Introduction

Forests, Water and People: 

Drinking water supply and forest lands 

in the Northeast and Midwest United States

United States Department of Agriculture

Forest Service

Northeastern Area State and Private Forestry

Newtown Square, PA 19073

NA-FR-01-08

June 2009

Martina C. Barnes, Albert H. Todd, Rebecca Whitney Lilja, and Paul K. Barten
The authors give special thanks to Robin Morgan, Northeastern Area State and Private Forestry (now 

with the Northern Research Station), Forest Service, U.S. Department of Agriculture, for her support 

and guidance in development of this project; and to Carl Reeverts, U.S. Environmental Protection 

Agency, for his help in providing critical data sets on drinking water consumers and supplies. Thanks 

go to Rebecca Whitney Lilja, Office of Knowledge Management, Northeastern Area State and Private 

Forestry, for producing all of the maps in this report, and for her patience through many rounds of 

changes. Thanks also go to Russ Lafayette, Eastern Region (R9) of the Forest Service, for his helpful 

comments on the draft report.

ACKNOWLEDGMENTS

Cover: The Quechee Gorge in Windsor County, Vermont, is part of the high-ranking Black-Ottauquechee 

watershed. Photo by Sherri Wormstead.
Authors

Martina C. Barnes, Regional Planner, 

Northeastern Area State and Private Forestry, 

Forest Service, U.S. Department of Agriculture

Albert H. Todd, Watershed Program Leader, 

Northeastern Area (now Assistant Director, 

Ecosystem Services and Markets), State 

and Private Forestry, Forest Service, U.S. 

Department of Agriculture

Rebecca Whitney Lilja, GIS Analyst, 

Northeastern Area State and Private Forestry, 

Forest Service, U.S. Department of Agriculture

Paul K. Barten, Ph.D., Associate Professor, 

University of Massachusetts, Amherst 

United States Department of Agriculture

Forest Service

Northeastern Area State and Private Forestry

11 Campus Boulevard, Suite 200

Newtown Square, PA 19073

NA-FR-01-08

June 2009

www.na.fs.fed.us

Forests, Water and People: 

Drinking water supply and forest lands 

in the Northeast and Midwest United States

Forests, Water, and People | i

Contents

CONTENTS

ABSTRACT ........................................................................................................................................................................... vi

INTRODUCTION..................................................................................................................................................................1

 Objectives of This Report.......................................................................................................................................1

 Multiple Barrier Approach to Water Protection..............................................................................................1

 Source Protection Versus Water Treatment .....................................................................................................2

STUDY AREA........................................................................................................................................................................4

 Land Use.......................................................................................................................................................................6

 Water Consumption .................................................................................................................................................7

 Large water supplies........................................................................................................................................7

 Medium-sized water supplies.......................................................................................................................8

 Small water supplies........................................................................................................................................8

ANALYSIS METHODS.........................................................................................................................................................9

 Step 1: Calculate Ability to Produce Clean Water (APCW).........................................................................10

 APCW index by 30-meter pixels................................................................................................................10

 Mean APCW for Watersheds.......................................................................................................................12

 Step 2: Add Data on Drinking Water Consumers.........................................................................................12

 Step 3: Add Data on Private Forest Land.........................................................................................................12

 Step 4: Add Data on Change in Housing Density ........................................................................................13

RESULTS AND DISCUSSION..........................................................................................................................................15

 Index of a Watershed’s Ability to Produce Clean Water (APCW) (Step 1).............................................15

 Importance of Watersheds for Drinking Water Supply (Step 2) .............................................................18

 Importance of Watersheds With Private Forests for Drinking Water Supply (Step 3) .....................20

 Top-scoring watersheds—Eastern portion of the study area.........................................................21
ii |

 Forests, Water, and People

Contents

 Top-scoring watersheds—Western portion of the study area ...............................................................................................22

 Development Pressure on Private Forests in Watersheds Important for Drinking Water Supply (Step 4).......................26

 Top-ranking watersheds—Eastern portion of the study area ................................................................................................28

 Top-ranking watersheds—Western portion of the study area...............................................................................................29

SUMMARY OF RESULTS..........................................................................................................................................................................................32

CLOSING COMMENTS.............................................................................................................................................................................................34

APPENDIX A: National Forest Lands Important for Drinking Water Supply.......................................................................................36

APPENDIX B: Technical Information Regarding Analysis and Metadata ..............................................................................................42

REFERENCES ..............................................................................................................................................................................................................69

Text Boxes

Case Study—Low-Scoring Watershed: South Fork Sangamon River, Springfield, IL....................................................................... 17

Case Study—Moderate-Scoring Watershed: James River, Springfield, MO........................................................................................25

Case Study—High-Ranking Watershed: Merrimack River, Manchester, NH.......................................................................................27

A Watershed Protection Forest Is... ...................................................................................................................................................................34

Tables

Table 1: Population and forest area in the Northeast and Midwest in 1900 and 2000, by State.................................................. 6

Table 2: Surface water supply systems in the Northeast and Midwest and population served in

2005, by State.............................................................................................................................................................................................................. 8

Table 3: Data sets used in the watershed analysis, by attribute ............................................................................................................10

Table 4: Biophysical characterization for 30-meter pixels, by attribute and ability to produce

clean water (APCW). Higher scores indicate greater ability to produce clean water ......................................................................12

Table 5: Summary of watershed analysis and prioritization, by steps in the GIS overlay process.............................................13
Forests, Water, and People | iii

Contents

Table 6: Top scoring watersheds for drinking water supply in the Northeast and Midwest, by composite score

and Hydrologic Unit Code (HUC).......................................................................................................................................................................19

Table 7: Top scoring watersheds in the eastern portion of the study area, in terms of private forests important for

drinking water supply, by label in Map 9 and composite score..............................................................................................................22

Table 8: Top scoring watersheds in the western portion of the study area, in terms of private forests important for

drinking water supply, by abel in Map 10 and composite score.............................................................................................................24

Table 9: Watersheds in the eastern portion of the study area with the greatest development pressure on

private forests important for drinking water supply, by label in Map 12 and composite score ..................................................29

Table 10: Watersheds in the western portion of the study area with the greatest development pressure on

private forests important for drinking water supply, by label in Map 13 and composite score ..................................................31

Table 11: Forest land ownership in the Northeast and Midwest, by number of surface water consumers...........................33

Figures

Figure 1: As in the watershed of the Quabbin Reservoir in western Massachusetts, sustainably managed

forests provide insurance against pollution from roads, sewers, and urban runoff.......................................................................... 2

Figure 2: New York City’s commitment to land management in its source watershed translates directly to abundant

quality drinking water for city residents, and annually avoids over $300 million in filtration costs. ........................................... 3

Figure 3: Nine layers of GIS data (boxes) were combined in stepwise fashion, to produce four indices (ovals) of

watershed importance for drinking water supplies and the need for private forest management

to protect those supplies......................................................................................................................................................................................14

Figure 4: Forest land ownerships in source water watersheds in the Northeast and Midwest..................................................32

Figure 5: Forest shelters this high-quality stream in Maryland. ............................................................................................................33

Maps

Map 1: Study area. The study area includes the District of Columbia and 20 States in the Northeast and

Midwest United States............................................................................................................................................................................................. 4
iv |

 Forests, Water, and People

Contents

Map 2: Private and protected forest land. Most forests in the Northeast and Midwest United States are

privately owned. ........................................................................................................................................................................................................ 5

Map 3: Change in housing density, 2000 - 2030. Housing density in the study area is

projected to increase from 2000 to 2030.......................................................................................................................................................... 5

Map 4: Surface water consumers. Most surface drinking water supply systems in the Northeast are small,

each serving less than 100,000 people... .......................................................................................................................................................... 7

Map 5: Index of the Ability to Produce Clean Water, 30-meter pixel view. The index of the ability to produce

clean water was developed by combining six layers of spatial data: forest and agricultural lands, riparian

forest cover, soil erodibility, road density, and housing density. Areas with higher scores have greater ability

to produce clean water..........................................................................................................................................................................................15

Map 6: Index of the Ability to Produce Clean Water, watershed view. The index of the ability to produce

clean water indicates the probability of finding surface waters of high quality in a watershed.

Higher scores indicate higher probability.......................................................................................................................................................16

Map 7: Importance of watersheds providing drinking water supply, watershed view. Watersheds with the

highest scores have the greatest ability to produce high quality water for the largest population .........................................18

Map 8: Importance of watersheds and private forests for drinking water supplies, watershed view.

Watersheds that scored high in their ability to produce high quality water for the largest population

also scored high in the amount of private forested land they encompassed. ..................................................................................20

Map 9: Importance of watersheds and private forests for drinking water supplies in the eastern portion

of the study area, watershed view. Watersheds that scored highest in terms of importance for drinking

water and for private forests important for drinking water supply were in the eastern portion of the

study area. See table 7 for more information about each watershed. ................................................................................................21

Map 10: Importance of watersheds and private forests for drinking water supplies in the western

portion of the study area, watershed view. High-scoring watersheds in terms of importance of watersheds and

private forests for drinking water supply in the western portion of the study area did not score as high as

watersheds in the eastern portion (Map 9). See Table 8 for more information about each watershed. . ................................23

Map 11: Development pressure on forests and drinking water supplies, watershed view. Watersheds with

the highest rank and the highest risk of future development are near major cities and metropolitan areas........................26

Map 12: Development pressure on private forests and drinking water supplies in the eastern portion of the

study area, watershed view. In the eastern portion of the study area, forested water supply watersheds subject

to the greatest development pressure are along the Interstate Highway 95 corridor. See Table 9 for more

information about each watershed. . ...............................................................................................................................................................28
Forests, Water, and People | v

Contents

Map 13: Development pressure on private forests and drinking water supplies in the western portion of

the study area, watershed view. Less private forest land in the western portion of the study area means there is less

development pressure on drinking water supply watersheds than in the eastern portion. See Table 10 for more

information about individual watersheds. .....................................................................................................................................................30
vi |

 Forests, Water, and People

Abstract

Forests are critically important to the supply of clean drinking water in the Northeast and Midwest 

portion of the United States. In this part of the country more than 52 million people depend on 

surface water supplies that are protected in large part by forested lands. The public is generally 

unaware of the threats to their water supplies or the connection between clean water and the extent 

and condition of forest lands in source water watersheds. The future security of water supplies will 

not be ensured by a focus on water treatment alone. Protecting and managing forests in source 

watersheds is an essential part of future strategies for providing clean, safe drinking water that 

citizens can afford. This analysis uses a GIS-based process and a series of maps to create a watershed 

condition index based on physical and biological attributes. Using a multi-step process, this index 

is then used to compare 540 watersheds across 20 States and the District of Columbia, in terms of 

their ability to produce clean water. The study also quantifies the magnitude and scope of forestdependent drinking water supplies, and their dependence on private forests; and it identifies 

watersheds that are threatened by land use change or are in need of management to sustain and 

improve forests that protect water supplies. The final maps and data display development pressure 

on private forests in watersheds important for drinking water.

ABSTRACT
Introduction

Forests, Water, and People | 1

Forests in the 20 States and Washington, DC, served by the Northeastern Area State and Private 

Forestry, Forest Service, U.S. Department of Agriculture, help to protect more than 1,600 drinking 

water supplies that are the source of water for more than 52 million Americans (U.S. Department 

of Agriculture, Forest Service 2005). More than two-thirds of the population in this region depend 

on water from streams, lakes, and reservoirs. The quality of this water depends, in part, on the 

forest lands in their watersheds. Besides providing this valuable public benefit, these forests are 

also often managed for timber products, wildlife, and recreation that help to conserve them as open 

space. While many citizens who depend on surface water from municipal sources live very close to 

their water supply system, the value of forests specifically to water quality and water supply is often 

overlooked by both the public and policymakers.

INTRODUCTION

Objectives of This 

Report

This project had two main objectives. 

The first was to illustrate the direct 

geographic connection between 

forests, water, and people—

sometimes called the “forest-tofaucet” connection. The maps and 

data for this objective display a 

watershed’s ability to produce clean 

water. The second objective was 

to demonstrate the importance of 

private forests to protecting surface 

drinking water quality and the 

potential threats to those forests. 

The maps and data for this objective 

display development pressure 

on private forests in watersheds 

important for drinking water. By 

looking at these relationships on 

a landscape scale, priorities for 

management action can be better 

determined.

The unique results of this analysis can be used in a 

number of ways: to guide strategies for forest land 

protection, outreach, and technical assistance to 

municipal water providers, and to refine and target 

assistance to individual forest landowners.

Multiple Barrier Approach to Water 

Protection

The time-tested multiple barrier approach to water 

protection remains vitally important to protecting 

drinking water supplies (National Research Council 

2000). Multiple barriers to disease agents provide the 

greatest protection to public health. This approach 

involves several consecutive and interrelated steps; (1) 

protecting source areas, (2) treating drinking water, and 

(3) monitoring the drinking water distribution system to 

ensure success. The single most important barrier has 

proven to be source water protection. 
2 |

 Forests, Water, and People

In the Northeast and Midwest United States and 

throughout much of the world, forests are the crucial first 

barrier for source water protection (Dudley and Stolton 

2003, National Research Council 2000, Platt and others 

2000). Some of the Northeast’s biggest cities, such as 

Boston, Hartford, and New York, took action more than a 

century ago to protect their water supplies by purchasing 

land in the watersheds that are the source of their 

drinking water. Even today, these cities are able to provide 

clean, safe water to millions of their citizens with minimal 

need for treatment (Barten and others 1998; Barten 2005). 

Yet, most people are unaware of the connection between 

clean water and the extent and condition of forest 

lands, or of the threats to their water supplies posed by 

development pressure (Ernst 2004).

Source Protection Versus Water 

Treatment

The future security of water supplies will not be ensured 

by a focus on water treatment alone. Protecting and 

managing forests in source watersheds are essential parts 

of future strategies for providing clean, safe drinking 

water that citizens can afford (Barten and Ernst 2004). 

One of the main reasons suppliers are revisiting the 

idea of source protection is the growing realization that 

allowing untreated water quality to degrade, in addition 

Figure 1: As in 

the watershed 

of the Quabbin 

Reservoir in western 

Massachusetts, 

sustainably 

managed forests 

provide insurance 

against pollution 

from roads, sewers, 

and urban runoff. 

Photo by Martina 

Barnes.

to threatening public health, also increases treatment and 

capital costs.

Advancements in the science of water treatment (filtration 

and disinfection) have enabled most cities to effectively 

treat water to remove known contaminants and provide 

safe drinking water. However, these same advancements 

have sometimes led to the false assumption that the quality 

of untreated water supplies is less critical today (Ernst 2006). 

Many small and medium-sized municipal water suppliers 

have been moving away from protecting and managing 

their source lands in favor of filtration and new treatment 

technologies. Some municipalities are even selling these 

lands, as they consider them unnecessary assets. 

As the degree of water treatment and disinfection has 

increased, so has concern over the potential health effects 

of exposure to the byproducts of extensive disinfection 

(Ernst 2004). A continually expanding list of diverse 

contaminants, coupled with greater pollutant loads and 

fewer natural barriers, has also made water treatment more 

expensive and increased the risk that contaminants may 

reach the faucet (Ernst and others 2004). Water suppliers 

who draw water from intensively used source lands face 

treatment challenges, such as these:
Introduction

Forests, Water, and People | 3

1. Emergence of new contaminants that suppliers may 

not be prepared to test for or treat, or that may be in 

the water long before they are identified as a threat to 

public health,

2. Spikes in pollutant loads after storms that make 

treatment more difficult,

3. Increased treatment and capital costs due to higher 

loads and changing regulations.

Reliance on treatment alone can also be a costly 

alternative in the long run (National Research Council 

2000). By protecting the watershed of the Quabbin 

Reservoir in western Massachusetts and practicing 

sustainable forestry since the 1930s, Boston made a 

cost-effective investment in clean source waters that will 

never be threatened by pollution from roads, sewers, or 

urban runoff (Figure 1). Allowing untreated water quality 

to degrade, in addition to threatening public health, also 

increases treatment and capital costs. New York City 

estimated the cost of installing filtration alone to be nearly 

$7 billion, with over $300 million in annual operating 

costs. As a result, New York City has chosen to sustain 

the quality of land management in its source watershed 

in order to sustain high water quality for a substantially 

lower investment (Figure 2).

Figure 2: New York 

City’s commitment 

to quality land 

management in its 

source watershed 

translates directly 

to abundant quality 

drinking water for 

city residents, and 

annually avoids 

over $300 million 

in filtration costs. 

Photo courtesy of 

George M. Aronson, 

photographer.

Current research on the public health impacts of urban 

and agricultural runoff in untreated water sources, 

and a recognition of the high costs and limitations of 

technological fixes reinforce two principles that were 

taken for granted a century ago: (1) the public water 

supply should be reasonably clean to begin with, and (2) 

forests and natural lands are critical to the quantity and 

quality of water supplies. 

A recent report from the World Bank, titled Running 

Pure, concluded that protecting forests around water 

catchment areas is no longer a luxury but a necessity 

(Dudley and Stolton 2003). Protecting forests—which 

reduce erosion and sediment, improve water purity, and 

in some cases capture and store water—is a cost-effective 

way to provide clean drinking water. When forests are 

removed, the costs of providing clean and safe drinking 

water to urban areas increase dramatically (Dudley and 

Stolton 2003).

A study of water suppliers conducted by the Trust for 

Public Land in association with the U.S. Forest Service and 

the American Water Works Association’s Source Water 

Protection Committee has found that operating treatment 

costs decrease as forest cover in a source area increases 

(Ernst and others 2004).
4 |

 Forests, Water, and People

Study Area

84

57

72

29

80

70

55

87

90

35

75

57

88

95

40

76

64

80

80

76

83

90

43

80

75

71

81

29

94

80

87

93

74

70

65

70

95

90

94

71

70

90

81

35

90

80

94

70

91

35 29

64

95

75

74

77

94

88

44

55

90

96

78

70

66

Providence

Hartford

Annapolis

Dover

Trenton Harrisburg

Charleston

Columbus

Springfield Indianapolis

Concord

Boston Albany

Montpelier

Augusta

Lansing

Madison

St. Paul

Jefferson City

Des Moines

Washington

New York

Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St. Louis

Memphis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas City

LEGEND

Northeastern Area

Freeway System

Nation's Capital

State Capital

Large Cities

1992 Landuse

Water

Developed

Forested

Agricultural

Map 1: Study area. The study area includes the District of Columbia and 20 States in the Northeast and Midwest 

United States.

STUDY AREA

The 20-State study area, including the District of Columbia, stretches from the mountains of northern 

Maine to the banks of the Mississippi River, and from the hills of Missouri to the Chesapeake Bay 

(Map 1). The area is both the most populated and the most forested part of the country. While the 

study area makes up only 18 percent of the land area of the United States (Smith and others 2004), 

it is home to over 43 percent of its population (U.S. Bureau of the Census 2000). Before European 

settlement, roughly 300 million acres of forest covered this region (Smith and others 2004). Today, 

about 4 out of every 10 acres in this region is covered by forest, representing some 170 million acres 

and 23 percent of the nation’s forest land. Of these forests, 92 percent are non-federally owned, 

with 76 percent owned by private landowners, which includes non-industrial private forest (NIPF) 

owners (Map 2; Smith and others 2004). 
Forests, Water, and People | 5

Study Area

New York Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St.

Louis

Memphis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas

City

Providence

Hartford

Annapolis

Dover

Trenton

Harrisburg

Charleston

Columbus

Indianapolis Springfield

Concord

Boston Albany

Montpelier

Augusta

Madison Lansing

St. Paul

Jefferson

City

Des

Moines

Washington

LEGEND

Protected Forest

Private Forest 

Non-Forest Land

Washington

Dover

Boston Albany

Trenton

Concord

Augusta

Madison Lansing

Hartford

Columbus

St. Paul

Annapolis

Providence

Harrisburg

Charleston

Montpelier

Des Moines

Springfield Indianapolis

Jefferson City

Omaha

Newark

Toledo Chicago

Memphis

Buffalo

Detroit

Baltimore

Cleveland

St. Louis

Milwaukee

Pittsburgh

Cincinnati

Louisville

Minneapolis

Kansas City

Philadelphia

LEGEND

CHANGE IN HOUSING DENSITY, 2000 to 2030

Exurban to Suburban/Urban

No Change in Housing Density Class

Rural to Exurban

Rural to Suburban/Urban

Suburban to Urban

Protected (PAD v.4)

Map 2: Private 

and protected 

forest land.

Most forests in the 

Northeast and 

Midwest United 

States are privately 

owned.

Map 3: Change in 

housing density, 

2000 – 2030.

Housing density in 

the study area is 

projected to increase 

from 2000 to 2030.
6 |

 Forests, Water, and People

Study Area

Land Use

Although forest acreage has been increasing for most of 

the last 100 years, a growing population and increasing 

consumption of water, wood, and energy have outpaced 

increases in forest cover. More than 2,000 acres of forest 

land are cleared for development each day in the United 

States, and growth projections suggest that as many as 

138 million acres of private forest land will be threatened 

by development between 2005 and 2030 (Stein and others 

2005). In the Northeast and Midwest States, nearly 3.8 

million acres of forest were lost to development between 

1982 and 1997, with another 12 million acres projected to 

be lost by the year 2030 (Lund 2005; Map 3). Much of this 

increase in development is occurring outside metropolitan 

centers and spreading across the landscape in what is 

often referred to as “sprawl.” Because of the need for 

dispersed transportation and business centers, this pattern 

of development tends to consume a much greater amount 

of open space than more compact and historic urban 

development. As a result, there were more people per 

square mile of forest in 2000 than in 1900 (Table 1).

Table 1: Population and forest area in the Northeast and Midwest in 1900 and 2000, by State

State Year Population* Forest area (mi2)† ‡ People per square mile of forest Forest acres per person

Connecticut 1900 910,000 3,305 275 2.3

2000 3,282,031 6,886 477 1.3

Delaware 1900 180,000 547 329 1.9

2000 754,000 598 1,261 0.5

Illinois 1900 4,800,000 3,906 1,229 0.5

2000 12,130,000 6,767 18,953 0.3

Indiana 1990 2,500,000 6,250 400 1.7

2000 5,940,000 7,033 845 0.8

Iowa 1900 2,200,000 3,906 563 1.1

2000 2,900,000 3,203 905 0.7

Maine 1900 694,466 23,730 29 21.9

2000 1,274,923 27,639 46 13.9

Maryland 1900 1,200,000 3,438 349 1.8

2000 5,200,000 4,009 1,297 0.5

Massachusetts 1900 2,788,000 5,824 479 1.3

2000 6,175,169 10,545 586 1.1

Michigan 1900 2,400,000 24,218 99 6.5

2000 9,860,000 30,127 327 1.9

Minnesota 1900 1,700,000 23,492 72 8.8

2000 4,920,000 26,094 188 3.3

Missouri 1900 3,100,000 28,594 108 5.9

2000 5,500,000 21,863 252 2.5

New Hampshire 1900 412,000 8,896 46 13.9

2000 1,201,134 18,240 66 9.7

New Jersey 1900 1,883,669 2,500 754 1.0

2000 8,414,350 3,672 2,292 0.3

New York 1900 7,283,000 9,445 771 0.8

2000 18,196,601 28,841 631 1.0

Ohio 1900 4,200,000 7,500 560 1.1

2000 11,260,000 12,273 918 0.7

Pennsylvania 1900 6,302,115 8,600 740 0.9

2000 12,281,054 26,562 462 1.4

Rhode Island 1900 430,000 754 570 1.1

2000 990,819 1,339 740 0.9

Vermont 1900 343,641 3,419 10 6.4

2000 608,827 7,233 84 7.6

West Virginia 1900 960,000 14,219 68 9.5

2000 1,800,000 18,919 95 6.7

Wisconsin 1900 2,100,000 25,000 84 7.6

2000 5,250,000 24,942 210 3.0

* Gibson and Lennon 1999; U.S. Bureau of the Census, Population Division 2000 (2000 population data).

† Barten 2007; Smith and others 2001, Table 3 (forest area for all non-New England states served by the Northeastern Area); Foster 1990 (forest area for 

Connecticut, Massachusetts, Maine, New Hampshire, Rhode Island, Vermont); Kellogg 1909 (1900 forest area data).

‡ 1 square mile (mi2) = 640 acres.
Forests, Water, and People | 7

Study Area

Water Consumption

Across the study area, daily household water use exceeds 

4 trillion gallons. This figure is based on water use of 

approximately 75 gallons daily per capita (household 

water use only, not including irrigation or power 

generation). This amounts to more than 27,000 gallons 

per person per year or more than three 20-foot diameter 

swimming pools! By comparison, per capita water use 

in 1900 was 5 to 10 gallons per day. In the New York City 

metropolitan area alone, water consumers use nearly 

2 billion gallons of water per day (National Research 

Council 2000). Major cities like New York and Boston have 

undertaken comprehensive programs to protect large 

forested watersheds far from these cities. Most small to 

mid-sized systems, however, are within 10 to 20 miles from 

the point of use, with limited opportunities for expansion 

to new forested watersheds (Sedell and Apple 2002). 

Large water supplies. 

Generally, large water supply systems serve more than 

1 million consumers and are owned and operated by 

public agencies with significant budgets and proactive 

management programs. The Catskill, Delaware, and 

Croton watersheds deliver 1.2 billion to 2.3 billion 

gallons per day to 9 million consumers in the New York 

metropolitan area. Most forest land in these watersheds is 

privately owned. The City and the Watershed Agricultural 

Council have promoted a program of outreach to forest 

landowners to improve timber management activities 

in the watershed. The Quabbin, Ware, and Wachusett 

Rivers serve over 2.2 million people in 47 communities 

and the City of Boston. This water supply system is 

predominantly publicly owned, and the Commonwealth 

of Massachusetts manages more than 100,000 acres of 

watershed forest. Of this acreage, 75 percent is actively 

managed, also producing nearly 10 million board feet of 

timber each year.

Map 4: Surface water consumers. Most surface drinking water supply systems in the Northeast and Midwest are small, each serving 

less than 100,000 people.

New York Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St.

Louis

Memphis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas

City

Providence

Hartford

Annapolis

Dover

Trenton

Harrisburg

Charleston

Columbus

Indianapolis

Springfield

Concord

Boston Albany

Montpelier

Augusta

Madison Lansing

St. Paul

Jefferson

City

Des

Moines

Washington

LEGEND

SURFACE WATER CONSUMERS

No Public Surface Water Consumers

Small Water Supplies (25-100,000 Consumers)

Medium Water Supplies (100,000-1,000,000 Consumers)

Large Water Supplies (> 1,000,000 Consumers)
8 |

 Forests, Water, and People

State Public water supplies* Population served†

Connecticut 36 2,231,610

Delaware ‡ 4,510

Illinois 87 1,657,750

Indiana 36 1,710,050

Iowa 29 632,860

Maine 59 393,240

Maryland and District of Columbia 40 4,085,850

Massachusetts 103 4,901,910

Michigan 17 1,295,335

Minnesota 15 973,828

Missouri 84 2,502,640

New Hampshire 40 480,780

New Jersey 30 3,482,340

New York 297 11,555,950

Ohio 126 3,133,310

Pennsylvania 305 7,530,110

Rhode Island 11 566,601

Vermont 63 261,710

West Virginia 139 1,621,140

Wisconsin 5 199,460

System intakes outside the Northeast 84 3,193,294

 Total 1,608 52,411,270

* Public water supplies are community or public drinking water systems as defined by the EPA, www.epa.gov/OGWDW/guide/

sen104.html.

† Water consumer data were provided by watershed, and then prorated by watershed area to estimate consumers by State.

‡ Part of Philadelphia’s water supply system.

Study Area

Table 2: Surface water supply systems in the Northeast and Midwest and population served in 2005, by State (U.S. 

Environmental Protection Agency 2005).

Medium-sized water supplies.

 Medium-sized surface water supply systems generally 

serve more than 100,000 people with a mix of public 

and private lands, and may have moderately funded 

systems with some ongoing planning, protection, and 

management.

Small water supplies. 

The majority of surface water supply systems are small, 

serving communities of 25 to less than 100,000 people 

(Map 4). These watersheds generally have minimal public 

ownership (except areas buffering small reservoirs), as well 

as minimal planning, and infrequent forest management. 

These smaller water supply systems often lack staffing 

or adequate management expertise and violate drinking 

water standards almost twice as often as those serving 

larger communities (Ernst and Hart 2005). The protection 

and proper management of forest lands for small and 

large systems alike is a critical and cost-effective approach 

to ensuring quality drinking water in the future.

Water supplies in the Northeast are finite and 

irreplaceable, and—with the exception of large rivers and 

lakes—most water sources have already been tapped. 

There are few ecologically or economically viable ways to 

dramatically augment current supplies. While they have 

been regular news in the West, water shortages have now 

taken center stage in the humid East as well. In addition 

to natural conditions such as drought, the primary threats 

to water supplies in the Northeast and Midwest are loss 

of forest to development, agriculture, or other land uses. 

If these threats are realized, the result is chronic erosion, 

altered and unstable streams, loss of riparian vegetation, 

and diminished forest health or watershed condition left 

by historic land use.
Forests, Water, and People | 9

Analysis Methods

ANALYSIS METHODS

The study used a GIS-based process and a series of maps to create a watershed condition index 

based on physical and biological attributes. Using a multi-step process, this index was then used 

to compare watersheds across the 20 States and District of Columbia, in terms of their ability 

to produce clean water. Through regional maps, this analysis also accomplishes the following: 

quantifies the magnitude and scope of forest-dependent drinking water supplies and their 

dependence on private forests; and identifies watersheds that are threatened by land use change 

or that are in need of forest management to sustain and improve forests that protect water 

supplies.

To score the importance of 

watersheds across the 20-State study 

area, four indices were developed for 

each watershed:

1. Ability to produce clean drinking 

water

2. Importance for drinking water 

supply

3. Dependence on unprotected 

private forest land for drinking 

water supply

4. Threat of forest conversion or 

need for management, to sustain 

and improve forest conditions to 

protect drinking water supply

Each index was created by overlaying 

spatial data in a Geographic 

Information System (GIS) (Figure 

3). Data layers were given equal 

weight in the overlay process to 

avoid potential bias; all resources 

were considered equally important. 

Each dataset was converted into 

a 30-meter resolution spatial 

grid and then summarized by 

watershed. Watersheds with eightdigit Hydrologic Unit Codes (HUCs) 

developed by the U.S. Geological 

Survey were selected as the summary 

units of the analysis, because 

they were large enough to ensure 

consistent data between units of analysis but small 

enough to identify priorities based on localized variations. 

The HUCs also facilitate the identification of problems 

and opportunities by hydrological boundaries rather than 

political ones. Within the study area are 540 separate 

HUC-8 watersheds. Where watersheds fell partly outside 

the political boundary of the study area, however, the 

entire watershed area was included in the analysis.

To maintain consistency across the 20-state area, nine 

standard nation-wide datasets were collected, scored, 

and overlaid to create the indices. While more current 

data was available for several states, this method used 

a seamless dataset to avoid dramatic changes from 

one State to another. A variety of other data sets were 

evaluated but were not used, due to problems identified 

with data consistency or appropriate scale. For example, 

the percent of impaired streams data provided by the U.S. 

Environmental Protection Agency were considered. Due 

to wide variations in State reporting of impaired streams, 

however, the layer was not included in this analysis 

(Table 3).
10 |

 Forests, Water, and People

Analysis Methods

Attribute Datasets Source

Forest land 1992 National Landcover Dataset U.S. Geological Survey 1999

Agricultural land by watershed 1992 National Landcover Dataset U.S. Geological Survey 1999

Riparian forest cover by watershed 1:100,000-scale National Hydrography 

Dataset, buffered to 30 meters

Hatfield 2005

Road density 2002 Bureau of Transportation Statistics (BTS) 

Roads 

U.S. Department of Transportation 2002

Soil erodibility STATSGO Soil Dataset, kffact Miller and White 1998

Housing density by watershed Housing density in 2000 Theobald 2004

Surface drinking water consumers 

per unit area

Public Drinking Water System (PWS) 

Consumers by eight-digit HUC; City 

Drinking water consumers for New York 

City, Philadelphia, St. Louis, St. Paul, and 

Washington DC

U.S. Environmental Protection Agency 

2005

Private forest by watershed Protected Areas Database, Version 4; 

Wisconsin Stewardship Data

Conservation Biology Institute 2006; 

U.S. Geological Survey, Upper Midwest 

Environmental Sciences Center 2005

Development pressure per unit area

Housing density in 2000 and 2030 Theobald 2004

Step 1: Calculate Ability to Produce 

Clean Water (APCW)

APCW Index by 30-meter pixels

The APCW Index characterized a variety of biophysical 

conditions in each watershed known to influence water 

quality. This index of water quality and watershed 

integrity uses six attributes: forest land, agricultural land, 

riparian forest cover, road density, soil erodibility, and 

housing density. Many other activities such as industrial 

pollution and mining, and natural variables such as 

climate change, floods, and fires, can also impact water 

quality. The evaluation of source water threats beyond 

traditional land use were not within the scope of this 

study but would be of value in more detailed source 

water analyses.

The forest land, agricultural land, and riparian forest 

buffer data were summarized by watershed and 

converted to a 30-meter spatial grid. The soil erodibility, 

road density, and housing density data were kept in their 

original 30-meter grid format and not summarized by 

Table 3: Data sets used in the watershed analysis, by attribute (Appendix B).

watershed. Each of the six attributes was rated from 1 

to 4 (low to very high) based on scientifically accepted 

standards (Table 4). Where standards or parameters were 

not available, the data was divided into quartiles for the 

purpose of analysis.

The six attributes were summed to determine the APCW 

Index for each 30-meter grid cell:

F + A + R + D + S + H = APCW

where,

F = Forest land (percent)

A = Agricultural land (percent)

R = Riparian forest cover (percent)

D = Road density (quartiles)

S = Soil erodibility (k factor)

H = Housing density (acres per housing unit, in 2000), and

APCW = Ability to Produce Clean Water

The resulting index has a total potential value of 6 to 24.
Forests, Water, and People | 11

Analysis Methods

The APCW attributes are surrogates for important 

watershed characteristics that influence water yield 

and water quality. The goal of this project was not to 

make deterministic predictions of changes in nutrient, 

sediment, or other nonpoint source pollutant loading 

or flow regime, but rather to rank the 540 watersheds 

in the study area on a common scale. This ranking 

required the normalization of several attributes to enable 

objective comparison on a unit area basis (i.e., very large 

watersheds were not “advantaged” and comparatively 

small watersheds were not “disadvantaged”). This 

normalization produced a sufficient range of numerical 

variation in scores and more clearly identified critical 

watersheds. Characteristics of critical watersheds are a 

very high APCW, a large number of water consumers (per 

unit area), a large proportion of private forest land (that 

is potentially available for development and conversion 

to other land uses), and a high rate of forest conversion 

projected for 2030 (Stein and others 2005).

The scoring (i.e., low, moderate, high, very high) of the 

APCW layer was derived from a comprehensive review 

of salient literature (de la Cretaz and Barten 2007, Ice and 

Stednick 2004; National Research Council 2000 Stein 

and others 2005) and results of the Northeastern Area 

State and Private Forestry’s Spatial Analysis Project and 

the Chesapeake Bay Resource Lands Assessment (U.S. 

Department of Agriculture, Forest Service 2006, U.S. 

Geological Survey 2000).

The following is an explanation of the basis for the APCW 

scores.

Forest land: Long-term watershed studies have shown 

that 20 to 30 percent of the catchment area must 

be treated (or forest biomass harvested) to produce 

measurable water yield increases and associated water 

quality changes. Hence, the “very high” score was 

defined as 75-100% forested—and the other scores were 

apportioned equally.

Agricultural land by watershed: Agricultural land use, 

especially row crops, typically generates more substantial 

changes in water yield and quality in relation to watershed 

area (de la Cretaz and Barten 2007, Chapters 7 and 9). 

The proportional areas reflect these thresholds and were 

successfully tested during a decade of earlier work in the 

Chesapeake Bay watershed.

Riparian forest cover by watershed: The area and continuity 

of riparian forest cover directly influences water quality in 

ways that parallel the effects of forest cover at the 

watershed scale (de la Cretaz and Barten 2007, Chapter 5).

Road density: Road density is the surrogate layer for 

“development” and the addition of impervious surfaces 

and pollution sources to watersheds. Since there is 

neither detailed spatial data nor a consistent relationship 

between development, streamflow, and water quality, 

a straightforward quartile division was used to score 

watersheds.

Soil erodibility: The soil erodibility layer uses commonly 

accepted categories within the USDA Natural Resouces 

Conservation Service’s National Soils Database to 

represent the likelihood that—other characteristics being 

equal—the combined effects of soil texture and structure 

influence surface erosion, sediment transport, and water 

quality degradation.

Housing density by watershed: The housing density layer 

used for this analysis was based on past and current 

statistics on housing density and population, road density, 

past growth patterns, and locations of urban areas. The 

same model was used in the Forests on the Edge study 

(Stein and others 2005), which was based on research 

published in a peer-reviewed article in Ecology and 

Society (Theobald 2005). To date, Theobald’s housing 

density research has been used in three research reports 

published by the Forest Service (Stein and others 2005, 

2006, 2007).
12 |

 Forests, Water, and People

Analysis Methods

Mean APCW for Watersheds

The APCW values were averaged to create a mean APCW 

for a watershed. This mean was divided into 10 quantiles, 

with the 1st quantile receiving a score of 10 (very high) and 

the 10th quantile receiving a score of 1 (low) (Table 5).

Step 2: Add Data on Drinking Water 

Consumers

Step 2 combined the results of Step 1, the watershed’s 

mean Ability to Produce Clean Water, with water use data 

from the U.S. Environmental Protection Agency’s (EPA) 

Surface Drinking Water Information System (SDWIS). 

Selecting only surface water consumers (reservoirs and 

streams), the total number of drinking water consumers 

was summed for each eight-digit watershed and divided 

by the watershed area. For cities that use large river 

or lake intakes, such as Philadelphia, St. Louis, St. Paul, 

Chicago, and Washington, DC, the number of drinking 

water consumers was allocated among all upstream 

watersheds in relation to the drainage area that 

contributes water to the point of intake or diversion. For 

cities with municipal systems with multiple reservoirs in 

different eight-digit watersheds—including the New York 

City Watershed (Croton and Catskill/Delaware systems), 

Bridgeport and surrounding communities in southwestern 

Connecticut (reservoirs managed by Aquarion Water), 

metropolitan Boston, MA (Quabbin and Wachusett 

Reservoirs), and Springfield, MO—water consumers were 

allocated to reservoirs in relation to their storage volume 

and contribution to the total system capacity. The result 

for all watersheds and water supply systems was divided 

into 10 quantiles and combined with the APCW quantiles 

to yield a potential composite score of 2 to 20 (Figure 3, 

Table 5).

Step 3: Add Data on Private Forest 

Land

Step 3 combines the results of Step 2 with the percent 

of private forests in the watershed to highlight those 

private forest areas important for surface water drinking 

supply. The private forest database was derived using a 

subset of the Conservation Biology Institute’s Protected 

Areas database and an updated Wisconsin dataset (U.S. 

Geological Survey 2005). Only permanently protected 

lands (Federal, State, county, local, or permanent 

conservation easements) were considered “protected;” 

all other lands were considered unprotected, having 

the potential to be developed. The percent private 

forest by watershed was divided into 10 quantiles, and 

then combined with the results of Step 2 to yield a total 

potential of 3 to 30 (Figure 3, Table 5).

Attribute Scoring for 30-meter grid cell 

Low

(1 point)

Moderate 

(2 points)

High 

(3 points)

Very high 

(4 points)

Percent forest land (F) 0 – 24 25 – 49 50 – 75 >75

Percent agricultural land (A) >30 21 – 30 10 – 20 <10

Percent riparian forest cover (R) 0 – 29 30 – 50 51 – 70 >70

Road density (D; quartiles) 75 – 100th

percentile

50 – 74th

percentile

25 – 49th

percentile

0 – 24th

percentile

Soil erodibility (S; k factor) >0.34 0.28 – 0.34 0.2 – 0.28 0 – 0.2

Housing density (H; acres per 

housing unit in 2000)

< 0.6 acre/

unit

0.6 – 5.0 acres/

unit

5.0 – 20.0 acres/unit (east)

5.0 – 40.0 acres/unit (west)

> 20.0 acres/unit (east)

> 40.0 acres/unit (west)

 Total APCW Potential value 6 – 24

Note: Letters in parentheses correspond to the equation in the text. For more detailed information on any of the above data 

layers, please refer to the technical information in appendix B.

Table 4: Biophysical characterization for 30-meter pixels, by attribute and ability to produce clean water (APCW). 

Higher scores indicate greater ability to produce clean water. 
Forests, Water, and People | 13

Analysis Methods

Table 5: Summary of watershed analysis and prioritization, by steps in the GIS overlay process

GIS Overlay Analysis result

Watershed scoring

Low

(1 

point)

Moderate-high

(2-9 points)

Very high

(10 points)

Potential 

composite 

score (points)

Step 1—Average APCW data 

for pixels in watershed Watershed mean APCW 10th quantile 2nd – 9th quantile 1st quantile 1– 10

Step 2—Add data on surface 

water consumers

Watershed importance to 

drinking water consumers

10th 

quantile 2nd – 9th quantile 1st quantile 2 – 20 

Step 3—Add data on private 

forest

Private forest in important 

watershed 

10th 

quantile 2nd – 9th quantile 1st quantile 3 – 30

Step 4—Add data on change 

in housing density

Development pressure on 

private forest in important 

watershed

10th 

quantile 2nd – 9th quantile 1st quantile 4 – 40

Note: For more detailed information on any of the above data layers, please refer to the technical information in appendix B.

Step 4: Add Data on Change in 

Housing Density

Step 4 combines the results of Step 3 with the 

development pressure of future housing density increase 

on forests. Development pressure was calculated by 

subtracting the housing density in 2000 from projections 

for 2030. If housing density would have increased from 

rural to exurban, rural to suburban/urban, or exurban to 

suburban/urban between 2000 and 2030, development 

pressure was said to occur (Stein and others 2005, 

Theobald 2004; see Appendix B for detailed definitions). 

The total acreage of land under development pressure in 

the watershed was divided by the watershed area, divided 

into 10 quantiles, and then combined with the results of 

Step 3 to yield a total potential score of 4 to 40 (Figure 3, 

Table 5). The use of 10 quantiles to map the four steps 

in this analysis satisfied the practical need to generate 

an objective numerical gradient that would describe the 

many possible combinations of biophysical characteristics, 

water use, current development, and projected forest 

conversion to the year 2030.

Watersheds with the highest scores have the greatest 

ability to produce clean water for the greatest number 

of drinking water consumers. High ranking watersheds 

also have the largest amount of private forest land that 

is under the greatest pressure for development and 

conversion to other uses.
14 |

 Forests, Water, and People

Analysis Methods

Figure 3: Nine layers of GIS data (boxes) were combined in stepwise fashion, to produce four indices (ovals) of 

watershed importance for drinking water supplies and the need for private forest management to protect those 

supplies.

Index: Development

pressure on private forests in

important watersheds

Index: Private forests

in important watersheds

Surface Water Consumers

Private Forests

Change in Housing Density

Forested

Land

Agricultural

Land

Riparian

Forest Cover

Soil

Erodibility

Road

Density

2000 Housing + + + + + Density

+

+

+

Ability to

Produce Clean

Water (APCW) by

30-m. pixels

Index: Mean APCW

for watersheds

Index: Important watersheds

for drinking water

Step 2: Add data on drinking water consumers.

Step 3: Add data on private forest land.

Step 4: Add data on change in housing density.

Step 1: Calculate ability to produce clean water.
Forests, Water, and People | 15

Results and Discussion

Results and Discussion

New York Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St.

Louis

Memphis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas

City

Providence

Hartford

Annapolis

Dover

Trenton

Harrisburg

Charleston

Columbus

Indianapolis

Springfield

Concord

Boston Albany

Montpelier

Augusta

Madison Lansing

St. Paul

Jefferson

City

Des

Moines

Washington

STEP 1 COMPOSITE SCORE, 30m VIEW

6 8 10 12 14 16 18 20 22 24

(Low APCW) (High APCW)

Map 5: Index of the 

Ability to Produce 

Clean Water, 

30-meter pixel 

view. The index 

of the ability to 

produce clean water

was developed 

by combining six 

layers of spatial 

data: forest and 

agricultural lands, 

riparian forest cover, 

soil erodibility, road 

density, and housing 

density. Areas with 

higher scores have 

greater ability to 

produce clean water.

Index of a Watershed’s 

Ability to Produce Clean 

Water (APCW) (Step 1)

Water quality is a function of 

biophysical conditions as well as the 

nature and intensity of land use in a 

watershed. Watersheds with a large 

proportion of forest land are more 

likely to be associated with good water 

quality. Forests provide the best land 

cover when it comes to protecting soil, 

moderating streamflow, supporting 

healthy aquatic systems, and sustaining 

good water quality. In the absence 

of mitigating actions, conversion 

of forest to other land uses leads to 

reduced water quality via a net increase 

in runoff, soil erosion, downstream 

flooding, and the flow of nutrients and 

other pollutants into rivers and streams 

(de la Cretaz and Barten 2007).

Land uses that tend to dramatically alter natural hydrologic 

and biological processes also have the greatest potential to 

negatively influence the flow and quality of water from these 

watersheds. For example, areas that contain a high percent 

of forested riparian buffers contribute positively to the ability 

to produce clean water, while higher amounts of cropland or 

development are expected to have a negative influence on 

watershed function and the ability to produce clean water. 

Each of the six GIS-based layers that were used to develop 

the index of APCW (percent forest land by watershed, 

percent agricultural land by watershed, percent riparian 

forest cover by watershed, road density, soil erodibility, and 

2000 housing density) were ranked from 1 (low APCW) to 4 

(very high APCW) according to scientifically accepted breaks 

or quartiles (Table 4). Map 5 displays the results of the spatial 

overlay of these six biophysical layers. Map 5 is textured with 

each 30-meter pixel shown by its composite score. 

Map 6 displays an average of these 30-meter pixel scores 

by eight-digit Hydrologic Unit Code (HUC) watershed, or 

subbasin, with scores normalized for watershed size on a 

relative scale of 1 to 10.
16 |

 Forests, Water, and People

Results and Discussion

New York Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St.

Louis

Memphis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas

City

Providence

Hartford

Annapolis

Dover

Trenton

Harrisburg

Charleston

Columbus

Indianapolis

Springfield

Concord

Boston Albany

Montpelier

Augusta

Madison Lansing

St. Paul

Jefferson

City

Des

Moines

Washington

STEP 1 COMPOSITE SCORE

1 5 10

(Low APCW) (High APCW)

Map 6: Index 

of the Ability to 

Produce Clean 

Water, watershed 

view. The index 

of the ability to 

produce clean 

water indicates 

the probability of 

finding surface 

waters of high 

quality in a 

watershed. Higher 

scores indicate 

higher probability.

Maps 5 and 6 highlight the differences across the 

Northeast and Midwest United States in terms of land-use 

characteristics. Watersheds in a darker shade of blue show 

where forest land is likely to have a positive influence on 

surface water supply. On the other hand, shades of gray 

indicate that intensive agriculture and imperviousness are 

likely to degrade water quality. Analysis at the eight-digit 

HUC scale does mask some of these influences. Within 

a single large watershed, water quality and land use 

may vary widely and be distributed in broadly disparate 

patterns. Averaging conditions across a large watershed 

area gives a general probability of finding good or poor 

conditions but not a true spatial representation of the 

precise on-the-ground conditions at any given point. 

Therefore, a high score in this index does not imply that 

no water quality problems exist in a given watershed, but 

rather that the probability of finding surface waters of 

high quality is greater than in a lower-ranked watershed. 

Areas that ranked highest for their ability to produce 

clean water are northern Minnesota and Wisconsin, 

Michigan’s Upper Peninsula, the Adirondack region of 

northern New York, central Pennsylvania, most of Maine, 

and northern New Hampshire. Other high scoring areas 

include upper Michigan, southern West Virginia, northcentral Pennsylvania near the Allegheny National Forest, 

eastern Vermont and western New Hampshire, western 

Massachusetts, and northeastern New York.

In contrast, forest and grassland ecosystems that have 

been converted to intensive agriculture ranked lowest 

in APCW. Across large areas of the Midwest, where 

groundwater systems are the primary supply for rural 

communities, the influence of agriculture on nutrient 

cycling, soil erosion, pesticide residues, and other 

contaminants dwarfs the influence of forests (which 

comprise a small proportion of the landscape) (de la 

Cretaz and Barten 2007).

Not all areas scoring low in the APCW have poor surface 

water quality; however, the likelihood of finding clean 

drinking water requiring less chemical treatment is 

greater in higher scoring areas. High density population 

centers, especially around St. Paul – Minneapolis, Chicago, 

Indianapolis, Cincinnati, and Columbus, ranked lowest 

due to their high level of development combined with 

low percent forest, high soil erodibility, and high percent 

agriculture.

The APCW index may have its greatest utility in 

characterizing the areas where a focus on protection 

or restoration or a combination of these strategies may 

be most appropriate. In addition, scoring of individual 

watersheds can be repeated at intervals to show trends 

in watershed condition in the broadest sense over time. 

Where finer scale watershed delineation and hydrography 

exist, these same data layers could be used to produce a 

more accurate local depiction of the index. 
Forests, Water, and People | 17

South Fork Sangamon River, Springfield, IL

Watershed Score for Step 1: 1 out of 10

Watershed Rank: 487 of 540

The 784-square-mile South Fork Sangamon River 

Watershed supplies over 22 million gallons per day 

(MGD) of water to over 150,000 people in the Springfield, 

IL, area. The local utility owns Lake Springfield, which 

serves as the primary storage and source of drinking 

water. Constructed in 1935, this 4,200-acre reservoir is 

the largest municipally owned lake in Illinois. Besides 

water supply, the lake is a major central Illinois recreation 

center, as well as the source of condenser cooling water 

for the utility’s lakeshore power plant complex. The 

lake, shoreline, and lake-area parks host some 600,000 

recreational visitors each year.

Land use in the watershed is primarily agricultural, with 

only 4 percent forest cover overall. Corn and soybeans 

are primary crops. Riparian buffers are also dominated 

by agricultural lands with less than 20 percent in forests.

This watershed ranked low in Step 1 of the analysis. This 

score means that in comparison to other watersheds in 

the study area, it has a lower ability to produce clean 

water because it has a high percent of agriculture and a 

low percent of forested land.

Water undergoes a rigorous purification and testing 

process, to ensure it is free of harmful bacteria and 

particulate matter. Untreated water quality is lower than 

average with average turbidity of 9.3 nepholometric 

turbidity units (NTUs). Turbidity is a water quality 

measure that reflects the level of fine suspended 

particles of clay, silt, organic and inorganic matter, 

plankton, and other microscopic organisms that are 

in the water. Water designated for drinking must have 

turbidities consistently below 1 NTU. The watershed may 

also have untreated water quality higher than typical 

agricultural watersheds, because the upper arms of Lake 

Springfield trap more than 50% of the sediment that 

enters the reservoir.

Generally, this water supply system has higher-thanaverage chemical treatment costs of $96.50/MGD on 

average. Plant operators noted that water quality has 

been stable or improving and that cooperation with 

agricultural partners was a reason for improvement. 

Farmers have reduced atrazine applications and planted 

600 acres of filter strips. Restoring lakeside prairie grass 

may also have contributed to improved water quality.

www.cwlp.com/lake_springfield/lake.htm

86%

6%

4%

2% 2%

0%

Agriculture

Urban

Forest

Non-Forest

Wetland

Landform features

Step 1 results show where the ability to produce clean water is 

greatest in the South Fork Sangamon River Watershed, a lowscoring watershed.

Land use in the low-scoring South Fork Sangamon River 

Watershed is primarily agricultural.

Case Study—Low-Scoring Watershed

Lake Springfied dam gates. Lake Springfield is the largest 

municipally owned lake in Illinois and is the primary storage 

reservoir for the South Fork Sangamon River Watershed. Photo by 

Ted Meckes, City, Water, Light and Power Co., Springfield, IL.
18 |

 Forests, Water, and People

Results and Discussion

Importance of Watersheds for 

Drinking Water Supply (Step 2) 

Map 7 combines the index of Ability to Produce Clean 

Water with the total number of drinking water consumers 

served by that watershed to highlight those areas that 

provide high quality water to the largest population. 

Watersheds scoring high on this map are important 

forested watersheds and highlight the location of leading 

municipal water providers, both public and private. This 

region-wide map displays the eight-digit HUC watershed 

scores on a relative scale of 2 to 20.

Map 7: Importance of watersheds providing drinking water supply, watershed view. Watersheds with the highest scores have the 

greatest ability to produce high quality water for the largest population.

New York Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St.

Louis

Memphis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas

City

Providence

Hartford

Annapolis

Dover

Trenton

Harrisburg

Charleston

Columbus

Indianapolis

Springfield

Concord

Boston Albany

Montpelier

Augusta

Madison Lansing

St. Paul

Jefferson

City

Des

Moines

Washington

STEP 2 COMPOSITE SCORE

2 5 10 15 19

(Low APCW and Small number 

of water consumers)

(High APCW and Large number 

of water consumers)
Forests, Water, and People | 19

Results and Discussion

Table 6: Top scoring watersheds for drinking water supply in the Northeast and Midwest, by composite score and 

Hydrologic Unit Code (HUC)

Composite score HUC Watershed States

19 01080204 Chicopee MA

19 01080206 Westfield CT MA

19 02040102 East Branch Delaware NY

19 02040104 Middle Delaware-Mongaup-Brodhead NJ NY PA

18 01070004 Nashua MA NH

18 01080207 Farmington CT MA

18 01090001 Charles MA

18 01090003 Blackstone MA RI

18 02020005 Schoharie NY

18 02020006 Middle Hudson CT MA NY

18 02030101 Lower Hudson CT NJ NY

18 02030103 Hackensack-Passaic NJ NY

18 02040101 Upper Delaware NY PA

18 02070002 North Branch Potomac MD PA WV

Areas scoring highest are likely to be forested watersheds 

near large population centers. Many of these watersheds 

with a high APCW are the same watersheds that serve 

drinking water consumers in the eastern United States. 

The top scoring watersheds include southeastern New 

York (the New York City watersheds), northeastern 

Pennsylvania (the Pocono Mountains), central and 

western Massachusetts (Quabbin Reservoir and Berkshire 

Mountains), northern Connecticut (Hartford), and the 

Highlands of New Jersey. Other high scoring areas reflect 

the large amount of forest cover in the Northeast, and 

include portions of New England—including coastal 

Maine, Massachusetts, Connecticut, and Rhode Island—

and large portions of Pennsylvania, western Maryland, 

and West Virginia. Relatively high scoring watersheds 

were also located in northern Minnesota (around St. Paul – 

Minneapolis) and eastern Missouri (west of St. Louis).

In general, States including and to the west of Ohio 

ranked lower than the New England and Mid-Atlantic 

States. The reasons for these results include (1) the 

smaller numbers of surface water consumers in States 

west of and including Ohio, since groundwater supplies 

are more common there; (2) the lower overall forest cover 

and higher percentage of agricultural lands in much of the 

region west of Ohio; and (3) the number of drinking water 

consumers is allocated among all upstream watersheds 

that rely on large intakes (such as the Upper Mississippi 

River or Lake Michigan) and systems where a large 

watershed contributes to the point of diversion.

Watersheds with a high score in Map 7 should be 

recognized as critically important to the health and 

welfare of a large percentage of the population in the 

Northeast and Midwest. These are the workhorses of 

water supply in the region.
20 |

 Forests, Water, and People

Map 8: Importance 

of watersheds and 

private forests 

for drinking 

water supplies, 

watershed view.

Watersheds that 

scored high in their 

ability to produce 

high quality water 

for the largest 

population also 

scored high in the 

amount of private 

forested land they 

encompassed.

New York Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St.

Louis

Memphis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas

City

Providence

Hartford

Annapolis

Dover

Trenton

Harrisburg

Charleston

Columbus

Indianapolis

Springfield

Concord

Boston Albany

Montpelier

Augusta

Madison Lansing

St. Paul

Jefferson

City

Des

Moines

Washington

STEP 3 COMPOSITE SCORE

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

National Forest System Land

(Low APCW; Small number 

of water consumers; and 

Low % private forest)

(High APCW; Large number 

of water consumers; and 

High % private forest)

Results and Discussion

Importance of Watersheds With 

Private Forests for Drinking Water 

Supply (Step 3)

It is a common misconception that all or most lands 

that supply public drinking water are publicly owned or 

otherwise protected. Some highly valued drinking water 

supplies do come from public or other lands that are 

protected from future development or land-use impacts. 

Other water supply system lands have limited protection 

zones, often surrounding reservoirs, lakes, or intakes, 

while the remainder of the watershed is vulnerable 

to land-use change. Many small watershed supplies, 

however, contain only private lands with little or no 

protective agreements or special land-use provisions. 

Map 8 combines the results of the Ability to Produce 

Clean Water, number of surface drinking water consumers 

served, and the percent private forest land, to illustrate 

the important role that private forest lands play in 

protecting water supplies. As described under Analysis 

Methods, each of the three data layers was ranked from 

1 to 4 in quartiles (very high = 4 points, high = 3 points, 

intermediate = 2 points, and low = 1 point), and then 

summed for each eight-digit HUC watershed, resulting in 

composite scores ranging from 3 to 30.

Map 8 shows that most of the watersheds that ranked 

highest for their ability to produce clean water for large 

numbers of water consumers are also characterized 

by a high percent of private forest land. In general, 

areas scoring highest (dark green) as private forested 

watersheds with surface drinking water supply areas 

are east of Ohio. The highest scoring watersheds in this 

part of the analysis were in southern Maine, eastern New 

Hampshire, central and western Massachusetts, western 

Connecticut, southeastern New York, northeastern 

Pennsylvania, western Maryland, and southern West 

Virginia. 

Map 8 illustrates those important water supplies where 

current protection relies primarily on the decisions 

made by hundreds or even thousands of private forest 

landowners. In other words, watersheds that score high 

on this map contain very little protected land and are 

highly dependent on the management of forests by 

private landowners in order to protect water quality.

Map 8 also illustrates the importance of the 1911 Weeks 

Act in establishing National Forests, by authorizing 

the Federal purchase of forest lands in and around the 

headwaters and watersheds of navigable streams. By 

1980, more than 12.1 million acres of land had been 
Forests, Water, and People | 21

Results and Discussion

added, through purchase, to the National Forest system 

within the study area boundary. Maps A-1, A-2, and A3 

show the National Forest System lands relative to the high 

scoring watersheds for drinking water. Although most of 

the forest land in the region is privately owned, passage 

of the Weeks Act helped to emphasize the importance of 

protecting lands near water supply watershed areas (Buie 

1979, p. 3).

Map 9: Importance 

of watersheds and 

private forests for 

drinking water 

supplies in the 

eastern portion 

of the study area, 

watershed view.

Watersheds that 

scored highest in 

terms of importance 

for drinking water 

and for private 

forests important 

for drinking water 

supply were in the 

eastern portion of 

the study area. See 

Table 7 for more 

information about 

each watershed.

Washington

Providence

Hartford

Annapolis

Dover

Trenton Harrisburg

Charleston

Columbus

Concord

Boston Albany

Montpelier

Augusta

New York

Newark

Philadelphia

Baltimore

Pittsburgh

Cleveland

Toledo

Cincinnati

Buffalo

28

18

27

17

16

26

6

25

15 5

1

4

14

24 23

13

12

22

11

10

3 2

21

20

9

19

8

7

STEP 3 COMPOSITE SCORE

3 5 10 15 20 25 29

National Forest System Land

(Low APCW; Small number 

of water consumers; and 

Low % private forest)

(High APCW; Large number 

of water consumers; and 

High % private forest)

Top scoring watersheds—Eastern portion of the 

study area

The study area was divided into eastern and western 

components for ease of discussion and to more easily 

view the geographic distribution of priority watersheds.

Most states in the eastern portion of the study area (Map 

9, Table 7) scored high because approximately 75 percent 

of the privately owned forested lands in the study area are 

found here. 
22 |

 Forests, Water, and People

Table 7: Top-scoring watersheds in the eastern portion of the study area in terms of private forests important for 

drinking water supply, by label in Map 9 and composite score.

Results and Discussion

Label in map 9 Composite score Watershed HUC State

1 29 East Branch Delaware 02040102 NY

2 28 Chicopee 01080204 MA

3 28 Westfield 01080206 CT MA

4 28 Upper Delaware 02040101 NY PA

5 28 Middle Delaware-Mongaup-Brodhead 02040104 NY PA NJ

6 28 North Branch Potomac 02070002 MD PA WV

7 27 Presumpscot 01060001 ME

8 27 Piscataqua-Salmon Falls 01060003 ME NH MA

9 27 Nashua 01070004 MA NH

10 27 Farmington 01080207 CT MA

11 27 Blackstone 01090003 MA RI

12 27 Schoharie 02020005 NY

13 27 Middle Hudson 02020006 CT MA NY

14 27 Lower Hudson 02030101 CT NY NJ

15 27 Lackawaxen 02040103 PA

16 27 Lower New 05050004 WV

17 27 Lower Kanawha 05050008 WV

18 27 Big Sandy 05070204 KY WV

19 26 Merrimack 01070002 MA NH

20 26 Middle Connecticut 01080201 MA NH VT

21 26 Miller 01080202 MA NH

22 26 Housatonic 01100005 CT MA NY

23 26 Rondout 02020007 NJ NY

24 26 Hudson-Wappinger 02020008 NY

25 26 South Branch Potomac 02070001 MD VA WV

26 26 Cacapon-Town 02070003 MD PA VA WV

27 26 Lower Guyandotte 05070102 WV

28 26 Little Scioto-Tygarts 05090103 KY OH WV

Top scoring watersheds—Western portion of the 

study area

Overall, watersheds in the western portion of the study 

area (Map 10, Table 8) scored lower than watersheds in the 

eastern portion. This result is not unexpected. 

The highest scores were in northern Minnesota, western 

Missouri, and Michigan’s Upper Peninsula. Much like 

the results of Step 2, these results are likely due to the 

fact that this part of the country is less forested overall 

and relies much less on small surface water supplies for 

drinking water sources. In the western half of the region, 

the areas that scored highest are aligned more closely 

with the watersheds in and around National Forest 

boundaries. For more information on National Forest 

System lands important for drinking water, see Appendix 

A. The protection of water quality is a high priority for 

management of these public lands; results of the analysis 

show that high priority watersheds are near public lands.

Soils, geology, geography, and land use have led to 

a greater dependence on large surface water supply 

systems such as the Great Lakes, or rivers such as the 

Mississippi or Ohio. These large lake or run-of-the-river 
Forests, Water, and People | 23

Results and Discussion

Map 10: Importance of watersheds and private forests for drinking water supplies in the western portion 

of the study area, watershed view. High scoring watersheds in terms of importance of watersheds and private 

forests for drinking water supply in the western portion of the study area did not score as high as watersheds in 

the eastern portion (Map 9). See Table 8 for more information about each watershed.

Charleston

Columbus

Indianapolis Springfield

Lansing

Madison

St. Paul

Jefferson City

Des Moines

Pittsburgh

Cleveland

Toledo

Cincinnati

Louisville

Chicago

St. Louis

Memphis

Detroit

Milwaukee

Minneapolis

Omaha

Kansas City

13

27

8

26

25

24

23

12 1

22

21

20

11 3

10

2 9

7

19

18

17

6 16

5

4

15

14

STEP 3 COMPOSITE SCORE

3 5 10 15 20 25 29

National Forest System Land

(Low APCW; Small number 

of water consumers; and 

Low % private forest)

(High APCW; Large number 

of water consumers; and 

High % private forest)

systems mean that the analysis spreads water demand 

across a large landscape, reducing the watershed scores. 

In addition, states west of Ohio depend to a much greater 

degree on groundwater sources for drinking water, both 

as a factor of geology and because ambient water quality 

problems in many tributaries are brought on by intensive 

agriculture as a dominant land use. Since this study 

focuses on surface water supply systems, watersheds that 

depend on groundwater scored lower and are inherently 

more difficult to link directly to the influence of forest 

cover.
24 |

 Forests, Water, and People

Results and Discussion

Table 8: Top-scoring watersheds in the western portion of the study area, in terms of private forests important for 

drinking water supply, by label in map 10 and composite score.

Label in map 10 Composite score Watershed HUC State

1 23 Meramec 07140102 MO

2 22 Prairie-Willow 07010103 MN

3 22 Pine 07010105 MN

4 21 Keweenaw Peninsula 04020103 MI

5 21 Dead-Kelsey 04020105 MI

6 21 Michigamme 04030107 MI

7 21 Mississippi Headwaters 07010101 MN

8 21 Lower Missouri 10300200 MO

9 20 Leach Lake 07010102 MN

10 20 Elk-Nokasippi 07010104 MN

11 20 Crow Wing 07010106 MN

12 20 Big 07140104 MO

13 20 Spring 11010010 AR MO

14 19 St. Louis 04010201 MN WI

15 19 Black-Presque Isle 04020101 MI WI

16 19 Brule 04030106 MI WI

17 19 Cedar-Ford 04030109 MI

18 19 Tacoosh-Whitefish 04030111 MI

19 19 Thunder Bay 04070006 MI

20 19 Rum 07010207 MN

21 19 Upper Wisconsin 07070001 MI WI

22 19 Cahokia-Joachim 07140101 IL MO

23 19 Upper St. Francis 08020202 MO

24 19 Vermilion 09030002 MN

25 19 Upper Rainy 09030004 MN

26 19 Big Piney 10290202 MO

27 19 North Fork White 11010006 AR MO
Forests, Water, and People | 25

James River, Springfield, MO

Watershed Score for Step 3: 12 out of 30

Watershed Rank: 330 of 540

The nearly 1,400-square-mile James River watershed 

in southwestern Missouri is on the Ozark plateau and 

contains the city of Springfield, MO. Land use is roughly 

split between forest (31%), agricultural cover (grazing 

land, 37%), and other land uses. Riparian buffers are 

about 50 percent forested with over one-third containing 

agricultural land. The watershed is characterized by 

grazing lands and residential development dominated 

by large lots of 5 – 10 acres. 

Approximately 80 percent of Springfield’s drinking water 

comes from surface waters (lakes, rivers) and the rest 

from ground water (wells, springs). The Blackman Water 

Treatment Plant in the southeastern corner of the city 

receives water from Fellows Lake, Stockton Lake, and the 

James River. Water from a tributary of the James River 

fills Stockton Lake—a primary reservoir for the water 

supply system. Water from this lake is then pumped 

uphill about 460 feet into Fellows Lake. Lake water 

makes up two-thirds or more of the plant’s intake water. 

At times, the plant also draws up to a third of its supply 

directly from the James River. 

This watershed scored in the moderate importance 

range in the analysis. This means that in comparison to 

other watersheds in the study area, it has a good ability 

to produce clean surface water because one-third of its 

land base is forested. Many people rely on the surface 

drinking water supplies, and the water supplies are 

located near forest lands that are privately owned and 

subject to a medium level of development pressure.

The Blackman Water Treatment Plant provides about 

18 million gallons per day (MGD) of treated water to its 

customers with per unit chemical treatment costs of 

$62.20/MGD. Although water supply reservoirs have 

improved dramatically since the 1930s, plant operators 

have reported a decline in untreated water quality in 

recent years attributed to eutrophication from increased 

turbidity in the James River, sedimentation of reservoirs, 

and increased development of the source watershed. 

Failing septic systems are also seen as one of the top 

issues in the watershed.

County planning and zoning laws have been improved 

to address better watershed protection. The Watershed 

Committee of the Ozarks, a nonprofit that has been 

partnering with local stakeholders, is working to protect 

some property in the source watershed and to educate 

the public.

www.watershedcommittee.org/

Case Study—Moderate-Scoring Watershed

Step 3 results show where the role of private forests in protecting 

water supplies is greatest in the James River Watershed, a 

moderate-scoring watershed.

Agriculture

Urban

Forest

Non-Forest

Wetland

Landform features

0%

0%

37%

31%

4%

28%

Land use is divided roughly among grazing, forest, and other 

land uses in the James River Watershed, a moderate-scoring 

watershed.

Springfield, MO, receives most of its water supply from areas 

outside the James River basin, but some of its water comes from 

James River intakes. Photo by Dave Ballou, City of Springfield, MO.
26 |

 Forests, Water, and People

Results and Discussion

Development Pressure on Private 

Forests in Watersheds Important 

for Drinking Water Supply (Step 4)

The fact that watersheds are protected predominantly 

by private forest lands means that those watersheds are 

vulnerable to land-use change if they fall within areas 

of projected future growth. According to the EPA, more 

than 60 percent of U.S. water pollution comes from 

runoff from lawns, farms, cities, and highways, as well as 

leachate from septic systems (U.S. EPA 2007). The loss of 

forest lands to development affects not only the quality of 

drinking water, and therefore the cost of treating it, but the 

quantity as well. While it increases demand and water use, 

development also reduces the ability of water to infiltrate 

and recharge water supplies, and reduces supply as well.

In this analysis, housing density data, derived from 

U.S. Census (2000) block data, served as an indicator of 

development pressure. Projections of housing density 

change from 2000 to 2030 (Theobold 2005) that were 

developed as part of the Forests on the Edge project (Stein 

and others 2005) were combined with private land to 

illustrate those unprotected forest areas where housing 

density is likely to increase. Areas where housing density 

increased were extracted and reclassified as “development 

pressure.” The acreage subject to development pressure 

was then calculated for each watershed and divided by the 

acreage of the watershed. This “development pressure per 

unit area” was then used to assign a value from low to high.

Map 11: 

Development 

pressure on forests 

and drinking 

water supplies, 

watershed view.

Watersheds with the 

highest scores and 

the highest risk of 

future development 

are near major cities 

and metropolitan 

areas.

Map 11 combines the results of the index of Ability to 

Produce Clean Water, surface drinking water consumers 

served, percent private forest land, and housing 

conversion pressure to highlight environmentally 

important water supply protection areas that are at the 

highest risk for future development. Areas that ranked 

high are near the major cities in the Northeastern United 

States. Many local water supplies were established “just 

outside of town,” and now development is encroaching 

upon them. In general the highest ranking watersheds in 

the western half of the study area fell well below those 

in the eastern half, with the highest ranked watersheds 

in northern Minnesota, northern Wisconsin, northern 

Michigan and the Upper Peninsula, and southern and 

eastern Missouri. The high scoring watersheds in the 

western half are near major cities or metropolitan areas, 

including Minneapolis – St. Paul, Lansing, Jefferson City, 

and St. Louis.

In general, areas scoring highest for risk of future 

development pressure ran along the eastern seaboard, 

from eastern Pennsylvania to southern Maine. Watersheds 

that scored highest are in southern Maine, eastern New 

Hampshire, central Massachusetts, and northeastern 

Pennsylvania. High-scoring watersheds were also found 

in southern Ohio, western West Virginia, northern New 

Jersey, southeastern New York, Rhode Island, central 

Massachusetts, and northern Vermont.
Forests, Water, and People | 27

Merrimack River, Manchester, NH

Watershed Score for Step 4: 36 out of 40

Watershed Rank: 4 of 540

The 43-square-mile Merrimack River watershed 

provides the drinking water supply for a number of 

small communities as well as the residents of the city 

of Manchester, NH. The watershed is primarily forested 

(about 70%) and typical of southern New England 

forest, with pine, hemlock, and northern hardwoods as 

dominant vegetation. Most streams are well buffered by 

forest and wetland.

The Manchester Water Works, the State of New 

Hampshire’s largest water utility, manages the Merrimack 

River intake and is responsible for providing drinking 

water to the City of Manchester and portions of Auburn, 

Bedford, Derry, Goffstown, Hooksett, and Londonderry. 

Located adjacent to Lake Massabesic, the Manchester 

Water Treatment Plant treats the water before it is 

distributed to homes and industries. The plant presently 

delivers in excess of 17.75 million gallons per day (MGD) 

to approximately 159,000 consumers in the greater 

Manchester area. 

This watershed was one of the highest scoring 

watersheds in steps 3 and 4 of the analysis. Compared 

with other watersheds in the study area, this watershed 

had a very high ability to produce clean water because 

it is has such a high percent of forest; and the forests 

around the drinking water supplies are mainly privately 

owned and at great risk of development pressure.

Watershed management in the source water watershed 

includes an active forestry program. Under the direction 

of a professional forester, the Manchester Water Works 

annually harvests about 500,000 board feet of timber. 

The purpose of this program is to develop the best tree 

cover for the forest environment and promote controlled 

water retention and runoff.

The Manchester area is growing substantially. The 

Manchester Water Treatment Plant purchased 8,000 

acres, or about 3 percent of the watershed, to protect 

source water quality; however, the remainder of 

watershed forests remain privately owned. Although 

better-than-average water would be expected, the 

Manchester water plant has reported declining water 

quality. In recent years, the per unit water treatment cost 

increased from $53.26/MGD to $82.50/MGD.

www.manchesternh.gov/website/Departments/

WaterWorks/WaterSupply/tabid/419/Default.aspx 

Step 4 results show where development pressure on important 

private forest is greatest in the Merrimack River watershed, a 

high-scoring watershed.

At 110 miles in length, the Merrimack River is an important 

regional focus in New Hampshire and Massachusetts. It flows 

through Manchester, the largest city in New Hampshire, and 

provides drinking water to the city of Nashua and surrounding 

towns. Photo by William Frament, U.S. Forest Service.

Land use is primarily forest in the Merrimack River Watershed, a 

high-scoring watershed. 

Agriculture

Urban

Forest

Non-Forest

Wetland

Landform features

3%

5%

8%

0% 13%

71%

Case Study—High-Scoring Watershed
28 |

 Forests, Water, and People

Results and Discussion

Top ranking watersheds—Eastern portion of the 

study area

The study area was divided into an eastern and western 

component for ease of discussion and to more easily view 

the geographic distribution of priority watersheds.

In the Northeast, many city water supplies were 

established “just outside of town,” and new development 

is encroaching upon them. A recent study in New 

Hampshire showed that lands contributing to water 

supply made up approximately 10 percent of the State, 

while 75 percent of the population and most major 

communities relied on these lands for drinking water. 

The study also found, however, that these lands were 

Map 12: 

Development 

pressure on 

private forests and 

drinking water 

supplies in the 

eastern portion 

of the study area, 

watershed view.

In the eastern 

portion of the 

study area, forested 

water supply 

watersheds subject 

to the greatest 

development 

pressure are along 

the Interstate 

Highway 95 corridor. 

See Table 9 for more 

information about 

each watershed

four times more likely to be developed than other forest 

land in the State as a whole, and only 12 percent of these 

critical areas were permanently protected (Society for the 

Protection of New Hampshire Forests 1998).

Not surprisingly, those watersheds containing substantial 

existing forest lands and rapidly expanding towns and 

cities received the highest ranking. For example, the 

Presumpscot watershed includes the rapidly growing 

city of Portland, ME, while the Nashua and Merrimack 

watersheds are rapidly developing for commuters to 

Boston, MA. It is in the eastern portion of the study area 

along the Interstate Highway 95 corridor, where drinking 

water watersheds are subject to the greatest pressure.
Forests, Water, and People | 29

Results and Discussion

Table 9: Watersheds in the eastern portion of the study area with the greatest development pressure on private forests 

important for drinking water supply, by label in map 12 and composite score.

Label in map 12 Composite score Watershed HUC State

1 37 Presumpscot 01060001 ME

2 37 Piscataqua-Salmon Falls 01060003 ME NH MA

3 37 Nashua 01070004 MA NH

4 36 Merrimack 01070002 MA NH

5 36 Blackstone 01090003 MA RI

6 36 Lackawaxen 02040103 PA

7 36 Middle Delaware-Mongaup-Brodhead 02040104 NJ NY PA

8 35 Pawcatuck-Wood 01090005 RI CT

9 35 Lower Hudson 02030101 CT NJ NY

10 34 St. George-Sheepscot 01050003 ME

11 34 Concord 01070005 MA

12 34 Chicopee 01080204 MA

13 34 Narragansett 01090004 MA RI

14 34 Winooski 02010003 VT NY

15 34 Middle Hudson 02020006 CT MA NY

16 34 Rondout 02020007 NJ NY

17 34 Lower Kanawha 05050008 WV

18 34 Little Scioto-Tygarts 05090103 KY OH WV

19 33 Middle Connecticut 01080201 MA NH VT

20 33 Miller 01080202 MA NH

21 33 Farmington 01080207 CT MA

22 33 Quinebaug 01100001 CT MA RI

23 33 Shetucket 01100002 CT MA

24 33 Quinnipiac 01100004 CT

25 33 Housatonic 01100005 CT MA NY

26 33 Hudson-Wappinger 02020008 NY

27 33 Middle Delaware-Musconetcong 02040105 NJ PA

28 33 Lehigh 02040106 PA

29 33 Mullica-Toms 02040301 NJ

30 33 North Branch Potomac 02070002 MD PA WV

31 33 Lower Potomac 02070011 MD VA

32 33 Upper Monongahela 05020003 PA WV

33 33 Big Sandy 05070204 KY WV

Top ranking watersheds—Western portion of the 

study area

Like the Step 3 analysis results, watersheds in the 

western portion of the study area rank lower overall than 

watersheds in the eastern portion, and for many of the 

same reasons. However, another factor in these lower 

rankings is the presence of less private forest land and less 

potential for development and impact on watersheds.

Conversely, these same conditions make it easier to 

identify and target those areas that are high priority 

in the western part of the study area. Three distinct 

regions stand out: the watersheds in Missouri between 

the growth centers of St. Louis and Jefferson City, the 

growing retirement and recreation-based communities 

along the upper portion of Lake Michigan and the 

suburbs of Detroit, and the “white collar” communities 

north of Minneapolis – St. Paul. All three regions provide 

challenges for future protection of water supplies. 

Although not ranked in the top 20, northeastern 

Wisconsin stands out clearly as a regional priority as well.
30 |

 Forests, Water, and People

Results and Discussion

Map 13: Development pressure on private forests and drinking water supplies in the western portion of 

the study area, watershed view. Less private forest land in the western portion of the study area means there is 

less development pressure on drinking water supply watersheds than in the eastern portion. See Table 10 for more 

information about individual watersheds.
Forests, Water, and People | 31

Results and Discussion

Table 10: Watersheds in the western portion of the study area with the greatest development pressure on private 

forests important for drinking water supply, by label in map 13 and composite score.

Label in map 13 Composite score Watershed HUC State

1 29 Meramec 07140102 MO

2 29 Lower Missouri 10300200 MO

3 28 Pine 04080202 MI

4 28 Huron 04090005 MI

5 28 Pine 07010105 MN

6 28 Rum 07010207 MN

7 27 Keweenaw Peninsula 04020103 MI

8 27 Cheboygan 04070004 MI

9 27 Thunder Bay 04070006 MI

10 27 Big 07140104 MO

11 26 Pere Marquette-White 04060101 MI

12 26 Manistee 04060103 MI

13 26 Betsie-Platte 04060104 MI

14 26 Boardman-Charlevoix 04060105 MI

15 26 Flint 04080204 MI

16 26 Mississippi Headwaters 07010101 MN

17 26 Prairie-Willow 07010103 MN

18 26 Clearwater-Elk 07010203 MN

19 26 Cahokia-Joachim 07140101 IL MO

20 26 North Fork White 11010006 AR MO
32 |

 Forests, Water, and People

Summary of Results

Water, in all its uses and permutations, is by far the most valuable commodity that comes from the 

forest land that we manage, assist others to manage, and/or regulate.

–National Association of State Foresters Policy Statement, 2004

SUMMARY OF RESULTS

The results of this analysis confirm 

that forests are critically important 

to the supply of clean drinking 

water in the Northeast and Midwest. 

Forests protect the reservoirs and 

water supplies for more than 52 

million people in over 1,600 drinking 

water supplies (U.S. Department of 

Agriculture, Forest Service 2005). 

The results provide a foundation on 

which protection and management 

strategies for water supply systems 

can be built.

Specifically, the results describe 

the magnitude and scope of forestdependent drinking water supplies 

and quantify the dependence of 

the population on forests in these 

watersheds. The maps identify largescale watersheds where strategic 

action and partnerships are likely 

needed to reduce the threat of land 

use change and to increase forest 

protection. The maps also show areas 

where forest management strategies 

aimed specifically at maintaining 

or enhancing the quality, quantity, 

and timing of water flow may be 

beneficial.

Of the 540 eight-digit watersheds in 

the Northeast and Midwest, 329 of 

them are surface water watersheds. 

Just 78 of these watersheds supply 

the drinking water for nearly 38 

million people. The forests in these 

drinking water supply watersheds are 

overwhelmingly in private ownership 

and are being converted to other uses at an estimated rate 

of 350 acres per day. This rate of loss could increase to as 

much as 900 acres per day by 2030. Growth projections 

suggest that as many as 12 million acres of private forest 

land in these States may be converted to other uses by 

2030.

11%

1%

1%

5%

82%

Private

State

NFS

Other Federal

Other

Figure 4: Forest land ownerships in source water watersheds in the 

Northeast and Midwest.
Forests, Water, and People | 33

Summary of Results

Table 11. Forest land ownership in the Northeast and Midwest, by number of surface water consumers.

Type and number of surface water 

consumers in the watershed

Type of land ownership (percent)

Private State National Forest 

System Other Federal Other*

No public surface water consumers 70 16 9 1 4

Small water supply systems 

(25 – 100,000 consumers)

80 12 6 1 1

Medium-sized water supply systems 

(100,001 – 1,000,000 consumers)

87 9 2 1 1

Large water supply systems 

(>1,000,000 consumers)

87 11 0 1 2

* Other ownerships include tribal, local, county, private-protected, joint, and unknown.

This analysis also shows significant differences between 

watersheds that supply drinking water. Water supply 

systems in the eastern half of the study area are more 

likely to be dominated by a dependence on surface water 

of high existing quality, using limited chemical treatment, 

and located in smaller forested watersheds—often with 

more numerous intakes. Water supply systems in the 

western half of the study area are more likely to use 

groundwater or be of lower existing water quality due to 

intense agricultural land use. These water supplies are 

also much more likely to draw from large rivers or lakes 

and rely upon extensive treatment to meet drinking 

water standards.

For every water supply system, 

there is someone who oversees 

it and a managing or governing 

entity who makes the decisions 

that affect its operation and its 

future. Throughout the study 

area, there is great diversity in the 

amount of oversight and available 

expertise. Many water supply 

systems are very small—serving 

only 500–3,500 people with few or 

no dedicated technical staff. Larger 

systems serving 50,000 or more 

may have engineers, foresters, 

consultants, and work crews on 

staff. In each case, however, the 

protection and management of 

forests play a role in the central 

mission—to provide reliable safe 

drinking water. 

This analysis highlights the need to address a number 

of issues that water suppliers face related to protection 

and management of water supply systems. The issues 

include—

•	 Conservation of forest land

•	 Sustainable management of forests

•	 Understanding the forest-to-faucet connection by

consumers and decisionmakers

•	 Appreciation of the actual cost of clean drinking water

•	 Communication between water providers and water

consumers

•	 Availability of forest information and data to water

providers

Figure 5: Forest shelters this high-quality 

stream in Maryland. Photo by Al Todd.
34 |

 Forests, Water, and People

A Watershed Protection 

Forest Is ...

Based on a centuries-old 

concept in European and North 

American forestry

A living filter that protects 

aquatic ecosystems, drinking 

water supplies, and human 

health

Comprised of layers (overstory, 

midstory, and regeneration) with 

diverse species and ages

Growing vigorously and 

assimilating nutrients and 

sequestering carbon

Critical in protecting areas, such 

as riparian zones and steep 

slopes

Deliberately patterned across 

the landscape to be resistant 

to and resilient after natural 

disturbances (wildfire, storms, 

insects, and diseases)

Monitored to inform adaptive 

management

CLOSING COMMENTS

The forest is connected to the faucet: the cleanest water flows from healthy forested watersheds 

(Dissmeyer 2000). A watershed protection forest provides services like filtering air and water, 

reducing floods and erosion, sustaining stream flows and aquatic species, ensuring watershed 

stability and resilience, and absorbing rain and refilling groundwater aquifers. Maintaining these 

watershed services is essential.

Aside from the economic value of forest products like 

wood and paper, if forests fall into poor health or are 

converted to other uses, society has to invest billions 

in technological alternatives to replace the natural 

ecosystem services that the forests provided essentially 

for free. 

The degradation of water supplies and widespread 

flooding and erosion, in large degree, inspired the 

creation of the Forest Service a century ago, along 

with the birth of the conservation era. When President 

Theodore Roosevelt and Gifford Pinchot, the first Chief of 

the Forest Service, set up a system of National Forests, it 

was primarily for “securing favorable conditions of water 

flows.” Pinchot and Federal policymakers of the time 

were most concerned about preserving the forests that 

sustained the function of watersheds. In his Primer on 

Forestry, Pinchot (1903) wrote, 

A forest, large or small, may render its service in 

many ways. It may reach its highest usefulness 

by standing as a safeguard against floods, winds, 

snow slides, or especially against the need of 

water in the streams. 

Abundant, clean drinking water is a precious resource 

for which there is no substitute. People can look for 

alternatives sources of energy, or change their diets to 

adjust to new sources of food. Without enough water, 

however, people must reduce their water use, find 

more water, or move. The United States has enjoyed 

an abundance of clean water, accessible to all of its 

citizens; however, drinking water scarcity is a growing 

concern. With projections of increasing U.S. population, 

competition for water is expected to grow. Water 

shortages, worsened by increasing demand, are becoming 

Closing Comments
Forests, Water, and People | 35

Closing Comments

commonplace even in the humid eastern states. Climate 

change and its potential effects on water quantity, quality, 

and timing add a serious and complicated challenge to 

already perplexing water issues.

For natural resource agencies, a renewed focus on forests 

and their connection to clean and abundant water will be 

critical. The Forest Service—in partnership with State and 

local governments, nonprofits, and private landowners—

has a shared responsibility to care not just for the land but 

for the nation’s liquid assets as well.

Forested watersheds in the study area provide clean water 

that fills rivers, streams, lakes, and wetlands, sustains 

fisheries, and flows from faucets of homes and businesses. 

Water may be the most valuable product produced by 

public and private forest lands.

For more information on watershed forestry, including 

projects and tools linking forestry and clean drinking 

water, go to these Web sites:

•	 Northeastern Area State and Private Forestry’s 

watershed Web site: www.na.fs.fed.us/watershed/

•	 Forest-to-Faucet Partnership’s Web site: 

www.wetpartnership.org/
36 |

 Forests, Water, and People

Appendix A

APPENDIX A: National Forest Lands 

Important for Drinking Water Supply

Contents

Tables

Table A-1: Forest land ownerships in the 65 source water watersheds containing National Forest

System (NFS) land in the Northeast and Midwest, by number of surface water consumers..... ..........38

Table A-2: National Forests and the top 50 percent of watersheds important for drinking water

supply in the eastern portion of the study area, by label in map A-2 and composite score ................39

Table A-3: National Forests and the top 50 percent of watersheds important for drinking water

supply in the western portion of the study area, by label in map A-3 and composite score...............41

Figure

Figure A-1: Overall percentage of all forest land ownerships in the 65 source water watersheds

that contain National Forest System (NFS) lands in the Northeast and Midwest.....................................38

Maps

Map A-1: National Forests and important watersheds. National Forest System lands are near

water supply systems that serve large numbers of consumers in the Northeast and Midwest..........37

Map A-2: National Forests and watersheds important for drinking water supply, eastern 

portion of the study area, watershed view. In the eastern portion of the study area, watersheds 

in National Forests scored moderate to high in their importance for drinking water supply. See

Table A-2 for more information about individual watersheds. ......................................................................39

Map A-3: National Forests and watersheds important for drinking water supply, western 

portion of the study area, watershed view. In the western portion of the study area, all 

watersheds that scored high in importance for drinking water supply contain some National Forest

lands. See Table A-3 for more information about individual watersheds. ..................................................40 
Forests, Water, and People | 37

The 1897 Organic Administrative Act, 

which authorized the establishment 

of much of the National Forest 

System, said that the forest reserves 

were to protect and enhance water 

supplies, reduce flooding, secure 

favorable conditions of water flow, 

protect the forest from fires and 

other depredations, and provide 

a continuous supply of timber. By 

1915, National Forests in the West 

had been established in much the 

Map A-1: National Forests and important watersheds. National Forest System lands are near water supply 

systems that serve large numbers of consumers in the Northeast and Midwest.

Providence

Hartford

Annapolis

Dover

Trenton

Harrisburg

Charleston

Columbus

Indianapolis

Springfield

Concord

Boston Albany

Montpelier

Augusta

Madison Lansing

St. Paul

Jefferson

City

Des

Moines

Washington

New York Newark

Philadelphia

Baltimore

Pittsburgh

Toledo Cleveland

Cincinnati

Louisville

Chicago

St.

Louis

Buffalo

Detroit

Milwaukee

Minneapolis

Omaha

Kansas

City

Legend

National Forest System Land

Surface Water Consumers by Watershed

No Public Surface Water Consumers

Small Water Supplies (25-100,000 Consumers)

Medium Water Supplies (100,000-1,000,000 Consumers)

Large Water Supplies (> 1,000,000 Consumers)

form they retain today. At the time, few Federal forests 

were designated in the East because of the lack of public 

domain. Demand for eastern National Forests resulted 

in the passage of the 1911 Weeks Act, authorizing the 

acquisition of Federal lands to protect the watersheds 

of navigable streams. From 1911 to 1945, about 24 

million acres of depleted farms, stumpfields, and burned 

woodlands were incorporated into the National Forest 

System. Map A-1 shows the National Forest System lands 

in the Northeast and Midwest in relation to source water 

watersheds and water consumers served.

Appendix A
38 |

 Forests, Water, and People

Maintaining supplies of clean drinking 

water and protecting watersheds 

from degradation are major reasons 

for management of the National 

Forests. Another notable issue 

regarding management is whether 

municipal watersheds should be 

placed under active or passive 

management in order to sustain 

supplies of high quality water. While 

natural resource professionals agree 

that active management can be 

compatible with or even desirable 

in sustaining water supplies, many 

people also believe that, in the 

interest of water quality, forests in 

watersheds should not be altered in 

any way. 

Another issue is development and 

loss of open space. Although the 

vast majority of National Forest 

lands are unlikely to be converted to 

any form of developed uses, these 

scenic protected lands also attract 

development to their borders. In the 

Northeast and Midwest, for example, 

as shown in Figure A-1, the majority 

of forest land ownerships in the 65 

source water watersheds that contain 

National Forest System lands are 

privately owned and subject to land 

use conversion. Moreover, the vast 

majority of drinking water consumers 

are supplied by privately owned 

lands, in comparison to the small 

percentage supplied by State and 

Federal lands (Table A-1). However, 

development adjacent to National 

Forest boundaries is still a serious 

concern in many parts of the country, 

including the northeast. These more 

intensive land uses on the fringe of 

public lands increase the risks for 

wildlife, contribute to the spread of 

invasive plants and pests, reduce 

access to recreation, fragment habitat, 

and impact water quality.

Figure A-1: Overall percentage of all forest land ownerships in the 65 source water 

watersheds that contain National Forest System (NFS) lands in the Northeast and 

Midwest.

11%

1%

1%

15%

72%

Private

State

NFS

Other Federal

Other

Table A-1. Forest land ownerships in the 65 source water watersheds 

containing National Forest System (NFS) land in the Northeast and Midwest, by 

number of surface water consumers (percent)

Number of surface 

water consumers in the 

watershed

Private State NFS Other 

Federal

Other*

Small water supplies 

(25 –100,000 consumers)

84% 8% 7% 0% 1%

Medium-sized water 

supplies (100,000 – 

1,000,000 consumers)

70% 12% 17% 1% 1%

Large water supplies 

(>1,000,000 consumers)

There are no large water supply systems in watersheds 

that contain National Forest land.

*

Other ownerships include tribal, local, county, private-protected, joint, and unknown.

Maps A-2 and A-3 show that the study area was split into eastern and western 

halves, displaying the National Forest boundaries overlaid on Step 2 analysis 

maps. In other words, these maps illustrate the relationship of National Forest 

lands and the relative importance of water supplies in terms of the ability to 

produce clean water for the greatest number of water consumers.

Overall, in the eastern portion of the study area, National Forests fall within 

the moderate- to high-scoring watersheds (Map A-2). In the western portion 

of the study area, the Chippewa and Mark Twain National Forests coincide 

with the highest scoring watersheds, and in general, all of the highest scoring 

watershed areas contain some National Forest lands (Map A-3).

These maps help to highlight areas where National Forests are important in 

surface drinking water supplies and areas where this relationship is reinforced 

by currently unprotected forested areas. These maps also highlight areas 

where the collaborative management of public and private lands may benefit 

water consumers.

Appendix A
Forests, Water, and People | 39

Appendix A

Map A-2: National Forests and 

watersheds important for drinking 

water supply, eastern portion of 

the study area, watershed view. In 

the eastern portion of the study area, 

watersheds in National Forests scored 

moderate to high in their importance for 

drinking water supply. See Table A-2 for 

information about individual watersheds.

Green

Mountain NF

Finger

Lakes NF

White 

Mountain NF

Wayne NF Monongahela NF

Jefferson NF

HuronManistee NF

George 

Washington NF

Allegheny NF

7

16

20

15

14

13

19

18

6

4 1

3

12

5

11

10

17

9 2

8

STEP 2 COMPOSITE SCORE

2 5 10 15 19

National Forest System Land

(Low APCW and Small number 

of water consumers)

(High APCW and Large number 

of water consumers)

Table A-2: National Forests and the top 50 percent of watersheds important for drinking water supply in the eastern 

portion of the study area, by label in map A-2 and composite score. 

Label 

in 

Map 

A-3

Step 2 

Composite 

Score

Hydrologic 

Unit Code

Watershed Land 

Acreage

National 

Forest 

Acreage

Percent of 

watershed 

in National 

Forest

NFS State(s)

1 18 02070002 North Branch Potomac 853,706 3 <1.0 Monongahela MD PA WV

2 17 01060001 Presumpscot 635,384 8,578 1.4 White Mountain ME

3 17 02070001 South Branch Potomac 946,664 152,164 16.1 George Washington/

Monongahela

MD VA WV

4 17 02070003 Cacapon-Town 766,584 51,778 6.8 George Washington MD PA VA WV

5 16 02010003 Winooski 737,226 12,783 1.7 Green Mountain VT NY

6 16 02070006 North Fork Shenandoah 655,235 3,068 <1.0 George Washington VA WV

7 16 05090103 Little Scioto-Tygarts 637,369 54,370 8.5 Daniel Boone/Wayne KY OH WV

8 15 01040002 Lower Androscoggin 1,264,856 60,660 4.8 White Mountain ME NH

9 15 01060002 Saco 1,055,962 244,824 23.2 White Mountain ME NH

10 15 01080104 Upper Connecticut-Mascoma 921,973 13,472 1.5 White Mountain NH VT

11 15 01080203 Deerfield 416,335 67,705 16.3 Green Mountain MA VT

12 15 02020003 Hudson-Hoosic 1,190,337 78,768 6.6 Green Mountain MA NY VT

13 15 05020001 Tygart Valley 874,687 28,475 3.3 Monongahela WV

14 15 05020006 Youghiogheny 1,121,664 6 <1.0 Monongahela MD PA WV

15 15 05030201 Little Musringum-Middle Island 1,152,085 63,998 5.6 Wayne OH WV

16 15 05090101 Raccoon-Symmes 920,885 54,337 5.9 Wayne OH WV

17 14 01080101 Upper Connecticut 1,250,729 190,772 15.3 White Mountain ME NH VT

18 14 04140201 Seneca 2,072,942 15,234 <1.0 Finger Lakes NY

19 14 05010005 Clarion 797,893 132,875 16.7 Allegheny PA

20 14 05050002 Middle New 1,067,967 1,366 <1.0 Jefferson VA WV
40 |

 Forests, Water, and People

Map A-3: National Forests and watersheds important for drinking water supply, western portion of 

the study area, watershed view. In the western portion of the study area, all watersheds that scored high in 

importance for drinking water supply contain some National Forest lands. See Table A-3 for information about 

individual watersheds.

Wayne NF

Superior NF

Shawnee NF

Ottawa NF

Midewin NTP

Mark

Twain NF

HuronManistee NF

Hoosier NF

Hiawatha NF

Chippewa NF

ChequamegonNicolet NF

38

37

11

36 35

34

6

10

33

32 31 4

30

29

28 27

5 3

2

1

9

26 25 8

23 24

22

21

20

18 19 17

16

14 15

13 7

12

STEP 2 COMPOSITE SCORE

2 5 10 15 19

National Forest System Land

(Low APCW and Small number 

of water consumers)

(High APCW and Large number 

of water consumers)

Appendix A
Forests, Water, and People | 41

Appendix A

Table A-3: National Forests and the top 50 percent of watersheds important for drinking water supply in the 

western portion of the study area, by label in map A-3 and composite score.

Label in 

Map A-3

Step 2 

Composite 

Score

Hydrologic 

Unit Code

Watershed Land 

Acreage

National 

Forest 

Acreage

Percent of 

watershed 

in National 

Forest

NFS State(s)

1 16 07010101 Mississippi 

Headwaters

1,087,518 239,048 22 Chippewa MN

2 16 07010102 Leach Lake 707,800 199,108 28 Chippewa MN

3 16 07010103 Prairie-Willow 1,241,431 44,988 4 Chippewa MN

4 15 07140102 Meramec 1,365,884 174,876 13 Mark Twain MO

5 14 07010106 Crow Wing 1,179,214 590 0.1 Chippewa MN

6 14 09030002 Vermilion 585,030 183,885 31 Superior MN

7 13 04010201 St. Louis 1,830,340 139,480 8 Superior MN WI

8 12 04060106 Manistique 882,840 190,471 22 Hiawatha MI

9 12 05120208 Lower East Fork White 1,276,450 189,773 15 Hoosier IN

10 12 09030001 Rainy Headwaters 1,386,415 1,175,226 85 Superior MN

11 12 10290202 Big Piney 481,091 89,001 18 Mark Twain MO

12 11 04010101 Baptism-Brule 952,729 579,097 61 Superior MN

13 11 04010202 Cloquet 486,460 65,275 13 Superior MN

14 11 04020101 Black-Presque Isle 632,267 182,131 29 Ottawa MI WI

15 11 04020102 Ontonagan 851,254 491,647 58 Ottawa MI WI

16 11 04020103 Keweenaw Peninsula 683,841 7,126 1 Ottawa MI

17 11 04020104 Sturgeon 452,263 120,164 27 Ottawa MI

18 11 04020105 Dead-Kelsey 575,108 12,963 2 Ottawa MI

19 11 04020201 Betsy-Chocolay 717,211 50,801 7 Hiawatha MI

20 11 04020202 Tahquamenon 517,930 67,121 13 Hiawatha MI

21 11 04030106 Brule 657,974 216,652 33 ChequamegonNicolet/OttawaMI WI

22 11 04030107 Michigamme 438,641 4,170 1 Ottawa MI

23 11 04030111 Tacoosh-Whitefish 401,708 86,219 21 Hiawatha MI

24 11 04030112 Fishdam-Sturgeon 349,846 160,860 46 Hiawatha MI

25 11 04060107 Brevoort-Millecoquins 341,555 27,373 8 Hiawatha MI

26 11 04070002 Carp-Pine 406,330 216,357 53 Hiawatha MI

27 11 07030002 Namekagon 599,774 31,231 5 ChequamegonNicoletWI

28 11 07050002 Flambeau 678,200 9,734 1 ChequamegonNicoletMI WI

29 11 07050003 South Fork Flambeau 467,663 134,914 29 ChequamegonNicoletWI

30 11 07070001 Upper Wisconsin 1,276,907 127,740 10 ChequamegonNicolet/OttawaMI WI

31 11 07140104 Big 616,759 33,751 5 Mark Twain MO

32 11 07140105 Upper MississippiCape Girardeau1,026,466 56,516 6 Mark Twain/

Shawnee

IL MO

33 11 08020202 Upper St. Francis 820,394 110,996 14 Mark Twain MO

34 11 09030003 Rainy Lake 477,779 48,796 10 Superior MN

35 11 09030005 Little Fork 1,140,280 80,769 7 Superior MN

36 11 09030006 Big Fork 1,234,864 181,169 15 Chippewa MN

37 11 10300102 Lower MissouriMoreau2,136,106 15,352 1 Mark Twain MO

38 11 11010007 Upper Black 1,214,146 225,267 19 Mark Twain AR MO
42 |

 Forests, Water, and People

Appendix B

APPENDIX B: Technical Information 

Regarding Analysis and Metadata

Contents

Introduction ..................................................................................................................................................................... 44

Definitions......................................................................................................................................................................... 44

Analysis Area .....................................................................................................................................................................45

Step 1: Calculate Ability to Produce Clean Water.................................................................................................46

 Forested land......................................................................................................................................................47

 Agricultural land................................................................................................................................................49

 Riparian forest cover ........................................................................................................................................50

 Road density .......................................................................................................................................................52

 Soil erodibility ....................................................................................................................................................53

 Housing density.................................................................................................................................................54

 Ability to produce clean water (APCW) index by 30-m pixels...........................................................56

 Step 1 composite score: Mean APCW for watersheds..........................................................................57

Step 2: Add Data on Drinking Water Consumers.................................................................................................58

 Step 1 composite score...................................................................................................................................58

 Surface drinking water consumers per unit area...................................................................................59

 Step 2 composite score: Importance of watersheds for drinking water supply .........................61

Step 3: Add Data on Private Forest Land.................................................................................................................62

 Step 2 composite score...................................................................................................................................62

 Private forest.......................................................................................................................................................62

 Step 3 composite score: Importance of watersheds with private forests for 

 drinking water supply......................................................................................................................................65

Step 4: Add Data on Change in Housing Density.................................................................................................66

 Step 3 composite score...................................................................................................................................66

 Development pressure per unit area .........................................................................................................66

 Step 4 composite score: Development pressure on private forests in watersheds

 important for drinking water supply..........................................................................................................68
Forests, Water, and People | 43

Appendix B

Tables

Table B-1: GIS overlay process to estimate Ability to Produce Clean Water (APCW) for eight-digit

HUC watersheds in the 20-State study area...........................................................................................................46

Table B-2: Prioritization method for Step 2 for eight-digit HUC watersheds in the 20-State study

area.......................................................................................................................................................................................58

Table B-3: Prioritization method for Step 3 for eight-digit HUC watersheds in the 20-State

study area...........................................................................................................................................................................62

Table B-4: Prioritization method for Step 4 for eight-digit HUC watersheds in the 20-State

study area.............................................................................................................................................................................6
44 |

 Forests, Water, and People

Introduction

The goal of the Forests, Water, and People analysis is to evaluate current and projected future conditions 

across the Northeast and Midwest in order to maximize the protection and enhancement of forests, drinking 

water supplies, public health, and aquatic ecosystems. The project involves compiling a GIS database using 

existing and available data to quantify the key connections between forests, land use, water, and people in 

the Northeastern United States. The database will permit evaluation and ranking of these connections and 

characteristics, to identify priority areas for forest conservation and restoration. Results are intended to help 

managers determine where their investments will have the greatest benefits. 

This appendix is intended to serve as a technical guide to the Forests, Water, and People analysis for GIS 

professionals and researchers.

Definitions

Proclamation boundaries are identified in the 

proclamation that establishes the outer boundary within 

which a national forest or grassland could be established. 

Administrative boundaries identify the specific lands 

actually owned by the Federal Government and managed 

by the national forest. Proclamation boundaries were used 

for this study because these are the only Forest Service 

boundary data available in a national-level database.

Private land was defined to include tribal, forest industry, 

and non-industrial private ownerships, excluding public 

lands and other private lands identified as protected 

through conservation easements. 

Housing density is defined as the number of acres per 

housing unit.

Increased housing density was defined to mean shifts 

from rural to exurban or from rural or exurban to urban.

Rural is defined for this project as private land with 

greater than 20 acres per housing unit in the east (the 12 

states in the study area that are east of but do not include 

Ohio, and including the Big Sandy watershed) and greater 

than 40 acres per housing unit in the west (the 8 states in 

the study area that are west of and include Ohio). Forest 

lands in this housing density can support timber, most 

wildlife, and water quality.

Exurban is defined for this project as private land with 

5 – 20 acres per housing unit in the east (the 12 states in 

the study area that are east of but do not include Ohio, 

and including the Big Sandy watershed) and land with 5 – 

40 acres per housing unit in the west (the 8 states in the 

study area that are west of and include Ohio) . Forest lands 

in this level of housing density can support many types of 

wildlife; however, commercial timber management is less 

likely.

Suburban is defined for this project as private land with 

0.6 – 5 acres per housing unit. These lands are less likely 

to contribute to timber production, wildlife habitat, 

or water quality because of increased road density, 

infrastructure, and human population levels. Forest 

patches, however, are valued for their aesthetics, and 

noise abatement properties.

Urban is defined for this project as private land with less 

than 0.6 acre per housing unit. These lands are not likely 

to contribute to timber production, wildlife habitat, or 

water quality because of high road density, infrastructure, 

and human population levels.

Appendix B
Forests, Water, and People | 45

Appendix B

Analysis Area

Data Source: U.S. Geological Survey. 1994. 1:250,000-scale 

hydrologic units of the United States. Open-File Report 

94-0236. Reston, VA. http://water.usgs.gov/GIS/metadata/

usgswrd/XML/huc250k.xml (August 10, 2007)

Description: This data set is based on the Hydrologic 

Unit Maps published by the U.S. Geological Survey 

Office of Water Data Coordination, together with the list 

descriptions and name of region, subregion, accounting 

units, and cataloging unit. The hydrologic units are 

encoded with an eight-digit number that indicates the 

hydrologic region (first two digits), hydrologic subregion 

(second two digits), accounting unit (third two digits), and 

cataloging unit (fourth two digits). 

GIS Process: 

1. All watersheds (HUCs) that touched the 20 states in the 

Northeastern United States were selected and a new 

polygon data layer, HUC_NA, was created. Note: Some 

of the HUCs only partially fall within the study area, 

however, for the purpose of this project, the hydrologic 

boundary was used rather than the administrative 

one. Watersheds that are considered water bodies (i.e., 

Great Lakes, Chesapeake Bay, and Delaware Bay) were 

eliminated from the final HUC data layer. A total of 540 

eight-digit HUC watersheds resulted in the Analysis 

Area for this project.

Result: See following map.
46 |

 Forests, Water, and People

Step 1 characterized the biophysical conditions in each 

watershed. This characterization, the ability to produce 

clean water (APCW), is an index of water quality and 

watershed integrity based on six attributes: forest land, 

agricultural land, riparian forest cover, road density, soil 

erodibility, and housing density. The forest land, agricultural 

land, and riparian forest buffer data was summarized by 

watershed and converted to a 30-meter (30-m) spatial 

grid. The road density, soil erodibility, and housing density 

data were kept in their original 30-m grid format and not 

summarized by watershed. Each of the six attributes was 

scored from 1 to 4 (see Table 4 of the main report for more 

detail on the attribute scoring) based on scientifically 

accepted standards. Where standards or parameters were 

not available, the data was divided into quartiles.

The six attributes in step 1 were then summed, resulting in 

a value of 6 to 24 for each 30-m grid cell. To summarize the 

data by watershed, the values for all 30-m pixels in each 

watershed were averaged to produce a single score, with a 

minimum score of 6 and a maximum score of 24.

The APCW values were averaged to create a mean APCW for 

a watershed. This mean was divided into 10 quantiles, with 

the 1st quantile receiving a score of 10 (very high) and the 

10th quantile receiving a score of 1 (low).

This step will generate a defensible and understandable 

analysis of current conditions. It also will highlight the 

watershed management challenges and opportunities on 

each site and across the entire region.

Table B-1: GIS overlay process to estimate ability to produce clean water (APCW) for eight-digit HUC watersheds in the 

20-State study area.

Attribute Scoring for 30-meter grid cell 

Low (1 point) Moderate (2 points) High (3 points) Very High (4 points)

Percent forest land (F) 0 – 24 25 – 49 50 – 75 >75

Percent agricultural land (A) >30 21 – 30 10 – 20 <10

Percent riparian forest cover (R) 0 – 29 30 – 50 51 – 70 >70

Road density (D, quartiles) 75 – 100th percentile 50 – 74th percentile 25 – 49th percentile 0 – 24th percentile

Soil erodibility (S, k factor) >0.34 0.28 – 0.34 0.2 – 0.28 0 – 0.2

Housing density (H, acres per 

housing unit in 2000)

< 0.6 acre/unit 0.6 – 5.0 acres/unit 5.0 – 20.0 acres/unit 

(east)

5.0 – 40.0 acres/unit 

(west)

> 20.0 acres/unit 

(east)

> 40.0 acres/unit 

(west)

Total APCW F + A + R + D + S + H = APCW

Potential score 6 – 24

Attribute Watershed Scoring

Low

(1 point)

High/moderate

(2-9 points)

High

(10 points)

Step 1 = Mean APCW for Watersheds 10th quantile 2nd – 9th quantile 1st quantile

Step 1: Calculate Ability to Produce Clean Water

Data projection

All data was projected into the following coordinate 

system prior to each of the four steps to maintain the best 

possible accuracy.

Projection: Albers Conical Equal Area

Standard Parallel: 29.500000

Standard Parallel: 45.500000

Longitude of Central Meridian: −96.000000

Latitude of Projection Origin: 23.000000

False Easting: 0.000000

False Northing: 0.000000

Horizontal Datum Name: North American Datum of 1983

Ellipsoid Name: Geodetic Reference System 80

Semi-major Axis: 6378137.000000

Denominator of Flattening Ratio: 298.257222

Appendix B
Forests, Water, and People | 47

Appendix B

Forested land

Data Source: U.S. Geological Survey (USGS). 1999. 1992 

National Land Cover Data. Sioux Falls, SD.

http://eros.usgs.gov/products/landcover/nlcd.html. 

(August 10, 2007)

Description: Forested land data was extracted from 

the National Land Cover Data (NLCD,1992) with a spatial 

resolution of 30 meters. The NLCD is compiled from 

Landsat satellite TM imagery and supplemented by 

various ancillary data (where available). The analysis and 

interpretation of the satellite imagery was conducted 

using very large, sometimes multi-State image mosaics 

(i.e., up to 18 Landsat scenes). Using a relatively small 

number of aerial photographs for “ground truth,” the 

thematic interpretations were necessarily conducted from 

a spatially broad perspective. Furthermore, the accuracy 

assessments (see below) correspond to “Federal regions” 

which are groupings of contiguous States. Thus, the 

reliability of the data is greatest at the State or multi-State 

level. The statistical accuracy of the data is known only 

for the region. The land cover data files are provided as a 

“Geo-TIFF” for each State.

GIS Process:

1. The raw, downloaded NLCD GeoTiffs were converted to 

GRID using ArcInfo workstation. 

2. Once each state file was a GRID, all the GRIDs were 

merged to create a single GRID, nlcd92_huc. 

3. Forested land was summarized using NLCD grid values 

33 Transitional, 41 Deciduous Forest, 42 Evergreen 

Forest, 43 Mixed Forest, 51 Shrubland, 91 Woody 

Wetlands. The GRID was reclassified so all forested grid 

codes equaled “1” and all other grid codes equaled “0,” 

nlcd_for.

4. Using the “Tabulate Areas” function, the acreage of 

forested land in each watershed was computed. The 

resulting table was then joined to the HUC_NA shapefile.

5. The percent of the watershed that is forested was 

calculated by dividing the acreage of forested land 

by the total watershed land acreage. The results were 

saved in the attribute field Per_FOR.

6. The percent forest was reclassified into the four 

categories summarized in Table B-1. The results were 

saved in the attribute field Per_FOR_R.

Excerpt 1 from Table B-1

Attribute

Scoring for 30-meter grid cell 

Low

(1 point)

Moderate

(2 points)

High

(3 points)

Very High 

(4 points)

Percent 

forest land 

(F)

0 – 24 25 – 49 50 – 75 >75

7. The HUC_NA shapefile was converted to a raster data 

set with a pixel size of 30 m and the value field set to 

the attribute Per_FOR_R.

Result: See following map.
48 |

 Forests, Water, and People

Appendix B
Forests, Water, and People | 49

Appendix B

Agricultural land

Data Source: Same as for percent forested land.

Description: Same as for percent forested land.

GIS Process:

1. The raw, downloaded NLCD GeoTiffs were converted to 

GRID using ArcInfo workstation. 

2. Once each state file was a GRID, all the GRIDs were 

merged to create a single GRID, nlcd92_huc. 

3. Agricultural Land was summarized using grid values 

61 Orchard/Vineyard; 71 Grasslands/Herbaceous; 

81 Pasture/Hay; 82 Row Crops; 83 Small Grains; 84 

Fallow; 85 Urban/Recreational Grasses. The GRID was 

reclassified so all agricultural land grid codes equaled 

“1” and all other grid codes equaled “0,” nlcd_ag.

4. Using the “Tabulate Areas” function, the acreage of 

agricultural land in each watershed was computed. 

The resulting table was then joined to the HUC_NA 

shapefile.

5. The percent of the watershed that is agricultural land 

was calculated by dividing the acreage of agricultural 

land by the total watershed land acreage. The results 

were saved in the attribute field Per_AG.

6. The percent agricultural land was reclassified into the 

four categories summarized in Table 1. The results were 

saved in the attribute field Per_A

Excerpt 2 from Table B-1

Attribute

Scoring for 30-meter grid cell 

Low

(1 point)

Moderate

(2 points)

High

(3 points)

Very High

(4 points)

Percent 

agricultural 

land (A)

>30 21 – 30 10 – 20 <10

7. The HUC_NA shapefile was converted to a raster data 

set with a pixel size of 30 m, and the value field was set 

to the attribute Per_AG_R.

Result: See following map.
50 |

 Forests, Water, and People

Riparian forest cover

Data Source: Hatfield, Mark. 2005. 30m Buffer of the 

1999 National Hydrography Data set (NHD). St. Paul, MN: 

U.S. Department of Agriculture, Forest Service, Northern 

Research Station. [unpublished digital data]

Description: The National Hydrography Data set (NHD; 

USGS) comes with several different layers. Three are 

of interest to this project: the waterbodies, areas, and 

flowlines. The waterbodies layer depicts any water that 

has area, such as lakes, swamps, and ocean. The area layer 

shows features, such as rivers, that become too large 

to represent with only a line. The flowline layer shows 

all linear features, and includes information about the 

direction of flow through its topology. All three layers 

include an FCode for each feature, to describe what the 

feature is. Flowline features coded as “Pipeline” were 

deleted because they were determined to be irrelevant 

to the project. Using the buffer tool, a 30-m buffer 

was created around each feature in the Flowline and 

Waterbody/Area layers. 

Data Source: Same as for percent forested land.

GIS Process:

1. The raw, downloaded NLCD GeoTiffs were converted to 

GRID using ArcInfo workstation. 

2. Once each state file was a GRID, all the GRIDs were 

merged to create a single GRID, nlcd92_huc. 

3. Forested Land was summarized using NLCD grid values 

33 Transitional; 41 Deciduous Forest; 42 Evergreen 

Forest; 43 Mixed Forest; 51 Shrubland; 91 Woody 

Wetlands. The GRID was reclassified so all forested grid 

codes equaled “1” and all other grid codes equaled “0,” 

nlcd_for.

4. Using the “Extract by Mask” command in ArcInfo, the 

nlcd_for GRID was clipped to the 30-m NHD buffer 

nlcd_rip30.

5. Using the “Tabulate Areas” function, the acreage 

of forested land within the riparian buffer for each 

watershed was computed using the nlcd_rip30 

GRID and HUC_NA polygon shapefile. The acreage 

of forested land was divided by the total acreage of 

riparian buffer in the watershed. The resulting table was 

then joined to the HUC_NA shapefile. The results were 

saved in the attribute field Per_RIP.

6. The percent riparian forest cover was reclassified into 

the four categories summarized in Table 1. The results 

were saved in the attribute field Per_RIP_R. See step 7.

Excerpt 3 from Table B-1

Attribute

Scoring for 30-meter grid cell 

Low

(1 point)

Moderate

(2 points)

High

(3 points)

Very High

(4 points)

Percent 

riparian forest 

cover (R)

0 – 29 30 – 50 51 – 70 >70

7. The HUC_NA shapefile was converted to a raster data 

set with a pixel size of 30 m and the value field set to 

the attribute Per_RIP_R. See step 6.

Result: See following map. 

Appendix B
Forests, Water, and People | 51

Appendix B
52 |

 Forests, Water, and People

Road density

Data Source: U.S. Department of Transportation. 2002. 

Bureau of Transportation Statistics (BTS): Roads. http://

seamless.usgs.gov/ (December 1, 2006)

Description: This data set portrays a Bureau of 

Transportation Statistics overview of the road networks 

for all 50 States, the District of Columbia, and Puerto Rico. 

GIS Process:

1. Removed ferry routes from road layer Feature Class 

Code (FCC) A65, A66, A68, and A69.

2. Ran “Line Density” function in ArcInfo. Parameters were 

set as follows: 

Cell size = 30 m 

Search radius = 564.3326 m (to equal a search area 

of 1 km2

) 

Units = square kilometer

3. The line density function had to be run in sections due 

to file size; therefore, each final line density grid had to 

be merged together. Where two grids overlapped, the 

average line density was computed.

4. The results were sorted into four quartiles, and 

reclassified with values 1-4.

Excerpt 4 from Table B-1

Attribute

Scoring for 30-meter grid cell 

Low

(1 point)

Moderate

(2 points)

High

(3 points)

Very High

(4 points)

Road 

density (D, 

quartiles)

75 – 100th

percentile

50 – 74th

percentile

25 – 49th

percentile

0 – 24th

percentile

Result: See following map.

Appendix B
Forests, Water, and People | 53

Appendix B

Soil erodibility

Data Source: Miller, Douglas A.; White, Richard A. (NRCS) 

1998. STATSGO: A conterminous United States multilayer soil characteristics data set for regional climate 

and hydrology modeling. www.soilinfo.psu.edu/index.

cgi?soil_data&conus (December 1, 2006)

GIS Process:

1. Clipped the STATSGO mapunit coverage to the HUC_NA 

boundary

2. Joined the mu_kfact table to the clipped STATSGO 

shapefile.

3. Converted shape to raster using the kffact field as the 

grid value.

4. Reclassified the grid, where 

Kffact =

a. 0-0.2 = 4

b. 0.2-0.28 = 3

c. 0.28-0.34 = 2

d. >0.34 = 1

Excerpt 5 from Table B-1

Attribute

Scoring for 30-meter grid cell 

Low

(1 point)

Moderate

(2 points)

High

(3 points)

Very High

(4 points)

Soil 

erodibility 

(S, k factor)

>0.34 0.28 – 0.34 0.2 – 

0.28 0 – 0.2

Result: See following map.
54 |

 Forests, Water, and People

Housing density

Data Source: Theobald, David M. 2004. Housing density 

in 2000 and 2030. [Digital Data]. Fort Collins, CO: Colorado 

State University, Natural Resource Ecology Lab. 

Description: This raster data set shows housing density in 

2000, based on 2000 U.S. Census Bureau block (SF1) data 

sets developed by the Natural Resource Ecology Lab. 

To reduce the overall file size, the continuous values 

(in units per hectare * 1,000) were reclassified into the 

following: Code: Units per hectare 

 1: ≤1 

 2: 2 – 8 

 3: 9 – 15 

 4: 16 – 31 

 5: 32 – 49 

 6: 50 – 62 

 7: 63 – 82 

 8: 83 – 124

 9: 125 – 247 

10: 248 – 494 

11: 495 – 1,454 

12: 1,455 – 4,118 

13: 4,119 – 9,884 

14: 9,885 – 24,711 

15: 24,712 – 9,999,999 

GIS Process:

1. The raw 2000 housing density data was clipped to the 

analysis area and resampled from a 100-m grid to a 

30-m grid.

2. The raw grid values in units per hectare were converted 

to acres/unit using the following formula:

((units/ha)/1,000) * 1 ha/2.47 acres = units/acre (invert) = 

acres/unit, so the 15 classes equaled:

15 classes (acres/unit)

 1: < 2,470 

 2: 309 – 1,235 

 3: 165 – 274 

 4: 80 – 154 

 5: 50 – 77 

 6: 40 – 50 

 7: 30 – 40 

 8: 20 – 30 

 9: 10 – 20 

10: 5 – 10 

11: 1.7 – 5 

12: 0.6 – 1.7 

13: 0.25 – 0.6 

14: 0.1 – 0.25 

15: > 0.10

3. The 15 value classes were reclassified into four housing 

density classes: rural, exurban, suburban, and urban, 

where:

West (eight States, west of and including Ohio)

Rural: 1 – 6 = 4

Exurban: 7 – 10 = 3

Suburban: 11 – 12 = 2

Urban: 13 – 15 = 1

East (12 States, east of, but not including Ohio (does 

include the Big Sandy Watershed)

Rural: 1 – 8 = 4

Exurban: 9 – 10 = 3

Suburban: 11 – 12 = 2

Urban: 13 – 15 = 1

Excerpt 6 from Table B-1

Attribute

Scoring for 30-meter grid cell 

Low

(1 point)

Moderate

(2 points)

High

(3 points)

Very High

(4 points)

Housing density 

(H, acres per 

housing unit in 

2000)

< 0.6 

acre/unit

0.6 – 5.0 

acres/unit

5.0 – 20.0 

acres/unit 

(east)

5.0 – 40.0 

acres/unit 

(west)

> 20.0 

acres/unit 

(east)

> 40.0 

acres/unit 

(west)

Result: See following map.

Appendix B
Forests, Water, and People | 55

Appendix B
56 |

 Forests, Water, and People

Ability to produce clean water (APCW) index 

by 30-m pixels

The six attributes in step 1 were summed, resulting in a 

value of 6 – 24 for each 30-m grid cell. 

F + A + R + D + S + H = APCW

where,

F = forest land (percent)

A = agricultural land (percent)

R = riparian forest cover (percent)

D = road density (quartiles) 

S = soil erodibility (k factor)

H = housing density (acres per housing unit in 2000)

Result: See following map.

Appendix B
Forests, Water, and People | 57

Appendix B

Step 1 composite score: Mean APCW for watersheds

Data Source: Ability to Produce Clean Water (APCW) 

Index by 30-m pixels 

Description: See previous step.

GIS Process

1. Using the “zonal statistics as table” function in ArcInfo, 

the average APCW score was computed for each 

watershed. 

2. The average scores were split into 10 quantiles and 

reclassified with a value of 1 – 10, with 1 being the 

lowest APCW and 10 the highest (See Table B-2.)

Result: See following map.
58 |

 Forests, Water, and People

(See Analysis Methods section of main report)

Step 2 combined the results of Step 1, the Ability to 

Produce Clean Water, with water use data from the U.S. 

Environmental Protection Agency’s (EPA) Surface Drinking 

Water Information System (SDWIS). The ability to produce 

clean water was divided into 10 quantiles, with the 1st

quantile receiving a score of 10 and the 10th quantile 

receiving a score of 1. 

Selecting only surface water consumers (reservoirs 

and streams), the total drinking water consumers was 

summed for each eight-digit watershed and divided by 

the watershed area. For large city watersheds that use 

river intakes, including Philadelphia, St. Louis, St. Paul, 

and Washington, DC, the drinking water consumers were 

redistributed among the upstream watersheds. The New 

York City Watershed was individually calculated using the 

latest drinking water consumer data from the water utility. 

The total number of drinking water consumers for each 

watershed was then divided by the watershed area. This 

result was divided into 10 quantiles and combined with 

the APCW to yield a total score ranging from 2 to 20.

Table B-2: Prioritization method for Step 2 for eight-digit HUC watersheds in the 20-State study area.

Attribute

Scoring for 30-meter grid cell

Low

(1 point)

Moderate/High

(2-9 points)

Very High

(10 points)

Step 1 = Mean APCW for Watersheds 10th quantile 2nd – 9th

quantile 1st quantile

DW = surface drinking water consumers 

per unit area 10th quantile

2nd – 9th

quantile 1st quantile

(90th percentile and higher = 1st quantile = 10; 80th to 89th percentile = 2nd quantile = 9; 70th to 79th percentile = 3rd

quantile = 8 ...)

Step 1 composite score

See result from Step 1.

Step 2: Add Data on Drinking Water Consumers

Appendix B
Forests, Water, and People | 59

Appendix B

Surface drinking water consumers per unit 

area 

Data Source: U.S. Environmental Protection Agency. 2005. 

Public drinking water system (PWS) consumers by 8-digit 

HUC (data extracted from Safe Drinking Water Information 

System (SDWIS) in the 4th quarter of 2004). http://www.

epa.gov/enviro/html/sdwis/sdwis_ov.html (August 10, 

2007) This information is proprietary. To request access 

and permission to this spatial dataset, contact the U.S. EPA 

Office of Wetlands, Oceans and Watersheds (OWOW) at 

202-566-1300.

Description: The public drinking water supply systems 

regulated by the EPA, and delegated states and tribes, 

provide drinking water to 90 percent of Americans. These 

public drinking water supply systems, which may be 

publicly or privately owned, serve at least 25 people or 15 

service connections for at least 60 days per year. 

GIS Process: 

1. The Public Drinking Water System (PWS) data from 

the Safe Drinking Water Information System (SDWIS) 

database catalogs all drinking water intakes, including 

groundwater wells. Only surface water intakes (code = 

SW) were considered.

2. Overlaying the 540 watershed boundaries over the 

drinking water intakes, each intake was attributed 

with the proper eight-digit watershed code, in which it 

resides. Intakes that fell within a watershed boundary 

but obtain their water from the one of the five Great 

Lakes or the St. Lawrence River were not included. 

3. The remaining intakes were evaluated by public water 

supply system. If a public water supply system spanned 

two watersheds, the total number of consumers was 

divided by 2 and half put in each watershed. 

4. For the major cities with river intakes (Philadelphia, St. 

Louis, Cincinnati, St. Paul, Minneapolis, Washington, 

DC), their consumers were assigned to the eight-digit 

watershed immediately upstream, not the entire 

Delaware, Mississippi and Missouri, Ohio, Mississippi, 

or Potomac watershed above their respective intakes. 

In other words, it overstates the importance of the 

nearby watersheds while failing to “credit” the other 

(nested) upstream watersheds. Each city was evaluated 

separately:

a. Cincinnati—The water consumers were prorated 

over the subwatersheds along the main stem Ohio 

River.

b. St. Louis—same rationale, except the distribution 

of water users was limited to the Mississippi 

tributaries that are largely in Missouri and the 

Missouri River “corridor.”

c. Philadelphia and Washington, DC, were distributed 

on the basis of subwatershed area.

5. New York City watersheds were corrected using current 

NYCDEP daily drinking water supply estimates. 

6. The water consumers were summed by HUC and 

divided by watershed land acreage. 

7. The watershed results were split into 10 quantiles and 

given a value 1 through 10 (table B-2).

Result: See following map.
60 |

 Forests, Water, and People

Appendix B
Forests, Water, and People | 61

Appendix B

Step 2 composite score: Importance of watersheds 

for drinking water supply 

The two attributes in step 2 were summed, resulting in 

values of 2 to 20 for each watershed. 

Mean APCW + DW = STEP 2

where,

Mean APCW = Ability to Produce Clean Water

DW = Surface Drinking Water consumers per unit area

Result: See following map.
62 |

 Forests, Water, and People

Step 3 combines the results of Step 2 with the watershed’s 

percent private forest to highlight those areas important 

for surface water drinking supply that contain private 

forest lands. The private forest database was derived using 

a subset of the Conservation Biology Institute’s Protected 

Areas database and an updated Wisconsin data set. Only 

permanently protected lands (Federal, State, county, local, 

or permanent conservation easements) were considered 

“protected,” all other lands were considered unprotected, 

having the potential to be developed. The percent private 

forest by watershed was divided into 10 quantiles and 

then combined with the results of Step 2 to yield a total 

score of 3 – 30.

Step 3: Add Data on Private Forest Land

Table B-3: Prioritization method for Step 3 for eight-digit HUC watersheds in the 20-State study area.

Attribute

Scoring for 30-meter grid cell

Low

(1 point)

High/moderate

(2-9 points)

High

(10 points)

Step 2 = Importance of watersheds for drinking water 

supply See results from Step 2

PF = Private Forest (%) 10th quantile 2nd – 9th quantile 1st quantile

(For example, 90th percentile and higher = 1st quantile = 10; 80th to 89th percentile = 2nd quantile = 9; 70th to 79th

percentile = 3rd quantile = 8; ...)

Step 2 Composite Score

See results from Step 2. 

Private forest

Data Source: Conservation Biology Institute. 2006. CBI 

Protected Areas Database, Version 4. [CD-ROM] Corvallis, 

OR. http://www.consbio.org (August 10, 2007). 

U.S. Geological Survey, Upper Midwest Environmental 

Sciences Center. 2005. Gap Analysis Program—Wisconsin 

Stewardship Data. [Digital Data] La Crosse, WI.

Description: The original CBI Protected Areas Database 

(PAD) was the product of a collaborative effort between 

the Conservation Biology Institute and World Wildlife 

Fund, USA. The second and third versions of the PAD 

represent updates of the first database. This fourth version 

of the PAD specifically includes a complete update of 20 

eastern and 5 western U.S. States. Polygons are assigned 

with a GAP Analysis Program (GAP) code of 1, 2, 3, or 4 

and IUCN category of I through VI, N/A or Unknown. We 

added an additional GAP code of 5 to designate bodies of 

water. MN GAP has assigned some additional GAP codes, 

which are described in their metadata file. Additionally, 

the database contains information about parcel type, 

ownership, size, and protection level. 

GAP Code 1: An area having permanent protection 

from conversion of natural land cover and a mandated 

management plan in operation to maintain a natural 

state within which disturbance events (of natural 

type, frequency, intensity, and legacy) are allowed to 

Appendix B
Forests, Water, and People | 63

Appendix B

proceed without interference or are mimicked through 

management. Examples: national parks, nature preserves, 

wilderness areas.

GAP Code 2: An area having permanent protection 

from conversion of natural land cover and a mandated 

management plan in operation to maintain a primarily 

natural state, but which may receive uses or management 

practices that degrade the quality of existing natural 

communities, including suppression of natural 

disturbance. Examples: State parks, national wildlife 

refuges, national recreation areas.

GAP Code 3: An area having permanent protection from 

conversion of natural land cover for the majority of the 

area, but subject to extractive uses of either a broad, 

low-intensity type (e.g., logging) or localized intense 

type (e.g., mining). It also confers protection to federally 

listed endangered and threatened species throughout 

the area. Examples: national forests, most Bureau of Land 

Management land, wildlife management areas.

GAP Code 4: There are no known public or private 

institutional mandates or legally recognized easements or 

deed restrictions held by the managing entity to prevent 

conversion of natural habitat types to anthropogenic 

habitat types. The area generally allows conversion to 

unnatural land cover throughout.

Data Source: U.S. Geological Survey (USGS). 1999. 

1992 National land cover data set. Sioux Falls, SD. http://

edcwww.cr.usgs.gov/programs/lccp/nationallandcover.

html (August 10, 2007)

Description: The National Land Cover Data set was 

compiled from Landsat satellite TM imagery (circa 1992) 

with a spatial resolution of 30 meters and supplemented 

by various ancillary data (where available). The analysis 

and interpretation of the satellite imagery was conducted 

using large, sometimes multi-State image mosaics (i.e., 

up to 18 Landsat scenes). Using a relatively small number 

of aerial photographs for “ground truth,” the thematic 

interpretations were necessarily conducted from a 

spatially broad perspective. Furthermore, the accuracy 

assessments (see below) correspond to “Federal regions,” 

which are groupings of contiguous States. Thus, the 

reliability of the data is greatest at the State or multi-State 

level. The statistical accuracy of the data is known only 

for the region. The land cover data files are provided as a 

“Geo-TIFF’’ for each State.

GIS Process:

1. The Protected Areas Database (PAD) contains properties 

that are not permanently protected; therefore, several 

polygons were removed before percent private forest 

was calculated. 

Properties retained:

All State, Federal, local, and county lands were 

considered permanently protected regardless 

of gap code (State and county parks and wildlife 

management areas are GAP code 4). 

Properties removed: 

Ownerships with a GAP code of 4 or above were 

removed, as were Gap codes of 3 that were 

designated as private industrial or private nonindustrial forest. 

2. Using the final edited PAD shapefile as the ERASE 

template, all the protected areas were erased from the 

analysis area resulting in a layer of private land.

3. The private land shapefile was used as a mask to clip 

the nlcd_for GRID (1992 forest land), to achieve a grid of 

private forest, pri_for.

4. To determine the acreage of private forest land in each 

watershed, the “tabulated areas” function was run using 

the pri_for GRID and HUC_NA polygon shapefile. 

5. The acreage of private forest was then divided by the 

total land acreage of the watershed to get the percent 

private forest by watershed. 

6. The results were split into 10 quantiles and given values 

of 1 through 10 (see Table B-3).

Result: See following map.
64 |

 Forests, Water, and People

Appendix B
Forests, Water, and People | 65

Appendix B

Step 3 composite score: Importance of watersheds 

with private forests for drinking water supply

The two attributes in Step 3 were summed, resulting in a 

potential composite score of 3 to 30 for each watershed. 

STEP 2 + PF = STEP 3

where,

Step 2 = Importance of watersheds for drinking water 

supply

PF = percent private forest

Result: See following map.
66 |

 Forests, Water, and People

Step 4: Add Data on Change in 

Housing Density

Step 4 combines the results of Step 3 with the 

development pressure of future housing density change 

on forests. Development pressure was calculated by 

subtracting the housing density in 2000 from the housing 

Step 3 Composite Score

See the results from Step 3.

Development pressure per unit area

Data Source: Theobald, David M. 2004. Housing density 

in 2000 and 2030 [Digital Data]. Fort Collins, CO: Colorado 

State University, Natural Resource Ecology Lab.

Description: This raster data set shows housing density 

in 2000, based on 2000 U.S. Census Bureau block (SF1) 

data sets developed by the Natural Resource Ecology 

Lab. Housing Density in 2030 was forecasted using the 

Spatially Explicit Regional Growth Model (SERGoM v2).

To reduce the overall file size, the continuous values 

(in units per hectare * 1,000) were reclassified into the 

following: Code: Units per hectare 

1: ≤1 

2: 2 – 8 

3: 9 – 15 

4: 16 – 31 

5: 32 – 49 

6: 50 – 62 

7: 63 – 82 

8: 83 – 124 

Table B-4: Prioritization method for Step 4 for eight-digit HUC watersheds in the 20-State study area.

Attribute

Score for 30-meter grid cell 

Low

(1 point)

High/moderate

(2-9 points)

High

(10 points)

Step 3 = Importance of watersheds and 

private forest for drinking water supply See results from Step 3

DP = Development pressure per unit area 10th quantile 2nd – 9th quantile 1st quantile

(90th percentile and higher = 1st quantile = 10; 80th to 89th percentile = 2nd quantile = 9; 

70th to 79th percentile = 3rd quantile = 8; ...)

GIS Process:

1. The raw 2000 housing density data was clipped to the 

analysis area and resampled from a 100 m grid to a 30 

m grid. 

2. The raw grid values in units per hectare were converted 

to acres/unit using the following formula:

((units/ha)/1,000) * 1 ha/2.47 acres = units/acre (invert) = 

acres/unit, so the 15 classes equaled:

15 classes (acres/unit)

1: < 2,470 

2: 309 – 1,235 

3: 165 – 274 

4: 80 – 154 

5: 50 – 77 

6: 40 – 50 

7: 30 – 40 

8: 20 – 30 

9: 125 – 247 

10: 248 – 494 

11: 495 – 1,454 

12: 1,455 – 4,118 

13: 4,119 – 9,884 

14: 9,885 – 24,711 

15: 24,712 – 9,999,999 

9: 10 – 20 

10: 5 – 10 

11: 1.7 – 5 

12: 0.6 – 1.7 

13: 0.25 – 0.6

14: 0.1 – 0.25 

15: > 0.10

density in 2030. If housing density increased between 

2000 and 2030, then development pressure was said 

to occur. The total acreage of land under development 

pressure in the watershed was divided by watershed area, 

divided into 10 quantiles, and then combined with the 

results of Step 3 to yield a total score ranging from 4 to 40.

Appendix B
Forests, Water, and People | 67

Appendix B

3. The 15 value classes were reclassified into four housing 

density classes: rural, exurban, suburban, and urban, 

where:

West (eight States, west of and including Ohio) 

Rural: 1 – 6 = 4 

Exurban: 7 – 10 = 3 

Suburban: 11 – 12 = 2 

Urban: 13 – 15 = 1

East (12 States, east of, but not including Ohio (does 

include the Big Sandy Watershed)) 

Rural: 1 – 8 = 4 

Exurban: 9 – 10 = 3 

Suburban: 11 – 12 = 2 

Urban: 13 – 15 = 1

4. Using the “Combine” function in ArcInfo, the values of 

the 2000 Housing density data set were combined with 

the 2030 housing density data set. Look at the output 

data set, areas that increased in housing density were 

extracted and reclassified as “development pressure.” 

Note: Areas that increased from suburban to urban 

and areas where housing density was not predicted to 

change were not included in the final data set. 

5. The acreage of areas experiencing “development 

pressure” was calculated for each watershed and 

divided the land acreage of the watershed. This 

“development pressure per unit area” fraction 

was split into 10 quantiles and given a value of 1 

(low development pressure) through 10 (very high 

development pressure; see Table B-4)

Result: See following map.
68 |

 Forests, Water, and People

Appendix B

Step 4 composite score: Development pressure on 

private forests in watersheds important for drinking 

water supply

Values for the two attributes in step 4 were summed, 

resulting in a potential composite score of 4 to 40 for each 

watershed. 

STEP 3 + DP = STEP 4

For more information about the technical process, contact:

Office of Knowledge Management 

Northeastern Area State and Private Forestry 

USDA Forest Service 

11 Campus Boulevard, Suite 200 

Newtown Square, PA 19073

where,

Step 3 = Importance of watersheds and private forest for 

drinking water supply

DP = Development Pressure per unit area

Result: See following map.
Forests, Water, and People | 69

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