Prepared in cooperation with Blue Water Baltimore

Factors Affecting Long-Term Trends in SurfaceWater Quality in the Gwynns Falls Watershed, 

Baltimore City and County, Maryland, 1998–2016

Open-File Report 2018–1038

U.S. Department of the Interior

U.S. Geological Survey
Cover. Blue Water Baltimore monitoring station in the main stem of the Gwynns Falls near the corner of Owings Mills Blvd. 

and Crondall Lane. (Photograph by Alice Volpitta, Blue Water Baltimore)
Factors Affecting Long-Term Trends in 

Surface-Water Quality in the Gwynns Falls 

Watershed, Baltimore City and County, 

Maryland, 1998–2016

By Emily H. Majcher, Ellen L. Woytowitz, Alexander J. Reisinger, and 

Peter M. Groffman

Prepared in cooperation with Blue Water Baltimore

Open-File Report 2018–1038

U.S. Department of the Interior

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

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Deputy Director

 exercising the authority of the Director

U.S. Geological Survey, Reston, Virginia: 2018

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Suggested citation:

Majcher, E.H., Woytowitz, E.L., Reisinger, A.J., and Groffman, P.M., 2018, Factors affecting long-term trends 

in surface-water quality in the Gwynns Falls watershed, Baltimore City and County, Maryland, 1998–2016: 

U.S. Geological Survey Open-File Report 2018–1038, 27 p., https://doi.org/10.3133/ofr20181038.

ISSN 2331-1258 (online)
iii

Acknowledgments

The authors wish to thank Lynn Heller of the Abell Foundation, as well as the Chesapeake Bay 

Trust Chesapeake Conservation Corps program and the leadership of the Baltimore Ecosystem 

Study for project funding and in-kind project support. The authors thank David Flores (Center 

for Progressive Reform, formerly of Blue Water Baltimore) for insights and project development 

assistance, and Alice Volpitta and Angela Haren of Blue Water Baltimore for their assistance. 

Thanks to the members of the Baltimore Urban Waters Partnership, Actionable Science workgroup, for input on prioritization, data sharing, and project feedback.

v

Contents

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

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

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

Urbanization Impacts ...........................................................................................................................2

Climate Impacts.....................................................................................................................................2

Impetus for the Study...........................................................................................................................3

Purpose and Scope ..............................................................................................................................3

Why the Gwynns Falls Watershed?............................................................................................................3

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

Land-Cover Change..............................................................................................................................5

Change in Precipitation .......................................................................................................................5

Is the Water Quality of the Gwynns Falls Watershed Changing?..........................................................6

Compilation of Data and Estimates of Water-Quality Trends ......................................................11

Summary of Streamflow and Water-Quality Trends .....................................................................12

What Factors are Affecting Water-Quality Trends in the Gwynns Falls?...........................................17

Evaluating Relations between Water Quality and Potential Factors of Influence...................17

Aging Gray Infrastructure and the Influence of Sanitary Sewer Overflows ............................18

Implementation of Green Infrastructure and Effects of Best Management Practices...........23

Conclusions...................................................................................................................................................25

References Cited..........................................................................................................................................25

Figures

1. Map showing location of Gwynns Falls watershed and subwatersheds, and 

Baltimore Ecosystem Study Sites, Baltimore County and Baltimore City, Maryland........4

2. Graph showing developed land cover and change in land cover, Gwynns Falls 

subwatersheds, Baltimore County and Baltimore City, Maryland, 2001–11 .......................6

3. Graph showing annual precipitation and annual count of runoff producing storms, 

Baltimore Washington International Airport meteorological station, Maryland, 

water years 1999–2016.................................................................................................................7

4. Graphs showing trends in seasonal precipitation, water years 1999–2016, 

Baltimore Washington International Airport meteorological station, Maryland: 

A, spring, B, summer, C, autumn, and D, winter ......................................................................8

5. Graphs showing trends in seasonal runoff producing storms, water years 

1999–2016, Baltimore Washington International Airport meteorological station, 

Maryland: A, spring, B, summer, C, autumn, and D, winter...................................................9

6. Graphs showing trends in contribution of seasonal runoff producing storms 

compared to total annual storms, water years 1999–2016, Baltimore Washington 

International Airport meteorological station, Maryland: A, spring, B, summer, 

C, autumn, and D, winter ...........................................................................................................10

7. Graph showing annual mean surface-water discharge, Baltimore Ecosystem 

Study sites, Baltimore County and Baltimore City, Maryland, water years 

1999–2013 .....................................................................................................................................13
vi

8. Graphs showing trends in A and C, loads and B and D, flow-weighted concentrations 

of nitrate (as nitrogen) and total nitrogen, Gwynns Falls Carroll Park subwatershed, 

Maryland, water years 1999–2013 ..............................................................................................15

9. Graphs showing trends in A and C, loads and B and D, flow-weighted concentrations 

of phosphate (as phosphorus) and total phosphorus, Gwynns Falls Carroll Park 

subwatershed, Maryland, water years 1999–2013....................................................................16

10. Graphs showing significant relation of median sanitary sewer overflow volume to 

A, annual nitrate (as nitrogen) load, and B, annual total coliform bacteria load, 

Gwynns Falls Carroll Park subwatershed, Maryland, water years 1999–2013 .......................21

11. Graphs showing relations of mean total sanitary sewer overflow duration (mean) to 

A, annual nitrate (as nitrogen) load, B, annual phosphate (as phosphorus) load, 

C, total nitrogen load, and D, total phosphorus load, Gwynns Falls Carroll Park 

subwatershed, water years 1999–2013 ......................................................................................22

12. Graphs showing significant and moderately significant relations of cumulative best 

management practices (drainage area) to A, annual phosphate (as phosphorus) load, 

B, total phosphorus load, C, annual phosphate (as phosphorus) flow-weighted 

concentration, and D, annual total phosphorus flow-weighted concentration, 

Gwynns Falls Carroll Park subwatershed, Maryland, water years 1999–2013 .......................24

Tables

1. Summary of water-quality constituents, data sources, and dates of data 

collection......................................................................................................................................11

2. Summary of water-quality constituent trends in Gwynns Falls subwatersheds, 

Maryland, water years 1999–2013 ...........................................................................................14

3. Summary of compiled factors of influence, data sources, and date ranges for 

factors...........................................................................................................................................18

4. Summary of relations between water-quality constituents and factors of 

influence.......................................................................................................................................19
vii

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd)

Area

square meter (m2) 0.0002471 acre

hectare (ha) 2.471 acre

square kilometer (km2) 247.1 acre

square centimeter (cm2) 0.001076 square foot (ft2)

square meter (m2) 10.76 square foot (ft2)

square centimeter (cm2) 0.1550 square inch (ft2)

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 0.2642 gallon (gal)

cubic meter (m3) 264.2 gallon (gal)

cubic meter (m3) 35.31 cubic foot (ft3)

cubic meter (m3) 1.308 cubic yard (yd3)

cubic meter (m3) 0.0008107 acre-foot (acre-ft)

liter (L) 61.02 cubic inch (in3)

Flow rate

liter per second (L/s) 15.85 gallon per minute (gal/min)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

metric ton 2,204.6 pound avoirdupois (lb)

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.

Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 

(NGVD 29).

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

or kilograms per liter (kg/L). Concentrations of bacteria in water are given in most probable 

number per liter (MPN/L).
viii

Abbreviations

BARN Baisman Run

BES Baltimore Ecosystem Study

BMP Best Management Practice

DEPS Department of Environmental Protection and Sustainability

DPW Department of Public Works

EPA U.S. Environmental Protection Agency

FWC Flow-weighted concentration

GFCP Gwynns Falls Carroll Park

GFGB Gwynns Falls Gwynnbrook

GFGL Gwynns Falls Glyndon

GFVN Gwynns Falls Villa Nova

LTER Long Term Ecological Research

MDE Maryland Department of Environment

MS4 Municipal separate storm sewer system

POBR Pond Branch

SSO Sanitary Sewer Overflow

TMDL Total Maximum Daily Load

USGS U.S. Geological Survey
Factors Affecting Long-Term Trends in Surface-Water 

Quality in the Gwynns Falls Watershed, Baltimore City and 

County, Maryland, 1998–2016

By Emily H. Majcher1, Ellen L. Woytowitz2, Alexander J. Reisinger3, and Peter M. Groffman4

Abstract

Factors affecting water-quality trends in urban streams 

are not well understood, despite current regulatory requirements and considerable ongoing investments in gray and green 

infrastructure. To address this gap, long-term water-quality 

trends and factors affecting these trends were examined in 

the Gwynns Falls, Maryland, watershed during 1998–2016 in 

cooperation with Blue Water Baltimore. Data on water-quality 

constituents and potential factors of influence were obtained 

from multiple sources and compiled for analysis, with a focus 

on data collected as part of the National Science Foundation 

funded Long-Term Ecological Research project, the Baltimore 

Ecosystem Study.

Variability in climate (specifically, precipitation) and 

land cover can overwhelm actions taken to improve water 

quality and can present challenges for meeting regulatory 

goals. Analysis of land cover during 2001–11 in the Gwynns 

Falls watershed indicated minimal change during the study 

time frame; therefore, land-cover change is likely not a factor 

affecting trends in water quality. However, a modest increase 

in annual precipitation and a significant increase in winter 

precipitation were apparent in the region. A higher proportion of runoff producing storms was observed in the winter 

and a lower proportion in the summer, indicating that climate 

change may affect water quality in the watershed. The increase 

in precipitation was not reflected in annual or seasonal trends 

of streamflow in the watershed. Nonetheless, these precipitation changes may exacerbate the inflow and infiltration of 

water to gray infrastructure and reduce the effectiveness of 

green infrastructure.

For streamflow and most water-quality constituents 

examined, no discernable trends were noted over the 

timeframe examined. Despite the increases in precipitation, 

no trends were observed for annual or seasonal discharge 

at the various sites within the study area. In some locations, 

nitrate, phosphate, and total nitrogen show downward trends, 

and total phosphorus and chloride show upward trends. 

Sanitary sewer overflows (gray infrastructure) and best 

management practices (green infrastructure) were identified 

as factors affecting water-quality change. The duration of 

sanitary sewer overflows was positively correlated with annual 

loads of nutrients and bacteria, and the drainage area of best 

management practices was negatively correlated with annual 

loads of phosphate and sulfate. Results of the study indicate 

that continued investments in gray and green infrastructure are 

necessary for urban water-quality improvement. Although this 

outcome is not unexpected, long-term datasets such as the one 

used in this study, allow the effects of gray and green infrastructures to be quantified.

Results of this study have implications for the Gwynns 

Falls watershed and its residents and Baltimore City and 

County managers. Moreover, outcomes are relevant to other 

watersheds in the metropolitan region that do not have the 

same long-term dataset. Further, this study has established 

a framework for ongoing statistical analysis of primary 

factors affecting urban water-quality trends as regulatory 

programs mature.

Introduction

Human activities have dramatically altered the input of 

contaminants, especially nutrients, to coastal waters. In order 

to address the problem of eutrophication (a condition that 

results in decreased oxygen and increased algal blooms largely 

as a result of nutrient runoff), numerous studies have begun 

to investigate the factors that control nitrogen and phosphorus 

export from coastal watersheds, including the Chesapeake 

Bay watershed. These investigations are particularly important 

and challenging in urban and suburban watersheds. Urban and 

suburban land cover is increasing across the United States. For 

example, between 1990 and 2000, developed land increased 

in the Chesapeake Bay watershed by 61 percent (Jantz and 

others, 2005).

1

U.S. Geological Survey.

2

Formerly U.S. Geological Survey.

3

Formerly Cary Institute of Ecosystem Studies, currently University of 

Florida.

4

City University of New York and Cary Institute of Ecosystem Studies.
2 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

Urbanization Impacts

Urbanization has complex effects on the behavior and 

overall impacts of nutrients in the landscape, resulting in loads 

of these nutrients entering waterways (Kaye and others, 2006; 

Wollheim and others, 2013). In addition to increasing inputs of 

contaminants from air pollution (Bettez and Groffman, 2013; 

Rao and others, 2014), fertilizers (Law and others, 2004), and 

sewage (Valiela and others, 1997), urbanization also affects 

the amount of nitrogen and phosphorus retained in the landscape by increasing runoff that results from increased impervious surfaces. Increasing impervious surface decreases the area 

in which runoff can naturally move into the soil and subsurface, which can prevent nitrogen from being cycled back 

into the environment or retained in the landscape. Further, 

increased runoff degrades streams and riparian areas, reducing 

the ability of these areas to function as retention “sinks” for 

nutrients in the watershed (Walsh and others, 2005). Stormwater control structures, including green infrastructure, temporarily hold, divert, and treat stormwater, resulting in more infiltration and thus retention of the nutrients in the soil (Bettez and 

Groffman, 2012).

Baltimore, Maryland, may be unique because, following 

a large fire in 1904, the stormwater sewer system was separated from the sanitary sewer system. Although this reduces 

problems with “combined sewer overflows,” it results in 

direct discharge of stormwater runoff to streams and receiving 

waters that increases with urban and suburban development 

and impervious surface. As a result, the only contamination 

source that is considered to be increasing in the Chesapeake 

Bay region is stormwater runoff (Chesapeake Bay Foundation, 2014). In Maryland, all large municipalities are required 

to obtain and comply with municipal separate storm sewer 

system (MS4) permits from Maryland Department of the 

Environment (MDE) to control stormwater contamination 

in the form of runoff to the “maximum extent practicable.” 

Jurisdictions are required by their permits to mitigate stormwater contamination by implementing best management 

practices (BMPs) and watershed restoration. Further, goals 

have been set to reduce the input of different contaminants, 

including nutrients, to waterways through the establishment of 

total maximum daily loads (TMDL)s, which were established 

to help communities comply with Maryland water-quality 

standards for waterways by using BMPs. BMPs are applied 

broadly (in the case of the Chesapeake Bay TMDL for nutrients and sediment) and locally to individual impaired waterways, such as the Gwynns Falls, which is considered impaired 

because of the presence of fecal bacteria and chlordane (a 

pesticide), in addition to sediment and nutrients. Examples 

of BMPs for TMDL compliance include bioretention facilities, constructed wetlands, green roofs, infiltration basins, rain 

gardens, and rain barrels. These measures, implemented by 

jurisdictions with the help of non-governmental organizations 

and other community groups, are variable in their effectiveness and cost. In Maryland, BMPs tend to be less cost effective in urban areas than in non-urban areas (Greg Busch, 

Maryland Department of Environment, written commun., 

November 2013). An assessment of the cost effectiveness 

of BMPs in the James River Basin (Virginia) indicates that 

detection and elimination of illegal connections to the stormwater discharge network (commonly called Illicit Discharge 

Detection and Elimination), and use of pet waste management and stream restoration practices, resulted in the greatest 

reductions in nutrients and sediment (Center for Watershed 

Protection, 2013).

The aging infrastructure of the Baltimore City and 

County sanitary sewer system has resulted in numerous 

unregulated discharges of untreated sewage to waterways (also 

called sanitary sewer overflows, or SSOs). Aging infrastructure is not unique to Baltimore. It is a common problem in the 

United States as a whole and can be considered a public health 

concern (American Society of Civil Engineers, 2017). SSOs 

occur when the capacity of the pipes is exceeded because of 

infiltration and inflow as a result of cracks and leaks, or when 

a blockage occurs as a result of the presence of foreign objects 

in the piping system (for example, fats, oils, grease, and 

wipes). In order to improve the aging infrastructure and reduce 

SSOs, Baltimore City (U.S. Department of Justice and others, 

2002, 2017) and Baltimore County (U.S. Department of Justice and others, 2005) entered into agreements with the U.S. 

Environmental Protection Agency and Maryland Department 

of Environment. These Consent Decree agreements require 

large capital investments estimated to be about $2 billion 

once completed (Baltimore City Department of Public Works, 

presentation from public meeting to discuss sewer Consent 

Decree, written commun., June 2016) to meet mandated goals. 

Climate Impacts

Along with urbanization, climate is expected to change 

for large parts of the United States over the 21st century. 

Although the changes are predicted to vary by region with 

high uncertainty in the long term, it is expected that the variability and incidence of climatic extremes, such as heavy 

precipitation [greater than 5 centimeters (cm) in less than 

48 hours], will increase (Walsh and others, 2014). Northern 

areas, especially the Northeastern United States, are expected 

to experience increases in precipitation, as well as warmer 

conditions during the winter months, resulting in more precipitation falling as rain and less as snow, which will decrease 

the magnitude of snowmelt events but increase the frequency 

of moderate runoff events (Hayhoe and others, 2007). Even 

modest increases in rain events can have substantial effects on 

urban streams because scouring floods can occur in response 

to small rain events (<0.75 cm; Murdock and others, 2004; 

Vietz and others, 2016) and subsequently alter ecosystem 

functions within urban streams (Reisinger and others, 2017). 

Numerous studies have shown that increased precipitation results in higher input of nitrogen to receiving waters 

and less natural uptake of nitrogen in the watershed (Morse 

and Wollheim, 2014; Kaushal and others, 2008; Chiwa and 

others, 2010).
Why the Gwynns Falls Watershed? 3

Impetus for the Study

Given the changing land use and precipitation, coupled 

with the rapid implementation of BMPs and ongoing sewer 

repairs, there is a critical need to determine whether the 

investments have produced the desired improvements in water 

quality. It is also important to have an understanding of the 

primary factors affecting water-quality trends. For example, it 

is important to understand whether water quality has improved 

not as a result of management interventions alone, but because 

dry weather has reduced the runoff of pollutants from land to 

water. Alternatively, long-term water-quality improvements 

may go undetected, not because interventions are ineffective, 

but because other factors such as continuing land-use change 

or increases in precipitation have overwhelmed the positive 

effects of these interventions. A wide variety of stakeholders, 

such as the general public, neighborhood groups, municipal authorities, and regulatory agencies, are interested and 

invested in the results of these analyses, which are complex 

and require a long-term record. This project, initiated in 2016 

in cooperation with Blue Water Baltimore, started to investigate these questions.

Purpose and Scope

The purpose of this report is to provide the preliminary 

results of a retrospective analysis of water quality and the 

factors affecting water quality in the Gwynns Falls watershed 

of Baltimore County and Baltimore City, Maryland, between 

1998 and 2016. Methods are presented for compilation of 

water-quality data constituents, as well as computation of 

loads from constituent concentration and streamflow (data 

presented elsewhere, Woytowitz and others, 2018). Methods 

for and results of analysis of relations between loads [and 

flow-weighted concentrations (FWC)] and factors affecting 

water quality are presented. Results of this study have implications for the Gwynns Falls watershed and receiving waters, 

the residents, and city and county managers. Outcomes are 

relevant to other watersheds in the metropolitan region that do 

not have the same long-term dataset. In summary, the purpose 

of this report is to improve the understanding of the following 

questions.

1. How have concentrations and loads of various constituents in the Gwynns Falls watershed changed over the 

past 15 years?

2. What are the dominant factors that have contributed to 

these changes, regionally and locally?

Why the Gwynns Falls Watershed?

A 2014 monitoring-focused workshop with stakeholders 

in the Greater Baltimore region identified the need to 

further understand factors affecting water-quality change, 

particularly at the watershed scale (Majcher, 2014). One 

reason this knowledge gap exists is that these investigations 

require a long-term dataset with monitoring performed at 

a high frequency, which is rather uncommon. The Gwynns 

Falls watershed was selected largely because it is the main 

study watershed of the U.S. National Science Foundation 

funded Baltimore Ecosystem Study (BES) urban Long-Term 

Ecological Research (LTER) project, which has conducted 

weekly sampling coupled with continuous streamflow 

measurement [in collaboration with the U.S. Geological 

Survey (USGS)] at several sites since 1998. The BES project 

is designed to answer basic science questions; however, the 

long-term data provide an opportunity to broaden the use of 

the basic scientific research by performing additional studies 

to inform local managers and residents.

The data collected by BES LTER and other organizations in the Gwynns Falls watershed, including Baltimore City 

Department of Public Works, Baltimore County Department of 

Environmental Protection and Sustainability, and the USGS, 

provide the long-term record to make these analyses possible. 

Further, Gwynns Falls provides a unique, intermediate size 

drainage area, in which factors that affect water-quality change 

can be examined. Typically, monitoring is required and conducted at a local scale (for example, BMP level evaluation per 

MS4 permit requirements) and at a broader Chesapeake Bay 

watershed scale (Moyer and Bloomquist, 2016). The intermediate scale represented by the Gwynns Falls study area allows 

us to focus on a larger drainage area than an individual BMP, 

while still being able to isolate specific management actions 

within the watershed.

The watershed is further described in this section, and 

its associated land use and climate variation over time are 

described. The effects of land cover and climate change in 

relation to the improvement or decline of water quality in the 

Gwynns Falls stream is presented.

Description of the Study Area

The Gwynns Falls watershed encompasses 24 percent 

of the Baltimore City and 7 percent of Baltimore County, and 

contributes 21 percent of the total water input and 25 percent 

of the contaminant load to the Baltimore Inner Harbor (MDE, 

2010). The 170.67 square kilometer (65.9-square mile) 

watershed extends from rural/suburban Baltimore County to 

the center of Baltimore City (fig. 1) and drains into the Middle 

Branch of the Patapsco River. The network of BES long-term 

monitoring sites in and around the Gwynns Falls ranges from 

completely forested “reference” sites to sites in suburban 

and urban subwatersheds in which impervious cover varies 

from 0–40 percent, based on 30-meter (m) resolution Digital 

Elevation Models (DEMs) and the 2011 USGS National Land 

Cover Dataset (updated 2014, https://landcover.usgs.gov/glc/).

The five BES Gwynns Falls monitoring sites included 

in this study are Glyndon in the suburban headwaters, 

Gwynnbrook farther downstream in a suburban area, Villa 
4 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

81°0'0"W 78°0'0"W 75°0'0"W

39°0'0"N

38°0'0"N

#

#

#

#

#

#

#

Carroll Park

(GFCP)

0158932

Baisman Run

(BARN)

01583580

Villa Nova

(GFVN)

01589300

Dead Run

(DRKR)

01589330

Pond Branch

(POBR)

Gwynnbrook 01583570

(GFGB)

01589197

Glyndon

(GFGL)

01589180

76°50'0"W 76°40'0"W

39°30'0"N

39°20'0"N

39°10'0"N

p

0 50 100 150 200 KILOMETERS

0 50 100 150 200 MILES

0 5 10 KILOMETERS

0 5 10 MILES

Middle Branch 

Patapsco River

EXPLANATION

Baltimore County

Baltimore City

Baltimore Washington 

 International (BWI) Airport 

 meterological station

Gwynns Falls stream

Baltimore Ecosystem Study 

 (BES) sites (Abbreviation)—

 numbers are USGS 

 streamgaging station numbers

#

Patapsco River

Dead Run

(DRKR)

01589330

Base modified from Esri, 1:250,000-scale, digital data, 2018

Base modified from Esri, 1:500,000,000-scale, digital data, 2017

Figure 1. Location of Gwynns Falls watershed and subwatersheds, and Baltimore Ecosystem Study Sites, Baltimore County and Baltimore City, Maryland. (BWI, Baltimore Washington International Airport; USGS, U.S. Geological Survey; BES, Baltimore Ecosystem Study)
Why the Gwynns Falls Watershed? 5

Nova at the suburban/urban boundary, and Dead Run and 

Carroll Park in urban areas (fig. 1). Glyndon, Gwynbrook, 

Villa Nova, and Carroll Park sites are on the main stem of the 

Gwynns Falls, whereas the Dead Run site is on a tributary 

to Gwynns Falls (fig. 1). Two more sites, Pond Branch in a 

forested area and Baisman Run in a suburban unsewered area, 

are used as reference sites and are within the Gunpowder 

Falls watershed (fig. 1). The watershed and subwatershed 

boundaries were delineated using a 30-m resolution USGS 

DEM (2011, updated 2014, https://landcover.usgs.gov/glc/).

Annual assessments conducted by MDE to fulfill requirements of the Clean Water Act (sections 305(b) and 303(d); 

MDE, 2014) show that the Gwynns Falls stream is impaired 

to varying degrees by several constituents, including fecal 

coliform bacteria, total phosphorus, total suspended solids, 

chloride, and polychlorinated biphenyls. In addition, specific 

goals for total nitrogen, total phosphorus, and total sediment 

are mandated by Maryland as a partner in the Chesapeake Bay 

Restoration Agreement (EPA, 2010). Impaired streams that do 

not meet the water-quality criteria designated by the State of 

Maryland are typically required to implement a management 

strategy and actions to bring streams into compliance.

Land-Cover Change

The spatial and temporal variation of land cover is an 

important factor to consider when studying water quality. 

To assess the amount of land-cover change that occurred 

within the Gwynns Falls subwatersheds during the study 

period, percent land-cover change from 2001 to 2011 was 

calculated for each subwatershed using National Land Cover 

Database 30-m resolution raster datasets (Homer and others, 

2007, 2015). The change in land cover (∆LC) was assessed 

by reclassifying raster values (RV) from 2001 and 2011 to 

one of eight general land-cover categories, including water, 

developed, barren, forest, shrubland, herbaceous, agriculture, 

and wetlands. The land-cover values were compared using the 

following equation: 

(RV2001 * 10) + RV2011 = ∆LC (1)

Change in land cover was assessed by examining the two 

digit number generated (∆LC) where the first digit represents 

the 2001 value, and a second digit represents the 2011 value. 

Land-cover change was identified by differences in the two 

digits of the land-cover number (for example, 42 = forest to 

developed land-cover change). For each category, the number 

of raster cells was summed to get a ∆LC raster count, then the 

number of cells for each category (∆LC) was multiplied by 

the area of an individual raster cell [900 square meters (m2

)] to 

calculate land-cover change area by using 

 Land-Cover Change Area = ∆LC Raster Count * 900 m2 (2)

Subsequently, the percent land-cover change for each category 

was calculated by using 

Percent LC Change = (Land-Cover Change Area/

 Subwatershed Area) * 100 (3)

where the subwatershed area was estimated using the sum of 

all areas for the eight categories.

Minimal land-cover change occurred in the watersheds of 

interest over the period analyzed (fig. 2), despite estimates of 

increasing urbanization across the Chesapeake Bay watershed, 

likely as a result of the already high percentage of impervious surface of the watershed at the start of the study. This is 

consistent with previous studies that estimated impervious 

surface change over roughly the same time frame, which 

indicated that older, established cities have relatively stable 

impervious surface over time (Sexton and others, 2013). The 

greatest percent change in land cover (7.65 percent) occurred 

in the Pond Branch forested reference subwatershed, and all of 

this change was from forested to herbaceous land cover, likely 

associated with natural gas line maintenance, which should 

have minimal effect on the hydrology and water quality of 

the subwatershed. Change in all other subwatersheds, except 

Villa Nova, was estimated to be about 2 percent; change was 

predominantly from forest and agricultural to developed land 

cover. Land-cover change within the Villa Nova subwatershed 

was estimated to be about 3.5 percent; change was predominantly from forest and agriculture to developed (fig. 2).

The minimal land-cover change of about 2 percent across 

the entire watershed (represented by the Carroll Park subwatershed) indicates that land-cover change is unlikely to be a 

major factor affecting water-quality trends across the watershed during the period of interest. If the subwatersheds were 

not as urbanized as they are, smaller changes in land cover 

could be more significant. However, nearly all subwatersheds 

in Gwynns Falls watershed were already dominated by developed land cover (fig. 2). There is, however, considerable variation in land cover and water quality between subwatersheds.

Change in Precipitation

Assessing changes in water quality over time in a changing climate can be difficult and can complicate evaluation of 

the effects of BMPs. Variability in climate and the incidence 

of climate extremes, such as increased heavy precipitation 

(greater than 1.9 centimeter (cm) in less than 48 hours), are 

increasing (Walsh and others, 2014). Increased precipitation 

has been shown to result in greater delivery of contaminants to 

receiving waters as a result of declines in residence time and 

uptake in the watershed (Morse and Wollheim, 2014; Kaushal 

and others, 2008; Chiwa and others, 2010; Bettez and others, 

2015). 

Owing to the lack of reliable meteorological stations 

within the watershed, data from a nearby regional meteorological station at Baltimore-Washington International (BWI) 
6 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

0

25.08

79.09 80.45

66.76

77.19

95.38

7.65

1.95 0 1.33 3.52 2.04 0.41

0

10

20

30

40

50

60

70

80

90

100

POBR BARN GFGL GFGB GFVN GFCP DRKR

Land cover, in percent

Gwynns Falls subwatersheds

Percent developed land cover Percent total land cover change

EXPLANATION

Figure 2. Developed land cover and change in land cover, Gwynns Falls subwatersheds, Baltimore County and Baltimore City, 

Maryland, 2001–11. (POBR, Pond Branch; BARN, Baisman Run; GFGL, Gylndon; GFGB, Gwynnbrook; GFVN, Villa Nova; GFCP, 

Carroll Park; DRDK, Dead Run)

Is the Water Quality of the Gwynns 

Falls Watershed Changing?

Water-quality trends were assessed in the Gwynns Falls 

subwatersheds and reference watersheds to establish a basis 

for evaluation of the primary factors affecting these trends. 

The data compilation effort and methods used are briefly 

described in the section. A summary of these trends is presented, and the trend results are discussed in relation to other 

publications that describe the conditions of Baltimore City and 

Baltimore County waterways. 

Loads and flow-weighted concentrations were calculated, and trends are described, for a range of water-quality 

constituents. For purposes of comparison to regulatory targets 

such as TMDLs, contaminant loads, or the cumulative mass 

of a constituent delivered to a location over a period of time 

(for example, 1,000 kilograms of total phosphorus per year), 

were calculated. However, loads are highly affected by flow 

rely on vegetation and other biological processes are often 

not as effective in the non-growing season as in the growing 

season. Increases in runoff producing storms can exacerbate 

the occurrence of wet weather SSOs.

opment Core Team, 2016).

Over the study period, the annual precipitation amount 

showed a moderately significant increase (fig. 3). This increase 

was driven by a significant increase in winter precipitation 

(fig. 4); winter is the season with the lowest precipitation. 

The number and proportion of storms occurring in the winter 

significantly increased (figs. 5 and 6), and the proportion of 

storms occurring in the summer significantly decreased during 

the time frame of the study (fig. 5).

Increased winter precipitation is important for area 

stakeholders to consider, largely because contaminant loads 

are dominantly transported in winter as a result of the lack of 

evapotranspiration during this season. Moreover, BMPs that 

Airport (fig. 1) operated by the National Oceanic and Atmospheric Administration (www.ncdc.gov, accessed January 

2017) was used to examine annual and seasonal trends in 

precipitation and runoff producing storms at the regional level. 

The total number and proportion of 48- and 24-hour runoff 

producing storms (defined as greater than 1.9 cm of precipitation over the time period; Lawrence and others, 2012) were 

calculated annually and seasonally. Annual time frames were 

based on the October 1–September 30 water year, and the seasons are fall (October–December), winter (January–March), 

spring (March–June) and summer (July–September). Statistical analysis was performed using R (Version 3.4.0; R Devel-
Is the Water Quality of the Gwynns Falls Watershed Changing? 7

EXPLANATION

Runoff producing storms

Trend line, annual precipitation

Annual precipitation

0

10

20

30

40

Annual precipitation, in centimeters

2000 2005 2010 2015

0

50

100

150

200

Annual number of storms

Years

r2=0.17

p=0.093

Figure 3. Annual precipitation and annual count of runoff producing storms, Baltimore Washington International 

Airport meteorological station, Maryland, water years 1999–2016. (Dashed line, 0.05<p<0.1).
8 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

0

20

40

60

80

A. Spring

p > 0.1

B. Summer

p > 0.1

2000 2005 2010 2015

0

20

40

60

80

C. Autumn

p > 0.1

2000 2005 2010 2015

D. Winter

r 2 = 0.27

Seasonal precipitation, in centimeters

p = 0.026

Years

EXPLANATION

Trend line

Figure 4. Trends in seasonal precipitation, water years 1999–2016, Baltimore Washington International Airport meteorological 

station, Maryland: A, spring, B, summer, C, autumn, and D, winter.
Is the Water Quality of the Gwynns Falls Watershed Changing? 9

A. Spring B. Summer

C. Autumn D. Winter

0

5

10

15

p > 0.1 p > 0.1

2000 2005 2010 2015

p > 0.1

0

5

10

15

2000 2005 2010 2015

r 2 = 0.38

p = 0.006

Number of seasonal storms

EXPLANATION

Trend line

Years

Figure 5. Trends in seasonal runoff producing storms, water years 1999–2016, Baltimore Washington International Airport 

meteorological station, Maryland: A, spring, B, summer, C, autumn, and D, winter.
10 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

0

20

40

60

p > 0.1

r 2 = 0.27

p = 0.029

2000 2005 2010 2015

0

20

40

60

p > 0.1

2005 2010 2015

r 2 = 0.26

p = 0.030

A. Spring B. Summer

C. Autumn D. Winter

2000

EXPLANATION

Trend line

Years

Seasonal storm count / annual storm count, in percent

Figure 6. Trends in contribution of seasonal runoff producing storms compared to total annual storms, water years 1999–2016, 

Baltimore Washington International Airport meteorological station, Maryland: A, spring, B, summer, C, autumn, and D, winter.
Is the Water Quality of the Gwynns Falls Watershed Changing? 11

rate (and therefore climate); therefore, in addition to loads, 

flow-weighted concentrations (FWCs) were calculated as the 

total constituent load divided by the total annual flow. Constituent FWCs are important to consider because they normalize for fluctuations in precipitation and streamflow. A FWC 

is the mass of a constituent relative to the volume of water 

(for example, 100 milligrams of total phosphorus per liter of 

water). By normalizing for flow conditions, it is possible to 

evaluate management changes (for example, implementation 

of BMPs) over time irrespective of relatively wet or dry years. 

Compilation of Data and Estimates of WaterQuality Trends

Streamflow and water-quality constituent data were 

compiled from various sources, including USGS (streamflow only), BES LTER, Baltimore City Department of Public 

Works (DPW), and Baltimore County Department of Environmental Protection and Sustainability (DEPS) (table 1). 

Sample frequency ranged from instantaneous (streamflow by 

USGS) to weekly (BES) to monthly (Baltimore City DPW 

and Baltimore County DEPS). Methods of data collection for 

the datasets are described elsewhere (Groffman and others, 

2004; USGS National Water Information System at 

https://waterdata.usgs.gov/md/nwis/sw for USGS 

streamgaging stations shown in fig. 1; Baltimore City DPW 

and Baltimore County DEPS, Sampling and Analysis Standard Operating Procedures, written commun., 2017). All BES 

monitoring is performed weekly with the collection of grab 

samples at USGS streamgaging stations (fig. 1) and has previously been shown to represent water quality over the full range 

of flow conditions (Groffman and others, 2004), whereas 

DPW and DEPS monitoring is not always directly associated 

with a streamgaging stations. From the compiled dataset from 

DPW and DEPS, monitoring sites at or in close proximity 

to [approximately within 152.4 m or 500 feet (ft)] a USGS 

streamgaging station were selected for load and FWC calculations. Sampling locations that were greater than 152.4 m 

from a streamgaging station or had tributaries entering the 

main stem of Gwynns Falls within 152.4 m of a streamgaging 

station were excluded. The results of the data compilation are 

provided in Woytowitz and others (2018).

Annual constituent loads and annual FWCs were calculated using daily mean discharge measurements from USGS 

streamgaging stations and corresponding constituent monitoring dates. Subsequently, loads and FWCs were extrapolated as 

a constant value between sampling events to estimate a daily 

load or concentration, and daily loads or concentrations were 

summed to estimate an annual load over the water year for a 

given constituent.

Table 1. Summary of water-quality constituents, data sources, and dates of data collectiona.

[GFCP, Carroll Park; GFVN, Villa Nova; GFGL, Glyndon; GFGB, Gwynnbrook; DRKR, Dead Run]

Water-quality constituent Subwatersheds Data source Dates of data collectiona

Streamflow All U.S. Geological Survey, https://waterdata.usgs.gov/md/nwis/

current/?type=flow&group_key=basin_cd

1998–2016, continuous 

record

Nitrate, phosphate, sulfate, 

total nitrogen, total 

phosphorus, chloride

All Baltimore Ecosystem Study Long-Term Ecological Research, 

http://beslter.org/data_browser.asp

1998–2013, weekly

Escherichia coli GFCP Baltimore City Department of Public Works, http://dpwapps.baltimorecity.gov/cleanwaterbaltimore/surface-water-quality-data/

2009–2012b, monthly

Fecal and total coliform GFCP Baltimore City Department of Public Works, http://dpwapps.baltimorecity.gov/cleanwaterbaltimore/surface-water-quality-data/1999–2008b, monthly

Ammonia and total 

suspended solids

GFCP Baltimore City Department of Public Works, http://dpwapps.baltimorecity.gov/cleanwaterbaltimore/surface-water-quality-data/1999–2015b, monthly

Total keldahl nitrogen and 

total suspended solids

GFVN, GFGL, 

GFGB, DRKR

Baltimore Department of Environmental Protection and Sustainability, https://www.baltimorecountymd.gov/Agencies/environment/watershedsandmonitoring.html2011–2016, monthly

a

Relevant water-quality monitoring stations and associated constituent data and sampling date summarized in Woytowitz and others (2018).

b

Shorter-term sampling duration and (or) less frequent sampling frequency.
12 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

Instantaneous load was calculated by using

Instantaneous Loadi (kilogram/second) = 

 Ci

 (kilogram/liter) × Qi (liter/second) (4)

where

Ci = Constituent concentration at time step i, and

Qi = mean daily discharge, time step i.

We assumed a constant instantaneous load between monitoring events, which allowed us to calculate a between-event load 

estimate by using

Between-event load (kilogram) = Instantaneous Loadi

 (kilogram/second) x [Ti+1 – Ti

] (second) (5)

where [Ti+1 – Ti

] = Time between sampling dates.

After calculating loads between each sampling event, annual 

loads were calculated by using

LoadA (kilogram) = Σ [Ci x Qi x [Ti+1 – Ti]] (6)

Annual FWCs were calculated by using

FWC (milligram/liter) = [Annual load (milligram/year)]/

 [Annual Q (liter/year)]. (7)

Trends were quantified using a linear regression model with 

the different constituents of interest as dependent variables 

and water year as the independent variable. Statistical analysis was performed in R (version 3.4.0; R Development Core 

Team, 2016). Regressions were assessed visually and using 

Q–Q plots to establish any issues with non-normality of 

residuals. No major issues with non-normality were found. It 

was expected that the simplicity of interpretation using nontransformed values outweighed any potential minor deviations 

from normality. 

Although monthly data for Escherichia coli (E. coli), 

total suspended solids (TSS), ammonia, and fecal and total 

coliform were compiled (table 1, Woytowitz and others, 2018), 

these data collection time frames ranged from 3 to 9 years, 

whereas weekly data for the other constituents were compiled 

for 15 years. Owing to the shortened timeframe and frequency 

of sampling, empirical trends for the five constituents are not 

reported, but fecal and total coliform datasets were retained for 

consideration in the factors analysis.

Summary of Streamflow and Water-Quality 

Trends

Despite the increases in precipitation described above, 

no trends were observed for annual or seasonal discharge at 

the seven sites within the study area (fig. 7). However, variability across sites was observed, with the discharge reflecting 

subwatershed size and high precipitation years. The greatest 

magnitude of discharge was measured at the Carroll Park 

streamgaging station, at the outlet of the watershed to the 

Middle Branch of the Patapsco River (fig. 7). The lack of 

any significant change in discharge is surprising, based on 

the observed increases in precipitation and storm events in 

this study period. It is uncertain what is causing this lack of 

response. It may be due to the installation of green infrastructure, which may alter precipitation–discharge relations, buffering these urban and suburban watersheds against increases in 

precipitation (Burgess, 2017). Although winter storms tend 

to be regional in nature, spring and summer storms can be 

spotty and vary widely with respect to duration, intensity, and 

amount of rainfall. Analysis of the data from meteorological 

station at BWI may affect consideration of this variability.

A summary of all water-quality trends in the seven 

subwatersheds in the study is provided in table 2. Most of 

the constituents and subwatersheds show no significant 

trends over time (greater than 90% of those considered). In 

some locations, nitrate, phosphate, and total nitrogen show 

downward trends and total phosphorus and chloride show 

upward trends (table 2, figs. 8 and 9). Results are consistent 

with previously published results examining nitrogen species 

(Kaushal and others, 2008, 2014; Bettez and others, 2015), 

and phosphate and total phosphorus (https://cbrim.er.usgs.gov/

trends_query.html? at USGS station Gwynns Falls at Villa 

Nova) for the same years.

Nitrate and total nitrogen loads follow patterns reflective 

of watershed size and appear to be largely affected by high 

precipitation and storm event years, including 2003 (Hurricane Isabel) and 2010 (multiple snow events and Tropical 

Storm Nicole) (fig. 3, figs. 8A, C). This is consistent with 

observations by Bettez and others (2015) and Kaushal and 

others (2008, 2014), where increased precipitation and storms 

were associated with increased export of constituents. Perhaps 

because of this strong correlation to precipitation variability, 

there were no significant trends in nitrate or total nitrogen 

loads in any subwatershed. When normalizing for the variability in streamflow using FWCs (figs. 8B, D), different patterns 

emerge, and the variability between years is dampened. A significant increase in nitrate FWC was observed for Dead Run 

(the most urban subwatershed) but not for the other subwatersheds. Total nitrogen showed a significant decline at the Glyndon headwaters site only (fig. 8D). The variability and magnitude of nitrate and total nitrogen FWCs do not follow the 

same patterns with urbanization and watershed size observed 

for loads (figs. 8A, C), likely owing to the effect of streamflow 

on loads. Variability in FWCs is likely driven by variation in 

nitrogen and nitrate sources (sanitary sewer overflows, septic 

systems, pet waste, fertilizers) in the seven watersheds.

Phosphate loads remained unchanged or declined at all 

sites (fig. 9A) with significant declines noted in the headwaters of Gwynns Falls stream at Glyndon, as well as at the 

urban Dead Run and Carroll Park sites. Total phosphorus 

significantly increased in the suburban Gwynn Brook site and 

significantly decreased at Carroll Park, with no significant 
Is the Water Quality of the Gwynns Falls Watershed Changing? 13

2000 2005 2010 2015

Annual mean discharge, in cubic meters 

GFCP

GFVN

GFGB

GFGL

DRKR

POBR

BARN

EXPLANATION

Years

10,000

100,000

1,000,000

10,000,000

100,000,000

Figure 7. Annual mean surface-water discharge, Baltimore Ecosystem Study sites, Baltimore County 

and Baltimore City, Maryland, water years 1999–2013. (Site abbreviations are defined in figures 1 and 2)
14 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed Table 2. Summary of water-quality constituent trends in Gwynns Falls subwatersheds, Maryland, water years 1999–2013. [POBR, Pond Branch; BARN, Baisman Run; GFGL, Glyndon; GFGB, Gwynnbrook; GFCP, Carroll Park; GFVN, Villa Nova; DRKR, Dead Run; FWC, flow-weighted concentration; ‘-’, no significant trend; ‘X’, trend could not be assessed with data set available; (+), upward trend; (-), downward trend; 

p value, level of marginal significance; r2, fitness to linear regression model; BOLD

p value less than, equal to 

0.05 or significant; 

p value between 0.05 and 0.1, marginally significant; kg/yr, kilograms per year; mg/L, milligrams per liter; trends for fecal and total coliform, ammonia, total suspended solids, total Keldahl 

nitrogen calculated but not presented because of monthly sampling over shortened timeframe]

Nitrate Ammonia Total nitrogen Phosphate Total phosphorus Chloride Sulfate Fecal coliforma Total coliforma

POBR Load (kg/yr) - X - - (+) 

p<0.01, r2=0.31 - - X

X

FWC (mg/L) - X - - (+) 

p<0.01, r2=0.54 (+) 

p=0.014, r2=0.38 - X

X

BARN Load (kg/yr) - X - - - - - X

X

FWC (mg/L) - X - - - (+) 

p<0.01, r2=0.46 - X

X

GFGL Load (kg/yr) - X - (-) 

p=0.049, r2=0.27 - - - X

X

FWC (mg/L) (-) 

p=0.087, r2=0.21

X (-) 

p=0.041, r2=0.3 (-) 

p=0.038, r2=0.29 - - - X

X

GFGB Load (kg/yr) - X - - - (+) 

p=0.018, r2=0.36 - X

X

FWC (mg/L) - X - - (+) 

p=0.018, r=0.36 (+) 

p=0.002, r2=0.52 - X

X

GFVN Load (kg/yr) - - - - - - - X (+) 

p=0.038, r2=0.63

FWC (mg/L) - - - - - - - X -

GFCP Load (kg/yr) - (-) 

p=0.07, r2=0.21 - (-) 

p=0.054, r2=0.26 (-) 

p=0.08, r2=0.23 - - - -

FWC (mg/L) - (-) 

p=0.08, r2=0.20 - (-) 

p=0.032, r2=0.31 (-) 

p=0.05, r2=0.28 - - - -

DRKR Load (kg/yr) - X - (-) 

p=0.088, r2=0.21 (+) 

p=0.02, r=0.38 - - X

X

FWC (mg/L) (+) 

p=0.049, r2=0.27

X - - (+) 

p=0.03, r2=0.33 - - X

X

aLoad expressed as MPN/yr, most probable number per year; FWC expressed as MPN/L, most probable number per liter.
Is the Water Quality of the Gwynns Falls Watershed Changing? 15

2000 2005 2010 2015

Load (kilograms per year) FWC (milligrams per liter) A

GFCP

GFVN

GFGB

GFGL

DRKR

POBR

BARN

2000 2005 2010 2015

Total nitrogen

C

2000 2005 2010 2015

B

2000 2005 2010 2015

D

EXPLANATION

Years Years

Years Years

Nitrate, as nitrogen

3.0

2.5

2.0

1.5

1.0

0.5

0.0

3.0

2.5

2.0

1.5

1.0

0.5

0.0

1,000,000

10,000

1

1,000

1,000,000

10,000

1

1,000

Figure 8. Trends in A and C, loads and B and D, flow-weighted concentrations of nitrate (as nitrogen) and total nitrogen, 

Gwynns Falls Carroll Park subwatershed, Maryland, water years 1999–2013. (FWC, flow-weighted concentration. Site 

abbreviations are defined in figures 1 and 2).
16 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

2000 2005 2010 2015

A

GFCP

GFVN

GFGB

GFGL

DRKR

POBR

BARN

2000 2005 2010 2015

C

2000 2005 2010 2015

B

2000 2005 2010 2015

D

EXPLANATION

Years Years

Years Years

Phosphate, as phosphorus Total phosphorus

0.08

0.04

0.04

0.02

0.0

0.08

0.04

0.04

0.02

0.0

1,000

10

0.1

1,000

10

0.1

Load (kilograms per year) FWC (milligrams per liter)

Figure 9. Trends in A and C, loads and B and D, flow-weighted concentrations of phosphate (as phosphorus) and 

total phosphorus, Gwynns Falls Carroll Park subwatershed, Maryland, water years 1999–2013. (FWC, flow-weighted 

concentration. Site abbreviations are defined in figures 1 and 2)
What Factors are Affecting Water-Quality Trends in the Gwynns Falls? 17

trends at other sites (figs. 9C, D). This may indicate that while 

variable inputs occur across the watershed, export from the 

overall watershed to receiving waters is declining for total 

phosphorus (table 2). Unlike nitrogen, these trends were 

consistent between loads and FWCs, indicating that phosphate 

and total phosphorus are not as dependent on the streamflow 

and climate variability as nitrogen. 

The variability and maximum load and FWCs of chloride 

differ from patterns observed for nitrogen and phosphorus 

(figs. 8, 9), indicating differing sources. There were no significant trends for sulfate at any location (table 2).

In 2011, non-governmental and governmental organizations in Baltimore City worked together to develop the 

Healthy Harbor Plan with the goal of working toward making the Inner Harbor fishable and swimmable by 2020. One 

outcome of this effort is the Healthy Harbor Report Card, 

which was first released in 2012. Although the basis for the 

report card is rooted in ecological and human health indicator 

thresholds, based on water-quality constituent concentrations 

(EcoCheck, 2011), rather than individual constituent loads, it 

is worthwhile to consider the results of the long-term trends 

at Carroll Park, the most downstream site in relation to these 

goals. From 2012 to 2014, the report cards indicated slightly 

improved annual scores for the Gwynns Falls watershed, with 

improvements mainly noted for total phosphorus (Waterfront 

Partnership, 2012, 2013, 2014), which are similar to the trends 

observed in this study. Despite investments in both gray (piped 

stormwater and sanitary sewer systems) and green (structural 

best management practices) infrastructure to improve water 

quality, total phosphorus is the only constituent identified as an 

impairment to the Gwynns Falls watershed. Chesapeake Bay 

showed improving water quality over the study time frame 

at Carroll Park. Other constituents of concern for impairment 

of the watershed either increased or decreased at Glyndon 

at the headwaters of the watershed but showed no change at 

the urban watershed outlet at Carroll Park. Given the limited 

datasets for TSS, ammonia, E. coli, and fecal coliform, no 

apparent trends could be determined for these constituents. 

Although the lack of improvement at Carroll Park may appear 

discouraging, there are no constituent values that indicate a 

degradation of water quality at Carroll Park (table 2), which 

is encouraging given the increases in precipitation that have 

occurred. Note that while the original gray infrastructure 

repairs were initiated during 2002–05, there are considerable 

improvements planned for the next 10 years (U.S. Department 

of Justice, 2017). Similarly, while jurisdictions have been 

monitoring and working under MS4 permits throughout the 

entire study time frame, the bulk of the watershed restoration 

(for example, reduction of impervious surface) occurred or is 

planned for post-2013 (Baltimore City Department of Public 

Works, 2015). This underscores the need for continued assessment during these ongoing efforts.

What Factors are Affecting WaterQuality Trends in the Gwynns Falls?

Following the empirical estimation of constituent loads 

and FWCs, statistical assessment was conducted to further 

explore the factors affecting water-quality trends in Gwynns 

Falls watershed. The data compilation effort, references to 

methods, and a summary of the primary factors determined to 

affect water quality are summarized in relation to regulatory 

requirements and economic investments. Of the various factors examined, aging gray infrastructure and the resulting SSO 

occurrences, as well as the implementation of green infrastructure BMPs, appeared to have the greatest effect on water 

quality at the watershed scale and are the focus of the sections 

that follow.

Evaluating Relations between Water Quality 

and Potential Factors of Influence

A summary of the compiled factors of influence along 

with the data sources and date ranges is provided in table 3 

and was published in Woytowitz and others (2018). Most factors can be categorized as either gray infrastructure (related 

to the piped stormwater and sanitary sewer systems) or green 

infrastructure (related to BMPs). There were several factors 

that were unavailable and were not included in the analyses. 

These include fertilizer use, sewer repairs corresponding to the 

Baltimore City Consent Decree prior to 2013, and BMPs in 

Baltimore City; these factors were few in number during the 

time frame of interest. 

Most information compiled from the city and county 

governments was georeferenced, and therefore Baltimore 

County BMPs, Baltimore City and County SSOs, water main 

breaks and leaks, and repairs were extracted to each subwatershed by using ArcMap (Version 10.5.0; ESRI, 2016) and then 

exported to Excel to assign data points to particular subwatersheds. Because of the minimal data density in many of the 

smaller subwatersheds and a desire to understand the possible 

influence of these factors on the entire watershed, analysis 

was focused on the largest, and farthest downstream subwatershed, Carroll Park. The data from Carroll Park encompass 

all upstream subwatershed actions and may be a good representation of the export of constituents in surface water from 

the watershed as a whole to the receiving waters of the Middle 

Branch Patapsco River. 

Relations between water-quality trends in Carroll Park 

subwatershed and SSOs or BMPs as factors were examined 

through multiple data fields, including count, mean, median, 

and total duration of SSO, and SSO volume, as well as count, 

mean, median, and total cumulative storage volume and 

drainage area for BMPs. It was assumed that once BMPs 

were implemented they would function as intended over time 

and therefore were considered cumulative over the period 

of interest.
18 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

Table 3. Summary of compiled factors of influence, data sources, and date ranges for factors.

Factor Data source Date range

Sewer repairs (count, length) Baltimore City Department of Public Works 2013–2015

Sanitary sewer overflows 

(count, duration, volume)

Baltimore City Department of Public Works 2003–2016

Water main breaks and leaks (count) Baltimore City Department of Public Works 2002–2016

Road salt (mass applied city wide) Baltimore City Department of Public Works 1999–2015

Sewer repairs (count, length) Baltimore County Department of Environmental Protection and Sustainability 2008–2016

Sanitary sewer overflows 

(count, duration, volume)

Baltimore County Department of Environmental Protection and Sustainability 2001–2016

Water main breaks and leaks (count) Baltimore County Department of Environmental Protection and Sustainability 2001–2016

Road salt (mass applied) Baltimore County Department of Environmental Protection and Sustainability 2000–2015

Best management practices 

(count, storage volume, drainage area)

Baltimore County Department of Environmental Protection and Sustainability 1998–2015

Rain barrels (count) Baltimore County Department of Environmental Protection and Sustainability 2009–2015

Road salt application was estimated on the basis of the 

total number of road lane miles (linear road miles multiplied 

by 2.5) within each individual subwatershed multiplied by an 

annual road salt application rate (metric tons per lane mile). 

The number of lane miles was quantified in each watershed 

as the sum of the Maryland State Highway Administration 

and National Highway System lane miles (Maryland State 

Highway Administration, 2015, https://data-maryland.opendata.arcgis.com/datasets/0db7280f6a024e498f9b706cd656

8cc6_3). These lane miles were divided into those managed 

by Baltimore County and those managed by Baltimore City. 

A per-lane-mile road salt application rate was calculated by 

dividing the total road salt applied (in metric tons, published 

in MS4 annual reports for each year in the study period) in a 

given year by the total lane miles within Baltimore County 

or Baltimore City. Finally, the watershed-specific county or 

city lane miles were multiplied by the county or city annual 

road salt application rate to estimate the watershed-specific 

annual mass of road salt applied by the county and city. Ultimately, the county and city road salt totals were added to get a 

watershed-specific estimate of total mass of road salt (metric 

tons) applied in a given watershed. 

Univariate relations between explanatory factors and constituent loads and FWCs were explored with linear regression 

models in R (version 3.4.0; R Development Core Team, 2016). 

Relations with p values of less than or equal to 0.05 were 

considered to be significant. Marginally significant relations, 

those correlations falling between 0.05 and 0.1 were retained. 

Although it is recognized that univariate analysis with linear 

regression is a simplified approach to this complex problem, 

it was the goal of this initial study to examine as many factors 

as possible throughout the watershed and initiate the analysis 

of the data. Follow-up studies would benefit from non-linear 

and multivariate analyses using the database that has been 

compiled, as well as expanding upon the time scale of the 

study and the factors analyzed.

Results of the analysis of factors influencing the waterquality trends are summarized in table 4. These relations, specifically those between gray infrastructure factors and nutrient 

(mainly nitrogen) and bacteria export, and green infrastructure 

and phosphorus export are described in more detail in the following sections.

Aging Gray Infrastructure and the Influence of 

Sanitary Sewer Overflows

Aging gray infrastructure and the resulting SSOs appear 

to be a major influence on the water quality of Gwynns Falls, 

particularly in relation to nitrogen export (nitrate and total N) 

and phosphate export, and to a lesser extent chloride export 

(table 4). Median SSO volume was significantly positively 

correlated with annual nitrate and total coliform loads 

(fig. 10). Similarly, longer duration SSOs were positively 

correlated with annual total nitrogen and nitrate loads, and 

annual total nitrogen FWCs (fig. 11). Phosphate load was also 

positively correlated with SSO duration (table 4, fig. 11B). The 

lack of correlation between SSOs and fecal bacteria, except for 

total coliform load (fig. 10B), was likely due to the use of different methods of fecal bacteria monitoring and the short data 

record (table 1). Therefore, it would be informative to evaluate 

the relation as the time frame of monitoring is extended. Interestingly, SSO volume was strongly positively correlated with 

sulfate concentrations, consistent with studies that showed 

increased export of sulfate was linked to drainage through 

storm drains in Baltimore City (Kaushal and Belt, 2012).

An additional indication of aging infrastructure is the 

occurrence of water main breaks and leaks. Unlike SSOs, 
What Factors are Affecting Water-Quality Trends in the Gwynns Falls? 19

Table 4. Summary of relations between water-quality constituents and factors of influence.

[SSO, sanitary sewer overflow; BMP, best management practice; FWC, flow-weighted concentration; cumul., cumulative; ‘-’, no significant relationship; (+), increase in constituent with an increase in factor; (-) decrease in constituent with increase in factor; ginal significance; r2, fitness to linear regression model; BOLD p p value, level of mar p value less than, equal to 0.05 or significant; value greater 

ginally significant; kg/yr than 0.05 and less than 0.1, mar , most probable number per year; MPN/L, most probable number per liter; h, hours; , kilograms per year; mg/L, milligrams per liter; MPN/yr

m3 , , square kilometers; yr, year] cubic meters; km2

Total nitrogen Total phosphorus Fecal coliform Total coliform Nitrate load Ammonia load Phosphate load Chloride load Sulfate load load load load load (kg/yr) (kg/yr) (kg/yr) (kg/yr) (kg/yr) (kg/yr) (kg/yr) (MPN/yr) (MPN/yr)

SSO duration (h) Mean (+) p=0.038, - (+) p=0.015, (+) p=0.008, - - - - (+) p=0.067, 

r2=0.34 r2=0.43 r2=0.49 r2=0.45

Median - - - - - - - - -

Total (+) p=0.016, - (+) p=0.037, - - - p=0.077, - -

r2=0.42 r2=0.34 r2=0.26

SSO volume (m3) Mean - - - - - (+) p=0.055, - - -

r2=0.30

Median (+) p=0.051, (+) p=0.026, (+) p=0.057, (+) p=0.039, (+) p=0.078, - (+) p<0.001, - (+) p=0.048, 

r2=0.30 r2=0.33 r2=0.29 r2=0.33 r2=0.26 r2=0.70 r2=0.51

Total - - - - - (+) p=0.098, (+) p=0.047, - -

r2=0.23 r2=0.31

BMP drainage area (km2) Mean - - - - - - - - -

Median - - - - - - - - -

Total - - - - - - - - -

Cumul. - (-) p=0.052, - (-) p=0.05, (-) p=0.038, (+) p=0.079, - - -

r2=0.23 r2=0.26 r2=0.31 r2=0.21

BMP storage volume (m3) Mean - - - - - (+) p=0.004, - - -

r2=0.48

Median - - - - - - - - -

Total - - - - - (+) p=0.010, - - -

r2=0.41

Cumul. - (-) p=0.0674, - - - - - -

r2=0.21

BMP count Cumul. - - - - - - - - -

Water main breaks/leaks Count - - - (+) p=0.0938 - (+) p=0.069, - - -

r2=0.25

Breaks - - - - - (+) p=0.093, - - -

r2=0.22

Leaks - - - - - (+) p=0.085, - - -

r2=0.23

Road salt applied (metric tons/yr) - - (+) p=0.044, - - - - - (+) p=0.062, 

r2=0.28 r2=0.37
20 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed Table 4. Summary of relations between water-quality constituents and factors of influence.—Continued [SSO, sanitary sewer overflow; BMP, best management practice; FWC, flow-weighted concentration; cumul., cumulative; ‘-’, no significant relationship; (+), increase in constituent with an increase in factor; (-) decrease in constituent with increase in factor; 

p value, level of marginal significance; r2, fitness to linear regression model; BOLD p value less than, equal to 0.05 or significant; 

p value greater 

than 0.05 and less than 0.1, marginally significant; kg/yr, kilograms per year; mg/L, milligrams per liter; MPN/yr, most probable number per year; MPN/L, most probable number per liter; h, hours; m3, cubic meters; km2, square kilometers; yr, year]

Total nitrogen Phosphate Total phosphorus Fecal coliform Total coliform Nitrate FWC Ammonia FWC Chloride FWC Sulfate FWC FWC FWC FWC FWC FWC (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (MPN/L) (MPN/L)

SSO duration (h) Mean (+) p=0.013, - (+) p=0.002 (+) p=0.005, - - - - -

r2=0.44 r2=0.58 r2=0.43

Median - - - - - - - - -

Total (+) p=0.013, - - - - - - - -

r2=0.44

SSO volume (m3

) Mean - - - - - - - - -

Median - (+) p=0.087, - - - - (+) p=0.017, - -

r2=0.21 r2=0.42

Total - - - - - - (+) p=0.062, - -

r2=0.28

BMP drainage area (km2) Mean - - - - - - - - -

Median - - - - - - - - -

Total - - - - - - - - -

Cumul. - - - (-) p=0.003, (-) p=0.003, - - - -

r2=0.50 r2=0.53

BMP storage volume (m3) Mean - - - - - - - - -

Median - - - - - - - - -

Total - - - - - - - - -

Cumul. - () p=0.077, - (-) p=0.008, - - (-) p=0.004, - -

r2=0.19 r2=0.42 r2=0.49

BMP count Cumul. - (-) p=0.072 - (-) p=0.00337 - - - - -Water main breaks/leaks Count - - - - (+) p=0.082, (+) p=0.028, - - -

r2=0.23 r2=0.34

Breaks - - - - (+) p=0.076, (+) p=0.031, - - -

r2=0.24 r2=0.33

Leaks - - - - - - - - -

Road salt applied (metric tons/yr) (+) p=0.013, - - - - 0.078, r2=0.22 (+) p=0.010, - -

r2=0.39 r2=0.44
What Factors are Affecting Water-Quality Trends in the Gwynns Falls? 21

0

50,000

100,000

150,000

200,000

r 2

 = 0.30

p = 0.051

A

0 2,500 5,000 7,500

0

300,00,000

60,000,000

90,000,000

120,000,000

r 2

 = 0.51

p = 0.048

B

Sanitary sewer median volume (liters)

Annual total coliform load, 

as most probable number per year Annual nitrate, as nitrogen load,

as kilograms per year

Figure 10. Significant relation of median sanitary sewer overflow volume to A, annual nitrate (as nitrogen) load, and B, annual 

total coliform bacteria load, Gwynns Falls Carroll Park subwatershed, Maryland, water years 1999–2013. (Dashed line, 0.05<p<0.1)
22 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

0

50,000

100,000

150,000

200,000

r 2 = 0.34

p = 0.038

A

0

500

1,000

1,500

2,000

B

r 2 = 0.49

p = 0.008

0

100,000

200,000

300,000

C

r 2 = 0.43

p = 0.015

10 15

0

2,500

5,000

7,500

10,000

D

Mean sanitary sewer overflow duration, in hours

Annual load, in kilograms per year

0 5

Total phosphorus Total nitrogen Phosphate, as phosphorus Nitrate, as nitrogen

Figure 11. Relations of mean total sanitary sewer overflow duration (mean) to A, annual nitrate (as nitrogen) load, B, annual 

phosphate (as phosphorus) load, C, total nitrogen load, and D, total phosphorus load, Gwynns Falls Carroll Park subwatershed, 

water years 1999–2013.
What Factors are Affecting Water-Quality Trends in the Gwynns Falls? 23

volume is not estimated for these events, rather the number of 

breaks and leaks are tracked by count. FWCs of chloride are 

positively related to the total count of water main breaks and 

leaks, and breaks alone, but not leaks alone (table 4). 

SSO reduction and elimination is a primary goal of the 

Consent Decree recently (2017) refiled for Baltimore City 

(U.S. Department of Justice and others, 2017; Pelton and others, 2015) and ongoing with Baltimore County (U.S. Department of Justice and others, 2005). Specifically, the city plans 

to reduce more than 99 percent of SSOs in a phased approach, 

to be completed by 2030, with 83-percent reduction by 2021 

(Baltimore City DPW, presentation from public meeting to 

discuss sewer Consent Decree, written commun., June 2016). 

Additional work will assess remaining inflow and infiltration 

in the sanitary sewer system and address sanitary discharges 

of unknown origin (Baltimore City DPW, presentation from 

public meeting to discuss sewer Consent Decree, written commun., June 2016) by the end of 2030. The gray infrastructure 

investment in Baltimore City through 2019 was estimated to 

be approximately $2 billion. 

The correlation of SSOs with nutrient export from the 

Gwynns Falls presents a challenge for city and county managers tasked with various regulatory requirements related to 

water quality under different programs with differing compliance time lines. The regulatory targets for nutrient loads are 

related to the TMDLs for total nitrogen and phosphorus, as 

specified in the city and county MS4 permits (Baltimore City 

Department of Public Works, 2015) and the Maryland Watershed Improvement Plan for compliance with the Chesapeake 

Bay TMDL, which are targeted for 2025. Specific reductions in these nutrient loads are expected to be addressed by 

reducing the impervious surface by 20 percent, as specified 

in the MS4 permits. These TMDLs are not explicitly tied to 

gray infrastructure repairs. Although Illicit Discharge Detection and Elimination is included as a major component of the 

MS4 permit program, the repairs to gray infrastructure and 

elimination of SSOs fall predominantly under the regulatory 

requirements of the Consent Decrees (U.S. Department of 

Justice and others, 2002, 2005, 2017), which are not expected 

to be completed until 2030. Many stakeholders, as part of 

the Healthy Harbor initiative, also have the goal of a fishable 

and swimmable harbor by 2020. The link between SSOs and 

nutrients indicates that improvement and repair of the gray 

infrastructure systems is critical for improvement of urban 

water quality. Continuing trends analysis could be a valuable 

tool for evaluating the effectiveness of the ongoing sewer 

repairs and SSO reductions.

Implementation of Green Infrastructure and 

Effects of Best Management Practices

In recent years, there has been an increased focus on 

rerouting stormwater from the piped, built environment using 

practices that focus on infiltration to enhance the natural 

filtering of stormwater, or green infrastructure (EPA, 2017). 

Baltimore City and County, as well as numerous non-governmental organizations in the region, have implemented green 

infrastructure to encourage infiltration and reduce stormwater 

runoff. Various green infrastructure practices are considered to 

be structural BMPs for water quality (for example, wetlands, 

bioretention, stream restoration) and can provide impervious surface “credits” for compliance with MS4 permit goals, 

which are based on assumed pollutant removal efficiencies 

(MDE, 2014). The presence of these structural BMPs within 

the county portion of the watershed appears to be a major 

factor affecting water quality in the Gwynns Falls watershed, 

particularly phosphorus export (table 4). BMP drainage area 

is significantly negatively correlated with phosphate and total 

phosphorus loads and FWC (fig. 12). The same relations were 

not observed for nitrogen however (table 4). These relations 

indicate that the county structural BMPs have had a positive 

downstream effect on phosphorus export from the watershed 

as a whole (as examined at Carroll Park). Mechanistically, this 

effect would be driven by the ability of green infrastructure 

features to retain sediment-bound phosphate. However, analysis does not rule out the possibility that another factor may be 

driving the relation between BMPs and phosphorus.

Although investments in the MS4 program have been 

relatively steady (estimated from MS4 annual fiscal reports 

to be approximately $10 million to $15 million since 2005), 

investment has increased to more than $20 million in the 

current permit cycle in Baltimore City, possibly owing to the 

passage of the stormwater remediation fee in 2013. The annual 

investment since 2005 in Baltimore County is slightly greater, 

averaging about $20 million per year (based on MS4 annual 

fiscal reports). This investment is expected to increase in the 

next several years as part of watershed restoration efforts 

outlined in the Watershed Implementation Plans for Baltimore 

City and County (for example, Baltimore City Department of 

Public Works, 2015). Nonetheless, approximately $100 million was estimated for MS4 investment, which is considerably 

less than the investment required for the gray infrastructure 

capital repairs.
24 Factors Affecting Long-Term Trends in Surface-Water Quality in the Gwynns Falls Watershed

0

500

1,000

1,500

2,000

2,500

A

r

2

 = 0.26

p = 0.050

Phosphate, as phosphorus

0.00

0.02

0.04

0.06

0.08

C

r

2

 = 0.30

p = 0.034

0 5 10 15 20

0

2,500

5,000

7,500

10,000

B

r

2

 = 0.31

p = 0.038

Best management practice drainage area (square kilometers)

Total phosphorus

0 5 10 15 20

0.000

0.025

0.050

0.075

0.100

D

r

2

 = 0.53

p = 0.003

Load (kilograms per year) FWC (milligrams per liter)

Figure 12. Significant and moderately significant relations of cumulative best management practices (drainage 

area) to A, annual phosphate (as phosphorus) load, B, total phosphorus load, C, annual phosphate (as phosphorus) 

flow-weighted concentration, and D, annual total phosphorus flow-weighted concentration, Gwynns Falls Carroll 

Park subwatershed, Maryland, water years 1999–2013. (Dashed line, 0.05<p<0.1; FWC, flow-weighted concentration)
References Cited 25

in accordance with the Baltimore City and Baltimore County 

municipal separate storm sewer system permits and watershed 

restoration plans.

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Conclusions

Long-term records of water quality are rare in urban 

environments, particularly for intermediate size drainage areas 

like the Gwynns Falls watershed (170.67 square kilometers) in 

Maryland. Considerable investments are being made in gray 

and green infrastructures with the expectation of water-quality 

benefits. There is a critical need for long-term assessment 

of the effects of these investments. This study, done in 

cooperation with Blue Water Baltimore, and other studies 

like it are important steps in understanding the complexity of 

these changes and can help stakeholders determine whether 

their investments are resulting in water-quality improvements. 

Changes in land use and climate, and the multiple sources of 

data (water-quality constituents and factors affecting these 

constituents), make these evaluations challenging.

As with other urban areas, the land-cover change was 

limited in the study subwatersheds as a result of the high 

proportion of developed land at the start of the study period 

(1998–2016). Therefore, land-cover change was not considered a factor of influence for water-quality change within 

the watershed. Precipitation and frequency of runoff producing storms did increase over the study period, particularly 

in the winter. Increased winter precipitation is an important 

consideration for area stakeholders because it can exacerbate 

limitations in gray (for example, wet weather sanitary sewer 

overflows) and green (for example, reliance on growing season) infrastructure.

In this study, despite the long data record, temporal trends 

were not apparent for the vast majority of the constituents 

examined. Despite a high percentage of impervious surface 

and increasing precipitation, degradation of water quality is 

not apparent for the most downstream and largest subwatershed (Carroll Park). In fact, improving water quality, determined on the basis of declining phosphate and total phosphorus flow-weighted concentrations, was noted at Carroll Park. 

The variability in trends throughout the Gwynns Falls watershed highlights the complexity of these systems. Similarly, 

many of the driving factors that were examined did not have 

statistically significant relations to the water-quality trends. 

However, sanitary sewer overflows and best management 

practices, are factors that appear to affect the water quality at 

Carroll Park, the most downstream location monitored in the 

Gwynns Falls watershed. The increasing duration of sanitary 

sewer overflows was related to increasing loads and concentrations of nutrients; sanitary sewer overflow volume was related 

to total coliform levels. In contrast, installation of structural 

best management practices appears to be related to declines in 

phosphorus. These outcomes, while preliminary, indicate that 

current efforts and investments in gray and green infrastructure improvements likely positively affect the water quality of 

the Gwynns Falls watershed. Further, this study has established a framework to evaluate the effect of future gray infrastructure repairs in accordance with the Baltimore City and 

County Consent Decrees and green infrastructure installation 
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Wollheim, W.M., Green, M.B., Pellerin, B.A., Morse, N.B., 

and Hopkinson, C.S., 2013, Causes and consequences of 

ecosystem service regionalization in a coastal suburban 

watershed: Estuaries and Coasts, v. 38, p. 19–34.

Woytowitz, E.L., Majcher, E.H., and Reisinger, A.J., 2018, 

Nutrient, bacteria, ammonia, total Kjeldahl nitrogen, total 

suspended solids annual loads; green and gray infrastructure; land cover change; and climate data in the Gwynns 

Falls subwatersheds, Baltimore, Maryland, 1998–2016: 

U.S. Geological Survey data release, https://doi.

org/10.5066/F76T0KTJ.

For more information, contact:

Director, MD-DE-DC Water Science Center

U.S. Geological Survey

5522 Research Park Drive

Baltimore, MD 21228

or visit our website at:

http://md.water.usgs.gov/

Publishing support provided by the West Trenton and 

Pembroke Publishing Service Centers
Majcher and others—Factors Affecting Long-Term Trends in the Gwynns Falls, Baltimore City and County, Maryland, 1998–2016—Open-File Report 2018–1038

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20181038 Printed on recycled paper