitrification. During wetter conditions similar to 

the December 2011 synoptic event, elevated nitrogen loadings 

were still contributed by Lacey Spring, but downstream of 

Lacey Spring, nitrogen concentrations decreased, and nitrogen 

loads continued to increase, likely because of relatively low 

nitrogen concentrations from the streams entering from the 

eastern edge of the watershed.

Phosphorus patterns in Smith Creek also demonstrated 

major spatial concentration and load patterns that were 

similar between synoptic sampling events (figs. 30 and 31). 

In general, low phosphorus concentrations were observed 

at the spring sites and the forested mountain sites. Relative 

to the low phosphorus concentrations in the headwaters, the 

monitoring site on Dry Fork (site 9) demonstrated slightly 

elevated phosphorus concentrations but insufficient discharge 

to be a major source of phosphorus. Phosphorus concentrations and loads appear to increase markedly in the lower part 

of Smith Creek, generally between stations 25 and 26, and 

these elevated loads persist throughout the remainder of the 

downstream part of Smith Creek.

The reach of Smith Creek between sites 25 and 26 was 

subsequently sampled at a much finer spatial resolution 

whereby water-quality samples were collected from all 

inflowing tributaries and point sources within the reach. 

Results of this water-quality sampling indicated that elevated 

phosphorus concentrations (0.34 mg/L) were being discharged 

from a permitted point source within this reach and that low 

concentrations (0.003 mg/L) of phosphorus were in the flowing 

tributaries. The sampled point source appears to be the cause 

of increased phosphorus loading in the lower part of Smith 

Creek. The discharge permit for this point source established 

limits for the release of flow, pH, chemical/biological oxygen 

demand, suspended solids, E. coli, total residual chlorine, oil, 

and grease; however, because of the size of the point source 

discharge (0.30 million gallons per day or less), no limitation 

has been placed on the concentrations of released nutrients.
48 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 28. Monitoring locations, total nitrogen concentrations measured during each synoptic 

sampling event, and the stream network in the Smith Creek watershed.

36 34**

35**

21 14

13

12

20

19*

17

18 16**

15**

4**

11

42*

10*

38

1* 2

39*

40* 6

7

8

9

78°50' 78°45' 78°40' 78°35'

38°45'

38°40'

38°35'

38°30'

38°25'

22

August 2011

22 ft3

/s

December 2011

61 ft3/s

May 2013

49 ft3

/s

May 2012

32 ft3/s

0 2 4 6 MILES

0 2 4 6 KILOMETERS

33

32

30

29 28

27 26

41** 25

24

23

EXPLANATION

32

Synoptic sampling events at site 32

 (USGS streamgage 01632900)

Total nitrogen, in milligrams per liter

Sample not collected

0 to 2.00

2.01 to 4.00

4.01 to 6.00

6.01 to 8.00

8.01 to 10.00

Synoptic sampling station

 and number—Spring- 

 dominated waters (*);

 streams that drain off of

 Massanutten Mountain (**)

Streamflow, in 

cubic feet per second (ft3

/s)

Streams from U.S. Geological Survey 

 National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983
Smith Creek Watershed Water-Quality Characterization 49

Figure 29. Monitoring locations, the percentage of the maximum total nitrogen load measured 

during each synoptic sampling event, and the stream network in the Smith Creek watershed. [Note 

that percentages are only valid for comparisons within a single synoptic event, not between synoptic 

events.]

36 34**

35**

21 14

13

12

20

19*

17

18 16**

15**

4**

11

38

1* 2

39*

40* 6

7

8

42*

10*

9

78°50' 78°45' 78°40' 78°35'

38°45'

38°40'

38°35'

38°30'

38°25'

22

August 2011

22 ft3

/s

506 lb/day

December 2011

61 ft3/s

1,199 lb/day

May 2013

49 ft3

/s

714 lb/day

May 2012

32 ft3/s

386 lb/day

Percent of maximum total 

nitrogen load by event

0 2 4 6 MILES

0 2 4 6 KILOMETERS

33

32

30

29 28

26 27

41** 25

24

23

Sample not collected

0 to 20.00

20.01 to 40.00

40.01 to 60.00

60.01 to 80.00

80.01 to 100 Streamflow, in

cubic feet per second (ft3

/s)

and maximum total nitrogen load,

in pounds per day (lb/day)

Streams from U.S. Geological Survey 

 National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

EXPLANATION

32

Synoptic sampling events at site 32

 (USGS streamgage 01632900)

Synoptic sampling station

 and number—Spring- 

 dominated waters (*);

 streams that drain off of

 Massanutten Mountain (**)
50 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 30. Monitoring locations, the total phosphorus concentration measured during each synoptic 

sampling event, and the stream network in the Smith Creek watershed.

36 34**

35**

21 14

13

12

20

19*

17

18 16**

15**

4**

11

38

1* 2

39*

40* 6

7

8

42*

10*

9

78°50' 78°45' 78°40' 78°35'

38°45'

38°40'

38°35'

38°30'

38°25'

22

August 2011

22 ft3

/s

December 2011

61 ft3/s

May 2013

49 ft3

/s

May 2012

32 ft3/s

Total phosphorus, in

 milligrams per liter

0 2 4 6 MILES

0 2 4 6 KILOMETERS

33

32

30

29 28

27 26

41** 25

24

23

Sample not collected

0 to 0.020

0.021 to 0.040

0.041 to 0.060

0.061 to 0.080

0.081 to 0.100

Streams from U.S. Geological Survey 

 National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

EXPLANATION

32

Synoptic sampling events at site 32

 (USGS streamgage 01632900)

Synoptic sampling station

 and number—Spring- 

 dominated waters (*);

 streams that drain off of

 Massanutten Mountain (**)

Streamflow, in 

cubic feet per second (ft3

/s)
Smith Creek Watershed Water-Quality Characterization 51

Figure 31. Monitoring locations, the maximum total phosphorus load measured during each synoptic 

sampling event, and the stream network in the Smith Creek watershed. [Note that percentages are only 

valid for comparisons within a single synoptic event, not between synoptic events.]

36 34**

35**

21 14

13

12

20

19*

17

18 16**

15**

4**

11

38

1* 2

39*

40* 6

7

8

42*

10*

9

78°50' 78°45' 78°40' 78°35'

38°45'

38°40'

38°35'

38°30'

38°25'

22

August 2011

22 ft3

/s

8.4 lb/day

December 2011

61 ft3/s

14.3 lb/day

May 2013

49 ft3

/s

20.1 lb/day

May 2012

32 ft3/s

10.4 lb/day

Percent of maximum total 

 phosphorus load by event

0 2 4 6 MILES

0 2 4 6 KILOMETERS

33

32

30

29 28

27 26

41** 25

24

23

Sample not collected

0 to 20.00

20.01 to 40.00

40.01 to 60.00

60.01 to 80.00

80.01 to 100

Streams from U.S. Geological Survey 

 National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Streamflow, in

cubic feet per second (ft3

/s)

and maximum total nitrogen load,

in pounds per day (lb/day)

EXPLANATION

32

Synoptic sampling events at site 32

 (USGS streamgage 01632900)

Synoptic sampling station

 and number—Spring- 

 dominated waters (*);

 streams that drain off of

 Massanutten Mountain (**)
52 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Because of the general consistency in the synoptic 

nutrient concentrations at each monitoring site, compared to 

the between-site variability, the water-quality samples for all 

four synoptic events were analyzed by using a single cluster 

analysis to explore spatial patterns in the water quality that 

persisted between synoptic events. Four distinct clusters were 

formed, with each cluster representing a different “water type” 

within the Smith Creek watershed (fig. 32). These water types 

are identified and described below:

Cluster #1 – Smith Creek type – This cluster included 

most sampling sites located on the mainstem of Smith Creek. 

The number of sampling locations included in this group 

across all sampling events suggests that the water quality of 

the Smith Creek main channel was relatively homogeneous, 

despite some unique conditions measured in the headwaters. 

The ionic strength of these waters was higher than that of the 

relatively dilute streams that flowed off of the eastern edge 

of the watershed (included in cluster #4), but lower than the 

headwater springs (cluster #2) and Dry Fork (cluster #3). The 

conditions within this group were likely influenced by contact 

with the carbonate bedrock in the upper part of the watershed 

and subsequent mixing with the other water sources.

Cluster #2 – Limestone springs type – This cluster 

included sites 39, 1, 19, 42, and 10, all associated with 

carbonate springs in the upper part of the Smith Creek 

watershed. Site 39 is the Smith Creek spring, and site 1 is 

located approximately 1,500 ft downstream of the spring. 

Site 19 is an unnamed tributary that drains into War Branch. 

Site 42 is Lacey Spring, with site 10 located approximately 

1,000 ft below Lacey Spring. These sites were all characterized by elevated concentrations of calcium, magnesium, 

potassium, bicarbonate, and nitrogen, as well as elevated 

pH

Orthophosphate

Total phosphorus

Ammonium

Manganese

Iron

Sulfate

Chloride

Sodium

Specific conductance

Calcium

Bicarbonate

Nitrate

Total nitrogen

Magnesium

Potassium

Dendrogram break point

Group 1

(Smith Creek type)

Group 2

(Limestone

springs type)

Group 3

(Dry Fork type)

Group 4

(Undelveloped, high-gradient,

forested site type)

Cluster group

EXPLANATION

Relative constituent value

Low High

Figure 32. Samples collected from each synoptic event within the Smith Creek watershed with a heat map of 

constituent values and the breakpoint used to produce four cluster groups.
Smith Creek Watershed Water-Quality Characterization 53

specific conductance. Cluster #2 was further characterized 

by low concentrations of phosphorus, ammonium, iron, and 

manganese, as well as low water temperatures. All of the 

previously described characteristics tend to be indicative 

of springs that issue from limestone and dolostone in the 

agricultural Shenandoah Valley (Nelms and Moberg, 2010). 

This cluster of sites included the sites with the highest nitrate 

concentrations in Smith Creek, and those sites with substantial 

flow were responsible for much of the nitrogen loading to 

Smith Creek.

Cluster #3 – Dry Fork type – Aptly named, Dry Fork was 

frequently a series of disconnected pools, except for during 

wet periods. Water quality within the Dry Fork watershed was 

quite different from that of Smith Creek and was characterized by extremely elevated specific conductance, as well as 

elevated concentrations of sodium, chloride, sulfate, calcium, 

bicarbonate. Dry Fork has a high density of sinkholes (fig. 2C) 

in its watershed that likely contribute to its intermittent flow 

and create a rapid connection between the land surface and a 

deeper groundwater reservoir that fails to support sustained 

base flow during all but the wettest periods in Dry Fork. 

The reason for the elevated specific conductance and total 

dissolved solids in Dry Fork remains unresolved. Potentially 

contributing to the observed salt and high dissolved solids 

in the Dry Fork watershed is the fairly extensive network of 

roadways that includes Interstate 81, Route 11, and many 

other tertiary roadways that bisect the Dry Fork watershed 

(fig. 7). Salt application to roadways during the winter may 

be one source of this elevated salt concentration. Overall, Dry 

Fork contributed very little direct surface flow to Smith Creek 

during base flow, although the stream was active during rainfall periods. Still, further investigation into the water resources 

of Dry Fork may be warranted to better understand the unusual 

water quality and potential groundwater vulnerability in 

this watershed.

Cluster #4 – Undeveloped, high-gradient, forested site 

type – This final cluster is composed of sites 4, 41, 34, 35, 15, 

and 16—all of which originated in the relatively undeveloped, 

high-gradient forested area along the eastern edge of the 

watershed. All sites in this cluster were characterized by 

low ionic strength waters with low nutrient concentrations, 

which generally confirms that the elevated nutrient concentrations in Smith Creek were likely related to anthropogenic 

activities or natural processes on the valley floor and not 

driven by atmospheric deposition or contributions from the 

forested areas.

While the cluster analysis demonstrated consistency 

among the site types, subtle mixing differences seem to be 

caused by varying hydrologic conditions (fig. 33). Note 

that because synoptic sites were added during the period 

of study, not all stations were sampled during all synoptic 

events. Overall, the spring sites and the forested mountain 

sites retained their membership during all events, but the 

chemistry of the stream sites downstream of these springs 

and mountain tributaries varied somewhat, with the greatest 

effects of the springs occurring during the driest period and 

the greatest effects of the mountain tributaries occurring 

during wetter periods. For example, during the August 2011 

event, the bulk of the Smith Creek headwaters resembled 

the main channel of Smith Creek, while during the wettest 

event in December 2011, the headwater sites were most like 

the mountain streams, presumably because of increased flow 

from these mountain streams. Overall, the water quality 

within Smith Creek appeared to be a mixture of springs and 

mountain streams, with the relative mixture of the two water 

types driving the upper Smith Creek chemistry until the 

addition of the permitted point source, which supplied greater 

phosphorus concentrations and could have initiated greater 

in-stream processing.

The synoptic spatial sampling of the Smith Creek 

watershed demonstrated a complex hydrogeology and 

geochemistry. In general, most base flow was generated by 

groundwater discharge in the upper part of Smith Creek that 

lies above site 11. Within this upper part of the watershed, the 

contributions of flow from Lacey Spring (site 42) often dominate discharge. Groundwater with high total nitrogen concentrations was discharging from several headwater springs, and 

downstream effects of these springs were governed by the 

discharge of each spring; the greatest loads originated from 

Lacey Spring (site 42), and the lowest originated from site 19. 

Because of the high density of sinkholes, most of the water 

in the Dry Fork watershed was recharged to groundwater and 

contributed little direct flow to Smith Creek. Relatively dilute, 

nutrient-poor water was contributed from the forested eastern 

boundary, and little nitrogen was likely entering along the 

lower part of Smith Creek underlain by shale. In contrast, the 

limestone headwaters had low phosphorus concentrations and 

were a less dominant source of phosphorus to the watershed. 

Instead, a point source with high phosphorus concentrations 

discharged to Smith Creek between station 25 and 26. Further 

study of the nitrogen and phosphorus sources to Smith 

Creek could improve the understanding of the sources and 

transport processes (including lag times) associated with 

these constituents.

Characterization of Nitrogen Sources

Characterization of the primary nitrogen sources in the 

Smith Creek watershed can inform watershed-management 

decisions related to the implementation of conservation 

BMPs. In this section, nitrogen sources as indicated by nitrate 

isotopes and other data are compared to results of land-use and 

model-based nutrient-source evaluations.

The synoptic nitrate isotope results indicated several 

patterns and processes that affect the distribution and export 

loads of nitrate in the Smith Creek watershed (fig. 34). The 

lowest nitrate concentrations and δ15N values were associated 

with sites draining undeveloped, high-gradient forested sites, 

while the highest nitrate concentrations and intermediate 

δ15N values were associated with limestone springs. The 

nitrogen from the undeveloped high-gradient forested sites 

was likely from a combination of atmospheric and natural 
54 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 33. Synoptic sampling events at monitoring locations in the Smith Creek watershed as grouped 

in a cluster analysis.

36 34**

35**

21 14

13

12

20

19*

17

18 16**

15**

4**

11

42*

10*

38

1* 2

39*

40* 6

7

8

9

78°50' 78°45' 78°40' 78°35'

38°45'

38°40'

38°35'

38°30'

38°25'

22

August 2011

22 ft3

/s

December 2011

61 ft3/s

May 2013

49 ft3

/s

May 2012

32 ft3/s

0 2 4 6 MILES

0 2 4 6 KILOMETERS

33

32

30

29 28

27 26

41** 25

24

23

Streams from U.S. Geological Survey 

 National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

EXPLANATION

32

Synoptic sampling events at site 32

 (USGS streamgage 01632900)

Synoptic sampling station

 and number—Spring- 

 dominated waters (*);

 streams that drain off of

 Massanutten Mountain (**)

Streamflow, in 

cubic feet per second (ft3

/s)

Cluster group

Sample not collected

Group 1—Smith Creek type

Group 2—Limestone springs type

Group 3—Dry Fork type

Group 4—Undeveloped, highgradient, forested site type
Smith Creek Watershed Water-Quality Characterization 55

soil sources, but the nitrogen from the limestone springs was 

most consistent with manure-derived sources, although the 

possibility of mixed sources cannot be excluded. The median 

δ15N value of nitrate from Lacey Spring (8.4 ‰) was similar 

to reported values for nitrate in discharge from a small spring 

in the nearby Muddy Creek watershed (+8.6 ‰ ± 0.7 ‰) 

where the nitrate was interpreted to be largely derived from 

animal-rearing activities (Hyer and others, 2001; Lindsey and 

others, 2003).

Regardless of the nitrogen source(s), δ18O data indicate 

that nearly all of the nitrate exported from Smith Creek 

originated from within-watershed nitrification, and not as 

unreacted atmospheric or synthetic nitrate, which would have 

low δ15N and high δ18O values (fig. 6 and associated text). 

Even during high-flow events, direct runoff of atmospheric 

nitrate generally was not sufficient to affect the isotopic 

composition of nitrate in total discharge. The main-stem 

Smith Creek sites were characterized by intermediate nitrate 

concentrations and by increasing δ15N and δ18O values in 

the downstream direction. The downstream trends may have 

resulted from partial nitrate reduction in the stream channels 

Figure 34. Nitrate isotope results from each synoptic sampling event within the Smith Creek watershed. 

Nitrate plus nitrite,

in milligrams per liter as nitrogen

10

2

4

6

8

0

0

0 5 10 15 20

0 2 4 6 8 10

2

4

6

8

10

EXPLANATION

Groups identified by the cluster analysis 

Smith Creek type 

Limestone springs type

Dry Fork type

Undeveloped, high-gradient, forested site type

Schematic changes potentially associated with 

*Typical range of error, 2-sigma: 

 delta N-15: 0.5 per mil

 delta O-18: 1.0 per mil

Dilution

Addition of atmospheric nitrate in runoff 

Nitrate reduction (denitrification, assimilation) 

Different nitrification N sources

Delta N-15 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

or in downgradient discharging groundwater. The Dry Fork 

sites had variable nitrate concentrations and δ15N and δ18O 

values, which indicated substantial biologic processing of the 

nitrate in some cases.

Land-use, fertilizer-use, and animal and human 

population census data were used to evaluate likely nitrogen 

sources within the Smith Creek watershed. Although a landuse analysis is informative about the sources and application 

of nutrients that are available for transport, it does not provide 

specific information regarding which sources of nitrogen 

are stored in the landscape and which are transported to the 

groundwater or stream system. Because most of the available 

datasets related to agricultural practices were developed and 

published at the county scale, the land-use nitrogen-source 

analysis was performed at the county scale. Results of a 

county-based analysis are only relevant to a given watershed 

of interest (in this case, Smith Creek) if land uses are similar 

in the basin of interest and the county. Fortunately for this 

analysis, land uses within the Smith Creek watershed and 

those of Rockingham and Shenandoah Counties (the Smith 

Creek watershed lies in both counties) are similar (fig. 35), 
56 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Smith Creek Watershed

Rockingham County

Shenandoah County

Cultivated crop

1.99 percent

Cultivated crop

3.42 percent

Cultivated crop

3.11 percent

Developed

7.23 percent

Developed

6.85 percent

Developed

7.80 percent

Pasture/hay

40.59 percent

Pasture/hay

30.24 percent

Pasture/hay

28.56 percent

Other <1 percent

Water 

0.36 percent

Water 0.06 percent

Water

0.58 percent

Forest

48.31 percent

Forest

58.92 percent

Forest

61.38 percent

Other <1 percent

Other <1 percent

Figure 35. Percentages of land-use types from Homer and others 

(2015) within the Smith Creek watershed, Rockingham County and 

Shenandoah County, Virginia. 

and consequently the land-use-derived information on 

nitrogen sources in Rockingham and Shenandoah Counties 

is likely representative of the nitrogen sources in the Smith 

Creek watershed.

The land-use-derived sources of nitrogen to the land 

surface were computed for Rockingham and Shenandoah 

Counties from 1987 through approximately 2006 (fig. 36A). 

Within both counties, manure sources of nitrogen dominated 

and were followed by commercial inorganic fertilizer 

application and atmospheric deposition. The manure source 

(from agricultural livestock, dominated by poultry and cattle 

sources) was approximately four times the commercial 

inorganic fertilizer source in both counties. In both counties, 

the amount of nitrogen generated by sewage and septic waste, 

as well as by residential fertilizer, was small compared to other 

sources. While these land-use-derived source data indicate 

only the relative magnitudes of nitrogen inputs on the landscape and do not necessarily indicate which nitrogen sources 

were routed to the stream system, they appear to corroborate 

the nitrate isotope results, which indicate that manure was the 

dominant source of nitrogen exported from the watershed. 

Based on this analysis, reductions in nitrogen sources have not 

been observed within either county, to date (fig. 36A).

Watershed-scale nitrogen sources for Smith Creek also 

were evaluated by using results from a SPAtially Referenced 

Regressions On Watershed attributes (SPARROW) model with 

parameter values derived for the Chesapeake Bay watershed 

(Ator and others, 2011). Land-surface loading data used by 

SPARROW indicated that nitrogen loading from manure was 

approximately 2 times the nitrogen loading from crop fertilizer 

plus nitrogen fixation in the Smith Creek watershed for 2002 

(fig. 37), similar to the land-use-derived pattern previously 

described (fig. 36B). However, when converted to percentages 

of nitrogen sources contributing to the TN export load of the 

stream, SPARROW model results indicated that nitrogen from 

manure was preferentially retained and nitrogen from fertilizer 

plus nitrogen fixation was preferentially exported, such that 

the nitrogen export load from manure was approximately 

0.5 times the nitrogen export load from fertilizer plus nitrogen 

fixation, a factor of 4 reversal in the relative fluxes between 

input and output (table 12). This result would seem to be in 

conflict with the nitrate isotope data, which favor manure over 

fertilizer and nitrogen fixation as dominant source of nitrate-N 

exported from Smith Creek. The reasons for this apparent 

discrepancy are unclear, but some possible contributing 

factors based on the SPARROW model results are (1) nitrogen 

from fertilizer and nitrogen fixation assimilated into plants in 

pastureland largely was used as animal feed and converted 

to manure before being nitrified and exported as nitrate; 

(2) relative source coefficients (the model terms representing 

the fraction of applied source material that is transported to the 

stream) for nitrogen from manure and fertilizer plus nitrogen 

fixation derived from the SPARROW model for the whole 

Chesapeake Bay watershed were not accurate in the Smith 

Creek watershed because of local conditions of hydrogeology 

(karst) or land-use practices (open pasture). Some other 
Smith Creek Watershed Water-Quality Characterization 57

Fertilizer (farm)

Sanitary

sewer

Septic

Rockingham County Shenandoah County

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

A. Kilograms of nitrogen per year

10,000

100,000

1,000,000

10,000,000

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

Rockingham County Shenandoah County

B. Nitrogen sources in 2002

Manure

Atmospheric deposition

3.46 percent

Atmospheric deposition

Atmospheric deposition

9.28 percent

Septic 0.78 percent Septic 1.22 percent

Manure

77.21 percent

Manure

70.40 percent

Fertilizer

(farm)

17.28%

Fertilizer

(farm)

17.84% Fertilizer (nonfarm)

0.35%

Fertilizer (nonfarm)

Fertilizer (nonfarm)

0.68%

Sanitary sewer

0.37 percent

Sanitary sewer

1.14 percent

Figure 36. (A) Nitrogen sources for Rockingham County and Shenandoah County, Virginia, from 1987 to 2012 and 

(B) nitrogen sources in 2002 for Rockingham County and Shenandoah County, Virginia.

Atmospheric 

deposition

8.53 percent

Manure

30.56 percent

Manure

66.17 percent

Fertilizer (farm)

29.10 percent

Fertilizer (farm)

Atmospheric 54.76 percent

deposition

3.98 percent

Urban area

0.75 percent

Urban area

6.14 percent

Input Output

Figure 37. Percentages of input and output sources of total nitrogen for the Smith Creek watershed 

as generated by the 2002 Chesapeake Bay Total Nitrogen SPARROW model as described in Ator and 

others (2011). 
58 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 12. Nitrogen delivery factors and source estimates identified by the SPARROW model for the Smith Creek watershed.

[HUCs, hydrologic unit codes; kg/yr–1, kilogram per year; km2, square kilometer; (kg/km–2)/yr–1, kilogram per square kilometer per year; %, percent]

Number of 8-digit HUCs Nitrogen delivery factor1

25

Minimum Median Maximum

0.553 0.701 0.875

Source explanatory variables

Source 

coefficient 

estimate2

Units

Inputs1 Outputs1

Sum % Sum %

Point sources (kg/yr–1) 0.774 — 0.00 — 0.00

Crop fertilizer and fixation (kg/yr–1) 0.237 898,739 29.10 150,079 54.76

Manure (kg/yr–1) 0.058 2,043,675 66.17 83,758 30.56

Atmospheric deposition (kg/yr–1) 0.267 122,814 3.98 23,387 8.53

Urban2 (km2) 1,090 (kg/km–2)/yr–1 23,293 0.75 16,834 6.14

Total 3,088,522 100.00 274,059 100.00

1

Nitrogen delivery factors, input and output data calculated for 25 8-digit HUCs that encompass the Smith Creek watershed. 

2

Source coefficient data adopted from the Chesapeake Bay watershed total nitrogen model as reported in Ator and others (2011).

reported SPARROW models yielded source coefficient ratios 

for manure to fertilizer nitrogen that were similar to those 

listed in table 12 (for example, Preston and others, 2011, 

for the northeastern United States); however, other models 

yielded ratios closer to 1 (for example, Preston and 

Brakebill, 1999, for the Chesapeake Bay watershed; Alexander 

and others, 2000, 2008, for the Mississippi River Basin), in 

which cases the isotope data and model export loads would be 

more consistent. Alternatively, elevated δ15N values may not 

be related to manure sources if mineralized soil and fertilizer 

N were isotopically fractionated more than usual in Smith

Creek watershed because calcareous (high pH) soils promoted 

volatilization of ammonia prior to nitrification (Kreitler, 1975; 

Kreitler and others, 1978); however, high δ15N values similar 

to those in Smith Creek have been reported for groundwater 

nitrate in other Chesapeake Bay subwatersheds containing 

pastures and confined animal feeding operations, not all of 

which have calcareous soils (Lindsey and others, 2003). 

More detailed analysis of SPARROW input variables and 

regression results could reveal new insights about the relative 

transmission of nitrogen from manure and other sources from 

landscapes to streams.
Smith Creek Watershed Water-Quality Characterization 59

Table 13. Number of USDA-compliant conservation practices implemented in water years 2007 through 2013 in the Smith Creek 

watershed, aggregated by sponsoring program.

[USDA, U.S. Department of Agriculture]

Program 2007 2008 2009 2010 2011 2012 2013 Total

Chesapeake Bay Watershed Initiative 0 0 0 1 60 121 189 371

Environmental Quality Incentives Program 7 3 9 10 7 5 14 55

Conservation Security Program 0 1 0 0 0 0 0 1

Conservation Reserve Program1 2 0 2 0 0 0 0 4

Total USDA-funded practices 9 4 11 11 67 126 203 431

Conservation Technical Assistance2 283 62 88 106 135 186 113 973

Total practices 292 66 99 117 202 312 316 1,404

1

Conservation Reserve Program and Conservation Reserve Enhancement Program practices administered by the USDA Farm Service Agency.

2

Technical assistance and verification provided by the USDA Natural Resources Conservation Service, without federal cost-share funding.

Table 14. Implementation of USDA-compliant conservation practices within the Smith Creek watershed for water 

years 2007 through 2013.

[USDA, U.S. Department of Agriculture; —, values are privacy protected due to fewer than five customers participating]

Practice 

code1 Practice name Lifespan (years) Units 2007 2008 2009 2010 2011 2012 2013

Aggregate 

implementation: 

2007 to 20132

328 Conservation Crop Rotation 1 acres 445 — — — — 518 342 2,119

340 Cover Crop 1 acres — — — — 735 736 1,494 3,878

590 Nutrient Management 1 acres — — 659 — 1,473 728 1,026 4,842

633 Waste Recycling 1 acres — — — 632 — 417 — 1,239

645 Upland Wildlife Habitat 

Management

1 acres 32 — — — — — — 138

512 Forage and Biomass Planting 5 acres — — — — — — 84 122

472 Access Control 10 acres — — — — — — 48 135

561 Heavy Use Area Protection 10 acres — — — — — 8 — 16

382 Fence 20 feet — — — — 8,191 23,410 28,660 76,375

516 Livestock Pipeline 20 feet — — — — — 6,310 17,594 31,164

614 Watering Facility 20 number — — — — — 13 22 56

1

Practice codes from the U.S. Department of Agriculture, Natural Resources Conservation Service (2016).

2

Aggregate implementation is greater than the sum of reported annual practices because privacy protections restrict the reporting of annual results for practices with fewer than five participating customers.

Implementation of Conservation Practices and 

Water-Quality Response

The designation of Smith Creek as an agricultural 

Showcase Watershed by USDA in 2010 and the subsequent 

commitment of implementation program funding to the 

Smith Creek watershed have substantially increased the 

implementation of conservation management actions over the 

past 5 years (table 13). Several State and Federal programs 

have been responsible for the increased level of conservation implementation, including cost-share and technical 

assistance from the NRCS, FSA, and Virginia Department 

of Conservation and Recreation (VADCR). The observed 

implementation intensity in Smith Creek is likely indicative 

of what can be achieved through concentrated grassroots local 

implementation efforts that are supported by these State and 

Federal programs.

Numerous Federal- and State-funded conservation 

practices have been implemented within Smith Creek 

to reduce the transport of sediment and nutrients 

(tables 13 through 15). In the Smith Creek watershed, 

1,404 USDA-compliant agricultural conservation practices 

were implemented between 2007 and 2013 (table 13). 

The USDA provided cost-share for 431 of those practices 

using funds from the Chesapeake Bay Watershed Initiative 

(371 practices), Environmental Quality Incentives Program 
60 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

(55 practices), and additional USDA programs (5 practices). 

For the remaining 973 conservation practices, the NRCS 

provided technical assistance, but funding was provided by 

non-Federal programs and (or) farmers.

The aggregated total of conservation practice 

implementations (new practices applied between October 2007 

and September 2013) is reported by NRCS practice code 

in table 14 for all practices with five or more participating 

farmers. State-funded conservation practices are included in 

table 15. Sediment-control measures tended to include fencing 

of cattle out of streams and marginal pastureland, along with 

the creation of alternative watering facilities. From 2007 

to 2013, more than 75,000 ft of fencing was installed in the 

Smith Creek watershed to reduce cattle access to sensitive 

areas and to reduce sediment transport. Nutrient-control 

measures tended to include the development of nutrient 

management plans, conservation crop rotation, and planting of 

cover crops, as well as excluding cattle from stream access.

Although intensive water-quality monitoring at the Smith 

Creek streamgage started for this project in 2010, historical 

water-quality monitoring data exist for the period 1979 

through 2004, and these historical data were coupled with 

the recent monitoring data to provide an initial assessment 

of trends in water quality in Smith Creek. These data were 

included in a trend analysis using Weighted Regression on 

Time, Discharge and Season (WRTDS) to provide trends in 

concentration and load (http://cbrim.er.usgs.gov/). Results 

of this analysis of the Smith Creek nitrate data indicate 

that flow-normalized nitrate concentrations have increased 

from 1985 through 2014 at a rate of 0.01 mg/L per year. 

While this is a small rate of change, it indicates that nitrate 

concentrations in the watershed have increased, despite the 

implementation of conservation practices to date. Even though 

nitrate concentrations have increased over this time period, 

flow-normalized nitrate fluxes (loads) have actually decreased 

by approximately 0.1 percent per year. Overall, these trends 

are small and are generally difficult to discern in the time 

series of observed concentration data (fig. 38). Differences 

in trend between flow-normalized concentration patterns and 

flow-normalized flux patterns are fairly common in WRTDS 

analyses and occur whenever the changes in water chemistry 

vary with flow. In the case of Smith Creek, WRTDS model 

output data indicate that nitrate concentrations increased 

during low-flow conditions, causing the increasing trend 

in concentration, but the nitrate concentrations during high 

flows have decreased, resulting in decreasing nitrate loads. 

Increasing nitrate concentrations during low-flow conditions 

suggests that concentrations of nitrate in the groundwater 

discharged to Smith Creek are actually increasing over time. 

Increased nitrate concentrations in groundwater may be 

related to stable or increasing nitrogen sources in Rockingham 

and Shenandoah Counties (fig. 36A), as well as potential 

lag effects related to groundwater residence and transport 

times. Enhanced implementation of agricultural conservation 

practices and continued beneficial farm practices will continue 

to be needed to reduce nitrate concentrations in Smith Creek.

Recent results from a Conservation Effects Assessment 

Project (CEAP) in the Chesapeake Bay Region (U.S. 

Department of Agriculture, Natural Resources Conservation 

Service, 2013) suggested that while implementation of 

conservation practices across the Chesapeake Bay watershed 

is increasing, the following trends in other cultivated cropland 

management practices, evaluated from 2003–2006 to 2011, 

were less encouraging:

• Annual nitrogen application increased 10 percent on 

cultivated cropland, including a 13-percent increase in 

manure nitrogen application;

• Annual phosphorus application increased 6 percent on 

cultivated cropland, including an 11-percent increase in 

manure nitrogen application; and

• Manure application rates on cultivated cropland 

increased by 25 percent.

Table 15. Implementation of state-funded conservation practices within the Smith Creek watershed for water years 2007 through 

2014. The table does not include practices for which the NRCS provided funds or technical assistance.

[NRCS, Natural Resources Conservation Service; —, values are privacy protected due to fewer than five customers participating]

Practice 

code1 Practice name Units 2007 2008 2009 2010 2011 2012 2013

Aggregate 

implementation: 

2007 to 20132

NM-3B Manure Application to Corn Using 

Pre-Application Nitrate Test

acres — 682 — 1,137 — — — 2,022

RB-1 Septic Tank Pumpout number — — — — — 6 41 47

RB-3 Septic Tank System Repair number — — — — — — 5 6

SL-8B Small Grain Cover Crop for 

Nutrient Management

acres 1,170 1,063 839 616 140 571 879 5,278

SL-8H Harvestable Cover Crop acres — 802 432 570 471 521 678 3,474

WP-4 Animal Waste Control Facility number — — — — — — 5 12

1

Practice codes from the Virginia Department of Conservation and Recreation (2016).

2

For some practices, aggregate implementation is greater than the sum of reported annual practices because privacy protections restrict the reporting of 

annual results for practices with fewer than five participating customers.
Smith Creek Watershed Water-Quality Characterization 61

These increases in nutrient inputs are associated with 

increased agricultural intensification, including higher 

yields and greater numbers of animals. While these overall 

CEAP trends are not specific to Smith Creek, they may help 

to explain why nitrate concentrations were increasing in 

the watershed, despite the widespread implementation of 

conservation practices.

The cumulative effects of BMP implementation within 

the Smith Creek watershed were difficult to quantify because 

of the many different types of BMP practices, the location 

of each, potential lag times between implementation and full 

functionality of the practice, and the relative efficiency of 

each practice/installation. Future monitoring will measure the 

overall changes in watershed nutrient and sediment transport. 

Another way to develop an interim set of expectations is 

with the Chesapeake Bay Program’s watershed model, by 

evaluating model output for model scenarios with and without 

the implementation of BMP practices. The results for the 

land-water segment that contains Smith Creek are presented 

in figure 39 (courtesy of the Chesapeake Bay Modeling Team, 

written commun., 2015, based on Watershed Model 5.3.2). 

This modeling scenario projects that nitrate loads have 

decreased approximately 16 percent since 2007 and that 

overall, the reductions in nitrate loading to this segment of the 

model are driven by agricultural BMPs, with little changes in 

the other source categories. These model results are generally 

in conflict with the empirical water-quality data that indicate 

a general increase in nitrate concentrations, raising concerns 

regarding the application of certain watershed model assumptions within the Smith Creek watershed. Subsequent investigations will explore in more detail the possible reasons for the 

discrepancy between the watershed model and the observed 

water-quality data.

Conceptual Watershed Model and Implications 

for Management Actions

Cumulatively, the intensive and extensive monitoring 

elements provide a process-level understanding of the Smith 

Creek nutrient and sediment transport processes that are 

summarized as follows.

• Substantial hydrologic and seasonal variability in 

the water-quality conditions of Smith Creek were 

observed. Concentrations of constituents associated 

with groundwater discharge were generally highest 

during base flow and were diluted during stormflow. 

During stormflow, concentrations of particulate 

constituents increased. Common seasonal patterns in 

water-quality constituents were observed.

• No systematic seasonal pattern was observed for 

nitrate concentrations; instead, maximum concentrations occurred throughout the year when flow was 

about 70–80 ft3/s. Dilution by runoff commonly caused 

lower nitrate concentrations at flows greater than this, 

while biologic processing may have contributed to 

lower concentrations at discharges less than this.

• The dominant geographic source of nitrate discharge 

to Smith Creek appears to be headwater springs, while 

the primary source of the nitrogen is likely agricultural 

manure, followed by commercial fertilizer.

• Most of the phosphorus load in Smith Creek at base 

flow appears to enter the stream from a local source 

near the town of New Market; during wet-weather 

conditions, nonpoint sources seemed to dominate 

phosphorus input.

>80 to 100

>60 to 80

>40 to 60

>20 to 40

0 to 20

EXPLANATION

Percentage of values

 equal to or less than the 

 maximum streamflow of

 4,250 cubic feet per second

Streamflow data

not available

Nitrate plus nitrite,

in milligrams per liter as nitrogen

Year

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

(where small values 

represent low-flow samples)

Figure 38. Nitrate concentrations from the Smith Creek streamgage for water years 1979 

through 2013.
62 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

• Tributaries draining the eastern forested edge of the 

watershed contributed relatively dilute, low ionic 

strength water that tended to dilute the composition 

of Smith Creek as it passed through the lower part of 

the watershed.

• During the driest, lowest flow conditions, springs 

provided the bulk of the water in Smith Creek; during wetter base-flow conditions, the eastern forested 

tributaries provided substantial flow to Smith Creek, 

and during wet weather events, various nonpoint 

sources contributed flow, sediment, and nutrients to 

Smith Creek.

• Implemented conservation practices increased 

substantially during 2007 through 2013. The primary 

practices for the reduction of sediment and nutrient 

transport included exclusion of cattle from streams by 

using fencing, as well as the development of nutrient 

management plans and planting of cover crops.

• During 1985–2014, nitrate concentrations in 

Smith Creek increased at a rate of approximately 

0.01 mg/L per year, despite the implementation of 

conservation practices. Increasing nitrate concentrations during low-flow conditions suggest that concentrations of nitrate in the groundwater discharged to 

Smith Creek are increasing over time.

The process-level understanding previously summarized 

is directly applicable to the development of effective conservation practices and is particularly important for informed 

management strategies. Decisions to manage the following 

constituents in the following ways are likely to generate the 

most efficient response.

• Management activities for nitrogen would likely be 

most effective in the upper part of the watershed 

in the areas that are underlain by karst geology. 

Management activities that reduce the amount of nitrogen transported to groundwater are important because 

subsequent groundwater discharge to the stream, 

Pounds of nitrate plus 

 nitrite as nitrogen from

 all sources with no best

 management practice

 implementation 

Source-specific pounds of 

nitrate plus nitrite as nitrogen

with best management

practice implementation

Percent difference per year

 in nitrate plus nitrite as nitrogen

 predicted by implementation 

 versus no implementation of

 best management practices

EXPLANATION

Agriculture

Urban area

Forest

Wastewater

–10

–8

–6

–4

–2

0

2

4

6

8

10

Year

1985 1990 1995 2000 2005 2010

Nitrate plus nitrite, in pounds of nitrogen

0

20,000

40,000

60,000

80,000

100,000

Annual percent change of nitrate plus nitrite as nitrogen

110,000

90,000

70,000

50,000

30,000

10,000

Figure 39. Nitrate loads estimated from the Chesapeake Bay watershed model for the model 

segment containing Smith Creek with and without best management practice implementation with 

input sources and the annual percentage difference of load attributed to implementation, 1985–2012.
Smith Creek Watershed Water-Quality Characterization 63

particularly in the upper part of the watershed, appears 

to be driving the increasing concentrations of nitrate 

observed in Smith Creek.

• The development of nutrient management plans that 

address the application of manure, as well as commercial inorganic fertilizer, could be important for 

reducing the overall nitrogen loading to the basin.

• Managing these manure and commercial inorganic 

fertilizer inputs could have a corollary benefit of 

reducing phosphorus inputs because nonpoint-source 

phosphorus inputs seem to dominate the phosphorus 

transport at high flow.

• Managing phosphorus within the Smith Creek 

watershed will involve both point-source and 

nonpoint-source contributions. A single point 

source is generally responsible for the majority of 

the phosphorus transported by Smith Creek under 

base-flow conditions. Nonpoint sources are generally 

responsible for the majority of the phosphorus during 

wet-weather periods.

• While modifications to the timing and incorporation 

of fertilizer and manure could alter the transport of 

nitrogen and phosphorus within the system, a reduction 

in total nutrient loading to the watershed is ultimately 

needed to reduce nutrient export from the basin.

• The increased in-stream nitrate concentrations were 

likely a result of stable or increasing nitrogen sources 

within the watershed, although groundwater residence 

times and transport processes may also be important.

• The collection of empirical nutrient and geochemical 

water-quality data in the Smith Creek watershed 

were critical for better understanding of the nitrogen 

sources and temporal responses within the watershed 

and for reconciling regional model discrepancies for 

nitrogen sources (SPARROW results) and trends in 

water-quality data (Chesapeake Bay Watershed Model 

results). Given the potential for model discrepancies at smaller scales, the collection and analysis of 

field water-quality data remain the primary tool for 

evaluating basins of interest.

Ongoing USGS water-quality monitoring efforts will 

be directed towards the further understanding of the sources, 

sinks, and transport processes in Smith Creek, as well as the 

detection of water-quality change that can be directly related 

to the implementation of management actions. Because large 

changes in water quality take time and sufficient management 

of the landscape to actually affect the system, different study 

elements will be used to detect change in the basin.

• Nutrient and sediment monitoring at the streamgage 

will provide a long-term record of the quality of the 

water exiting the basin. With enough years of monitoring, a trend analysis on an expanded list of constituents 

can be performed to determine whether statistically 

significant trends exist. Generally, 10 years of data 

is considered sufficient to attempt a trend test. This 

is a classical approach for trend analysis; however, 

it is least sensitive to change because the intensive 

monitoring site is located well down in the watershed, 

miles below much of the interesting geochemistry and 

anticipated change.

• A continuous nitrate record is likely one of the best 

tools for determining whether the Smith Creek system 

is changing. With 15-minute interval, continuous 

data being collected, sufficient data are available to 

measure the change over time, rather than estimating this change with regression models, such as 

LOADEST and WRTDS. Furthermore, shifts in the 

nitrate C-Q relationships might be an early indicator of water-quality change because the increased 

management of the watershed is likely to fundamentally change nutrient transport dynamics without actually changing the hydrology; hence, the C-Q relationships might evolve over time with enhanced nutrient 

management.

• Ratios of various chemical tracers might produce 

indicators of change at an early stage because concentrations of some constituents are controlled largely by 

dissolution and reaction chemistry in aquifers, whereas 

others are more strongly related to land-use practices 

on the land surface. For example, implementation of 

sufficient nutrient management practices could drive 

changes in the ratios of nitrate to calcium and other 

constituents. With additional years of data collection, 

these patterns in constituent ratios will be evaluated.

• Direct monitoring of the headwater springs that 

contribute significant flows and elevated nitrogen 

concentrations represent an effective way to monitor 

changes in the chemistry and nutrient transport from 

the important agricultural sources to Smith Creek.
64 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Upper Chester River Watershed 

Water-Quality Characterization

Lead Author: Judith M. Denver

The Upper Chester River watershed includes the 

watershed area around several small nontidal tributaries that 

drain into the tidal portion of the Chester River (fig. 40). The 

study area was selected in 2010 as a Showcase Watershed by 

the USDA NRCS with the intent of targeting the area with 

intensive installation of BMPs aimed at improving water 

quality throughout the watershed. The USGS was charged 

with monitoring the long-term effects of this concentrated 

BMP implementation. Current (2015) monitoring includes 

an intensively gaged monitoring site on Chesterville Branch 

and 17 additional sites on Chesterville Branch and several 

other tributaries that were monitored during synoptic surveys 

of water quality (fig. 40). These data are supported by 

previous data collection and analyses from intensive studies 

of groundwater and surface-water quality, particularly by the 

USGS National Water-Quality Assessment (NAWQA), on the 

northern side of the Chester River (Böhlke and Denver, 1995; 

Shedlock and others, 1999; Ator and others, 2004).

Streamflow and Water-Quality Characterization 

at the Intensive Monitoring Site

Chesterville Branch, which drains approximately 

6.12 mi2, is the largest nontidal subwatershed of the Upper 

Chester River watershed (fig. 40). Land use is row-crop 

production in the southern part of the watershed; in the 

northern part, a large plant nursery covers about 40 percent of 

the total watershed area. This site has been monitored for flow 

and water quality intermittently since 1990, when samples 

were collected in 1990 and 1991 to support a NAWQA 

groundwater flow-path study and for routine monitoring 

and storm sampling from 1996 through 2001 as part of 

the Chesapeake Bay nontidal network. Stream gaging and 

sampling began again in July 2011 as part of the current study, 

with sampling during base-flow and storm conditions (fig. 41). 

Most of the studies of nutrient and sediment transport in the 

Bay area have been conducted in much larger watersheds, 

which makes studying a stream of this size somewhat unusual. 

Hydrogeologic setting and land use in the Chesterville Branch 

subwatershed are similar to those in large areas of the Eastern 

Shore that have not been monitored intensively. Results from 

the study in this watershed can be extended to understanding 

processes affecting nutrient and sediment transport in other 

similar small Coastal Plain watersheds in this area.

Characterization of the Physical Hydrology

Based on the base-flow index, about three-quarters of 

the flow to Chesterville Branch is from groundwater that 

continually discharges through the streambed, as is typical of 

most of the Coastal Plain (table 16). Base flow ranged from 

about 10 ft3/s during winter months when the water table is 

generally highest and groundwater discharge is greatest to as 

low as 3 ft3/s during dry periods, which generally occur in the 

late summer (fig. 41). Runoff is rapid during storms, and the 

stream returns to base-flow conditions within a relatively short 

time period because little water is retained in the floodplain of 

this small watershed. During the study period from July 2011 

through September 2013, streamflow was below normal (as 

represented by median flow) at the beginning, increased to 

be above normal in the late summer and fall after a period 

of intensive storms, returned to normal in January 2012, 

and remained normal throughout the remainder of the study 

period (fig. 41).
Upper Chester River Watershed Water-Quality Characterization 65

Figure 40. Monitoring stations, select subwatershed boundaries, and the stream network within the 

Upper Chester watershed, Maryland.

KENT COUNTY

QUEEN ANNE’S COUNTY

76°00' 75°57' 75°54'

39°18'

39°15'

39°12'

19 18

Outlet of flow

0 1 2 3 MILES

0 1 2 3 KILOMETERS

Watershed

Upper Chester River

 (area=36.54 mi2

)

Upper Chester River

Tributary to Upper Chester River Foreman Branch

Pearl Creek

Chesterville Branch

VIRGINIA

DELAWARE

MARYLAND NEW

JERSEY

Study

area

Atlantic Ocean

Chesapeake Bay

15 14

16

21

20

13

10 12

8 9

26

27

3

2

6

17

20

EXPLANATION

Monitoring station and identifier

USGS streamgage 01493112

21

Chesterville

Foreman Branch

Chester

River

Branch

Pearl Creek

Streams from U.S. Geological Survey National Hydrography Dataset

Drainage area from U.S. Department of Agriculture

 Natural Resources Conservation Service 

 unpub. data for HUC 020600020605, 2005

Roads are from U.S. Census Bureau TIGER/Line shapefiles (2015) 

North American Datum of 1983

Synoptic sampling location

 and identifier

Delmarva Peninsula
66 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed Streamflow, in cubic feet per second

2,000

1,000

100

10

1

 WY 2011 WY 2012 WY 2013

Minimum daily flow to 5th percentile

5th percentile to 10th percentile

10th percentile to 25th percentile

25th percentile to 75th percentile

75th percentile to 90th percentile

90th percentile to 95th percentile

95th percentile to maximum daily flow

Median flow

EXPLANATION

Monthly flow statistics—Historical daily 

 flows from WY 1991 to WY 2013

Streamflow, in cubic feet 

 per second

WY Water year

Sampling event

Routine sampling

Storm sampling

Synoptic sampling

J A S O N D J F M A M J J A S O N D J F M A M J J A S

Figure 41. Chesterville Branch streamgage and quantiles that represent historical monthly flow data with the timing of various 

sampling events from July 2011 through September 2013 within the Upper Chester River watershed. 

Table 16. Streamflow partitioning for streamgage station 01493112 in the Upper Chester River watershed.

[mi2, square mile; ft3/s, cubic foot per second; in/yr, inch per year; %, percent; data were computed using the software package PART (Rutledge, 1998)]

Watershed Drainage area 

(mi2

) Period of record

Mean 

streamflow Mean base flow Base-flow 

index (%)

ft3

/s in/yr ft3/s in/yr

Upper Chester 

River

6.12 1997–2001 7.71 17.12 5.79 12.84 75.0

2012–2013 8.64 19.17 6.23 13.83 72.1
Upper Chester River Watershed Water-Quality Characterization 67

Characterization of Continuous 

Water-Quality Data

Continuous data collected at Chesterville Branch 

illustrate seasonal and diel patterns in water chemistry, as well 

as the effects of variations in streamflow (figs. 42 and 43). 

Water temperature ranged from 0 °C to about 25 °C over the 

course of the study period (fig. 42). Concentrations of DO 

were highest in the cooler months and stayed above about 

6 mg/L during base flow, reflecting a consistent input of DO 

in discharging groundwater or reaeration processes (fig. 42). 

The lowest DO concentrations occurred during the warmer 

months when biologic activity and the potential for DO to be 

affected by plant and microbial respiration was greatest. Also 

during the warmer months, streamflow is generally lowest 

on an annual basis, and a larger proportion of discharging 

groundwater is older, calcareous water that has relatively little 

DO (Böhlke and Denver, 1995). The amplitude of diel DO 

fluctuations reached about 5 mg/L in the spring when biologic 

activity increased, but was generally about 2 mg/L over the 

rest of the year (fig. 42).

Specific conductance, pH, nitrate concentrations, 

and turbidity were strongly related to flow in Chesterville 

Branch (figs. 44 through 46). Nitrate concentrations and 

specific conductance decrease with increases in flow because 

runoff has much lower concentrations of constituents than 

the groundwater that makes up most of the stream base 

flow. As flow decreases, nitrate concentrations and specific 

conductance begin to increase until returning to base-flow 

levels. Previous studies in this area have shown that chemicals 

from agricultural sources, including nitrate, calcium, and

magnesium, dominate water chemistry and contribute to the 

higher specific conductance in groundwater and base flow 

than in runoff (Böhlke and Denver, 1995; Shedlock, and 

others, 1999; Denver and others, 2014). Seasonal variations 

in specific conductance are minor during base flow, with 

the majority of change occurring during stormflow dilution 

(fig. 44). Stream water is circumneutral to slightly acidic, and 

pH decreases with runoff during storms. The pH also is related 

to seasonal changes in DO diel cycles, with the highest pH 

readings occurring during periods when the diel fluctuation of 

DO is greatest in the spring (figs. 42B, 42C, 44, and 45).

Concentrations of nitrate ranged from about 8 mg/L as 

N to more than 10 mg/L as N in base flow during the study 

period (fig. 45). The median nitrate concentrations were higher 

during cooler months when base flow was greatest, indicating 

the potential for a larger proportion of young groundwater 

containing nitrate to enter the stream and also a lower potential 

for uptake of nitrate by aquatic plants and algae (fig. 45). 

Groundwater discharge to Chesterville Branch includes the 

nitrate-rich water from the unconfined surficial aquifer and 

older water from near the base of the surficial aquifer which is 

anoxic, had higher bicarbonate than water from the unconfined 

surficial aquifer, and did not contain nitrate (Böhlke and 

Denver, 1995). The contribution of older water from the 

deeper aquifer is greatest during the dry season when the water 

table is low (Ator and others, 2004).

Turbidity data did not have much seasonal difference 

during the study period with continuous data collection. The 

study period was limited, however, and does not represent a 

large number of storms (fig. 45).
Figure 42. Continuous (A) water temperature, (B) dissolved oxygen, and (C) pH data with the hydrograph 

from the Chesterville Branch streamgage, 2012–2013.

EX

Water tempera

2.70 to 6.56.51 to 8.08.01 to 9.6

9.61 to 11

11.71 to 114.71 to 1

Streamflo

Dissolved oxyg

4.40 to 7.57.51 to 7.97.91 to 8.3

8.31 to 8.7

8.71 to 9.19.11 to 9.7

Streamflo

pH, in 

6.00 to 6.36.31 to 6.46.41 to 6.5

6.51 to 6.6

6.61 to 6.76.71 to 6.8

Streamflo

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

Water year 2012 Water year 2013

1011001,0002,0001011001,0002,0001011001,0002,000

Streamflow, incubic feet per second

ABC

June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct.
Figure 43. Continuous (A) specific conductance, (B) nitrate, and (C) turbidity data with the hydrograph from the Chesterville Branch streamgage, 2012– 2013.

EXPLA

Specific conducta per centimeter a

37.0 to 179.0179.1 to 188.0188.1 to 193.0

193.1 to 196.0

196.1 to 198.0198.1 to 199.0

Streamflow, 

Nitrate plus nitrite per liter as nitro

0.64 to 7.137.14 to 7.917.92 to 8.24

8.25 to 8.42

8.43 to 8.588.59 to 8.74

Streamflow, 

Turbidity, in forma

0 to 2.402.41 to 3.203.21 to 4.20

4.21 to 5.20

5.21 to 6.506.51 to 8.10

Streamflow, 

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

1011001,0002,000 A

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

101001,0002,000

B

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

1011001,0002,000

C

Water year 2012 Water year 2013

June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct.

Streamflow, incubic feet per second
70 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 44. (A) Water temperature, (B) specific conductance, and (C) pH data 

from continuous monitoring and discrete storm and routine samples for warm 

(April to September) and cool (October to March) seasons, Chesterville Branch 

streamgage, 2012–2013. 

0

5

10

15

20

25

30

Water temperature, in degrees Celsius

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

0

50

100

150

200

250

pH, in standard units

Specific conductance, in microsiemens 

per centimeter at 25 degrees Celsius

1 10 100 1,000 1 10 100 1,000 3,000

Streamflow, in cubic feet per second

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

5.8

6.0

6.2

6.4

6.6

6.8

7.0

7.2

7.4

A

B

C
Upper Chester River Watershed Water-Quality Characterization 71

0

5

10

15

Dissolved oxygen, in milligrams per liter

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

0.01

0.1

1

10

100

1,000

Turbidity, in formazin nephelometric units Nitrate plus nitrite, in milligrams per liter as nitrogen

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

0

2

4

6

8

10

12

A

B

C

1 10 100 1,000 1 10 100 1,000 3,000

Streamflow, in cubic feet per second

Figure 45. (A) Dissolved oxygen, (B) turbidity, and (C) nitrate data from continuous 

monitoring and discrete storm and routine samples for warm (April to September) and 

cool (October to March) seasons, Chesterville Branch streamgage, 2012–2013. 
72 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Characterization of Discrete Water-Quality Data

Differences in water quality related to flow at Chesterville 

Branch are further supported by correlations between discrete 

water-quality data and streamflow (table 17). Constituents 

transported primarily in runoff or associated with near-stream 

processes, such as the degradation of organic matter and development of reducing conditions in floodplain areas and ponds, 

are positively correlated with flow. Constituents primarily 

associated with groundwater input are negatively correlated 

with flow. These relations, shown graphically on figure 47 

and statistically in table 17, are important to understanding 

nitrogen and phosphorus transport in Chesterville Branch. As 

flow increases, concentrations of ammonium, orthophosphate, 

and TP increase, but concentrations of nitrate and TN decrease. 

Concentrations of TP are considerably higher than those of 

orthophosphate, the dissolved form of phosphorus, because 

much of the phosphorus measured is in the particulate phase 

adsorbed onto sediment particles. Other constituents associated with overland flow include potassium, sulfate, iron, and 

manganese, which are likely contributed from near-stream 

areas with higher organic-matter content, and suspended 

sediment. In contrast, most of the TN in the stream is in the 

form of nitrate, which is dissolved in groundwater. Calcium 

and magnesium applied as lime commonly are correlated with 

nitrate in groundwater affected by agriculture on the Delmarva 

Peninsula (Shedlock and others, 1999) and also decrease as 

flow increases. Silica, dissolved in groundwater from aquifer 

sediments, also decreases as stream water is diluted by runoff, 

as do bicarbonate and pH as the stream water is diluted by the 

more acidic precipitation. Non-significant C-Q relationships are 

provided in the appendix as supplemental material (fig. 1–3).

Seasonal differences also are evident in discrete samples 

collected during base flow (table 17). Many of these differences are related to seasonal changes in biologic activity that 

affects the uptake and release of chemical constituents in 

the stream and near-stream areas. Processes associated with 

redox reactions are evident in the trends in concentrations of 

bicarbonate and sulfate during base-flow conditions (represented by routine samples) over time (table 17; fig. 48). In the 

spring, sulfate is high and bicarbonate is low, with the reverse 

occurring in autumn months. The pattern is likely related to 

processes occurring in highly eutrophic ponds with heavy 

algal growth and other aquatic vegetation that are located in 

the watershed upgradient of the sampling site—the largest of 

which is behind a dam in the stream channel. Similar patterns 

were seen in a study of eutrophic ponds by Mann (1958), who 

found that sulfate derived from sulfides produced by organic 

decay in anaerobic pond bottom sediments in winter and early 

spring was reduced when the ponds became anoxic in the 

later spring and summer. Lower concentrations of bicarbonate 

in the cool season could be partly due to removal of carbon 

dioxide from the water during photosynthesis and also to the 

formation of sulfuric acid when the sulfide oxidizes in water 

(Mann, 1958). These processes could also affect concentrations of manganese which is redox sensitive and more mobile 

in more acidic conditions (Millaleo and others, 2010) and 

shows a similar seasonal pattern as sulfate (table 17). The 

effects of anoxic conditions during the warmer months 

are also indicated by higher concentrations of dissolved 

orthophosphate released from exchange sites on streambed or 

near-stream sediments (table 17). Despite the effect of these 

ponds on redox-sensitive species, nitrate concentrations do 

not appear to respond to these processes, possibly because 

Nitrate plus nitrite, in milligrams per liter as nitrogen

0

10 11 12 13

July 2013

14 15 16

1

2

3

4

5

6

7

8

9

Streamflo

w, in cubic feet per second

0

10

20

30

40

50

060

70

80

90

100

110

120

130

Specific conductance, in microsiemens per centimeter

at 25 degrees Celsius

0

50

100

150

200

250

Nitrate-N Streamflow

Specific

conductance

Figure 46. Chesterville Branch hydrograph, continuous nitrate concentrations, and 

continuous specific conductance data, July 10 through July 16, 2013.
Upper Chester River Watershed Water-Quality Characterization 73

Table 17. Summary of hydrologic and seasonal correlations for discrete water-quality constituents collected at the Chesterville 

Branch streamgage.

[Results in bold are significant at α=0.05; n, number of observations; APR, April; SEPT, September; OCT, October; MAR, March; <, less than]

Parameter

Streamflow Season3

n rho1 p-value1 nAPR-SEPT nOCT-MAR p-value2 Season with 

higher values

Turbidity 33 0.75 <0.001 11 8 0.741

Suspended-sediment 

concentration

41 0.73 <0.001 14 11 0.545

Total phosphorus 45 0.72 <0.001 16 12 0.417

Iron 32 0.62 <0.001 11 10 0.504

Ammonium 45 0.52 <0.001 16 12 0.659

Manganese 32 0.51 0.003 11 10 0.002 OCT-MAR

Orthophosphate 45 0.50 <0.001 16 12 0.031 APR-SEPT

Potassium 32 0.47 0.007 11 10 0.860

Sulfate 32 0.46 0.008 11 10 <0.001 OCT-MAR

Delta O-18 of nitrate, 

per mil

35 0.20 0.257 11 10 0.860

Delta N-15 of nitrate, 

per mil

35 0.13 0.459 11 10 0.048 APR-SEPT

Sodium/chloride 32 0.07 0.697 11 10 0.038 APR-SEPT

Calcium 32 –0.81 <0.001 11 10 0.916

Calcium/magnesium 32 –0.78 <0.001 11 10 0.170

Specific conductance 47 –0.69 <0.001 18 13 0.388

Nitrate-N 45 –0.62 <0.001 17 12 0.020 OCT-MAR

Silica 32 –0.61 <0.001 11 10 0.699

pH 47 –0.60 <0.001 18 13 0.172

Total nitrogen 44 –0.56 <0.001 16 11 0.018 OCT-MAR

Bicarbonate 29 –0.54 0.003 8 11 <0.001 APR-SEPT

Sodium 32 –0.36 0.042 11 10 0.550 APR-SEPT

Chloride 32 –0.24 0.191 11 10 0.193

Dissolved oxygen 47 –0.21 0.162 18 13 0.001 OCT-MAR

Water temperature 47 –0.07 –0.623 18 13 <0.001 APR-SEPT

Magnesium 32 –0.05 0.778 11 10 0.008 OCT-MAR

1

rho is Spearman’s rho; p-value is from Spearman’s test for correlation.

2

p-value is from the Wilcoxon test of rank scores.

3

Only routine samples included in the Wilcoxon test of rank scores.
74 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 47. Concentration-discharge data for discrete samples from the Chesterville Branch streamgage 

by warm and cool seasons, 2011–2013.

4

8

12

16

20

24

0

Calcium,

in milligrams per liter

2.5

3.0

3.5

4.0

4.5

2.0

Ratio of calcium to magnesium

2

4

6

8

10

12

0

Sodium,

in milligrams per liter

1

2

3

4

5

6

7

0

Potassium,

in milligrams per liter

5

10

15

20

0

Nitrate plus nitrite, 

in milligrams per liter as nitrogen

5

10

15

20

0

Total nitrogen,

in milligrams per liter

0.05

0.10

0.15

0.20

0.25

0

Ammonium,

in milligrams per liter as nitrogen

0.05

0.10

0.15

0.20

0.25

0.30

0

Orthophosphate,

in milligrams per liter as phosphorus

0.1

1

10

0.01

Total phosphorus,

in milligrams per liter

10

100

1,000

Suspended-sediment concentration,

1

in milligrams per liter

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

10 100 1,000 1 10 100 1,000
Upper Chester River Watershed Water-Quality Characterization 75

EXPLANATION

Season

April to September

October to March

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)

2

4

6

8

10

0

Sulfate,

in milligrams per liter

10

20

30

40

50

0

Bicarbonate,

in milligrams per liter

Streamflow, in cubic feet per second

1 10

Streamflow, in cubic feet per second

1 10

2

4

6

8

10

12

14

0

Silica,

in milligrams per liter as SiO2

100

200

300

400

500

600

700

0

Iron,

in micrograms per liter

100

200

300

400

0

Manganese,

in micrograms per liter

6.2

6.4

6.6

6.8

7.0

7.2

7.4

6.0

pH,

in standard units

50

100

150

200

250

0

Specific conductance, 

in microsiemens per centimeter

10

100

1,000

0

Turbidity,

in formazin nephelometric units

100 1,000 100 1,000

Figure 47. Concentration-discharge data for discrete samples from the Chesterville Branch streamgage 

by warm and cool seasons, 2011–2013.—Continued
76 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 48. Concentrations of various constituents in discrete samples from the Chesterville Branch streamgage, 2011–2013.

20

15

10

5

0

Nitrate plus nitrite,

in milligrams per liter

as nitrogen

Sodium,

in milligrams per liter

Bicarbonate, 

in milligrams per liter

J J S

WY 2011 WY 2012 WY 2013 WY 2011 WY 2012 WY 2013

A O N D J F M A M J J A S O N D J F M A M J J A S O J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

20

15

10

5

0

Total nitrogen,

in milligrams per liter

7.0

6.5

6.5

5.5

5.0

4.5

4.0

Delta N-15 of nitrate,

per mil

0.3

0.2

0.1

0

Orthophosphate,

in milligrams per liter

as phosphorus

12

10

8

6

4

2

0

7

6

5

4

3

2

1

0

Magnesium,

in milligrams per liter

Sodium to chloride ratio

0.46

0.44

0.42

0.40

0.38

0.36

0.34

10

8

6

4

2

0

Sulfate,

in milligrams per liter

50

40

30

20

10

0

400

300

200

100

0

Manganese, 

in micrograms per liter

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year
Upper Chester River Watershed Water-Quality Characterization 77

of the high concentrations of nitrate in the stream. Other 

ions contributed from groundwater that are not affected by 

redox reactions in ponds and the near-stream area, including 

silica, calcium, sodium, potassium, and chloride, do not show 

significant seasonal fluctuations in base flow because groundwater containing these constituents is continuously discharged 

throughout the year (fig. 1–4).

Nutrient concentrations and nitrate isotopes also show 

seasonal trends, though variations are relatively small 

compared to trends in the other watersheds in this study 

(table 17 and fig. 48). Overall, δ15N values were consistent 

with artificial fertilizer as a major source of excess nitrogen. 

Some degree of uptake and (or) denitrification is indicated 

during the warmer months by slight increases in δ15N 

(figs. 48 and 49) and increases in δ18O of nitrate (fig. 49). The 

high concentrations of nitrate in base flow at Chesterville 

Branch likely dampen observation of the effects of near-stream 

or in-stream denitrification and uptake at this site, compared to 

what might be seen at a site with low nitrate inputs. High-flow 

events that caused substantial dilution of nitrate and other 

major constituents in the Chesterville Branch caused little or 

no change in values of δ15N or δ18O of nitrate (table 17). This 

result is consistent with the relatively low concentration of 

atmospheric nitrate in precipitation compared to the much 

higher concentration of agricultural nitrate in the base flow. 

Some of the change in nitrate concentrations also may be 

EXPLANATION

Routine sample

Storm sample

Dilution

Addition of atmospheric nitrate in runoff

Nitrate reduction (denitrification, assimilation)

Different nitrification N sources

Nitrate plus nitrite,

in milligrams per liter as nitrogen

3

0

4

1

2

5

0

8

10

12

2

4

6

Hydrologic conditions

Schematic changes potentially associated with

*Typical range of error, 2-sigma:

 delta N-15: 0.5 per mil,

 delta O-18: 1.0 per mil Delta N-15 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

0 2 4 6 8 10

1 2 3 4 5 6

Figure 49. Variability in nitrate isotopes in discrete samples from the Chesterville Branch streamgage and 

potential causes for variability, 2011–2013. 
78 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 18. Loading of water-quality constituents collected at the Chesterville Branch 

streamgage on principal component axes 1 and 2.

[%, percent; PC1, first principal components axis; PC2, second principal components axis; loadings with 

absolute value greater than 0.70 are shown in bold; those with absolute value less than |0.40| are omitted]

Constituent PC1 

(44.2%)1

PC2 

(17.5%)1

pH 0.51

Specific conductance 0.84

Dissolved oxygen 0.72

Bicarbonate 0.41 –0.59

Sulfate 0.75

Chloride 0.48

Calcium 0.82

Magnesium 0.69

Sodium 0.61

Potassium –0.83

Nitrate-N 0.89

Ammonium –0.43 0.42

Total nitrogen 0.83

Orthophosphate –0.80 –0.40

Total phosphorus –0.90

Iron –0.71

Manganese –0.51 0.60

Silica 0.77

1

Percent of overall variance explained.

related to proportionally less young groundwater containing 

higher concentrations of nitrate and magnesium from agricultural sources discharging to Chesterville Branch in the warmer 

months when the water table is typically lowest.

Cluster and PCA of geochemical parameters also clearly 

illustrate the strong differences in water chemistry related 

to flow and season in the discrete samples from Chesterville 

Branch (table 18; figs. 50 and 51). The two clusters containing 

multiple samples are related to flow regime (fig. 50): base 

flow (cluster 1) is related to groundwater inputs, and high 

flow (cluster 2) is related to rapid overland runoff. A third 

cluster comprises one very dilute high-flow sample (fig. 50). 

Further analysis of these data using PCA developed from the 

same discrete dataset with the plotted points colored by their 

assigned cluster (fig. 51) shows similar patterns as seen in the 

Spearman’s test and the cluster analysis. The first principal 

components axis relates to flow and shows the strong relationship of chemicals transported in groundwater (44.2 percent 

of the variance explained; during base flow, +) and high 

flows (in runoff, –). The second principal components axis 

appears to relate to seasonal variability in base-flow samples. 

These results are similar to those seen in a PCA based on 

earlier data collected at this site, where 47 percent of the 

variance in the data was explained by flow and 15 percent was 

attributed to seasonal variations in water chemistry (Ator and 

others, 2004).
Upper Chester River Watershed Water-Quality Characterization 79

Figure 50. Discrete samples from the Chesterville Branch streamgage with a heat map of constituent 

values and the breakpoint used to produce three cluster groups, 2011–2013.

Total nitrogen

Nitrate

Iron

Total phosphorus

Ammonium

Silica

Orthophosphate

Potassium

Sodium

Chloride

Sulfate

Magnesium

Calcium

Bicarbonate

Specific conductance

Manganese

Dissolved oxygen

pH

Dendrogram break point

Cluster group

EXPLANATION

Relative constituent value

Low High

Group 1

(Base-flow samples)

Group 3

(High-flow sample 

with dilute

water chemistry)

Group 2

(High-flow samples)
80 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 51. First two principal component axis scores for discrete samples from the Chesterville 

Branch streamgage along with time and streamflow, 2011–2013.

*Principal component 1 explains 

 44.2 percent of the variability in the data

**Principal component 2 explains 

 17.5 percent of the variability in the data

EXPLANATION

Cluster analysis group

Group 1—Base-flow sample

Group 2—High-flow sample

Group 3—High-flow sample 

 

 with dilute water chemistry 

1

4

2

0

–2

–4

–6

2

1

0

–1

–2

–3

–4

Principal component 1* Principal component 2**

July 1

2011

Jan.

2012

July

2012

July

2013

Jan.

2013

10 100 1,000

Date Streamflow, in cubic feet per second
Upper Chester River Watershed Water-Quality Characterization 81

Table 19. Details of the top-ranked suspended sediment, total nitrogen, and total phosphorus concentration estimation models 

for the Chesterville Branch streamgage with an alternative model presented for each constituent.

[ln, natural logarithm; top-ranked models are listed in bold; <, less than]

Explanatory variables Model 

coefficient p-value Variance inflation factor

Number of 

observations

Adjusted 

coefficient of 

determination

Mallows’ Cp

Suspended-sediment concentration

Intercept 2.192 0.005

30 0.951 7.08 ln(Flow) 0.315 0.007 2.42

Dissolved oxygen –0.100 0.005 1.11

ln(Turbidity) 0.790 <0.001 2.47

Intercept 0.132 0.506

ln(Flow) 0.335 0.011 2.41 30 0.936 15.37

ln(Turbidity) 0.823 <0.001 2.41

Total nitrogen

Intercept 0.683 <0.001

ln(Flow) 0.046 0.058 3.05 31 0.973 4.11

ln(Nitrate-N) 0.723 <0.001 3.05

Total phosphorus

Intercept –2.455 <0.001

32 0.959 2.09 ln(Flow) 0.316 <0.001 2.41

Dissolved oxygen –0.145 <0.001 1.11

ln(Turbidity) 0.592 <0.001 2.46

Intercept –5.013 <0.001

ln(Flow) 0.344 0.007 2.40 32 0.907 4.48

ln(Turbidity) 0.639 <0.001 2.40

Surrogate Models for the Computation of 

Sediment and Nutrient Loads

The discrete and continuous water-quality data were 

subsequently analyzed to explore the development of 

surrogate regression models to predict SSC and TN and TP 

concentrations. The development of these surrogate models 

is critical to the computation of constituent loads and an 

improved understanding of the watershed function.

Model Development

Predictions of SSC, TN, and TP were explored by using 

surrogate regression models of the discrete and continuous 

water-quality data. For each constituent, the strongest predictive model (as indicated by lowest Mallows’ Cp) and model 

diagnostics are presented (table 19; figs. 52 through 54).

For SSC and TP, alternative model forms are presented that 

contain only flow and turbidity as explanatory variables. Flow 

and turbidity were significant in both suspended-sediment 

models, as would be expected, but the addition of DO, which 

had a negative relation to flow and turbidity, improved the 

predictions in the best model. Continuous data from this site 

indicate that DO decreases during high-flow events when 

sediment is transported (fig. 45). Both of the phosphorus 

models included flow and turbidity, and as with suspended 

sediment, the addition of DO improved the model. In this 

case, DO had a greater significance (lower p-value) than in the 

SSC model, which could be related to the potential for more 

transport of phosphorus from parts of the stream channel and 

ponds with reducing conditions during high-flow events. For 

the TN model, nitrate was the most important predictor, which 

is expected because nitrate makes up approximately 90 percent 

of the total nitrogen concentration during base flow.
82 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 52. Observed and estimated values from the (A) best and (B) alternative suspended-sediment surrogate models 

for the Chesterville Branch streamgage and the residual plot of model predictions, 2012–2013.

1,000

100

10

1

1

Observed suspended sediment,

in milligrams per liter

Estimated suspended sediment, in milligrams per liter

Streamflow, in cubic feet per second

1 10 100 1,000 1 10 100

1 10 100 1 10 100

1,000

EXPLANATION

Observed concentration

Estimated concentration

A B

EXPLANATION

Observed concentration

Estimated concentration

1.0

0.8

0.6

0.4

0.2

0

–0.2

–0.4

–0.6

–0.8

–1.0

Residual error

Estimated suspended sediment, in milligrams per liter

Estimated suspended sediment, in milligrams per liter

Streamflow, in cubic feet per second

Estimated suspended sediment, in milligrams per liter

1 10 100 1,000 1 10 100 1,000

1,000

100

Suspended sediment, 

10

in milligrams per liter
Upper Chester River Watershed Water-Quality Characterization 83

Figure 53. Observed and estimated values from the (A) best and (B) alternative total phosphorus surrogate models for 

the Chesterville Branch streamgage and the residual plot of model predictions, 2012–2013. 

1

0.1

0.01

1

Observed total phosphorus,

in milligrams per liter

Estimated total phosphorus, in milligrams per liter

Streamflow, in cubic feet per second

0.01 0.1 1 0.01 0.1 1

1 10 100 1 10 100

A B

1.0

0.8

0.6

0.4

0.2

0

–0.2

–0.4

–0.6

–0.8

–1.0

Residual error

Estimated total phosphorus, in milligrams per liter

Estimated total phosphorus, in milligrams per liter

Streamflow, in cubic feet per second

Estimated total phosphorus, in milligrams per liter

0.01 0.1 1 0.01 0.1 1

1,000

100

10

 Total phosphorus,

in milligrams per liter

EXPLANATION

Observed concentration

Estimated concentration

EXPLANATION

Observed concentration

Estimated concentration
84 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

12

Observed total nitrogen,

in milligrams per liter

Estimated total nitrogen, in milligrams per liter

Streamflow, in cubic feet per second

Residual error

Estimated total nitrogen, in milligrams per liter

Total nitrogen,

in milligrams per liter

EXPLANATION

Observed concentration

Estimated concentration

10

8

6

4

2

0

12

10

8

6

4

2

0

0 2 4 6 8 10 12

1 10 100 1,000

0 2 4 6 8 10 12

0.20

0.15

0.10

0.05

0

–0.05

–0.10

–0.15

–0.20

Figure 54. Observed and estimated values from the 

best total nitrogen surrogate model for the Chesterville 

Branch streamgage and the residual plot of model 

predictions, 2012–2013. 

Interpretation of the Computed Loads

After developing surrogate models for estimating 

concentrations of suspended sediment, TN, and TP, water-year 

loads of these constituents were computed by using the record 

of continuous flow (from the streamgage) and predicted 

water quality (from the regression equations presented 

above). Methods for the replacement of missing records were 

described in the Development of Surrogate Water-Quality 

Models and Computation of Loads section of this report. 

The USGS software program LOADEST was used for these 

computations because the computed load could be calculated 

with confidence intervals around the predicted load, which 

was needed for comparisons between water years and sites.

The annual sediment load for the 2013 water year was 

estimated to be about 2,600 tons (fig. 55). The greatest loads 

were episodic and occurred during the two highest flows, 

particularly during one major storm early in the 2013 water 

year that contributed more than 65 percent of the total load; 

otherwise, the sediment load was minor during the smaller 

high-flow events (fig. 56).

About 13,000 lb of phosphorus entered Chesterville Branch 

during the 2013 water year (fig. 57). The greatest loads of TP 

also occurred during major storm events, and the pattern in load 

accumulation was almost identical to that of SSC (fig. 58).

The estimated load of TN during the 2013 water year 

was about 140,000 lb (fig. 59). TN, most of which comes 

from input of groundwater containing nitrate, had a constant 

input during most of the year, rather than large episodic 

event-related inputs as seen with SSC and TP. The major 

storm that contributed the majority of SSC and TP loads 

to Chesterville Branch (about 70 percent and 65 percent, 

respectively) contributed about 20 percent of the TN load; 

otherwise, the total load increased relatively steadily during 

the remainder of the year as nitrate in groundwater continuously entered the stream (fig. 60). Compared to the other study 

watersheds, intermittent fouling of the nitrate sensor resulted 

in a greater frequency of missing data that were replaced for 

the computation of TN loads.
Upper Chester River Watershed Water-Quality Characterization 85

2013

Water year

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

0

50

100

150

200

250

300

350

Annual total streamflow, in millions of cubic feet

0

1,000

2,000

3,000

4,000

5,000

Annual suspended-sediment load, in tons

Figure 55. Suspended-sediment load and total annual 

streamflow at the Chesterville Branch streamgage for 

the 2013 water year.

1

Streamflow, 

in cubic feet per second

10

100

1,000

2,000

0

10

20

30

40

50

60

70

80

90

100

Percent of total suspended-sediment load

estimated during the study period

EXPLANATION

Suspended-sediment 

load

Streamflow 

A

B

0

300

600

900

1,200

2,700

1,500

1,800

2,100

2,400

Annual suspended-sediment load, 

in tons

0

320

280

Total annual streamflow, 

in millions of cubic feet

40

80

120

160

200

240

Water year 2012 Water year 2013

O N D J F M A M J J A S O N D J F M A M J J A S

Figure 56. Suspended-sediment load at the Chesterville Branch streamgage for water years 

2012 and 2013 along with (A) the percentage of load accumulated and the hydrograph during 

the period of study and (B) the accumulation of load and streamflow in each water year. 
86 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

0

5,000

10,000

15,000

20,000

Annual total phosphorus load, in pounds 

0

50

100

150

200

250

300

350

Annual total streamflo

w, in millions of cubic feet

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

2013

Water year

Figure 57. Total phosphorus load and total annual 

streamflow at the Chesterville Branch streamgage for 

the 2013 water year.

Water year 2012 Water year 2013

O N D J F M A M J J A S O N D J F M A M J J A S

1

Streamflo

w, 

in cubic feet per second

10

100

1,000

2,000

0

320

280

Total annual streamflo

w, 

in millions of cubic feet

40

80

120

160

200

240

EXPLANATION

Total phosphorus load

Streamflow 

A

B

0

10

20

30

40

50

60

70

80

90

100

Percent of total phosphorus load

estimated during the study period

0

14,000

12,000

in pounds

2,000

4,000

6,000

8,000

10,000

Annual total phosphorus load, 

Figure 58. (A) The percentage of total phosphorus load accumulated and the hydrograph 

during the period of study and (B) the accumulation of load and streamflow at the Chesterville 

Branch streamgage for water years 2012 and 2013.
Upper Chester River Watershed Water-Quality Characterization 87

2013

Water year

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Annual total nitrogen load, in pounds

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

0

50

100

150

200

250

300

350

Annual total streamflow, in millions of cubic feet

Figure 59. Total nitrogen load and annual total 

streamflow at the Chesterville Branch streamgage for 

the 2013 water year.

Annual total nitrogen load,

in pounds

0

140,000

20,000

40,000

60,000

80,000

100,000

120,000

160,000

B

EXPLANATION

Total nitrogen load

Streamflow 

A

Percent of total nitrogen load

estimated during the study period

0

10

20

30

40

50

60

70

80

90

100

Streamflow, 

in cubic feet per second

1

10

100

1,000

2,000

Total annual streamflow, 

in millions of cubic feet

0

350

50

100

150

200

250

300

Water year 2012 Water year 2013

O N D J F M A M J J A S O N D J F M A M J J A S

Figure 60. (A) The percentage of total nitrogen load accumulated and the hydrograph during the period 

of study and (B) the accumulation of load and streamflow at the Chesterville Branch streamgage for 

water years 2012 and 2013.
88 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Spatial Patterns in Water Quality

Water-quality samples were collected during base-flow 

conditions at 18 individual sites throughout the watershed in 

a synoptic manner (all sites were sampled within a short timeframe) 4 times during the study period (fig. 61). The sampled 

locations include multiple sites on the three main tributaries—

Chesterville Branch (6.12 mi2

), Foreman Branch (5.27 mi2), 

and Pearl Creek (3.08 mi2

)—and single sites on seven smaller 

tributaries. These sampling events were intended to provide an 

understanding of the spatial patterns in water quality and the 

processes controlling the transport of nutrients throughout the 

watershed. Sampling occurred during a range of hydrologic 

conditions from very low summer base flow (July 2011) to 

high winter base flow (December 2011) and intermediate 

spring base flow (May 2012 and April 2013; fig. 61).

Nitrogen concentrations in stream water varied across 

the watershed between sampling locations and between the 

different sampling events (fig. 62) and were affected by 

differences in the amounts of agricultural land use in each 

subwatershed and hydrogeologic conditions. In the absence of 

agricultural or other anthropogenic effects, the predominance 

of highly weathered sediments composed mostly of quartz and 

other silicate minerals that are resistant to weathering results 

in groundwater that generally has a specific conductance less 

than 100 μS/cm (Denver, 1989; Hamilton and others, 1993). 

In water affected by agriculture, calcium and magnesium from 

lime, nitrate and chloride from nitrogen-based fertilizer and 

manure, and potassium and chloride from potash fertilizer (KCl) 

are the predominant ions; specific conductance also is elevated 

above natural conditions. Water of this type is common across 

the Delmarva Peninsula in areas with agricultural land use 

(Shedlock and others, 1999; Debrewer and others, 2007).

As agricultural inputs increase, the amounts of 

agricultural chemicals in groundwater also increase. In the 

Upper Chester River watershed, this is illustrated in water 

chemistry from the three major subwatersheds—Chesterville 

Branch, Foreman Branch, and Pearl Creek (fig. 63). Base-flow 

water quality in Chesterville Branch and Foreman Branch, 

which have about 82 percent and 61 percent of agricultural 

land use, respectively, is affected by ions from agricultural 

fertilizers and manure. The soils and surficial aquifer sediments are well drained and oxic in most of the watershed, thus 

contributing to the formation of nitrate in the soil zone and 

the transport of nitrate and other soluble fertilizer components 

(including calcium, magnesium, potassium, and chloride) 

through groundwater to streams. More than 90 percent of the 

TN in Chesterville Branch and Foreman Branch is nitrate that 

originates in groundwater. During base-flow conditions in 

December 2011, concentrations of total nitrogen in the most 

downstream sites were about 8 mg/L in Chesterville Branch 

and about 5 mg/L in Foreman Branch (fig. 62). Chesterville 

Figure 61. Streamflow and drainage area for samples collected from the Upper 

Chester River watershed during each synoptic event. 

December 2011

0

2

4

6

8

10

12

May 2012

0

0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7

2

4

6

8

10

12

April 2013

July 2011

EXPLANATION

Chesterville Branch streamgage

 (site 20)

All other synoptic sampling locations 

Synoptic sampling location 

Most downstream sampling 

 location on:

Foreman Branch (site 6)

Pearl Creek (site 8)

Drainage area, in square miles

Streamflow, in cubic feet per second
Upper Chester River Watershed Water-Quality Characterization 89

14

15

21

16

17

20 13 19 18

10

12

8

9

26

27

3

2

6

76°00' 75°58' 75°56' 75°54' 75°52'

39°18'

39°16'

39°14'

39°12'

0 1 2 3 MILES

0 1 2 3 KILOMETERS

July 2011

3.4 ft3

/s

December 2011

10.8 ft3

/s

April 2013

6.5 ft3

/s

May 2012

7.1 ft3

/s

Total nitrogen, in

 milligrams per liter

20

Synoptic sampling station 

 and number

EXPLANATION

Synoptic sampling events at site 20

 (USGS streamgage 01493112)

Streamflow, in 

cubic feet per second (ft3

/s)

Sample not collected

0 to 2.60

2.61 to 5.20

5.21 to 7.80

7.81 to 10.40

10.41 to 13.00

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Department of Agriculture, Natural Resources Conservation Service

 unpub. data for HUC 020600020605, 2005. North American Datum of 1983

Figure 62. Monitoring locations, total nitrogen concentrations measured during each synoptic sampling event, 

and the stream network in the Upper Chester River watershed.

1 0 1

Cations Anions

Milliequivalents per liter

Major-ion

chemistry 

Land

use EXPLANATION

Chesterville Branch

Foreman Branch

Pearl Creek

Calcium

Magnesium

Sodium

Potassium

Bicarbonate

Sulfate

Chloride

Nitrate

Nitrate-N

Cultivated crop

Percent land use

Cations/anions

Specific conductance

8.3 mg/L

177 μS/cm

mg/L milligrams per liter

μS/cm microsiemens per centimeter 

 at 25 degrees Celsius

137 μS/cm

69 μS/cm

5.4 mg/L

1.2 mg/L

Developed

Forest

Pasture/hay

Wetland

Nitrate-N

Specific conductance

Nitrate-N

Specific conductance

Figure 63. Major-ion chemistry from the December 2011 synoptic sampling event and pie charts 

representing percentages of land use within the Chesterville Branch, Foreman Branch, and Pearl Creek 

subwatersheds.
90 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

14

15

21

16

17

20 13 19 18

10

12

8

9

26

27

3

2

6

76°00' 75°58' 75°56' 75°54' 75°52'

39°18'

39°16'

39°14'

39°12'

July 2011

3.4 ft3

/s

December 2011

10.8 ft3

/s

April 2013

6.5 ft3

/s

May 2012

7.1 ft3

/s

Total nitrogen, in

 pounds per year

20

Synoptic sampling station 

 and number

EXPLANATION

Synoptic sampling events at site 20

 (USGS streamgage 01493112)

Streamflow, in 

cubic feet per second (ft3

/s)

0 1 2 3 MILES

0 1 2 3 KILOMETERS

Sample not collected

0 to 106.0

106.1 to 212.0

212.1 to 318.0

318.1 to 424.0

424.1 to 530.0

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Department of Agriculture, Natural Resources Conservation Service

 unpub. data for HUC 020600020605, 2005. North American Datum of 1983 

Figure 64. Monitoring locations, total nitrogen load measured during each synoptic sampling event, and the 

stream network in the Upper Chester River watershed.

Branch, which has the highest concentrations of nitrate in the 

part of the Upper Chester River watershed studied, also has a 

higher percentage of agricultural land use, including a large 

nursery in its headwaters, than Foreman Branch where some 

of the headwaters areas are forested, which would contribute 

little nitrate to the stream (fig. 63). Nitrogen loadings are also 

greatest in these two streams compared to other small streams 

in the Upper Chester River watershed because of the combination of higher flows and higher concentrations of nitrogen 

(fig. 64). While Chesterville Branch consistently contributes 

the highest loads to the Chester River, loads from Foreman 

Branch are more variable over time, and were particularly low 

during July 2011, the driest period sampled.

The Pearl Creek subwatershed has generally poorly 

drained soils, a thinner surficial aquifer, and less agricultural 

land use than the Chesterville Branch and Foreman Branch 

subwatersheds. Nitrate concentrations are about 1 mg/L, and 

water is more dilute and similar to water in areas without 

anthropogenic effects (specific conductance less than 

100 μS/cm) than water in the other major branches where 

agriculture dominates (fig. 63). Nitrogen load is similarly 

low, with nitrate composing about 75 percent of the TN load. 

Although calcium and magnesium are still the major cations, 

sulfate and chloride are the major anions instead of nitrate 

as in the other streams (fig. 63). During low-flow conditions, 

such as observed in July 2011, Pearl Creek can go dry.

Concentrations of nitrogen in the smaller watersheds 

with single sampling sites were usually lower than those in 

Chesterville Branch and Foreman Branch. When combined 

with very low flows, concentrations of nitrogen had relatively 

insignificant loadings on an individual basis (figs. 62 and 64).

TP concentrations during base flow at the synoptic sites 

ranged from below detection (0.004 mg/L) to about 0.2 mg/L 

(fig. 65). Concentrations were greatest on the northern side 

of the Upper Chester River where there is more relief and a 

greater opportunity for phosphorus transport during runoff 

from agricultural fields. The highest concentrations were 

present in small tributaries, but because of low flows, loads 

were relatively small compared to loads in streams with higher 

flows, such as Chesterville Branch and Foreman Branch, even 

though concentrations in Foreman Branch also were relatively 

low (figs. 65 and 66). The synoptic samples were collected 

during base flow when phosphorus transport from the land 

surface was minimal. Higher concentrations under low baseflow conditions could indicate the release of phosphorus from 

anoxic sediments in the streambed or ponds.
Upper Chester River Watershed Water-Quality Characterization 91

14

15

21

16

17

20 13 19 18

10

12

8

9

26

27

3

2

6

76°00' 75°58' 75°56' 75°54' 75°52'

39°18'

39°16'

39°14'

39°12'

July 2011

3.4 ft3

/s

December 2011

10.8 ft3

/s

April 2013

6.5 ft3

/s

May 2012

7.1 ft3

/s

Total phosphorus, in

 milligrams per liter

20

Synoptic sampling station 

 and number

EXPLANATION

Synoptic sampling events at site 20

 (USGS streamgage 01493112)

Streamflow, in 

cubic feet per second (ft3

/s)

0 1 2 3 MILES

0 1 2 3 KILOMETERS

Sample not collected

0 to 0.040

0.041 to 0.080

0.081 to 0.120

0.121 to 0.160

0.161 to 0.200

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Department of Agriculture, Natural Resources Conservation Service

 unpub. data for HUC 020600020605, 2005. North American Datum of 1983

Figure 65. Monitoring locations, the total phosphorus concentration measured during each synoptic sampling 

event, and the stream network in the Upper Chester River watershed.

14

15

21

16

17

20 13 19 18

10

12

8

9

26

27

3

2

6

76°00' 75°58' 75°56' 75°54' 75°52'

39°18'

39°16'

39°14'

39°12'

July 2011

3.4 ft3

/s

December 2011

10.8 ft3

/s

April 2013

6.5 ft3

/s

May 2012

7.1 ft3

/s

Total phosphorus, in

 pounds per year

20

Synoptic sampling station 

 and number

EXPLANATION

Synoptic sampling events at site 20

 (USGS streamgage 01493112)

Streamflow, in 

cubic feet per second (ft3

/s)

0 1 2 3 MILES

0 1 2 3 KILOMETERS

Sample not collected

0 to 0.66

0.67 to 1.32

1.33 to 1.98

1.99 to 2.64

2.65 to 3.30

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Department of Agriculture, Natural Resources Conservation Service

 unpub. data for HUC 020600020605, 2005. North American Datum of 1983

Figure 66. Monitoring locations, the total phosphorus load measured during each synoptic sampling event, and 

the stream network in the Upper Chester River watershed.
92 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 67. Samples collected from each synoptic event within the Upper Chester River watershed 

with a heat map of constituent values and the breakpoint used to produce four cluster groups.

Total nitrogen

Nitrate

Total phosphorus

Iron

Ammonium

Orthophosphate

Potassium

Sodium

Chloride

Sulfate

Magnesium

Calcium

Bicarbonate

Specific conductance

Manganese

pH

Dendrogram break point

Group 1

(Oxic, high agricultural type)

Group 2

(Northern, high

total phosphorus type)

Group 3

(High-NaCl type)

Group 4

(Anoxic, low agricultural type)

Cluster group

EXPLANATION

Relative constituent value

Low High

Cluster analysis of the data from the synoptic sampling 

events was used to group the small watersheds into four 

groups with similar water-quality characteristics and to 

explore spatial patterns in nitrate and phosphorus across the 

landscape in the Upper Chester River watershed (fig. 67). This 

analysis brings out several features of the watershed that are 

not evident by looking only at nutrient concentrations and also 

helps to further explain the differences among Chesterville 

Branch, Foreman Branch, and Pearl Creek.

Cluster 1 includes the majority of samples from 

Chesterville Branch and from Foreman Branch (fig. 68). This 

cluster represents water affected by agriculture with dominantly 

oxic conditions leading to higher nitrate concentrations, as seen 

in the higher cluster loadings (red and pink) with the constituents contributed from agriculture including nitrate, calcium, 

magnesium, and potassium than in the other clusters (fig. 67). 

The difference in calcium and bicarbonate loadings between 

some samples in Cluster 1 is likely related to the hydrogeologic 

differences above and below the Upper Chester River. There is 

a greater influence of deeper calcareous groundwater discharge 

into Chesterville Branch on the northern side of the river than 

into Foreman Branch on the southern side.

Cluster 2 represents samples from the northern side of the 

Upper Chester River with high phosphorus loadings during 

one or more synoptic sampling events (fig. 68). Elevated 

loadings of ammonium, manganese, and iron in the water 

samples from this group indicate that anoxic conditions are 

likely controlling the presence of higher loadings of dissolved 

phosphorus. The tributaries that contribute phosphorus include 

some of the smallest tributaries with agricultural land use.

Cluster 3 contains samples from one site with high sodium 

chloride, a condition that most likely originates from a nearby 
Upper Chester River Watershed Water-Quality Characterization 93

Figure 68. Monitoring locations as identified in a cluster analysis for each synoptic sampling event and the 

stream network of the Upper Chester River watershed.

14

15

21

16

17

20 13 19 18

10

12

8

9

26

27

3

2

6

76°00' 75°58' 75°56' 75°54' 75°52'

39°18'

39°16'

39°14'

39°12'

July 2011

3.4 ft3

/s

December 2011

10.8 ft3

/s

April 2013

6.5 ft3

/s

May 2012

7.1 ft3

/s

20

Synoptic sampling station 

 and number

EXPLANATION

Synoptic sampling events at site 20

 (USGS streamgage 01493112)

Streamflow, in 

cubic feet per second (ft3

/s)

Cluster group

Sample not collected

Group 1—Oxic, high agriculture type

Group 2—Northern, high total phosphorus type

Group 3—High-NaCl type

Group 4—Anoxic, low agriculture type

0 1 2 3 MILES

0 1 2 3 KILOMETERS

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Department of Agriculture, Natural Resources Conservation Service

 unpub. data for HUC 020600020605, 2005. North American Datum of 1983

road. This sampling location has relatively little anthropogenic 

additions of agricultural constituents to affect water chemistry.

Cluster 4 represents waters where the effects of anoxic 

conditions can be seen in higher loadings of constituents such 

as iron, phosphorus, ammonium, sulfate, and manganese 

and lower loadings of chemicals typically associated with 

agricultural additions of fertilizer and lime (such as nitrate, 

magnesium, and calcium) (fig. 67). This cluster includes all 

samples from Pearl Creek, the other major tributary in the 

Upper Chester River study area (fig. 68).

With some exceptions, δ15N and δ18O values of nitrate 

at the Upper Chester River watershed sites cluster around 

mean values of 5 per mil (‰) and 3 ‰, respectively (fig. 69). 

Whereas most samples plotted in a tight cluster near the low 

end of the range, a few samples had relatively high δ15 N and 

δ18O values that may indicate nitrate reduction (denitrification, 

assimilation). Excepting samples with relatively high 

δ18O values, the median δ18O value indicates that nitrification of reduced N was the dominant source of nitrate. The 

δ15N data are similar to those reported previously for nitrate 

in groundwater recharged beneath fertilized agricultural land 

in this region (Böhlke and Denver, 1995; Böhlke, 2003). 

Despite high nitrate concentrations and association of nitrate 

with other constituents attributed to agricultural fertilizer 

applications, δ15N values of nitrate in recharge generally 

were higher than δ15N values of major fertilizers used on 

crops. A δ15N shift of about +4 ‰ ± 2 ‰ from fertilizer to 

nitrate recharged beneath fertilized fields may be attributed to 

isotopic fractionation between fertilizer loading and recharge; 

fractionation can occur, for example, by ammonia volatilization, uptake by plants and soil microbiota, loss of nitrogen gas, 

nitrous oxide, and other gases during nitrification or denitrification in soils. Thus, Upper Chester River nitrate δ15N values 

can be explained by the dominance of a fractionated “fertilized 

agriculture” source of nitrate. Elevated δ15N and δ18O values 

were occasionally detected in samples from different parts of 

the watershed, most commonly from Pearl Creek. Variations in 

isotope values indicating nitrate loss could be related to variable seasonal flow conditions and uptake or to anoxia in ponds 

in near-stream areas associated with the small watersheds.
94 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 69. Nitrate isotope results from each synoptic sampling event within the Upper Chester River watershed. 

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

EXPLANATION

Groups identified by the cluster analysis 

Oxic, high agriculture type 

Northern, high total phosphorus type

High NaCl type

Anoxic low agriculture type

Schematic changes potentially associated with 

*Typical range of error, 2-sigma:

delta N-15: 0.5 per mil

delta O-18: 1.0 per mil

Dilution

Addition of atmospheric nitrate in runoff 

Nitrate reduction (denitrification, assimilation) 

Different nitrification N sources

Nitrate plus nitrite,

in milligrams per liter as nitrogen

6

0

8

2

4

0

12

15

3

6

9

Delta N-15 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

0 2 4 6 8 10 12

2 4 6 8 10
Upper Chester River Watershed Water-Quality Characterization 95

Characterization of Nitrogen Sources

Fifty-four percent of the land area of the Upper Chester 

River watershed is cultivated cropland, and about 10 percent 

is pasture, making agricultural activities the major human 

land use in the area (fig. 70). Also, a large plant nursery on the 

northern side of the watershed covers about 7 percent of the 

land area. Forests cover 13.35 percent of the area, with most of 

the forested areas located adjacent to streams. Many forested 

areas are also wetlands (10.75 percent of the area) located 

in riparian zones (fig. 3A). The subwatersheds with the most 

intensive agricultural land use are on the northern side of the 

Upper Chester River in Kent County, Maryland, and subwatersheds with a greater amount of forested land are on the 

southeastern side of the river in Queen Anne’s County, Maryland. Overall, however, the Upper Chester River study area has 

a similar land-use distribution to that of both counties (fig. 70).

Land-use nitrogen-source analysis indicates that 

nitrogen additions to cropland and the watershed as a whole 

are primarily through inorganic fertilizers (fig. 71). This 

observation is supported by isotopes of nitrogen from samples 

in this area that are consistent with an inorganic fertilizer 

source (fig. 69). Manure is the second most commonly added 

source of nitrogen, although its estimated use is an order of 

magnitude less than that of fertilizer in this area (fig. 71). 

In 2015, local sources of manure have been limited to only 

a few poultry producers and small dairy operations. Manure 

application is greater in southern parts of the Delmarva 

Peninsula closer to areas with concentrated poultry production. SPARROW model input data attribute almost 80 percent 

of the nitrogen sources in the Upper Chester River watershed 

to fertilizer and fixation of atmospheric nitrogen by legumes 

(fig. 72) (Wieczorek and LaMotte, 2010a,b,c,d). The amount 

of biologic nitrogen fixation, mostly by soybeans, which is the 

major nitrogen-fixing crop in this area, has not been quantified 

locally; however, estimates of nitrogen added by fixation 

of soybeans range from 15 to 60 pounds per acre (lb/acre) 

(Killpack and Buchholz, 1993).

Developed

7.12 percent

Cultivated crop

54.00 percent

Pasture/hay

10.24 percent

Water

4.35 percent

Upper Chester River watershed

Queen Anne’s County

Kent County

Wetland

10.75 percent

Forest

12.44 percent

Developed

10.24 percent

Cultivated crop

46.02 percent

Pasture/hay

14.82 percent

Water

0.07 percent

Wetland

15.78 percent

Forest

13.76 percent

Developed

6.55 percent

Cultivated crop

55.58 percent

Pasture/hay

9.93 percent Water

0.89 percent

Wetland

13.29 percent

Forest

13.35 percent

Figure 70. Percentages of land-use types from Homer and others 

(2015) within the Upper Chester River watershed, Queen Anne’s 

County, and Kent County, Maryland.
96 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Kent County Queen Anne’s County

Kent County Queen Anne’s County

10,000

100,000

1,000,000

10,000,000

5,000

Fertilizer (farm)

Sanitary sewer

Septic

A. Kilograms of nitrogen per year

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

B. Nitrogen sources in 2002

Manure

Atmospheric

deposition

4.75 percent

Atmospheric deposition

Manure 13.87 percent

Fertilizer (farm)

79.29 percent

Fertilizer (farm)

77.15 percent

Fertilizer (nonfarm)

0.36 percent

Fertilizer (nonfarm)

Manure

16.22 percent

Atmospheric

deposition

4.99 percent

Septic 1.19 percent Septic 0.89 percent

Sanitary sewer Sanitary sewer 0.53 percent

0.54 percent

Fertilizer (nonfarm)

0.21 percent

Figure 71. (A) Nitrogen sources for Kent County and Queen Anne’s County, Maryland, from 1987 to 2012, and 

(B) nitrogen sources in 2002 for Kent County and Queen Anne’s County, Maryland.

Atmospheric 

deposition

6.62 percent

Manure

14.56 percent

Fertilizer/fixation

78.56 percent

Urban area

0.26 percent

Atmospheric 

deposition

8.61 percent

Manure

3.89 percent

Fertilizer/fixation

86.18 percent

Urban area

1.32 percent

Input Output

Figure 72. Percentages of input and output sources of total nitrogen for the Upper Chester River 

watershed as generated by the 2002 Chesapeake Bay Total Nitrogen SPARROW model.
Upper Chester River Watershed Water-Quality Characterization 97

Table 20. Number of USDA-compliant conservation practices implemented in water years 2007 through 2013 in the Upper Chester 

River watershed, aggregated by sponsoring program.

[USDA, U.S. Department of Agriculture]

Program 2007 2008 2009 2010 2011 2012 2013 Total

Chesapeake Bay Watershed Initiative 0 0 0 24 115 79 36 254

Environmental Quality Incentives Program 1 30 4 1 31 1 2 70

Conservation Security Program 0 0 0 12 0 0 0 12

Conservation Reserve Program1 16 16 14 21 7 12 9 95

Wildlife Habitat Incentives Program 1 0 1 2 1 2 0 7

Agricultural Management Assistance 4 0 2 0 0 0 0 6

Total USDA-funded practices 22 46 21 60 154 94 47 444

Conservation Technical Assistance2 161 74 96 150 46 182 41 750

Total practices 183 120 117 210 200 276 88 1,194

1

Conservation Reserve Program and Conservation Reserve Enhancement Program practices administered by the USDA Farm Service Agency.

2

Technical assistance and verification provided by the USDA Natural Resources Conservation Service, without federal cost-share funding.

Table 21. Implementation of USDA-compliant conservation practices within the Upper Chester River watershed for water years 2007 

through 2013. 

[USDA, U.S. Department of Agriculture; —, values are privacy protected due to fewer than five customers participating; the most frequently implemented 

practices oriented toward controlling nutrients and sediment are listed in bold]

Practice 

code Practice name Lifespan(years) Units 2007 2008 2009 2010 2011 2012 2013

Aggregate 

implementation: 

2007 to 20131

2

340 Cover Crop 1 acres 1,211 732 — 646 2,774 2,647 — 8,170

2

590 Nutrient Management 1 acres 766 — 152 1,790 — 685 — 4,125

2

328 Conservation Crop Rotation 1 acres — — — 1,936 — 1,243 — 4,747

2

595 Integrated Pest Management 1 acres 551 — — 891 — — — 1,746

2

645 Upland Wildlife Habitat 

Management

1 acres 412 — 494 — — — — 1,451

2

329 Residue and Tillage 

Management, No-Till

1 acres — — — — — 663 — 1,839

3

CP21 Filter Strips 10 acres 19 30 56 69 37 76 127 414

1

Aggregate implementation is greater than the sum of reported annual practices because privacy protections restrict the reporting of annual results for practices with fewer than five participating customers. 

2

Practice codes from the U.S. Department of Agriculture, Natural Resources Conservation Service (2016).

3

Conservation reserve practice codes from the U.S. Department of Agriculture, Farm Service Agency (2016).

Implementation of Conservation Practices and 

Water-Quality Response

In the Upper Chester River watershed, 1,194 USDAcompliant agricultural conservation practices were 

implemented between 2007 and 2013 (table 20). The 

NRCS provided cost-share for 363 of those practices 

using funds from the Chesapeake Bay Watershed Initiative (254 practices), Environmental Quality Incentives 

Program (70 practices), Conservation Reserve Program 

(95 practices) and additional USDA programs (25 practices). 

For the remaining 750 conservation practices, the NRCS 

provided technical assistance, and funding was provided 

by non-Federal programs and (or) farmers. The aggregated 

total of conservation practice implementation (new practices 

applied between October 2007 and September 2013) is 

reported by NRCS practice code in table 21 for all practices 

with five or more participating farmers. Additional practices 

were implemented through the Maryland Agricultural 

Cost-Share (MACS) Program, other State programs, and 

voluntary practices implemented by farmers (table 22). The 

overall implementation of conservation practices increased 

substantially over this time period, especially after 2010 when 

the area was targeted as a Showcase Watershed by the USDA. 

The most frequently used practices oriented to nutrient and 

sediment control included cover crops, nutrient management, 

conservation crop rotation, conservation tillage, and residue 

and tillage management (tables 21 and 22).
98 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 22. Implementation of conservation practices sponsored by state programs or adopted on a voluntary basis within the Upper 

Chester River watershed for water years 2007 through 2013. The table does not include practices for which the NRCS provided funds or 

technical assistance.

[MACS, Maryland Agricultural Water Quality Cost Share; USDA, U.S. Department of Agriculture; NRCS, Natural Resources Conservation Service; the most 

frequently implemented practices oriented toward controlling nutrients and sediment are listed in bold; —, values are privacy protected due to fewer than five 

customers participating]

NRCS 

code1 Practice description Units 2007 2008 2009 2010 2011 2012 2013 Aggregate implementation: 2007 to 20132

340 Cover Crop–MACS acres 3,032 2,718 2,674 4,796 1,652 4,398 1,458 20,728

412 Grassed Waterway acres — 9 7 — — — 8 26

1

Maryland Department of Agriculture uses identical codes to the USDA NRCS to track the implementation of State-supported conservation practices.

2

Aggregate implementation is greater than the sum of reported annual practices because privacy protections restrict the reporting of annual results for practices with fewer than five participating customers.

A “farm assessment survey” covering 87 percent of the 

agricultural land in the watershed was conducted between 

December 2010 and March 2011 as part of the USDA-led 

Showcase Watershed effort (Maryland Department of 

Agriculture, 2011). Results of the assessment indicated that 

97 percent of the acreage was covered by a nutrient management plan and 89 percent of cropland acreage was regularly 

planted in cover crop (when feasible due to crop rotations and 

weather). These frequently implemented practices are among 

the most important to limiting nutrient transport to streams. 

Nutrient management is intended to help farmers determine 

the amount, placement, and timing of plant nutrients to obtain 

optimum yields and minimize the risk of surface-water and 

groundwater pollution. Cover crops, when planted in newly 

harvested fields, recycle unused plant nutrients remaining 

in the soil from previous summer crops, protect the field 

against wind and water erosion, and reduce leaching of nitrate 

to groundwater (U.S. Department of Agriculture, Natural 

Resources Conservation Service, 2013).

The time series of observed nitrate concentrations 

in Chesterville Branch indicates that, overall, nitrate 

concentrations have been increasing (fig. 73), despite the 

implementation of conservation practices to date. Waterquality monitoring at the streamgage started for this project 

in 2010 coupled with historical water-quality monitoring data 

for the period 1996 through 2002 provide an initial assessment 

of trends in water quality in Chesterville Branch. Because 

the streamgage was discontinued between 2002 and 2011, 

insufficient data are available to perform a trend analysis 

using Weighted Regression on Time, Discharge, and Season 

(Moyer and others, 2012). Increasing nitrate concentrations 

may still be occurring because of the time period required 

for water carrying nitrate to move through the groundwater 

system and discharge to the stream. Nitrate concentrations in 

groundwater have increased over time, especially since the 

early 1970s, in response to increases in nitrogen applications 

at the land surface (Böhlke and Denver, 1995; Debrewer 

and others, 2007). Since that time, the groundwater carrying 

higher concentrations of nitrate has reached Chesterville 

Branch causing nitrate concentrations in the stream to 

increase. The timeframe of increased implementation of 

practices intended to reduce nitrate in groundwater may not be 

sufficient yet to see any changes in stream quality that result 

from those practices, or even to evaluate if they will result in 

significant change.

Recent results from a CEAP report in the Chesapeake 

Bay Region (U.S. Department of Agriculture, Natural 

Resources Conservation Service, 2013) suggest that while 

implementation of conservation practices across the basin is 

increasing, trends in other cropland management practices, 

evaluated from 2003–2006 to 2011 were less encouraging:

• Annual nitrogen application increased 10 percent on 

cultivated cropland, including a 9-percent increase in 

commercial fertilizer application;

• Annual phosphorus application increased 6 percent on 

cultivated cropland, including a 5-percent increase in 

commercial fertilizer application; and

• Appropriate nitrogen application rate on all crops 

in rotation decreased 9 percent from 32 percent to 

23 percent of cropped acres.

These increases in nutrient inputs are associated with 

increased agricultural intensification, including higher 

yields and greater number of animals. While these overall 

CEAP trends are not specific to Chesterville Branch, they 

may help to explain why nitrate concentrations are increasing in the basin, despite the widespread implementation of 

conservation practices.

The implementation and continuation of widespread 

conservation practices in the Upper Chester River watershed 

could have long-term benefits to farming and water-quality 

in this area. The current Chesapeake Bay model estimates a 

9-percent decrease between 2007 and 2012 in overall delivery 

of nitrate to the model segment containing Chesterville 

Branch (source: Chesapeake Bay Modeling Team, written 

commun., 2015, based on Watershed Model 5.3.2) (fig. 74). 

As mentioned previously, the time period of monitoring has 

not been sufficient for changes that may be occurring to be 

measured in surface water. In addition, groundwater, the 

major source of nitrate to streams and the first place changes 

would be seen, is not currently monitored; therefore, no direct 
Upper Chester River Watershed Water-Quality Characterization 99

>80 to 100

>60 to 80

>40 to 60

>20 to 40

0 to 20

EXPLANATION

Percentage of values

 equal to or less than the 

 maximum streamflow of

 1,730 cubic feet per second

Streamflow data

not available

Nitrate plus nitrite,

in milligrams per liter as nitrogen

1990 1992 1994 1996 1998 2000 2002

Year

2004 2006 2008 2010 2012

0

2

4

6

8

10

12

(where small values 

represent low flow samples)

Figure 73. Nitrate concentrations from the Chesterville Branch streamgage between 

1990 and 2013.

–10

–8

–6

–4

–2

0

2

4

6

8

10

EXPLANATION

Agriculture

Urban area

Forest

Wastewater

Atmospheric deposition

1985 1990 1995 2000 2005 2010

Year

0

20,000

40,000

60,000

80,000

100,000

Nitrate plus nitrite, in pounds of nitrogen

Pounds of nitrate plus 

 nitrite as nitrogen from

 all sources with no best

 management practice

 implementation 

Source-specific pounds of 

nitrate plus nitrite as nitrogen

with best management

practice implementation

Percent difference per year

 in nitrate plus nitrite as nitrogen

 predicted by implementation 

 versus no implementation of

 best management practices

Annual percent change of nitrate plus nitrite as nitrogen

Figure 74. Nitrate load estimated from the Chesapeake Bay watershed model for the 

model segment containing Chesterville Branch with and without best management practice 

implementation with input sources and the annual percentage difference of load attributed to 

implementation, 1985–2012. 
100 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

data support estimates of nutrient reduction in the current 

Chesapeake Bay model. Previous estimates of the apparent 

ages of groundwater in the surficial aquifer discharging to 

Chesterville Branch ranged from relatively young near the 

water table to between 30 and 50 years at deeper depths in 

the aquifer and at discharge sites (Böhlke and Denver, 1995; 

Denver and others, 2010). According to a recent groundwater 

flow model for the Delmarva Peninsula, the median age of 

groundwater discharging to Chesterville Branch is about 

25 years (Sanford, Pope, and others, 2012), similar to that 

derived previously from local groundwater dating (Böhlke 

and Denver, 1995). Less than 20 percent of the groundwater 

discharging to streams recharged the groundwater system 

within the last 10 years, the time period when most cover 

crops and nutrient management plans have been implemented. 

Continued long-term monitoring of surface water and groundwater could help to verify any improvement to water quality 

related to agricultural conservation practices.

Conceptual Watershed Model and Implications 

for Management Actions

Cumulatively, the intensive monitoring at Chesterville 

Branch and extensive monitoring throughout the watershed 

combine to provide a process-level understanding of nutrient 

and sediment transport in the Upper Chester River watershed. 

These processes are summarized as follows:

• Intensive data collected at Chesterville Branch indicate 

that constituents associated with groundwater dominated water chemistry during base flow and were 

diluted during stormflow. Nitrate ranged from 8 to 

about 11 mg/L as N during base flow all year, with the 

lowest concentrations during the summer when biologic 

uptake is greatest and the highest concentrations in 

the colder months. Dissolved oxygen also was lower 

during the summer, but still above 5 mg/L, the level 

of concern for aquatic life, because of the continuous 

discharge of oxic groundwater into the stream.

• The predominant sources of nitrogen and phosphorus 

in the Upper Chester River watershed are inorganic 

fertilizers and nitrogen fixation by legume crops. 

Manure is also a source of these nutrients, but it is not 

as widely used as in other parts of the Delmarva Peninsula. Total nitrogen in streams is predominantly in the 

form of nitrate from groundwater. Ratios of nitrogen 

and oxygen isotopes of nitrate also identify inorganic 

fertilizer and nitrogen fixation as the primary sources 

of nitrogen in this watershed.

• Nitrate concentrations have increased in Chesterville 

Branch since the early 1990s. This increase is 

associated with an increase of nitrogen use in this 

watershed over time that resulted in increases in 

concentrations of nitrate in groundwater that has been 

discharging to the stream over time. The median age 

of groundwater discharging to Chesterville Branch 

is about 25 years, and the effects of conservation 

practices implemented in the last 10 years to more 

effectively utilize nitrogen applied to cropland, which 

should reduce nitrate leaching to groundwater, are 

not yet great enough or of long enough duration to be 

evident in stream water quality.

• In 2013, more than 90 percent of the phosphorus in 

Chesterville Branch entered the stream during two 

large storms with runoff. Concentrations of dissolved 

phosphorus were much lower during storms than 

those of TP, indicating that most of the phosphorus 

in streams was transported while attached to 

soil particles.

• Phosphorus is typically low or below detection in 

Chesterville Branch and other small tributaries during base-flow conditions with concentrations of TP 

ranging from below detection to about 0.2 mg/L. 

Some of the small tributaries on the northern side of 

the Upper Chester River had higher concentrations 

of phosphorus than those on the southern side. Sites 

with higher phosphorus also had more dissolved iron 

and ammonium than in other tributaries—conditions 

indicative of reducing conditions where phosphorus is 

more readily dissolved in water.

• Variability associated with hydrogeologic conditions 

was observed in base-flow water-quality conditions 

from samples collected throughout the Upper Chester 

River watershed. Concentrations of nitrate entering the small subwatersheds of the Upper Chester 

River watershed were greatest in the two largest 

streams, Chesterville Branch and Foreman Branch. 

The watersheds for these streams have predominantly 

agricultural land use and are underlain by thick sandy 

surficial aquifer sediments through which nitrate is 

transported to streams relatively unaltered. In the Pearl 

Creek subwatershed, the third largest subwatershed, 

soil and aquifer sediments are not as well drained, and 

the surficial aquifer is thin. Land use is a mix of forest, 

wetland, and agriculture. Nitrate leaving Pearl Creek is 

about 1 mg/L as N. In addition, because the aquifer is 

very thin, Pearl Creek can be ephemeral under certain 

low-flow conditions.

Knowledge gained from process-level understanding 

is important for the development of the most effective 

conservation management practices and informed decisionmaking. Decisions to manage the nutrients by considering 

their modes of transport in the environment are likely to have 

the greatest positive effect on water quality:

• Managing nitrogen in surface water will require a 

decrease in the amount of nitrate reaching groundwater. Ways to accomplish this reduction include reduced 

application of nitrogen fertilizer on crop land and 

expanded use of cover crops that retain nitrogen in the 
Upper Chester River Watershed Water-Quality Characterization 101

soil zone for use by the next crop. The effectiveness of 

cover crops, however, varies with the species planted, 

previous crop, the method and date of planting, and 

soil types. Because most nitrogen reaching streams is 

nitrate from groundwater, practices that limit runoff 

and soil losses, while effective at limiting nitrogen in 

runoff, may actually cause more infiltration of nitrogen 

to groundwater and may have little overall effect on 

nitrate concentrations in streams.

• Understanding subsurface flow paths is important to 

determining the effectiveness of management practices 

such as riparian buffers intended to increase the likelihood of nitrogen losses through denitrification. In areas 

of the Upper Chester River watershed with a thick 

sandy aquifer, nitrate in groundwater can bypass these 

areas and discharge relatively unaltered to streams.

• Changes in management actions today that affect 

nitrogen concentrations will take several years or 

decades to be fully realized in streams because most 

nitrogen is from groundwater sources and it will take 

time for water currently recharging the system to reach 

local streams.

• Developing and following nutrient management plans 

that address the amounts and timing of fertilizer application customized to meet crop needs can substantially 

reduce nutrient applications to crops in excess of plant 

needs. 

• Phosphorus transport during high-flow events can be 

limited by practices that limit soil erosion, including conservation tillage, riparian buffers, grassed 

waterways, and sediment-control ponds. Reduction 

of phosphorus use to the minimum required for crop 

production will also reduce phosphorus storage in soils 

over time. 

• Because of the large stores of phosphorus currently in 

soils and streambed sediments, reductions in phosphorus available for transport, particularly during large 

storms, will also take many years to be realized.

• The collection of empirical nutrient and geochemical 

water-quality data in the Upper Chester River watershed was critical for better understanding the nitrogen 

sources and temporal responses within the basin, and 

for verifying regional model performance (SPARROW 

results) and assessing trends in water-quality data 

(Chesapeake Bay Watershed Model results).

Ongoing USGS water-quality monitoring efforts will be 

directed towards improved understanding of nutrient sources 

and transport processes in the Upper Chester River watershed, 

as well as the detection of water-quality change that can be 

directly related to the implementation of management actions. 

Large changes in water quality take time and sufficient 

implementation of conservation management practices on the 

landscape to be fully realized in water quality. To accomplish 

this goal, monitoring of both surface water and groundwater for 

trends is needed. Ongoing monitoring in Chesterville Branch 

and the small watersheds located throughout the Upper Chester 

River watershed is intended to continue to help meet this goal. 

• Monitoring of nutrients and sediment at the streamgage 

on Chesterville Branch will provide a long-term record 

of water quality with respect to changes in nutrient management practices. With sufficient data, an 

expanded trend analysis can be performed to determine 

whether statistically significant trends exist. Previous surface-water monitoring has shown nitrate has 

increased from a high of about 9 mg/L as N in 1997 to 

about 11 mg/L in 2013. A similar amount of time may 

be needed to see the effects of changes in management 

practices that began in the last 10 years.

• The continuous nitrate record at Chesterville Branch 

is likely one of the best tools for determining whether 

water quality is changing in Chesterville Branch. With 

15-minute interval, continuous data, sufficient data can 

be collected to measure change over time, rather than 

estimating it with regression models such as LOADEST and WRTDS. Furthermore, shifts in the relationship between nitrate and flow might be an early indicator of change because increased nutrient management 

in the watershed will change the transport of nutrients 

without changing hydrology. Even though it will take 

several decades for the entire groundwater system that 

contributes to Chesterville Branch to show the effect 

of current management practices, gradual changes in 

the relationship between flow and nitrate may be seen 

sooner in water that travels along shorter flow paths.

• Monitoring the current network of small tributaries 

to the Upper Chester River began in 2011 and will 

continue on a yearly basis during high base-flow conditions in the spring to provide a long-term record of 

water quality in small first-order streams so that trends 

can be studied on a watershed basis.

• If conservation practices implemented in the Upper 

Chester River watershed are improving groundwater quality, monitoring of shallow groundwater over 

time could show changes in nitrate concentrations 

in recently recharged groundwater long before those 

changes are observed in the streams and would also be 

an indicator of how much change to expect. Development of a network of monitoring wells associated with 

selected tributaries to the Upper Chester River would 

be needed to accomplish this. 
102 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Conewago Creek Watershed 

Water-Quality Characterization

Lead Author: Michael J. Langland

In June 2010, the Conewago Creek watershed was named 

a Showcase Watershed by the USDA. The USGS employed 

an upstream/downstream monitoring strategy by building 

and operating one streamflow station near the middle of the 

watershed and another near the outlet into the Susquehanna 

River (fig. 75). The intensity of land-based management 

practices and water-quality data-collection activities within the 

basin was increased when the monitoring sites were funded 

and instrumented as part of the Chesapeake Bay Program 

Nontidal Network.

Streamflow and Water-Quality Characterization 

at the Intensive Monitoring Site

Monthly water-quality sampling, continuous 

water-quality monitoring for field parameters, and wetweather (storm) monitoring were initiated in January 2012 

at the upstream site and in June 2011 at the downstream 

site. The timeline associated with the collection of different 

water-resources data in the Conewago Creek watershed is 

summarized in figure 76. High-flow synoptic sampling of an 

additional 12 sites occurred in May 2012. 

Characterization of the Physical Hydrology

No long-term hydrologic record is available in the 

Conewago Creek watershed. The hydrologic record is limited 

to complete water years for 2012 and 2013 and indicates 

a wide range in instantaneous flows from lows of 1.5 and 

4.7 ft3/s to highs of 776 and 9,610 ft3/s at the upstream and 

downstream sites, respectively (fig. 76). The maximum 

downstream flow occurred during one notable storm event on 

September 7, 2011, shortly after the downstream gage was 

built, when more than 9 inches of rain fell in the watershed. 

The hydrographical record was examined by using the hydrograph separation software package, PART, to compute a BFI 

for the watershed over multiple time periods (Rutledge, 1998). 

The BFI represents the ratio of the total base-flow volume 

to the total flow volume for a given year and is indicative 

of whether a given stream is dominated by groundwater 

discharge or direct runoff during storms. The results indicate a 

BFI of 54.9 percent for the upstream site and 43.1 percent for 

the downstream site for the 2012–2013 water years (table 23). 

Approximately 43 percent of the Conewago Creek flow exited 

the watershed as base flow during the study period. This 

suggests that Conewago Creek hydrology is dominated by 

stormflow discharge rather than groundwater discharge, which 

is characteristic of underlying geology dominated by less 

permeable mudstone, and sandstone with volcanic (diabase) 

intrusions instead of more permeable carbonate rock (fig. 4B). 

The influence of more carbonate rock on the BFI from below 

the upstream site to the downstream site was not observed, 

likely due to having only 2 years of record.
Conewago Creek Watershed Water-Quality Characterization 103

Figure 75. Stream network, monitoring stations, subwatershed boundaries, and sampling locations 

within the Conewago Creek watershed, Pennsylvania. 

76°40' 76°35' 76°30'

40°20'

40°15'

40°10'

40°05'

1 2

3

4

5A

5

7

14

8

10

11

12

13

Direction of flow

9

DAUPHIN COUNTY

LEBANON COUNTY

LANCASTER COUNTY

0 1 2 5 MILES

0 1 2

3 4

3 4 5 KILOMETERS

VIRGINIA Atlantic Ocean

Chesapeake Bay

MARYLAND

PENNSYLVANIA

Study area

NEW

JERSEY

DELAWARE

EXPLANATION

Monitoring station and identifier

21

Conewago Creek

 (area=52.53 mi

2

)

12–USGS streamgage Falmouth

 (01573710)

4–USGS streamgage Bellaire

 (01573695)

Subwatershed

Watershed boundary

Conewago Creek

Tributary to Conewago Creek

4

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Geological Survey StreamStats program (2012b)

Roads are from U.S. Census Bureau TIGER/Line shapefiles (2015) 

North American Datum of 1983

Synoptic sampling location

 and identifier

Susquehanna River

Brills Run

Lynch Run

Hoffer Creek
104 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 76. (A) Conewago Creek Bellaire and (B) Conewago Creek Falmouth streamgages with the timing of various 

sampling events.

A. Bellaire

B. Falmouth

EXPLANATION

Streamflow, in cubic feet per second

WY Water year

Sampling event

Routine sampling

Storm sampling

Synoptic sampling

1,000

100

10

1

10,000

1,000

100

10

WY 2012 WY 2013

Streamflow, in cubic feet per second

 WY 2011

J J A S O N D J F M A M J J A S O N D J F M A M J J A S
Conewago Creek Watershed Water-Quality Characterization 105

Characterization of Continuous 

Water-Quality Data

The temporal variability in the continuously monitored 

water-quality constituents is presented for the monitoring 

period as multidimensional contour plots in figures 77 and 78. 

Seasonal, diel, and flow-driven fluctuations in most of the 

continuously monitored constituents were observed at both 

monitoring locations. 

Water temperature had some of the strongest seasonality, 

with summertime temperatures commonly reaching greater 

than 20 °C and wintertime temperatures falling below 5 °C. 

A slight increase in average temperature (0.5 °C) is noted 

between the upstream and downstream monitoring locations 

in the summer months, and the reverse happened in the winter 

months. This can be attributed to a greater amount of forest 

canopy (shading) above the upstream site in the summer and 

greater exposure to colder air at the downstream site in winter. 

Specific conductance values were generally about 

250 μS/cm at the upstream location and 300 μS/cm at the 

downstream location. The higher conductance values are 

likely influenced by some thin carbonate rock layers present 

in the lower half of the watershed. Specific conductance is 

inversely related to streamflow, with the highest specific 

conductance levels observed during relatively low-flow 

periods and the lowest levels observed during stormflow 

periods due to dilution from overland runoff and precipitation 

with lower specific conductance. 

Hydrologic variability in the continuously monitored 

constituents was explored by using a series of concentrationdischarge plots (known as C-Q plots) that were subset into two 

classifications—warm periods (April–September) and cool 

periods (October–March). For this assessment, the continuously monitored parameters (temperature and specific conductivity) were combined with discrete water-quality samples 

collected within the same timeframe. Seasonal characterization for each of the continuously monitored parameters was 

based on the C-Q plot and seasonal boxplots (figs. 79 and 80). 

Water temperature indicated the most seasonality, with greater 

temperatures observed during the April–September warmer 

seasons. Specific conductance values showed little seasonal 

variability, with both seasons demonstrating substantial 

dilution during elevated flow conditions. 

Table 23. Streamflow partitioning for streamgage stations 01573695 and 01573710 in the Conewago Creek watershed.

[mi2, square miles; ft3/s, cubic foot per second; in/yr, inch per year; %, percent; data were computed using the software package PART 

(Rutledge, 1998)]

Watershed Drainage area 

(mi2)

Period of 

record

Mean streamflow Mean base flow Base-flow 

index (%) ft3

/s in/yr ft3/s in/yr

Conewago Creek Bellaire 20.5 2013–2013 26.14 17.32 14.35 9.51 54.9

Conewago Creek Falmouth 47.3 2012–2013 60.06 17.25 25.89 7.44 43.1
Figure 77. Continuous (A) water temperature and (B) specific conductance data with the hydrograph from the Conewago Creek Bellaire streamgage, 2012–2013.

EXP

Water temperat

0 to 1.301.31 to 2.502.51 to 4.20

4.21 to 6.60

6.61 to 11.411.41 to 14Streamflow

86.0 to 203203.1 to 21216.1 to 23

231.1 to 24

241.1 to 24247.1 to 25Streamflow

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

Specific conduc per centimete

1011001,000

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

J F M A M J J A S O N D J F M A M J J A S

1011001,000

Streamflow, incubic feet per second

Water year 2012 Water year 2013

AB
Figure 78. Continuous (A) water temperature and (B) specific conductance data with the hydrograph from the Conewago Creek Falmouth streamgage, 2011–2013.

EXP

Water temperatu

0 .00 to 1.301.31 to 2.602.61 to 4.80

4.81 to 7.20

7.21 to 11.811.81 to 15.Streamflow

Specific conduc per centimeter 

70.0 to 206.206.1 to 238238.1 to 269

269.1 to 287287.1 to 297Streamflow

297.1 to 305

1011001,00010,000

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

J J A S O N D J F M A M J J A S O N D J F M A M J J A S

Water year 2011 Water year 2012 Water year 2013

1011001,00010,000

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

AB

Streamflow, incubic feet per second
108 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 79. (A) Water temperature and (B) specific conductance data from continuous 

monitoring and discrete storm and routine samples for warm (April to September) and cool 

(October to March) seasons, Conewago Creek Bellaire streamgage, 2012–2013.

–5

10

15

20

25

30

35

Water temperature, in degrees Celsius

A

B

5

0

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

0

100

200

300

400

500

Specific conductance, in microsiemens 

per centimeter at 25 degrees Celsius

1 10 100 1 10 100 1,000

Streamflow, in cubic feet per second
Conewago Creek Watershed Water-Quality Characterization 109

Figure 80. (A) Water temperature and (B) specific conductance data from continuous 

monitoring and discrete storm and routine samples for warm (April to September) and cool 

(October to March) seasons, Conewago Creek Falmouth streamgage, 2011–2013.

–5

0

5

10

15

20

25

30

35

Water temperature, in degrees Celsius

0

100

200

300

500

400

Specific conductance, in microsiemens

per centimeter at 25 degrees Celsius 

A

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

1 10 100 1 10 100 1,000

Streamflow, in cubic feet per second

1,000 10,000

Characterization of Discrete Water-Quality Data

A total of 85 water-quality samples were collected at 

the monitoring sites during the study period, and field and 

laboratory parameters were analyzed from each discrete 

water-quality sample collected. Systematic patterns in the 

discrete water-quality samples were explored by using simple 

statistics, cluster analysis, and PCA because these tests offer 

additional insights in complex water-quality datasets. 

Similar to the continuous water-quality data, the discrete 

water-quality data demonstrated temporal variations that are 

likely related to flow, seasonality, temperature, nutrient uptake, 

and natural in-stream processes. A slight seasonal pattern 

in 2012 and 2013 is observed in the discharge data associated 

with water-quality sampling with elevated flow levels in late 

winter and lower flow levels in late summer. Because the annual 

flows were similar in 2012 and 2013, a longer monitoring 

period that would include more hydrologic variability is likely 

needed to provide additional information on seasonal patterns. 

Simple statistics based on constituent concentrations 

(maximum, minimum, and mean) for the upstream (Bellaire) 

and downstream (Falmouth) monitoring locations are 

presented in tables 24 and 25, respectively. Based on a 

comparable dataset of 38 samples from each monitoring 

location, nearly every constituent increased in mean 

concentration between the two sites. The largest increase 

was in flow. The increases in mean concentrations from 

upstream to downstream are predominantly related to drainage 

area (flow) and land use. Areas upstream of the upstream 

monitoring gage have a greater percentage of land classified 

as forest, less agricultural land, and minimal developed land 

(fig. 4A). The downstream watershed contains less forest, with 

more agricultural and urban land (fig. 4A). All of the impaired 

tributaries are below the upstream monitoring location.

Conewago Creek pH ranged from 7.0 to 8.9, averaging 

7.7 and 7.8 at the upstream and downstream monitoring locations, respectively (tables 24 and 25). The pH levels indicate 

some dilution by more acidic runoff during stormflow. At 

each location, the difference between the storm and non-storm 

samples was about 1 standard unit, which represents a ten-fold 

change in acidity. 

DO ranged from about 5 mg/L to 16 mg/L with a 

mean about 10 mg/L at each of the monitoring locations 

(tables 24 and 25). Some variation is indicated between storm 

and non-storm samples, but correlations between DO and flow, 

temperature, or nutrient data were statistically insignificant. 
110 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 24. Summary statistics for discrete water-quality constituents collected at the Conewago Creek Bellaire streamgage with a 

comparison between storm and base-flow samples.

[USGS, U.S. Geological Survey; n, number; deg C, degrees Celsius; mg/L, milligram per liter; N, nitrogen; P, phosphorus; CaCO3, calcium carbonate; μg/L, 

microgram per liter; %, percent; <, less than; mm, millimeter; μS/cm, microsiemens per centimeter; ft3/s, cubic foot per second; —, no samples collected]

Parameter name

USGS 

parameter 

code

Reporting units

All samples Storm 

samples

(n = 16)

mean

Non-storm 

samples

(n = 22)

mean

(n = 38)

min max mean

Discharge 00061 ft3/sec 5 850 93 211 17

Water temperature 00010 deg C 1.6 23.7 12.8 12.9 12.8

Specific conductance 00095 μS/cm at 25 deg C 198 1310 1242 1261 1209

Dissolved oxygen 00300 mg/L 4.8 15.8 10.5 10.5 10.5

pH 00400 Standard units 7.0 8.9 7.7 7.6 7.8

Total nitrogen 00600 mg/L 1.49 4.27 2.34 2.66 2.13

Ammonium 00608 mg/L as N <0.02 0.32 0.07 0.11 0.04

Nitrate-N 00631 mg/L as N 0.89 3.77 1.79 1.64 1.90

Total phosphorus 00665 mg/L 0.03 0.84 0.18 0.35 0.07

Orthophosphate 00671 mg/L as P 0.02 0.41 0.11 0.19 0.05

Hardness 00900 mg/L as CaCO3 38 115 81 66 90

Calcium 00916 mg/L 8.17 29.50 20.53 17.11 22.76

Magnesium 00927 mg/L 3.26 9.97 7.11 6.10 7.78

Chloride 00940 mg/L 5.25 57.88 22.91 20.66 24.38

Sulfate 00945 mg/L 5.13 19.58 12.68 10.41 14.17

Aluminum 01105 μg/L <200 14,100 1,272 2,931 <200

Copper 01042 μg/L <4 30 6 10 <4

Iron 01045 μg/L 98 19,400 1,663 3,747 304

Manganese 01055 μg/L 10 560 92 168 43

Zinc 01092 μg/L <10 73 14 23 <10

Organic carbon 00680 mg/L 1.61 14.50 5.70 9.83 3.01

Total suspended solids 00530 mg/L 5 574 46 106 7

Suspended-sediment 

concentration

80154 mg/L 29 2535 2131 2131 —

1

Statistics for specific conductance are based on 33 total samples, 14 storm samples, and 19 non-storm samples.

2

Statistics for suspended-sediment concentration are based on 14 total samples, 14 storm samples, and 0 non-storm samples.
Conewago Creek Watershed Water-Quality Characterization 111

Table 25. Summary statistics for discrete water-quality constituents collected at the Conewago Creek Falmouth streamgage with a 

comparison between storm and base-flow samples.

[USGS, U.S. Geological Survey; n, number; deg C, degrees Celsius; mg/L, milligram per liter; N, nitrogen; P, phosphorus; CaCO3, calcium carbonate; μg/L, 

microgram per liter; %, percent; <, less than; mm, millimeter; μS/cm, microsiemens per centimeter; ft3/s, cubic foot per second; —, no samples collected]

Parameter name

USGS 

parameter 

code

Reporting units

All Samples Storm 

samples

(n = 18)

mean

Non-storm 

samples

(n = 28)

mean

(n = 46)

Min Max Mean

Discharge 00061 ft3/sec 9 1,990 243 562 39

Water temperature 00010 deg C 1.5 23.9 14.2 14.0 14.3

Specific conductance 00095 μS/cm at 25 deg C 1110 1388 1269.3 1222.6 1298

Dissolved oxygen 00300 mg/L 2.1 18.9 9.8 9.0 10.3

pH 00400 Standard units 7.0 8.5 7.8 7.7 7.7

Total nitrogen 00600 mg/L 1.18 8.40 3.28 3.68 3.03

Ammonium 00608 mg/L as N <0.02 1.37 0.12 0.25 0.03

Nitrate-N 00631 mg/L as N 0.44 4.83 2.54 2.27 2.70

Total phosphorus 00665 mg/L 0.02 1.38 0.28 0.57 0.10

Orthophosphate 00671 mg/L as P 0.02 0.74 0.19 0.32 0.10

Hardness 00900 mg/L as CaCO3 24 119 95 73 108

Calcium 00916 mg/L 6.79 30.50 24.48 19.94 27.40

Magnesium 00927 mg/L 1.74 10.80 8.32 6.71 9.35

Chloride 00940 mg/L 2.37 60.40 24.41 19.38 27.64

Sulfate 00945 mg/L 3.76 21.10 14.88 11.33 17.16

Aluminum 01105 μg/L <200 10,500 1,255 2,848 232

Copper 01042 μg/L <4 67 8 13 4 

Iron 01045 μg/L 54 11,700 1,474 3,318 289

Manganese 01055 μg/L <10 550 76 153 27

Zinc 01092 μg/L <10 142 22 33 15

Organic carbon 00680 mg/L 1.97 18.32 6.24 10.29 3.63

Total suspended solids 00530 mg/L <5 402 53 126 7

Suspended-sediment 

concentration

80154 mg/L 210 2411 2161 2161 —

1

Statistics for specific conductance are based on 42 total samples, 16 storm samples, and 26 non-storm samples.

2

Statistics for suspended-sediment concentration are based on 14 total samples, 14 storm samples, and 0 non-storm samples.

Based on the observation that mean concentrations 

increased downstream, a cluster analysis was run on the 

geochemical results for the 74 discrete samples for which a 

complete set of analyses was available (fig. 81). Variables that 

are strongly correlated to flow (such as sediment) or season 

(such as water temperature) were excluded from this analysis 

to better examine the stream geochemistry and provide 

information on subtle patterns in the data. Four cluster groups 

resulted, representing both site locations and hydrologic 

variability (fig. 82). All the Group 1 and the Group 2 samples 

were collected during low- to medium-flow conditions at each 

monitoring location. Groups 1 and 2 represent nearly each 

site, exclusively. Group 3 included the samples collected at the 

highest flows during the monitoring period and demonstrated 

strongly diluted major-ion (calcium, magnesium, and chloride) 

and iron and manganese concentrations. Group 4 represents 

the samples collected at “elevated” flow, a transition from 

lower flows to the highest sampled flows. Group 4 samples 

had the highest concentrations of particulate-associated 

constituents (iron, manganese, TN, and TP), and during 

“elevated” flows, water quality in Conewago Creek showed 

the greatest change in concentration. This response is likely 

due to the rapid transport of particulate-associated constituents 

that become diluted at the highest flows (Group 3) due to 

supply limitation.

A PCA was run using all 74 discrete samples collected at 

the two monitoring locations with complete analytical results 

to further explore the patterns in discrete concentration data. 

Similar to the cluster analysis, water temperature and sediment 

were withheld from the analysis. Two principal components 
112 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

(the first and second principal components axes) explained 

66 percent of the variability in the data (table 26). The first 

component explained 48.5 percent of the data variability and 

had high positive axis loadings for specific conductance, 

sulfate, chloride, calcium, magnesium, and nitrate, with large 

negative axis loadings for TP, ammonium, orthophosphate, 

iron, and manganese. The first principal components axis is 

a flow axis; at low flow, calcium, magnesium, sulfate, and 

specific conductance are elevated, while concentrations of 

iron and TP are relatively low. At higher flows and under 

runoff conditions, concentrations of iron, magnesium, and 

TP increase. The second principal components axis explains 

less variability (17.7 percent); however, the occurrence of 

TN and dissolved nitrate (primary species of TN) suggests 

an additional influence other than flow. An examination of 

the second principal components axis over time suggests the 

cycling of nitrogen does not follow a traditional seasonal 

pattern with higher nitrogen concentrations in the fall and 

winter due to less biologic uptake. Some weak seasonal 

patterns are evident during base-flow conditions at each location. As previously mentioned, the data record may not be long 

enough to detect a persistent seasonal signature. The presence 

of a flow and a seasonal (although weak) signature in the 

discrete data is typical of most stream systems and indicates 

that hydrology and seasonality both contribute to water-quality 

variability in Conewago Creek.

Figure 81. Discrete samples from the Conewago Creek Bellaire and Falmouth streamgages with a heat map of 

constituent values and the breakpoint used to produce four cluster groups, 2011–2013.

pH

Dissolved oxygen

Nitrate

Total nitrogen

Specific conductance

Sulfate

Calcium

Magnesium

Chloride

Ammonium

Orthophosphate

Total phosphorus

Iron

Manganese

EXPLANATION Cluster group

Relative constituent values

Low High

Group 1

(Falmouth base-flow samples)

Group 2

(Bellaire base-flow samples)

Group 3

(Highest-flow

samples)

Group 4

(Elevated-flow

samples)

Watershed

Conewago Creek Falmouth

Conewago Creek Bellaire

Dendrogram break point
Conewago Creek Watershed Water-Quality Characterization 113

Figure 82. First two principal component axis scores for discrete samples from the Conewago Creek 

Bellaire and Falmouth streamgages along with time and streamflow, 2011–2013.

Principal component 1* Principal component 2**

July 1

2011

Jan.

2012

July

2012

July

2013

Jan.

2013

10

6

4

2

0

–2

–4

–6

3

2

1

0

–1

–2

–3

–4

100 1,000 10,000

EXPLANATION

Cluster analysis group

Group 1—Falmouth base-flow sample

Group 2—Bellaire base-flow sample

Group 3—Highest-flow sample

Group 4—Elevated-flow sample

*Principal component 1 explains 

 48.5 percent of the variability in the data

**Principal component 2 explains 

 17.7 percent of the variability in the data

–5

4

Date Streamflow, in cubic feet per second
114 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 26. Loading of water-quality constituents collected at the Conewago Creek Bellaire and 

Falmouth streamgages on principal component axes 1 and 2.

[%, percent; PC1, first principal components axis; PC2, second principal components axis; loadings with absolute 

value greater than 0.70 are shown in bold; those with absolute value less than |0.40| are omitted]

Parameter PC1 

(48.5%)1

PC2 

(17.7%)1

pH

Specific conductance 0.82

Dissolved oxygen

Sulfate 0.89

Chloride 0.74

Calcium 0.87

Magnesium 0.87

Nitrate-N 0.67 0.56

Ammonium –0.73

Total nitrogen 0.82

Orthophosphate –0.62 0.61

Total phosphorus –0.73 0.54

Iron –0.76 0.44

Manganese –0.76

1

Percent of overall variance explained.
Conewago Creek Watershed Water-Quality Characterization 115

The cluster analysis and the PCA indicated correlations 

with flow, and to a lesser extent, the seasonal variability 

of the parameters in Conewago Creek; therefore, many 

constituents were tested individually for relationships to flow 

and seasonality (tables 27 and 28). Highly correlated (termed 

large, Spearman’s rho > 0.7) significant positive relations with 

flow are indicative of constituents that are transported during 

higher flow and runoff conditions. Large positive relations with 

flow were observed for iron at the upstream location (table 27), 

while smaller but still statistically significant positive 

relationships were observed at both sites for TP, orthophosphate, ammonium, manganese, and iron (at the downstream 

site) (table 28). Large (Spearman’s rho < –0.7) inverse 

relationships with flow were observed for two ions (calcium 

and magnesium) at both locations, while moderate inverse 

correlations were observed for specific conductance, sulfate, 

chloride, and water temperature (tables 27 and 28). These negative relationships are consistent with dilution of streamflow by 

lower ionic strength waters (rainfall) during storm events. Total 

nitrogen results indicate significant positive correlations with 

flow at the upstream location and non-significant correlations at 

the downstream location, most likely due to less variability in 

flow and concentration at the upstream location.

The general relations between flow and various 

water-quality constituents exhibited in the data for Conewago 

Creek (fig. 83) are typical of many streams in the Piedmont 

Physiographic Province, where sediments and particulate 

nitrogen and phosphorus concentrations typically increase 

during storm events, while groundwater-derived constituents 

(such as calcium, magnesium, chloride, and sulfate), which 

are more prominent during lower flows, tend to decrease 

during storms. Groundwater-derived streamflow has a longer 

residence time and contact with bedrock, resulting in water 

with a higher concentration of major ions. Dilution occurs 

when rainfall-derived runoff from the landscape mixes with 

groundwater-derived streamflow with higher concentrations 

of major ions. Non-significant seasonal relationships are 

provided in the appendix as supplemental material (fig. 1–5).

Statistically significant seasonal patterns exist for only 

a few of the monitored constituents (fig. 84). The most 

significant seasonal patterns occurred in the warm season 

(April–September) for water temperature at both locations. At 

the upstream station, TN and nitrate concentrations increased in 

the cool season (October–March), and TP and orthophosphate 

concentrations increased in the warm season. At the downstream station, nitrate increased in the cool season, whereas 

orthophosphate increased in the warm season. Seasonal 

patterns in water temperature were expected and demonstrated 

previously with C-Q plots. Many of the remaining constituents 

showed insignificant seasonal patterns, which may be the result 

of a short data record; a longer monitoring record could result 

in clearer seasonal patterns (fig. 1–6).

Table 27. Summary of hydrologic and seasonal correlations for discrete water-quality constituents collected at the Conewago Creek 

Bellaire streamgage.

[results in bold are significant at α=0.05; n, number of observations; APR, April; SEPT, September; OCT, October; MAR, March; <, less than]

Parameter

Streamflow Season1

n rho2 p-value2 nAPR-SEPT nOCT-MAR p-value3 Season with 

higher values

Water temperature 38 –0.35 0.032 12 11 <0.001 APR-SEPT

pH 38 –0.18 0.271 12 11 0.156

Specific conductance 34 –0.33 0.063 11 9 0.703

Dissolved oxygen 38 –0.02 0.906 12 11 0.479

Sulfate 38 –0.47 0.0031 12 11 0.310

Chloride 38 –0.43 0.007 12 11 0.372

Calcium 38 –0.81 <0.001 12 11 0.124

Magnesium 38 –0.80 <0.001 12 11 0.079

Calcium/magnesium 38 –0.15 0.382 12 11 0.018

Nitrate-N 38 –0.13 0.421 12 11 0.018 OCT-MAR

Ammonium 38 0.56 <0.001 12 11 0.616

Total nitrogen 38 0.41 0.011 12 11 0.011 OCT-MAR

Orthophosphate 38 0.61 <0.001 12 11 0.013 APR-SEPT

Total phosphorus 38 0.65 <0.001 12 11 0.010 APR-SEPT

Iron 38 0.81 <0.001 12 11 0.829

Manganese 38 0.63 <0.001 12 11 0.175

1

Only routine samples included in the Wilcoxon test of rank scores. 

2

rho is Spearman’s rho; p-value is from Spearman’s test for correlation.

3

p-value is from the Wilcoxon test of rank scores.
116 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 28. Summary of hydrologic and seasonal correlations for discrete water-quality constituents collected at the Conewago 

Creek Falmouth streamgage.

[results in bold are significant at α=0.05; n, number of observations; APR, April; SEPT, September; OCT, October; MAR, March; <, less than]

Parameter

Streamflow Season1

n rho2 p-value2 nAPR-SEPT nOCT-MAR p-value3 Season with 

higher values

Water temperature 46 –0.45 0.002 17 11 <0.001 APR-SEPT

pH 46 –0.12 0.439 17 11 0.126

Specific conductance 42 –0.57 <0.001 16 10 1.000

Dissolved oxygen 46 –0.17 0.273 17 11 0.747

Sulfate 46 –0.61 <0.001 17 11 0.204

Chloride 46 –0.51 0.000 17 11 0.742

Calcium 46 –0.73 <0.001 17 11 0.410

Magnesium 46 –0.78 <0.001 17 11 0.540

Calcium/magnesium 46 0.29 0.051 17 11 0.888

Nitrate-N 46 –0.18 0.235 17 11 0.029 OCT-MAR

Ammonium 45 0.62 <0.001 16 11 0.745

Total nitrogen 46 0.18 0.235 17 11 0.063

Orthophosphate 46 0.49 0.001 17 11 0.043 APR-SEPT

Total phosphorus 46 0.66 <0.001 17 11 0.070

Iron 46 0.48 0.001 17 11 0.925

Manganese 46 0.38 0.01 17 11 0.094

1

Only routine samples included in the Wilcoxon test of rank scores. 

2

rho is Spearman’s rho; p-value is from Spearman’s test for correlation.

3

p-value is from the Wilcoxon test of rank scores.
Conewago Creek Watershed Water-Quality Characterization 117

10

8

6

4

2

0

1

0.1

0.01

0.001

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Total nitrogen,

in milligrams per liter

Ammonium,

in milligrams per liter as nitrogen

Orthophosphate,

in milligrams per liter as phosphorus

1

0.1

0.01

Total phosphorus,

in milligrams per liter

Streamflow, in cubic feet per second Streamflow, in cubic feet per second

1 10 100 1,000 10,000 1 10 100 1,000 10,000

EXPLANATION

Season

April to September

October to March

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)

A B

Figure 83. Concentration-discharge data for discrete samples from the Conewago Creek 

(A) Bellaire and (B) Falmouth streamgages by warm (April to September) and cool (October to 

March) seasons.
118 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

1,000

100

10

1

Sediment concentration, 

in milligrams per liter

35

30

25

20

15

10

5

0

12

10

8

6

4

2

0

Calcium,

in milligrams per liter

Magnesium,

in milligrams per liter

70

60

50

40

30

20

10

0

Chloride,

in milligrams per liter

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

10 100 1,000 10,000 1 10 100 1,000 10,000

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

10 100 1,000 10,000 1 10 100 1,000 10,000

EXPLANATION

Season

April to September

October to March

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)

A B

Figure 83. Concentration-discharge data for discrete samples from the Conewago Creek 

(A) Bellaire and (B) Falmouth streamgages by warm (April to September) and cool (October to 

March) seasons.—Continued
Conewago Creek Watershed Water-Quality Characterization 119

Sulfate,

in milligrams per liter

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

10 100 1,000 10,000 1 10 100 1,000 10,000

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

10 100 1,000 10,000 1 10 100 1,000 10,000

24

20

16

12

8

4

0

Iron,

in micrograms per liter

10,000

1,000

100

10

Manganese,

in micrograms per liter

600

500

400

300

200

100

0

EXPLANATION

Season

April to September

October to March

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)

A B

Figure 83. Concentration-discharge data for discrete samples from the Conewago Creek 

(A) Bellaire and (B) Falmouth streamgages by warm (April to September) and cool (October to 

March) seasons.—Continued
120 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

EXPLANATION

Hydologic conditions

Routine sample

Storm sample

Season

“Warm” (April to September)

“Cool” (October to March)

10

8

6

4

2

0

Nitrate plus nitrite, in 

milligrams per liter as nitrogen

A B

10

8

6

4

2

0

Total nitrogen,

in milligrams per liter

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Orthophosphate, in milligrams 

per liter as phosphorus

1

0.1

0.01

Total phosphorus, in

milligrams per liter

O N

WY 2012

Water year

WY 2013

D J F M A M J J A S O N D J F M A M J J A S O

WY 2011 WY 2012

WY

WY 2013

M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

Figure 84. Concentrations of various constituents in discrete samples from the Conewago Creek (A) Bellaire and (B) 

Falmouth streamgages.
Conewago Creek Watershed Water-Quality Characterization 121

Surrogate Models for the Computation of 

Sediment and Nutrient Loads

The discrete and continuous water-quality data were 

subsequently analyzed to explore the development of surrogate regression models to predict total sediment concentration 

(TSC) and TN and TP concentrations. The development 

of these surrogate models is critical to the computation of 

constituent loads and an improved understanding of the 

watershed function.

Model Development

Sediment samples from Conewago Creek were analyzed 

at two different labs depending on flow conditions. During 

low to medium flows, the sediment samples were analyzed 

for total suspended solids (TSS) at the PADEP laboratory. 

Samples collected during high flows were analyzed for SSC 

at the USGS Sediment Laboratory in Kentucky. Previous 

studies have examined the bias between TSS and SSC 

samples collected concurrently and concluded that total 

sediment load is under-predicted based on TSS alone (Gray 

and others, 2000). The TSS and SSC records were combined 

(concatenated) into a new variable, referred to as total 

sediment concentration (TSC) for load estimation.

 Standard USGS multiple linear regression methods 

were used to develop the best surrogate models (Helsel and 

Hirsch, 2002; Jastram and others, 2009; Rasmussen and 

others, 2009), and a detailed overview of the model selection 

process is presented in the Study Approach and Methods 

section. The best regression model prediction of TSC and TN 

and TP concentrations, as well as basic model diagnostics, is 

presented in table 29. Figures 85, 86, and 87 present observed 

versus predicted concentrations, observed and predicted 

concentrations relative to flow, and residuals versus the 

predicted concentrations for the best TSC, TN, and TP models 

at the upstream and downstream sites. 

Table 29. Details of the top-ranked suspended sediment, total nitrogen, and total phosphorus concentration estimation models for the 

Conewago Creek Bellaire and Falmouth streamgages.

[dtime, decimal time; <, less than; ln, natural logarithm]

Watershed Explanatory 

variables

Model 

coefficient p-value

Variance 

inflation 

factor

Number of 

observations

Adjusted 

coefficient of 

determination

Mallows’ Cp

Total suspended solids and suspended-sediment concentration

Bellaire Intercept 1.634 0.004

Data 

unavailable 38 84.32 2.19 ln(Flow) 1.479 <0.001

ln(Flow)2 –0.284 0.002

Water temperature 0.147 <0.001

Falmouth Intercept 1.407 0.019

Data 

unavailable 46 74.27 7.43 ln(Flow) 1.281 <0.001

Water temperature 0.115 <0.001

dtime 0.651 0.014

Total nitrogen

Bellaire Intercept 0.953 <0.001

Data 

unavailable ln(Flow) 0.065 0.023 38 23.94 0.30

ln(Flow)2 –0.052 0.015

Falmouth Intercept –1.957 0.081

Data 

unavailable 42 36.80 6.31 ln(Flow) 0.116 0.006

ln(Flow)2 –0.043 0.026

ln(Specific conductance) 0.587 0.005

Total phosphorus

Bellaire Intercept –2.686 <0.001

ln(Flow) 0.628 <0.001 1.11 38 0.82 2.71

Water temperature 0.068 <0.001 1.11

Falmouth Intercept –2.361 <0.001

ln(Flow) 0.626 <0.001 1.13 46 0.70 3.24

Water temperature 0.065 <0.001 1.13
122 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 85. Observed and estimated values from the best suspended-sediment surrogate models for the Conewago 

Creek (A) Bellaire and (B) Falmouth streamgages, the observed and estimated values along with streamflow, and the 

residual plot of model predictions.

2 10 100 1,000

1,000

100

10

2

Observed total sediment concentration,

in milligrams per liter

1,000

100

10

2

Total sediment concentration,

in milligrams per liter

Estimated total sediment concentration, in milligrams per liter

Streamflow, in cubic feet per second

–1.0

0

2.5

2.0

1.5

0.5

1.0

–0.5

–1.5

–2.0

–2.5

Residual error

Streamflow, in cubic feet per second

2 10 100 1,000

2 10 100 1,000 4,000 2 10 100 1,000 4,000

Estimated total sediment concentration, in milligrams per liter

2 10 100 1,000

Estimated total sediment concentration, in milligrams per liter

2 10 100 1,000

Estimated total sediment concentration, in milligrams per liter

A B

EXPLANATION

Observed concentration

Estimated concentration

EXPLANATION

Observed concentration

Estimated concentration
Conewago Creek Watershed Water-Quality Characterization 123

Figure 86. Observed and estimated values from the best total nitrogen surrogate models for the Conewago Creek

(A) Bellaire and (B) Falmouth streamgages, the observed and estimated values along with streamflow, and the 

residual plot of model predictions.

A B

9

8

7

6

5

4

3

2

1

0

0 1 2 3 4 5 6 7 8 9

Estimated total nitrogen, in milligrams per liter

9

8

7

6

5

4

3

2

1

0

Streamflow, in cubic feet per second

1 10 100 1,000 10,000

EXPLANATION

Observed concentration

Estimated concentration

–1.0

0

0.5

1.0

–0.5

Estimated total nitrogen, in milligrams per liter

0 1 2 3 4 5

0 1 2 3 4 5

Estimated total nitrogen, in milligrams per liter

5

4

3

2

1

0

Observed total nitrogen, in milligrams per liter Total nitrogen, in milligrams per liter

Streamflow, in cubic feet per second

5

4

3

2

1

0

0 1 2 3 4 5

Estimated total nitrogen, in milligrams per liter

–0.75

0

0.25

0.50

–0.25

0.75

–0.50

Residual error

EXPLANATION

Observed concentration

Estimated concentration

1 10 100 1,000
124 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 87. Observed and estimated values from the best total phosphorus surrogate models for the Conewago 

Creek (A) Bellaire and (B) Falmouth streamgages, the observed and estimated values along with streamflow, and the 

residual plot of model predictions.

0.01 0.1 1

0.01

0.1

1

0.01

0.1

1

Observed total phosphorus, in milligrams per liter

0.01

0.1

1

0.01

0.1

1

Total phosphorus, in milligrams per liter

Estimated total phosphorus, in milligrams per liter

0.01 0.1 1

Estimated total phosphorus, in milligrams per liter

Streamflow, in cubic feet per second

0

0.5

1.0

–0.5

Residual error

Streamflow, in cubic feet per second

2 10 100 1,000 4,000 2 10 100 1,000 4,000

0.01 0.1 1 0.01

Estimated total phosphorus, in milligrams per liter

0.1 1

Estimated total phosphorus, in milligrams per liter

A B

EXPLANATION

Observed concentration

Estimated concentration

EXPLANATION

Observed concentration

Estimated concentration

–1.5

–1.0

–0.5

0.0

0.5

1.0

1.5
Conewago Creek Watershed Water-Quality Characterization 125

The best sediment models included flow and water 

temperature at the upstream site, and flow, water temperature, 

and time at the downstream site. At the downstream site 

(Falmouth location), the model diagnostics indicate the 

possibility of slight overprediction of load at the highest flows 

(predicted values higher than observed values). Continued 

sampling and the addition of continuous turbidity data could 

improve this regression model. For the prediction of TN, the 

Bellaire model contains only flow and flow-squared terms, 

while the Falmouth model includes flow, flow squared and 

specific conductance. Both models explained relatively little 

of the variability in the TN concentrations. The deployment 

of a continuous nitrate monitor could help improve TN 

predictions. The best prediction models for TP concentrations 

included flow and water temperature and accounted for at least 

70 percent of the variability in TP at each of the sites. 

Interpretation of the Computed Loads 

After developing surrogate models for estimating 

concentrations of total sediment, TN, and TP, water-year 

loads of these constituents were computed using the record of 

continuous flow (from the streamgages) and predicted water 

quality (from the regression equations presented above). 

Methods for the replacement of missing records are provided 

in the Development of Surrogate Water-Quality Models and 

Computation of Loads section of this report. The USGS 

software program LOADEST was used for daily load 

computations with estimation of error between computed and 

predicted loads. 

The water-year total sediment loads are presented in 

figure 88, along with the total annual flow. The 2013 Bellaire 

sediment load was approximately 2,800 tons, while the 

Falmouth sediment loads were 9,900 tons in 2012 and 

18,900 tons in 2013. Generally, the total mass of sediment 

transported is highly related to the total flow in a given year; 

however, factors in addition to total annual flow (such as 

rainfall intensity, duration, season, and antecedent conditions) 

can affect the amount of sediment transported. For example, 

the accumulated total flows in 2012 and 2013 at the downstream location are nearly the same; however, the 2013 annual 

sediment load is nearly double that of 2012. 

Sediment loadings within Conewago Creek are highly 

episodic, with the majority of the sediment transported in 

only a few events (figs. 89 and 90). In 2013, at the upstream 

location, approximately 60 percent of the sediment load 

was transported in three high-flow events (fig. 89), and 

the downstream location had approximately 80 percent 

transported in the same three high-flow events (fig. 90). Based 

on the 2013 data, approximately 15 percent of the sediment 

load originated from above the upstream monitoring location, 

which composes approximately 43 percent of the monitored 

Conewago Creek watershed. Conversely, 85 percent of 

sediment load was transported from the remaining 57 percent 

of the downstream Conewago Creek watershed (fig. 90). This 

disproportionate amount of transported sediment is likely 

caused by the land-use distribution of more forest land in the 

upper headwaters and more agricultural and mixed land uses 

in the lower part of the watershed (fig. 4A). 

Annual TN loads were not estimated because of poor 

model performance and results of model diagnostics. Additional discrete monitoring in conjunction with a continuous 

nitrate sensor (a surrogate for TN), could produce a better 

model and more robust predictions. 

The water-year TP loads are presented in figure 91, along 

with the total flow for each water year. Note that 2012 was 

a partial monitoring year at the upstream site. As reported 

earlier, the annual flows in 2012 and 2013 were nearly the 

same; however, unlike total sediment loads, the TP loads were 

only slightly higher in 2013 with an additional 3,200 pounds 

transported. A total of 51,500 pounds was transported past 

the downstream monitoring location during the 2013 water 

year (fig. 91). The cumulative loading plot for TP is similar 

to that of sediment, with the three largest storm events 

in 2013 transporting the majority (75 percent) of the TP load 

(fig. 92 and 93). Based on the 2013 data, approximately 

25 percent of the TP load originated from above the upstream 

monitoring location. Similar to the total sediment loads, a 

disproportionate amount of TP load (75 percent of TP load; 

57 percent of the watershed) was mobilized between the 

upstream and downstream monitoring locations.

 In summary, the sediment and TP loadings were largely 

driven by storm events, but base-flow contributions were still 

substantial. More management actions targeting minimizing 

effects of the high-flow events by keeping more water on 

the landscape (terracing) and more runoff-control structures, 

in addition to streambank stabilization, could help reduce 

sediment and TP loads.
126 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

6,000

5,000

4,000

3,000

2,000

1,000

0

2013

Annual total sediment load, in tons

A. Bellaire

Water year

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

B. Falmouth

2012 2013

Annual total streamflow, in millions of cubic feet

Annual total sediment load, in tons

50,000

40,000

30,000

20,000

10,000

2,500

2,000

1,500

1,000

500

0

Water year

0

900

800

700

600

500

400

300

200

100

0

Annual total streamflow, in millions of cubic feet

Figure 88. Total sediment load and total annual streamflow at the 

Conewago Creek (A) Bellaire and (B) Falmouth streamgages for water 

years 2012 and 2013.

EXPLANATION

Total sediment 

load

Streamflow 

A

B

800

600

400

200

0

Streamflow, 

in cubic feet per second

100

90

80

70

60

50

40

30

20

10

0

Percent of total sediment load

estimated during the study period

3,000

2,500

2,000

1,500

1,000

500

0

Annual total sediment load, 

in tons

Water year 2012 Water year 2013

1,000

800

600

400

200

0

Total annual streamflow, 

in millions of cubic feet

O N D J F M A M J J A S O N D J F M A M J J A S

Figure 89. Total sediment load at the Conewago Creek Bellaire streamgage for water years 2012 

and 2013 along with (A) the percentage of load accumulated and the hydrograph during the period 

of study and (B) the accumulation of load and streamflow in each water year.
Conewago Creek Watershed Water-Quality Characterization 127

Figure 90. Total sediment load at the Conewago Creek Falmouth streamgage for water years 2011 

through 2013 along with (A) the percentage of load accumulated and the hydrograph during the 

period of study and (B) the accumulation of load and streamflow in each water year.

40,000

35,000

20,000

10,000

0

Annual total sediment load, 

in tons

B

EXPLANATION

Total sediment 

 load 

Streamflow 

A

100

90

80

70

60

50

40

30

20

10

0

Percent of total sediment load

estimated during the study period

Water year 2011

O N D J F M A M J J A S

Water year 2012

O N D J F M A M J J A S

Water year 2013

O N D J F M A M J J A S

Streamflo

w, 

in cubic feet per second

10,000

1,000

100

10

1

30,000

25,000

15,000

5,000

2,000

1,000

500

0

Total annual streamflo

w, 

in millions of cubic feet

1,500
128 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

900

800

700

600

500

400

300

200

100

0

2013

Annual total streamflow, in millions of cubic feet

A. Bellaire B. Falmouth

2012 2013

Annual total streamflow, in millions of cubic feet

Water year Water year

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

0

Annual total phosphorus load, in pounds

2,500

2,000

1,500

1,000

500

0

Annual total phosphorus load, in pounds

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

0

2,000

Figure 91. Total phosphorus load and total annual streamflow at the 

Conewago Creek (A) Bellaire and (B) Falmouth streamgages for the 2012 

and 2013 water years.

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

1,000

800

600

400

200

0

Annual total phosphorus load, 

in pounds

Total annual streamflow, 

in millions of cubic feet

B

100

90

80

70

60

50

40

30

20

10

0

Percent of total phosphorus load

estimated during the study period

800

700

600

500

400

300

200

100

0

Streamflow, 

in cubic feet per second

EXPLANATION

Total phosphorus load

Streamflow 

A

Water year 2012 Water year 2013

O N D J F M A M J J A S O N D J F M A M J J A S

Figure 92. Total phosphorus load at the Conewago Creek Bellaire streamgage for water years 

2012 and 2013 along with (A) the percentage of load accumulated and the hydrograph during the 

period of study and (B) the accumulation of load and streamflow in each water year.
Conewago Creek Watershed Water-Quality Characterization 129

Figure 93. Total phosphorus load at the Conewago Creek Falmouth streamgage for water years 

2011 through 2013 along with (A) the percentage of load accumulated and the hydrograph during 

the period of study and (B) the accumulation of load and streamflow in each water year.

EXPLANATION

Total phosphorus 

load

Streamflow 

A

100

90

80

70

60

50

40

30

20

10

0

Percent of total phosphorus load

estimated during the study period

10,000

1,000

100

10

1

Streamflo

w, 

in cubic feet per second

B

Water year 2011

O N D J F M A M J J A S

Water year 2012

O N D J F M A M J J A S

Water year 2013

O N D J F M A M J J A S

Annual total phosphorus load,

in pounds

120,000

90,000

60,000

30,000

0

2,000

1,000

500

0

Total annual streamflo

w, 

in millions of cubic feet

1,500
130 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Spatial Patterns in Water Quality 

In addition to routine sample collection at the two 

monitoring locations, 14 locations (including the 2 monitoring 

locations) were sampled during 1 high-flow synoptic event 

in Conewago Creek (fig. 94). The purpose of the sampling 

was to provide information (a snapshot) of the chemical and 

hydrologic spatial variability within the watershed, as well as 

provide information related to sources and transport processes 

for nutrients and sediments. Sites were selected to isolate 

various tributaries and stream reaches. Each of the major 

subwatersheds had at least one location (many had two), and 

four locations were on the mainstem of Conewago Creek. The 

high-flow synoptic sampling was conducted on May 15, 2012, 

following a watershed-wide 1.5- to 2-inch rainfall event. All 

sites were sampled within a 3-hour period. At each location, field measurements were recorded and discharge was 

measured at locations where conditions were safe. At four 

locations, discharge was estimated by using drainage ratios. 

Significant variability in discharge and in nitrogen, 

phosphorus, and sediment concentrations were observed in 

the Conewago Creek watershed during the high-flow synoptic 

sampling (table 30). Discharges ranged from 11 to 412 ft3/s, 

and not surprisingly, were highly dependent on drainage 

area. TN concentrations ranged from 2.16 to 11.7 mg/L (the 

highest observed in Conewago Creek during the study period), 

TP concentrations ranged from 0.11 to 1.59 mg/L, and SSC 

ranged from 57 to 250 mg/L. 

Mean nutrient concentrations were lower (TN 3.34 mg/L 

and TP 0.37 mg/L) for sites that drain the mainly forested tributaries (sites 1, 2, and 3) above the upstream monitoring location 

(site 4) and were higher downstream (TN 6.0 mg/L and TP 

0.78 mg/L). Average SSCs were about equal above and below 

the upstream monitoring locations. Expectations were that 

sediment concentrations would be higher downstream, similar to 

the TP concentration patterns. The lower than expected sediment 

concentrations could have been the result of missing peak 

sediment concentrations on several tributaries downstream.

Differences in nutrient and sediment concentrations 

and loads by subwatershed for the high-flow synoptic 

sampling are shown in figure 94. TN has a general pattern of 

increasing concentrations and loads downstream as shown 

by the stream-reach line color and stream-reach thickness 

between monitoring sites. The TN concentration at site 9 

(Brills Run) was 11.7 mg/L (fig. 94A; table 30), nearly double 

the next highest TN concentration. The area above this site is 

dominated by agricultural activities (42 percent). The second 

highest TN concentration occurred at site 10 (Lynch Run), 

which coincides with dense multiple land uses. A trailer park, 

residential homes, and small businesses dominate the western 

side of Lynch Run, and agriculture and a golf course dominate 

the eastern side of Lynch Run. Based on the daily flow, 

approximately 14,500 pounds of TN were discharged from the 

Conewago Creek watershed on May 15, 2012 (fig. 94A). 

The pattern of increasing TP concentrations and load 

patterns downstream is similar to that exhibited by TN (fig. 94B). 

The lowest phosphorus concentrations were generally observed at 

sites with greater percentages of forested land. Based on the daily 

flow, approximately 1,500 pounds of TP were discharged from 

the Conewago Creek watershed on May 15, 2012 (fig. 94B).

Sediment concentration and load patterns differed from 

the TP patterns (fig. 94C). Out of the five sites with the highest 

sediment concentrations, only one also had one of the highest 

TP concentrations (site 5A). Because TP is bound to sediment, 

a closer correspondence in spatial patterns was expected. The 

highest concentrations of sediment did occur in the agricultural basins. Figure 94 does suggest some sediment deposition 

in the lowest area of the basin based on the measured load 

at site 12. Based on the daily flow, approximately 124 tons 

of sediment were discharged from the Conewago Creek 

watershed (site 12) on May 15, 2012 (fig. 94C).
Conewago Creek Watershed Water-Quality Characterization 131

Figure 94. Instantaneous (A) total nitrogen, (B) total phosphorus, and (C) suspended-sediment 

concentrations and loads at the Conewago Creek watershed monitoring locations, May 15, 2012.

76°40' 76°36' 76°32' 76°28'

40°16'

40°12'

40°08'

EXPLANATION

Total nitrogen load, in 

pounds per day

186 to 3,089

3,090 to 5,991

5,992 to 8,893

8,894 to 11,795

11,796 to 14,698

Total nitrogen, in

milligrams per liter

2.16 to 4.07

4.08 to 5.99

6.00 to 7.90

7.91 to 9.82

9.83 to 11.74

Streamflow, in

cubic feet per second

11 428

Total phosphorus, in

milligrams per liter

Total phosphorus load,

in pounds per day

0.11 to 0.41

0.42 to 0.71

0.72 to 1.02

1.03 to 1.32

1.33 to 1.63

20 to 328

329 to 635

636 to 943

944 to 1,250

1,251 to 1,558

Suspended-sediment

load, in tons per day

3 to 44

45 to 85

86 to 126

127 to 167

168 to 209

Suspended sediment, in

milligrams per liter

57 to 96

97 to 134

135 to 173

174 to 211

212 to 250

B. Total phosphorus

C. Suspended sediment

1

1

2

3

4

5A

5

7

14

9

8

10

11

12

13

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from U.S. Geological Survey StreamStats program (2012b)

North American Datum of 1983

0 1 2 5 MILES

0 1 2

3 4

3 4 5 KILOMETERS

Synoptic sampling location and identifier

1

2

3

4

5A

5

7

14

9

8

10

11

12

13

A. Total nitrogen

Conewago Creek

 watershed

1

2

3

4

5A

5

7

14

9

8

10

11

12

13
132 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 30. Station information, sampling time, and water-quality results from the high-flow synoptic samples collected in the 

Conewago Creek watershed.

[USGS, U.S. Geological Survey; ft3/s, cubic foot per second; mg/L, milligram per liter; mi2, square mile; SSC, suspended-sediment concentration]

Station ID

USGS 

local 

ID

USGS site name

Drainage 

area 

(mi2)

Mainstem or 

subwatershed Time Discharge (ft3/s)

Total 

nitrogen 

(mg/L)

Nitrate-N 

(mg/L)

Total 

phosphorus 

(mg/L)

SSC 

(mg/L)

01573670 1 Conewago Creek at Colebrook, PA5 Mainstem 8:30 34 2.18 1.63 0.11 142

01573680 2 Unnamed tributary to Little 

Conewago Creek near 

Upper Lawn, PA

5.15 Upper 

Conewago 

Creek

9:45 29 4.38 2.76 0.60 193

01573690 3 Conewago Creek near 

Lawn, PA

18.1 Mainstem 10:50 119 3.47 2.19 0.40 225

01573695 4 Conewago Cr near Bellaire, PA20.5 Mainstem 11:30 252 3.83 2.65 0.43 168

01573702 5 Hoffer Creek near Bellaire, 

PA

2.25 Hoffer Run 12:15 22 5.59 4.27 0.36 158

01573703 5a Unnamed trib to Hoffer 

Creek

2.98 Hoffer Run 13:00 13 6.27 4.20 0.91 198

01573704 7 Unnamed tributary to 

Conewago Creek near 

Elizabethtown, PA

1.93 Gallaghar 

Run

13:40 43 5.15 3.83 0.46 57

01573705 14 Conewago Ck at Aberdeen 

Mills, Pa

33 Mainstem 8:30 280 4.69 3.82 0.48 250

01573706 8 Brills Run near Deodate, 

PA

2.32 Brills Run 9:50 32 5.00 3.15 0.57 199

01573707 9 Brills Run near Elizabethtown, PA3.61 Brills Run 9:10 50 11.70 9.57 1.59 126

01573708 10 Lynch Run at Londonderry 

Township, PA

1.58 Lynch Run 10:45 24 7.25 4.83 1.42 96

01573709 11 Lynch Run near Elizabethtown, PA4.24 Lynch Run 11:40 11 5.41 3.13 1.07 95

01573710 12 Conewago Cr near Falmouth, PA47.3 Mainstem 13:40 412 6.53 4.54 0.69 112

01573713 13

Unnamed tributary to 

Conewago Creek near 

Royalton, PA

2.42 Unnamed 

tributary 12:40 16 2.16 1.08 0.28 61
Conewago Creek Watershed Water-Quality Characterization 133

Characterization of Nitrogen Sources

Knowledge of the primary nitrogen sources in the 

Conewago Creek watershed is critical for making informed 

watershed-management decisions regarding BMP implementation. To explore the likely sources of the nitrogen observed 

in Conewago Creek, two different approaches were used: 

(1) an analysis of land-use and agricultural census data, and 

(2) SPARROW model results. A better understanding of 

nitrogen sources in Conewago Creek might make it easier to 

more effectively target the locations of agricultural conservation 

practices to provide the highest return on investment. 

Compiled agricultural practices and land-use data can 

provide evidence of the nitrogen sources within a given 

region. While an analysis of agricultural practices and land-use 

data cannot provide detailed information regarding nitrogen 

storage and potential transport to groundwater or surface 

water, the analysis can provide information about the sources 

of material that are available for transport. Because most of 

the available datasets related to agricultural practices were 

developed at the county scale, the land-use nitrogen-source 

analysis was also performed at the county scale. Results of 

a county-based source analysis are only relevant to a given 

watershed of interest (Conewago Creek) if land uses are 

similar in the basin of interest and the county. The NLCD 

land use for Conewago Creek was compared to the land use 

of Lebanon and Dauphin Counties (fig. 95). As expected, the 

Conewago Creek watershed has a mix of both counties’ land 

uses, with some percentages of categories closely matching 

those of each county. For example, the category of cultivated 

crop is more indicative of Lebanon County, and the category 

of forest is more indicative of Dauphin County. This corresponds to the geographical locations of each county. Areas 

above the Conewago Creek upstream monitoring location are 

dominated by forest and are nearly all in Lebanon County, but 

the cultivated areas are located most often in Dauphin County.

The land-use-derived sources of nitrogen applied to the 

land surface were similar in Dauphin and Lebanon Counties 

from 1987 through approximately 2006 (fig. 96A; results 

for 2002 displayed in fig. 96B). Within both counties, manure 

sources of nitrogen dominate, followed by a mixture of other 

sources, including agricultural fertilizer usage, atmospheric 

deposition, sanitary sewage, septic waste, and nonfarm 

fertilizer. While these land-use-derived source data provide 

information on the relative magnitude of the nitrogen inputs 

on the landscape, the data do not provide information on the 

amount routed to the stream system. 

 Watershed-scale nitrogen sources for Conewago 

Creek were further evaluated by using the output from the 

SPARROW model with parameters derived for the Chesapeake Bay watershed (Ator and others, 2011). The SPARROW 

model’s relative sources of nitrogen input and output source 

data are shown in figure 97. The output represents the 

percentage of the total nitrogen reaching the stream, by source. 

Within the Conewago Creek watershed, the primary sources of 

in-stream nitrogen are from fertilizer and fixation, with lesser 

contributions from manure, atmospheric deposition, and urban 

sources (fig. 97). These results are in broad agreement with 

the county land-use data that indicated agricultural sources are 

dominant, followed by atmospheric and urban sources. Similar 

to observations from the Smith Creek watershed, as watershed 

inputs were converted to fractions of N sources contributing 

to the TN export load of the stream, SPARROW model results 

indicated that N from manure was preferentially retained and 

N from fertilizer plus nitrogen fixation was preferentially 

exported, resulting in a factor of 4 reversal in the relative 

fluxes between input and export. The dominant sources of 

in-stream nitrogen in Conewago Creek are likely a combination of manure as well as fertilizer plus nitrogen fixation; 

further investigation could provide more resolution regarding 

the dominant nitrogen source.
134 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 95. Percentages of land-use types from Homer and others 

(2015) within the Conewago Creek watershed, Dauphin County, 

and Lebanon County.

Conewago Creek watershed

Dauphin County

Lebanon County

Other 0.73 percent

Other 0.38 percent

Other 0.28 percent

Forest

43.78 percent

Forest

46.44 percent

Forest

32.64 percent

Developed

16.09 percent

Developed

21.36 percent

Developed

19.26 percent

Cultivated crop

20.07 percent

Cultivated crop

9.56 percent

Cultivated crop

22.42 percent

Pasture/hay

17.32 percent

Pasture/hay

16.11 percent

Pasture/hay

23.97 percent

Water

0.10 percent

Water

5.87 percent

Wetland

1.91 percent

Wetland

0.28 percent

Water

0.72 percent

Wetland

0.71 percent
Conewago Creek Watershed Water-Quality Characterization 135

Fertilizer (farm)

Sanitary

sewer

Septic

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

A. Kilograms of nitrogen per year

B. Nitrogen sources in 2002

Manure

Atmospheric

deposition

3.02 percent

Atmospheric 

deposition

Fertilizer (nonfarm)

8.18 percent

Fertilizer (nonfarm)

Septic

4.20 percent

Sanitary sewer

17.55 percent

Atmospheric

deposition

1.42 percent

Septic

1.51 percent

Sanitary sewer

3.01 percent

Dauphin County Lebanon County

Dauphin County Lebanon County

100,000

1,000,000

10,000,000

30,000

Fertilizer

(farm)

22.76 percent

Fertilizer

(farm)

19.20 percent

Manure

44.29 percent Manure

73.30 percent

Fertilizer (nonfarm)

1.56 percent

Figure 96. (A) Nitrogen sources for Dauphin County and Lebanon County from 1987 to 2012 and (B) nitrogen sources 

in 2002 for Dauphin County and Lebanon County, Pennsylvania.

Atmospheric 

deposition

8.85 percent

Atmospheric 

deposition

15.03 percent

Manure

53.14 percent

Manure

19.90 percent

Fertilizer/fixation

36.40 percent Fertilizer/fixation

54.65 percent

Urban area

1.61 percent

Urban area

10.43 percent

Input Output

Figure 97. Percentages of input and output sources of total nitrogen for the Conewago Creek 

watershed as generated by the 2002 Chesapeake Bay Total Nitrogen SPARROW model. 
136 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Implementation of Conservation Practices and 

Water-Quality Response 

The designation of Conewago Creek as an agricultural 

Showcase Watershed by USDA, and the subsequent commitment of program funding to the Conewago Creek watershed 

resulted in increased implementation of conservation management actions from 2009 to 2013 (table 31). Several Federal 

and State programs have been responsible for the increased 

level of conservation implementation, including cost-share 

and technical assistance from the NRCS, the FSA, and the 

EPA Section 319 Nonpoint Source Management Program. 

Implementation efforts in the Conewago Creek watershed are 

indicative of what can be done through local, communitybased support for these programs. An extensive outreach 

effort by the Conewago Creek watershed group helped to 

coordinate efforts. During the last 5 years, representatives 

from the USDA, NRCS, and local conservation districts have 

visited nearlzy all of the farmers within the Conewago Creek 

watershed to conduct surveys, discuss farm management, and 

plan possible implementation of conservation practices.

In the Conewago Creek watershed, 682 USDA-compliant 

agricultural conservation practices were implemented 

between 2009 and 2013 (table 31). The USDA provided 

cost-share for 198 of those practices using funds from 

the Chesapeake Bay Watershed Initiative (131 practices), 

Environmental Quality Incentives Program (60 practices), and 

additional USDA programs (7 practices). For the remaining 

484 conservation practices, the NRCS provided technical 

assistance but funding was provided by non-Federal programs 

and (or) farmers. While the total number of conservation 

practices implemented per year recorded in the USDA 

database remained relatively steady at around 100 per year, the 

designation of the Conewago Creek watershed as a Showcase 

Watershed led to yearly increases in USDA cost-shared 

conservation implementation, from 9 in 2009 to 47 in 2013, 

through the Chesapeake Bay Watershed Initiative.

The aggregated total of conservation practice 

implementations (new practices applied between October 2007 

and September 2013) is reported by NRCS practice code 

in table 32 for all practices with five or more participating 

farmers. State-funded conservation practices sponsored by 

the EPA Section 319 Nonpoint Source Management Program 

are included in table 33. Common practices implemented 

included grassed waterways, residue and tillage management, 

nutrient management, conservation crop rotation, terraces, 

and construction related to water control. Crop residue and 

tillage management affect sediment transport from the land 

surface, and nutrient management and crop rotation affect 

nitrogen and phosphorus by controlling applications to the 

field and enhancing nutrient uptake. Because no historical 

water-quality data exist for either Conewago Creek monitoring 

station, trends in water quality cannot be evaluated until about 

10 years of data collection is completed.

The cumulative effects of all the BMP implementation 

within the Conewago Creek watershed are difficult to quantify 

because of the many different types of BMP practices, the 

location of each practice, potential lag times between implementation and full functionality of the practice, and the relative 

efficiency of each practice/installation. Factors such as location, lifespan, number of conservation practices, and amount 

of land affected by the practice all influence downstream water 

quality. Another important factor in detecting changes in water 

quality related to conservation implementation is the time lag 

from implementation of a practice to a measurable resultant 

change. The lag could be years to decades depending on the 

constituent (nitrogen, phosphorus, or sediment) and primary 

delivery source (groundwater or surface water). Further 

water-quality monitoring could measure the overall changes in 

Conewago Creek nutrient and sediment transport.

Table 31. Number of USDA-compliant conservation practices implemented in water years 2007 through 2013 in the Conewago Creek 

watershed, aggregated by sponsoring program.

[USDA, U.S. Department of Agriculture]

Program 2007 2008 2009 2010 2011 2012 2013 Total

Chesapeake Bay Watershed Initiative 0 0 3 28 28 34 38 131

Environmental Quality Incentives Program 17 5 6 17 7 1 7 60

Conservation Stewardship Program 0 0 0 0 3 0 2 5

Conservation Reserve Program1 0 2 0 0 0 0 0 2

Total USDA-funded practices 17 7 9 45 38 35 47 198

Conservation Technical Assistance2 114 43 101 45 84 51 46 484

Total practices 131 50 110 90 122 86 93 682

1

Conservation Reserve Program and Conservation Reserve Enhancement Program practices administered by the USDA Farm Service Agency.

2

Technical assistance and verification provided by the USDA Natural Resources Conservation Service, without federal cost-share funding.
Conewago Creek Watershed Water-Quality Characterization 137

Table 32. Implementation of USDA-compliant conservation practices within the Conewago Creek watershed for water years 2007 

through 2013.

[—, values are privacy protected due to fewer than five customers participating]

Practice 

code1 Practice name Lifespan(years) Units 2007 2008 2009 2010 2011 2012 2013

Aggregate 

implementation: 

2007 to 20132

412 Grassed Waterway 10 acres 8 — — — 6 — — 30

329 Residue and Tillage 

Management, No-Till

1 acres — — — 864 453 304 — 2,160

328 Conservation Crop Rotation 1 acres — — — — 366 — — 2,284

620 Underground Outlet 20 feet — — — — 7,773 — — 17,487

590 Nutrient Management 1 acres 535 — — — — — — 1,379

600 Terrace 10 feet — — — — 19,912 — — 32,377

606 Subsurface Drain 20 feet — — — — 6,418 — — 20,961

1

Practice codes from the U.S. Department of Agriculture, Natural Resources Conservation Service (2016).

2

Aggregate implementation is greater than the sum of reported annual practices because privacy protections restrict the reporting of annual results for practices with fewer than five participating customers.

Table 33. Implementation of State-level conservation practices sponsored by the EPA Section 319 Nonpoint Source Management 

Program within the Conewago Creek watershed.

[EPA, U.S. Environmental Protection Agency]

Practice name Units Amount

Access Road feet 218 

Animal Trails and Walkways feet 4,799 

Diversion acres 424 

Grassed Waterway acres 11 

Grazing Planned Systems acres 29 

Heavy Use Area Protection acres 0 

Nutrient Management acres 660 

Riparian Forest Buffer acres 18 

Stream Channel Stabilization feet 4,840 

Stream Exclusion with Grazing Land Management feet 6,310 

Stream Habitat Improvement and Management feet 3,370 

Streambank and Shoreline Protection feet 9,680 

Terrace feet 12,425 

Waste Storage Facility number 2 

Wetland Restoration acres 16 
138 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

One way to develop an interim set of expectations for 

future improvements in water quality is with the Chesapeake 

Bay Program’s watershed model. Evaluating model output 

for model scenarios with and without the implementation 

of BMPs provides an indication of expected improvement in the watershed. The results for the land-water 

segments that contain Conewago Creek are presented in 

figures 98 and 99 (source: Chesapeake Bay Modeling Team, 

written commun., 2015, based on Watershed Model 5.3.2). 

Despite the implementation of BMPs, these modeling 

scenarios project that the nitrate load has increased since 2007 

and is primarily driven by increases in wastewater sources. 

Between 1985 and 2012, agricultural sources of nitrate have 

decreased in the land-river segment that contains the entire 

Conewago Creek watershed (fig. 99).

Results from a recent CEAP report in the Chesapeake 

Bay Region (U.S. Department of Agriculture, Natural 

Resources Conservation Service, 2013) indicate that 

while implementation of conservation practices across the 

basin is increasing, trends in other cropland management 

practices that were evaluated from 2003–2006 to 2011 were 

less encouraging.

• Annual nitrogen application increased 10 percent on 

cultivated cropland, including a 9-percent increase 

in commercial fertilizer application and a 13-percent 

increase in manure nitrogen application.

• Annual phosphorus application increased 6 percent on 

cultivated cropland, including a 5-percent increase in 

commercial fertilizer application and an 11-percent 

increase in manure nitrogen application.

• Manure application rates on cultivated cropland 

increased by 25 percent.

• Use of appropriate nitrogen application rate on all 

crops in rotation decreased 9 percent from 32 percent 

to 23 percent of cropped acres.

These increases in nutrient inputs are associated with 

increased agricultural intensification, including higher yields 

and greater numbers of animals. These overall CEAP trends 

are not specific to Conewago Creek; however, they do reflect 

regional trends occurring in Chesapeake Bay agricultural 

practices. If the increases in commercial fertilizer applications 

are similar in Conewago Creek from farms and other land 

uses, then expectations for water-quality change will be 

further delayed and harder to detect, despite the widespread 

implementation of conservation practices.

Conceptual Watershed Model and Implications 

for Management Actions 

The intensive and extensive monitoring components 

combine to provide a process-level understanding of the 

Conewago Creek watershed. Major nutrient and sediment 

characterizations and transport processes are summarized 

below as follows:

• The geology of the watershed is a complex mix of 

sedimentary rocks with diabase intrusions. The more 

resistant sandstones are in the upper watershed, and 

softer, finer textured mudstones are in the middle and 

lower watershed. About 57 percent of the streamflow 

leaving the watershed is from runoff.

• Land use is strongly associated with geology. 

Agricultural land use dominates the areas underlain 

by sandstone and mudstone, and forested land use 

dominates the areas underlain by diabase.

• Extensive hydrologic and, to a lesser extent, seasonal 

variability in the water-quality conditions of Conewago 

Creek were observed at the upstream and downstream 

locations. Constituents associated with groundwater 

discharge (calcium, magnesium, and chloride) were 

generally greatest during base flow and were diluted during stormflow. During stormflow, particulate constituents 

(metals, total nutrients, and sediment) increased. Seasonal 

patterns in water temperature, specific conductance, and 

other water-quality constituents were observed.

• Mean concentrations of nearly every measured 

constituent increased from upstream to downstream, 

indicative of the changing land uses.

• Sediment loads are highly episodic; the majority of the 

sediment is transported in only a few high-flow events. 

Phosphorus transport is usually similar to sediment transport because of its affinity to attach to sediment; however, 

while phosphorus transport was elevated in high-flow 

events, it was not as closely related to sediment. 

• Two approaches were used to examine nitrogen inputs 

into Conewago Creek—county-level agricultural 

land-use and census data and SPARROW model data. 

As expected, the county-level data represented the two 

counties composing most of the watershed. The three 

leading inputs were agriculture manure, commercial 

fertilizer, and atmospheric deposition. The SPARROW 

model was also used to discern primary sources of 

instream nitrogen. The primary sources of instream 

nitrogen are commercial fertilizer and fixation, manure, 

and atmospheric deposition.

 The process-level understanding is directly applicable 

in developing the most effective conservation practices and 

plans by providing useful results to managers for informed 

decisionmaking. Managing the following constituents in the 

following ways could generate the greatest benefit. 

• Management activities for nitrogen will likely be 

most effective in the middle part of the watershed 

in the areas that tend to have the greater amount of 

agricultural land.
Conewago Creek Watershed Water-Quality Characterization 139

10

8

6

4

2

0

–2

–4

–6

–8

–10

Year

1985 1990 1995 2000 2005 2010

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Nitrate plus nitrite, in pounds of nitrogen

EXPLANATION

Pounds of nitrate plus 

 nitrite as nitrogen from

 all sources with no best

 management practice

 implementation 

Agriculture

Urban area

Forest

Wastewater

Source-specific pounds of 

 nitrate plus nitrite as 

 nitrogen with best 

 management practice 

 implementation

Percent difference per year

 in nitrate plus nitrite as 

 nitrogen predicted by 

 implementation versus no 

 implementation of best 

 management practices

Annual percent change of nitrate plus nitrite as nitrogen

Figure 98. Nitrate loads estimated from the Chesapeake Bay watershed model for 

the Conewago Creek Bellaire land-river segment with and without best management 

practice (BMP) implementation with input sources and the annual percentage 

difference of load attributed to BMP implementation, 1985–2012.

10

8

6

4

2

0

–2

–4

–6

–8

–10

Year

1985 1990 1995 2000 2005 2010

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

Nitrate plus nitrite, in pounds of nitrogen

EXPLANATION

Pounds of nitrate plus 

 nitrite as nitrogen from

 all sources with no best

 management practice

 implementation 

Agriculture

Atmospheric 

 deposition

Urban area

Forest

Wastewater

Source-specific pounds of 

 nitrate plus nitrite as 

 nitrogen with best 

 management practice 

 implementation

Percent difference per year

 in nitrate plus nitrite as 

 nitrogen predicted by 

 implementation versus no 

 implementation of best 

 management practices

Annual percent change of nitrate plus nitrite as nitrogen

Figure 99. Nitrate loads estimated from the Chesapeake Bay watershed model for 

the Conewago Creek Falmouth land-river segment with and without best management 

practice (BMP) implementation with input sources and the annual percentage 

difference of load attributed to BMP implementation, 1985–2012. 
140 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

• The development of nutrient management plans that 

address especially the application of manures, as 

well as commercial fertilizer should be important for 

reducing the overall nitrogen loading to the basin. 

• Managing these manure and fertilizer inputs should 

have a corollary benefit of reducing phosphorus inputs 

because nonpoint-source phosphorus inputs seem to 

dominate the phosphorus transport. 

• While modifications to the timing and incorporation 

of fertilizer and manure could alter the transport of 

nitrogen and phosphorus within the system, ultimately 

a reduction in nutrient loading is needed to reduce 

nutrient export from the watershed. 

• Many of the streams are impaired by sediment. Erosion 

and sediment controls are needed to improve the health 

of the streams and similar to nutrients and also to 

reduce sediment load exported from the watershed.

• The collection of empirical nutrient and geochemical 

water-quality data in the Conewago Creek watershed 

was critical for better understanding the nitrogen 

sources and temporal responses within the basin, for 

verifying regional model performance (SPARROW 

results), and for assessing trends in water-quality data 

(Chesapeake Bay Watershed Model results).

Ongoing USGS water-quality monitoring efforts will 

be directed towards the further understanding of the sources, 

sinks, and transport processes in Conewago Creek, as well as 

the detection of water-quality change that can be related to the 

implementation of management actions. Large changes in water 

quality take time and sufficient management of the landscape to 

actually affect the system; a number of different study elements 

will be used to help detect future change in the basin.

• Nutrient and sediment monitoring at the streamgages 

will provide a long-term record of the water quality 

in the basin. With sufficient data, trend analysis can 

be performed to determine whether a statistically 

significant trend exists, and comparisons from 

upstream to downstream can be further evaluated.

• The addition of a continuous nitrate monitor and a 

longer monitoring period will permit the estimation of 

a TN load. To investigate in-stream nitrate dynamics 

such as stream respiration and diel cycles, nitrate sensor data collected at a 15-minute interval are necessary.

• Changes in the nutrient and sediment concentration 

compared to flow relation might be an early indicator of 

water-quality change because increased management in 

the watershed is likely to produce a change in nutrient 

transport dynamics with minimal effects on hydrology. In 

other words, concentration and flow relations might evolve 

over time with continued and enhanced management.
Difficult Run Watershed Water-Quality Characterization 141

Difficult Run Watershed Water-Quality 

Characterization

Lead Author: James S. Webber

Water quality was characterized within the Difficult Run 

watershed through the use of a streamgage, site 30, located 

near the outlet of the basin and by extensive synoptic sampling 

at 35 sites throughout the basin (fig. 100). The synoptic sites 

were selected to characterize the water-chemical variability in 

the tributaries and upstream section of Difficult Run. Various 

stream orders, land-use activities, and surrounding hydrologic 

features were captured by the network of synoptic locations.

Streamflow and Water-Quality Characterization 

at the Intensive Monitoring Site

The intensive Difficult Run monitoring location is located 

approximately eight-tenths of a mile above the confluence 

of Difficult Run with the Potomac River (station 01646000). 

Discrete sample collection began at this site in July 2011, and 

continuous water-quality monitoring began in October 2012. 

A streamgage has been in operation at this location since 1935, 

which allows the findings and hydrologic conditions of this 

study period to be placed into a historical context. This section 

presents an overview and characterization of the physical 

hydrology and water-chemistry data from the various sampling 

activities at this location.

Characterization of the Physical Hydrology 

The hydrology of Difficult Run during the sample period 

and the long-term record of flow is presented in figure 101. 

Seasonal variation in flow followed historical patterns, with

elevated base flows of about 50 ft3/s in early- to mid-spring 

followed by a decrease through late summer. Historical 

flows during summer were between 5 and 15 ft3/s, which 

coincide with the conditions measured during this study. The 

total volume of water transported in water years 2011, 2012, 

and 2013 was higher than the historical median annual 

total streamflow of 1,200 million ft3 (fig. 102). Total annual 

discharge is mostly controlled by precipitation quantity and 

intensity. A comparison of annual rainfall totals at two weather 

stations adjacent to the Difficult Run watershed demonstrates 

that total precipitation was greater in 2013 than historical 

averages (table 34). Precipitation during 2012 was below longterm averages and was only higher in 2011 at the Dalecarlia 

Reservoir station. Rainfall intensity resulting from Tropical 

Storm Lee was mostly responsible for the large total discharge 

in 2011. Annual instantaneous flow peaked at 17,000 ft3/s in 

September 2011, and the substantial effect of that storm event 

on streamflow is seen in the cumulative annual streamflow 

(fig. 101). By contrast, maximum annual instantaneous flows 

were 2,100 ft3/s in 2012 and 2,650 ft3/s in 2013.

The influences of storm-related flows in the basin can 

be described by using the hydrograph separation tool, PART. 

The results indicate that 42.4 percent of the streamflow left 

the basin as base flow during the study period (table 35), 

which means that a majority of flow can be attributed to 

storm runoff. This is consistent with a traditional conceptual 

model of streamflow generation in an urban basin where 

imperviousness is known to cause an increased frequency, 

magnitude, and duration of extreme flow events (McMahon 

and others, 2003). A shift in historical flow records occurred 

in this basin following the development of Reston, Virginia, 

in the mid-1960s that resulted in greater mean annual peak 

discharges (Hupp and others, 2013). This continued development of the watershed is reflected in a lower BFI, 50.8 percent 

from 1990–2010, compared to the long-term historical value 

of 57.9 percent (table 35).
142 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

City of Fairfax

Outlet

of flow

Tysons

Corner

Reston

FAIRFAX COUNTY

FAIRFAX COUNTY

Piney Branch

Piney Run

Colvin Run

Hickory Run

Captain

Snakeden Branch

Rocky

Branch

S Fork Little Difficult Run

Wolftrap Creek

Lake

Fairfax

Lake

Anne

Lake

Thoreau

Lake 

Audubon

77°21' 77°18' 77°15'

39°00'

38°57'

38°54'

38°51'

Potomac River

17

14

15

13 31 12

11

10

8

6

7

1

2

35

36 3

4

9

5

18

29

30

24

28

27

25

34

33

26

21 22 23

16

20

19

Rocky Run

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

D.C.

VIRGINIA Atlantic Ocean

Chesapeake Bay

MARYLAND

Study

area

NEW

JERSEY

DELAWARE

EXPLANATION

Watershed 

Difficult Run

Tributary to Difficult Run

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Land use from Homer and others (2015)

Drainage area from Hayes and Wiegand (2006)

Roads are from U.S. Census Bureau TIGER/Line shapefiles (2015)

North American Datum of 1983

Monitoring station and identifier

USGS streamgage 01646000

18 Difficult Run

(area=57.82 mi2

)

Captain Hickory Run

Land use

Developed

30

Synoptic sampling location

 and identifier

Figure 100. Monitoring stations and the stream network within the Difficult Run watershed, Virginia.
Difficult Run Watershed Water-Quality Characterization 143

Streamflow, in cubic feet per second

100,000

10,000

1,000

100

10

1

0.1

0.01

Minimum daily flow to 5th percentile

5th percentile to 10th percentile

10th percentile to 25th percentile

25th percentile to 75th percentile

75th percentile to 90th percentile

90th percentile to 95th percentile

Median flow

EXPLANATION

Monthly flow statistics—Historical daily 

 flows from WY 1935 to WY 2013

Streamflow, in cubic feet 

 per second

WY Water year

Sampling event

Routine sampling

Storm sampling

Synoptic sampling

95th percentile to maximum daily flow

 WY 2011 WY 2012 WY 2013

J J A S O N D F M A M J J A S J O N D J F M A M J J A S O

Figure 101. Difficult Run streamgage and quantiles that represent historical monthly flow data with the timing of various 

sampling events from June 2011 through September 2013 within the Difficult Run watershed.

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Cumulative daily streamflow, in million cubic feet

2,500

2,000

1,500

1,000

500

0

Month

EXPLANATION

Historical streamflow Water year

2011

2012

2013

75th quantile of 

 observed values

25th quantile of 

 observed values

Median of 

 observed values

Figure 102. Cumulative daily streamflow values from the Difficult Run streamgage for water years 2011 through 

2013 with the median, 75th quantile, and 25th quantile of daily streamflow values observed from water years 1935 

through 2010.
144 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 34. Rainfall data from two NOAA weather monitoring stations including average precipitation for 

two historical time periods and total precipitation for water years 2011 through 2013.

[numbers in bold indicate water years that were greater than the historical averages]

Weather station Water year Annual precipitation (inches)

Mean Total

Dalecarlia Reservoir

1949–2010 43.3 —

1990–2010 45.9 —

2011 — 48.6

2012 — 38.0

2013 — 50.4

Dulles

1964–2010 41.3 —

1990–2010 40.3 —

2011 — 39.7

2012 — 35.7

2013 — 42.4

Table 35. Streamflow partitioning for streamgage station 01646000 in the Difficult Run watershed.

[mi2, square mile; ft3/s, cubic foot per second; in/yr, inch per year; %, percent; data were computed using the software package PART (Rutledge, 1998)]

Watershed Drainage area 

(mi2)

Period of 

record

Mean streamflow Mean base flow Base-flow 

index (%) ft3

/s in/yr ft3/s in/yr

Difficult Run 57.8 1936–2010 62.51 14.69 36.22 8.51 57.9

1936–1965 53.6 12.56 35.98 8.46 67.1

1990–2010 71.13 16.72 36.16 8.50 50.8

2011–2013 65.63 15.42 27.84 6.54 42.4
Difficult Run Watershed Water-Quality Characterization 145

Characterization of Continuous 

Water-Quality Data

Continuous data collected at the streamgage 

demonstrated seasonal and hydrologic variability in a number 

of parameters that are presented in the contour plots in 

figures 103 and 104 and in C-Q plots separated by “warm” 

(April through September) and “cool” (October through 

March) seasons in figures 105 and 106.

DO solubility varies inversely with water temperature; 

consequently, observed concentrations of DO are inversely 

related to the water temperature. Water temperatures peaked 

at about 27 °C in the summer and decreased to below 5 °C in 

the winter. DO concentrations were lowest in July and August 

with most measurements about 7 mg/L, and concentrations 

increased in the winter to a maximum of 15 mg/L. These 

constituents followed a diel cycle that is driven by light 

availability. Photosynthetic production of oxygen occurred 

during the day, resulting in peak concentrations in early to 

mid-afternoon and minimums during the early morning. This 

diel pattern was interrupted during periods of stormflow. 

Temperature and DO responded inconsistently to increased 

flows, but some inferences were made during the largest 

events. High flows had a neutralizing influence on water 

temperature, with relatively cooler water added during 

warm months and warmer precipitation added during cooler 

periods. Large events appeared to have no effect on DO 

during warm periods when concentrations were already low, 

but concentrations decreased during cool months with the 

addition of relatively warmer stormwater.

Specific conductance exhibited a relationship with 

flow that appears to be influenced by the application of road 

salts. These chloride-based compounds are applied during 

the winter months and caused a spike in conductivity values 

when they were delivered to the stream. Evidence for this 

pattern was seen in the varying seasonal response to flow. 

Conductivity values in early spring months (represented by 

the October through March season in fig. 105) peaked during 

large storm events and then quickly subsided. The maximum 

conductivity measurement of 2,660 μS/cm was recorded 

during this period. These findings are consistent with 

previous work done in the basin and suggest that the chloride 

concentrations associated with periods of peak conductivity 

could negatively affect aquatic health (Jastram, 2014). A more 

typical dilution response is exhibited outside of these early 

spring months.

Most of the pH values were between 7.0 and 

7.5 standard units, the expected range of pH in natural waters 

that are unaffected by pollution (Hem, 1985). Values of pH 

had a negative relation with streamflow that was likely caused 

by the addition of relatively acidic rainfall and storm runoff. 

Values of pH of up to 8.0 were observed during base-flow 

conditions in late April and early May. The uptake of carbon 

dioxide by aquatic organisms during the growing season 

likely caused these increased values (Hem, 1985). The effect 

of biologic processing also was seen in the diel cycling of pH.

Turbidity is a measure of the cloudiness of water and is 

directly related to the amount and composition of sediment 

in the water column. This parameter was directly correlated 

with streamflow and displayed no obvious seasonal patterns. 

Values greater than 300 FNU were commonly observed 

during storm events, and readings less than 5 FNU were 

typical for periods of base flow.

Nitrate concentrations generally ranged between 

0.5 and 2.0 mg/L and demonstrated a seasonally dependent 

flow response. The average nitrate concentration between 

October and March was 1.46 mg/L and was 1.17 mg/L for 

April through September. Lower concentrations during 

the growing season may be related to low-flow conditions, combined with denitrification or biologic uptake in 

the watershed or within the stream network. A decrease 

in biologic processing within the streams and watershed 

during the cooler months could explain the increased 

in-stream nitrate concentrations during these months. Nitrate 

demonstrated a strong dilution response during high-flow 

events during the cooler months that was less evident 

during the growing season when base-flow concentrations 

were lower.
Figure 103. Continuous (A) water temperature, (B) dissolved oxygen, and (C) specific conductance data with the hydrograph from the Difficult Run streamgage, 2013 water year.

EXP

Water temperatu

0 to 3.903.91 to 5.405.41 to 7.10

7.11 to 9.00

9.01 to 14.014.01 to 16.Streamflow

Dissolved oxyge

5.30 to 7.707.71 to 8.108.11 to 8.50

8.51 to 9.00

9.01 to 9.709.71 to 11.2

Streamflow

Specific conduc per centimeter 

68.0 to 177177.1 to 20201.1 to 21

215.1 to 223

223.1 to 233233.1 to 245Streamflow

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Water year 2013

Oct.

1011001,00010,0001011001,00010,000101001,00010,0001

Streamflow, in cubic feet per second

ABC
Figure 104. Continuous (A) pH, (B) turbidity, and (C) nitrate data with the hydrograph from the Difficult Run streamgage, 2013 water year.

EXP

pH, in 

6.60 to 7.007.01 to 7.057.06 to 7.10

7.11 to 7.15

7.16 to 7.207.21 to 7.25Streamflow

Turbidity, in form

0 to 1.401.41 to 1.801.81 to 2.20

2.21 to 2.60

2.61 to 3.303.31 to 4.60

Streamflow

Nitrate plus nitrite, in m

0.15 to 0.810.82 to 0.980.99 to 1.13

1.14 to 1.26

1.27 to 1.401.41 to 1.49Streamflow

12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM12:00 AM

6:00 AM

12:00 PM

6:00 PM

12:00 AM

Nitrate data

not available

Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Water year 2013

Oct.

1011001,00010,0001011001,00010,000101001,00010,0001

Streamflow, incubic feet per second

ABC
148 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

–5

0

5

10

15

20

25

30

Water temperature, in degrees Celsius

9.0

8.5

8.0

7.5

7.0

6.5

6.0

1,000

100

pH, in standard units

Specific conductance, in microsiemens 

per centimeter at 25 degrees Celsius

A

B

C

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

1 10 100 1,000 1 10 100 1,000

Streamflow, in cubic feet per second

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Figure 105. (A) Water temperature, (B) specific conductance, and (C) pH data from 

continuous monitoring and discrete storm and routine samples for warm (April to 

September) and cool (October to March) seasons, Difficult Run streamgage, 2012–2013.
Difficult Run Watershed Water-Quality Characterization 149

Figure 106. (A) Dissolved oxygen, (B) turbidity, and (C) nitrate data from continuous 

monitoring and discrete storm and routine samples for warm (April to September) and 

cool (October to March) seasons, Difficult Run streamgage, 2012–2013.

0

5

10

15

2.5

2.0

1.5

1.0

0.5

0

1,000

100

10

1

0.1

Dissolved oxygen, in milligrams per liter Turbidity, in formazin nephelometric units Nitrate plus nitrite, in milligrams per liter as nitrogen

EXPLANATION

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)

Discrete storm sample

Discrete routine sample

Continuous sample

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

Apr.–Sept. Oct.–Mar. Apr.–Sept. Oct.–Mar.

A

B

C

1 10 100 1,000 1 10 100 1,000

Streamflow, in cubic feet per second
150 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Characterization of Discrete Water-Quality Data

Discrete samples and targeted stormflow samples were 

analyzed for a broad suite of water-quality parameters. 

Cluster analysis and PCA were used to identify patterns 

in water-quality constituents that could be attributed to 

changes in flow or seasonality. All possible C-Q and temporal 

plots are provided in the appendix and those of interest are 

discussed below.

Three groups were identified from the cluster analysis 

of 25 discrete samples (fig. 107). Seven of the eight samples 

assigned to group 1 were collected between December and 

May. In general, these cool-season samples had higher values 

of nitrate, TN, DO, conductivity, sodium, and chloride than 

the other two groups. These characteristics coincide with 

cooler stream water that has limited biologic activity and is 

influenced by the runoff of high conductivity road salts. The 

14 samples assigned to group 2 were collected between May 

and November. These samples were more dilute and had lower 

nitrogen concentrations than those in group 1 and represent 

the influence of the growing season on the in-stream water 

chemistry. The remaining four samples were collected when 

streamflow was greater than 150 ft3/s and represent a dilution 

response to stormflow. These samples had the lowest specific 

conductance values and the highest TP concentrations of the 

25 discrete samples. 

The amount of variability explained by streamflow 

and seasonality was quantified by using a PCA. The first 

principal components axis demonstrated that a large portion 

(48.5 percent) of this variability could be attributed to 

streamflow. This axis had a strong, negative correlation with 

flow, and the parameter loadings indicated a dilution response 

Total nitrogen

Nitrate

Iron

Total phosphorus

Ammonium

Silica

Orthophosphate

Potassium

Sodium

Chloride

Sulfate

Magnesium

Calcium

Bicarbonate

Specific conductance

Manganese

Dissolved oxygen

pH

Group 1

(Cool-season samples)

Group 2

(Warm-season samples)

Group 3

(High-flow samples)

Cluster group

Dendrogram break point

EXPLANATION

Relative constituent value

Low High

Figure 107. Discrete samples from the Difficult Run streamgage with a heat map of 

constituent values and the breakpoint used to produce three cluster groups, 2012–2013.
Difficult Run Watershed Water-Quality Characterization 151

Table 36. Loading of water-quality constituents collected at the 

Difficult Run streamgage on principal component axes 1 and 2.

[%, percent; PC1, first principal components axis; PC2, second principal 

components axis; loadings with absolute value greater than 0.70 are shown 

in bold; those with absolute value less than |0.40| are omitted]

Constituent PC1 

(48.5%)1

PC2 

(13.7%)1

pH 0.66

Specific conductance 0.81

Dissolved oxygen 0.72

Bicarbonate 0.67 –0.58

Sulfate 0.54

Chloride 0.78 0.41

Calcium 0.90

Magnesium 0.91

Sodium 0.76 0.43

Potassium –0.83

Nitrate-N 0.88

Ammonium –0.58

Total nitrogen 0.44 0.61

Orthophosphate –0.69

Total phosphorus –0.87

Iron

Manganese 0.51

Silica 0.77

1

Percent of overall variance explained.

(table 36; fig. 108). Constituents with strong, positive loadings 

had a negative relation with flow and included magnesium, 

calcium, nitrate, specific conductance, chloride, sodium, and 

silica. Parameters with strong, negative loadings increased 

with flow and included potassium and TP. 

The second principal components axis accounted for only 

13 percent of the variability within the dataset and represented 

the effects of seasonality. The sinusoidal relationship between 

this axis and the timing of sample collection demonstrated that 

higher axis scores were assigned during cooler months and 

lower values during warmer months. DO concentrations had 

the strongest positive relationship on this axis, which indicates 

higher concentrations during cooler seasons. Most parameters 

loaded weakly on this axis, which suggests that they do not 

experience seasonal variations.

The significance (p<0.05) of streamflow correlations and 

seasonal differences were calculated for individual parameters 

based on the importance that these effects have on in-stream 

water chemistry (table 37). These relationships are characteristic of a stormflow-dominated, urbanized watershed. The 

significant negative relations between flow and bicarbonate, 

calcium, magnesium, and silica represent a dilution response 

to storm events (fig. 109). The concentrations of these groundwater-derived constituents were higher during base-flow 

conditions because the stream water has been in contact with 

bedrock longer than water contributed from overland flow. 

Specific conductance is a representation of the ionic strength 

of water and had a weaker negative relation with flow due 

to the varied response of sodium and chloride. Sodium and 

chloride concentrations had no significant relations with flow 

because the dilution response demonstrated by these constituents is interrupted in winter and early spring. Sodium-chloride 

salts are commonly used to deice roads, and they cause a 

spike in conductivity when they enter streams during these 

seasons. Turbidity and suspended-sediment concentrations 

had significant positive relations with flow, which is expected 

with these correlated parameters that represent the mobilization of streambed and streambank material (table 37). Total 

phosphorus sorbs to the surface of minerals and displays 

a similar relationship with flow as turbidity (fig. 109) 

(Hem, 1985). Orthophosphate is the biologically available 

form of phosphorus, and its concentrations had a significant 

positive correlation to flow. This constituent can be transported 

from groundwater, but concentrations were likely from 

overland runoff. Ammonium concentrations respond similarly 

to streamflow and had a positive significant correlation with 

orthophosphate concentrations. Nitrate concentrations have a 

significant negative relation with streamflow, and the nature 

of this response is described in the previous discussion 

about the characterization of continuous water-quality data 

(fig. 106). Non-significant C-Q relationships are provided in 

the appendix as supplemental material (fig. 1–7).

Water temperature, DO, nitrate, ammonium, 

orthophosphate, δ15N, and manganese demonstrated a significant (p<0.05) difference between seasons (table 37). Water 

temperature and DO patterns were described in the previous 

section, Characterization of Continuous Water-Quality Data 

(figs. 105 and 106). Nitrate concentrations were significantly 

higher in the cool season during relatively high base-flow 

conditions; biologic activity and uptake processes during 

warmer months result in reduced concentrations (fig. 110). The 

seasonality displayed by orthophosphate concentrations may 

be a dilution response related to higher base flows in October 

through March. Manganese concentrations were significantly 

lower during the warm season because it can be accumulated 

by aquatic vegetation for metabolic use (Hem, 1985). 

Seasonally variable δ15N values are likely related to in-stream 

and (or) watershed nutrient processing resulting in elevated 

δ15N values during warmer periods and lower values during 

cooler, less biologically active periods (fig. 110). Nonsignificant seasonal relationships are provided in the appendix 

as supplemental material (fig. 1–8).
152 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 108. First two principal component axis scores for discrete samples from the Difficult Run 

streamgage along with time and streamflow, 2012–2013. 

May 10

2012

Sept.

2012

Jan.

2013

Sept.

2013

May

2013

100 1,000 10,000

Principal component 1* Principal component 2**

EXPLANATION

Cluster analysis group

Group 1—Cool-season sample

Group 2—Warm-season sample

Group 3—High-flow sample

*Principal component 1 explains 

 48.5 percent of the variability in the data

**Principal component 2 explains 

 13.7 percent of the variability in the data

Date Streamflow, in cubic feet per second

0

0

–2

–2

–6

–4

2

4

2

4

6
Difficult Run Watershed Water-Quality Characterization 153

Table 37. Summary of hydrologic and seasonal correlations for discrete water-quality constituents collected at the Difficult Run 

streamgage.

[<, less than; results in bold are significant at α=0.05; n, number of observations; APR, April; SEPT, September; OCT, October; MAR, March]

Parameter

Streamflow Season1

n rho2 p-value2 nAPR-SEPT nOCT-MAR p-value3 Season with 

higher values

Water temperature 49 –0.33 0.02 19 12 <0.001 APR-SEPT

pH 49 –0.34 0.02 19 12 0.968

Specific conductance 49 –0.39 0.01 19 12 0.641

Dissolved oxygen 49 0.30 0.04 19 12 <0.001 OCT-MAR

Turbidity 49 0.78 <0.001 19 12 0.761

Bicarbonate 29 –0.74 <0.001 13 6 0.455

Sulfate 29 –0.16 0.42 13 6 0.072

Chloride 29 0.02 0.93 13 6 0.895

Calcium 41 –0.59 <0.001 16 11 0.278

Magnesium 41 –0.56 <0.001 16 11 0.388

Calcium/magnesium 41 0.40 0.012 16 11 0.748

Sodium/chloride 29 0.68 <0.001 13 6 0.630

Sodium 41 –0.14 0.39 16 11 0.902

Potassium 41 0.19 0.23 16 11 0.246

Nitrate-N 49 –0.34 0.02 19 12 0.008 OCT-MAR

Ammonium 31 0.33 0.02 19 12 0.002 APR-SEPT

Total nitrogen 31 0 0.98 19 12 0.011

Orthophosphate 31 0.37 0.01 19 12 <0.001 APR-SEPT

Total phosphorus 31 0.66 <0.001 19 12 0.435

Delta N-15 of nitrate, per mil 26 –0.77 <0.001 16 10 0.006 APR-SEPT

Delta O-18 of nitrate, per mil 26 0.23 0.17 16 10 0.108

Suspended-sediment concentration 26 0.83 <0.001 15 11 0.213

Iron 27 0.38 0.02 16 11 0.570

Manganese 27 0.15 0.35 16 11 0.032 OCT-MAR

Silica 24 –0.55 <0.001 14 10 0.121

1

Only routine samples included in the Wilcoxon test of rank scores. 

2

rho is Spearman’s rho; p-value is from Spearman’s test for correlation.

3

p-value is from the Wilcoxon test of rank scores.
154 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

3.0

2.5

2.0

1.5

1.0

0.5

0

0.12

0.10

0.08

0.06

0.04

0.02

0

0.035

0.030

0.025

0.020

0.015

0.010

0.005

0

Nitrate plus nitrite, 

in milligrams per liter as nitrogen

Ammonium,

in milligrams per liter as nitrogen

Orthophosphate,

in milligrams per liter as phosphorus

8

6

4

2

0

Magnesium,

in milligrams per liter

12

10

8

6

4

2

0.30

0.25

0.20

0.15

0.10

0.05

0

Delta N-15 of nitrate,

per mil

Total phosphorus,

in milligrams per liter

18

16

14

12

10

8

6

4

2

0

Calcium,

in milligrams per liter

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

10 100 1,000 10,000 1 10 100 1,000 10,000

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

10 100 1,000 10,000 1 10 100 1,000 10,000

0.01

0.001

0.1

1

10

100

1,000

Suspended sediment concentration,

in milligrams per liter

Figure 109. Concentration-discharge data for discrete samples from the Difficult Run streamgage by 

warm (April to September) and cool (October to March) seasons, 2011–2013.
Difficult Run Watershed Water-Quality Characterization 155

Figure 109. Graphs showing concentration-discharge data for discrete samples from the Difficult Run 

streamgage by warm (April to September) and cool (October to March) seasons, 2011–2013.—Continued

2.6

2.4

2.2

2.0

1.8

Ratio of calcium to magnesium

50

40

30

20

10

0

700

600

500

400

300

200

100

0

Bicarbonate, 

in milligrams per liter

0.65

0.60

0.55

0.50

0.45

0.40

0.35

Sodium to chloride ratio

16

12

8

4

0

Silica, 

in milligrams per liter as SiO

Iron, 

in micrograms per liter

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

1 10 100 1,000 10,000

10 100 1,000 10,000

Streamflow, in cubic feet per second

1

Streamflow, in cubic feet per second

1 10 100 1,000 10,000

10 100 1,000 10,000

EXPLANATION

Season

April to September

October to March

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)

2
156 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

EXPLANATION

Hydologic conditions

Routine sample

Storm sample

3.0

2.5

2.0

1.5

1.0

0.5

0

Nitrate plus nitrite, 

in milligrams per liter as nitrogen

0.12

0.10

0.08

0.06

0.04

0.02

0

Ammonium, 

in milligrams per liter as nitrogen

12

10

8

6

4

2

Delta N-15 of nitrate,

per mil

0.035

0.030

0.25

0.020

0.015

0.010

0.005

0

Orthophosphate,

in milligrams per liter as phosphorus

200

150

100

50

0

Manganese, 

in micrograms per liter

2011 2012

Water year

2013

J A S O N D J F M A M J J A S O N D J F M A M J J A S O

2011 2012

Water year

2013

J A S O N D J F M A M J J A S O N D J F M A M J J A S O

Figure 110. Concentrations of various constituents in discrete samples from the Difficult Run streamgage, 2011–2013.
Difficult Run Watershed Water-Quality Characterization 157

Nitrate plus nitrite,

in milligrams per liter as nitrogen

0

10

5

15

0

2

1

3

EXPLANATION

Routine sample

Storm sample

Dilution

Addition of atmospheric nitrate in runoff

Nitrate reduction (denitrification, assimilation)

Different nitrification N sources

Hydrologic conditions

Schematic changes potentially associated with

*Typical range of error, 2-sigma:

 delta N-15: 0.5 per mil,

 delta O-18: 1.0 per mil

0 2 4 6 8 10 12

5 10 15 20

Delta N-15 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

Figure 111. Variability in nitrate isotopes in discrete samples from the Difficult Run streamgage and potential 

causes for variability, 2011–2013.

The isotopic composition of nitrate in Difficult Run 

at the streamgage was variable at seasonal and event time 

scales (fig. 111). The δ15N values ranged from about +2 ‰ to 

+10 ‰, and δ18O values ranged from about +2 ‰ to +18 ‰. 

Routine (base-flow) samples had relatively uniform isotopic 

composition; minor variations exhibited a weak pattern of 

positively correlated δ15N and δ18O values, with higher values 

typically associated with relatively low nitrate concentrations 

in summer and fall seasons. These subdued patterns are 

qualitatively consistent with isotopic fractionation caused by 

partial nitrate reduction that was more pronounced during 

seasonal low-flow periods of older groundwater discharge 

and more in-stream biologic activity. In contrast, stormflow 

samples typically had lower nitrate concentrations, lower 

δ15N values, and higher δ18O values than routine (base-flow) 

samples, indicating contributions of unaltered atmospheric 

nitrate in precipitation runoff during high-flow events.

Although precipitation runoff generally had lower nitrate 

concentrations than stream water, and therefore generally 

diluted stream nitrate concentrations, the distinctive isotopic 

composition of atmospheric nitrate could be detected in the 

diluted mixtures. The stream sample with the highest nitrate 

δ18O value (+18 ‰), when compared to the base-flow stream 

value (+3 ‰) and typical atmospheric value (+75 ‰), would 

be consistent with approximately 20 percent of new precipitation nitrate in the sample. In samples least likely to have been 

affected by either nitrate reduction or precipitation runoff, 

values of δ15N were about +7 to +8 ‰, and δ18O values were 

about around +3 ‰, respectively. Those values are consistent 

with nitrification as the source of the base-flow nitrate, with 

some mixture of organic sources of the nitrified nitrogen, as 

discussed subsequently in the Characterization of Nitrogen 

Sources section of this report.
158 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 38. Details of the top-ranked suspended sediment, total nitrogen, and total phosphorus concentration estimation models for the 

Difficult Run streamgage.

[<, less than; ln, natural logarithm]

Explanatory 

variables

Model 

coefficient p-value Variance inflation factor

Number of 

observations

Adjusted coefficient 

of determination Mallows’ Cp

Suspended-sediment concentration

Intercept 0.070 0.025

ln(Flow) 0.223 0.194 4.49 42 0.848 16.55

ln(Turbidity) 0.952 <0.001 4.49

Total nitrogen

Intercept –0.143 <0.001

ln(Flow) 0.098 <0.001 1.17 42 0.867 9.71

Nitrate-N 0.525 <0.001 1.17

Total phosphorus

Intercept –4.841 <0.001

ln(Flow) 0.005 0.954 4.49 42 0.882 39.84

ln(Turbidity) 0.649 <0.001 4.49

Surrogate Models for the Computation of 

Sediment and Nutrient Loads

The discrete and continuous water-quality data were 

subsequently analyzed to explore the development of 

surrogate regression models to predict SSC and TN and TP 

concentrations. The development of these surrogate models 

is critical to the computation of constituent loads and an 

improved understanding of the watershed function.

Model Development

Standard USGS multiple linear-regression methods 

were used to develop the best surrogate models (Helsel and 

Hirsch, 2002; Jastram and others, 2009; Rasmussen and 

others, 2009); a detailed overview of the model selection 

process is offered in the Development of Surrogate WaterQuality Models and Computation of Loads section of this 

report. For Difficult Run, relatively common load model 

formulations were observed to provide the strongest predictive 

models for SSC and TN and TP concentrations; consequently, 

no other common model formulations are presented 

(table 38; figs. 112–114). 

For SSC, the best model included turbidity and flow 

terms, which is a typical surrogate model formulation SSC. 

For prediction of TN concentrations, the best predictor always 

included the continuous nitrate data, which was expected 

because the vast majority of the nitrogen in Difficult Run was 

in the form of nitrate at the intensive monitoring site. The best 

nitrate model also included a significant flow term, indicating 

that TN transport in Difficult Run was best predicted by a 

nitrate concentration component and a flow component. For 

the prediction of TP concentrations, the best concentration 

model included flow and turbidity, which is a common 

surrogate model formulation for TP (Jastram and others, 2009). 

Interpretation of the Computed Loads

After developing surrogate models for estimating 

concentrations of suspended sediment, TN, and TP, 2013 

water-year loads of these constituents were computed by 

using the record of continuous flow (from the streamgage) 

and predicted water quality (from the regression equations 

in table 38). Because the continuous water-quality monitors 

at Difficult Run were not installed until early October 2012, 

only the loads for the 2013 water year are presented. Methods 

for the replacement of missing records are provided in the 

Development of Surrogate Water-Quality Models and Computation of Loads section of this report. The USGS software 

program LOADEST was used for these computations because 

the computed load can be calculated with confidence intervals 

around the predicted load, which is needed for comparisons 

between water years and sites. 

The 2013 water-year SSC load is presented in figure 115, 

along with the total flow for the water year. Because the 

hydrology during the 2013 water year was fairly typical, 

the surrogate SSC loads computed for the 2013 water year 

are likely fairly typical for the Difficult Run watershed. The 

total sediment load is estimated to be just over 8,000 tons of 

sediment during the water year. Normalizing the Difficult 

Run loads to the watershed area (to compute a sediment 

yield) results in a sediment yield of approximately 143 tons 

per square mile (tons/mi2), which is almost a factor of 4 less 

than the 546-tons/mi2 sediment-loading rates that have been 

documented for headwater streams in Fairfax County, Virginia 

(Jastram, 2014, based on an average of sediment-loading 

rates from USGS stations 01645762 and 01645704). The 

reduced sediment export rates measured at the downstream 

Difficult Run site (relative to the headwater site studies by 

Jastram, 2014) are likely related to deposition of eroded 
Difficult Run Watershed Water-Quality Characterization 159

1 10 100 1,000

1 10 100 1,000

10 100 1,000

1

10

100

1,000

1

10

100

1,000

Observed suspended sediment,

in milligrams per liter

Suspended sediment,

in milligrams per liter

Estimated suspended sediment, in milligrams per liter

Estimated suspended sediment, in milligrams per liter

Streamflow, in cubic feet per second

Residual error

EXPLANATION

Observed concentration

Estimated concentration

–4

–3

–2

–1

0

1

2

3

4

Figure 112. Observed and estimated values from the 

best suspended-sediment surrogate model for the 

Difficult Run streamgage and the residual plot of model 

predictions, 2011–2013.

10 100 1,000

3.0

2.5

2.0

1.5

1.0

0.5

0

0 0.5 1.0 1.5 2.0 2.5 3.0

0 0.5 1.0 1.5 2.0 2.5 3.0

3.0

2.5

2.0

1.5

1.0

0.5

0

0.5

0.4

0.3

0.2

0.1

0

–0.1

–0.2

–0.3

–0.4

–0.5

Observed total nitrogen,

in milligrams per liter

Total nitrogen,

in milligrams per liter

Estimated total nitrogen, in milligrams per liter

Streamflow, in cubic feet per second

Residual error

Estimated total nitrogen, in milligrams per liter

EXPLANATION

Observed concentration

Estimated concentration

Figure 113. Observed and estimated values from the 

best total nitrogen surrogate model for the Difficult Run 

streamgage and the residual plot of model predictions, 

2011–2013.
160 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 114. Observed and estimated values from 

the best total phosphorus surrogate model for the 

Difficult Run streamgage and the residual plot of model 

predictions, 2011–2013.

0.01 0.1 1

10 100 1,000

0.01 0.1 1

0.01

0.1

1

0.01

0.1

1

Observed total phosphorus,

in milligrams per liter

Total phosphorus,

in milligrams per liter

Streamflow, in cubic feet per second

–1.0

–1.5

0

0.5

1.0

1.5

–0.5

Residual error

Estimated total phosphorus, in milligrams per liter

Estimated total phosphorus, in milligrams per liter

EXPLANATION

Observed concentration

Estimated concentration

sediment on the well-developed floodplains in the lower 

part of the watershed (Noe and others, 2013). Sediment 

loading within Difficult Run was highly episodic, with the 

seven largest storms generating nearly 95 percent of the 

estimated sediment load (fig. 116). The largest storm event 

(October 2012) generated more than 20 percent of the annual 

sediment load. Additional years of data will be required before 

comparisons between water years can be made to better 

understand intra-annual variability.

The 2013 water-year TN load for Difficult Run is 

presented in figure 117 along with the total flow for the water 

year. Because the hydrology during the 2013 water year was 

fairly typical, the surrogate TN loads computed for the 2013 

water year were likely fairly typical for the Difficult Run 

watershed, and the annual total nitrogen loading for Difficult 

Run is approximately 140,000 lb/yr. This estimated load is 

based on 288 days of the water year because the site was 

brought online on October 19 and 2 months of nitrate data 

were unavailable between May and July due to instrumentation fouling. This fouling was a result of increased turbidity 

caused by frequent stormflows that blocked the optic window 

on the nitrate monitor. Streamflow exceeded the 95 percent 

percentile of 153 ft3/s eight times during this period, with 

a peak flow of 1,470 ft3/s on June 11. Based on previously 

measured high-flow events, the 2013 TN load may be 

underpredicted by about 10 percent due to this lapse in data 

collection. The collection of quality data through high-flow 

events was improved with the addition of a wiper system in 

July 2013. The 2013 nitrogen load can be normalized by basin 

area to compute a yield of approximately 2,500 pounds per 

square mile (lb/mi2) of nitrogen, which is substantially less 

than the median yield of 5,700 lb/mi2 that was observed in 

the headwater streams of Fairfax County by Jastram (2014), 

theoretically because of spatial variations in Difficult Run 

nitrogen sources, as well as in-stream processing and 

stream interaction with floodplains during storms (Noe and 

others, 2013). Interestingly, the TN load accumulation seemed 

to occur more gradually and is less driven by episodic inputs 

than the SSC accumulation (fig. 118). This more gradual 

accumulation rate is likely caused by the persistent nitrogen 

loading that occurred during base-flow conditions that support 

relatively steady nitrogen loading that is only occasionally 

altered by significant flow events. Additional years of 

monitoring will be needed before total nitrogen loading rates 

between water years can be compared. 
2013*

0

Annual total streamflow, in millions of cubic feet

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0

500

1,000

1,500

2,000

Annual suspended-sediment load, in tons

*Sediment load is 

 computed based

 on 354 days of 

 water year 2013

Water year

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

Figure 115. Suspended-sediment load and total 

annual streamflow at the Difficult Run streamgage for 

the 2013 water year.

Difficult Run Watershed Water-Quality Characterization 161

100

90

80

70

60

50

40

30

20

10

0

10,000

8,000

6,000

4,000

2,000

0

2,700

2,400

2,100

1,800

1,500

1,200

900

600

300

0

1,800

1,500

1,200

900

600

300

0

Annual suspended-sediment load,

in tons

Streamflow,

in cubic feet per second

Total annual streamflow,

in millions of cubic feet

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Water year 2013

EXPLANATION

Suspended-

 sediment 

 load

Streamflow 

A

B

Percent of suspended-sediment load

estimated during the study period

Figure 116. (A) The estimated percentage of suspended-sediment load accumulated 

and the hydrograph during the period of study and (B) the accumulation of load and 

streamflow at the Difficult Run streamgage for the 2013 water year.
162 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

160,000

120,000

80,000

40,000

0

2,000

1,500

1,000

5000

0

2013*

Annual total streamflow, in millions of cubic feet

*Total nitrogen load

 is computed based

 on 288 days of 

 water year 2013

Annual total nitrogen load, in pounds

Water year

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

Figure 117. Total nitrogen load and total annual 

streamflow at the Difficult Run streamgage for the 2013 

water year.

100

90

80

70

60

50

40

30

20

10

0

140,000

120,000

100,000

80,000

60,000

40,000

20,000

0

2,700

2,400

2,100

1,800

1,500

1,200

900

600

300

0

1,800

1,500

1,200

900

600

300

0

Annual total nitrogen load,

 in pounds

Data not 

available

Data not 

available

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Water year 2013

EXPLANATION

Total nitrogen load

Streamflow 

Streamflow, 

in cubic feet per second

Total annual streamflow, 

in millions of cubic feet

A

B

Percent of total nitrogen load 

estimated during the study period

Figure 118. (A) The estimated percentage of total nitrogen load accumulated and the 

hydrograph during the study period and (B) the accumulation of load and streamflow at 

the Difficult Run streamgage for the 2013 water year.
20,000

15,000

10,000

5,000

0

2,000

1,500

1,000

5000

0

Annual total phosphorus load, in pounds

2013*

Annual total streamflow, in millions of cubic feet

*Phosphorus load is 

 computed based

 on 354 days of 

 water year 2013

Water year

EXPLANATION

Upper 95-percent 

confidence interval

Lower 95-percent 

confidence interval 

Annual total streamflow

Load estimate

Figure 119. Total phosphorus load and total annual 

streamflow at the Difficult Run streamgage for the 2013 

water year.

Difficult Run Watershed Water-Quality Characterization 163

100

90

80

70

60

50

40

30

20

10

0

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

2,700

2,400

2,100

1,800

1,500

1,200

900

600

300

0

2,700

2,400

2,100

1,800

1,500

1,200

900

600

300

0

Annual total phosphorus load, 

in pounds

Streamflow,

in cubic feet per second

Total annual streamflow,

in millions of cubic feet

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Water year 2013

Percent of total phosphorus load

estimated during the study period

EXPLANATION

Total phosphorus load

Streamflow 

A

B

Figure 120. (A) The estimated percentage of total phosphorus load accumulated 

and the hydrograph during the period of study and (B) the accumulation of load and 

streamflow at the Difficult Run streamgage for the 2013 water year.

The 2013 water-year TP load is presented in figure 119, 

along with the total flow for the water year. Given the typical 

discharge associated with 2013, the typical TP load for 

Difficult Run is estimated to be about 14,000 lb/yr. Relative 

to the headwater contributions throughout Fairfax County 

(Jastram, 2014), the Difficult Run TP yield at the downstream 

gage (250 lb/mi2) is much less than the typical headwater 

yields (median of 485 lb/mi2, based on an average of USGS 

stations 01645762 and 01645704), likely because of variations in Difficult Run erosion rates and floodplain-deposition 

patterns, as well as in-stream processing. The cumulative 

loading plot (fig. 120) for TP is similar to that of suspended 

sediment, likely because much of the phosphorus is moving 

in association with sediment. Unlike Smith Creek, which 

demonstrates a base-flow loading of phosphorus, nearly all the 

TP loading in Difficult Run occurs in association with storm 

events and not during base-flow conditions.
164 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Spatial Patterns in Water Quality

Synoptic sampling throughout the Difficult Run watershed 

was completed in September 2011, December 2011, April 2012, 

and May 2013 during base-flow conditions. The relative 

wetness of the basin varied during these events, with the 

highest flows measured in December 2011 and lowest flows in 

September 2011 (fig. 121). Flow increased in the downstream 

direction with the addition of each contributing tributary, and 

the gain in streamflow between sampling stations was directly 

proportional to drainage area in all but the driest event. 

Streamflow measurements were higher than expected 

at some headwater sites following a localized rain event 

during the September 2011 synoptic sampling. Opportunities 

to perform such time-intensive sampling during base-flow 

conditions were limited, and water-chemistry results from this 

synoptic sampling were rigorously reviewed for any potential 

biases. Six headwater sites with disproportionately high 

discharges were removed from the basin-wide load analyses 

because they misrepresented the spatial base-flow patterns 

of nitrogen and phosphorus accumulation. These samples 

were excluded because their ratio of discharge to drainage 

area was higher than the 95th percentile of samples from all 

events (fig. 122). These six samples were included in all 

other analyses because the majority of their water-chemistry 

results were minimally affected. Large-scale spatial patterns 

and a conceptual understanding of basin processes were still 

achieved from sampling after this rain event.

In general, total nitrogen loads increased downstream 

along the main channel of Difficult Run and ranged from 

216 lb/day during the driest synoptic sampling event of 

September 2011 to 463 lb/day during the wettest synoptic 

sampling event of December 2011 (fig. 123). TN concentrations remained relatively constant along the stream channel 

(fig. 124), indicating that nitrogen loads were mostly 

controlled by changes in flow. Loads were highest at the 

streamgage, site 30, during all events (fig. 123). This is the 

most downstream sampling location that had the highest 

streamflow. Loads from sampled tributaries were comparatively low, but some locations, such as Captain Hickory 

Run (sites 27, 25, 33, and 34), had elevated total nitrogen 

concentrations (fig. 124).

TP loads also accumulated along the Difficult Run main 

channel and ranged from to 1.8 lb/day during the driest 

synoptic sampling event of September 2011 to 2.7 lb/day 

during the wettest event of December 2011 (fig. 125). 

Phosphorus concentrations were generally less than 

0.02 mg/L, but elevated concentrations occurred occasionally in some tributaries (fig. 126). Phosphorus was significantly correlated (p<0.05) with turbidity measurements, so 

these elevated concentrations were likely influenced by the 

quantity of sediment present during sampling. Sites with 

elevated TP concentrations were variable between events, 

but low flows resulted in loads that were consistently small 

at these sites.
Difficult Run Watershed Water-Quality Characterization 165

April 2012 May 2013

September 2011 December 2011

40

30

20

10

0

40

30

20

10

0

EXPLANATION

Synoptic sampling location

USGS Difficult Run 

 streamgage 01646000

Other synoptic sampling 

 location

0 10 20 30 40 50 60 0 10 20 30 40 50 60

Drainage area, in square miles

Streamflow, in cubic feet per second

Figure 121. Streamflow and drainage area for samples collected from the Difficult Run watershed during each 

synoptic event.

Ratio of streamflow to drainage area

0

1

2

3

4

5

September 2011 December 2011

Date

April 2012 May 2013

95th percentile of streamflow

to drainage area ratios

of all synoptic samples

Figure 122. Ratio of streamflow to drainage area for each 

monitoring location during the synoptic sampling events in 

the Difficult Run watershed.
166 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

77°22' 77°18' 77°14'

39°00'

38°56'

38°52'

September 2011

23 ft3

/s

216 lb/day

December 2011

39 ft3

/s

463 lb/day

May 2013

34 ft3

/s

319 lb/day

May 2012

33 ft3/s

340 lb/day

35

36

2

1

4 7

6

10

19

16 20

24

28

23

21 22

26

33 34

25

27

30

29

18

17

3

14

15

9 31

13 11

12

8 5

Percent of maximum total 

 nitrogen load by event

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

30

Synoptic sampling station 

 and number

EXPLANATION

Sample not collected

0 to 20.00

20.01 to 40.00

40.01 to 60.00

60.01 to 80.00

80.01 to 100.00

Streamflow, in

cubic feet per second (ft3

/s)

and maximum total nitrogen load,

in pounds per day (lb/day)

Synoptic sampling events at site 30

 (USGS streamgage 01646000)

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Figure 123. Monitoring locations, the percentage of the maximum total nitrogen load measured during 

each synoptic sampling event, and the streamflow network in the Difficult Run watershed.

77°22' 77°18' 77°14'

39°00'

38°56'

38°52'

September 2011

23 ft3

/s

December 2011

39 ft3

/s

May 2013

34 ft3

/s

May 2012

33 ft3/s

35

36

2

1

4 7

6

10

19

16 20

24

28

23

21 22

26

33 34

25

27

30

29

18

17

5

3

14

15

9 31

13 11

12

8 Total nitrogen, in

 milligrams per liter

Synoptic sampling events at site 30

 (USGS streamgage 01646000)

Streamflow, in 

cubic feet per second (ft3

/s)

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

Sample not collected

0 to 1.00

1.01 to 2.00

2.01 to 3.00

3.01 to 4.00

4.01 to 5.00

30

Synoptic sampling station 

 and number

EXPLANATION

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Figure 124. Monitoring locations, the total nitrogen concentration measured during each synoptic 

sampling event, and the streamflow network in the Difficult Run watershed.
Difficult Run Watershed Water-Quality Characterization 167

77°22' 77°18' 77°14'

39°00'

38°56'

38°52'

September 2011

23 ft3

/s

1.8 lb/day

December 2011

39 ft3

/s

2.7 lb/day

May 2013

34 ft3

/s

1.8 lb/day

May 2012

33 ft3/s

1.8 lb/day

35

36

2

1

4 7

6

10

19

16 20

24

28

23

21 22

26

33 34

25

27

30

29

18

17

3

14

15

9 31

13 11

12

8 5

Percent of maximum total 

 phosphorus load by event

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

30

Synoptic sampling station 

 and number

EXPLANATION

Sample not collected

0 to 20.00

20.01 to 40.00

40.01 to 60.00

60.01 to 80.00

80.01 to 100.00

Streamflow, in

cubic feet per second (ft3

/s)

and maximum total phosphorus load,

in pounds per day (lb/day)

Synoptic sampling events at site 30

 (USGS streamgage 01646000)

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Figure 125. Monitoring locations, the percentage of the maximum total phosphorus load measured during 

each synoptic sampling event, and the streamflow network in the Difficult Run watershed.

77°22' 77°18' 77°14'

39°00'

38°56'

38°52'

September 2011

23 ft3

/s

December 2011

39 ft3

/s

May 2013

34 ft3

/s

May 2012

33 ft3/s

35

36

2

1

4 7

6

10

19

16 20

24

28

23

21 22

26

33 34

25

27

30

29

18

17

5

3

14

15

9 31

13 11

12

8 Total phosphorus, in

 milligrams per liter

Streamflow, in 

cubic feet per second (ft3

/s)

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

Sample not collected

0 to 0.013

0.014 to 0.026

0.027 to 0.039

0.040 to 0.052

0.053 to 0.065

30

Synoptic sampling station 

 and number

EXPLANATION

Synoptic sampling events at site 30

 (USGS streamgage 01646000)

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Figure 126. Monitoring locations, the total phosphorus concentration measured during each synoptic 

sampling event, and the streamflow network in the Difficult Run watershed.
168 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Total nitrogen

Nitrate

Manganese

Iron

Ammonium

Total phosphorus

Orthophosphate

Potassium

Sodium 

Chloride

Specific conductacne

Magnesium

Calcium

Bicarbonate

Sulfate

pH

Group 1

(Difficult Run type)

Group 2

(High-nitrate

type)

Group 3

 (Low-nitrate

type)

Group 4

(Urban-area

type)

Cluster group

Dendrogram

break point

EXPLANATION

Relative constituent value

Low High

Figure 127. Samples collected from each synoptic event within the Difficult Run watershed with a heat map of 

constituent values and the breakpoint used to produce four cluster groups.

Water-quality samples from all four synoptic events were 

analyzed by using a single cluster analysis (fig. 127). Four 

groups were identified, and the relative geochemical composition of each group is characterized below. The interpretation 

of the resulting groups of sites provided a better understanding 

of spatial water-quality patterns throughout the Difficult 

Run watershed. 

Cluster #1 – Difficult Run Type – The largest group 

identified from the cluster analysis contained 76 of the 

129 samples (fig. 127). Samples within this group are 

represented by stations along the Difficult Run stream channel 

in the upper and lower parts of the watershed (fig. 128). 

Water-quality patterns are variable in the headwaters of the 

watershed, but these patterns are masked in the well mixed 

and homogeneous flows of the main channel. This homogenization effect is likely amplified by the lack of topographic 

or geologic variability in the watershed. The remaining 

cluster groups capture the variability in the headwater 

sampling stations.

Cluster #2 – High Nitrate Type – Membership within 

the second cluster was assigned to all Captain Hickory Run 

and Little Difficult Run sampling locations (fig. 128). These 

stations are distinguished by elevated nitrate concentrations 

that are in contrast to an otherwise dilute water chemistry 

(fig. 127). Because these subwatersheds are characterized by 

low-density and estate-residential land uses (Jastram, 2014), 

the elevated nitrate concentrations were unexpected and 

may be the result of wastewater inputs (from septic systems) 

or increased fertilizer application rates on these larger lots 

(see the Characterization of Nitrogen Sources section, below, 

for further information). The less pronounced urban effect 

on these streams is likely reflected in dilute water chemistry; 

Captain Hickory Run and Little Difficult Run have relatively 

low levels of sodium, chloride, and specific conductance.

Cluster #3 – Low Nitrate Type – Five sampling stations 

were assigned membership to the third cluster group (fig. 127). 

This group had relatively dilute nitrate concentrations 

and more variable measures of TP, iron, ammonium, and 
Difficult Run Watershed Water-Quality Characterization 169

77°22' 77°18' 77°14'

39°00'

38°56'

38°52'

September 2011

23 ft3

/s

December 2011

39 ft3

/s

May 2013

34 ft3

/s

May 2012

33 ft3/s

35

36

2

1

4 7

6

10

19

16 20

24

28

23

21 22

26

33 34

25

27

30

29

18

17

5

3

14

15

9 31

13 11

12

8

Synoptic sampling events at site 30

 (USGS streamgage 01646000)

Streamflow, in 

cubic feet per second (ft3

/s)

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

30

Synoptic sampling station 

 and number

EXPLANATION

Streams from U.S. Geological Survey National Hydrography Geodatabase (2013)

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Cluster group

Sample not collected

Group 1—Difficult Run type

Group 2—High nitrate type

Group 3—Low nitrate type

Group 4—Urban area type

Figure 128. Synoptic sampling events at monitoring locations in the Difficult Run watershed as grouped in 

a cluster analysis.

manganese than the other groups. Curiously, all synoptic 

sampling sites within Snakeden Branch were contained within 

this cluster, indicating that this watershed had lower nitrogen 

inputs or watershed processes that favored uptake and denitrification processes. Lower nitrogen inputs to this watershed could 

be related to the extensive amounts of stream manipulation 

and restoration within this watershed that may have enhanced 

conditions for nitrogen loss within the watershed, as well as 

other watershed factors (wastewater infrastructure, for example) 

that could reduce nitrogen inputs to these subwatersheds. 

Cluster #4 – Urban Area Type – These sites are 

distinguished by high specific conductivities and appear to 

be related to the imperviousness of the surrounding region 

(figs. 127 and 128). Sampling stations within this group 

are located within the headwaters of the watershed and are 

immediately downstream of Fairfax City, Tysons Corner, 

and Reston. The high sodium and chloride concentrations in samples from sites within this group are likely 

caused by runoff of deicing salts applied to the dense 

roadway network.
170 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Characterization of Nitrogen Sources

Characterization of the primary nitrogen sources in the 

Difficult Run watershed is important for making informed 

watershed-management decisions related to the implementation of BMPs. Nitrate concentration and isotope data were 

evaluated in combination with land-use data and model-based 

estimates to determine likely sources of surface-water nitrate. 

Based on the overall assessment, elevated nitrate concentrations appear to be caused by high septic system density in 

some Difficult Run subwatersheds. 

The highest observed TN and nitrate concentrations were 

at sites 25, 33, and 34, which are within the Captain Hickory 

Run subwatershed (fig. 124). The Captain Hickory Run 

subwatershed is predominantly characterized by low-intensity 

residential (32 percent of watershed) and estate residential 

(47 percent of watershed) land uses and has the lowest 

population density of all subwatersheds within Difficult Run 

(Jastram, 2014; U.S. Census Bureau, 2012). Lot sizes within 

the Captain Hickory Run subwatershed are generally larger 

than those in other watersheds in Difficult Run, and this area 

has no sewage infrastructure in this area because all houses 

are served by septic systems. Subsequent analyses were 

performed to explore whether elevated nitrate concentrations 

in Captain Hickory Run and other Difficult Run subwatersheds 

were likely related to the presence of septic systems, or were 

possibly caused by excessive use of commercial fertilizers 

on lawns.

Spatial nitrate concentration patterns in Difficult Run 

correlated well with the presence of septic systems throughout 

the watershed (fig. 129), with higher concentrations of 

nitrate observed in areas that are served by septic systems. 

Subsequent analyses incorporating data from Difficult Run 

and other watersheds within Fairfax County (Jastram, 2014) 

demonstrated a statistically significant positive relation 

(p-value <0.0001) between watershed nitrate concentrations 

and the septic system density (fig. 130). In-stream nitrate 

concentrations increase steadily with increasing septic density, 

but concentrations appeared to sharply increase once septic 

density exceeded 100 units per square mile, indicating that 

wastewater leachate from septic systems may be a substantial 

source of nitrate in these suburban streams. Monthly waterquality data from two subwatersheds outside of the sanitary 

sewer service area, Captain Hickory Run (site 25) and Little 

Difficult Run (site 6), were available for seasonal analysis as 

part of another water-quality project (Jastram, 2014). Nitrate 

concentrations exhibited no statistically significant seasonal 

pattern at either of these sites; for example, no evidence was 

found for seasonally elevated concentrations that might have 

been caused by runoff from excessive lawn fertilization in 

the spring or fall (fig. 131). Instead, during wet weather, the 

concentrations of nitrate in both streams generally decreased, 

which is a pattern more consistent with dilution of a 

groundwater source during storms. 

The synoptic nitrate isotope results demonstrate a broad 

range of values and a number of patterns and processes to 

provide a basic understanding of the nitrogen sources within 

the Difficult Run watershed (fig. 132). Sites with relatively 

high nitrate concentrations typically had δ15N values from 

+7 ‰ to +9 ‰, similar to those commonly attributed to 

sewage or septic system sources, although more complex 

mixtures including nitrate derived from other sources cannot 

be excluded. Nitrate isotope data generally do not indicate 

major fractionating losses along the main stream channel; 

δ18O values were relatively constant and similar to biogenic 

source values except during some high-flow events. The 

sites with the lowest nitrate concentrations also generally 

had relatively low δ15N values, which may indicate recycled 

atmospheric and soil sources of nitrate and, in some cases, 

nitrate formed in upstream lakes. Only modest amounts of 

biologic processing are apparent in the data, because the 

greatest δ15N values are generally not associated with elevated 

δ18O values. A statistically significant (p-value = 0.0002) 

positive correlation was found between nitrate concentrations 

and δ15N values—a pattern that would be more consistent 

with varying excess contributions from wastewater rather 

than from commercial lawn fertilizers (fig. 132). Furthermore, a positive correlation between the density of septic 

systems and δ15N values supports an interpretation of excess 

contributions from wastewater (fig. 133). Some of the lowest 

nitrate δ18O values (< 2 ‰) were from Captain Hickory Run. 

Some studies indicate that δ18O of nitrate values from septic 

systems may be slightly lower than δ18O of nitrate values 

formed in soils, possibly because of rapid reactions and the 

presence of transient intermediate species in septic system 

drain fields (for example, Hinkle and others, 2008). Previous 

studies also indicate δ15N values of +7 ‰ (similar to Captain 

Hickory Run) are common for nitrate from septic systems 

(see figure 6 and associated references in the Interpretation of 

Nitrate Isotopes section of this report). 

A mass-balance computation of nitrogen sources in 

the Captain Hickory Run watershed was prepared by using 

U.S. Census data, Fairfax County land-use data, and numerous 

publications that focus on septic and fertilizer leachate 

(table 39). While various assumptions related to application 

rates and leachate amounts were required, the mass-balance 

computation indicates that the mass of nitrogen available 

for export from potential septic system leachate could be 

approximately an order of magnitude greater than the mass 

of nitrogen expected to be available from inorganic fertilizer 

applications. Even though input loading of nitrogen to the 

watershed was generally similar between septic systems and 

fertilizer application in this mass-balance calculation, most of 

the fertilizer nitrogen presumably was assimilated by plants 

and cycled in soils, whereas nitrogen in septic system recharge 

was delivered to streams relatively efficiently in the absence of 

groundwater denitrification. 

Historical land use within the Captain Hickory watershed 

has not always been low-density residential, which could 

affect present-day nitrate concentrations. Legacy land use 

included the presence of cattle farms in some areas, which 

could be a persistent source of nitrate leaching from soils 
Difficult Run Watershed Water-Quality Characterization 171

Rocky

Branch

Rocky Run

Captain Hickory Run

Piney Branch

Piney Run

Colvin Run

Snakeden Branch

S Fk Little Difficult Run

Wolftrap Creek

77°21' 77°18' 77°15'

39°00'

38°57'

38°54'

38°51'

17

14

15

31 12 13

11 10

8

6

7

1

2

35

36 3

4

9

5

18

29

30

24

28

27

25

34

33

26

21 22

23

16

20

19

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

EXPLANATION

Difficult Run

Tributary to Difficult Run

Parcel served by sewer Subwatershed boundary

Parcel not served by sewer

Septic/sewage coverage

(from Fairfax County GIS Branch, unpub. data, 2014)

Streams from U.S. Geological Survey National Hydrography Geodatabase, 2013

Drainage area from Hayes and Wiegand (2006)

North American Datum of 1983

Monitoring site and identifier

USGS streamgage 01646000

18

30

Synoptic sampling location 

 and identifier

Figure 129. Streamflow network, watershed boundaries of each monitoring location, and 

county-level tax parcels served and not served by sewage infrastructure in the Difficult Run 

watershed.
172 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

EXPLANATION

Site within Difficult Run Watershed

Site outside of Difficult Run Watershed

Nitrate plus nitrite, in milligrams per liter as nitrogen

0

1

2

3

4

5

Number of septic tanks per square mile

10 100 1,000

Figure 130. Median nitrate concentration results from 

synoptic sampling events within the Difficult Run watershed 

and from additional U.S. Geological Survey base-flow 

monitoring in Fairfax County compared to the septic tank 

density within the drainage area of each monitoring 

location. 

Routine sample

Storm sample

Hydrologic condition

Water year

EXPLANATION

Captain Hickory Run

South Fork LIttle Difficult Run

Nitrate plus nitrite, in milligrams per liter as nitrogen 0

1

2

3

4

5

0

1

2

3

4

5

6

6

WY 2007

WY

WY 2008 WY 2009 WY 2010 WY 2011 WY 2012

Figure 131. Nitrate concentrations by hydrologic event from Captain Hickory Run (site number 25) and South 

Fork Little Difficult Run (site number 7) for water years 2007 through 2012.

or in groundwater; however, most of the basin transitioned 

from agricultural to residential land use during the 1970s and 

1980s, and legacy land use from 30+ years ago is considered 

relatively unlikely to be the primary driver of nitrate patterns 

or sources within the watershed. 

Dominant nitrogen sources indicated by land-use-based 

estimates and nitrate isotopes were compared with results 

from the SPARROW Chesapeake Bay TN model (fig. 134). 

According to the model, urban sources contributed the most 

nitrogen to the watershed, followed by atmospheric deposition. 

The category of urban sources can include inputs from residential fertilizer, septic systems, leaky sewer lines, and local 

deposition from automobiles (Ator and others, 2011). Because 

these sources were not differentiated in the SPARROW model, 

however, the relative importance of different urban sources 

that might affect the isotopic composition of exported nitrate 

in stream base flow, such as wastewater or lawn fertilizer, 

could not be resolved from the model. 

Earlier studies (Heisig, 2000; Burns and others, 2005; 

Landers and Ankcorn, 2008) have demonstrated that an 

increased density of septic systems within unsewered 

communities can result in hydrologic and chemical alteration 

to downstream receiving waters. The potential contribution 

of nitrate from septic systems to the Chesapeake Bay itself, 

however, has generally not been well understood.
Difficult Run Watershed Water-Quality Characterization 173

Dilution

Addition of atmospheric nitrate in runoff

Nitrate reduction (denitrification, assimilation)

Different nitrification N sources

Schematic changes potentially associated with

*Typical range of error, 2-sigma:

 delta N-15: 0.5 per mil,

 delta O-18: 1.0 per mil 0 2 4 6 8 10

2 4 6 8 10

Nitrate plus nitrite,

in milligrams per liter as nitrogen

0

4

2

6

8

0

2

1

3

5

4

6

EXPLANATION

Difficult Run type

High-nitrate type

Groups identified by the cluster analysis

Low-nitrate type

Urban-area type

Delta N-15 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

Figure 132. Nitrate isotope results from each synoptic sampling event within the Difficult Run watershed.

Number of septic tanks per square mile

600

500

400

300

200

100

0

*Typical range of error, 2-sigma: 

 delta N-15: 0.5 per mil and

 delta O-18: 1.0 per mil

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

Delta N-15 of nitrate, per mil* Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

Figure 133. Median nitrate isotope results from synoptic sampling events within the Difficult Run watershed compared to the 

septic tank density within the drainage area of each monitoring location with dashed lines indicating literature values of nitrate 

sources.
174 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Table 39. Estimated annual nitrate loads contributed by septic system leachate and residential fertilizer application at the Captain 

Hickory Run monitoring location (01645940).

[TN, total nitrogen; lb, pound; yr, year; mi2, square mile; %, percent]

Population1 Number of septic 

systems2

Septic system TN 

loading rate at 

the edge of the 

drainfield

(lb/person/yr)3

Residential area

(mi2

)

4

Fertilizer 

application rate 

(lb/yr/mi2

)

5

Percent of 

fertilizer 

nitrogen leached 

from turfgrass

(%)6

Annual TN load

(lb/yr)

Septic7 Fertilizer8

1,724 668 9.33 1.14 15,560 10 16,085 1,774

1

U.S. Census Bureau, 2012.

2

Fairfax County GIS Branch, unpub. data, 2014. 

3

Maizel and others, 1997.

4

Fairfax County Stormwater Planning Division, 2007.

5

Law and others, 2004.

6

Petrovic, 2008.

7

Computed as the population (1,724) multiplied by the septic TN loading rate at the edge of the drainfield (9.33).

8

Computed as the residential area (1.14) multiplied by the fertilizer application rate (15,560) and the fertilizer leachate rate (10 percent).

Manure

9.35 percent

Manure

1.07 percent

Fertilizer

(fixation)

21.36 percent

Fertilizer (fixation)

9.96 percent

Atmospheric

deposition

39.74 percent

Atmospheric

deposition

22.18 percent

Urban area

29.55 percent

Urban area

66.79 percent

Input Output

Figure 134. Percentages of input and output sources of total nitrogen for the Difficult 

Run watershed as generated by the 2002 Chesapeake Bay Total Nitrogen SPARROW 

model.

Implementation of Conservation Practices and 

Water-Quality Response

 Implementation of conservation practices within the 

Difficult Run watershed has increased from 2007 to 2014 

(table 40), with stream restoration becoming one of the 

principal implementation activities within the watershed. 

In theory, stream restoration should reduce sediment and 

nutrient export from the watershed by reducing streambank 

erosion, enhancing denitrification processes, and improving 

the connectivity between the stream and the floodplain. Other 

implementation practices were also used during the last 

6 years to reduce stormwater runoff and transport of sediment 

and nutrients.

 Although intensive water-quality monitoring 

at the streamgage started for this project in 2010, 

historical water-quality monitoring data exist for the 

period 1972 through 2007, and these historical data were 

coupled with the recent monitoring data to provide an initial 

assessment of trends in water quality in Difficult Run. These 

data were included in a trend analysis using Weighted Regression on Time, Discharge and Season (WRTDS) to provide 

trends in concentration and load (http://cbrim.er.usgs.gov/). 

Analysis of the Difficult Run nitrate data using WRTDS 

indicated that overall nitrate concentrations have increased 

from 1985 through 2014 at a rate of approximately 0.02 mg/L 

per year. Flow normalized nitrate fluxes over this same 

period have increased by approximately 2.8 percent per year. 

While this is a rather small rate of change, it indicates that 

nitrate concentrations and in-stream loads have increased, 

which is evident in the time series of observed concentration 

data (fig. 135). Enhanced implementation of conservation 

practices could be necessary to reduce nitrate concentrations 

in Difficult Run.
Difficult Run Watershed Water-Quality Characterization 175

Table 40. The classes/types of conservation practices that were implemented in the Difficult Run watershed for water years 2009 

through 2014.

Practice type 2009 2010 2011 2012 2013 2014

Stream Restoration 1 1 1 5

Extended Detention Pond 2 1 1

Soil Compost Amendment 2 2 2

Bioretention 3 1

Permeable Pavement 1 1

Constructed Wetland 2 1 2

Infiltration 2

Dry Swale 1 Nitrate plus nitrite, in milligrams per liter as nitrogen

0

0.5

1.0

1.5

2.0

2.5

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

>80 to 100

>60 to 80

>40 to 60

>20 to 40

0 to 20

EXPLANATION

Percentage of values

 equal to or less than the

 maximum streamflow of

 3,900 cubic feet per second

 (where small values

 represent low-flow samples)

Streamflow data

not available Year

Figure 135. Nitrate concentrations for the Difficult Run watershed water years 1972 through 2013.

The cumulative effects of all the BMP implementation 

within the Difficult Run watershed are difficult to quantify 

because of the many different types of BMP practices, the 

location of each, potential lag times between implementation 

and full functionality of the practice, and the relative efficiency 

of each practice/installation. Future monitoring will be 

designed to measure the overall changes in watershed nutrient 

and sediment transport. An interim set of expectations was 

developed by using the Chesapeake Bay Program’s watershed 

model to evaluating model output for model scenarios with and 

without the implementation of BMP practices. The results for 

the land-water segment that contain Difficult Run are presented 

in figure 136 (source: Chesapeake Bay Modeling Team, written 

commun., 2015, based on Watershed Model 5.3.2). The model 

results indicate that the implementation of BMPs has been 

responsible for a 1 to 2 percent annual change in nitrate, with a 

reduction of approximately 15 percent between 1985 and 2012. 

This annual rate of change would be difficult to detect in a 

short-term study, but could be detected through a long-term 

water-quality monitoring program.

Conceptual Watershed Model and Implications 

for Management Actions 

Intensive monitoring of a downstream gage and extensive 

sampling throughout the basin has provided an overview 

of nutrient processing, seasonal dynamics, and spatial 

water-quality patterns within the Difficult Run watershed. 

These findings can inform future management actions related 

to controlling nutrient and sediment sources. Some of the more 

pertinent findings are summarized below to highlight their 

significance in our understanding of the watershed.

• A majority of the total flow in Difficult Run occurs 

as stormflow. The prominence of high-flow events is 

likely influenced by the degree of imperviousness of 

the watershed, which magnifies the effect of overland 

runoff on water quality.

• Most atmospheric nitrate was cycled through biota 

before being transported to the stream; however, direct 

runoff of atmospheric nitrate during storm events was 

more evident in Difficult Run than in the other watersheds, partly because base-flow nitrate concentrations 

were relatively low and partly because of enhanced 

runoff from urbanized parts of the watershed.

• The runoff of deicing salts applied to the dense road 

network has a seasonal effect on the water quality of 

the basin. Specific conductance values often spike 

during late winter and early spring storms because of 

increased concentrations of chloride. Previous work 

has demonstrated the effect of this process and has 

suggested that elevated chloride concentrations could 

negatively affect aquatic health (Jastram, 2014).
176 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

10

8

6

4

2

0

–2

–4

–6

–8

–10

EXPLANATION

Agriculture

Urban area

Forest

Wastewater

Year

1985 1990 1995 2000 2005 2010

0

50,000

100,000

150,000

200,000

250,000

300,000

Atmospheric deposition

Nitrate plus nitrite, in pounds of nitrogen

Pounds of nitrate plus 

 nitrite as nitrogen from

 all sources with no best

 management practice

 implementation 

Source-specific pounds of 

nitrate plus nitrite as nitrogen

with best management

practice implementation

Percent difference per year

 in nitrate plus nitrite as nitrogen

 predicted by implementation 

 versus no implementation of

 best management practices

Annual percent change of nitrate plus nitrite as nitrogen

Figure 136. Nitrate loads estimated from the Chesapeake Bay watershed model for the Difficult 

Run land-river segment with and without best management practice implementation with input 

sources and the annual percentage difference of load attributed to implementation, 1985–2012.
Difficult Run Watershed Water-Quality Characterization 177

• Continuous nitrate data display a seasonal pattern that 

likely results from a combination of biologic in-stream 

and watershed processing, as well as changes in 

groundwater inputs. 

• Suspended-sediment loading was highly episodic; 

95 percent of the load was generated by the seven 

largest storms. This pattern is similar to the loads for 

headwater sites within the basin (Jastram, 2014). The 

sediment yield at the streamgage was almost a factor 

of 2 less than the yields measured at upstream sites, 

which indicates significant deposition of sediment on 

the Difficult Run floodplain, as described by Schenk 

and others (2013).

• TN loads accumulated steadily throughout the water 

year, indicating a persistent source during base-flow 

conditions. Annual nitrogen yields at the streamgage 

are less than those in the headwaters, which may be 

a result of biologic processing, variations in spatial 

sources, and interaction with the floodplain.

• TP accumulation patterns mirrored the episodic nature 

of suspended sediment and annual yields that are 

higher in the headwaters than the downstream monitoring location at site 30. Some spatial processing and 

floodplain deposition are likely responsible for this 

downstream reduction in load.

• The Captain Hickory Run subwatershed had high 

nitrate and TN concentrations during all sampling 

events. These concentrations are likely affected by 

elevated density of septic systems contributing to 

groundwater discharge of nitrate, though other nitrogen 

sources also may contribute.

• The unique chemical composition of some headwater 

sampling locations was almost entirely diluted along 

the main channel of Difficult Run, resulting in relatively homogenous conditions throughout the majority 

of the basin. 

• The largest water-quality variations were attributed 

to surrounding land-use or hydrologic features. Sites 

directly downstream of urban areas had the highest 

measures of conductivity, which are likely related to 

the application of road salts. 

• Land-use-based assumptions, nitrate isotopes, and 

SPARROW modeling results suggest that a mix of 

sources, including septic system leachate, atmospheric 

deposition, and commercial fertilizer application, may 

be significant sources of nitrogen within the basin.

This process-level understanding is directly applicable in 

the development of the most effective conservation practices 

and provides some insight for informed decisionmaking. Decisions to manage the following constituents in the following 

ways could generate the most efficient results. 

• Management activities for nitrogen would likely be 

most effective by the ongoing maintenance of septic 

systems, the management of fertilizer applications, 

and the possible expansion of the sanitary-sewer 

infrastructure.

• For the management of sediment and phosphorus, most 

loading occurs during the few relatively large storm 

events that occur each year; management strategies 

that target these few large events are critical.

• The collection of empirical nutrient and geochemical 

water-quality data in the Difficult Run watershed were 

critical for better understanding the nitrogen sources 

and temporal responses within the basin and for assessing trends in water-quality data (Chesapeake Bay 

Watershed Model results).

Ongoing USGS water-quality monitoring efforts will 

be directed towards the further understanding of the sources, 

sinks, and transport processes in Difficult Run, as well as the 

detection of water-quality change that can be directly related 

to the implementation of management actions. Because large 

changes in water quality take time and sufficient management 

of the landscape to actually affect the system, a number of 

methods could be used to detect change in the basin.

• Nutrient and sediment monitoring at the streamgage 

could provide a long-term record of the water quality that is leaving the basin. With sufficient data, 

expanded trend analyses can be performed to determine whether statistically significant trends exist for 

more constituents. 

• The continuous nitrate, turbidity, and specific 

conductance records are likely some of the best tools 

for determining whether the Difficult Run system is 

changing. With 15-minute-interval data, change can 

be measured over time, rather than estimated with 

regressions models such as LOADEST and WRTDS. 

Furthermore, shifts in the nitrate C-Q relationships 

might be an early indicator of water-quality change 

because increased management of the watershed is 

likely to fundamentally change nutrient transport 

dynamics without necessarily changing the hydrology 

much; hence, the C-Q relationships might evolve over 

time and with enhanced management.

• Ratios of various chemical tracers might produce 

indicators of change at an early stage because the concentrations of some constituents are determined more 

by dissolution and reaction chemistry in the aquifers 

and others by nutrient management practices within the 

basin. For example, implementation of BMPs might 

result in changes in ratios of nitrate to calcium or other 

ions in base flow or runoff. 

• Control of direct surface runoff from urban areas 

might be detected by a reduction in the frequency 

or magnitude of high-δ18O nitrate peaks during 

precipitation events.
178 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Comparison of Water-Quality Patterns 

Among Study Watersheds

Comparison of water-quality patterns among the four 

study watersheds provides an opportunity to explore similarities and differences among these watersheds that provide a 

range of land uses and hydrogeology. 

Continuous water-quality data were collected at 

the streamgage(s) within each watershed, and these 

continuous water-quality data demonstrate a range of patterns 

(figs. 137 through 139). The greatest specific conductance 

values were from the predominantly agricultural sites during 

low-flow conditions, with subsequent dilution occurring 

during higher flows (fig. 137). Within the agricultural basins, 

the greatest conductance values were observed in the Smith 

Creek watershed, followed by the Conewago Creek watersheds, likely because of the carbonate bedrock that underlies 

parts of both watersheds. Lower conductance values were 

generally observed in the Chesterville Branch watershed, 

which yields lesser conductance from weathering. At the 

predominantly urbanized Difficult Run watershed, specific 

conductance sometimes decreased during higher flows, but 

more often increased. During the cooler season, in particular, a 

limited number of elevated Difficult Run specific conductance 

values were observed during higher flow periods, which 

seems to be attributable to the runoff of roadway salts and the 

generally higher roadway density in this watershed, relative to 

the other watersheds.

Data from continuous nitrate monitors demonstrate 

tremendous variability between water-quality sites (fig. 138). 

Chesterville Branch nitrate values fluctuate the most among 

the three monitored sites, ranging from almost 10 mg/L during 

low-flow periods to 1 mg/L during wet-weather periods, when 

nitrate from groundwater is diluted with event runoff. Smith 

Creek and Difficult Run nitrate concentrations are much less 

than those in Chesterville Branch and therefore have less 

variability. In contrast to the dilution response observed in 

Chesterville Branch, however, maximum nitrate concentrations at Smith Creek and Difficult Run were generally reached 

during intermediate flow conditions, with lower concentrations 

of nitrate observed during relatively low and high flows. 

Lower concentrations during high flows are attributed to 

dilution during storm events. Lower concentrations during 

low flow in Smith Creek and Difficult Run are likely related to 

in-stream uptake and loss of nitrate during relatively low-flow 

conditions, as well as to potential shifts in the contributions of 

groundwater from different aquifer sources. The high concentrations of nitrate in base flow at Chesterville Branch likely 

dampen observation of the effects of near-stream or in-stream 

denitrification and uptake at this site, compared to what might 

be seen at a site with low nitrate inputs.

Relatively similar patterns in DO concentrations were 

observed in Smith Creek, Difficult Run, and Chesterville 

Branch (fig. 139). All three watersheds displayed higher 

DO concentrations during the winter months, which is 

related to the increased solubility of oxygen in cooler water. 

All three sites showed relatively similar variability in DO 

concentrations over the monitoring period (about 7 mg/L), and 

concentrations remained above 4 mg/L for all streams.

For further comparison between watersheds, the discrete 

water-quality data collected at the streamgage(s) in each 

watershed have been summarized (fig. 140; table 41). While 

a similar distribution of observed values is evident for water 

temperature and DO concentrations (fig. 140), the other 

monitored constituents show more variability, even for the two 

water-quality monitoring stations in Conewago Creek. These 

differences were explored by using cluster analyses.

Cluster analyses indicate more between-basin 

variability than within-basin variability in water quality 

among the study watersheds, with some interesting patterns. 

Four of the six major clusters tended to be dominated by 

water-quality samples from a single watershed (fig. 141). 

Group 1 included nearly all the samples from the Smith 

Creek watershed and was characterized as having relatively 

high pH, specific conductance, calcium, magnesium, and 

sulfate levels (fig. 141). Groups 2 and 3 included nearly 

all the water-quality samples from the two monitoring 

sites in Conewago Creek, as well as a pair of higher flow 

samples from Smith Creek (figs. 141 and 142). Group 2 was 

characterized as having higher phosphorus concentrations and 

were generally associated with higher flows. Group 3 was 

characterized by elevated sulfate concentrations, as well as 

pH, specific conductance, calcium, and magnesium levels that 

are slightly less than those in group 1. Given that Conewago 

Creek is partially underlain by carbonate rocks, but to a lesser 

extent than Smith Creek, this cluster makes sense. Group 4 

contains high-flow samples from all watersheds, as well as 

a few Difficult Run base-flow samples, and is characterized 

by relatively dilute water chemistry. The Difficult Run 

base-flow samples in group 4 tended to be collected during 

the summer months and had lower chloride concentrations. 

Group 5 includes only samples from Difficult Run, and are 

generally characterized as having elevated chloride (figs. 141 

and 142). Group 6 contains only samples from Chesterville 

Branch and is characterized by elevated nitrogen concentrations in samples collected during low- and intermediate-flow 

conditions (figs. 141 and 142). 

Given extensive plans for implementation of BMPs 

across all four watersheds, as well as the somewhat varied 

nitrogen and phosphorus patterns among these basins and 

during different flow conditions, patterns in dissolved and 

total nutrients were examined. Theoretically, the effects of 

implemented BMP practices might first appear as subtle shifts 

in the ratios of certain nutrient constituents, rather than as 

statistically significant changes in loads or nutrient concentrations. The current findings provide a baseline to compare 

to future water-quality patterns at these sites. The highest 
Comparison of Water-Quality Patterns Among Study Watersheds 179

nitrogen concentrations were observed in Chesterville Branch 

(up to 10 mg/L), with the remaining four monitoring sites 

having relatively similar nitrogen concentrations that range 

from 1 to 4 mg/L (fig. 143). For all five monitoring sites, the 

vast majority of the low-flow TN was present as nitrate, which 

is almost entirely contributed by discharging groundwater, 

with nitrate composing an incrementally smaller proportion of 

the TN as flow and runoff increased (fig. 143). Orthophosphate 

and TP concentrations were more variable than those of TN; 

lower phosphorus concentrations overall were observed at 

Difficult Run and Smith Creek, relative to those at either 

Chesterville Branch or the Conewago Creek monitoring 

stations (fig. 144). During low-flow conditions, much of the 

TP was present as orthophosphate, but during higher flow 

conditions, TP was dominated by particulate or dissolved 

organic forms (fig. 144). Subsequent research could further 

explain phosphorus sources in these watersheds.

Nitrate isotope data for samples collected in Smith 

Creek, Difficult Run, and Chesterville Branch indicated 

variations in the proportions of nitrate sources and the 

relative effects of biogeochemical processes (fig. 145). 

Nitrate δ15N values were largely consistent with commercial 

fertilizer inputs at Chesterville Branch, predominantly 

manure inputs at Smith Creek, and mixtures including 

domestic wastewater at Difficult Run. Nitrate δ18O values 

indicate that almost all of the nitrate exported from these 

watersheds was produced by nitrification, regardless of the 

source of nitrogen. Exceptions to this were most evident in 

Difficult Run, where nitrate commonly exhibited substantial 

isotopic variations during stormflow that were consistent 

with contributions of new atmospheric nitrate in runoff (high 

δ18O, low δ15N). In comparison to the other watersheds, more 

frequent detections of atmospheric nitrate contributions 

in Difficult Run can be attributed to (1) a relatively large 

amount of impervious surface and stormwater engineering 

features that routed precipitation rapidly to streams and 

minimized contact with biogeochemically active soils, and 

(2) base-flow nitrate concentrations that were relatively low, 

so atmospheric nitrate was a relatively large fraction of the 

total nitrate during stormflow. Chesterville Branch nitrate 

exhibited relatively little isotopic variability at high flow, 

despite large variations in concentration. In this case, nitrate 

discharging from groundwater was diluted by precipitation 

runoff, but because of the high concentrations of nitrate 

from groundwater, atmospheric nitrate in the runoff was not 

a substantial fraction of the diluted stream mixtures. Smith 

Creek nitrate exhibited moderate isotopic variability, including 

a shift to lower δ15N values during stormflow periods (some 

combination of shallow groundwater flowpaths and ephemeral 

contributions from high-elevation forested watersheds) and a 

tendency towards slightly higher δ15N and δ18O values during 

low-flow periods that may indicate partial nitrate reduction 

in the stream channel or variations in groundwater flow paths 

contributing to discharge. Isotopic results from the spatial 

synoptic sampling events (fig. 146) supported the observations 

associated with the temporal discrete samples and revealed 

more variability related to local source-specific signatures 

from different parts of each watershed. 

The 2013 water year sediment and nutrient loads for 

each basin were converted to yields to allow among-basin 

comparisons. Unexpectedly, sediment yields (fig. 147) were 

greatest for Chesterville Branch, while the lowest sediment 

yield was observed in Smith Creek, with Difficult Run 

and Conewago Creek basins having intermediate yield. In 

contrast to these results, Gellis and others (2009) documented relatively high sediment yields from the Piedmont 

Physiographic Province and relatively low sediment yields 

from the Coastal Plain Physiographic Province. Elevated 

sediment yields in Chesterville Branch may be related to basin 

scale and land use, as this is a relatively small headwater 

basin that is similar to the small headwater streams described 

by Jastram (2014), tremendous sediment erosion may come 

from the headwaters of this system. The upper one-third of 

the Chesterville Branch watershed is an active nursery, and 

activities related to this land-use practice could be increasing 

the sediment concentrations in a way that is not representative 

of other small Coastal Plain watersheds. As described earlier, 

the Difficult Run sediment yield was approximately 25 percent 

of what is mobilized from the Difficult Run headwaters 

(Jastram, 2014; J.D. Jastram, U.S. Geological Survey, oral 

commun., 2014), highlighting that much of the sediment that 

is liberated from the Difficult Run headwaters ends up in 

storage (either on the floodplains or in the stream channel) 

and is not immediately exported from the watershed (Noe and 

others, 2013). The Conewago Creek sediment yields had large 

uncertainties associated with them, but a source of sediment 

lower in the watershed was indicated that increased the 

sediment load from the watershed. The collection of additional 

water-quality data in future years could be used to refine 

the surrogate sediment models and to reduce the confidence 

intervals associated with these estimates. 

TN loads were computed for all watersheds except 

Conewago Creek (fig. 148). Chesterville Branch yielded the 

most nitrogen, and Difficult Run yielded the least nitrogen, 

consistent with the relative differences in nitrate and TN 

concentrations. Despite the dramatically different land uses 

and geology in the Smith Creek and Difficult Run watersheds, 

their TN yields were relatively similar. Additional years of data 

would be needed to see if these patterns are consistent over time.

TP yields (fig. 149) were greatest for Chesterville Branch, 

intermediate for Conewago Creek, and lowest for Smith Creek 

and Difficult Run. Similar to the sediment patterns observed in 

Conewago Creek, phosphorus yields were greater at Falmouth 

and smaller at Bellaire, indicating the presence of a phosphorus 

source lower in the watershed that increased the phosphorus 

load from the basin. Because these yields were computed 

for a single year of data, climatological variability may be 

responsible for some of the relative differences between sites.
180 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 137. Continuous specific conductance data compared to normalized streamflow quantiles 

measured at the Conewago Creek Bellaire and Falmouth, Smith Creek, Chesterville Branch, and 

Difficult Run streamgages with colored bands that represent the density of data points for a “warm” 

(April through September) and “cool” (October through March) season.

100

200

300

400

500

Conewago Creek Bellaire Conewago Creek Falmouth Smith Creek

50

100

150

200

250

Chesterville Branch

100

1,000

3,000

–4 –3 –2 –1 0 1 2 –4 –2 –1 0 1 2 3 4

Difficult Run

Specific conductance at 25 degrees Celsius, in microsiemens per centimeter

Normalized streamflow quantile*

3 –3

100

200

300

400

500

100

200

300

400

500

600

0

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Percent of measured values

*One normalized quantile equates

 to one standard deviation

April–September October–March
Comparison of Water-Quality Patterns Among Study Watersheds 181

Smith Creek Chesterville Branch

–4 –3 –2 –1 0 1 2 –4 –2 –1 0 1 2 3 4

Difficult Run

Nitrate plus nitrite, in milligrams per liter as nitrogen

Normalized streamflow quantile*

3 –3

12

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Percent of measured values

*One normalized quantile equates

 to one standard deviation

10

8

6

4

2

2.5

2.0

1.5

1.0

0.5

1.0

0.5

0

3.5

3.0

2.5

2.0

1.5

April–September October–March

Figure 138. Continuous nitrate data compared to normalized streamflow quantiles measured at the 

Smith Creek, Chesterville Branch, and Difficult Run streamgages with colored bands that represent 

the density of data points for a “warm” (April through September) and “cool” (October through 

March) season. 
182 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

0

4

8

12

16

20

Smith Creek

Dissolved oxygen, in milligrams per liter

0

4

8

12

Chesterville Branch

0

5

10

Difficult Run

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Percent of measured values

*One normalized quantile equates

 to one standard deviation

Normalized streamflow quantile*

–4 –3 –2 –1 0 1 2 3 –4 –3 –2 –1 0 1 2 3 4

April–September October–March

Figure 139. Continuous dissolved oxygen data compared to normalized streamflow quantiles 

measured at the Smith Creek, Chesterville Branch, and Difficult Run streamgages with colored bands 

that represent the density of data points for a “warm” (April through September) and “cool” (October 

through March) season.
Comparison of Water-Quality Patterns Among Study Watersheds 183

Water temperature, 

in degrees Celsius

Specific conductance, in

microsiemens per centimeter

pH,

in standard units

Calcium,

in milligrams per liter

Magnesium,

in milligrams per liter

Sodium,

in milligrams per liter

Bicarbonate,

in milligrams per liter

Sulfate,

in milligrams per liter

Chloride,

in milligrams per liter

Nitrate plus nitrite,

in milligrams per liter as nitrogen

Total nitrogen,

in milligrams per liter

Ammonium,

in milligrams per liter as nitrogen

100

80

60

40

20

0

0 5 10 15 20 25

0 10 20 30 40 50 60 70 80 5 10 15 20 0 10 20 30 40 50

0 50 100 150 200

0 5 10 15 0 5 10 15 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

250 0 5 10 15 20 0 20 40 60 80 0

0

0 100 200 300 400 500 6.0 6.5 7.0 7.5 8.0 8.5 9.0

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

Cumulative percent

Figure 140. Cumulative distribution of various water-quality constituents collected from the Smith Creek, 

Conewago Creek Bellaire and Falmouth, Chesterville Branch, and Difficult Run streamgages.
184 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Dissolved oxygen,

in milligrams per liter

Potassium,

in milligrams per liter

Orthophosphate,

in milligrams per liter as phosphorus

Total phosphorus,

in milligrams per liter

Iron,

in micrograms per liter

Manganese,

in micrograms per liter

Silica,

in milligrams per liter as SiO

Streamflow,

in cubic feet per second

EXPLANATION

Median value

Smith Creek

Chesterville Branch

Conewago Creek Bellaire

Conewago Creek Falmouth

Difficult Run

Watershed

1 10 100 1,000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Cumulative percent

0 5 10 15 0 1 2 3 4 5 6 7

100

80

60

40

20

0

100

80

60

40

20

0

0 100 200 300 400 500 600 0 50 100 150 200 250 300 350 0 2 4 6 8 10 12 14

100

80

60

40

20

0

2

Figure 140. Cumulative distribution of various water-quality constituents collected from the Smith Creek, 

Conewago Creek Bellaire and Falmouth, Chesterville Branch, and Difficult Run streamgages.—Continued
Comparison of Water-Quality Patterns Among Study Watersheds 185

Table 41. Median concentrations of select discrete water-quality constituents at the Smith Creek, Difficult Run, Chesterville Branch, 

and Conewago Creek streamgages.

[deg C, degrees Celsius; mg/L, milligram per liter; μS/cm, microsiemens per centimeter; —, parameters not collected]

Parameter Reporting units Smith Creek Difficult Run Chesterville 

Branch

Conewago Creek 

Bellaire

Conewago 

Creek 

Falmouth

Water temperature deg C 12.46 16.44 12.89 11.60 15.20

pH Standard units 8.03 7.14 6.69 7.67 7.78

Specific conductance μS/cm at 25 deg C 472.0 222.5 194.0 256.0 299.5

Dissolved oxygen mg/L 9.50 9.21 9.20 10.10 9.20

Calcium mg/L 65.95 11.29 18.80 21.30 26.20

Magnesium mg/L 16.76 4.90 5.27 7.42 8.80

Sodium mg/L 9.94 16.56 5.56 — —

Potassium mg/L 2.96 2.61 3.78 — —

Bicarbonate mg/L 256.06 37.62 29.15 — —

Sulfate mg/L 17.50 4.97 5.33 13.31 16.38

Chloride mg/L 18.21 41.47 14.97 22.70 25.80

Nitrate-N mg/L 2.16 0.86 8.73 1.82 2.46

Ammonium mg/L 0.02 0.02 0.03 0.05 0.04

Total nitrogen mg/L 2.82 1.40 8.94 2.20 3.06

Orthophosphate mg/L 0.04 0.01 0.01 0.09 0.12

Total phosphorus mg/L 0.10 0.03 0.03 0.10 0.16

Iron μg/L 19.2 197.0 143.8 392.5 265.0

Manganese μg/L 10.79 52.90 82.77 53.50 28.00

Silica mg/L 6.36 8.23 10.40 — —
186 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

EXPLANATION

Relative constituent value

Low High

Total phosphorus

Orthophosphate

Ammonium

Total nitrogen

Nitrate-N

Chloride

Dissolved oxygen

Calcium

Magnesium

Specific conductance

Sulfate

pH

Group 1

(Smith Creek

type)

Group 2

(Elevated

phosphorus type)

Group 3

(Conewago Creek type)

Group 4

(Weak ionic

strength type)

Group 5

(Difficult Run

type)

Dendrogram break point

Group 6

(Chesterville Branch

type)

Figure 141. Discrete samples collected from the Smith Creek, Conewago Creek Bellaire and Falmouth, 

Chesterville Branch, and Difficult Run streamgages with a heat map of constituent values and the 

breakpoint used to produce five clusters.

Streamflow, in cubic feet per second

1

10

100

1,000

10,000

Cluster group

1 2 3 4 5 6

EXPLANATION

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)

Cluster group

Group 1—Smith Creek type

Group 2—Elevated phosphorus type

Group 3—Conewago Creek type

Group 4—Weak ionic strength type

Group 5—Difficult Run type

Streamgage

Smith Creek

Chesterville Branch

Conewago Creek Bellaire

Conewago Creek Falmouth

Difficult Run

Group 6—Chesterville Branch type

Figure 142. Streamflow for the five groups identified in a cluster analysis 

along with individual samples from the Smith Creek, Conewago Creek 

Bellaire and Falmouth, Upper Chester River, and Difficult Run watersheds.
Comparison of Water-Quality Patterns Among Study Watersheds 187

0.2

0.4

0.6

0.8

1.0

1.2

Smith Creek Difficult Run Chesterville Branch

Conewago Creek

Bellaire Falmouth

1,000

Streamflow, in cubic feet per second

1 01 100 1 01 100 1,000 1 01 100 1,000 1 01 100 1,000 1 01 100 1,000

Ratio of nitrate

to total nitrogen

0

A

2

4

6

8

10

12

Smith Creek Difficult Run Chesterville Branch

Conewago Creek

Bellaire Falmouth

0

B

1:1 line

Nitrate plus nitrite in

milligrams per liter as nitrogen

Total nitrogen, in milligrams per liter

02511050511050511050511050511050

Figure 143. Discrete data from the Smith Creek, Difficult Run, Chesterville Branch, and Conewago Creek Bellaire and 

Falmouth streamgages representing (A) the ratio of nitrate to total nitrogen compared to streamflow and (B) nitrate compared 

to total nitrogen concentrations, 2010 through 2013.

Total phosphorus, in milligrams per liter

0.5

1.0

1.5

2.0

2.5

Smith Creek Difficult Run Chesterville Branch

Conewago Creek

Bellaire Falmouth

0

1 10 100 1,000 1 10 100 1,000 1 10 100 1,000 1 10 100 1,000 1 10 100 1,000

Ratio of orthophosphate

to total phosphorus

Streamflow, in cubic feet per second

0

0.2

0.4

0.6

0.8

Smith Creek Difficult Run Chesterville Branch

Conewago Creek

Bellaire Falmouth

0.25.11.00.505.11.00.505.11.00.505.11.00.505.11.00.50

Orthophosphate, in

milligrams per liter as phosphorus

1:1 line

A

B

Figure 144. Discrete data from the Smith Creek, Difficult Run, Chesterville Branch, and Conewago Creek Bellaire and 

Falmouth streamgages representing (A) the ratio of orthophosphate to total phosphorus compared to streamflow and 

(B) orthophosphate compared to total phosphorus concentrations, 2010 through 2013
188 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed Nitrate plus nitrite, in milligrams per liter as nitrogen

0

2

4

6

8

10

0

5

10

15

12

0 5 10 15 20

2 64 8 10 12 14 16 18 20

EXPLANATION

Streamgage

Smith Creek

Difficult Run

Chesterville Branch

Routine sample

Storm sample

Dilution

Addition of atmospheric nitrate in runoff

Nitrate reduction (denitrification, assimilation)

Different nitrification N sources

Hydrologic conditions

Schematic changes potentially associated with

*Typical range of error, 2-sigma:

 delta N-15: 0.5 per mil,

Delta N-15 of nitrate, per mil* delta O-18: 1.0 per mil

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

Figure 145. Discrete data from the Smith Creek, Difficult Run, and Chesterville Branch 

streamgages, and the relationships between δ15N, δ18O, and nitrate results. 

0 5 10 15 20

1 2 3 4 5 6 7 8 9 10

Nitrate plus nitrite,

in milligrams per liter as nitrogen

0

2

4

6

8

10

0

2

4

6

7

8

9

1

3

5

12

14

Watershed

Delta N-15 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Delta O-18 of nitrate, per mil*

Artificial fertilizer Manure

Forest soils Septic systems

Common delta N-15 values of nitrate sources

Smith Creek

Difficult Run

Upper Chester River

EXPLANATION

Dilution

Addition of atmospheric nitrate in runoff

Nitrate reduction (denitrification, assimilation)

Different nitrification N sources

Schematic changes potentially associated with

*Typical range of error, 2-sigma:

 delta N-15: 0.5 per mil,

 delta O-18: 1.0 per mil

Figure 146. Data from the Smith Creek, Difficult Run, and Upper Chester River watersheds from 

each synoptic sampling event and the relationships between δ15N, δ18O, and nitrate results. 
Comparison of Water-Quality Patterns Among Study Watersheds 189

0

100

200

300

400

500

600

700

800

900

1,000

0

10

20

30

40

50

60

Streamflow, in millions of cubic feet per square mile

B

Smith

Creek

Chesterville

Branch

Watershed

Conewago

Creek

(Bellaire)

Conewago

Creek

(Falmouth)

Difficult

Run

EXPLANATION

In millions of cubic feet

Total streamflow

Upper 95 percentile confidence interval

Lower 95 percentile confidence interval

Yield estimate

In millions of cubic feet per square mile

0

500

1,000

1,500

2,000

2,500

Streamflow, in millions of cubic feet 

A

 Sediment yield, in tons per square mile

0

100

200

300

400

500

600

700

800

900

1,000

 Sediment yield, in tons per square mile

Figure 147. (A) The suspended-sediment 

yield and total annual streamflow and (B) the 

suspended-sediment yield and streamflow yield 

for the 2013 water year from the Smith Creek, 

Chesterville Branch, Conewago Creek Bellaire 

and Falmouth, and Difficult Run streamgages.

Total nitrogen yield, in pounds per square mile 

Streamflow, in millions of cubic feet Streamflow, in millions of cubic feet per square mile

Total nitrogen yield, in pounds per square mile

A

B

Watershed

Smith

Creek

Chesterville

Branch

Difficult

Run

0

5,000

10,000

15,000

20,000

25,000

30,000

0

300

600

900

1,200

1,500

1,800

2,100

2,400

0

5,000

10,000

15,000

20,000

25,000

30,000

0

10

20

30

40

50

60

EXPLANATION

In millions of cubic feet

Total streamflow

Upper 95 percentile confidence interval

Lower 95 percentile confidence interval

Yield estimate

In millions of cubic feet per square mile

Figure 148. (A) Total nitrogen yield and total 

annual streamflow and (B) the total nitrogen yield 

and streamflow yield for the 2013 water year from 

the Smith Creek, Chesterville Branch, and Difficult 

Run streamgages.
190 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Figure 149. (A) Total phosphorus yield and total 

annual streamflow and (B) the total phosphorus 

yield and streamflow yield for the 2013 water 

year from the Smith Creek, Chesterville Branch, 

Conewago Creek Bellaire and Falmouth, and 

Difficult Run streamgages 

0

500

1,000

1,500

2,000

2,500

3,000

Total phosphorus yield, in pounds per square mile

0

500

1,000

1,500

2,000

2,500

3,000

0

10

20

30

40

50

60

Total phosphorus yield, in pounds per square mile

Streamflo

w, in millions of cubic feet per square mile

A

Smith

Creek

Chesterville

Branch

Watershed

Conewago

Creek

(Bellaire)

Conewago

Creek

(Falmouth)

Difficult

Run

0

500

1,000

1,500

2,000

2,500

Streamflo

w, in millions of cubic feet 

B

EXPLANATION

In millions of cubic feet

Total streamflow

Upper 95 percentile confidence interval

Lower 95 percentile confidence interval

Yield estimate

In millions of cubic feet per square mile

Future Directions

This report on the results of the small watershed studies 

in Smith Creek, Upper Chester River, Conewago Creek, and 

Difficult Run describes the first 3 years of data collection in 

these watersheds and contributes to understanding baseline 

conditions in these four watersheds. The report also begins 

to characterize the sources, sinks, and transport processes of 

sediment and nutrients in these watersheds. Future work may 

focus on the following areas of study:

1. Expand studies to improve understanding of sources, 

sinks, and transport processes in these watersheds. 

Improved understanding of sediment and nutrient sources 

and transport processes could lead to improved recommendations for implementation of the most beneficial 

BMPs as well as improved monitoring strategies to detect 

responses of water quality to BMPs. This expanded 

source and transport work will include such activities 

as (1) age dating of groundwater resources, (2) shallow 

groundwater monitoring, and (3) increased use of other 

geochemical tracers, including isotopes, emerging contaminants, and endocrine-disrupting chemicals.

2. Improve understanding of the effects of the 

implementation of conservation practices across these 

watersheds. While the BMPs that have been applied 

across the landscape to date can be characterized by 

type and quantity, the tools for characterizing how these 

actions should affect water quality are rather weak 

and are commonly limited to evaluations of watershed 

models with and without conservation practices. 

3. Continue core water-quality monitoring and synoptic 

sampling in these watersheds to maintain a network that 

is capable of detecting and characterizing long-term 

improvements in water quality that result from the implementation of conservation practices. All five discrete 

water-quality monitoring sites described in this report 

currently are included in the Chesapeake Bay Nontidal 

Network, and therefore, trends in water-quality constituents will be computed as soon as sufficient data are available. The presence of the synoptic station network provides an additional trend-detection network that might be 

more sensitive than the five streamgages for the detection 

of water-quality improvements because the synopticmonitoring sites represent smaller subwatersheds that 

might be subjected to particularly intense local BMP 

implementation; therefore, these smaller synoptic sites 

could respond to BMP implementation sooner than the 

streamgage monitoring sites. 

4. Finally, in an effort to better understand how activities 

on the landscape affect water quality, and ultimately 

ecological health, within these watersheds, a relatively 

new initiative has begun within these basins. Since 2014, 

the small watershed water-quality team and the fishhealth team of the USGS Chesapeake Bay Program have 
Summary and Conclusions 191

been collaborating to coordinate traditional water-quality 

nutrient and sediment monitoring with enhanced spatial 

and temporal monitoring of hormone activity (estrogenicity, androgenicity, glucocorticoid) in an effort to better understand the human and environmental factors that 

drive potential endocrine disruption in the environment. 

Detailed characterization of water quality, hydrogeology, and nutrient sources that has already been initiated 

within the small watershed basins will strengthen the 

interpretation of these data. 

Summary and Conclusions

In 2010, the U.S. Geological Survey partnered with the 

U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in 

four diverse small watersheds that were targeted for increased 

implementation of conservation practices. The objective of 

this study was to investigate spatial and temporal variations 

in water chemistry and suspended sediment in these four 

watersheds that represent a range of land-use patterns and 

underlying geology to (1) characterize current water-quality 

conditions in these watersheds, and (2) identify the dominant 

sources, sinks, and transport processes in each watershed. 

The results of monitoring performed from April 2010 through 

September 2013 are discussed by watershed. 

Smith Creek Watershed

Smith Creek is a 105.39-mi2 watershed within the 

Shenandoah Valley that drains to the North Fork Shenandoah 

River. The long-term Smith Creek base-flow index is 

72.3 percent, indicating that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. 

A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek 

water quality could be attributed to hydrologic and seasonal 

variability. Statistically significant positive correlations with 

flow were observed for turbidity, suspended sediments, total 

nitrogen, ammonium, orthophosphate, iron, total phosphorus, 

and the ratio of calcium to magnesium concentrations. Statistically significant inverse correlations with flow were observed 

for specific conductance, magnesium, δ15N of nitrate, δ18O of 

nitrate, pH, bicarbonate, and calcium. Of particular note, flow 

and nitrate were not significantly correlated, likely because of 

the relatively complex concentration-discharge relationship 

observed in continuous and discrete datasets. Statistically 

significant seasonal patterns were observed for numerous 

water-quality constituents. Water temperature, orthophosphate, 

total phosphorus, turbidity, suspended-sediment concentration, 

and silica were higher during the warm season than during 

the cool season, and dissolved oxygen, pH, and sulfate were 

higher during the cool season. Surrogate-regression models 

were developed to compute sediment and nutrient loads in 

Smith Creek using the continuous water-quality monitors. 

The Smith Creek in-stream sediment load was approximately 

6,900 tons per year, with nearly 90 percent of the sediment 

load over the 3-year study period contributed during the eight 

largest storm events of that period. The Smith Creek phosphorus load was approximately 21,000 pounds per year, with 

the majority of the load contributed during stormflow periods, 

although a substantial phosphorus load still occurred during 

base-flow conditions. Smith Creek total nitrogen load was 

about 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions and strongly 

affected by base-flow export of nitrogen from the watershed. 

Extensive water-quality monitoring throughout the 

Smith Creek watershed revealed how a complex geology 

and hydrology interact to result in variable water chemistry 

throughout the Smith Creek watershed. During relatively dry 

and low base-flow periods, much of the discharge in Smith 

Creek was contributed by a single dominant spring—Lacey 

Spring. During wetter base-flow periods, the flows in Smith 

Creek were largely generated by a mixture of headwater 

springs and forested mountain tributaries with very different 

geochemical composition. The headwater springs were generally underlain by carbonate bedrock and were characterized as 

having relatively high nitrate, specific conductance, calcium, 

and magnesium, as well as relatively low concentrations of 

phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally 

characterized by low ionic strength waters with low nutrient 

concentrations. Analysis of the nitrate isotope data generally 

indicates that the nitrogen from the limestone springs was 

largely consistent with manure-derived sources (cattle and 

poultry), although the possibility of other mixed sources 

cannot be excluded. The nitrogen from the undeveloped, highgradient, forested mountain sites was consistent with atmospheric and natural soil sources of nitrogen. Land-use data 

indicate that manure sources of nitrogen dominated the input 

of nitrogen to the watershed. Phosphorus sources were less 

well studied. The presence of a single point-source discharge 

in the watershed near the town of New Market contributed the 

majority of the phosphorus to Smith Creek under base-flow 

conditions, but nonpoint sources of phosphorus dominated the 

loading to Smith Creek during stormflow periods. 

Implementation of conservation practices increased in 

the Smith Creek watershed during the study period, and even 

though a broad range of practice types were implemented, the 

most common practices included stream fencing (for cattle 

exclusion), the development of nutrient management plans, 

conservation crop rotation, and the planting of cover crops. 

While the implementation of these conservation practices 

is encouraging, results indicate small increases in nitrate 

concentrations at the streamgage during the past several 

decades, concurrent with small decreases in nitrate fluxes. The 

cumulative effect of these practices may not be detected in the 

Smith Creek water quality for many years, and the magnitude 

of the effect of these conservation practices in Smith Creek 

will depend largely on whether nutrient loading (of manure 

and commercial fertilizer) is reduced over time. 
192 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Upper Chester River Watershed

The Upper Chester River watershed includes the 

36.54-square-mile (mi2) watershed area around several 

nontidal tributaries that drain into the tidal Chester River. 

The streamgage is on Chesterville Branch, the largest 

nontidal tributary (approximately 6.12 mi2) and the site for 

continuous water-quality monitoring during this project. The 

base-flow index at Chesterville Branch is about 72 percent and 

indicates that, as in most of the Coastal Plain, groundwater 

is the greatest contributor to streamflow. As such, more 

than 90 percent of the nitrogen in the stream is in the form 

of nitrate from groundwater. Continuous and discrete data 

collected at Chesterville Branch show the effects of streamflow and season on water quality. Significant positive correlations with flow were observed for ammonium, orthophosphate, 

total phosphorus, sediment, and turbidity as runoff carried 

these constituents from the land surface into Chesterville 

Branch. Other constituents that increased significantly with 

flow include potassium, sulfate, iron, and manganese, which 

were likely contributed from near-stream areas and ponds with 

higher organic-matter content. Total nitrogen, pH, and specific 

conductance, along with chemical constituents associated 

with groundwater inputs including nitrate, calcium, silica, 

bicarbonate, ratio of calcium to magnesium, and sodium, were 

negatively correlated with flow because concentrations of 

these constituents were diluted by runoff. 

Seasonal differences in water chemistry most likely 

related to increased biologic effects on the uptake and release 

of chemicals in the stream and near-stream areas also were 

observed. Water temperature, orthophosphate, δ15N of nitrate, 

bicarbonate, ratio of sodium to chloride, and sodium were 

higher during the warm season, and dissolved oxygen, total 

nitrogen, nitrate, magnesium, sulfate, and manganese were 

higher during the cool season. 

Surrogate regression models developed by using 

continuous water-quality data showed that the annual sediment 

load for the 2013 water year to be about 2,600 tons, with more 

than 90 percent of the sediment contributed during two storms. 

The total phosphorus load in 2013 water year was about 

13,000 pounds, with more than 90 percent of the load contributed during the same two storms. The load of total nitrogen 

in the 2013 water year, about 140,000 pounds, accumulated 

steadily throughout the year as nitrate in groundwater continuously discharged into the stream. The same two large storms 

that contributed 90 percent of the suspended-sediment and 

total phosphorus load only contributed about 20 percent of the 

annual total nitrogen load.

Extensive water-quality monitoring of stream base flow 

throughout the Upper Chester River watershed identified 

how differences in land use and hydrogeology affect water 

chemistry. In parts of the watershed with well-drained soil and 

thick sandy aquifer sediments, concentrations of nitrate and 

other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. 

More than 90 percent of the nitrogen in streams from these 

areas was in the form of nitrate, and concentrations ranged 

from about 6 to 10 milligrams per liter as nitrogen in the two 

largest tributaries. Stream nitrate concentrations were about 

1 milligram per liter as nitrogen where soils were more poorly 

drained, the surficial aquifer sediments were thinner, and 

forests and wetlands were more widespread than agriculture. 

Nitrogen and oxygen isotopes of nitrate were consistent with 

soil nitrification in areas receiving large quantities of excess 

nitrogen from artificial fertilizers or atmospheric nitrogen 

fixation. Some denitrification was indicated by the isotopic 

signatures in water from the poorly drained streams. An 

analysis of land-use data and SPARROW modeling input 

data attribute almost 90 percent of the nitrogen sources in 

the Upper Chester River watershed to inorganic fertilizer 

and nitrogen fixation of atmospheric nitrogen by legumes, 

which agrees with the isotopic signature of nitrate in this 

watershed. Local sources of manure are limited in this area. 

Total phosphorus concentrations during base flow ranged from 

below detection to about 0.2 milligram per liter. Phosphorus 

concentrations present in streams during base-flow conditions 

were generally low compared to concentrations measured 

during storms because most phosphorus transport occurs 

as phosphorus attached to sediment particles during runoff. 

Because manure is not widely used in this area, the probable 

major source of phosphorus is fertilizer.

The implementation of conservation practices in the 

Upper Chester River watershed increased substantially during 

the study period. Out of the 1,194 USDA-compliant practices 

that were implemented, the most frequently used practices 

were oriented towards nutrient and sediment control, including 

cover crops, nutrient management planning, conservation crop 

rotation, conservation tillage, and irrigation management. The 

current Chesapeake Bay model for this area estimates that 

implementation of best management practices has caused a 

9-percent decrease in overall delivery of nitrate to the Upper 

Chester River since 1985. Because most nitrogen travels 

through the groundwater system for years to decades before 

being discharged to streams, the time period of monitoring 

was not sufficient to see the effects of these practices on water 

quality. The magnitude of the effect that may eventually be 

detected depends on the degree to which leaching of nitrate 

into the groundwater system is reduced over time. Loadings 

of phosphorus and sediment are primarily transported during 

large runoff events and are difficult to control and analyze for 

trends because of their timing and episodic nature. 

Conewago Creek Watershed 

Conewago Creek has two monitoring locations—one 

near the middle of the 47-square-mile watershed, and the other 

near the outlet just upstream of the Susquehanna River. The 

base-flow index was 43.1 percent for 2012–2013, indicating 

that on average, approximately 57 percent of the streamflow 

in Conewago Creek exited the watershed as stormflow, which 

suggests that stormwater runoff was greater than groundwater 

discharge (base flow). A series of cluster and principal 
Summary and Conclusions 193

components analyses demonstrated that the majority of the 

variability in the Conewago Creek water quality could be 

attributed to hydrologic and seasonal variability. Statistically 

significant positive correlations with flow were observed for 

total nitrogen (at the upstream site only), ammonium, total 

phosphorus, orthophosphate, iron, and manganese. Statistically 

significant inverse correlations with flow were observed for 

water temperature, specific conductance (at the downstream 

site only), sulfate, chloride, calcium, and magnesium. The only 

difference between the monitoring locations was that flow and 

total nitrogen were not statistically significantly correlated at 

the downstream site. Statistically significant seasonal patterns 

were observed for several water-quality constituents. Water 

temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season than during the cool 

season, and nitrate nitrogen and TN (upstream site only) were 

higher during the cool season. 

Surrogate regression models were developed to compute 

sediment and nutrient loads in Conewago Creek by using the 

continuous water-quality monitors and water-quality samples. 

Conewago Creek sediment load was approximately 9,900 tons 

in 2012 and 18,900 tons in 2013, with nearly 80 percent of the 

sediment load in 2013 contributed by the three largest storm 

events. Annual total nitrogen loads could not be estimated 

due to poor model performance. Additional monitoring or 

a continuous nitrate sensor might improve future estimates 

of TN loads. Conewago Creek phosphorus loads were 

approximately 50,000 pounds in 2012 and 2013. 

Combining data from one high-flow synoptic sampling 

event with the data from routine sampling revealed how the 

geology and hydrology interact to result in variable water 

chemistry throughout the Conewago Creek watershed. 

The areas above the upstream gage in the headwaters are 

generally underlain by forested non-carbonate bedrock 

and are characterized by low nitrate, specific conductance, 

calcium, and magnesium, as well as low concentrations of 

phosphorus, ammonium, iron, and manganese. The more 

developed, agricultural areas below the upstream site are 

generally characterized by higher ionic strength waters with 

higher nutrient and metal concentrations. An analysis of 

land-use data and SPARROW modeling data indicates that 

manure sources of nitrogen dominate the input of nitrogen to 

the watershed. 

Implementation of conservation practices increased in the 

Conewago Creek watershed during the study period, and while 

a broad range of practice types were implemented, the most 

common practices included residue and tillage management 

(no-till with cover crops), nutrient management, terracing, 

and stream fencing (for animal exclusion or bank restoration). 

While the implementation of these conservation practices is 

encouraging, the cumulative effect of these practices may 

not be detected in Conewago Creek water quality for years. 

The magnitude of the effect of these conservation practices 

depends largely on whether or not nutrient loading (manure 

and commercial fertilizer) is reduced and if sedimentmanagement activities are intensified over time. 

Difficult Run Watershed

The Difficult Run watershed is a 57.82-mi2 watershed 

that drains to the Potomac River. The long-term Difficult Run 

base-flow index was 57.9 percent, indicating that approximately 58 percent of the flow left the watershed as base 

flow and 42 percent as stormflow; however, with continued 

development and urbanization of the watershed, the base-flow 

index has decreased to 50.8 percent during the last 20 years. 

This base-flow index was smaller than those of the other study 

watersheds with long-term streamflow data, likely because 

the Difficult Run watershed is mostly underlain by crystalline Piedmont metamorphic rocks and has a relatively large 

proportion of impervious urban land cover. A series of cluster 

and principal components analyses indicated that most of the 

variability in Difficult Run water quality could be attributed to 

hydrologic variability and seasonality. Statistically significant 

positive correlations with flow were observed for turbidity, 

dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant 

inverse correlations with flow were observed for water 

temperature, pH, specific conductance, bicarbonate, calcium, 

magnesium, nitrate, δ15N of nitrate, and silica. Statistically 

significant seasonal patterns were observed for numerous 

water-quality constituents. Water temperature, ammonium, 

orthophosphate, and δ15N of nitrate were higher during the 

warm season, and dissolved oxygen, nitrate, and manganese 

were higher during the cool season. Surrogate regression 

models were developed to compute sediment and nutrient 

loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of 

the sediment load in the 2013 water year contributed by the 

seven largest storm events. The phosphorus load in Difficult 

Run was approximately 14,000 pounds per year, with the 

majority of the load contributed during stormflow periods. The 

total nitrogen load in Difficult Run is estimated to have been 

approximately 140,000 pounds per year, with total nitrogen 

accumulation less dominated by stormflow contributions than 

that of phosphorus and strongly affected by base-flow export 

of nitrogen from the basin.

Extensive water-quality monitoring throughout the 

Difficult Run watershed revealed relatively uniform generation 

of flow per unit of watershed area, as well as spatial clusters 

in water quality that appear to be strongly related to land-use 

activities. One cluster of monitoring sites is composed of 

stations with elevated nitrate concentrations. The elevated 

nitrate concentrations are inversely correlated with population 

density and positively correlated with septic system density 

within each subwatershed. The majority of the elevated nitrate 

concentrations for sites in this cluster may have resulted 

from nitrate leaching by septic systems, whereas homeowner 

fertilizer usage in these lower density subwatersheds was less 

likely to be the principal source of nitrate. Nitrate isotope 

data, temporal patterns in the water-quality data, mass-balance 

computations, and a separate land-use analysis all generally 

indicate that leachate from septic systems was the most 

likely source of the elevated nitrate. Another water-quality 
194 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

cluster is composed of sites with relatively low nitrogen 

concentrations, and these sites are generally clustered in areas 

that are served by city sewer lines and have had significant 

stream restoration activities. A third cluster drained the areas 

with the highest imperviousness and consisted of sites with 

strongly elevated specific conductance, chloride, and sodium, 

which were likely caused by a combination of road salting and 

other anthropogenic sources draining these urbanized areas 

in the watershed. A fourth water-quality cluster represents a 

mixture of water sources and had water quality similar to that 

at the Difficult Run streamgage. Nitrate isotope data indicate 

variations in the proportions of different sources. Implementation of conservation practices increased in the Difficult Run 

watershed during the study period, and while a broad range of 

practice types were implemented, the most common practices 

included stream restoration. While the implementation of these 

conservation practices is encouraging, the cumulative effect of 

these practices likely will not be detected in the Difficult Run 

water quality for years.

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Appendix 1 Nitrate plus nitrite, in milligrams per liter as nitrogen

Potassium,

in milligrams per liter

Sodium,

in milligrams per liter

Chloride,

in milligrams per liter

Sodium to chloride ratio

Sulfate,

in milligrams per liter

Manganese,

in micrograms per liter

0

1

2

3

4

5

6

0

2

4

6

8

0

5

10

15

0

4

8

12

16

20

24

28

0.4

0.5

0.6

0.7

0

4

8

12

16

20

24

10 100 1,000

10 100 1,000

0

10

20

30

40

Streamflow, in cubic feet per second

Streamflow, in cubic feet per second

EXPLANATION

Season

April to September

October to March

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)

Figure 1–1. Concentration-discharge data for discrete samples from the Smith Creek streamgage by 

warm and cool season.
202 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

1

10

100

1,000

10,000

Streamflo

w,

in cubic feet per second

0

1

2

3

4

5

6

0

1

2

3

4

5

6

Nitrate plus nitrite,

in milligrams per liter as nitrogen

Total nitrogen,

in milligrams per liter

5

10

15

0

2

4

6

Delta N-15 of nitrate,

per mil

Delta O-18 of nitrate,

per mil

0.001

0.01

0.1

1

Ammonium,

in milligrams per liter as nitrogen

WY 2010 WY 2011 WY 2012 WY 2013

A J O J A J O J A J O J A J O

WY 2010 WY 2011 WY 2012 WY 2013

A J O J A J O J A J O J A J O

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year

Figure 1-2. Concentrations of various constituents in discrete samples from the Smith Creek streamgage, 2011–2013.
Appendix 1 203

0

20

40

60

80

100

0

5

10

15

20

25

Calcium,

in milligrams per liter

Magnesium, in 

milligrams per liter

0

2

4

6

8

2

3

4

5

6

7

Potassium,

in milligrams per liter

Ratio of calcium

to magnesium

0

5

10

15

0

4

8

12

16

20

24

28

Sodium,

in milligrams per liter

Chloride,

in milligrams per liter

0.4

0.5

0.6

0.7

Sodium to

chloride ratio

0

50

100

150

200

250

300

Bicarbonate,

in milligrams per liter

WY 2010 WY 2011 WY 2012 WY 2013

A J O J A J O J A J O J A J O

WY 2010 WY 2011 WY 2012 WY 2013

A J O J A J O J A J O J A J O

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year

Figure 1-2. Concentrations of various constituents in discrete samples from the Smith Creek streamgage, 

2011–2013.—Continued
204 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

1

2

3

4

5

6

7

Magnesium,

in milligrams per liter

0

4

8

12

16

20

24

0

Chloride,

in milligrams per liter

0.36

0.38

0.40

0.42

0.44

0.46

0.34

Sodium to chloride ratio

4.5

5.0

5.5

6.0

6.5

7.0

4.0

Delta N-15 of nitrate,

per mil

Streamflow, in cubic feet per second

1 10 100 1,000

Streamflow, in cubic feet per second

1 10 100 1,000

1

2

3

4

5

6

0

Delta O-18 of nitrate,

per mil

10

15

20

25

5

Water temperature,

in degrees Celsius

5

10

15

0

Dissolved oxygen,

in milligrams per liter

EXPLANATION

Season

April to September

October to March

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)

Figure 1-3. Concentration-discharge data for discrete samples from the Chesterville Branch streamgage by warm 

and cool season, 2011–2013.
Appendix 1 205

0

1.0

2.0

3.0

4.0

5.0

6.0

Delta O-18 of nitrate,

per mil

1

10

100

1,000

0

0.05

0.10

0.15

0.20

0.25

Suspended-sediment 

concentration,

in milligrams per liter

Ammonium,

in milligrams per liter as nitrogen

0.01

0.1

1

Total phosphorus,

in milligrams per liter

0

4

8

12

16

20

24

Calcium,

in milligrams per liter

2.0

2.5

3.0

3.5

4.0

4.5

1

2

3

4

5

6

7

Ratio of calcium

to magnesium

Potassium,

in milligrams per liter

0

4

8

12

16

20

24

Chloride,

in milligrams per liter

0 0

0

2

4

6

8

10

12

14

Silica,

in milligrams per liter as SiO

100

200

300

400

500

600

700

Iron,

in micrograms per liter

WY 2011 WY 2012 WY 2013

J OJAJ O J A J O

WY 2011 WY 2012 WY 2013

J OJAJ O JAJ O

2

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year

Figure 1-4. Concentrations of various constituents in discrete samples from the Chesterville Branch streamgage, 

2011–2013. 
206 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

Streamflow, in cubic feet per second Streamflow, in cubic feet per second

EXPLANATION

Season

April to September

October to March

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)

1 10 100 1,000 10,000 1 10 100 1,000 10,000

0

2

4

6

8

10

Nitrate plus nitrite,

in milligrams per liter as nitrogen

A B

2.0

2.5

3.0

3.5

4.0

Ratio of calcium

to magnesium

A B

Figure 1-5. Concentration-discharge data for discrete samples from the Conewago Creek 

(A) Bellaire and (B) Falmouth streamgages by warm and cool season.
Appendix 1 207

0

4

8

12

16

20

24

Sulfate,

in milligrams per liter

A B

0

5

10

15

20

25

30

35

Calcium,

in milligrams per liter

0

2

4

6

8

10

12

Magnesium,

in milligrams per liter

2.5

0

3.0

3.5

4.0

Ratio of calcium

to magnesium

WY 2011 WY 2012 WY 2013

J O J A J O J A J O

WY 2012 WY 2013

OJAJOJ A J O

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year

Figure 1-6. Concentrations of various constituents in discrete samples from the Conewago Creek (A) Bellaire and 

(B) Falmouth streamgages.
208 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

0.001

0.01

0.1

1

Ammonium,

in milligrams per liter

as nitrogen

1

10

100

1,000

Sediment concentration,

in milligrams per liter

10

100

1,000

10,000

0

100

200

300

400

500

600

Iron,

in micrograms per liter

Manganese,

in micrograms per liter

A B

WY 2011 WY 2012 WY 2013

J O J A J O J A J O

WY 2012 WY 2013

O J AJOJAJ O

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year

Figure 1-6. Concentrations of various constituents in discrete samples from the Conewago Creek (A) Bellaire and 

(B) Falmouth streamgages.—Continued
Appendix 1 209

EXPLANATION

Season

April to September

October to March

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)

Streamflow, in cubic feet per second

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Total nitrogen,

in milligrams per liter

1 10 100 1,000 10,000

Streamflow, in cubic feet per second

1 10 100 1,000 10,000

0

5

10

15

20

Delta O-18 of nitrate,

per mil

0

1

2

3

4

Potassium,

in milligrams per liter

0

10

20

30

40

50

0

20

40

60

80

100

Sodium,

in milligrams per liter

Chloride,

in milligrams per liter

0

2

4

6

8

Sulfate,

in milligrams per liter

0

50

100

150

200

Manganese,

in micrograms per liter

Figure 1-7. Concentration-discharge data for discrete samples from the Difficult Run streamgage by 

warm and cool season, 2011–2013.
210 Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0

10

20

30

40

50

0

10

20

30

40

50

0

100

200

300

400

500

600

700

20

0

40

60

80

100

0

2

4

6

8

0

4

8

12

16

Sodium to

chloride ratio

Sodium,

in milligrams per liter

Bicarbonate,

in milligrams per liter

Iron,

in micrograms per liter

Chloride,

in milligrams per liter

Sulfate,

in milligrams per liter

Silica,

in milligrams per liter as SiO

WY 2011 WY 2012 WY 2013

J O J A J O J A J O

WY 2011 WY 2012 WY 2013

J O J A J O J A J O

2

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year

Figure 1-8. Concentrations of various constituents in discrete samples from the Difficult Run streamgage, 

2011–2013.
Appendix 1 211

0

0.5

1.0

1.5

2.0

2.5

3.0

0.001

0.01

0.1

1

10

100

1,000

0

2

4

6

8

10

12

14

16

18

1.8

2.0

2.2

2.4

2.6

0

5

10

15

20

0

0.05

0.10

0.15

0.20

0.25

0.30

0

2

4

6

8

0

1

2

3

4

Total nitrogen,

in milligrams per liter

Suspended-sediment 

concentration,

in milligrams per liter

Calcium,

in milligrams per liter

Ratio of calcium

to magnesium

Delta O-18 of nitrate,

per mil

Total phosphorus,

in milligrams per liter

Magnesium,

in milligrams per liter

Potassium,

in milligrams per liter

WY 2011 WY 2012 WY 2013

J O J A J O J A J O

WY 2011 WY 2012 WY 2013

J O J A J O J A J O

EXPLANATION

Routine sample

Storm sample

Hydrologic conditions

WY Water year

Figure 1-8. Concentrations of various constituents in discrete samples from the Difficult Run streamgage, 

2011–2013.—Continued

Manuscript approved June 25, 2016

Prepared by the Reston and Lafayette 

Publishing Service Centers

For more information about this publication, contact:

Director 

U.S. Geological Survey

1730 East Parham Road

Richmond, VA 23228

(804) 261-2600

or visit our Web site at

http://va.water.usgs.gov/
ISBN 978-1-4113-4085-5

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Hyer and others—Spatial and Temporal Variation of Stream Chemistry, Chesapeake Bay Watershed—Scientific Investigations Report 2016–5093

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20165093 Printed on recycled paper