Risk of Impaired Condition 

of Watersheds Containing 

National Forest Lands

Thomas C. Brown and Pamela Froemke
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Brown, Thomas C.; Froemke, Pamela. 2010. Risk of impaired condition of watersheds 

containing National Forest lands. Gen. Tech. Rep. RMRS-GTR-251. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

57 p.

Abstract

We assessed the risk of impaired condition of the nearly 3700 5th-level watersheds in 

the contiguous 48 states containing the national forests and grasslands that make up the 

U.S. Forest Service’s National Forest System (NFS). The assessment was based on readily 

available, relatively consistent nationwide data sets for a series of indicators representing 

watershed stressors and resources at risk of watershed impairment. Using a set of weights 

that express the relative importance of the indicators, a summary measure of relative risk 

of watershed impairment was computed for each entire watershed, each NFS part of each 

watershed, and each non-NFS part of each watershed. The summary measure reflects 

the assumption that indicators are linearly related to risk of watershed impairment. 

The orderings based on these measures provide a first-cut at a consistent nationwide 

comparison of watersheds with NFS land. Users of the spreadsheets that contain 

the detailed results of the assessment may alter the weights according to their own 

understanding of the relative importance of the indicators, producing their own ratings 

and rankings. Among other things, we find that the non-NFS parts of the watersheds are 

consistently under much greater stress than the NFS parts, but that the resources at risk 

are more evenly spread across the NFS and non-NFS parts of the watersheds; and that risk 

is unevenly spread across the NFS, with most units in the two eastern regions at higher 

risk than nearly all units in the western regions. The results of this assessment offer a 

starting point for deciding about risk mitigation efforts, one that could be supplemented 

by locally available data on additional indicators and by a comparison of the costs and 

benefits of mitigation options.

Keywords: watershed condition, sediment, nutrients, toxics, national forests

Authors

Thomas C. Brown is an economist with the U.S. Forest Service, Rocky Mountain 

Research Station.

Pamela Froemke is a GIS analyst with the U.S. Forest Service, Rocky Mountain Research 

Station.
Preface

This report was produced with two objectives in mind. First, we sought to provide a 

nationwide assessment based on relatively good quality, consistent data, which could 

serve as a point of reference for pending decisions about agency watershed condition 

assessment. Second, we hoped to offer information that would be useful in making broadscale decisions about where more in-depth assessment would be most helpful. Our end 

products—this report and its associated spreadsheets of data and metrics of risk to watershed condition—offer a great deal of information about nearly 3700 5th-level watersheds 

across the lower 48 states. Two qualifications about this information are most important. First, this is a nationwide analysis, which most appropriately supports broad-scale 

comparisons and decisions. Because good quality nationwide data sets are not available 

for some variables that may be of interest in specific locations, and because even good 

quality nationwide data sets sometimes fail to accurately characterize local situations, 

the results will probably provide only a starting point for watershed assessments at local 

scales, such as that of a national forest. Also, although we performed the analysis using 

what for a nationwide assessment is considered a very fine spatial scale, that scale may 

not be sufficiently fine for local planning needs. Second, because consistent nationwide 

data on soil quality, on aquatic species populations, and on water quantity, quality, and 

timing were not available, this assessment examines the risk of impaired watershed condition, not the actual condition of watersheds.

Acknowledgments

This study was initiated and partially funded by Strategic Planning and Resource Assessment, Programs and Legislation, U.S. Forest Service, Washington, D.C. The majority of 

the funding has been provided by the Rocky Mountain Research Station.

The work plan benefited from review and suggestions from Alan Clingenpeel, Mike 

Furniss, Mark Hudy, Bruce McCammon, John Potyondy, and Kurt Riitters of the Forest 

Service, and Bruce Jones of the USGS. In addition, we thank Alan Clingenpeel and Mark 

Hudy for encouragement and numerous hints, techniques, and ideas as we struggled with 

obtaining and reconciling early versions of the boundaries of 5th-level watersheds and 

with analyzing broad spatial data sets; Susan Ruzicka of the Forest Service’s Automated 

Lands Project for supplying us with the latest forest ownership data; Raymond Watts of 

the USGS in Fort Collins for the roads data; Peter Landres of the Leopold Wilderness 

Research Institute for information about activities in wilderness areas; Ted Geier, Bill 

Hansen, Sherry Hazelhurst, Suzanne Krieger, David Merritt, Bruce Sims, and Brett Roper 

of the Forest Service, plus Dave Theobald of Colorado State University, for comments on 

earlier draft; and Tom Crow, Charlie Luce, and Keith Reynolds of the research branch of 

the Forest Service and John Potyondy of the Forest Service’s Stream Team for comments 

on a subsequent draft.
Contents

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

Methods ..................................................................................................................................4

Characterizing Risk of Impaired Watershed Condition .......................................4

Delineating Areas of Consideration .........................................................................5

Identified Problems .....................................................................................................6

Selecting Indicators .....................................................................................................9

Scaling and Weighting Procedures .........................................................................15

Ordering of Watersheds ...........................................................................................19

Data Processing...........................................................................................................21

Results and Analysis............................................................................................................22

Relations Among Components of the Conceptual Model ................................22

Comparison of NFS Regions: Indicators and Background Variables ...............27

How Levels of Indicators Differ by Land Ownership..........................................29

Risk of Impaired Watershed Condition .................................................................34

Summary of Major Findings..............................................................................................43

Future Possibilities..............................................................................................................45

Final Comments...................................................................................................................46

Literature Cited....................................................................................................................47

Appendix A: Watershed Area, Elevation, and Ownership..........................................51

Appendix B: Incidence of Threatened and Endangered Species..............................54

Methods .......................................................................................................................54

Results...........................................................................................................................55

ii
USDA Forest Service RMRS-GTR-251. 2010. 1

Introduction________________

Although watershed condition is an ongoing 

concern for the U.S. Forest Service,1 the agency lacks 

a systematic nationwide assessment of the condition 

of its watersheds. The objective of this study was to 

develop a consistent nationwide assessment of the 

risk of impaired watershed condition for all watersheds containing National Forest System (NFS) land. 

The assessment could inform Forest Service decisions 

on where to focus future, more in-depth watershed 

assessment,2 which would in turn help to determine 

where watershed protection actions would be most effective. The assessment would also provide consistent 

data for others to use in national or regional watershed 

analyses of their own. And the assessment would 

provide an example of what is currently possible in 

assessing risk of impaired watershed condition at the 

national scale, which may inform efforts within the 

agency to develop a process for periodic nationwide 

watershed assessments.

“Watershed condition” is not a precise term, and 

means different things to different people. For some, 

watershed condition refers to the condition of both the 

soil and the water within the watershed, whereas for 

others it refers only to those aspects of the watershed 

affecting the quality, quantity, or timing of the water 

flowing in or from the watershed. For some, watershed 

condition includes the habitats of terrestrial plants and 

animals, even those outside of riparian zones, whereas 

for others only aquatic and perhaps also riparian biota 

are of importance. The U.S. Forest Service includes 

both soil and water functions in its official definition of 

watershed condition, and in recent practice the agency 

has also emphasized the importance of ecological conditions throughout the watershed.3 This assessment of 

watersheds with NFS land adopts the traditional focus 

on soil and water conditions.

The national forest preserves were originally set 

aside for provision of two key resources, water and 

timber, and for the protection of the watershed functions that supported those resources. Today we would 

also call those resources “ecosystem goods” and those 

functions “ecosystem functions.” Over time the goals 

of the national forests have been expanded to the 

provision of other ecosystem goods including wildlife, 

range forage, and recreation opportunities. As goals 

for the national forests have expanded, so also has our 

understanding of ecosystem functions and the services they provide. Key ecosystem services provided 

in watersheds include not only water purification 

and mitigation of floods and droughts, but also soil 

retention, translocation of nutrients, and maintenance 

of habitats.4 An assessment of watershed condition is 

essentially an assessment of either the current capacity of watersheds to provide ecosystem goods and 

services, or of the threats to that capacity.

Watersheds of the United States are delineated using a system that divides and subdivides the country into successively smaller basins. At the grossest 

level there are 20 large basins called “water resource 

regions” in the United States, each designated by a 

2-digit numeric code. Until recently, the finest breakdown for which certified boundaries were available 

for the entire United States was the 4th-level division, 

for which there are 2139 hydrologic units, each identified by an 8-digit numeric code (called a hydrologic 

1 In recent years the NFS has spent roughly $50 million per year to “maintain and improve watershed conditions.”

2 An example of a more in-depth assessment is the Aquatic and Riparian Effectiveness Monitoring Plan (AREMP) developed as 

part of the Northwest Forest Plan (Reeves and others, undated). Under this plan, 6th-level watersheds are assessed in three 

parts: upland area, riparian zone, and stream channel. The small scale allows a detailed assessment that combines onsite 

inventory with remotely sensed data.

3 The U.S. Forest Service Manual, chapter 2520, defines watershed condition as “the state of a watershed based upon physical 

and biological characteristics and processes affecting hydrologic and soil functions.” Three condition classes are defined. In 

class 1 the “physical, chemical, and biologic conditions suggest that soil, aquatic, and riparian systems are predominantly 

functional in terms of supporting beneficial uses.” In classes 2 and 3 those conditions are at risk in being able to, or 

are unable to, support the beneficial uses, respectively. However, some recent Forest Service descriptions of watershed 

condition take an even broader view. For example, consider this (dated February 2009) from a Region 8 website: “The 

ecological significance of ecosystem processes is recognized as essential to good watershed condition. A watershed in 

good condition has physical structures and biotic resources that support productive ecosystems, the capacity to quickly 

recover from episodic and chronic disturbances, and conditions that support the natural expression of ecosystem functions 

and structures (e.g., plant succession, diversity of species and habitats).”

4 The distinction between goods and services employed here, taken from economic theory, is that goods (in most cases) are 

tangible, material products (whether or not they are traded in markets), whereas services tend to provide improvements in 

the condition or location of things of value. Thus, for example, water is a good and water purification a service. Recreation 

opportunities do not fit neatly into this dichotomy, but typically are treated as goods (Brown and others 2007).
2 USDA Forest Service RMRS-GTR-251. 2010.

unit code, or HUC).5 However, even 4th-level units 

are too large for a useful characterization of watershed condition. Fortunately, certified boundaries for 

the next finer breakdown, 5th-level hydrologic units, 

became available for the coterminous United States in 

early 2009.6 We base this assessment on 5th-level hydrologic units, which are each identified by a 10-digit 

HUC and are called “watersheds” herein.

In addition to limiting our effort to watersheds 

containing NFS land, we further limited the study area 

to the 48 contiguous states because good quality, comparable data on some variables of interest were not 

available elsewhere.7 In the 48 contiguous states there 

are 18 water resource regions, 2112 4th-level units, and 

roughly 21,000 5th-level units, of which nearly 3700 

have NFS land (table 1).

Our approach in assessing watershed condition 

emphasizes stressors that tend to impair the condition of watersheds (for example, roads) and resources 

within the watersheds that are sensitive to such 

stressors (for example, lakes and reservoirs subject to 

sedimentation), but not direct measures of watershed 

condition as indicated by the condition of soil, water, 

and aquatic organisms. The reason for focusing on 

stressors and at-risk resources rather than directly on 

watershed condition variables is not a lack of interest in gauging actual watershed condition; rather it 

is simply the lack of consistent and readily available 

nationwide data on soil quality, on water as it leaves 

the watersheds, and on aquatic populations living in 

the lakes and streams of the watersheds. It will also 

be seen that some important stressors (for example, 

recent severe wildfires) and at-risk resources (for example, presence of pristine fishing stream reaches) are 

not included in the analysis, again because of the lack 

of good quality nationwide data sets. We placed a high 

priority on data quality and consistency across the 

full set of watersheds. Thus, whereas soil quality and 

instream measures would allow an assessment of the 

result of watershed impairment, our approach, which 

focuses on watershed stressors and at-risk resources, 

can be best characterized as an assessment of the risk

of watershed impairment.

5 The Water Resources Council, and later the USGS, called these 8-digit basins “cataloguing units” (U.S. Water Resources 

Council 1978). Recently an inter-agency committee renamed the different levels of hydrologic units, calling 8-digit units 

“sub-basins” and 10-digit units “watersheds.” The inter-agency report is found at:

ftp://ftp-fc.sc.egov.usda.gov/NCGC/products/watershed/hu-standards.pdf.

6 Fifth-level hydrologic units probably provide sufficient detail for a national-level assessment but are generally considered too 

large for watershed planning at the regional and local scales. At those finer scales, 6th-level (12-digit) units are desired.

7 Hawaii has no national forests, and although Alaska and Puerto Rico do, data for many variables were lacking there. For 

example, as of December 2008 we lacked comparable data for Alaska on road density, housing density, drinking water 

intakes, fire hazard, precipitation, and erosivity.

Table 1. Watersheds and administrative areas analyzed.

Number of Area (km2)

NFS region Watershedsa NFS unitsb Wilderness areasc Watershed NFS Wilderness

1 494 13 13 238,230 103,424 21,052

2 460 15 46 260,034 87,589 19,005

3 356 11 52 234,535 82,162 10,940

4 624 13 41 315,495 128,167 22,972

5 433 18 56 181,206 82,033 18,281

6 466 17 59 216,591 99,880 18,512

8 489 14 76 239,365 53,645 2,832

9 350 15 50 188,906 48,579 5,356

Total 3672 116 393 1,874,362 685,478 118,949

a Watersheds with at least 1% of the area managed by the NFS. Watersheds that span regional boundaries are listed with the 

region with the greater amount of NFS area within the watershed. b Forest codes were obtained from the Forest Service’s Automated Lands Project (ALP) in July 2009. The individual units are 

listed in the accompanying online material (see footnote 20 for the website address). c Only designated NFS wilderness areas are included. Wilderness areas that span regional boundaries are listed with the region 

with the greater amount of area within the region.
USDA Forest Service RMRS-GTR-251. 2010. 3

The inability to assess watershed condition using data on the condition of soils, water, and aquatic 

organisms highlights an obvious need for improved 

inventory of soils, lakes, and streams. With data on 

soil, lake, and stream condition variables, or data on 

additional stressors and resources at risk, nationwide 

watershed condition assessments would become 

more useful. But it must be recognized that there will 

always be variables of importance in some regional 

or more local settings that are relatively unimportant 

in other settings and for which data will therefore be 

missing in some regions of the country. Budgets will 

not support collecting all the data we might wish to 

have. Regional or local analyses, however, allow for 

more variables and better data. Because of these data 

and scale issues, assessments at different geographical 

scales support different kinds of decisions. The limited 

data available for nationwide assessments support 

comparisons of broad areas of the country, perhaps 

aiding in budget allocation among those broad areas, 

but only approximate the assessments that would 

be possible at finer scales. Finer-scale analyses using 

additional, more regionally relevant data along with 

some first-hand knowledge of field conditions allow 

enhanced comparison of areas such as watersheds 

within a NFS unit. And finally, even finer-scale analyses, aided by on-the-ground verification of conditions, 

allow effective comparison of even smaller areas, such 

as areas within individual 5th-level watersheds. Finerscale analysis may, of course, find it useful to lower the 

spatial scale to 6th-level watersheds.

We provide an ordering of watersheds from lowest to highest level of concern but make no attempt 

to go to the next step and establish a reference condition against which the watersheds can be compared. 

A reference condition would, for example, specify the 

desired minimum state of risk of watershed impairment, allowing us to then separate the watersheds into 

two groups, those that measure up to the reference 

condition and those that do not. We forego this logical 

extension of our analysis for two reasons. First, there 

are many possible reference conditions—examples 

include pre-colonial, pre-industrial, and pre-WW II 

conditions, as well as the best available current condition within a designated geographic area such as an 

ecoregion—and the choice of one is a value judgment. 

Second, even if we chose some basis for specifying a 

reference condition, precisely characterizing reference 

conditions for all the various physical and ecological situations across the United States where national 

forests and grasslands are found would be far beyond 

our ability. Thus, we restrict our assessment to an 

ordering of watersheds. Such an ordering places no 

value judgments on the condition of any one watershed, but it does provide clear relative information, 

showing, for example, where the watersheds of greatest risk of impairment are found.

This assessment covers only one point in time—or, 

more precisely, the various recent points in time when 

the different variables we used were inventoried.8 An 

attractive feature of analyses of watershed condition 

is the possibility of periodic reassessments, thereby allowing measurement of trends in risk of watershed impairment. Indeed, trend analysis is perhaps the most 

useful role of such an assessment. However, trend 

analysis requires periodic and consistent data collection for all important variables. This assessment could 

provide a starting point for nationwide measurement 

of trends in watershed condition but a less than ideal 

starting point because of three factors: (1) the variables 

we used were not all inventoried at the same point in 

time, thus providing a temporally inconsistent picture 

of existing (i.e., recent past) watershed conditions; (2) 

the same variables will be re-inventoried at various 

times in the future; and (3) the time between inventories may be longer than desired for trend analysis.9

The assessment—limited though it is to supporting 

only broad-scale planning, and relying completely on 

data sets developed by others—required a great deal 

of analysis. The assessment involved over 184,500 NFS 

and non-NFS polygons for which we computed information on over 20 indicators and about 30 background 

8 In general, we did not attempt to estimate risk from activities that occurred prior to recent inventories, which may have 

enhanced the risk of water quality problems, such as agricultural cultivation prior to 2001 when our land use data were 

amassed. This is not to say that so-called legacy sediment or nutrients are not still impairing water quality; rather, our lack 

of attention to earlier activities reflects a combination of lack of comparable data and the judgment that earlier activities 

are less important than more recent activities in characterizing what is currently occurring and what is therefore subject to 

change. One exception to this general rule, however, is the inclusion of abandoned mines, which may still have important 

effects on water quality.

9 A promising possibility is that fine-scale remote sensing data capture and analysis will quickly advance to the point where 

relevant stressors can be inexpensively monitored on an annual basis. 
4 USDA Forest Service RMRS-GTR-251. 2010.

variables. And the indicators were analyzed at a fine 

scale—for example, we used a 30 m DEM to calculate 

slope for isolating steep roads, and a 100 m buffer 

with the 1:100,000 NHD medium-resolution streams 

layer to map riparian areas—allowing fairly accurate 

measures of the indicators, as seen in more detail in 

the next section.

Methods___________________

We first developed a conceptual framework for 

characterizing watershed condition and then settled 

on a procedure for completing the assessment. The 

procedure has the following five steps:

• Given a consistent set of 5th-level watershed boundaries, delineate NFS and non-NFS polygons within 

the watersheds.

• Select watershed problems of interest.

• Identify variables relevant to those problems, and 

obtain geo-referenced data for indicators to characterize those variables.

• Add data for each indicator to the polygon layer, 

and compute average values of the variables for 

each polygon.

• Finalize weights, compute scale values, and order 

the watersheds.

In the following subsections we describe our framework and explain the five steps.

Characterizing Risk of Impaired 

Watershed Condition

Our underlying goal in characterizing watershed 

condition was to provide initial information that would 

eventually help guide watershed management. With 

this practical goal in mind, the conceptual framework 

we used to organize our thinking about watershed 

condition has four basic parts: stressor, problem as 

indicated by soil and aquatic conditions, affected resources, and mitigating management action (figure 1). 

A stressor creates or amplifies a problem, which has 

on-site, down-slope, downstream, or downwind impacts on resources. Management actions can affect the 

stressor and thereby lessen the impact of the problem.

To illustrate the conceptual framework, consider a 

common problem in forested watersheds: sediment in 

streams (figure 1). Excess sediment in streams has various deleterious effects, including damage to aquatic 

life, sedimentation of reservoirs, and increased costs of 

treating water for municipal use. Existence of a problem is indicated in measurements of soil and instream 

conditions or revealed in damage to resources at risk. 

Potential causes of this problem (i.e., stressors) include 

vehicle use on unpaved roads and skid trails, severe 

wildfire, and excessive livestock grazing in sensitive 

areas. Potential actions, or management options, available to land managers fall into three groups: road and 

trail management, forest fuels and vegetation management, and riparian area management. Specific management actions include constraints on OHV use, waterbars for skid trails, prescribed burning so as to avoid 

potentially more destructive wildfire, and livestock 

management including fencing of riparian areas.10

Figure 1. Conceptual framework.
USDA Forest Service RMRS-GTR-251. 2010. 5

Given the practical objective of this effort, characterizations of watershed condition must reflect an identified problem affected by at least one identified stressor, 

and there must be at least one possible action that 

could mitigate the impacts by affecting the stressor. 

Problems without identifiable stressors, and problems 

that are not amenable to mitigation via management 

action, are not of primary interest. That said, in this 

effort we are not concerned with which management 

action should be taken. Our focus is on obtaining an 

initial estimate of the degree to which causes of identified problems amenable to mitigation are present. This 

focus on individual problems amenable to mitigation 

is different from most prior assessments of watershed 

condition. Prior assessments (Clingenpeel 2003; Jones 

and others 1997; U.S. Forest Service 2000a, 2000b) have 

typically produced general watershed characterizations or rankings that give users an overall idea of the 

condition of the assessed watersheds, not information 

about individual problems.

Our conceptual framework emphasizes humancaused stressors. Purely natural levels of soil movement, nutrient leaching, or other natural processes—

although sometimes undesirable—are not of primary 

interest because avoiding natural stressors is generally 

not an option. Within this framework, wildfire is a 

special case, for although wildfire is a natural phenomenon, the severity of wildfire has been enhanced by 

past management actions in many forests with short 

fire recurrence intervals. For this reason, potentially 

damaging wildfire is included as a stressor. In addition, to the extent that natural processes exacerbate 

the effect of human actions—as high erosion potential 

exacerbates the deleterious effect of road construction—they too are of interest. For example, we include 

stressors for cultivation on highly erosive soils and 

roads on steep slopes.

Primarily we are concerned with stressors involving human actions that occur on the watershed at 

issue. This is both because actions that could stress a 

watershed often occur on that watershed, and because 

a local link of cause to effect increases the likelihood 

that the action causing the stress will be addressed. 

However, one important exception to this general rule 

is atmospheric deposition, which is an important cause 

of stress for some watersheds and almost always results from actions (for example, burning of fossil fuels 

at power plants) off of the watershed.

Delineating Areas of Consideration

For this assessment we used the 5th-level watershed 

boundaries provided by the Natural Resources Conservation Service (NRCS) and NFS boundaries from 

the Forest Service’s Automated Lands Project (ALP). 

The full set of 5th-level watershed boundaries for the 

contiguous 48 states became available in March 2009. 

The ALP boundaries were obtained in July of 2009 

(figure 2).

10 This simple model is not limited to watershed applications. For example, a person may be stressed by overwork, poor 

nutrition, and a sedentary life style, leading to problems such as hardening of the arteries or diabetes, which may be 

indicated by measurements of the body’s condition such as of blood pressure and blood chemistry, and which manifest 

in effects on resources at risk such as disposable income (based on the logic that increases in health care expenses reduce 

disposable income) or one’s ability to enjoy outdoor activities.

Figure 2. NFS parcels, also 

showing region numbers 

and boundaries.
6 USDA Forest Service RMRS-GTR-251. 2010.

Following watershed boundary delineation, we 

eliminated from consideration all watersheds with less 

than 1 percent NFS land, for two reasons. First, when 

very little of a watershed is in NFS land, management 

on that land can have little impact on overall conditions within the watershed. Second, when only a small 

amount of NFS land is being analyzed, any errors in 

the data on the indicator variables may overly influence the characterization of that watershed and the 

ranking of the watersheds. Applying our 1 percent 

rule resulted in a total of 3672 watersheds (figure 3).

Within each watershed we distinguished between 

NFS land and non-NFS land. Any land not designated 

as NFS was considered non-NFS land. In addition, 

within NFS land we distinguish between designated 

wilderness areas and other NFS land, again using ALP 

boundary data. Thus, each watershed contains up to 

three categories of land, with some containing only 

one or two categories. 11

The 3672 watersheds range in size from 27 to 1652 

km2 and have a median area of 474 km2, with 81 percent being from 200 to 800 km2 in size (figure 4). The 

NFS regions contain from 350 to 624 watersheds each, 

from 11 to 18 NFS units, and from 13 to 76 wilderness 

areas (table 1). Across the regions (as may be computed from data in table 1), the average watershed size 

varies from 418 km2 in Region 5 to 659 km2 in Region 

3; the average NFS unit size varies from 10,067 km2 in 

Region 5 to 24,269 km2 in Region 4; and the average 

wilderness area size varies from 37 km2 in Region 8 to 

1460 km2 in Region 1. Thus, the regions vary relatively 

little in typical watershed size but greatly in unit and 

wilderness area size.

Figure 5 shows the area in each region that is NFS 

wilderness, other NFS land, and non-NFS land. Across 

all of the watersheds, 63 percent is non-NFS of which 

23 percent is other federal land, and 37 percent is in 

the NFS of which 17 percent is designated wilderness.

Identified Problems

There are many soil- and water-related watershed 

problems that could be studied, including sediment 

and nutrients in lakes and streams; dissolved metals 

and other toxic chemicals (which together we will 

call toxics) in lakes, streams, and soils; alteration of 

stream temperature;12 diminished water flows; pathogens in lakes and streams; invasive aquatic 

Figure 3. 3672 5th-level 

watersheds with at 

least 1% NFS land, also 

showing NFS region 

numbers and boundaries.

11 When delineating these land categories within a watershed, isolated slivers of land often appeared along boundaries, which 

may occur by chance or because of errors or inconsistencies in boundary delineation. For example, a national forest 

boundary may proceed along a ridge line at the edge of a watershed but may not match the watershed boundary exactly, 

leaving isolated narrow strips of non-NFS land along the boundary. In an attempt to remove erroneous or trivially small 

parcels, we eliminated a polygon if the total area of the polygon parcel within the watershed had a ratio of area (in m2) to 

perimeter (in m) ≤ 100 (a smaller criterion of 50 was used for the Superior National Forest because of the preponderance of 

small private parcels within the national forest). Using these rules, we eliminated 1220 slivers.
USDA Forest Service RMRS-GTR-251. 2010. 7

species; soil compaction; stream channel modification; and loss of hydrologic connectivity. Lacking 

good quality nationwide data sets for variables 

relevant to some of these problems, we limited our 

effort to three problems—sediments, nutrients, and 

toxics—all of which can end up in lakes and streams 

and some of which can harm soils, thereby damaging valuable resources (table 2). These three problems 

capture four of the five most frequently cited causes 

of fresh water impairment.13

4

0

100

200

300

400

500

600

700

Number of watersheds

Upper bound on interval (km2)

Figure 4. Size distribution 

of the 3672 watersheds.

5

0

50

100

150

200

250

300

350

1 2 3 4 5 6 8 9

Thousand km2

Region

State & private

Other federal

NFS wilderness

NFS non-wilderness

Figure 5. Watershed 

area by region.

12 Stream temperature is a major issue in some forest areas, such as in the Pacific Northwest. Because alteration of stream 

temperature occurs primarily via removal of riparian vegetation, stream temperature is largely a riparian area issue, not a 

watershed-wide issue. In any case, obtaining good nationwide data on riparian vegetation density and water temperature at 

the 5th-level watershed scale is problematic.

13 In November 2007 the EPA (http://oaspub.epa.gov/waters/national_rept.control) listed 36 possible causes of impaired waters 

on which states report pursuant to section 303(d) of the Clean Water Act. The five most frequently cited causes, in order of 

declining frequency, are mercury, pathogens, sediment, metals other than mercury, and nutrients.
8 USDA Forest Service RMRS-GTR-251. 2010.

Table 2. Variables for assessing watershed condition and potential indicators.

Variable Potential indicator

Stressor

Human presence Population density

Rate of population increase

Housing density

Urban land cover

Housing in riparian areas

Industry Number of industrial sites

Roads Road density 

Road-stream crossings 

Roads in riparian areas

Roads on steep slopes 

OHV use levels

Agriculture Cultivation on gentle slopes

Cultivation on steep slopes

Cultivation on highly erosive soils

Cultivation in riparian areas

Timber harvest Recent harvest

Mining Number of mines (active and inactive)

Livestock and poultry Animal density 

Feedlots Number of feedlots

Atmospheric deposition Precipitation chemistry data (e.g., SO4

2−, NO3−, Hg deposition)

Dams Number of dams

Natural processes

Wildfire Recent severe burns

Area of potentially damaging wildfire

Precipitation Erosivity

Erosion RUSLE estimates or erosive soil coverage

Landslides Susceptibility to landslides

At-risk resource

Water bodies and stream segments Lakes and reservoirs

Pristine streams

Recreational fishing visitor days

Designated wild and scenic rivers

Drinking water supply Municipal water intakes

Animal and plant species Aquatic animal and plant T&E species

Terrestrial animal and plant T&E species

Stream and soil condition

Water quality Average concentration of selected constituents

Recent listing of impaired streams and water bodies from the EPA’s 303(d) 

TMDS tracking system

Water temperature Average temperature readings at all available USGS stations in watershed

Water quantity Recent mean annual flow as a percent of pre-settlement mean annual flow

Fish populations Fish population by species, compared with unimpaired potential

Soil quality Measures of key constituents and indicators
USDA Forest Service RMRS-GTR-251. 2010. 9

Selecting Indicators

Three groups of variables were identified as useful 

for the conceptual framework outlined above: stressors, 

at-risk resources, and watershed condition variables 

(left-hand column of table 2). Stressors are either human 

activities or influences, such as mining, that may place 

stress on the watershed and that, being human influences, are amenable to change via human action; or 

natural events or susceptibilities that may intersect with 

human activities, such as wildfire or soil erosion. At-risk 

resources are human uses and concerns that are vulnerable to stressors. Examples include reservoirs that are 

susceptible to sedimentation, pristine streams subject 

to deterioration, and endangered species whose habitat 

may be harmed. Finally, condition variables are instream 

or soil measures indicating whether or not a problem 

exists, such as measures of water quality.

The variables of each group are represented by a 

set of indicators, which together characterize watershed condition according to our conceptual model. As 

explained below, we first identified a set of potential 

indicators for each group of variables (table 2), and then 

selected a subset of those indicators for which good 

quality, spatially consistent data were available.

Stressors

The largest, most complex set of indicators in the 

conceptual model is associated with watershed stressors. Each identified problem—sediment, nutrients, and 

toxics—provides a context and rationale for an associated set of stressors as discussed below. Notice that the 

importance of some stressors depends on where the 

activity occurs. For example, the effect of agricultural 

cultivation is most serious when it occurs on steeper 

slopes, and OHV use is more serious when it occurs 

on highly erosive soils or near streams. Thus several 

of the stressor variables are specific to slopes, soils, or 

hydrologic networks within a watershed.

Sediments consist of soil particles that are carried 

along in stream flow, some of which settle on the 

stream bottom or in lakes and reservoirs. Suspended 

sediments increase turbidity and transport plant nutrients, toxics, pathogens, and other potential pollutants 

attached to the soil particles. Sustained high turbidity 

can reduce photosynthesis by algae, reduce the success 

of sight-feeding fish, and degrade the quality of drinking water (or increase the cost of water treatment). 

Settling particles reduce the porosity of gravel beds, 

causing anaerobic conditions unsuitable for spawning 

and blocking emergence of fry from the gravels, lower 

storage capacity of lakes and reservoirs, and interfere 

with navigation.

Based on 1982 NRI (National Resource Inventory) 

estimates of erosion on nonfederal rural lands, in 

light of sediment transport and delivery predictions, 

discharge rates into rivers and streams from cropland 

were estimated to be more than five times the rate 

from forest land (Gianessi and others 1986). Of the 

total sediment discharge from non-federal lands, 57 

percent was estimated to originate on cropland, 16 

percent on rangeland, 10 percent on forest land, 5 percent on pastures, and 12 percent on other lands (mines, 

quarries, farmsteads, and other uses). Given the relative amounts of land of these different land covers, 

cropland and other lands are clearly the land covers 

of greatest concern. Jones and others (2001) found that 

stream suspended sediment loads across the United 

States were positively associated with the percent of 

the watershed in urban cover and negatively associated with wetland and riparian forest covers. In addition 

to activities associated with the land covers of concern 

(plowing and other soil disturbances with agriculture, 

construction in urban areas, and mining), several other 

activities are recognized as important causes of suspended sediment. Perhaps the most important of these 

is road construction, which both exposes soil surfaces 

and concentrates surface runoff, thereby increasing 

sediment transport capacity (Forman and Alexander 

1998). These effects were found to increase with slope 

and with loss of vegetation on the cutslope and in 

roadside ditches (Luce and Black 1999).

For forest management activities, Binkley and 

Brown (1993) concluded based on a review of many 

empirical studies that concentrations of suspended 

sediment often increase after management activities 

such as road construction, timber harvest, and forest 

site preparation following harvest, but that effects are 

highly variable across soils and slopes. Severe forest 

fires (for example, Benavides-Solorio and MacDonald 

2001; Cannon and others 2001; Moody and Martin 

2001; Neary and others 2005) and livestock grazing, 

especially in riparian areas (for example, Johnson and 

others 1978; Kauffman and Krueger 1984; Wohl and 

Carline 1996), can also add to suspended sediment. 

The impact of severe wildfire on sediment movement 

depends critically on the timing of precipitation events. 

Heavy rain following the Buffalo Creek (Moody and 

Martin 2001) and Hayman (Graham 2003) fires along 
10 USDA Forest Service RMRS-GTR-251. 2010.

the Colorado Front Range provided vivid examples of 

the potential for erosion following wildfire.

Livestock grazing is an important cause of soil disturbance and related suspended sediment, especially 

in arid environments, where water is a limiting factor 

and grazing animals naturally congregate near streams 

and other water bodies (Bartley and others 2010). In 

rangeland environments, where crop agriculture and 

other land use activities are less common, most of the 

damage to streams and riparian systems is thought to 

result from livestock grazing (Belsky and others 1999).

Based on this evidence, we propose that increases 

in suspended sediment will be associated with the 

following stressors: urban and suburban land uses 

including building construction; agriculture; mines, 

quarries, gravel pits, and other such concentrated rural 

land disturbances; roads, especially in riparian areas; 

timber harvest; livestock grazing, especially near 

streams; and severe wildfire (table 2). These activities 

tend to cause more erosion if they occur on steeper 

slopes, on highly erosive soils, and in areas of high 

erosivity (i.e., high precipitation volume and intensity).

Nutrients, primarily forms of nitrogen and phosphorous, are essential for primary food production in 

aquatic ecosystems, but at high levels they can cause 

excessive growth of aquatic plants and animals, which 

in turn cause murky water, algal blooms, and dense 

mats of aquatic plants, all characteristics of eutrophication. The U.S. Environmental Protection Agency (1990) 

concluded that eutrophication was the most common 

impairment of surface waters in the United States. 

Background levels of nutrients tend to be low for natural land covers not subject to atmospheric nitrogen 

deposition, but land uses and atmospheric deposition 

can cause levels to rise much above those background 

levels (Mueller and Helsel 1996).14

Nonpoint source discharges of nutrients are estimated to be over five times as great as point source 

discharges (Carpenter and others 1998). In a nationwide analysis, Omernik (1977) found that annual 

nutrient concentrations in streams draining 

predominantly agricultural watersheds were about 

nine times higher than in streams draining predominantly forested watersheds, and about four times 

higher than in streams draining predominantly rangeland watersheds. Smith and others (1987) reported that 

changes in agricultural fertilizer use and atmospheric 

deposition accounted for the significant increases in 

nitrogen concentrations across the United States observed over the period 1974 to 1981. Similarly, Driscoll 

and others (2003) found that the main anthropogenic 

nitrogen inputs to Northeastern watersheds were from 

atmospheric deposition, agriculture, and human food 

importation and consumption (and related sewage). 

Jones and others (2001) found for the Chesapeake 

Bay Basin that high nutrient levels were associated 

mainly with agricultural and urban land covers and 

with atmospheric deposition of nitrogen. Carpenter 

and others (1998) and Wickham and others (2005) both 

concluded that nitrogen and phosphorous concentrations across the United States were largely associated 

with agricultural and urban land uses. Concentrated 

animal feeding operations (feedlots) are also a significant source of nutrients (Mallin 2000). Conversely, forest cover, especially in riparian areas, was negatively 

associated with stream nutrient levels (Jones and others 2001) and timber harvest tends to have only a small 

impact on stream nutrient levels (Binkley and Brown 

1993). Based on all this evidence, we propose that the 

following stressors are important for stream nutrients: 

human presence indicated by urban land cover and 

housing density (the latter will pick up rural as well as 

urban inputs), agriculture, feedlots, and atmospheric 

deposition (table 2).15

Toxics are chemicals that cause damage to plants 

and animals (both vertebrates and invertebrates, and 

including humans) at low levels. They include toxic 

heavy metals (for example, mercury, lead, cadmium, 

and arsenic), some pesticides (including herbicides), 

some industrial chemicals (for example, PCBs), 

some pharmaceuticals, and acids. Heavy metals exist naturally and are essential for life but are toxic at 

elevated levels. Mining can greatly increase heavy 

14 Chronically elevated nitrogen levels in soils can also inhibit growth of alpine and subalpine plant species, including 

evergreen forests, and continual acid deposition can increase rates of nutrient leaching from the soil profile (Driscoll and 

others 2003). However, in most locations nitrogen is a limiting factor and nitrogen from atmospheric deposition acts as a 

fertilizer.

15 Most atmospheric deposition results from activities over which watershed managers have no control. Atmospheric 

deposition is included here nevertheless because such deposition can exacerbate the nutrient problem, making land-based 

mitigation all the more important.
USDA Forest Service RMRS-GTR-251. 2010. 11

metal discharge, and long-abandoned mining sites 

may continue to contribute excess levels of such metals to the stream as metal-bearing fine sediments are 

washed out of tailings by heavy rains or enter the 

surface water from polluted shallow groundwater 

near the mines (for example, Courtney and Clements 

2002; Roline 1988; Rosner 1998). Of course, the pollution potential of mines differs greatly with the geology 

of the area (and thus the substances mined) and the 

mining processes used. Other sources of toxic levels of 

heavy metals are some activities, such as some manufacturing and refining processes, common in urban 

areas (for example, Kelly and others 1996; Pouyat 

and others 1995), vehicle traffic (for example, Albasel 

and Cottenie 1985; Forman and Alexander 1998), and 

thermo-electric power plant emissions.16 Pesticides are 

used primarily on agricultural lands and in urban and 

suburban areas. Pesticide application rates (kg/km2/

yr) on agricultural land tend to be roughly 1000 times 

greater than rates on forested land (Brown and Binkley 

1994). Although uses of herbicides in urban areas do 

not approach those in agriculture, uses of insecticides 

have been found to be similar (for example, Hoffman 

and others 2000; Paul and Meyer 2001). Industrial 

chemicals tend, of course, to be used at industrial and 

mining sites. Pharmaceuticals are mainly associated 

with the presence of humans or domesticated animals. 

Acids (primarily sulfuric and nitric acid) reach the 

soil or water bodies in acidic precipitation resulting 

from the burning of fossil fuels (for example, at power 

plants, in vehicles). These acids can damage leaves and 

needles and lower the pH of water bodies into which 

they fall. Further, when washed into the soil, the acids, 

if not buffered sufficiently, can leach soil nutrients 

and toxic substances from the soil, hampering plant 

growth and, when washed on to receiving waters, 

aquatic life as well (Driscoll and others 2003). Based on 

this evidence, we suggest that the following stressors 

are relevant to toxics: mining of heavy metals and 

other toxic substances; agriculture; activities associated 

with housing, landscape maintenance, and commercial 

enterprise; industrial sites; feedlots; and atmospheric 

deposition of acids and heavy metals (table 2).

Resources at risk

Resources at risk of damage from the stressors just 

mentioned include (1) water bodies (mainly lakes, reservoirs, and marshes), which may support a variety of 

uses including water storage, recreation, scenic viewing, species habitat, and water supply for various uses, 

and which are subject to damage from sedimentation 

or water quality degradation; (2) stream segments that 

support one or more of a variety of uses including species habitat, preservation goals (perhaps as part of the 

Wild and Scenic River system), recreation (including 

fishing, swimming, boating, hiking, and picnicking), 

water supply, and navigation, all of which are subject 

to the three aforementioned watershed problems; (3) 

drinking water intakes; and (4) sensitive animal and 

plant species (table 2). For each resource at risk, one 

ideally would measure a trend, the rate of change in 

the quantity or quality of the resource, such as the 

sedimentation rate of reservoirs, the gain or loss in 

length of pristine streams, the change in the quality of 

water at drinking water intakes, or the rate at which 

threatened and endangered (T&E) species are listed 

or delisted. Lacking data to support measurement of 

trends, one could merely identify the presence of such 

resources at risk, such as the area of lakes, length of 

pristine streams, or number of T&E species.

Measures of watershed condition

The condition of soils, streams, lakes, and reservoirs 

is indicated by measures of soil quality and productivity and of water quantity (volume and timing), quality, 

and temperature as well as measures of aquatic species 

populations (table 2). Of course there are many water 

quality constituents and many aquatic species that 

may be of interest.

Indicators

To assess watershed condition, variables must be 

represented by measurable indicators. For example, 

the roads stressor might be represented by a measure 

of road density such as kilometers of road per square 

kilometer of watershed, and general human presence 

might be represented by population density, rate of 

16 Power plants affect mercury in watersheds in two ways. First, mercury is a direct by-product of coal burning (roughly onehalf of the electricity produced in the U.S. is from coal-fired plants). Mercury and other substances from coal burning reach 

land downwind in wet or dry deposition. Second, acidic atmospheric deposition—which forms when emissions of SO2- 

and nitrogen oxides (NO2, NO) convert to acids when they come in contact with water in the atmosphere—may dissolve 

mercury and other toxic elements (e.g., aluminum) in the soil, which then may be taken up by plants or leached from the 

soil and flow on to rivers, lakes, and estuaries.
12 USDA Forest Service RMRS-GTR-251. 2010.

population change, or housing density. Table 2 lists 

many of the indicators that were considered for representing the identified variables.

The indicators in table 2 are listed without consideration of data availability. Table 3 lists the indicators 

that we ultimately chose to characterize risk of one or 

more problems, along with their associated spatial data 

sets and the methods we used to compute the indicator 

variables. Selection of indicators was constrained by 

the availability of good quality, comprehensive data.

Table 4 lists indicators we did not use along with 

the reason for exclusion, which in most cases was the 

lack of comprehensive and consistent data.17 Unfortunately, and most importantly, we did not find or 

obtain adequate data on several important stressors 

(for example, recent fires, recent timber harvests, feedlots, OHV use levels), some key resources at risk (for 

example, important streams, presence of native fish 

populations), or any stream or soil condition variables.

Table 5 lists background variables that are used 

to describe the watersheds, but are not themselves 

indicators used to order watersheds. These variables 

include purely descriptive measures (for example, 

watershed mean precipitation rate and mean elevation), variables used in the computation of indicators 

(for example, watershed population, total length of 

streams in the watershed), and miscellaneous other 

variables (for example, length of impaired streams as 

listed in the EPA TMDS tracking system).

The stressors fall into five groups (table 3). For 

example, human population density and growth, 

housing density, and percent urban land cover fall 

within the development group. Indicators within a 

group may be significantly correlated. Yet each may 

make a unique contribution to the problem at hand, 

as does housing density in capturing the location of 

sources of nutrients from second homes that are not 

reflected in population data. We attempt to control for 

Table 3. Approach to indicator measurement.a

Indicator Approach

Stressors by group

Development group

Population density Area-weighted population per km2 from 2000 census tract data.

Population growth Percent increase in area-weighted density per km2 from year 1990 to year 2000 

using county census data (census tract boundaries changed from 1990 to 2000).

Developed land cover Percent of watershed in developed land cover (classes 21, 22, 23, and 24) from 

2001 300m National Land Cover Dataset (NLCD) data, released by the U.S. 

Geological Survey.

Housing density Housing units per km2 in year 2000 based on 2000 US Census Bureau block 

(SF1) datasets, from Dave Theobald’s 100 m2 / cell data set.b

Roads group

Road density Meters of road (paved & unpaved together) per km2 of watershed land (excluding 

water bodies) based on the 2000 Geographic Data Technologies (GDT) roads 

database.c

Road-stream crossings Number of road-stream crossings per km of stream, from GDT roads intersected 

with the USGS 2004 National Hydrography Dataset (NHD) medium-resolution 

(1-to-100,000) stream coverage.d

Roads in riparian areas Total road length in meters (m) within riparian areas per km2 of riparian area 

(riparian area defined as 100m each side of stream), using GDT roads and 

medium-resolution NHD stream coverage.e

Roads on steep slopes Total length of roads (km) in areas with slope >45% per km2 of watershed, based 

on GDT roads and 30m, 1 degree DEM (1998). Potentially weighted by 

erosivity.

17 Hudy and others (2008) use four criteria for selecting independent variables: completeness, range, redundancy, and 

responsiveness to the dependent variable. Because we lack a quantitative dependent variable, we could not gauge 

responsiveness. Completeness refers to comprehensive and consistent data, a criterion on which we placed considerable 

emphasis. Range refers to the ability of the variable to distinguish among watersheds; the ranges we observed were 

sufficient that we did not eliminate any variables on this basis. Redundancy is measured by the correlations among 

prospective dependent variables. We deal with redundancy via our weighting procedure.
USDA Forest Service RMRS-GTR-251. 2010. 13

Table 3. Continued.

Indicator Approach

Stressors by group

Farm and ranch group

Cultivation on gentle slopes Percent of watershed area in agricultural land cover (i.e., cultivation) on slopes 

 ≤ 3% (Jones and others 2000; Wischmeier and Smith 1978). Cultivation from 

300m 2001 NLCD. Cultivated area defined as NLCD classes 81 (pasture and 

hay) and 82 (cultivated crops). Slopes based on 300m, 1 degree DEM (1998). 

Potentially weighted by erosivity.

Cultivation on steep slopes Percent of watershed area in cultivation on slopes > 3%; same sources as 

cultivation on gentle slopes. Potentially weighted by erosivity.

Cultivation—all slopes Percent of watershed in cultivation; same source as cultivation on gentle slopes.

Cultivation on highly erosive soils Percent of watershed area in cultivation on erosive soils, with erosive soils 

specified from the 1998 BASINS STATSGO data, where erosive soils are those 

with K factor times the square root of maximum slope range exceeding 

a threshold of 1.64; cultivated area determined as described above.

Potentially weighted by erosivity.

Livestock grazing Animal units per km2 in year 2007 based on USDA Census of Agriculture county 

data, released by the National Agricultural Statistics Service.f

Confined animal feeding Animal units per km2 in year 2007 based on USDA Census of Agriculture county 

data, released by the National Agricultural Statistics Service.g

Mining group

Mining land cover Percent of watershed in mining land cover (class 32, quarries, mines, gravel pits) 

from 1994 300m NLCD data.h

Mines Total number of active and inactive mine sites potentially yielding toxics per 

1000 km2 of watershed, from the 1998 US Bureau of Mines MAS/MILS 

database.i,j

Other group

Area of potentially damaging Percent of area with high risk of losing key ecosystem components in a fire, from 

wildfire the 1 km grid Fire Regime Current Condition Classes (Schmidt and others 2000),

condition class 3 (“fire regimes have been significantly altered from their 

historical range; the risk of losing key ecosystem components is high”).k

Atmospheric deposition—acid Mean annual (2000-2006) input of NO3

− and SO42− in kg/ha, in wet

forming compounds atmospheric deposition from the National Atmospheric Deposition Program 

data.l

Dams Number of dams per km2 of watershed, from the 2009 version of the National 

Inventory of Dams (NID) database maintained by the Army Corps of 

Engineers.m

At-risk resources

Water bodies (lakes, reservoirs, etc.) Presence of water bodies (m of water body perimeter per km2 of watershed) 

based on the medium-resolution NHD water bodies data released by the USGS 

in 1999.n In the NHD, water bodies include lakes, ponds, reservoirs, playas, 

swamps, marshes, and ice masses; we excluded playas and ice masses for this 

indicator.

Municipal water intakes Total number of intakes per km2 of watershed, from the 2009 EPA’s Safe Drinking 

Water Information System, Federal Version.

Wild and scenic rivers Length (m) of wild and scenic rivers per km2 of watershed, with length from the 

data set Federal Land Features of the United States—Parkways and Scenic 

Rivers (1:2,000,000), of the National Atlas of the United States, 2006, 

originally extracted from USGS digital line graph data.

T&E aquatic species Number of aquatic animal and plant species with an ESA status of threatened or 

endangered, from the NatureServe database.o,p

T&E terrestrial species Number of terrestrial animal and plant species with an ESA status of threatened 

or endangered that are not also aquatic species, from the NatureServe 

database.o,q

a This approach is applied to entire watersheds, to NFS land within the watersheds, and to non-NFS land within the watersheds (where 

“watershed” is used in this table, the designation may refer to either whole watersheds or each of these two portions of watersheds). b The data are reported in 15 classes, with each class representing a range in units per 1000 hectares. For example, class 2 is 2−8 units/1000 

ha and class 10 is 248−494 units/1000 ha. The smallest (1st) and largest (15th) classes are unbounded, at <= 1 and >24,711 units/1000 ha, 

respectively. We converted classes to numbers of units per thousand hectares by using the midpoint of the range of each class (for the 1st and 

15th classes we assigned 0.5 and 30,000 units/1000 ha), and computed the average density across the cells within an area of interest.
14 USDA Forest Service RMRS-GTR-251. 2010.

c The GDT roads coverage is an offspring of TIGER, which was assembled in the late 1980s from a variety of source materials that reflected quad 

mapping over many years. Thus, the GDT roads coverage is both generally out-of-date and inconsistent across space in terms of when the 

roads were inventoried. However, roads are such an important variable that a road coverage is essential for watershed condition assessment. 

The GDT road coverage is considered sufficiently precise to support only broad-scale comparisons. d The NHD stream coverage is based on topographic contour maps, which generally do not include all streams. For example, in one careful 

comparison, Hansen (2001) found for a 728 km2 watershed in the Southeast that the 1-to-100,000 NHD coverage captured only about onehalf of the total perennial stream length (the 1-to-24,000 coverage captured about three-fourths of perennial stream length). Further, almost 

none of the intermittent and ephemeral streams were captured by the NHD coverage. Hansen’s findings may not apply to other regions of 

the U.S. We examined the 2009 NHD as a replacement for the 2004 NHD, but at the time we tested it the newer version had many missing 

records and was therefore not used. e Using a 200m buffer around all streams is a simplification that fails to reflect how the actual size of riparian areas varies across different 

topography. In mountainous terrain the riparian area may be much narrower than 200m, whereas on flat land where streams are poorly 

differentiated the riparian area may be much wider. f

 The following livestock categories were included (listed here with the Ag Census tables from which the data came): cattle (table 11); horses 

(table 15); sheep (table 16); goats (table 17); llamas, alpacas, bison, deer, elk, and mules (table 24). Animal unit computations were based on 

the following conversion factors: cattle (1), horses (1.25); sheep (0.2); goats (0.2); llamas (0.2), alpacas (0.2), bison (1.25), deer (0.2), elk (0.5), 

and mules (0.8). Animals were assumed to be found only on slopes of less than 30%. The grazing density on forest land was assumed to be 

0.4 times the density on other lands. Only cattle, horses, and sheep were assumed to be found on NFS land, whereas all listed species were 

potentially found on non-NFS land. The following animal species were assumed to occur only on the following cover classes: cattle, horses, 

and sheep on forest (classes 41, 42, and 43); all species on range (classes 52 and 71) and on agriculture pasture (class 81); cattle, sheep, 

goats, llamas, and alpacas on agriculture crop land (class 82); and horses, mules, alpacas, and llamas on low-density developed land (classes 

21 and 22 only). Within available land areas, animals were assumed to be spread evenly across the area. The density for a watershed or part 

of a watershed was computed as an area-weighted average of respective county-level densities. g The following livestock and poultry categories were included (listed here with the Ag Census tables from which the data came): fed cattle 

(table 11); hogs (table 12); chickens (layers, pullets, broilers, table 13); turkeys (table 13); mink (table 22); rabbits (table 24). Animal unit 

computations were based on the following conversion factors: fed cattle (1); hogs (0.3); chickens (0.003); turkeys (0.015); mink (0.01); and 

rabbits (0.01). Operations were restricted to slopes of less than 30%, and to non-NFS land. Fed cattle was assumed to occur only on range 

(classes 52 and 71) and agricultural lands (classes 81 and 82); hogs, chickens, turkey, and mink were assumed to occur only on agricultural 

lands (classes 81 and 82); rabbits were assumed to occur only on agricultural lands (classes 81 and 82) and low-density developed land 

(classes 21 and 22). Within available land areas, animals were assumed to be spread evenly across the area. The density for a watershed or 

part of a watershed was computed as an area-weighted average of respective county-level densities. h The 2001 NLCD includes quarries, mines, and gravel pits within the barren land class, which also includes bedrock, desert pavement, scarps, 

talus, volcanic material, and sand dunes. We used the earlier (1992) NLCD for this stressor because it isolated mining impacts. i

 The MAS/MILS data set relies in part on state-level data collection efforts that sometimes differed in methods, resulting in inconsistencies across 

states (Shields and others 1995). The data set was included here nonetheless because of the importance of mining to water quality. We 

removed what were, for our purposes, duplicate records by dissolving on SEQNUM and filtered using “bad” mines criteria.

j

 To select toxic commodities we relied on the draft unpublished report “Guidance for Implementation of Trace Elements National Synthesis 

Within the National Water-Quality Assessment Program,” table 2b (Trace Elements of Interest to the National Water-Quality Assessment 

Program—Water), produced by the Trace Elements National Synthesis Planning Team, USGS, 1998 (226 pages). A mine was included if 

it produced at least one of the following commodities: aluminum, antimony, arsenic, barium, beryllium, boron, chromium, coal, cobalt, 

copper, gold, iron, lead, manganese, mercury, molybdenum, nickel, silver, sulfur, titanium, uranium, vanadium, or zinc. k An option, not employed herein, is to weight the wildfire stressor by erosivity.

l

 The National Atmospheric Deposition Program data we used were in the form of a nationwide spatially interpolated grid at the 2.5 km level of 

resolution available at http://NADP.sws.uiuc.edu. m This stressor was not actually used (i.e., it was assigned zero weight) because dams do not directly cause the selected problems. It is included 

here nonetheless because it is such a significant stressor for other problems and is available in RWI.xls if users would like to assign it a 

positive weight in their own analyses. n This indicator is intended to capture the potential vulnerability of water quality of standing water bodies to activities on the land. Perimeter was 

used instead of area because perimeter was thought to better reflect the connectivity of water bodies to the surrounding land. The correlation 

of perimeter to area across all water bodies in the full set of watersheds is 0.86. o This indicator uses NatureServe’s Element Occurrence species location database. The data we obtained track species with federal status 

assigned pursuant to the Endangered Species Act, including not only listed threatened and listed endangered (T&E) species but also 

species proposed for listing or species that are candidates for listing. This indicator, however, includes only listed endangered and listed 

threatened species (plants and animals together). A species was assigned a habitat value by NatureServe if the habitat is known to contribute 

significantly to the survival or reproduction of the species at some point in its live cycle. Thus, some species were assigned to both aquatic 

and terrestrial habitats (double-counting of species occurred in 802 watersheds). Information on non-listed species and listed species not yet 

assigned habitat values is presented in table B1. Species’ geographical ranges are imprecisely mapped. Mapping accuracy is categorized in 

the NatureServe data as high, medium, or low. We used rules for accepting a species as present in the watershed that differed by accuracy 

class, as explained in Appendix B. p The aquatic indicator includes species assigned aquatic habitat values. Some T&E species have not yet been assigned the habitat values 

necessary to label them as being either aquatic or terrestrial; unlabeled species occurrences account for 2.6% of all listed species 

occurrences (and 3% of all non-listed species occurrences). The number of aquatic T&E species includes the subset of aquatic “obligate” 

species, those species that spend their lives in water, including crayfishes, fairy, clam, and tadpole shrimps, freshwater and anadromous 

fishes, freshwater mussels, and freshwater snails; and non-obligate species, which include plants, amphibians, or reptiles such as turtles 

(some of which are more aquatic than others). q The terrestrial indicator was computed as the total number of listed species minus the number of aquatic listed species and is therefore equal to 

the number of terrestrial listed species that are not also aquatic species plus the number of unassigned species. This computation was used in 

place of the number of terrestrial species in order to avoid double-counting of species assigned to both the aquatic and terrestrial categories.
USDA Forest Service RMRS-GTR-251. 2010. 15

Table 4. Indicators not used.

Potential indicator Limitation

Stressor

Housing in riparian areas Thought to be largely captured by roads in riparian areas.

Industrial sites Have not attempted to incorporate this vast database.

OHV use level Have not found national data on this.

Cultivation in riparian areas Thought to be largely captured by roads in riparian areas

Recent timber harvest Have not found national data on this.

Feedlots Have not found detailed national dataa.

Atmospheric deposition—Hg Mercury deposition was only available for the East in the NADP data.

Recent severe burns Some polygon coverages exist, but are incomplete and inconsistent. Most recent 

data are in point coverages.

Susceptibility to landslides A data set is available (Godt 2001; Radbruch-Hall and others 1982), but it was 

judged to lack sufficient accuracy.

Stream and soil condition

Average concentration of selected Insufficient number of gages measuring water quality within 

constituents watersheds.

Length of stream on EPA impaired 303(d) list of impaired waters is of inconsistent data quality.

waters list

Average temperature readings Insufficient number of gages measuring water quality within watersheds

Recent mean annual flow as a Inconsistent, incomplete recent national data for 5th-level watersheds,

percent of pre-settlement mean missing data for pre-settlement flows.

annual flow

Fish population by species Lack of data

Soil quality Lack of consistent and comprehensive data.

At-risk resource

Trends in availability or quality of Lack of consistent and comprehensive data.

resources

Length of pristine streams Lack of consistent and comprehensive data.

Recreational fishing visitor days Lack of consistent and comprehensive data.

a The EPA maintains a Contained Animal Feeding Operations database, but it does not suit our purposes because not all CAFOs are listed and 

because the location data are of mailing addresses, which may be different from the locations of the actual operations.

the colinearity in two ways. First, in assessing a given 

problem, we do not include indicators that, across 

all watersheds, are strongly correlated with another 

included indicator, where we define strongly correlated as having a Pearson correlation coefficient (R) 

greater than 0.6. Second, the weights we assign to the 

individual indicators (described in the next section) 

were set partially in light of the correlations among the 

indicators. For example, if indicators for three human 

presence stressors are included and, in aggregate, are 

judged to account for 30 percent of the problem, the 

weights assigned to the individual indicators sum to 

reflect the 30 percent judgment.

Scaling and Weighting Procedures

When characterizing watershed condition, indicator variables must be combined to reach an overall 

assessment of the relative positions of the watersheds. 

Ideally a multivariate model would be available that, 

across all important ecosystems, accounted for all 

relationships between stressors and watershed condition as well as for interactions among the stressors. 

Because watersheds are so complex and onsite inventory is expensive, such a comprehensive model will be 

an unmet goal for many years to come. In the absence 

of such a model we would wish to at least quantify, 

for some of the most prevalent ecosystems, the relationship of each stressor to each resource at risk and 

to each major stream and soil condition variable. For 

example, the effect of road density on suspended 

sediment in streams would be described in a series 

of equations, one for each major soil type and physiographic class. Such equations would probably be 

nonlinear, reflecting critical points in road density 

beyond which suspended sediment increases more 

rapidly than at lower densities. Of course, such relations would depend not only on slope and geologic 

considerations, but also on the design, surface, and 

maintenance of the roads. As this example suggests, 

however, such quantification is also very complex. 
16 USDA Forest Service RMRS-GTR-251. 2010.

Although such relationships are fairly well understood 

for some stressors in a few well-studied zones, they 

are not well understood across most stressors or across 

the full geographic scope of the current assessment.

Without detailed models showing the quantitative 

relationships between the independent variables and 

the measure of watershed condition (or, in our case, 

the measure of risk of impaired watershed condition), 

we are left relying on simplifying assumptions. Our 

principal simplifying assumption is that the relation 

between all indicators and watershed condition is linear—that the scales differ only by a constant slope and 

intercept. In other words, we assume that two different 

but equal-sized moves along an indicator scale (say in 

km of roads per km2 of watershed) cause two corresponding equal-sized moves along the index of risk of 

impaired watershed condition.

Numerous procedures have been proposed for 

combining across stressors or other relevant variables 

when a quantitative multivariate model is unavailable 

(for analyses of several, see, for example, Ebert and 

Welsch 2004; Smith and others 2003; Zhou and others 2006). Such procedures would combine indicators 

in some way to achieve an overall measure of risk of 

impaired watershed condition. The procedures should 

deal with three issues that are encountered in creating 

an aggregate index: the units in which the indicators 

are measured may differ from one indicator to the 

next; the indicators may to some extent be correlated 

with each other; and the indicators may be of unequal 

importance in characterizing the dimension at issue, 

which in our case is risk of impaired watershed condition. We employ a rather simple procedure, roughly 

similar to that used in other recent assessments of 

watershed condition, which relies on scaling (also 

called normalization) to deal with the units of measurement issue, avoidance of indicators that are highly 

inter-correlated, and weighting to reflect the relative 

Table 5. Background variables.

Variable Approach

Size of watershed Total area and total land area (excluding water bodies), in km2.

Watershed elevation Minimum, maximum, and mean elevation based on 30m DEM.

Precipitation 1961-1990 average annual precipitation from 2 km grid generated by the PRISM model.

Erosivitya Rainfall-runoff erosivity factor R, in hundred ft-ton/ac/hour (Renard and others 1997). Erosivity 

is used not as an independent stressor, but rather to weight some other stressors that

are exacerbated by erosivity.

Population in year 2000 Total area-weighted population from 2000 census tract data.

Land ownership Percent of watershed in NFS land, other federal land, and private land based on 2003 USGS 

Federal Lands and Indian Reservations proclamation boundaries for non-NFS land and on 

detailed forest land ownership boundaries obtained in July, 2009, from the Forest Service’s 

Automated Lands Project (ALP) for NFS land.

Land cover type Percent of watershed in each of seven major land cover types (forest, rangeland, water, wetland, 

agriculture, developed, and barren) based on 300m 2001 NLCD (two of these are used as 

stressors).b

Riparian buffer Percent of area in riparian buffer (see table 3 for details).

Slopes Percent of area in slopes greater than 3% and greater than 45% (see table 3 for details).

Erosive soils Percent of area in highly erosive soils (see table 3 for details).

Stream length From medium resolution USGS 2004 NHD streams.

ESA species The following categories of ESA federal status species from the NatureServe database (see table 3 

for source): aquatic species other than listed, terrestrial species other than listed, listed species 

not assigned habitat values, other than listed species not assigned habitat values, listed aquatic 

obligate species, other than listed aquatic obligate species, and total number of federal status 

species (see Appendix B).

Impaired waters Km of steam and area of water body within the watershed failing to meet criteria for designated 

uses, as listed under 303(d) in the EPA’s TMDS tracking system, 2002.

a The R-factor (which is used in the revised USLE) is the long-term average of the annual sums across all significant storms during the year of the 

product of E and I, where E is the kinetic energy of a storm and I is rainfall intensity, measured as maximum 30 minute rainfall depth during 

the storm. The energy component is meant to represent sediment transport capacity, whereas the intensity component is meant to represent 

particle detachment capacity. We began with a digitized isoquant map (the isoquants are called isoerodents) developed in about 1998 by the 

NRCS and then used the ArcGIS “Topo to Raster” tool to interpolate the projected polyline shapefile to a 1km raster with continuous values 

for R-Factor, using iterative finite difference interpolation. This variable is used to weight selected stressors affecting sediment. b The seven cover types are constructed from the following 2001 NLCD classes: forest (41, 42, 43), rangeland (52, 71), water (11, 12), wetland 

(90, 95), agriculture (81, 82), developed (21, 22, 23, 24), and barren (31).
USDA Forest Service RMRS-GTR-251. 2010. 17

importance of the different indicators and to account 

for remaining inter-correlation.18

Very generally then, the procedure for forming the 

composite measure of risk involves: (1) measuring 

each watershed for each indicator; (2) selecting a set of 

watersheds (or portions of watersheds) to be compared and then scaling of values for each indicator in 

order to provide comparability of value range across 

indicators for watersheds within the set; (3) multiplying the scale values by weights, with the weights 

expressing principally the relative importance of the 

indicators to overall risk of impaired watershed condition (with higher weights indicating greater importance); and (4) additively combining the weighted 

values for the indicators for each watershed. This is 

sometimes called the “weighted sum” method.

With proper scaling of the indicator values, the scale 

values are dimensionless and the resulting aggregated 

index of risk is invariant to a positive linear transformation of the original indicator values.19 For example, 

it will make no difference to the aggregate index 

whether developed land cover is measured in acres or 

in percent of total area, or roads in riparian areas are 

measured in meters per km2 or miles per mi2. Similarly, to take an interval scale variable, it would make 

no difference whether temperature were measured in 

degrees Celsius or degrees Fahrenheit.

We implement the procedure in an Excel workbook 

(RWI.xls, as described in the “Results and Analysis” 

section), where the individual steps of the procedure 

can be observed.20 The procedure, described here in 

terms of whole watersheds, has the following six steps 

(note that this procedure was also used for the NFS 

portions of watersheds and for non-NFS portions of 

watersheds):

1. For each watershed, the value for each indicator 

(table 3) is computed. For all indicators, a higher 

value indicates a greater level of concern.

2. For each indicator, the watershed values are normalized. That is, they are transformed linearly to a 

scale ranging from 0 to 1 using xi,n = (vi,n - vi

min) / 

(vi

max - vimin) where xi,n is the scale value for indicator i in watershed n, vi,n is the value of indicator i

in watershed n in whatever units were used for the 

indicator, and vi

min and vimax are the minimum and 

maximum values for the indicator, respectively.21,22

The values of vi

min and vimax represent whatever 

set of watersheds is chosen to provide the context 

for comparison. When the basis for comparison is 

the set of watersheds being assessed, the resulting 

scale values range from 0 to 1. For ease of notation, 

this procedure for linearly scaling an indicator is 

indicated by Ω; thus xi,n = Ω (vi,n).

3. Separately for each set of indicators (stressors and 

at-risk resources) and each problem, the scale values 

for the indicators are each multiplied by weights 

and the products are summed for each watershed, 

producing two sums for each problem for each watershed, one for each set of indicators. The weights 

specify the relative importance of the indicators. 

As implemented in RWI.xls, the weights of each set 

must sum to 1.

4. For each watershed and problem, the stressor and 

at-risk resource values of step 3 are each multiplied 

by weights and the products are summed.23

18 Although we limit the set of indicators to those that are not highly inter-correlated, we do not go the next step and attempt 

to select a parsimonious set of indicators. Such an effort, which could be achieved using Principal Component Analysis or 

similar approaches, should be pursued if this assessment were to be followed over time by other national-level efforts to 

assess the risk of impaired watershed condition. 

19 By “positive” linear transformation we mean a linear transformation with a slope coefficient > 0.

20 RWI.xlsx (for Risk of Watershed Impairment) and related material are available at http://www.fs.fed.us/rm/value/

watershedcondition.

21 This transformation does not work if all of the values for a given indicator are identical, for in this case the minimum equals 

the maximum, resulting in division by zero.

22 The scale values provide an interval scale metric. With an interval scale metric, the zero point has no absolute meaning (i.e., 

it does not represent the absence of the thing being measured), but differences between numbers have meaning (e.g., a 

difference of 0.4 in scale value is twice as large as a difference of 0.2).

23 An alternative to the weighted sum approach would be to multiply the scale value of stressors by the scale value of at-risk 

resources. This approach essentially weights the stressor value by the at-risk resource value and vice versa. Compared 

with the weighted sum method, this approach more effectively separates those watersheds with a confluence of high stress 

and high at-risk resources from watersheds without such a confluence. However, a disadvantage of this approach is that 

differential weights for stressors and at-risk resources cannot be used (the multiplication of the stressor and at-risk resource 

scale values essentially weights each component equally).
18 USDA Forest Service RMRS-GTR-251. 2010.

The weights specify the relative importance of the 

two sets of indicators. The weights must sum to 1.

5. The previous two steps were used separately for 

each identified problem (sediments, nutrients, and 

toxics). Step 5 combines results of step 3 across 

problems, separately for each of the two sets of 

indicators. That is, for stressors and at-risk resources separately, the values of step 3 for the three 

problems are each multiplied by weights and the 

products are summed. The weights express the relative importance of the problems in characterizing 

overall risk of watershed impairment. The weights 

must sum to 1. The result is an index for stressors 

and another index for at-risk resources. Each index 

combines across problems, providing an overall 

indication of the relative position of the watersheds.

6. Then, to provide an overall watershed summary, 

for each watershed the stressor and at-risk resource 

values of step 5 are each multiplied by weights and 

the products are summed. The weights, which sum 

to 1, specify the relative importance of the two sets 

of indicators, as in step 4. (An identical watershed 

summary value can be computed by applying the 

weighting procedure of step 5 to the problem-specific values produced in step 4.)

The procedure is presented more precisely in 

table 6, assuming a population of watersheds (n), a 

set of stressor variables (i), a set of at-risk resources 

(j), a set of watershed condition problems (k), weights 

(w), a procedure for linearly scaling an indicator (Ω), 

and that s indicates stressors and r indicates at-risk 

resources.

The results of steps 3 to 6 (sums of weighted values, 

y, see table 6) are summarized for presentation in 

terms of scale values (x), ranks (R), percentile ranks 

(Pr), and risk levels (G). The scale values (x) are computed from y as they were from v in step 2, linearly 

transforming the y scale to a scale ranging from 0 to 1. 

Table 6. Procedure.

Step Computation

1 vi n,

s = value of watershed n for stressor i

v j,n

r = value of watershed n for at-risk resource j

2 x , , n in vs i

s = X^ h = scale value of watershed n based on stressor i

x j, , n n v r j

r = X^ h = scale value of watershed n based on at-risk resource j

3 y , ,, n ki w x s i n

s

i

k

s = /^ : h = sum of weighted scale values for stressors for problem k of watershed n

yk, ,, n kn w x r j

r jr

j

= /^ : h = sum of weighted scale values for at-risk resources for problem k of watershed n

4 , ,,

ss rr

kn k kn k kn y wy wy =⋅ +⋅ = sum of weighted values for problem k of watershed n

5 yn w y k k n, s s

k

= /^ : h= sum across problems of weighted values for stressors of watershed n

y wy n , r

k

k k n

r = R^ : h= sum across problems of weighted values for at-risk resources of watershed n

6 y wy wy wx n , s n

s r nr

k

=+= :: : R^ k knh= sum of weighted values of watershed n

Summary of results of step 6 (similar summaries are possible of the results of steps 3-5)

xn = Ω(yn) = scale value of watershed n

Rn = rank (yn) = rank of watershed n

Prn = Φ(Rn) = percentile rank of watershed n

Gn = Ψ(xn) = risk level of watershed n
USDA Forest Service RMRS-GTR-251. 2010. 19

Thus, for example, each y from step 4 is transformed as 

xk,n = Ω(yk,n) and each y from step 6 is transformed as 

xn = Ω(yn). Ranks are computed directly from y; identical ranks could be computed from x. Percentile ranks 

(Pr) simply group watersheds into categories; here we 

use five categories each containing 20 percent of the 

watersheds. Given 3672 watersheds, the 734 watersheds with the lowest y values, indicating a relatively 

low level of risk, would receive a Pr of 1, and the 735 

watersheds with the highest y values would receive a 

Pr of 5. To summarize by risk level, the scale values (x) 

are partitioned using a procedure we designate as Ψ. 

Thus, for example, Gn = Ψ(xn), where G indicates risk 

level. RWI.xls allows up to a 6-point risk scale, with 

the partitioning of the 0 to 1 range of scale values into 

the risk categories defined by the user. For presentation here we use all six categories with the first five 

categories representing equally sized intervals across 

the bottom half of the scale value range and the last 

category representing the top half of the scale value 

range, as follows:

Rating Scale value

1 0 to .1

2 >0.1 to 0.2

3 >0.2 to 0.3

4 >0.3 to 0.4

5 >0.4 to 0.5

6 >0.5 to 1

Using six equally sized intervals would preserve the 

interval scale information contained in the scale values. We deviate from the equal-interval approach for 

the top (highest risk) interval because, as will be seen 

in the results section, the upper half of the scale value 

range is populated by very few watersheds, due to the 

highly skewed scale value distribution. Compressing 

the top half of the scale into one risk category allows 

for additional distinctions within the part of the scale 

where the great majority of watersheds are found.

An objective procedure for assigning the weights 

( s wi , r wj , s w , r w , and wk ) would require a precise 

definition of watershed condition and a quantitative 

model expressing the relative contribution of each 

dependent variable to the overall measure of watershed condition (or to risk of watershed impairment). 

Lacking a precise definition and a quantitative model, 

the assignment of weights must rely on professional 

judgment in light of the available evidence. For the 

purpose of illustration, and for application in lieu of 

something better, we use the weights listed in table 7. 

The stressor weights within a group sum to the group 

weights listed in table 8. The weights in table 7 express our understanding of the relative contribution 

of each of the available indicators to risk of impaired 

watershed condition. The weights can be altered easily 

in RWI.xls. Users may wish to enter their own sets of 

weights.

Erosivity, the impact of rainfall on the soil, is a 

factor like slope, in that it accentuates the effect of 

a stressor. The higher the erosivity of an area, the 

greater is the amount of erosion that is likely to occur, 

all else equal. Unlike slope, which is treated here as a 

dichotomous variable in its role in the effect of roads 

or cultivation on sediment movement (see table 3), we 

treat erosivity as a continuous weighting variable but 

an optional one. The user has the option in RWI.xls to 

weight the road, cultivation, grazing, mining, and fire 

hazard stressors by erosivity using a simple multiplication of the erosivity value times the stressor value 

(vi) before the scale value of the stressor is computed.

Ordering of Watersheds

The ordering (rating and ranking) of watersheds 

was performed by problem (sediment, nutrients, toxics) for three geographical scales and for four land 

ownership categories. The geographical scales are 

individual NFS unit (national forest or grassland), NFS 

region, and the entire study area (the 48 contiguous 

states). Thus, for example, watersheds are ordered for 

the sediment problem (1) by NFS unit (producing 116 

sets of orderings), (2) by NFS region (producing eight 

sets of orderings), and (3) for the entire study area 

(producing one ordering). The ownership categories 

are entire watersheds, NFS land, non-NFS land, and 

non-wilderness NFS land. Orderings for NFS land are 

critical because the agency’s management actions are 

largely limited to NFS land. And within NFS land, 

orderings for non-wilderness land are most relevant 

both because non-wilderness land faces greater stresses than wilderness and because managers have more 

options on such land. The condition of non-NFS land 

is also relevant, because the agency may take action 

on NFS land to compensate for a problem on non-NFS 

land downstream, or may wish to work with partners to resolve problems off of NFS land. In addition, 

overall watershed orderings are useful for determining 

to what extent the combination of NFS and other land 

presents watershed-level problems. Crossing these two 

ordering breakdowns, based on geographic scale and 
20 USDA Forest Service RMRS-GTR-251. 2010.

Table 8. Aggregate weights of stressor groups.

Problem

Stressor group Sediment Nutrients Toxics

Development 0.20 0.30 0.20

Roads 0.37 0.00 0.05

Farm and ranch 0.30 0.55 0.35

Mining 0.05 0.00 0.30

Other 0.08 0.15 0.10

 Sum 1.00 1.00 1.00

Table 7. Weights.

Problem

Indicator variable Sediment Nutrients Toxics

A. Indicator variables

Stressor

Population density 0.10 0.05

Population growth 0.05

Developed land cover 0.15 0.10

Housing density 0.10 0.05

Road density 0.10 0.05

Road-stream crossings 0.15

Roads in riparian areas 0.05

Roads on steep slopes 0.07

Cultivation on gentle slopes 0.05

Cultivation on steep slopes 0.15

Cultivation – all slopes 0.45 0.30

Cultivation on highly erosive soils 0.08

Livestock grazing 0.10 0.05

Confined animal feeding 0.10 0.05

Other (mines, quarries, etc.) land cover 0.05

Mines 0.30

Area of potential severe wildfire 0.05

Atmospheric deposition 0.15 0.10

Dams

At-risk resource

Water bodies 0.40 0.40 0.30

Municipal water intakes 0.30 0.30 0.25

Wild & scenic rivers 0.10 0.10 0.10

T&E aquatic species 0.20 0.20 0.30

T&E terrestrial species 0.05

B. Indicator categories

Stressors 0.7 0.7 0.7

At-risk resources 0.3 0.3 0.3

C. Problems 0.6 0.3 0.1
USDA Forest Service RMRS-GTR-251. 2010. 21

land ownership, results in 3 x 4 = 12 sets of orderings 

per problem, or 48 sets in all (table 9). In addition, the 

orderings for all three problems together were performed separately for stressors and at-risk resources, 

producing another 24 sets of orderings (see RWI.xls).

Watershed boundaries rarely coincide with NFS 

unit boundaries. Some watersheds are entirely contained within NFS units, but most watersheds also 

contain non-NFS land. Some watersheds contain land 

from two or three NFS units or even two or three NFS 

regions, but most watersheds are associated with only 

one NFS unit.24 Further, NFS units may be made up 

of numerous non-contiguous parcels. The existence of 

multiple NFS units or regions within a single watershed complicates the process of ordering watersheds 

within a given NFS unit or region. To keep things simple, when more than one NFS unit was found within 

a watershed, the watershed was assigned to the unit 

with the largest amount of land in the watershed.25

Grouping a set of watersheds and comparing them 

for risk of watershed impairment begs the question, 

should the watersheds in the set be compared? One 

might argue that it is not helpful or even “fair” to 

compare watersheds that cannot be brought up to 

the same level of watershed condition. For example, 

should watersheds without attractive mineral deposits 

be compared with watersheds where mining is common and very profitable, or should watersheds with 

little erosion potential be compared with watersheds 

containing highly erodible soils? It may even be the 

case that the maximum potential level of risk in one 

watershed is below the minimum potential level of 

another watershed; is it not misleading to compare 

such watersheds? Our response is that these concerns 

are really about how the orderings might be used, and 

not about the orderings themselves. The orderings we 

present are orderings of what is, not of what might be. 

The orderings are useful in understanding the range of 

conditions that exist; they provide a context for beginning to consider watershed improvement decisions. 

However, the orderings are insufficient for deciding 

a future course of action. The decision of what to do 

about risk of watershed impairment must take into 

account not only what is, but also what can be and 

how much it would cost to get there. It may even be, 

depending on the relative costs of making improvements, that a given expenditure to improve watershed 

condition would be more effective on a low-risk watershed than on a high-risk watershed. Consideration of 

the benefits and costs of efforts to improve watershed 

condition is complex and best performed at regional 

and local scales.

Data Processing

The projection for all data layers is “USA Contiguous 

Albers Equal Area Conic” (ESRI ArcGIS version 9.3).

We used overlay analyses in ArcGIS to combine indicator data layers with the watershed polygons. Data 

were then summarized and totaled for each watershed 

and relevant subset (for example, NFS land) of the 

watershed.

Because of constraints imposed by computer 

hardware and software, the large datasets we had for 

some indicators could not be analyzed in whole. To 

circumvent this problem, we used a “While Loop” in a 

Table 9. Sets of watershed orderings.

48 sets of watershed orderings are produced by crossing the 

following 11 breakdowns for the weighted combination of 

stressors and at-risk resources:

Problems

 Sediment

 Nutrients

 Toxics

 All three together

Geographic scales

 Individual national forest or grassland

 NFS region

 Entire study area (coterminous U.S.)

Land ownership categories

 Non-wilderness NFS land within the watershed

 NFS land within the watershed

 Non-NFS land within the watershed

 All land within the watershed

24 Based on our analysis of the 3672 watersheds at issue, 131 watersheds are made up entirely of NFS land and the other 3541 

watersheds also have non-NFS land. Of the 3672 watersheds, 318 have land of more than one NFS unit (303 watersheds 

have land of two units and 15 watersheds have land of three units). Note, however, that these counts rely on our decision 

to exclude NFS units within a given watershed if those units accounted for very little of the basin area or appeared to result 

from an error in boundary delineation.

25 An unavoidable result of this rule is that the surface area of NFS land in a given watershed, which gets assigned to a 

particular NFS unit, may exceed the actual surface area of that NFS unit within that watershed (because it includes surface 

area from other NFS units).
22 USDA Forest Service RMRS-GTR-251. 2010.

Python script to divide the datasets into smaller parts, 

perform analyses on each part, and then recombine the 

parts.

Results and Analysis_________

Results are presented in this report, in accompanying online tables and maps, and in the online Excel 

file called RWI.xls (see footnote 20 for the website 

address). Ratings and ranks for each combination of 

problem (sediment, nutrients, toxics) and geographic 

scale (NFS unit, NFS region, or entire 48 states) are 

available in the ordering sheets of the file RWI.xls. 

Unless stated otherwise, the results presented here are 

based on the weights listed in table 7 and do not use 

erosivity to further weight selected stressors.

A great many graphs or figures could be produced 

summarizing the data and related watershed orderings available in RWI.xls. In the accompanying online 

material, in addition to RWI.xls, we present the following information: a description of RWI.xls, tables of risk 

scale values and ranks by NFS unit, tables of numbers 

of threatened and endangered species by NFS unit, 

national maps showing levels of the indicators, national maps showing risk levels for whole watersheds, 

national and regional maps showing risk levels for 

non-wilderness NFS parts of watersheds, and regionlevel graphs of NFS unit scale values.

In considering the results that follow, it is important to realize that, although all regions contain some 

watersheds with very little NFS land and others that 

are completely made up of NFS land, the NFS regions 

differ in land ownership distributions, and fall roughly 

into three groups. The watersheds of the eastern regions tend to contain the least amount of NFS land; the 

median percent NFS is 18 percent in Region 8 and 21 

percent in Region 9. The watersheds of Regions 2 and 

3 contain slightly more NFS land; the median percent 

NFS is 25 percent in Region 3 and 30 percent in Region 

2. The watersheds of the remaining regions tend to 

contain considerably more NFS land; median percentages NFS are 42 percent, 40 percent, 43 percent, and 

46 percent in Regions 1, 4, 5, and 6, respectively. These 

percentages of NFS land reflect the history of land 

settlement prior to the parcels being set aside, which 

in turn reflects not only the human migration patterns 

and policies that influenced settlement, but also the 

climate and the topography, soils, and vegetative cover 

of the land, among other things.

Background information on watershed sizes, elevations, and ownership by region is given in Appendix A.

Relations Among Components of the 

Conceptual Model

Our approach to assessing risk of impaired watershed condition recognizes different watershed 

management problems, two broad sets of indicators 

(stressors and at-risk resources), and a set of individual indicators. Here we report on relations among these 

components of the approach.

Relations among problems

As described in the Methods section, the scale 

value range is partitioned into six intervals representing levels of risk. Combining across all stressors and 

at-risk resources, the whole watershed scale values 

(yk,n, table 6) across the full set of 3672 watersheds form 

unimodal distributions for each of the three problems, 

as indicated by the assignment of watersheds to risk 

levels shown in table 10. Interestingly, the distributions for all three problems are highly skewed, with 

the mode at either the lowest risk level (nutrients and 

toxics) or the second risk level (sediment). Across all 

three problems, at least 60 percent of the watersheds 

fall within the lowest two risk levels (1 or 2) and fewer 

than 9 percent fall within the highest two risk levels 

(5 or 6). Clearly, a few watersheds stand out as facing 

relatively high risk in comparison to the bulk of the 

watersheds.

Across all 3672 whole watersheds, values across 

all stressors ( ,

s

k n y ) for sediment are quite highly 

correlated with those of the other two problems (the 

correlations are 0.55 for stressors versus nutrients 

Table 10. Distribution of whole watersheds by risk level and 

problem (Gk,n).

Risk All 3

level Sediment Nutrients Toxics problems

1 521 2206 1885 850

2 1667 481 695 1559

3 1015 444 470 667

4 344 272 306 342

5 82 140 174 175

6 43 129 142 79

All 3672 3672 3672 3672
USDA Forest Service RMRS-GTR-251. 2010. 23

and 0.58 for stressors versus toxics), and the values 

for nutrients versus toxics are very highly correlated 

(R = 0.98).26 This indicates that the nutrient and toxic 

scales are not each making unique contributions to the 

overall characterization of watershed stressors. The 

correlations reflect in part the facts that some stressors 

contribute to more than one problem (for example, 

cultivation contributes to all three problems), and 

that different stressors tend to occur in tandem (for 

example, cultivation, housing, and mines all require 

roads). In addition, the correlations reflect the stressors 

and weights we used (listed in table 7), which in turn 

reflect our judgments about the risks that the stressors 

pose to watershed condition.

Turning now to at-risk resources, all three correlations among ,

r

k n y are very high (1.00, 0.96, and 0.96, 

respectively), which is not unexpected given that we 

used only five at-risk resources and that the weights 

we used are very similar across the three problems. 

If data on more at-risk resources were available, and 

if the additional at-risk resources were each uniquely 

affected by the different problems, the at-risk resource 

category of indicators would contribute more to the 

distinctions among watersheds in the overall assessment of risk of impaired watershed condition.

Although the overall correlations across problems 

are rather high, this does not necessarily mean that the 

watersheds, or even the NFS units, rank the same for 

all three problems. To see this, consider as an example 

the NFS units of Region 2 for the three problems using 

the weights of table 7, and focusing on whole watersheds. Figure 6 presents the combined (stressors plus 

at-risk resources) scale values (yk,n) for the 15 units 

of Region 2, where the scale values were computed 

relative to the range in scale values across the full set 

of 116 NFS units in the contiguous 48 states for each 

of the problems (the scale values for all of the regions 

are listed in the accompanying online material). Notice 

that the scale values are nearly all below 0.3, indicating 

that the watersheds associated with units of Region 

2 fall in the lower part of the full range among units 

nationwide for each of the three problems. The nutrient scale values are particularly low, with all but four 

falling below a scale value of 0.1. As seen, the Region 2 

6

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Sediment

Nutrients

Toxics

26 Respectively, the corresponding correlations are 0.35, 0.44, and 0.93 for NFS parts of watersheds and 0.56, 0.60, and 0.97 

for non-NFS parts of watersheds. The stronger relations for non-NFS lands result from the much greater importance of 

development and cultivation stressors, which both cause erosion and contribute nutrients, on non-NFS lands than on the 

NFS lands.

Figure 6. Scale 

values of whole 

watersheds 

containing NFS 

units of Region 2.
24 USDA Forest Service RMRS-GTR-251. 2010.

units rank quite differently depending on which problem is chosen. Note that the ranking based on the three 

problems together (indicated by the order of the units 

along the horizontal axis) is approximated by the order for the sediment problem, reflecting the relatively 

high weight given to the sediment problem (table 7).

Graphs similar to figure 6, but for non-wilderness 

NFS parts of watersheds, for the full set of regions are 

found in the accompanying online material, as are 

tables of the associated scale values. These graphs are 

also found in RWI.xls. The user may wish to enter a 

custom set of weights in RWI.xls and recompute the 

graphs.

Relations among indicators

Much could be said about the relations among the 

many indicators we investigated for the different sets 

of watersheds or parts of watersheds, but a thorough 

investigation of the relations among the indicators is 

beyond the scope of this report. Here we offer only 

a simple look at associations among indicators, to 

provide an initial understanding of how the stressors 

and at-risk resources relate to each other. For this purpose, we present correlation coefficients for two sets 

of watersheds or parts of watersheds, one for the full 

set of whole watersheds (table 11) and the other for 

NFS parts of the full set of watersheds (table 12). Both 

sets show simple correlations between pairs of indicators (both stressors, ,

s

i n y , and at-risk resources, ,

r

i n y ). 

By and large, the correlations are rather low, indicating that in most cases the indicators are not simply 

duplicating each other, at least not across the full set of 

watersheds. However, some relations show expectedly 

higher correlations, especially for whole watersheds. 

These higher correlations generally reflect the fact 

that stressors often occur together (in proximity)—the 

result of the multifarious impacts that humans tend to 

impose on the land.

For whole watersheds, for stressors, the following 

correlations are of particular interest (table 11):

• Population density correlates highly with developed land cover (0.82), housing density (0.88), and 

road density (0.64), and quite highly with roadstream crossings (0.41).

• Road density is, as would be expected, quite strongly correlated with other road-related measures, such 

as road-stream crossings (0.78) and length of roads 

in riparian areas (0.62).

• Density of road-stream crossings, of course, is 

highly correlated with density of roads in riparian 

areas (0.81), but is also fairly strongly correlated 

with housing density (0.42).

• Livestock grazing and confined animal feeding are 

both quite highly correlated with measures of agricultural activity.

• Erosivity is highly correlated with atmospheric 

deposition (0.72). However, this correlation largely 

reflects broad regional differences, with high levels 

atmospheric deposition occurring where erosivity is 

also highest, and vice versa.27

• Fire condition class 3, mines, and “other” land cover 

are not highly correlated with any other stressors.

Among resources at risk, for whole watersheds the 

correlations are all very low except for that between 

listed aquatic T&E species and listed terrestrial T&E 

species (0.51) (table 11).

And comparing stressors with at-risk resources for 

whole watersheds, the highest correlations are those 

for drinking water intakes with population density 

(0.60), developed land cover (0.62), housing density 

(0.62), road density (0.52), and road-stream crossings 

(0.37).

Comparison of tables 11 and 12 reveals an interesting general difference: the correlations for whole watersheds tend to be higher than those for NFS parts of 

watersheds. For example, there are 64 correlations of 

at least 0.2 in table 11, but only 24 such correlations in 

table 12. The differences in magnitude of the correlations are confined to stressors and reflect two basic differences between the land areas being compared. First, 

stressors are more common and more interrelated on 

private land (which makes up the bulk of the non-NFS 

land) than on NFS land, in part because private land 

lacks many of the constraints on human pursuits that 

characterize NFS land management. Some activities 

on private land that cause stress to watersheds often 

occur in proximity, as do cultivation and livestock 

27 In watersheds of Regions 1-6, watershed average erosivity generally falls below an R-factor of 100 (although it reaches 

above an R-factor of 200 in a few watersheds of Regions 5 and 6) and atmospheric deposition is below 10 kg/ha, whereas 

in the two eastern regions erosivity is often above 200 (it ranges from 120 to 650 in Region 8 and from 75 to 259 in Region 

9) and atmospheric deposition ranges from 11 to 47 kg/ha.
USDA Forest Service RMRS-GTR-251. 2010. 25 Table 11. Correlations among indicators for 3672 whole watersheds.a

sity den ulation Pop

wth gro ulation Pop

Cover developed 

sity den sing Hou

sity d den oaR

ssings Rxs cro

arian d rip oaR

p d stee oaR

tle Ag gen p Ag stee

Ag all

ive Ag eros

Grazing

Animal feeding

Other cover

nes Mi

 class 3 Fire

ep d pheric Atmos

Erosivity

Dams

ies bod er Wat

Drink intakes

enic Wild & sc

tic T&E aqua

terrestrial T&E 

Pop. density 1.00Pop. growth 0.01 1.00Cover,devel. 0.82 -0.03 1.00Housing den 0.88 0.06 0.80 1.00Road den 0.64 -0.06 0.80 0.62 1.00Rxs cross 0.41 -0.01 0.60 0.42 0.78 1.00Roads ripar. 0.26 0.00 0.37 0.26 0.62 0.81 1.00Roads steep 0.04 -0.03 0.06 0.04 0.20 0.15 0.37 1.00Ag gentle 0.03 -0.15 0.21 0.00 0.20 0.16 -0.03 -0.20 1.00Ag steep 0.02 -0.08 0.18 0.00 0.17 0.26 0.26 -0.06 0.33 1.00Ag all 0.03 -0.16 0.24 0.00 0.22 0.22 0.07 -0.19 0.94 0.62 1.00Ag erosive 0.00 -0.02 0.02 -0.01 0.02 0.05 0.07 -0.02 0.13 0.42 0.25 1.00Grazing -0.01 -0.08 0.09 -0.04 0.09 0.10 0.01 -0.16 0.38 0.38 0.45 0.02 1.00Feeding 0.00 0.00 0.08 -0.01 0.07 0.05 -0.04 -0.10 0.32 0.20 0.34 0.00 0.56 1.00Other cover 0.02 -0.04 0.06 0.03 0.04 0.06 0.03 -0.02 -0.01 0.01 -0.01 -0.01 -0.03 -0.01 1.00Mines -0.01 0.05 -0.03 0.00 -0.02 0.04 0.13 0.15 -0.09 -0.07 -0.09 -0.03 -0.09 -0.06 0.09 1.00Fire class 3 -0.06 -0.03 -0.10 -0.03 0.02 -0.02 0.09 0.20 -0.17 -0.08 -0.17 -0.05 -0.18 -0.08 -0.01 0.06 1.00Atmos. dep 0.03 -0.18 0.29 0.05 0.26 0.23 0.10 -0.14 0.28 0.39 0.37 -0.02 0.22 0.18 0.07 -0.10 -0.02 1.00Erosivityb 0.03 -0.13 0.26 0.05 0.25 0.07 -0.11 -0.07 0.26 0.15 0.27 -0.04 0.18 0.18 0.03 -0.08 -0.09 0.72 1.00Dams 0.18 0.00 0.32 0.18 0.27 0.21 0.09 -0.10 0.20 0.14 0.22 -0.01 0.11 0.07 0.09 0.02 -0.08 0.33 0.28 1.00Water -0.01 -0.06 0.04 0.03 0.05 -0.01 -0.14 -0.12 0.01 -0.06 -0.03 -0.03 -0.07 -0.02 0.07 -0.06 0.16 0.22 0.23 0.01 1.00Drink intakes 0.60 0.02 0.62 0.62 0.52 0.37 0.25 0.08 -0.01 0.01 -0.01 -0.01 0.03 -0.02 0.01 0.00 -0.01 0.05 0.03 0.15 0.08 1.00Wild & scen -0.02 -0.06 -0.01 -0.01 -0.03 -0.09 -0.05 0.16 -0.08 -0.04 -0.08 -0.02 -0.04 -0.04 -0.02 0.01 0.15 -0.01 0.01 -0.05 -0.02 -0.01 1.00T&E aquatic 0.08 0.04 0.15 0.11 0.09 0.07 0.05 0.09 -0.02 0.03 0.00 -0.01 -0.06 -0.03 0.00 0.05 -0.06 0.10 0.13 0.03 -0.02 0.09 0.07 1.00T&E terrest. 0.13 0.06 0.23 0.18 0.14 0.10 0.06 0.13 -0.08 -0.02 -0.07 -0.04 -0.08 -0.06 0.03 0.04 -0.11 0.11 0.15 -0.01 0.00 0.22 0.03 0.51 1.00a Correlations of at least 0.034 and 0.045 are significant at the 0.05 and 0.01 probability levels, respectively.b Erosivity is included here because it may be used in RWI.xls to weight selected stressors.
26 USDA Forest Service RMRS-GTR-251. 2010. Table 12. Correlations among indicators for NFS parts of the 3672 watersheds.a

sity den ulation Pop

wth gro ulation Pop

Cover developed 

sity den sing Hou

sity d den oaR

ssings Rxs cro

arian d rip oaR

p d stee oaR

tle Ag gen p Ag stee

Ag all

ive Ag eros

Grazing

Other cover

Mines 

 class 3 Fire

ep d pheric Atmos

Erosivity

Dams

ies bod er Wat

Drink intakes

enic Wild & sc

Pop. density 1.00Pop. growth 0.04 1.00Cover, devel. 0.35 -0.12 1.00Housing den 0.13 0.03 0.15 1.00Road density 0.04 -0.11 0.43 0.05 1.00Rxs crossing 0.00 -0.01 0.17 0.03 0.52 1.00Roads ripar. -0.02 0.00 0.06 0.00 0.50 0.56 1.00Roads steep 0.03 -0.01 0.18 0.00 0.34 0.13 0.24 1.00Ag gentle 0.09 -0.07 0.24 0.00 0.14 0.07 0.09 -0.09 1.00Ag steep 0.18 -0.07 0.17 -0.01 0.00 -0.03 -0.03 -0.06 0.19 1.00Ag all 0.13 -0.08 0.27 0.00 0.12 0.06 0.07 -0.09 0.95 0.48 1.00Ag erosive 0.02 -0.04 0.05 0.00 0.00 0.01 0.03 -0.02 0.02 0.17 0.07 1.00

Grazing 0.06 -0.07 0.12 -0.01 0.03 0.00 -0.05 -0.12 0.11 0.21 0.17 0.01 1.00

Other cover 0.07 -0.04 0.06 0.00 0.05 0.04 0.01 -0.02 0.01 0.02 0.01 0.01 -0.01 1.00

Mines -0.02 0.03 -0.04 0.00 -0.02 0.03 0.05 0.12 -0.04 -0.03 -0.05 -0.02 -0.07 0.01 1.00

Fire class 3 -0.07 0.00 -0.05 0.01 0.17 0.10 0.14 0.09 -0.07 0.04 -0.05 -0.03 -0.13 -0.01 0.04 1.00

Atmos. dep 0.36 -0.17 0.49 0.07 0.10 -0.03 -0.07 -0.15 0.18 0.37 0.27 0.09 0.18 0.06 -0.12 -0.05 1.00

Erosivityb 0.29 -0.13 0.53 0.08 0.18 -0.03 -0.15 -0.06 0.19 0.22 0.23 0.01 0.16 0.06 -0.07 -0.12 0.70 1.00

Dams 0.06 0.01 0.08 0.02 0.06 0.11 0.01 -0.06 0.05 0.06 0.07 0.00 0.06 0.00 -0.01 -0.05 0.07 0.04 1.00

Water 0.03 -0.06 0.11 0.04 0.06 0.02 -0.08 -0.11 0.02 -0.03 0.01 -0.01 -0.07 0.00 -0.07 0.08 0.22 0.21 0.03 1.00

Drink intakes 0.14 0.00 0.24 0.13 0.06 0.09 0.04 0.08 0.00 -0.03 -0.01 -0.01 0.02 0.00 0.03 0.01 -0.01 0.00 0.08 0.03 1.00Wild & scenic 0.08 -0.05 0.05 0.01 0.00 -0.05 -0.04 0.07 -0.03 0.00 -0.02 -0.01 0.00 -0.01 -0.01 0.07 0.05 0.03 -0.01 0.00 -0.01 1.00a Correlations of at least 0.034 and 0.045 are significant at the 0.05 and 0.01 probability levels, respectively.b Erosivity is included here because it may be used in RWI.xls to weight selected stressors.
USDA Forest Service RMRS-GTR-251. 2010. 27

feeding, tending to raise the inter-indicator correlations on non-NFS land. Second, stressors are least 

common on designated wilderness lands, which make 

up 17 percent of the NFS lands, tending to lower the 

correlations for NFS land.

It should be remembered that these correlations are 

for the entire set of 3672 watersheds and that correlations across watersheds of individual regions or NFS 

units may be quite different.

Relations between stressors and at-risk resources

We move now to a comparison of the two components of the conceptual model, stressors to at-risk 

resources. Are at-risk resources most numerous where 

stressors are most threatening, or are these two components of our conceptual model unrelated or possibly 

even negatively related? We explore these questions 

by examining the correlations of the summary values 

of stressors ( ,

s

k n y ) with the summary values of at-risk 

resources ( ,

r

k n y ) for the three problems (k). For whole 

watersheds, these correlations are 0.11, 0.10, and 0.12 

for the sediment, nutrients, and toxics problems, 

respectively. For NFS parts of watersheds, the correlations are 0.02, 0.18, and 0.18, respectively. These low 

correlations indicate that the relation of stressors to 

at-risk resources is generally positive but weak, and 

thus that including both stressors and at-risk resources 

in the analysis is important, all else equal.

However, this finding does not apply to all regions, 

as there are significant regional differences. As shown 

in table 13, the correlations of stressors to at-risk 

resources tend to be positive in the western regions 

but are consistently negative in the eastern regions. 

Further examination reveals that the negative correlations result largely from the location of water bodies 

in the eastern regions. Watersheds with large numbers of water bodies in the eastern regions tend to be 

located away from development pressures, especially 

pressures from housing, roads, cultivation, dams, 

and atmospheric deposition. For example, consider 

the relation of water bodies to housing. In the eastern 

regions high water body density tends to occur at low 

housing densities, with little evidence of such a relation in the western regions. In Region 9, the most obvious case of this phenomenon is that of the lake country 

of northern Michigan, Wisconsin, and Minnesota, 

where nearly 40 percent of the Region’s watersheds 

are found. In Region 8, this combination of high lake 

density and relatively low housing density is found in 

areas of northern Florida and along the coasts of North 

and South Carolina.

In the western regions the correlations of stressors 

to at-risk resources are positive but generally quite 

low, with the exception of Region 5, where the correlations among the three problems range from 0.45 

to 0.55 (table 13). The higher correlations for Region 5 

are attributed largely to the stronger relations between 

the development, road, and cultivation stressors and 

aquatic T&E species.

Comparison of NFS Regions: 

Indicators and Background Variables

The NFS regions differ markedly in terms of many 

of the variables we measured. As a very general comparison of the regions, we present two tables for whole 

watersheds: one of background variables and the other 

of indicators.

First, table 14 lists area-weighted average values for 

background variables, providing a rough comparison 

of the ownership, cover, topography, and other basic 

characteristics of the regions. Some general observations from the table are:

•	 Land ownership. Only about one-fourth of the land 

area of the watersheds in Regions 8 and 9 is part of 

the NFS, far less than for the other regions, which 

range from 34 percent (Region 2) to 46 percent 

(Region 6) in the NFS. Other (non-NFS) federal 

ownership is greatest in Regions 3 (22 percent) and 

4 (34 percent), lowest in the eastern regions (3 percent) (see also figure 5). For more detail on land 

ownership, see Appendix A.

Table 13. Correlation of scale value of stressors to scale value 

of at-risk resources, whole watersheds.

Problem

 All 

Region Sediment Nutrients Toxics three

1 0.02 -0.08 -0.11 -0.05

2 0.22 -0.07 0.03 0.09

3 0.02 -0.02 0.01 0.00

4 0.08 0.17 0.11 0.13

5 0.45 0.50 0.55 0.52

6 -0.13 0.08 0.11 -0.05

8 -0.12 -0.22 -0.16 -0.18

9 -0.39 -0.53 -0.52 -0.52

All 0.11 0.10 0.12 0.12
28 USDA Forest Service RMRS-GTR-251. 2010. Table 14. Values of background variables for NFS regions, whole watersheds. NFS Region All Variable 1 2 3 4 5 6 8 9 watersheds Surface area (km2) 238,230 260,034 234,535 315,495 181,206 216,591 239,365 188,906 1,874,362 Precipitation (mm/yr)a 683 512 434 503 830 1215 1330 942 782 Population in year 2000 (1000s) 682 1,904 3,630 3,333 11,880 1,405 5,366 2,411 30,612 Erosivity (hundreds ft∙tonf∙in∙(ac∙h) -1)a 17.55 21.40 32.87 8.27 34.40 38.66 308.65 126.82 70.70 % of area in NFS ownership 43.41 33.68 35.03 40.62 45.27 46.11 22.41 25.72 36.57 % of area in other federal ownership 8.40 14.90 21.58 33.71 13.43 12.16 2.59 2.95 14.84 % of area state & private ownership 48.19 51.42 43.39 25.66 41.30 41.73 75.00 71.33 48.59 % of area in forest cover 45.81 33.47 29.82 32.09 41.83 57.69 61.19 66.35 44.81 % of area in range cover 41.99 55.48 65.53 57.49 46.13 31.85 7.90 3.87 40.45 % of area in water cover 0.96 0.63 0.22 0.56 1.01 1.19 2.07 3.54 1.19 % of area in wetland cover 1.20 1.92 0.27 1.16 0.43 1.30 8.17 10.21 2.91 % of area in agricultural cover 8.17 4.97 1.43 5.16 2.59 4.06 14.40 11.28 6.47 % of area in developed cover 1.18 1.40 1.76 1.67 5.12 2.29 5.96 4.36 2.81 % of area in other (mines, etc.) cover 0.01 0.03 0.13 0.04 0.01 0.00 0.08 0.15 0.05 % of area in barren (rock, sand, clay) cover 0.67 2.11 0.84 1.84 2.88 1.62 0.25 0.24 1.31 % of area in riparian buffer 15.35 13.27 14.76 14.45 14.47 13.04 15.46 11.30 14.09 % of area with slopes > 3% 76.17 68.10 62.05 76.85 82.55 85.45 43.39 37.63 67.01 % of area with slopes > 45% 17.67 9.92 8.15 16.02 20.99 21.26 5.71 1.93 12.75 % of area with highly erosive soils 4.58 4.90 0.46 2.75 0.04 4.34 0.46 2.18 2.57 NHD stream length (1000 km) 188 177 176 232 133 143 190 110 1,349 303(d) Impaired streams (1000 km) 16 6 4 20 24 14 12 15 112 303(d) Impaired water bodies (km2) 881 77 76 213 421 128 1,055 2,762 5,611 a Area-weighted average.
USDA Forest Service RMRS-GTR-251. 2010. 29

•	 Land cover. The watersheds of the two eastern 

regions (8 and 9) have the highest levels of forest 

cover (over 60 percent of the area), wetland cover 

(about 9 percent), and agricultural cover (roughly 

12 percent), and the least amount of rangeland 

cover (less than 8 percent). At the other extreme, 

Region 3 has the least amount of forest cover 

(30 percent) and the greatest amount of rangeland 

cover (66 percent). Regions 2 and 4 have slightly 

more forest than does Region 3 (about 33 percent), 

followed by Regions 1 and 5 at roughly 44 percent 

and then Region 6 at 58 percent, with rangeland 

cover correspondingly decreasing as forest cover 

increases. Wetlands cover less than 2 percent of the 

western region watersheds, whereas agricultural 

cover ranges from 1 percent (Region 3) to 8 percent 

(Region 1).

•	 Riparian buffer. The portion of the watersheds 

within the riparian buffer zone differs little across 

the regions, ranging only from 11 percent to 15 percent of the watershed area.

•	 Topography. The watersheds of the eastern regions 

have the gentlest slopes, with roughly 60 percent of 

the land having a slope no greater than a 3 percent. 

The watersheds in Regions 5 and 6 have generally 

the steepest slopes; in each region over 80 percent of 

the slopes exceed 3 percent and over 20 percent of 

the slopes exceed 45 percent. Regions 1 and 4 form 

the next group, where about 16 percent of the land 

is on slopes exceeding 45 percent, and Regions 2 

and 3 form the final group, where about 9 percent of 

the land is on slopes exceeding 45 percent.

Table 15 lists area-weighted average values of the 

indicators by region for whole watersheds. Examination of table 15 reveals much about how the regions 

compare, offering a preview of the scale value results 

to follow. For stressors, Regions 5 and 8 have the highest levels of population density, developed land area, 

and housing density; Regions 5−9 have the highest 

levels of road density, road-stream crossings, and 

roads in riparian areas; Regions 5 and 6 have the highest densities of roads on steep slopes; Regions 8 and 

9 have the highest percentages of land in cultivation; 

Region 8 has by far the highest densities of livestock 

grazing and confined animal feeding; Regions 5, 6, 

and 9 have the highest densities of land in fire condition class 3; Regions 8 and 9 have the highest levels of 

atmospheric deposition; and Region 8 has the highest 

density of dams. Regions 1−4 stand out only for 

population change (Regions 2−4) and density of mines 

(with Regions 5 and 6 joining Regions 1−4 on this 

stressor). And for resources at risk, the greater densities are found in Regions 8 and 9 for water bodies, 

Regions 5 and 9 for drinking water intakes, Regions 5 

and 6 for wild and scenic rivers, and Regions 6 and 8 

for aquatic T&E species. Region 3 joins Regions 5−8 for 

high numbers of terrestrial T&E species. To summarize, in terms of regional averages, Regions 5−9 exhibit 

the greater levels of stress and at-risk resources.

As mentioned, tables 14 and 15 report on whole 

watersheds. Comparable values for NFS parts and 

non-NFS parts of watersheds are found in RWI.xls. 

Note that the regions do not differ one to the next as 

much in their NFS parts as in their non-NFS parts.

Focusing on whole watersheds, this section has examined variability across regions, which may be quite 

different from variability across NFS units within a region. To see this, consider the NFS units of one region, 

Region 2, for two indicators. Whereas mean density of 

roads in riparian areas varies from 1092 to 1632 among 

the eight regions (table 15), mean density of roads in 

riparian areas varies from 535 to 1604 m/km2 among 

the 15 NFS units of Region 2. Similarly, whereas average density of mines varies from 8 to 28 mines per 

1000 km2 among the regions, it varies from 0 to 115 

among the units of Region 2. Regional averages give 

an idea of how broad regions of the country differ, but 

mask the great variability within their boundaries.

How Levels of Indicators Differ by 

Land Ownership

Our data allow an important comparison between 

the risks faced on non-NFS portions of watersheds 

and the risks faced on non-wilderness NFS portions 

of watersheds (because many activities are prohibited 

in wilderness areas, they of course face less stress than 

other areas and are excluded from this comparison). 

This comparison provides a rough indication of the 

extent to which national forest designation provides 

a higher level of watershed protection than otherwise 

tends to be maintained. It is important to remember 

that non-NFS land is a mixture of state and private 

(77 percent) and federal non-NFS (23 percent) land, so 

that the comparison is not strictly a public-private one.

First we examine whether non-wilderness NFS 

parts of the watersheds are really different from the 

non-NFS parts. For the indicators and some additional 
30 USDA Forest Service RMRS-GTR-251. 2010. Table 15. Values of indicators for NFS regions, whole watersheds. NFS Region All Variable 1 2 3 4 5 6 8 9 watersheds Population density (people / km2) 2.89 7.35 15.51 10.64 66.38 6.56 23.14 14.06 16.65 Annual population change (%)a 1.83 2.65 2.75 3.05 1.12 1.89 1.48 0.95 1.60 Land cover – developed (% of area) 1.18 1.40 1.76 1.67 5.12 2.29 5.96 4.36 2.81 Housing density (units/km2) 3.20 5.90 11.99 9.90 30.26 6.77 14.67 11.08 11.09 Road density (m/km2) 772.01 852.11 882.06 724.01 1257.43 1159.50 1344.94 1173.38 991.20 Roads-stream crossings (#/stream km) 0.31 0.33 0.38 0.32 0.45 0.40 0.48 0.43 0.38 Roads in riparian areas (m/km2) 1096.92 1139.32 1092.27 1114.09 1408.71 1631.64 1348.58 1234.67 1239.38 Roads on steep slopes (m/km2) 55.45 13.62 8.96 22.51 81.84 106.86 26.70 8.72 38.61 Cultivation-gentle slopes (% of area) 6.55 3.92 1.34 3.86 2.09 2.36 10.19 8.21 4.75 Cultivation-steep slopes (% of area) 1.61 1.05 0.08 1.31 0.49 1.70 4.33 3.36 1.69 Cultivation (% of area) 8.17 4.97 1.43 5.16 2.59 4.06 14.40 11.28 6.40 Cultivation-erosive soils (% of area) 0.25 0.06 0.00 0.56 0.00 0.50 0.04 0.15 0.21 Livestock grazing (animal units/km2) 5.55 7.23 4.03 4.54 6.14 4.04 11.99 6.79 6.23 Confined animal feeding (animal units/km2) 0.05 0.83 0.63 0.17 0.02 0.05 1.74 0.51 0.51 Other land cover (mines, etc.) (% of area) 0.01 0.03 0.13 0.04 0.01 0.00 0.08 0.15 0.12 Mines (#/1000 km2) 21.94 28.07 19.29 20.26 24.13 21.14 7.99 15.42 19.93 Fire condition class 3 (% of area) 18.76 11.21 17.35 9.38 23.88 22.60 8.33 36.56 17.21 Atmospheric deposition (NO3 + SO4 kg / ha) a 4.24 6.30 5.59 3.78 3.45 5.21 24.57 23.08 9.15 Dams (#/1000 km2) 3.79 8.29 1.89 3.60 4.12 3.02 13.93 8.23 5.83 Water bodies (m of perimeter/km2 of area) 51.39 38.84 16.16 42.63 65.79 44.56 211.36 610.64 121.16 Drinking water intakes (#/1000 km2) 10.11 12.79 14.50 9.04 103.96 23.39 26.58 49.35 27.28 Wild and scenic rivers (m/km2 of area) 3.65 0.63 1.37 1.05 10.95 9.19 4.19 5.65 4.12 T&E aquatic species (#/watershed) 1.08 0.90 2.56 0.91 1.71 2.63 2.82 0.79 1.65 T&E terrestrial species (#/watershed) 0.86 1.10 3.04 1.39 2.48 3.21 2.89 1.48 2.01 a Area-weighted average.
USDA Forest Service RMRS-GTR-251. 2010. 31

variables, table 16 summarizes the comparison of nonwilderness NFS and non-NFS parts of the 3541 watersheds that contain land of both categories of ownership. For all of the variables listed in table 16, the mean 

value for the NFS parts of watersheds is significantly 

different from that of the non-NFS parts. Useful findings summarized in table 16 include the following:

• As one would expect, non-NFS parts of watersheds 

tend to have the greater densities of population, 

housing, roads, road-stream crossings, roads in 

riparian areas, livestock grazing, mines, dams, and 

drinking water intakes, as well as the greater land 

covers in cultivation and mining.28,29

• In contrast, NFS parts of watersheds have the 

greater density of roads on steep slopes and greater 

occurrence of forests in fire condition class 3.30

• As expected, the correlations for indicators of NFS 

parts versus non-NFS parts of watersheds are positive, suggesting that where the non-NFS portions 

are under higher levels of stress or contain higher 

numbers of resources at risk, so also are the NFS 

portions.

Looking beyond the measures of central tendency 

in table 16, with our data we can examine how the 

distributions for individual indicators differ between NFS and non-NFS parts of watersheds. As an 

Table 16. Summary of non-wilderness NFS parts and non-NFS parts of watersheds.

NFS Non-NFS

Correlation

Variablea Meanb Median Meanb Median t-testc (R)

Area (km2) 152.47 111.21 335.75 292.21 0.000 -0.31

Population density (p/km2) 5.27 1.76 21.09 2.24 0.000 0.33

Annual population change (%) 1.64 1.35 1.65 1.35 0.047 0.94

Land cover – developed (% of area) 1.15 0.43 3.92 2.12 0.000 0.38

Housing density (units/km2) 6.26 1.08 14.27 3.06 0.000 0.14

Road density (m/km2) 746.37 666.84 1281.92 1148.73 0.000 0.23

Roads-stream crossings (#/stream km) 0.29 0.24 0.48 0.42 0.000 0.15

Roads in riparian areas (m/km2) 1093.25 924.65 1555.57 1372.20 0.000 0.30

Roads on steep slopes (m/km2) 62.54 16.16 46.05 7.92 0.000 0.58

Cultivation-gentle slopes (% of area) 0.40 0.00 5.95 1.25 0.000 0.47

Cultivation-steep slopes (% of area) 0.23 0.00 2.56 0.50 0.000 0.45

Cultivation (% of area) 0.63 0.00 8.43 2.65 0.000 0.45

Cultivation-erosive soils (% of area) 0.01 0.00 0.35 0.00 0.000 0.20

Livestock grazing (animal units/km2) 4.99 2.94 6.76 4.29 0.000 0.85

Land cover – other (mines, etc.) (% of area) 0.01 0.00 0.08 0.00 0.000 0.12

Mines (#/100 km2) 25.35 0.00 33.49 0.00 0.003 0.28

Fire condition class 3 (% of area) 22.59 11.70 15.89 4.68 0.000 0.71

Atmospheric deposition (NO3 + SO4 kg/ha) 9.09 5.10 9.25 5.25 0.000 0.96

Dams (#/100 km2) 3.56 0.00 8.51 2.13 0.000 0.12

Water bodies (m of perimeter/km2 of area) 115.41 0.00 146.96 21.02 0.000 0.71

Drinking water intakes (#/1000 km2) 10.83 0.00 57.94 8.96 0.000 0.40

Wild and scenic rivers (m/km2 of area) 4.15 0.00 6.48 0.00 0.000 0.38

a T&E species indicators are not included here because we have T&E data only for whole watersheds.

b Simple average across all 3541 watersheds with some non-NFS land.

c Probability for paired samples, two-tailed Student’s t-tests comparing means. 

28 The population density on NFS lands is an over-estimate resulting from our methods. Recall that population density was 

computed by averaging across census tracts. When census tracts span a boundary between NFS and non-NFS land, some of 

the population residing outside the national forest will unavoidably be counted as being in the national forest. 

29 The estimate of cultivation on NFS lands (0.25%) is perhaps slightly over-estimated because the land area coverage is a 

grid, and grid cell boundaries do not necessarily match NFS boundaries. However, some cultivation on NFS land would 

remain even without this problem. For example, some areas of the NFS were formerly cropped or mined and are still being 

rehabilitated, and some recent land acquisitions have yet to undergo rehabilitation.

30 This finding reflects in part the differences in topography between NFS and non-NFS parts of the watersheds. Across all 

watersheds, 7% of the non-NFS land and 20% of non-wilderness NFS land is on slopes > 45%.
32 USDA Forest Service RMRS-GTR-251. 2010.

example we present a graph depicting how average 

road density compares on non-wilderness NFS and 

non-NFS lands across the 3541 watersheds with both 

categories of land (figure 7). A few extreme points are 

excluded, so that the range can be reduced sufficiently 

to more clearly show the distribution for the majority 

of watersheds. As expected, road density tends to be 

considerably greater on as opposed to off NFS land. 

Mean road density averages 746 and 1282 m/km2 on 

non-wilderness NFS and non-NFS land, respectively 

(table 16). Mean road density is less on NFS land than 

on non-NFS land on 81 percent of the 3541 watersheds. 

It is perhaps surprising that road density is greater on 

than off of NFS land on 19 percent of the watersheds. 

Certainly the presence of other public land or large 

private holdings helps explain this finding, but it must 

also be noted that some NFS areas also have high road 

densities, particularly in Region 6.31

Moving from the national to the regional scale, 

table 17 contains a comparison of non-wilderness NFS 

and non-NFS lands for a selection of indicators, this 

time based on all available watersheds (i.e., the full set 

of 3672 watersheds for non-wilderness NFS land and 

the 3541-watershed subset for non-NFS land). Considerable variation is found. Specific findings from table 

17 include the following:

• Mean density of roads and road-stream crossings is 

greater off than on NFS lands in all regions. However, the regions differ in the extent to which these 

two categories of land differ. Mean road density on 

non-wilderness NFS lands is one-half or less than 

half of what it is on non-NFS lands in Regions 1, 

2, 4, 5, and 8, but reaches to 80 percent of non-NFS 

density in Region 6. Region 6 also has the smallest difference between NFS and non-NFS lands in 

density of road-stream crossings.

• The average density of roads in riparian areas is 

greater on non-NFS lands than on non-wilderness 

NFS lands in all regions.

• Density of roads on steep (> 45 percent) slopes 

is greater on non-wilderness NFS lands than on 

non-NFS lands in all regions except Region 9. The 

highest densities for both categories of land ownership are in Region 6 (156 and 87 m per km2 on nonwilderness NFS and non-NFS lands, respectively).

7

0

500

1000

1500

2000

2500

3000

3500

4000

0 500 1000 1500 2000 2500 3000 3500 4000

Non-NFS parts of watersheds

Non-wilderness NFS parts of watersheds

Figure 7. Comparing 

NFS and non-NFS 

parts of watersheds 

for road density (m/

km2).

31 For example, over half of the watersheds containing parts of six Region 6 NFS units (Fremont-Winema, Malheur, Ochoco, 

Olympic, Umatilla and Wallowa-Whitman NFs) have higher road densities on the national forests than off. It should be 

added, however, that in a few cases in these national forests as well as others the portions of the watersheds within the NFS 

are so small that the density estimate may not be representative of the condition of the national forest to which the specific 

NFS parts belong.
USDA Forest Service RMRS-GTR-251. 2010. 33 Table 17. Area weighted average values of selected indicators for NFS regions, non-wilderness NFS parts and non-NFS parts of watersheds.a NFS Region All Indicator 1 2 3 4 5 6 8 9 watersheds Road density (m/km2) NFS 622 563 670 471 922 1236 783 863 747 Non-NFS 984 1057 1040 951 1698 1282 1514 1312 1204 Roads-stream crossings (#/stream km) NFS 0.24 0.23 0.32 0.24 0.31 0.40 0.26 0.24 0.28 Non-NFS 0.39 0.38 0.43 0.40 0.60 0.45 0.53 0.48 0.45 Roads in riparian areas (m/km2) NFS 1030 979 1093 1080 1125 1666 869 862 1117 Non-NFS 1263 1277 1165 1236 1819 1819 1481 1354 1392 Roads on steep slopes (m/km2) NFS 106 22 16 41 121 156 36 9 68 Non-NFS 32 11 5 14 69 87 24 9 27 Livestock grazing (animal units/km2) NFS 3.03 4.78 2.19 3.51 5.17 3.15 8.46 4.19 4.05 Non-NFS 7.67 8.80 5.03 5.52 6.22 4.96 13.02 7.78 7.60 Other land cover (mines, etc.) (% of area) NFS 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.01 0.00 Non-NFS 0.03 0.03 0.19 0.06 0.01 0.01 0.10 0.20 0.08 Mines (#/1000 km2) NFS 29 27 23 28 32 24 6 8 24 Non-NFS 20 31 18 16 21 17 9 18 19 Fire condition class 3 (% of area) NFS 30 21 29 14 34 32 8 44 26 Non-NFS 9 7 10 6 17 17 8 33 13 Atmospheric deposition (NO3 + SO4 kg/ha) NFS 4.0 6.4 5.2 3.6 3.2 5.0 24.5 22.5 7.7 Non-NFS 4.5 6.3 5.8 3.9 3.6 5.1 24.6 23.5 10.2 Dams (#/1000 km2) NFS 1.2 8.0 1.1 2.5 2.9 1.9 3.8 3.4 3.0 Non-NFS 5.7 9.1 2.4 4.5 5.5 4.0 16.9 10.0 7.7 Water bodies (m of perimeter/km2 of area) NFS 31 29 14 30 54 28 246 722 103 Non-NFS 63 40 18 46 75 54 202 549 130 Drinking water intakes (#/1000 km2) NFS 2.4 7.5 4.4 4.2 35.7 7.4 6.8 12.1 9.2 Non-NFS 16.5 16.2 20.2 12.8 167.1 38.3 32.4 62.9 38.7 Wild and scenic rivers (m/km2 of area) NFS 4.1 1.3 1.3 0.8 13.4 10.8 7.5 11.7 5.7 Non-NFS 1.4 0.3 1.1 0.2 8.0 6.8 3.0 3.9 2.6 a Based on all available watersheds (thus, the full set of 3672 watersheds for NFS land and the 3541-watershed subset for non-NFS land). Because area-weighted averages are reported here, the averages for non-NFS parts of watersheds will not tend to agree with the corresponding estimates in table 16, which are simple averages.
34 USDA Forest Service RMRS-GTR-251. 2010.

• Averaging over all watersheds, grazing density is 

nearly twice as high off of the NFS as it is in nonwilderness NFS parts of the watersheds. Average 

livestock grazing density is greater off, as opposed 

to on, NFS lands in all regions. Of course, these 

calculations depend in part on our assumptions in 

allocating county-wide data to the watersheds (see 

table 3).

• The density of mines is greater on non-wilderness 

NFS lands than off of the NFS in five regions, with 

the reverse being true in Regions 2, 8, and 9.

• The prevalence of drinking water intakes is much 

greater off than on NFS lands in all regions.

• Water bodies are more common in the eastern than 

the western regions. The average density of water 

bodies is greater on non-wilderness NFS than on 

non-NFS parts of the watersheds in the eastern regions, but the reverse is true in the western regions.

• Region 3 stands out as having the smallest difference between NFS and non-NFS lands for five 

indicators: density of roads in riparian areas, mines, 

dams, water bodies, and wild and scenic rivers.

Although we do not provide a detailed comparison 

of wilderness land versus non-wilderness land, we 

do not wish to leave the impression that designated 

wilderness is devoid of present or past activities that 

may affect current watershed condition. For example, 

of the 105 NFS units with wilderness acreage, our 

data show that 103 have roads, 75 have mines, 39 have 

dams, and 31 have drinking water intakes within one 

or more wilderness areas. Many of the roads may now 

be closed to all use, but some are used for specified 

purposes such as wildlife or livestock management or 

to access mines or other facilities. Most of the mines 

are abandoned, but some are still active. Details of the 

exceptions to the normal operating procedures for wilderness areas are written into the legislation authorizing the wilderness areas.32

Risk of Impaired Watershed Condition

As described in the Methods section, two ways to 

succinctly summarize watershed scale values are to (1) 

divide the scale value range into intervals and observe 

which watersheds fall within each interval (the G

scale), and to (2) divide the watersheds into essentially 

equal-sized subsets based on the scale values (the Pr

scale). Both approaches yield useful information about 

the relative risk levels of the watersheds. The former 

approach highlights the interval scale nature of the 

scale values, wherein the watersheds may not be evenly spread along the range. Each interval represents a 

separate risk level that is directly comparable to the 

other risk levels. The latter approach emphasizes the 

ordinal position of the watersheds and provides what 

is perhaps the most easily interpreted summary of the 

findings. We apply both approaches here based on 

the summary scale values of the watersheds or parts 

of watersheds. Summary scale values are those that 

combine across the three problems (yn, table 6 ). This 

is done across the coterminous United States for the 

following three sets of watersheds or parts of watersheds: whole watersheds, non-wilderness NFS parts of 

watersheds, and non-NFS parts of watersheds.

As explained earlier, for the former approach we 

have divided the 0 to 1 scale value range into the 

following six intervals: 0 to 0.1, >0.1 to 0.2, >0.2 to 

0.3, >0.3 to 0.4, >0.4 to 0.5, and >0.5 to 1. Watersheds 

whose summary scale values fall into these intervals 

are assigned risk levels of 1, 2, 3, 4, 5, and 6, respectively. And for the percentile rank approach, we have 

used five quantiles (called quintiles) each representing 

20 percent of the watersheds (or parts of watersheds) 

in the set. The 20 percent of the watersheds at lowest 

risk fall in quintile 1, the next 20 percent fall in quintile 2, etc. Assignment to quintile 5 indicates that the 

watershed is among the 20 percent of the watersheds 

at greatest risk of impaired condition.

Comparison of watersheds

Using the first approach just described, figure 8 

shows the risk levels for the non-wilderness NFS parts 

of all watersheds, and table 18 summarizes the risk 

levels by region. In the table it is seen that the bulk of 

the watersheds fall in the lower two risk levels (thus, 

representing a scale value ≤ 0.2). For example, focusing on non-wilderness NFS parts of watersheds, only 

29 percent the watersheds receive a risk level > 0.2 

(table 18b). A few of the watersheds stand out as facing 

an unusual level of risk of impaired watershed condition, leaving most watersheds to fall in the bottom two 

32 An excellent searchable database of information about the special provisions affecting wilderness area management is 

available at www.wilderness.net, “tools for managers,” “special provisions.”
USDA Forest Service RMRS-GTR-251. 2010. 35

risk levels. For non-wilderness NFS land, these few 

watersheds are found largely in Regions 8 and 9.

It is important to remember that for this analysis the 

watersheds, or parts of watersheds (such as the nonwilderness NFS parts), in each set are scaled relative to 

others in the set, not relative to watersheds of another 

set, and thus that results for the different sets are not 

directly comparable across set.

Because the scale value distribution is so skewed, 

dividing the watersheds into only six groups—even 

after collapsing the top half of the scale value range 

into one risk category—does not allow for much 

distinction among the bulk of the watersheds, leading 

us to the quantile approach, presented in figure 9 and 

table 19. Examination of table 19 shows that the distributions of watersheds among the regions tend to fall 

into the following three groups: the eastern regions (8 

and 9), with many watersheds in the higher quintiles, 

the intermountain regions (1 to 4), with relatively few 

watersheds in the higher quintiles, and the West Coast 

regions (5 and 6), which fall in between these two 

extremes. These three groups contain 839, 1934, and 

899 watersheds, respectively. Figure 10 summarizes 

the results for these three regional groups for nonwilderness NFS parts of watersheds. As seen in the 

figure, watersheds in the eastern regions account for 

nearly three-quarters (73 percent) of the watersheds in 

the fifth (highest risk) quintile (64 percent of the watersheds of the eastern regions fall in the fifth quintile). 

Watersheds falling in the fourth quintile are rather 

evenly spread among the three groups. Watersheds 

falling in the lowest three quintiles are mainly from 

the intermountain regions; these watersheds make up 

nearly all of the lowest two risk quintiles and most of 

the third quintile (note, however, that over half of the 

3672 watersheds are in the intermountain regions). 

Sixty-three percent of the West Coast watersheds fall 

in the third and fourth quintiles.

Figure 8. Comprehensive assessment of risk levels for non-wilderness NFS parts of watersheds (Gn), with scale values computed 

relative to the range across the full set of 3672 watersheds.
36 USDA Forest Service RMRS-GTR-251. 2010.

How risk values differ by land ownership

In the previous section about summary scale values, 

the different sets of watersheds and parts of watersheds 

were scaled separately. In this section we combine all 

non-wilderness NFS parts and all non-NFS parts of 

watersheds in one data set and scale them together, 

allowing a direct comparison of these categories of land 

ownership across the coterminous United States. 

Figure 11 shows the basic result of this rescaling, using 

the former (risk level) approach of the previous section, which places the watersheds into six groups each 

representing an interval along the scale value range. As 

seen in the figure, the distributions of the two sets of 

watershed parts are quite different. 

The top four risk levels are largely populated by nonNFS parts of watersheds (indeed, 93 percent of the 

Table 18a. Whole watersheds.

Risk level

Region 1 2 3 4 5 6 Total

1 156 244 66 21 3 4 494

2 142 250 44 18 5 1 460

3 125 204 17 6 3 1 356

4 299 229 52 27 12 5 624

5 79 220 90 20 12 12 433

6 47 294 100 12 11 2 466

8 0 70 182 127 72 38 489

9 2 48 116 111 57 16 350

Total 850 1559 667 342 175 79 3672

Table 18b. Non-wilderness NFS parts of watersheds.

Risk level

Region 1 2 3 4 5 6 Total

1 226 239 25 2 2 0 494

2 122 296 31 10 1 0 460

3 74 258 23 1 0 0 356

4 313 284 26 1 0 0 624

5 54 247 107 20 3 2 433

6 21 301 133 9 1 1 466

8 3 76 308 85 15 2 489

9 10 78 129 106 20 7 350

Total 823 1779 782 234 42 12 3672

Table 18c. Non-NFS parts of watersheds.

Risk level

Region 1 2 3 4 5 Total

1 73 237 72 31 17 8 438

2 89 226 92 29 9 7 452

3 137 161 29 17 5 3 352

4 222 219 80 21 16 30 588

5 69 176 109 34 13 23 424

6 62 210 115 38 13 13 451

8 1 41 129 139 95 82 487

9 2 27 99 109 66 46 349

Total 655 1297 725 418 234 212 3541

a The watersheds of each set were separated into six groups based on where their summary scale 

values fall along the scale value range. A risk value of 6 indicates the highest risk level.

Table 18. Number of watersheds by risk level by region, watershed sets scaled separately.a
USDA Forest Service RMRS-GTR-251. 2010. 37

Table 19a. Whole watersheds.

Risk quintile

Region 1 2 3 4 5 Total

1 138 119 116 84 37 494

2 114 136 123 57 30 460

3 102 125 89 30 10 356

4 266 161 84 58 55 624

5 73 80 108 117 55 433

6 39 92 166 125 44 466

8 0 15 27 166 281 489

9 2 6 22 97 223 350

Total 734 734 735 734 735 3672

Table 19b. Non-wilderness NFS parts of watersheds.

Risk quintile

Region 1 2 3 4 5 Total

1 206 122 103 49 14 494

2 108 159 125 48 20 460

3 66 113 116 49 12 356

4 274 209 89 45 7 624

5 50 57 113 141 72 433

6 19 59 134 180 74 466

8 2 1 21 150 315 489

9 9 14 34 72 221 350

Total 734 734 735 734 735 3672

Table 19c. Non-NFS parts of watersheds.

Risk quintile

Region 1 2 3 4 5 Total

1 83 119 124 69 43 438

2 100 132 108 76 36 452

3 146 109 54 25 18 352

4 234 140 87 64 63 588

5 74 88 110 97 55 424

6 67 95 147 97 45 451

8 1 17 38 163 268 487

9 3 8 40 117 181 349

Total 708 708 708 708 709 3541

a The watersheds of each set were separated into five approximately equal-sized groups based on 

summary scale values of risk. The 20% of the watersheds at lowest risk are assigned a risk level 

of 1, the next 20% are assigned a risk level of 2, etc. Thus, a risk level of 5 indicates that the 

watershed is among the 20% of the watersheds at greatest risk of impaired condition.

watershed parts with a risk level above 3 are on nonNFS land), and the two lowest risk levels are dominated 

by non-wilderness NFS parts. However, although NFS 

parts dominate at the lower risk levels, many non-NFS 

parts are also found at these low risk levels. For example, although 36 percent of the NFS parts (representing 

1325 watersheds) fall in the lowest risk category, so too 

do 16 percent of the non-NFS parts (representing 560 

watersheds). Thus, these 560 non-NFS parts of watersheds are found to be at lower risk than 64 percent of 

the non-wilderness NFS parts. Table 20 breaks the result 

down by region, showing, for example, that nearly all 

watershed parts, whether NFS or non-NFS, assigned a 

risk level above 1 are found in Regions 1−6.

Table 19. Number of watersheds by risk quintile by region, watershed sets scaled separately.a
38 USDA Forest Service RMRS-GTR-251. 2010.

Figure 9. Risk quintiles for non-wilderness NFS parts of watersheds (Gn), with scale values computed relative to the range across 

the full set of 3672 watersheds.

10

0

100

200

300

400

500

600

700

800

1 2 3 4 5

Number of watersheds

Risk quintile

East

West Coast

Intermountain

Figure 10. Number of 

watersheds by risk 

quintile for three 

regional groups, nonwilderness NFS parts 

of watersheds.
USDA Forest Service RMRS-GTR-251. 2010. 39

11

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1 2 3 4 5 6

Number of watersheds

Risk level

Non-wilderness NFS

Non-NFS Figure 11. Number of 

watersheds by risk 

level.

Table 20a. Non-wilderness NFS parts of watersheds.

Risk level

Region 1 2 3 4 5 6 Total

1 296 189 7 2 0 0 494

2 223 219 16 2 0 0 460

3 137 213 6 0 0 0 356

4 443 180 1 0 0 0 624

5 126 282 25 0 0 0 433

6 79 357 29 0 0 1 466

8 4 270 197 14 4 0 489

9 17 120 170 34 2 7 350

Total 1325 1830 451 52 6 8 3672

Table 20b. Non-NFS parts of watersheds.

Risk level

Region 1 2 3 4 5 6 Total

1 60 240 76 31 20 11 438

2 67 237 99 29 12 8 452

3 121 181 25 18 4 3 352

4 192 249 78 22 13 34 588

5 61 195 97 33 18 20 424

6 57 220 111 37 13 13 451

8 1 35 122 142 94 93 487

9 1 40 111 89 52 56 349

Total 560 1397 719 401 226 238 3541

a All watershed parts (i.e., both sets) were scaled together and then separated into six groups based on where 

the summary scale values fell along the scale value range. A risk value of 6 indicates the highest risk level. 

Table 20. Number of watersheds by risk level by region, watershed sets scaled together.a
40 USDA Forest Service RMRS-GTR-251. 2010.

Comparison of NFS units

Unlike the prior two sections, which report on 

the number of watersheds by region at different risk 

levels, here we report on average values across the 

watersheds within a NFS unit. Perhaps the most parsimonious way to compare NFS units is to observe summary scale values (that is, values combining across 

the three problems) with the units scaled to range 

from 0 to 1 relative to the range across the full set of 

116 units (yn, where n is a NFS unit). Figure 12 depicts 

these summary scale values, one dot per unit with the 

units arranged by region in order of increasing scale 

value from left to right. Figure 12a depicts scale values 

for whole watersheds, and figure 12b depicts scale 

values for non-wilderness NFS parts of watersheds. 

Note that in both figures the scale values range from 

0, indicating the unit with the lowest risk of impaired 

watershed condition, to 1, indicating the unit with the 

highest risk. The scale values of each figure are internally consistent but not comparable across figures (for 

example, a scale value of 1 in figure 12a does not necessarily indicate the same level of risk as does a scale 

value of 1 in figure 12b).

As seen in figure 12a, only 15 units, found in 

Regions 5 through 9, have a scale value above 0.5. Regions 1, 3, and 4 exhibit a narrow range in scale value 

across their respective units, with no units having a 

scale value above 0.3. At the other extreme, 

12b

0.0

0.1

0.2

0.3

0.4

0.5

0.6

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NFS units in order of increasing scale value by region

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Figure 12. Summary scale values of NFS units by region.

Figure 12a. Whole 

watersheds.

Figure 12b. Nonwilderness NFS 

parts of watersheds.
USDA Forest Service RMRS-GTR-251. 2010. 41

Regions 5 through 9 show a large range in scale value 

across their respective units, demonstrating a great variety in average risk across the units of those regions.

Restricting our purview now to the non-wilderness 

NFS parts of watersheds (figure 12b), we see that 

Regions 5 and 9 exhibit a much larger range in scale 

value among their respective units than do the other 

regions, but we also see that the unusually large ranges for these two regions are due to only a few units at 

the high-risk end of the scale. Figure 12b shows that 

all but nine of the 65 units with an average scale value 

above 0.2 are found in Regions 5 through 9; these 56 

units are quite evenly spread among the four regions.

The reasons that Regions 5 through 9 contain a 

disproportionate share of the units at highest risk of 

watershed impairment on their non-wilderness NFS 

lands are seen in their relative levels of stressors and 

at-risk resources. As reported above, the following 

regions have on average the highest levels of the following stressors: population and livestock grazing 

(Region 8); road density and road-stream crossings 

(Region 6), roads in riparian areas and on steep slopes 

(Regions 5 and 6), cultivation (regions 8 and 9), and 

atmospheric deposition (Regions 8 and 9). And for 

resources at risk, the following regions have on average the highest levels: drinking water intakes (Region 

5), water bodies (Regions 8 and 9); and density of wild 

and scenic river stretches (Regions 5, 6, and 9). For the 

other stressors and resources at risk, specific regions 

do not stand out when looking at regional averages.

Scale values for the individual NFS units, both for 

whole watersheds and non-wilderness NFS parts of 

watersheds, are listed in the accompanying online material (see footnote 20 for the website address). Similar 

results for non-NSF parts of watersheds are available 

in RWI.xls, which is also available online.

Changing the weights

The RWI.xls workbook was designed to easily 

produce results for different characterizations of risk 

to watershed condition than that summarized in 

figure 12. Alternative characterizations could involve 

a different environmental problem, a different balance 

between the stressor and at-risk resource components 

of the model, or a more limited set of stressors or 

at-risk resources. To see the effect of a new characterization of risk of impaired watershed condition, one 

merely needs to change the weights entered in the 

“Weights” worksheet of RWI.xls and then recompute 

the workbook. The only constraint on this process is 

that, at this point, it is limited to the set of indicators 

described in table 3.

As an example, we computed a new set of NFS unit 

scale values based on a more limited set of indicators, one that ignores the development and cultivation 

stressors (see table 3). Assigning zero weight to the 

development and cultivation stressors will affect some 

watersheds—those with high levels of development 

and cultivation—more than others. This characterization of risk of impaired watershed condition is of 

interest because it avoids any problems introduced 

by inaccuracies in the spatial data for the development and cultivation stressors, which in any case are 

stressors over which NFS managers are likely to have 

limited control. For this characterization the relative 

weights of the remaining stressors are maintained 

as they were in the original characterization (table 7) 

and all other features of the original characterization 

remain as described earlier, including the inclusion of 

the three environmental problems with weights of 0.6, 

0.3, and 0.1, respectively, and the inclusion of both the 

stressor and at-risk-resource components of the model 

with weights of 0.7 and 0.3, respectively (table 7). We 

call this the “NFS-focused” characterization of risk of 

impaired watershed condition.

Figure 13 displays the results of this new characterization of risk of impaired watershed condition.33

As with figure 12, the scale values depicted in figure 

13 are computed with the NFS units scaled relative 

to the range in yn across the full set of 116 NFS units. 

As in figure 12, the units are shown one per dot, arranged by region in order of increasing scale value 

from left to right. As seen in the figure, removing the 

development and cultivation stressors, which are 

some of the dominate causes or risk (found especially 

in Regions 5, 8 and 9), reduces the risk levels of the 

highest-risk watersheds, bringing them closer to some 

other watersheds and allowing the units to be more 

spread out across the range in scale value. Whereas 

before (with the full set of stressors) only 15 NFS units 

had a whole watershed scale value above 0.5, now 26 

units have a scale value above 0.5. Or, restricting our 

33 A table in the accompanying online material contains the scale values for the individual NFS units based on the NFS-focused 

characterization of risk of impaired watershed condition (that are depicted in figure 13). This is done for summary scale 

values and for the individual problems, for NFS parts of watersheds. See footnote 20 for the website address.
42 USDA Forest Service RMRS-GTR-251. 2010.

purview to non-wilderness NFS parts of watersheds, 

whereas only five NFS units in two regions had a scale 

value above 0.5 based on the full set of indicators, 

now 40 units spread across five regions have a scale 

value above 0.5. Interestingly, 21 of the 40 units are 

in the eastern regions and another 17 are in the West 

Coast regions. Thus, this look at the non-wilderness 

NFS parts of watersheds still shows a very unequal 

distribution among the regions of risk of impaired 

watershed condition, despite the fact that the effect of 

removing the development and cultivation stressors 

was felt largely in selected units of Regions 5, 8, and 9.

Comparison of figures 12 and 13 reveals that the 

two characterizations produce similar pictures of relative risk among the NFS units. For whole watersheds, 

13

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NFS units in order of increasing scale value by region

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Figure 13. Summary scale values of NFS units by region, using a NFS-focused set of indicators.

Figure 13a. Whole 

watersheds.

Figure 13b. Nonwilderness NFS parts of 

watersheds.

the Spearman rank order correlation between the two 

orderings is 0.96, and for non-wilderness NFS parts of 

watersheds the correlation is 0.95. This similarity in 

ranking generally holds for individual regions. However, the relative scale values may shift considerably 

when the development and cultivation stressors are 

removed from the set. For example, consider Region 6 

for non-wilderness NFS parts of watersheds, shown in 

figure 14. The units of Region 6 are arranged from left 

to right in order of increasing scale value using the full 

set of indicators. As seen, using the NFS-focused set 

of indicators shifts the scale values upward; the scale 

values range from 0 to 0.37 using the full set and from 

0 to 0.65 using the limited set. The general increase in 

scale value reflects the shifting of the scale following 
USDA Forest Service RMRS-GTR-251. 2010. 43

As expected given how erosivity varies across the 

regions—with Regions 8 and 9 having much higher 

erosivity values than the other regions (table 14)—the 

primary effect of weighting by erosivity is to increase 

the scale values of units in the eastern regions relative 

to the other regions. For example, considering only 

non-wilderness NFS parts of the watersheds, when 

not using erosivity 42 percent of the units with a scale 

value above 0.2 are in Regions 8 and 9 (figure 12), but 

when weighting by erosivity 70 percent of the units 

with a scale value above 0.2 are in those two regions 

(figure 15). We leave it to others to decide whether 

weighting by erosivity is an important addition to the 

assessment of risk of watershed impairment.

Summary of Major Findings__

We have summarized and analyzed a great deal of 

spatially explicit data, which reveals much about how 

watershed conditions vary across the country. These 

findings include the following:

• The NFS regions differ considerably in the degree 

of control that the agency has over the condition of 

the watersheds where the NFS units are found. In 

Regions 1, 4, 5, and 6 the NFS units tend to occupy 

about 44 percent of the watershed area; in Regions 

2, 3, and 4 the NFS units tend to occupy about 34 

percent of the watershed area; and in Regions 8 and 

9 the NFS units tend to occupy only about 24 percent of the watershed area. Of course, within each 

region there is considerable variability, with some 

removal of the effects of development and cultivation 

(these effects having caused units of other regions to 

capture the high end of the scale). However, as seen 

in figure 14, levels of development and cultivation, as 

we were able to measure those levels, are sufficiently 

prevalent and unequally distributed on NFS land in 

Region 6 to lead to some re-ordering of the units when 

stressors reflecting those impacts are removed from 

consideration.

Although the scale values of figures 12 and 13 were 

carefully derived and potentially are useful for decision making, we wish to emphasize that the full set 

of indicators of table 3 is limited by the availability 

of credible nationwide data, and that the weights of 

table 7 are based on our own judgment. If a process 

such as outlined herein were used to compare NFS 

units within regions, or watersheds within units, for 

the purpose of allocating budget or workloads, it 

should use indicators and weights chosen as a result of 

careful deliberation by the managers supervising the 

process.

Effect of erosivity

The use of erosivity to weight stressors affecting 

sediment is an option in RWI.xls. The erosivity value 

may be multiplied by the stressor value (vi) before the 

scale value of the stressor is computed. In a separate 

analysis we used erosivity to weight the road, cultivation, grazing, mining, and fire hazard stressors, using 

the full set of indicators. The results for NFS unit scale 

values are presented in figure 15, which is comparable 

to figure 12 in all respects except for the erosivity 

weighting.

14

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Scale value

Full set

NFS-focused set

Figure 14. Comparison 

of summary scale 

values for NFS units 

in Region 6 using the 

full set and a NFSmanagement-focused 

set of indicators, nonwilderness NFS parts of 

watersheds.
44 USDA Forest Service RMRS-GTR-251. 2010.

watersheds falling completely within a NFS unit 

and others containing very little NFS land.

• The regions differ greatly in the population density 

of their watersheds, ranging from 66 people per 

km2 on average in Region 5 to less than 8 people per 

km2 on average in Regions 1, 2, and 6. As with land 

ownership, there is great variation in population 

density among NFS units within a given region.

• Topography also varies greatly across the regions. 

The watersheds of the eastern regions tend to have 

the gentlest slopes, with roughly 60 percent of the 

watershed area having a slope less than 3 percent. 

The watersheds of Regions 5 and 6 tend to have 

15b

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NFS units in order of increasing scale value by region

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Scale value

NFS units in order of increasing scale value by region

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Figure 15. Summary scale values of NFS units by region, with erosivity used to weight selected stressors. 

Figure 15a. Whole 

watersheds.

Figure 15b. Nonwilderness 

NFS parts of 

watersheds.

the steepest slopes; in these regions only about 15 

percent of the slopes are less than 3 percent and 21 

percent of the slopes exceed 45 percent.

• The regions differ considerably in vegetative cover. 

Extent of forest cover ranges from about 60 percent 

or more of the watershed area in Regions 6, 8, and 

9 to about one-third of watershed area in Regions 2, 

3 and 4. Extent of rangeland cover varies from twothirds of watershed area in Region 3 and about 56 

percent in Regions 2 and 4 to less than 8 percent in 

the eastern regions.

• Non-NFS parts of watersheds generally have 

greater road density, greater density of roads in 
USDA Forest Service RMRS-GTR-251. 2010. 45

higher levels of stress and higher levels of at-risk 

resources than do Regions 1 through 4.

• Regional averages mask considerable variation 

within regions. At the NFS unit level, and focusing on non-wilderness NFS parts of watersheds 

and on the stressors most important on NFS land, 

this variation is smallest in Regions 1, 3, and 4 and 

greatest in Regions 6 and 9 (figure 13b).

• Although NFS parts of watersheds generally are at 

lower risk than non-NFS parts, this is not always 

the case. When non-wilderness NFS and non-NFS 

parts of watersheds across all regions are scaled 

together, making their scale values directly comparable, we find, for example, that while 36 percent 

of the non-wilderness NFS parts of watersheds are 

assigned to the lowest risk category, so also are 16 

percent of the non-NFS parts. Thus, this 16 percent 

of the non-NFS parts of watersheds is at lower risk 

than 64 percent of the non-wilderness NFS parts 

(table 20). The 64 percent of the NFS parts at a risk 

level of 2 or higher are found in all regions, with 

30 percent in Regions 5 and 6 and 35 percent in 

Regions 8 and 9.

Future Possibilities__________

Several improvements to this assessment are possible. The improvements fall into two groups: those 

possible without new data or models, and those that 

would require new data or models. Possible improvements that fall in the first group include the following: expanding the assessment to all watersheds in 

the coterminous United States; convening groups of 

watershed experts and managers to determine weights 

to be used in the assessment; separating private from 

other non-NFS land; accounting for the importance of 

location of water bodies within a watershed; accounting for resources at risk that are located downstream 

from the watershed at issue; and analyzing the relation 

of stressors to the EPA’s list of impaired waters. Improvements that await new data or models include the 

following: adding stressors such as industrial sites and 

feedlots to improve the treatment of the three problems addressed herein; adding stressors such as water 

diversions or soil compaction to allow the assessment 

to address additional problems; adding additional 

resources at risk such as pristine stream stretches and 

riparian areas, and more road-stream crossings than 

do NFS parts of the watersheds, but NFS parts tend 

to have steeper slopes and a higher density of roads 

on very steep slopes. Based on regional averages 

for the four road stressors, and looking only at 

non-wilderness NFS parts of the watersheds (table 

17), Regions 2 and 4 face relatively low road-related 

risk, followed by Region 1, whereas Region 6 faces 

high road-related risk. Of course, regional averages 

mask great diversity within regions.

• The densities of both grazing and confined animal 

feeding vary considerably across the regions. Watersheds of Region 8 tend to have the highest densities of both grazing and confined feeding, whereas 

watersheds in Regions 3, 4, and 6 typically have the 

lowest grazing densities and Regions 1, 4, 5, and 6 

tend to have the lowest confined feeding densities 

(table 15).

• Sixty-one percent the watersheds have no listed 

aquatic T&E species, and 56 percent of the watersheds have no listed terrestrial species. Among 

the watersheds with listed species, the average 

number of listed T&E species per watershed is 1.8 

for aquatic species and 1.9 for terrestrial species. 

Regions 6 and 8 tend to have the highest densities 

of listed species.

• At the watershed scale, stressors do not tend to be 

located in concert with at-risk resources. Across the 

watersheds of the West, the correlations of stressors 

to at-risk resources are positive but generally weak 

(Region 5 is an exception).

• The risk level distributions of the three problems 

are unimodal and skewed. For nutrients and toxics, 

most watersheds received a risk level of 1 (on a 

6-point scale), whereas for sediment the mode is a 

risk level of 2. For all three problems, fewer than 17 

percent of the watersheds fall at a risk level of 4 or 

higher (representing a scale value above 0.30). Thus, 

for all three problems, the upper two-thirds of the 

scale value range is occupied by only a few watersheds of highest risk. These findings apply to whole 

watersheds and also to non-wilderness NFS parts of 

watersheds.

• In terms of regional averages, Regions 5 through 

9 have higher levels of risk than do Regions 1 to 4, 

whether we focus on whole watersheds or nonwilderness NFS parts of watersheds (see figures 

10 and 12). Regions 5 through 9 tend to have both 
46 USDA Forest Service RMRS-GTR-251. 2010.

highly valued recreational stream stretches; adapting 

data sources that rely on recent remote sensing efforts; 

and using models to improve the utility of stressors 

(for example, using a distributed erosion prediction 

model with our topography, soils, precipitation, and 

other data to improve the estimate of erosion potential).

Final Comments_____________

The analysis confirmed what one would expect—

that threats to watershed condition are generally much 

lower on NFS land than on the composite of other 

lands. The differences between NFS land and non-NFS 

lands that we measured would undoubtedly be greater if non-NFS lands had not included other protected 

lands. The substantial difference in risk of impaired 

watershed condition on NFS as opposed to non-NFS 

lands (figure 11) offers strong evidence that ecosystem processes and the goods and services that flow 

from those processes are under reduced risk on public 

lands, even if those lands are managed for multiple 

uses. Given the increase in development of private 

lands that is expected as the U.S. population continues 

to grow, the difference in risk of impaired watershed 

condition between public and private lands is likely to 

grow, thus increasing the value of the protected lands 

(Nie and Miller 2010).

Some of the differences between NFS and non-NFS 

lands were obvious from the start, such as the differences in cultivated area, population density, and 

housing density. Other differences were not so obvious, such as the much lower number of road-stream 

crossings or density of roads in riparian areas on NFS 

lands. However, some of the differences do not favor 

the NFS lands, such as the greater density of roads on 

steep slopes or of lands in fire condition class 3 on NFS 

lands (table 16).

The NFS units are not evenly spread across the 

range in scale value. For whole watersheds we find 

that only a small minority of the NFS units (15 out of 

116) have a summary scale value in the top half of the 

scale value range (figure 12a). Ten of these 15 units are 

found in Regions 8 and 9 and four of the remaining 

five are in Region 5. Many factors contribute to this 

finding, but clearly among the most important of those 

factors are population and related road and housing 

pressures, plus cultivation.

Knowing about the risk of impaired watershed 

condition for whole watersheds, when only portions 

the watersheds are in the NFS, is useful background 

knowledge that may influence what actions are taken 

on the NFS lands, but most pertinent for NFS managers is the information about non-wilderness NFS 

lands. Further, because development and cultivation 

stressors tend to pose the greatest risk of impairment but are the least amenable to change by public 

land management, perhaps the cleanest approach to 

comparing the NFS lands is to ignore the development 

and cultivation sets of stressors. When the weights 

placed on the stressors reflect this reasoning, many of 

the most worrisome watersheds (those with unusually 

dense human populations or cultivation) are no longer 

so worrisome, reducing the range in scale value and 

allowing more watersheds to move up in scale value 

(and thus in risk of impaired watershed condition). 

Considering only non-wilderness NFS parts of watersheds and using this restricted set of stressors, 40 NFS 

units have a summary scale value above 0.5, the midpoint of the scale (figure 13b). These 40 units include 

21 of the 29 units in Regions 8 and 9, 17 of the 35 units 

in Regions 5 and 6, and only two of the 52 units in Regions 1 through 4. Clearly, risk of impaired watershed 

condition is not evenly spread across the regions. Of 

course, we must remember that the watersheds within 

an individual NFS unit may vary considerably in risk 

level; for example, it is not uncommon in units containing a watershed with a risk level of 5 or 6 to also 

contain watersheds with risk levels of 1 or 2.

We do not wish, however, to overstate the utility 

of the information we present. This assessment was 

intended primarily to provide an understanding of 

how watershed condition varies across large sections 

of the United States. The scale of the analysis restricted 

us to using only existing nationwide data sets, such 

that we were unable to include some variables that are 

undoubtedly important in assessing risk of watershed 

impairment (for example, feedlots, OHV use). Thus, 

the information included in this nationwide assessment may be too coarse or limited for local planning 

needs (although the data amassed here may serve as a 

starting point for others who wish to add more locally 

available data). For example, it is certainly possible 
USDA Forest Service RMRS-GTR-251. 2010. 47

that an assessment of risk of impaired watershed 

condition performed by a NFS unit would place heavy 

emphasis on a stressor not included here, such as OHV 

use. OHV use is unlikely to be strongly related to any 

of the stressors we used, causing the watershed ranking based on that local assessment to differ markedly 

from the ranking reported here. Finally, it is probably 

worth repeating the point that because of a lack of consistent, broad-scale data on soil quality, water quality, 

and aquatic species populations, this assessment produces a measure of the risk of watershed impairment, 

not a measure of actual watershed impairment.

Given that this analysis focuses on risk, rather than 

on objective measures of watershed condition or the 

degree to which ecosystem processes are compromised, one may ask: Is risk of a problem a sufficient 

basis for mitigation? The answer to this question is 

of course “no.” Assessing risk is only the first step 

towards achieving a basis for mitigation. In addition, 

we must be sufficiently certain that the risk indeed has 

the potential to result in real harm, and we must be 

sufficiently convinced that the benefits of avoiding the 

harm outweigh the costs. Many studies, such as those 

cited in the Stressors subsection, have examined the 

relation between stressors and downstream, downslope, or down-wind environmental quality, and a 

good portion of them have found significant relations, 

although the strength of the relations undoubtedly 

varies by location. And still other studies have attempted to estimate the benefits and costs of mitigation efforts. Such studies should be consulted, and 

additional analyses would undoubtedly be helpful.

In addition to these additional steps, we must 

consider the scale at which mitigation decisions are 

best made. The appropriate scale for making decisions 

about on-the-ground mitigation actions is a much 

more regional or local scale than that represented by 

the huge land base assessed herein. National-level 

analyses provide a broad picture of the extent of a 

problem and some indication of how different areas of 

the country compare, and also provide a context and 

starting point for making local decisions, but they lack 

sufficient data and knowledge about local conditions 

to be a sufficient basis for on-the-ground management 

decisions. These more local analyses may take advantage of modeling efforts that are possible with more 

site-specific data (for an example, see Arabi and others 

2006) and may involve local efforts to assign weights 

(for an example, see Shriver and Randhir 2006).

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Remortel, Rick D. Van; Wickham, James D.; Riitters, 

Kurt H.; [and others]. 2001. Predicting nutrient and 

sediment loadings to streams from landscape metrics: a multiple watershed study from the United 

States Mid-Atlantic region. Landscape Ecology 16: 

301-312.

Jones, K. Bruce; Ritters, Kurt H.; Wichham, James 

D.; Tankersley, Roger D., Jr.; O’Neill, Robert B.; 

Chaloud, Deborah J.; [and others]. 1997. An ecological assessment of the United States Mid-Atlantic 

region: A landscape atlas. EPA/600/R-97/130. 

Washington, DC: U.S. Environmental Protection 

Agency.

Kauffman, J. Boone; Krueger, W. C. 1984. Livestock 

impacts on riparian ecosystems and streamside 

management implications ... a review. Journal of 

Range Management 37(5): 430-438.

Kelly, J.; Thornton, I.; Simpson, P. R. 1996. Urban 

geochemistry: a study of the influence of anthropogenic activity on the heavy metal content of soils 

in traditionally industrial and non-industrial areas 

of Britain. Environmental Geochemistry 11(1-2): 

363-370.

Luce, Charles H.; Black, Thomas A. 1999. Sediment 

production from forest roads in western Oregon. 

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production on rivers and estuaries. American 

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geomorphic response following a wildfire in the 

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Mueller, David K.; Helsel, Dennis R. 1996. Nutrients 

in the nation’s waters: too much of a good thing? 

Circular 1136. Denver, CO: U.S. Geological Survey.

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F. 2005. Wildland fire in ecosystems: effects of fire 

on soil and water. Gen. Tech. Rep. RMRS-GTR-42-

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urban landscape. Annual Review of Ecology and 

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USDA Forest Service RMRS-GTR-251. 2010. 51

The watersheds range in size from 27 to 1652 km2, 

with a mean of 511 km2. Mean watershed surface area 

varies across the regions from 418 km2 in Region 5 to 

659 km2 in Region 3 (table A1). The NFS portions of 

the watersheds range in size from about 1 to 1175 km2, 

with a mean of 187 km2. Mean surface area of the NFS 

portions of the watersheds varies across the regions 

from 110 km2 in Region 8 to 231 km2 in Region 3. The 

non-NFS portions of the watersheds, for the subset 

of watersheds with non-NFS land, range in size from 

below 1 to 1542 km2, with a mean of 336 km2. Mean 

non-NFS surface area varies across the regions from 

234 km2 in Region 5 to 433 km2 in Region 3.

Across all 3672 watersheds, mean elevation ranges 

from -67 to 4410 m, with a mean of 1371 m (table A2). 

Mean elevation varies across the regions from 290 m in 

Region 8 to 2134 m in Region 2. Mean elevation of the 

NFS portions of the watersheds is greater than that of 

the non-NFS portions of the watersheds in all regions; 

mean NFS elevation exceeds mean non-NFS elevation by from 119 m in Region 9 to 805 m in Region 2; 

this difference averages 547 m across all watersheds. 

A cleaner comparison of NFS and non-NFS portions 

of the watersheds than that presented in table A2 is 

possible if we restrict the watershed set to the 3541 

watersheds with both classes of land ownership. This 

comparison is shown in figure A1, where we see that 

the difference between the mean elevation of NFS 

portions of the watersheds and the mean elevation of 

non-NFS portions diminishes with overall elevation 

as we move away from the central Rocky Mountains, 

with the smallest differences found in the eastern 

regions. Also shown in figure A1 is the percent of the 

watersheds of each region where the elevation of the 

Appendix A: Watershed Area, Elevation, and Ownership

NFS portion exceeds that of the non-NFS portion. For 

example, the elevation of the NFS portion exceeds that 

of the non-NFS portion in 89% of the watersheds of 

Region 1.

Within each NFS region, watersheds vary across 

the land ownership spectrum, from those with only 

1% of their land in the NFS (our cutoff for inclusion) 

to watersheds that are completely NFS land. The NFS 

portion of the watersheds tends to be smaller than the 

non-NFS portion; across the full set of watersheds, the 

median NFS portion is only 32%. However, the regions 

differ markedly in how much of their watersheds 

are typically in the NFS and fall into three groups, as 

shown in figure A2, which presents the cumulative 

proportions of watersheds in each region with given 

levels of NFS land in the watersheds.1 The figure 

shows, for each region, the proportion of the watersheds that contain less than any given percent NFS, 

or, in other words, the proportion of the watersheds 

that are at least a given percent NFS. For example, 

the dotted line in the figure, which is at the 50% point 

along the horizontal axis, intersects the lines for the 

two eastern regions at about 0.85, indicating that about 

15% of the watersheds are greater than one-half NFS 

land. However, in Regions 2 and 3 about 30% of the 

watersheds are greater than one-half NFS land, and in 

Regions 1, 4, 5, and 6 about 55% of the watersheds are 

greater than one-half NFS land (with Region 4 slightly 

above the other three regions in the vicinity of the dotted 50% line). Thus, the regions differ considerably in 

how much control the agency tends to have over the 

condition of the watersheds, with the regions in the 

northern Rockies and along the West coast tending to 

have the greatest control.

1 Figure A2 is produced by arranging the watersheds of each region in order of increasing percentage of the watershed in the 

NFS.
52 USDA Forest Service RMRS-GTR-251. 2010. Table A1. Surface area of watersheds by NFS region (km2). NFS Region All 1 2 3 4 5 6 8 9 watersheds Whole watershedsa Minimum 65 89 112 58 106 27 85 74 27 Maximum 1116 1478 1432 1652 1295 1435 1324 1347 1652 Mean 482 565 659 506 418 465 489 540 510 NFS parts of watershedsa Minimum 3 4 5 <1 1 2 2 2 <1 Maximum 943 1175 1113 1175 808 909 516 760 1175 Mean 209 190 231 205 189 214 110 139 187 Non-NFS parts of watershedsb Minimum 0 3 0 0 0 1 0 7 0 Maximum 1061 1365 1379 1542 973 1220 1255 1131 1542 Mean 308 382 433 319 234 259 381 402 336 a 3672 watersheds. b 3541 watersheds. Table A2. Elevation of watersheds by NFS region (m above sea level). NFS Region All 1 2 3 4 5 6 8 9 watersheds Whole watershedsa Minimum 266 432 283 512 -67 -1 -14 0 -67 Maximum 3898 4397 4168 4210 4410 4394 2037 1915 4410 Meanc 1474 2134 1795 2018 1270 1088 290 376 1371 NFS parts of watershedsa Minimum 288 462 398 512 -1 0 -3 64 -3 Maximum 3898 4397 4014 4210 4410 3746 2012 1882 4410 Meanc 1728 2667 2011 2325 1583 1300 383 452 1718 Non-NFS parts of watershedsb Minimum 266 432 283 513 -67 -1 -14 0 -67 Maximum 3466 4370 4168 4192 4403 4394 2037 1915 4403 Meanc 1279 1863 1678 1808 1012 906 264 350 1171 a 3672 watersheds. b 3541 watersheds. c Area-weighted mean.
USDA Forest Service RMRS-GTR-251. 2010. 53

a1

0

500

1000

1500

2000

2500

3000

1 2 3 4 5 6 8 9

Meters

Region

NFS parts

non-NFS parts

89%

89%

93% 97%

90%

90%

78% 79%

a2

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50 60 70 80 90 100

Proportion 

Percent of watershed area in NFS land

8 9

1

2

4

3

6

5

Figure A1. Mean 

elevation of 

portions of 

watersheds 

(percentages are 

of the number 

of watersheds 

within a region 

with average 

NFS elevation > 

average non-NFS 

elevation).

Figure A2. Cumulative 

proportion of 

watersheds with given 

levels of NFS land 

(numbers indicate 

NFS regions, using 

ALP boundaries for all 

regions).
54 USDA Forest Service RMRS-GTR-251. 2010.

Listed threatened and endangered (T&E) species (aquatic and non-aquatic) are included as at-risk 

resources in the above analysis of risks of impaired 

watershed condition. The T&E species counts were 

provided by NatureServe in early 2010 for all 3672 

whole watersheds. NatureServe also provided information about non-listed species. This section presents 

more detail about the NatureServe data, both listed 

and non-listed, for the watersheds.

Methods

Counts of the number of T&E species in each of the 

3672 whole watersheds were computed by NatureServe using watershed boundaries that we provided to 

them for this project. NatureServe overlaid the watershed boundaries on its “Element Occurrence” (EO) 

species location data, which includes both global level 

(range-wide) tracking data developed centrally at NatureServe as well as state level tracking data provided 

by natural heritage programs across the United States.2

The T&E data include all federal status species. Federal status species are those that have been assigned 

the following status designations by the U.S. Fish 

and Wildlife Service under the Endangered Species 

Act: listed endangered, listed threatened, proposed 

endangered, proposed threatened, candidate, special 

concern, proposed for delisting, listed endangered or 

threatened because of similarity of appearance, proposed endangered or threatened because of similarity 

of appearance, essential experimental population, and 

nonessential experimental population. In our data, 

these different status designations are combined into 

two groups: listed (both endangered and threatened) 

and non-listed (all other categories).

It is important to notice that the species counts provided by NatureServe are counts per watershed. Thus, 

Appendix B: Incidence of Threatened and Endangered Species

the data do not indicate which species are found in a 

watershed and may include the same species in different watersheds. We have summarized the data for 

larger areas, such as NFS regions, by simply summing 

across watersheds. We have no way to know how 

many different species are included in the total count 

for a multi-watershed area. Across all 3672 watersheds 

the total count is 5151, 85% of which (4399) is for listed 

species.

The EO is the mapping unit developed by Natural 

Heritage Programs for documenting the distribution 

of species populations. Formally defined as “an area of 

land and/or water in which a species or natural community is, or was, present,” an element occurrence ideally reflects species population units, either a distinct 

population, part of a population (sub-population), or a 

group of populations (meta-population).

The completeness of the EO data varies among species. As NatureServe explains, the data are particularly 

strong and complete for terrestrial and freshwater 

vertebrate species and vascular plants, and for entities with T&E status. Many invertebrate groups are 

completely tracked, but the data on these elements 

continue to expand. The non-vascular plant data 

(lichens, mosses, liverworts and hornworts, and fungi) 

are being actively developed and EOs for these groups 

will expand over the next few years.3,4

EOs were filtered out of the analysis that are known 

to be extirpated or that have not been last observed 

since 1970. One exception is with the Washington animal data—the Washington DFW only tracks records 

observed no later than 1976.

The accuracy of the mapping of EO polygons varies and is categorized in the NatureServe database as 

high, medium, or low. Using this accuracy information, and in light of the fact that EOs are nearly always 

considerably smaller than 5th-level watersheds, the 

following overlap rules were applied for the spatial 

2 NatureServe does not have precise location data in-house for Arizona, Massachusetts, New Hampshire, and Pennsylvania. 

To fill those data gaps for this analysis, each of those state programs intersected their EOs of federal status species with the 

watershed layer we provided (and NatureServe then sent to the states). The results of these separate state-level analyses 

were then incorporated with the EO overlay results for the rest of the country.

3 NatureServe performs a data exchange with each Heritage Program in the United States on an annual basis, but NatureServe 

does not guarantee the currentness or completeness of any data provided. Because data are constantly being revised and 

new data are constantly being developed, the data presented here may soon be out of date.

4 NatureServe provided a detailed state-by-state list of the data limitations, which is available by contacting the authors.
USDA Forest Service RMRS-GTR-251. 2010. 55

overlay in order for a species to be counted for a 

watershed. For EO records that were assigned a high 

mapping accuracy value, a species was counted for a 

watershed if there was any intersection at all between 

the watershed and the EO polygon. For EO records 

that were assigned a medium to low mapping accuracy value, or for which a mapping accuracy value has 

not yet been assigned (the majority of the data fell into 

this category), a species was counted for a watershed 

if 25% or more of the area of its representative EO(s) 

overlapped with the watershed.5

NatureServe distinguishes between two habitat categories, aquatic associated and terrestrial associated, 

depending on whether a habitat is known to “contribute significantly to the survival or reproduction of the 

species at some point in its lifecycle,” which does not 

necessarily mean that the habitat is critical to a species’ 

survival. These two habitat categories are not mutually exclusive, and many species are listed as belonging to both categories. For example, birds that utilize 

aquatic habitats for hunting or nesting, such as the 

osprey, would be assigned to the “aquatic associated” 

category even though they do not actually live in the 

water and are also assigned to the terrestrial associated 

category. However, not all species have as yet been assigned to one of these two habitat categories. Unclassified species are listed below as “unassigned.”

Because the collection of aquatic associated species 

includes many species not principally reliant on water 

flows, aquatic species were also categorized based on 

their taxonomic group in a category that NatureServe 

calls aquatic “obligate” species. Aquatic obligate species spend their lives in the water and do not include 

plants (which can be adaptable to different hydrologic 

regimes in different areas), amphibians, or reptiles 

such as turtles (some of which are more aquatic than 

others). Aquatic obligate species belong to the following taxonomic groups: crayfishes; fairy, clam, and 

tadpole shrimps; freshwater and anadromous fishes; 

freshwater mussels; and freshwater snails.6

Results

Table B1 lists the species counts by region for the 

different listing and habitat categories described 

above. Note that if the total number of species (those 

in the “All” rows of the table) is less than the sum of 

aquatic associated and terrestrial associated rows, it 

is because one or more species has been listed as both 

aquatic associated and terrestrial associated. This 

double counting occurs with listed species in 819 watersheds and with non-listed species in 89 watersheds.

As seen in table B1, most (85%) of the T&E species 

count data pertain to listed species. Across all watersheds the total species count for listed species is 4399, 

with 2357 for aquatic associated species, 2933 for terrestrial associated species, and 134 for unassigned species. Of the total count of 2357 for aquatic associated 

species, 45% (1049) is for aquatic obligate species.

Region 8 has the largest count of listed species, with 

978, or 22% of the total, followed by Region 6 with 19% 

(table B1). These two regions have the largest counts 

of aquatic associated, aquatic obligate, and terrestrial 

associated species. At the other end of the spectrum, 

Region 2 has the smallest count of listed species, with 

224, or 5% of the total, followed by Region 9 with 6%.

Only 2048 watersheds, 56% of the full set of watersheds, have a listed species. Further, the regions 

vary greatly in the likelihood that a watershed has a 

listed T&E species; across the regions, the percentage 

of watersheds with at least one listed species ranges 

from 36% for Region 2 to 77% for Region 6 (figure B1). 

For aquatic associated species, only 39% of the watersheds have a listed species, with regional percentages 

varying from 24% for Region 9 to 61% for Region 6 

(figure B1). And for aquatic obligate species, only 19% 

of the watersheds have a listed species, with regional 

percentages varying from 6% for Regions 2 and 9 to 

32% for Region 6. Finally, for terrestrial associated species, 46% of the watersheds have a listed species, with 

regional percentages varying from 27% for Region 1 to 

5 Because mapped territories are nearly always considerably smaller than 10-digit watersheds, it was not considered necessary 

to apply the converse of this rule, where a mapped territory would be reported if it covered at least 25% of the total area of 

the watershed.

6 In the original NatureServe data, some species in the aquatic obligate taxonomic groups were not also listed as aquatic 

associated species, causing the number of aquatic obligate species to sometimes exceed the number of aquatic species. 

This was corrected by entering aquatic in the habitat field for all species included in the aquatic obligate taxonomic group.
56 USDA Forest Service RMRS-GTR-251. 2010.

65% for Region 3 (with Regions 6 and 8 close by at 64% 

and 62%, respectively) (figure B1).

Figure B2 shows the species counts per watershed 

for watersheds with at least one listed T&E species for 

the respective species category. For example, in Region 

3 there are 253 watersheds with at least one listed 

aquatic associated species (39% of the total of 648 

watersheds). Across these 253 watersheds the average 

listed aquatic associated T&E species count is 2.56 per 

watershed. As seen in figure B2, Region 5 has the highest concentration of listed T&E species (3.28 per watershed among the 202 watersheds with at least one listed 

species) and listed terrestrial associated T&E species 

(2.89 per watershed among the 145 watersheds with at 

least one listed terrestrial species), whereas Region 8 

has the highest concentration of listed aquatic associated T&E species (2.38 per watershed among the 236 

watersheds with at least one listed aquatic species).7

The lowest concentrations of listed T&E species occur 

in Regions 1, 2, and 4.

Of the 116 units, only four have no listed species in 

the watersheds containing NFS land, and only eight 

7 Concentrations vary considerably across NFS units within a region. For example, the high concentrations in Region 5 are 

due largely to just a few of the 18 NFS units in the region. For total (aquatic plus terrestrial) counts of listed species, only 

three national forests (the Angeles, Cleveland, and San Bernardino National Forests, all in southern California) have 

concentrations above 3 species per watershed.

Table B1. T&E species counts by region.

NFS Region

 All

Variable 1 2 3 4 5 6 8 9 watersheds

Number of watersheds

All 494 460 356 624 433 466 489 350 3672

With ≥ one listed species 242 167 253 311 202 361 362 150 2048

With ≥ one species (listed or non-listed) 245 179 256 341 300 398 379 156 2254

Numbers of species (sum of counts across all watersheds in region)

All categories (listed and non-listed) 325 241 761 532 850 1035 1089 318 5151

Listed

All 316 224 648 466 663 832 978 272 4399

Aquatic associated 225 144 314 239 287 467 562 119 2357

Terrestrial associated 183 184 396 382 419 577 576 216 2933

Unassigned 9 9 7 7 52 17 28 5 134

Aquatic obligate 124 31 156 76 122 238 268 34 1049

Not assigned aquatica 91 80 334 227 376 365 416 153 2042

Non-listed

All 9 17 113 66 187 203 111 46 752

Aquatic associated 1 1 28 35 68 106 71 46 356

Terrestrial associated 3 13 85 42 142 105 43 19 452

Unassigned 5 3 0 20 2 1 2 0 33

Aquatic obligate 0 1 27 1 0 33 52 18 132

a Either unassigned (could be terrestrial or aquatic) or terrestrial but not also aquatic.

have no listed aquatic species. For example, 17 of the 

18 watersheds where land of the Bitterroot National 

Forest is found have at least one listed T&E species. A 

table in Section C of the material posted on the website (see footnote 20) lists by NFS unit the number of 

watersheds that have listed T&E species.

Among the 116 NFS units, the median number of 

listed aquatic or terrestrial species is 4, and the median total number of listed species is 7. The George 

Washington-Jefferson National Forests have the greatest number of listed aquatic species (35), and the San 

Bernardino National Forest has the greatest number of 

listed terrestrial species (32). The George WashingtonJefferson National Forests also have the greatest total 

number of listed species (53). A table in the accompaning online material lists by NFS unit the number of 

separate T&E species found in the watersheds containing NFS land.

Maps in the accompanying online material show 

the number of listed T&E species for each of the 3672 

watersheds, for aquatic and terrestrial species separately.
USDA Forest Service RMRS-GTR-251. 2010. 57

b1

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 8 9

Percent

Region

Aquatic associated

Terrestrial associated

All species

b2

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1 2 3 4 5 6 8 9

Species count

Region

Aquatic associated

Terrestrial associated

All species Figure B2. Average 

number of listed 

species per watershed 

among watersheds 

with at least one 

listed species. 

Figure B1. Percent of 

watersheds with 

at least one listed 

species.
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United States Department of Agriculture

Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-251

November 2010