Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies:

USDA FOREST SERVICE USDA Forest Service

11 CAMPUS BLVD., SUITE 200 Publications Distribution

NEWTOWN SQUARE, PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

October 2013 Fax: 740-368-0152

Manuscript received for publication January 2013

Abstract

Future projections of tree cover and climate change are useful to natural resource managers 

from the local to national level as they illustrate potential changes to our natural resources and 

the ecosystem services they provide. By understanding these potential changes, management 

strategies can be set to sustain desired levels of healthy natural resources for future generations. 

In support of the 2010 Renewable Resources Planning Act (RPA) Assessment, this report 

a) details three projections of tree cover change across the conterminous United States based on 

predicted land-use changes from 2000 to 2060; b) evaluates nine climate projections for the same 

period to assess which areas of the country may become more or less arid; and c) provides an 

index of combined tree-cover and aridity change for nine modeled projections to illustrate which 

areas of the United States are projected to experience the greatest impact from tree-cover loss and 

increasing aridity. The index illustrates a new approach to highlight areas of ecological vulnerability 

or concern that may develop at the nexus of projected land use and climate change. We found 

that in all projections the conterminous United States loses tree cover by 2060, ranging from a 

1.1 to 1.6 percent decline; and that the conterminous United States is becoming more arid by 2060, 

ranging from a 0.05 to 0.19 decrease in the aridity ratio. Overall, the frequency and magnitude of 

percent tree cover losses and aridity increases among the counties of the conterminous United 

States are greater than percent tree cover gains and decreases in aridity. The index illustrates that 

the areas at greatest risk of ecological change from tree loss and increased aridity generally are 

rapidly urbanizing regions of high tree cover and low aridity such as those found in the metropolitan 

regions of the Pacific Northwest, Southeast, and Northeast.

Authors

ERIC J. GREENFIELD is a forester with the U.S. Forest Service’s Northern Research Station 

at Syracuse, New York.

DAVID J. NOWAK is a research forester and project leader with the U.S. Forest Service’s 

Northern Research Station at Syracuse, New York.
Tree Cover and Aridity Projections to 2060: 

A Technical Document Supporting the 

Forest Service 2010 RPA Assessment

Eric J. Greenfield

David J. Nowak
Contents

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

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

 2010 RPA Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

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

 Tree Cover Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

 Aridity Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

 Aridity and Tree Cover Change Index 2000 to 2060 . . . . . . . . . . . . . . . . . . . . 6

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 Projected Tree Cover 2000 to 2060 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 Tree Cover Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 Projected Aridity 2000 to 2060 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

 Scenario RPA A1B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

 Scenario RPA A2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

 Scenario RPA B2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

 Aridity Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

 Aridity and Tree Cover Change Index.............................. 28

 Scenario RPA A1B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

 Scenario RPA A2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

 Scenario RPA B2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

 Index Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
List of Tables

Table 1. Key characteristics of the RPA scenarios . . . . . . . . . . . . . . . . . . . . . . 3

Table 2. IPCC scenarios and GCMs used for the 2010 RPA Assessment 

 climate projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Table 3. Reconciling NRI and NLCD land-use/cover classes . . . . . . . . . . . . . . . 5

Table 4. Tree-cover projections and change for RPA scenarios by state, 

 2000 to 2060 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Table 5. Aridity projections and change for RPA scenario-climate 

 combinations by state, 2000 to 2060 . . . . . . . . . . . . . . . . . . . . . . . . . 13

Table 6. Land area distribution of categories of aridity for 

 the conterminous United States, 2000 to 2060 . . . . . . . . . . . . . . . . . 16

List of Figures

Figure 1. RPA assessment regions of the United States . . . . . . . . . . . . . . . . . . 2

Figure 2. 2000 percent tree cover by county . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Figure 3. 2060 percent tree cover by county for RPA scenarios 

 A1B, A2, and B2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Figure 4. Difference of percent tree cover by county for RPA scenarios 

 A1B, A2, and B2 from 2000 to 2060 . . . . . . . . . . . . . . . . . . . . . . . . . 11

Figure 5. 2000 aridity by county . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 6. 2060 aridity by county for RPA A1B-CGCM3.1, 

 A1B-CSIRO-Mk3.5, and A1B-MIROC3.2......................18

Figure 7. Difference in aridity ratio by county for RPA A1B-CGCM3.1, 

 A1B-CSIRO-Mk3.5, and A1B-MIROC3.2 from 2000 to 2060 . . . . . . 20

Figure 8. 2060 aridity by county for RPA A2-CGCM3.1, 

 A2-CSIRO-Mk3.5, and A2-MIROC3.2 . . . . . . . . . . . . . . . . . . . . . . . 21

Figure 9. Difference in aridity ratio by county for RPA A2-CGCM3.1, 

 A2-CSIRO-Mk3.5, and A2-MIROC3.2 from 2000 to 2060 . . . . . . . . 23

Figure 10. 2060 aridity by county for RPA B2-CGCM2, B2-CSIRO-Mk2, 

 and B2-HADCM3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 11. Difference in aridity ratio by county for RPA B2-CGCM2, 

 B2-CSIRO-Mk2, and B2- HADCM3 from 2000 to 2060 . . . . . . . . . . 26

Figure 12. Distribution of categories of aridity for 

 the conterminous United States from 2000 to 2060 

 across all nine climate change projections . . . . . . . . . . . . . . . . . . . 27

Figure 13. Aridity and tree cover change index for RPA A1B-CGCM3.1, 

 A1B-CSIRO-Mk3.5, and A1B-MIROC3.2 from 2000 to 2060 . . . . . 29

Figure 14. Aridity and tree cover change index for RPA A2-CGCM3.1, 

 A2-CSIRO-Mk3.5, and A2-MIROC3.2 from 2000 to 2060 . . . . . . . 31

Figure 15. Aridity and tree cover change index for RPA B2-CGCM2, 

 B2-CSIRO-Mk2, and B2-HADCM3 from 2000 to 2060 . . . . . . . . . . 32

i
Acknowledgments

Funding for this project was provided, in part, by the U.S. Forest Service’s RPA 

Assessment Staff and State & Private Forestry’s Urban and Community Forestry 

Program.

The authors would also like to thank and acknowledge the following: Linda L.

Langner, RPA Assessment National Program Leader at the U.S. Forest Service’s 

Research and Development Washington Office for her coordination, review, and 

editing; and David P. Coulson, Linda A. Joyce, and David N. Wear for their review 

of the original manuscript.
Tree Cover and Aridity Projections to 2060: 

A Technical Document Supporting the 

Forest Service 2010 RPA Assessment

Eric J. Greenfield

David J. Nowak

Introduction

Forests are an essential component of America’s 

“green infrastructure” and trees play particularly 

important roles in providing ecosystem services 

and enhancing human health. Trees provide various 

ecosystem services such as improvements in air and 

water quality, building energy conservation, cooler 

air temperatures, reductions in ultraviolet radiation, 

and many other environmental, economic and social 

benefits (e.g., Kuo and Sullivan 2001, Nowak and 

Dwyer 2007, Ulrich 1984, Westphal 2003, Wolf 2003). 

Costs associated with trees can be economic (e.g., 

planting and maintenance, increased building energy 

costs), social (obstructed views, litter, storm debris), 

and environmental (e.g., pollen, volatile organic 

compound emissions) (Nowak and Dwyer 2007). In 

addition, there can be transaction costs associated 

with the necessary institutional arrangements (setting, 

communicating, adapting policy) that aid forest 

management (Hardy and Koontz 2010, Ostrom 1990).

This report provides supporting documentation to 

the Forest Service 2010 Resources Planning Act 

(RPA) Assessment, Future of America’s Forests and 

Rangelands (U.S. Forest Service 2012a). The 2010 

RPA Assessment, the fifth report prepared in response 

to the mandate in the Forest and Rangeland Renewable 

Resources Planning Act (RPA 1974), summarizes 

findings about the status, trends, and projected future 

of forests, rangelands, wildlife and fish, biodiversity, 

water, outdoor recreation, wilderness, and urban 

forests, and the effects of climate change upon these 

resources. The RPA Assessment provides information 

to resource managers and policymakers from the 

national down to the local scale so they can develop 

strategies to sustain natural resources into the future. 

The key findings from the assessment document are 

a) land development will continue to threaten the 

integrity of natural ecosystems; b) climate change 

will alter natural ecosystems and affect their ability to 

provide goods and services; c) competition for goods 

and services from natural ecosystems will increase; 

and d) geographic variation in resource responses 

to drivers of change will require regional and local 

strategies to address resource management issues.

The future tree cover in the United States will change 

with the changes in land uses and climates outlined 

within the RPA Assessment, affecting the amount 

of benefits derived from this resource and the costs 

associated with managing or maintaining forests. This 

report details three projections of tree cover change 

across the conterminous United States based on 

predicted land-use changes from 2000 to 2060. It also 

evaluates nine climate projections for the same period 

to assess which areas of the country may become 

more or less arid. Finally this report provides an index 

of combined tree-cover and aridity change for nine 

modeled projections to illustrate which areas of the 

United States are projected to experience the greatest 

impact from tree-cover loss and increasing aridity. 

The index illustrates a new approach to highlight 
Tree Cover and Aridity Projections to 2060

areas of ecological vulnerability or concern that 

may develop at the nexus of projected land use and 

climate change. We found that in all projections, the 

conterminous United States loses tree cover by 2060, 

ranging from a 1.1 to 1.6 percent decline; and that the 

conterminous United States is becoming more arid 

by 2060, ranging from a 0.05 to 0.19 decrease in the 

aridity ratio. Overall, the frequency and magnitude of 

percent tree cover losses and aridity increases among 

the counties of the conterminous United States are 

greater than percent tree cover gains and decreases in 

aridity. The index illustrates that the areas at greatest 

risk of ecological change from tree loss and increased 

aridity generally are rapidly urbanizing regions of 

high tree cover and low aridity such as those found 

in the metropolitan regions of the Pacific Northwest, 

Southeast, and Northeast (Fig. 1). 

2010 RPA Scenarios

The RPA Assessment addresses a wide range of 

economic and ecological phenomena. Because there 

are uncertainties about future political, economic, 

social, and environmental change, we used scenarios 

to explore a range of possible futures for U.S. 

renewable natural resources. The projection period 

was 2010 to 2060. 

Figure 1.―RPA assessment regions of the United States
A Technical Document Supporting the Forest Service 2010 RPA Assessment 

Three scenarios1 were used to characterize common 

demographic, socioeconomic, and technological 

driving forces that underlay changes in resource 

conditions and to evaluate the sensitivity of resource 

trends to a feasible future range of these driving forces. 

These three RPA scenarios (Table 1) were linked to 

global scenarios developed for the Intergovernmental 

Panel on Climate Change (IPCC) Third Assessment 

Report (TAR) and Fourth Assessment Report (AR4) 

through IPCC assumptions and projections of global 

population growth, economic growth, bioenergy use, 

and climate (IPCC 2007). For continuity, we retained 

the scenario designations used in the IPCC TAR and 

AR4, with the addition of “RPA” to remind readers 

that these scenarios are tied to IPCC assumptions 

described in the Special Report on Emission Scenarios 

(SRES) (Nakicenovic and Swart 2000), but that 

some adjustments were made that are described in 

U.S. Forest Service (2012b). The RPA scenarios are 

therefore designated as RPA A1B, RPA A2, and RPA 

B2. Multiple climate projections were made for each 

IPCC scenario. The climate projections vary across 

scenarios in response to the associated levels of 

greenhouse gas (GHG) emissions, but they also vary 

within a scenario because general circulation models 

(GCMs) differ in their approaches to modeling climate 

dynamics. Therefore, we selected climate projections 

from three GCMs for each of the three RPA scenarios 

to capture a range of future climates. Table 2 lists the 

IPCC scenarios and associated GCM projections that 

were used to develop climate projections for the RPA 

scenarios. 

Land use change was projected by Wear (2011) for all 

counties in the conterminous United States for five 

major Natural Resource Inventory (NRI) land use 

classes: forest land, cropland, rangeland, urban and 

built-up areas, and pastureland and native pasture. 

Wear developed three land use distribution forecasts 

based on the three RPA scenarios using econometric 

models fit to historical data to allocate rural land 

among competing uses that incorporate projected 

changes in population and income at the county scale. 

Details of the methods and results can be found in 

Wear (2011). All land use change was assumed to 

occur on nonfederal land within these NRI categories; 

all other uses are held constant during the projection 

period, including Federal land, water area, enrolled 

Conservation Reserve Program lands, and utility 

corridors. The land use projections do not assume any 

1

 A fourth scenario was developed to address variation in 

future bioenergy use, but that scenario was not used for the 

analysis described in this document. 

a Numbers in parenthesis are the factors of change in the projection period. For example, U.S. GDP increases by a factor of 3.3 times between 

2010 and 2060 for scenario A1B.

GDP = gross domestic product.

IPCC = Intergovernmental Panel on Climate Change

Characteristic Scenario RPA A1B Scenario A2 Scenario B2

IPCC general global description Globalization, 

economic convergence

Regionalism, 

less trade

Slow change, 

localized solutions

IPCC global real GDP growth 2010-2060 High (6.2X) Low (3.2X) Medium (3.5X)

IPCC global population growth 2010-2060 Medium (1.3X) High (1.7X) Medium (1.4X)

IPCC global expansion of primary biomass energy production High Medium Medium

U.S. GDP growth 2006-2060 Medium (3.3X) Low (2.6X) Low (2.2X)

U.S. population growth 2006-2060 Medium (1.5X) High (1.7X) Low (1.3X)

Table 1.—Key characteristics of the RPA scenariosa
 Tree Cover and Aridity Projections to 2060

Scenario GCM Model vintage

A1B, A2 CGCM3.1(T47) (Canadian Centre for Climate Modeling and Analysis Third Generation 

Coupled Global Climate Model Version 3.1

AR4

MIROC3.2(medres) (Australian Commonwealth Scientific and Industrial Research 

Organisation Mk 3.5 Climate System Model)

CSIRO-Mk3.5 (Japanese Centre for Climate System Research, University of Tokyo, 

National Institute for Environmental Studies and Frontier Research Center for Global 

Change Model for Interdisciplinary Research on Climate Version 3.2)

B2 CGCM2 (Second Generation Coupled Global Climate Model) TAR

CSIRO-Mk2 (Mk 2 Climate System Model)

UKMO-HadCM3 (Hadley Centre for Climate Prediction and Research, UK)

a AR4 climate projections were downloaded from the web portal for the World Climate Research Program Coupled Model Intercomparison 

Project phase 3, and TAR climate projections were downloaded from the IPCC Data Distribution Centre. See Joyce et al. (in review) for details 

on the climate data and the downscaling procedures used.

IPCC=Intergovernmental Panel on Climate Change

GCM=general circulation model

Table 2.—IPCC scenarios and GCMs used for the 2010 RPA Assessment climate projectionsa

significant change in land use policy or regulations, 

i.e., projections are policy-neutral, based on historical 

land use relationships driven by future population and 

economic growth assumptions. The forecasted land 

uses are not influenced by climate.

In all RPA scenarios, increased urban and developed 

use is the dominant force in land use change, and 

all other land uses are projected to lose area. Urban 

and developed area increases the most in scenario 

RPA A1B, almost doubling the amount of urban area 

between 2010 and 2060. Forest land declines the most 

across the other land uses, with more than 30 million 

acres projected to be lost in RPA A1B. The largest 

forest land declines occur in the South, reflecting both 

an abundant forest resource and the highest projected 

population growth and urbanization (U.S. Forest 

Service 2012a).

Methods

Three types of analyses were conducted to understand 

the possible effects of changes in land use and climate 

on tree cover: a) projected change in percent tree 

cover from 2000 to 2060; b) projected change in 

aridity from 2000 to 2060; and c) combined index of 

change in percent tree cover and aridity from 2000 

to 2060. 

Tree Cover Projections

Tree cover projections in 2060 were derived by 

applying base year (2000) percent tree cover by land 

cover classes to projected land use changes. The model 

assumes that these land cover percent tree cover values 

will remain constant within their respective land uses 

over time. The data for this model comes from 2001 

National Land Cover Database (NLCD) land cover 

and adjusted tree-cover data (Nowak and Greenfield 

2010, USGS 2008), and models of projected land uses 

under the three future RPA scenarios A1B, A2, and B2 

for the conterminous United States from 1997 to 2060 

described previously (Wear 2011). For this analysis, 

both 2001 values from the NLCD and 1997 values 

from Wear (2011) were designated as the base year 

2000. Adjusted NLCD tree cover values were used 

because original tree-cover estimates from the NLCD 

underestimated percent tree cover compared with 

photo-interpreted estimates (Nowak and Greenfield 

2010). NLCD percent tree cover was modified 

according to the Nowak and Greenfield (2010) 

photo-interpreted values categorized by generalized 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 

NLCD land cover classes within individual mapping 

zones. 

The NRI land use classes used by Wear (2011) and 

the NLCD land cover categories are similar, but 

do not exactly match. The two sets of classes were 

reconciled with each other as follows (Table 3). The 

NLCD classes of developed land were associated 

with the NRI urban and built-up land category. The 

NLCD forested land cover classes were related with 

the NRI forest land category. The NLCD classes 

for agriculture were summed and associated with 

a summed agriculture category calculated from 

NRI cropland, and pastureland and native pasture 

categories. All other NLCD classes and NRI categories 

were related together as an “other” category to allow 

100 percent distribution within each county. From 

these data, the proportion of each county land area 

within the combined categories of developed/urban, 

forest, agriculture, and other was calculated for the 

years 2000 and 2060. The adjusted average percent 

tree cover by NLCD land cover class was extracted by 

county and applied to the 2000 land use distribution in 

the county. The 2060 percent tree cover in each county 

was then calculated by weighting county-specific 

land use tree cover values by 2060 land use area. 

Tree-cover changes from 2000 to 2060 were projected 

for each of the three RPA scenarios: A1B, A2, and 

B2. Changes in percent tree cover were calculated 

as county percent tree cover in 2060 minus county 

percent tree cover in 2000.

Aridity Projections

Aridity is a climate attribute that describes the amount 

of dryness of a region. Aridity influences ecosystem 

properties of a region, and changes in aridity may 

indicate areas of concern for ecosystem change. The 

aridity index used in this analysis utilizes precipitation 

and potential evapotranspiration data from Coulson 

et al. (2010a and 2010b). Changes in aridity were 

projected from 2000 to 2060 using the three RPA

scenarios (A1B, A2, B2), each having climate 

projections based on different models (Coulson et al. 

2010a, 2010b; Joyce et al. in review) (Table 2). The 

base year 2000 climate estimate used in this analysis 

was derived from the historical climate data found 

in Coulson and Joyce (2010). These climate models 

contain the following data downscaled to the counties 

of the conterminous United States: 

• Monthly totals of precipitation (mm) 

• Monthly means of maximum and minimum 

daily temperatures (°C) 

• Monthly mean of daily potential 

evapotranspiration (mm)

To provide decadal projections beginning in 2000, 

the individual county climate data were summarized 

into mean annual values based on decadal averages 

summed from monthly to annual values (e.g., average 

annual climate data for 2000 was based on average 

values from 1995 to 2004 inclusive, and 2060 was 

based on average values from 2055 to 2064 inclusive). 

Based on the climatic averages, an aridity index 

was calculated as the ratio of precipitation (P) to 

potential evapotranspiration (PE) (i.e., P/PE). In this 

dataset, potential evapotranspiration is calculated 

from temperature, elevation, dew point, and latitude 

variables (Coulson et al. 2010a, 2010b; Coulson and 

Joyce 2010). Aridity values were summarized within 

Analysis Categories NRI Categories NLCD Categories

Forest Forest land Deciduous forest, evergreen forest, mixed forest

Agriculture Cropland, pastureland 

and native pasture

Cultivated crops, pasture/hay

Developed/urban Urban and built-up areas Developed-open space, developed-low intensity, developedmedium intensity, developed-high intensity

Table 3.—Reconciling NRI and NLCD land use/cover classes
Tree Cover and Aridity Projections to 2060

the following categories (Middleton and Thomas 

1997):

• Hyperarid (P/PE < 0.05)

• Arid (0.05 to < 0.2)

• Semi-arid (0.2 to < 0.5)

• Dry subhumid (0.5 to < 0.65)

• Humid (P/PE > 0.65)

For the purposes of clearer illustration in this report 

and in the figures, changes in aridity from 2000 to 

2060 by county were calculated as county aridity 

index (P/PE) in 2000 minus aridity index in 2060, so 

that increasing aridity is indicated by positive values. 

Likewise, a decrease in the aridity ratio means an 

increase in aridity.

Aridity and Tree Cover Change Index 

2000 to 2060

The purpose of a combined aridity and tree cover 

change index is to provide additional information 

from the interaction of climate and land use change 

by mapping the simultaneous impacts of both of those 

changes across the conterminous United States. This 

index illustrates counties with the greatest potential of 

negative environmental conditions due to concurrent 

tree cover loss and/or increasing aridity. 

To determine areas that have relatively high amounts 

of environmental and/or climatic changes that are 

considered to be negative (i.e., tree loss and/or 

increased aridity), an aridity and tree cover change 

index was produced. In contrast with the changes in 

aridity described in the previous section, the changes 

in aridity from 2000 to 2060 by county were calculated 

as county aridity value (P/PE) in 2060 minus aridity 

value in 2000, so that increasing aridity is indicated 

by negative values, which indicate a negative 

environmental change. Similarly, changes in percent 

tree cover were calculated as county percent tree cover 

in 2060 minus county percent tree cover in 2000 so 

that the negative environmental change of tree cover 

loss is indicated by negative values. To calculate the 

aridity and tree cover change index, tree cover and 

aridity change values (2000 to 2060 using the decadal 

averages) were standardized by county across the 

conterminous United States based on the greatest 

change observed among all counties (i.e., all county 

values were divided by the maximum absolute value 

of change). In all scenarios, the maximum changes 

in both percent tree cover (loss of tree cover) and 

aridity (increasing aridity) were negative numbers, 

which produced standardized values of change 

ranging from -1 (greatest negative change) to some 

positive value less than 1, which indicated increased 

tree cover and/or decreased aridity. These two 

standardized values (percent tree cover and aridity) 

were added and then standardized again to produce 

values between -1 and some positive value less than 

1. With this final standard index value, counties with 

values approaching -1 indicate areas that have the 

greatest increase in aridity and/or the greatest decrease 

in percent tree cover. Counties with positive values 

indicate areas with decreasing aridity and/or increased 

percent tree cover.

Results and Discussion

The following text is divided into sections reporting 

and discussing the results for tree cover projections, 

aridity projections, and the combined aridity and tree 

cover change index for each of the climate change/

RPA scenarios.

Projected Tree Cover 2000 to 2060

The percent tree cover for the conterminous United 

States in 2000 is 34.1 percent, slightly higher than the 

amount of forest land at 27.1 percent, which reveals 

the substantial tree cover that exists in the other land 

uses such as urban and agricultural land. The state 

with the lowest percent tree cover is North Dakota 

(3.0 percent), and the state with the greatest percent 

tree cover is New Hampshire (84.5 percent) (Table 4). 

The county with the lowest percent tree cover is 

Greeley County, Kansas (1.0 percent), and the county 

with the greatest percent tree cover is Hamilton 

County, New York (94.7 percent) (Fig. 2). As 

illustrated in Figure 2, the extreme tree-cover values 

listed above are representative of the regional tree 

cover in those areas, with many nearby counties and 

states having very similar values of percent tree cover. 

That is, these are the expected naturally occurring 

regional tree cover patterns associated with forest, 

grassland and desert biomes. For example, the top 

10 counties with the highest percent tree cover are in 

New York, North Carolina, Wisconsin, West Virginia, 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 

2060

2000 RPA scenario A1B RPA scenario A2 RPA scenario B2

State tree cover tree cover changea tree cover changea tree cover changea

Table 4.—Tree cover projections and change for RPA scenarios by state, 2000 to 2060

Alabama 70.1 67.0 -3.1 67.7 -2.4 68.2 -1.9

Arizona 19.1 19.2 0.1 19.2 0.1 19.2 0.1

Arkansas 55.4 50.6 -4.8 51.8 -3.6 52.1 -3.3

Californiab 35.6 34.2 -1.4 34.2 -1.4 34.6 -1.0

Coloradoc 24.7 23.1 -1.6 23.2 -1.5 23.6 -1.1

Connecticut 72.0 69.1 -2.9 68.7 -3.3 70.3 -1.7

Delaware 40.8 38.4 -2.4 38.2 -2.6 39.2 -1.6

Florida 54.3 49.7 -4.6 49.3 -5.0 50.6 -3.7

Georgia 66.6 60.0 -6.6 61.6 -5.0 62.1 -4.5

Idaho 39.6 39.2 -0.4 39.3 -0.3 39.4 -0.2

Illinois 16.3 15.8 -0.5 16.0 -0.3 15.9 -0.4

Indiana 27.4 25.1 -2.3 25.8 -1.6 26.0 -1.4

Iowa 11.4 11.2 -0.2 11.3 -0.1 11.2 -0.2

Kansas 7.3 7.3 0.0 7.3 0.0 7.3 0.0

Kentucky 55.4 50.4 -5.0 51.5 -3.9 52.3 -3.1

Louisiana 51.6 48.6 -3.0 49.3 -2.3 49.7 -1.9

Maine 84.4 82.9 -1.5 83.2 -1.2 83.5 -0.9

Marylandd 51.3 45.0 -6.3 45.4 -5.9 46.8 -4.5

Massachusetts 69.2 65.1 -4.1 64.7 -4.5 66.5 -2.7

Michigan 59.2 57.3 -1.9 57.6 -1.6 58.0 -1.2

Minnesota 33.7 32.2 -1.5 32.5 -1.2 32.7 -1.0

Mississippi 62.5 59.5 -3.0 60.4 -2.1 60.6 -1.9

Missourie 41.4 38.4 -3.0 39.2 -2.2 39.4 -2.0

Montana 27.3 26.1 -1.2 26.4 -0.9 26.5 -0.8

Nebraska 4.8 4.6 -0.2 4.7 -0.1 4.7 -0.1

Nevada 13.0 12.7 -0.3 12.7 -0.3 12.8 -0.2

New Hampshire 84.5 81.1 -3.4 81.5 -3.0 82.2 -2.3

New Jersey 55.8 48.5 -7.3 47.4 -8.4 50.9 -4.9

New Mexico 18.2 18.0 -0.2 18.0 -0.2 18.0 -0.2

New York 67.0 65.7 -1.3 65.8 -1.2 66.3 -0.7

North Carolina 64.2 60.3 -3.9 60.9 -3.3 61.5 -2.7

North Dakota 3.0 3.0 0.0 3.0 0.0 3.0 0.0

Ohio 39.1 38.2 -0.9 38.3 -0.8 38.6 -0.5

Oklahoma 25.9 24.6 -1.3 24.8 -1.1 25.0 -0.9

Oregon 40.2 39.2 -1.0 39.2 -1.0 39.5 -0.7

Pennsylvania 65.9 63.2 -2.7 63.7 -2.2 64.4 -1.5

Rhode Island 62.5 53.2 -9.3 52.4 -10.1 56.9 -5.6

South Carolina 64.2 60.0 -4.2 60.5 -3.7 61.4 -2.8

South Dakota 4.1 4.3 0.2 4.3 0.2 4.3 0.2

Tennessee 59.3 55.0 -4.3 55.4 -3.9 56.5 -2.8

Texas 21.8 20.6 -1.2 21.1 -0.7 21.0 -0.8

Utah 19.0 17.8 -1.2 17.8 -1.2 18.1 -0.9

Vermont 78.2 74.6 -3.6 75.7 -2.5 75.9 -2.3

Virginiaf 68.2 65.1 -3.1 65.8 -2.4 66.0 -2.2

Washington 47.3 45.7 -1.6 45.6 -1.7 46.2 -1.1

West Virginia 82.0 77.4 -4.6 79.0 -3.0 79.3 -2.7

Wisconsin 49.0 48.1 -0.9 48.3 -0.7 48.4 -0.6

Wyoming 15.1 14.9 -0.2 14.9 -0.2 15.0 -0.1

Conterminous U.S. totalg 34.1 32.5 -1.6 32.7 -1.4 33.0 -1.1

a Change = (tree cover year 2060 – tree cover year 2000)

b California analysis covers 57 of 58 counties of state (Wear 2011)

c Colorado analysis covers 62 of 63 counties of state (Wear 2011)

d Maryland analysis covers 114 of 115 counties of state (Wear 2011)

e Missouri analysis covers 23 of 24 counties of state (Wear 2011)

f

 Virginia analysis covers 97 of 135 counties of state (Wear 2011) g Summary for lower 48 states excluding Washington, D.C.

% % % % % % %
 Tree Cover and Aridity Projections to 2060

and New Hampshire, and the next four states behind 

New Hampshire for the highest percent state tree 

cover are Maine, West Virginia, Vermont, and 

Connecticut. The bottom 10 counties with the least 

percent tree cover are in Kansas, Colorado, Nebraska, 

and Montana, and the next four states ahead of North 

Dakota for the lowest percent state tree cover are 

South Dakota, Nebraska, Kansas, and Iowa (Table 4).

In all three scenarios, percent tree cover in the 

conterminous United States declines between 2000 

and 2060 (Fig. 3). In the RPA A1B scenario, with the 

largest increase in urbanization, percent tree cover 

declines by 1.6 percent from 2000 to 32.5 percent 

(Table 4). The minimum and maximum county percent 

tree cover also declines from 2000 from a range of 

1.0 to 94.7 percent to a range of 0.3 to 93.9 percent. 

RPA A2 has a decline of 1.4 percent and a range of 

0.5 to 94.2 percent tree cover. RPA B2 has a decline of 

1.1 percent tree cover with the same range as in RPA 

A2. These small changes result in little discernible 

change in the national patterns of tree cover. In all 

future scenarios, North Dakota remains the state with 

the lowest percent tree cover, at 3.0 percent. The 

bottom five states with the lowest percent tree cover 

remain the same as 2000 in all three scenarios with 

very small or no change in 2060. Maine becomes 

the state with the highest percent tree cover in all 

three scenarios (82.0 to 83.5 percent) replacing New 

Hampshire from 2000, but the top five states with the 

highest percent tree cover remain the same as 2000, 

each with small decreases in percent tree cover in 

2060. In 2060, Phillips County, Colorado, has the 

lowest percent tree cover in all three scenarios, ranging 

from 0.3 percent in RPA A1B to 0.5 percent in RPA 

A2 and B2. Hamilton County, New York, remains the 

county with the highest percent tree cover in all future 

scenarios, ranging from 93.9 percent in RPA A1B to 

94.2 percent in both RPA A2 and B2. Generally, states 

that contain the top 10 and bottom 10 counties remain 

relatively consistent with the 2000 rankings, but with 

the additions of Michigan, Virginia, and Kentucky 

for highest percent tree cover, and South Dakota for 

lowest percent tree cover. 

Figure 2.—2000 percent tree cover by county. Maximum and minimum values are represented in the legend.
A Technical Document Supporting the Forest Service 2010 RPA Assessment 

Figure 3.—2060 percent 

tree cover by county for RPA 

scenarios A1B, A2, and B2.

Maximum and minimum values 

are represented in the legends.
10 Tree Cover and Aridity Projections to 2060

The pattern of changes in tree cover is clearer in the 

figures that display the percent change in tree cover 

by county (Fig. 4). The greatest loss in percent tree 

cover for a county across the conterminous United 

States among all three RPA scenarios is 44.2 percent, 

and the greatest increase is in the A2 scenario with 

15.1 percent. At the state level, South Dakota has the 

greatest increase in percent tree cover, a small increase 

of 0.2 percent in all three RPA scenarios (Table 4). 

Like South Dakota, Arizona has a small increase 

followed by no changes in Kansas and North Dakota. 

As illustrated in the figures, most of the increase is 

minor and found in the grassland and desert regions 

of the conterminous United States. Rhode Island had 

the largest decrease in percent tree cover, ranging from 

5.6 percent (RPA B2) to 10.1 percent (RPA A2). The 

highest amount of loss in percent tree cover was found 

among the heavily forested eastern states of New 

Jersey, Georgia, Maryland, Florida, and Kentucky. 

These areas are losing tree cover within and around 

the urban areas of those states. The county with the 

greatest loss in percent tree cover between 2000 and 

2060 in all three scenarios is Dare County, North 

Carolina, with a loss of 44.2 percent in all scenarios. 

The county with the greatest increase in percent tree 

cover varied by scenario: Lincoln County, Washington, 

gained 9.2 percent in RPA A1B and 8.1 percent in 

RPA B2, while Val Verde County, Texas, gained 

15.1 percent in RPA A2. Generally, states that 

contain the top 10 and bottom 10 counties of change 

include North Carolina, Georgia, Virginia, Maryland, 

Arkansas, Florida, and New Jersey for the greatest 

decrease in percent tree cover, and Washington, Texas, 

Idaho, South Dakota, Wisconsin, and Minnesota for 

the gains in percent tree cover. 

Tree Cover Discussion

Tree cover projections reflect the overall trend of 

increased urban land use at the expense of forested, 

agricultural, and other land use categories throughout 

the conterminous United States in all three RPA 

scenarios (Wear 2011). In addition, the projections also 

relate to the original allocation of percent tree cover 

by land use category in 2000, which is dependent on 

local and current ecological conditions. In the more 

forested areas, the percent tree cover tends to decrease 

with expansion of urban land as tree cover is often 

displaced with more impervious surfaces or lawn 

space (e.g., Nowak and Greenfield 2012). In contrast, 

in grassland and desert areas, urban land use can 

increase tree cover due to active human management 

associated with urbanization (e.g., tree planting). 

However, this projection of increased percent tree 

cover is often dependent on local ecosystem conditions 

such as availability of sufficient water resources to 

sustain tree populations. These tree cover increases 

may not occur if necessary resources, such as water 

for irrigation, become scarce. As documented in the 

RPA Assessment (U.S. Forest Service 2012a), many 

of the areas projected to increase tree cover, especially 

in the Intermountain and Great Plains states, are also 

projected to be vulnerable to water shortage in the 

future. 

Because of the expanding urbanization at the expense 

of forested and agricultural land uses, the percent tree 

cover decreases in the more forested counties of the 

Pacific Northwest and east of the Mississippi River, 

while the percent tree cover increases in developing 

grassland and desert counties of the Great Plains and 

Intermountain regions. In all three RPA scenarios 

across the conterminous United States, the declines 

in percent tree cover are of greater frequency and 

magnitude than the gains in percent tree cover. 

Generally, the eastern United States loses more forest 

land than the west because of greater amounts of forest 

land, higher population density, higher urbanization, 

and lower amounts of protected forest lands. The 

exceptions are the areas of higher population densities 

in the west, and areas that project low density 

development into rural areas neighboring protected 

forest lands. The implications of this development 

include concerns for declining forest inventories 

and carbon stocks, habitat loss and degradation, and 

increased competition for goods and services from the 

natural ecosystems. In addition, areas of urban forest 

will increase and have greater competition for their 

ecosystem services (U.S. Forest Service 2012a). 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 11

Figure 4.—Difference of 

percent tree cover by county 

for RPA scenarios A1B, A2, 

and B2 from 2000 to 2060.

Maximum and minimum values 

are represented in the legends.
12 Tree Cover and Aridity Projections to 2060

The largest decline in forest land is in the South 

Region (up to 21 million acres) (U.S. Forest Service 

2012a), and this analysis reflects those higher 

magnitude tree cover losses (maximum tree cover 

loss of 44.2 percent among all three RPA scenarios) 

in and around the established and growing urban 

regions found within heavily tree covered and rapidly 

urbanizing regions such as metropolitan Atlanta, 

Georgia, which has several counties projected to lose 

20 percent or more of their tree cover in all three RPA 

scenarios. 

In contrast, the expansion of development in the desert 

and grassland regions may increase tree cover, but the 

magnitude of increases is less (maximum tree cover 

gain ranges from 15.1 to 8.1 percent at the county 

scale among the three RPA scenarios) than the losses 

illustrated above. For example, three developing rural 

counties in the eastern region of South Dakota have a 

range of increases across the three scenarios of 

2.5 to 4.3 percent. However, these increases may not 

be realized due to projected increases in competition 

for water resources.

The tree cover projections assume that the average 

percent tree cover within a county land cover class will 

remain constant through time, as well as the current 

natural resource policies and ecosystem conditions. 

Though the use of average cover by land use type 

can provide reasonable projections, there are various 

conditions or factors that may change to alter these 

projections. These factors include changes in aridity/

climate in the future that may affect regeneration 

and the ability or desire to water urban trees, future 

storms, wildfires, or insect outbreaks that can lead 

to significant losses in tree cover, and tree planting 

campaigns or natural resource protection policies that 

may stabilize and/or increase tree cover. The greater 

the deviation from current and recent past conditions, 

and from the projected land cover changes, the more 

the projected cover estimates will vary from the given 

projections. 

All three scenarios exhibit similar patterns of future 

changes in percent tree cover across the United States. 

The RPA A1B scenario, which has the most urban 

development outside of the more urbanized counties, 

has the largest change with a 1.6 percent decrease in 

tree cover in the conterminous United States from 

2000 to 2060. The B2 scenario, which has the most 

urban growth within the already urbanized counties, 

has the smallest change, with a 1.1 percent decrease. 

Even though tree cover changes within a state or 

nationwide are relatively small (mostly less than 

10 percent), changes at the county level can be much 

higher and more variable. For example, the county 

with the greatest projected percent tree cover loss, 

Dare County, North Carolina, loses tree cover because 

of projected urban and other land use increases at 

the expense of forest and agricultural land uses. The 

counties projected to gain the most percent tree cover 

(Lincoln County, WA, and Val Verde County, TX) had 

increased cover mostly due to projected increases in 

urban land in Lincoln County and agricultural land in 

Val Verde County.

Projected Aridity 2000 to 2060

In 2000, the aridity ratio (P/PE) for the conterminous 

United States is 0.49, or semi-arid (Table 5). In 2000, 

the conterminous United States has 0.5 percent of its 

area classified as hyperarid, 26.3 percent as arid, 

35.3 percent as semi-arid, 25.2 percent as dry 

subhumid, and 12.8 percent as humid (Table 6). 

Generally, the hyperarid and arid regions are found 

in the Intermountain region and extending from west 

Texas to southern California. Semi-arid areas are 

found in the Southeast, a large percentage of the center 

of the conterminous United States, and scattered in a 

crescent pattern from the Intermountain region into 

Pacific Coast states. Dry subhumid regions cover the 

eastern states, and humid areas are found along the 

northern Pacific coast, western Rocky Mountains, 

northern Gulf coast, and into the highlands of the 

eastern states. The most arid state is Arizona at 0.10 

(arid), and the least arid state is Washington at 0.97 

(humid). Imperial County, California, is the most arid 

county, with an aridity value of 0.02 (hyperarid), while 

the least arid county is Jefferson County, Washington, 

with a value of 3.15 (humid) (Fig. 5). 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 13

Alabama 0.61

Arizona 0.10

Arkansas 0.54

California 0.40

Colorado 0.24

Connecticut 0.78

Delaware 0.63

District of Columbia 0.57

Florida 0.54

Georgia 0.53

Idaho 0.35

Illinois 0.53

Indiana 0.60

Iowa 0.46

Kansas 0.32

Kentucky 0.63

Louisiana 0.61

Maine 0.78

Maryland 0.62

Massachusetts 0.78

Michigan 0.57

Minnesota 0.43

Mississippi 0.62

Missouri 0.51

Montana 0.27

Nebraska 0.29

Nevada 0.13

New Hampshire 0.77

New Jersey 0.67

New Mexico 0.14

New York 0.71

North Carolina 0.61

North Dakota 0.28

Ohio 0.60

Oklahoma 0.37

Oregon 0.86

Pennsylvania 0.69

Rhode Island 0.79

South Carolina 0.51

South Dakota 0.29

Tennessee 0.67

Texas 0.30

Utah 0.21

Vermont 0.77

Virginia 0.59

Washington 0.97

West Virginia 0.66

Wisconsin 0.53

Wyoming 0.23

Conterminous U.S.

e 0.49

Table 5.—Aridity projections and change for RPA scenario-climate combinations by state, 2000 to 2060

0.54 0.07 0.54 0.07 0.34 0.27

0.09 0.01 0.10 0.00 0.07 0.03

0.50 0.04 0.44 0.10 0.29 0.25

0.32 0.08 0.34 0.06 0.32 0.08

0.22 0.02 0.21 0.03 0.15 0.09

0.68 0.10 0.64 0.14 0.64 0.14

0.55 0.08 0.55 0.08 0.45 0.18

0.51 0.06 0.53 0.04 0.40 0.17

0.50 0.04 0.56 -0.02 0.25 0.29

0.50 0.03 0.51 0.02 0.30 0.23

0.33 0.02 0.32 0.03 0.28 0.07

0.46 0.07 0.39 0.14 0.31 0.22

0.49 0.11 0.45 0.15 0.35 0.25

0.44 0.02 0.37 0.09 0.29 0.17

0.29 0.03 0.25 0.07 0.19 0.13

0.53 0.10 0.52 0.11 0.38 0.25

0.55 0.06 0.55 0.06 0.33 0.28

0.70 0.08 0.71 0.07 0.66 0.12

0.54 0.08 0.55 0.07 0.43 0.19

0.68 0.10 0.64 0.14 0.64 0.14

0.48 0.09 0.45 0.12 0.35 0.22

0.41 0.02 0.35 0.08 0.30 0.13

0.54 0.08 0.53 0.09 0.34 0.28

0.45 0.06 0.37 0.14 0.28 0.23

0.28 -0.01 0.25 0.02 0.23 0.04

0.30 -0.01 0.27 0.02 0.19 0.10

0.12 0.01 0.13 0.00 0.10 0.03

0.67 0.10 0.64 0.13 0.63 0.14

0.61 0.06 0.60 0.07 0.53 0.14

0.12 0.02 0.13 0.01 0.09 0.05

0.61 0.10 0.58 0.13 0.52 0.19

0.56 0.05 0.57 0.04 0.40 0.21

0.27 0.01 0.20 0.08 0.20 0.08

0.49 0.11 0.46 0.14 0.37 0.23

0.32 0.05 0.30 0.07 0.21 0.16

0.78 0.08 0.71 0.15 0.73 0.13

0.60 0.09 0.59 0.10 0.48 0.21

0.70 0.09 0.66 0.13 0.66 0.13

0.50 0.01 0.53 -0.02 0.32 0.19

0.29 0.00 0.23 0.06 0.19 0.10

0.57 0.10 0.58 0.09 0.38 0.29

0.27 0.03 0.25 0.05 0.16 0.14

0.20 0.01 0.19 0.02 0.14 0.07

0.66 0.11 0.65 0.12 0.61 0.16

0.52 0.07 0.52 0.07 0.39 0.20

0.87 0.10 0.76 0.21 0.81 0.16

0.56 0.10 0.56 0.10 0.42 0.24

0.46 0.07 0.44 0.09 0.34 0.19

0.23 0.00 0.20 0.03 0.15 0.08

0.44 0.05 0.41 0.08 0.30 0.19

2060b

A1B

2000a CGCM3.1 CSIRO-Mk3.5 MIROC3.2

State aridityc aridityc changed aridityc changed aridityc changed

a 2000 is the modeled base year (annual values from decadal average of 1995 to 2004)

b 2060 is the modeled year for projections (annual values from decadal average of 2055 to 2064)

c Aridity is the ratio of precipitation (P) to potential evapotranspiration (PET); P/PE (Middleton and Thomas, 1997) 

d Change = aridity 2000 – aridity 2060

e Summary for lower 48 states including Washington, DC

(Table 5 continued on next page)
14 Tree Cover and Aridity Projections to 2060

Alabama 0.61

Arizona 0.10

Arkansas 0.54

California 0.40

Colorado 0.24

Connecticut 0.78

Delaware 0.63

District of Columbia 0.57

Florida 0.54

Georgia 0.53

Idaho 0.35

Illinois 0.53

Indiana 0.60

Iowa 0.46

Kansas 0.32

Kentucky 0.63

Louisiana 0.61

Maine 0.78

Maryland 0.62

Massachusetts 0.78

Michigan 0.57

Minnesota 0.43

Mississippi 0.62

Missouri 0.51

Montana 0.27

Nebraska 0.29

Nevada 0.13

New Hampshire 0.77

New Jersey 0.67

New Mexico 0.14

New York 0.71

North Carolina 0.61

North Dakota 0.28

Ohio 0.60

Oklahoma 0.37

Oregon 0.86

Pennsylvania 0.69

Rhode Island 0.79

South Carolina 0.51

South Dakota 0.29

Tennessee 0.67

Texas 0.30

Utah 0.21

Vermont 0.77

Virginia 0.59

Washington 0.97

West Virginia 0.66

Wisconsin 0.53

Wyoming 0.23

Conterminous U.S.

e 0.49

Table 5 (continued).

0.47 0.14 0.57 0.04 0.39 0.22

0.09 0.01 0.12 -0.02 0.07 0.03

0.44 0.10 0.52 0.02 0.30 0.24

0.26 0.14 0.39 0.01 0.30 0.10

0.20 0.04 0.23 0.01 0.17 0.07

0.68 0.10 0.60 0.18 0.67 0.11

0.54 0.09 0.47 0.16 0.47 0.16

0.50 0.07 0.44 0.13 0.42 0.15

0.44 0.10 0.48 0.06 0.29 0.25

0.43 0.10 0.50 0.03 0.35 0.18

0.31 0.04 0.33 0.02 0.28 0.07

0.43 0.10 0.52 0.01 0.34 0.19

0.48 0.12 0.56 0.04 0.39 0.21

0.39 0.07 0.44 0.02 0.31 0.15

0.26 0.06 0.30 0.02 0.20 0.12

0.52 0.11 0.56 0.07 0.40 0.23

0.49 0.12 0.53 0.08 0.36 0.25

0.69 0.09 0.67 0.11 0.71 0.07

0.53 0.09 0.47 0.15 0.45 0.17

0.68 0.10 0.60 0.18 0.68 0.10

0.45 0.12 0.49 0.08 0.43 0.14

0.36 0.07 0.38 0.05 0.31 0.12

0.47 0.15 0.56 0.06 0.37 0.25

0.41 0.10 0.47 0.04 0.30 0.21

0.27 0.00 0.28 -0.01 0.23 0.04

0.28 0.01 0.30 -0.01 0.20 0.09

0.09 0.04 0.15 -0.02 0.09 0.04

0.66 0.11 0.61 0.16 0.68 0.09

0.61 0.06 0.53 0.14 0.55 0.12

0.12 0.02 0.16 -0.02 0.09 0.05

0.59 0.12 0.57 0.14 0.56 0.15

0.52 0.09 0.53 0.08 0.42 0.19

0.26 0.02 0.25 0.03 0.22 0.06

0.49 0.11 0.54 0.06 0.41 0.19

0.29 0.08 0.33 0.04 0.20 0.17

0.73 0.13 0.71 0.15 0.74 0.12

0.59 0.10 0.58 0.11 0.52 0.17

0.70 0.09 0.61 0.18 0.70 0.09

0.45 0.06 0.48 0.03 0.35 0.16

0.27 0.02 0.25 0.04 0.20 0.09

0.53 0.14 0.60 0.07 0.41 0.26

0.24 0.06 0.29 0.01 0.15 0.15

0.16 0.05 0.22 -0.01 0.15 0.06

0.64 0.13 0.62 0.15 0.66 0.11

0.50 0.09 0.46 0.13 0.41 0.18

0.85 0.12 0.77 0.20 0.79 0.18

0.55 0.11 0.55 0.11 0.45 0.21

0.41 0.12 0.47 0.06 0.38 0.15

0.21 0.02 0.22 0.01 0.17 0.06

0.40 0.09 0.44 0.05 0.32 0.17

2060b

A2

2000a CGCM3.1 CSIRO-Mk3.5 MIROC3.2

State aridityc aridityc changed aridityc changed aridityc changed

a 2000 is the modeled base year (annual values from decadal average of 1995 to 2004)

b 2060 is the modeled year for projections (annual values from decadal average of 2055 to 2064)

c Aridity is the ratio of precipitation (P) to potential evapotranspiration (PET); P/PE (Middleton and Thomas, 1997) 

d Change = aridity 2000 – aridity 2060

e Summary for lower 48 states including Washington, DC

(Table 5 continued on next page)
A Technical Document Supporting the Forest Service 2010 RPA Assessment 15

Alabama 0.61

Arizona 0.10

Arkansas 0.54

California 0.40

Colorado 0.24

Connecticut 0.78

Delaware 0.63

District of Columbia 0.57

Florida 0.54

Georgia 0.53

Idaho 0.35

Illinois 0.53

Indiana 0.60

Iowa 0.46

Kansas 0.32

Kentucky 0.63

Louisiana 0.61

Maine 0.78

Maryland 0.62

Massachusetts 0.78

Michigan 0.57

Minnesota 0.43

Mississippi 0.62

Missouri 0.51

Montana 0.27

Nebraska 0.29

Nevada 0.13

New Hampshire 0.77

New Jersey 0.67

New Mexico 0.14

New York 0.71

North Carolina 0.61

North Dakota 0.28

Ohio 0.60

Oklahoma 0.37

Oregon 0.86

Pennsylvania 0.69

Rhode Island 0.79

South Carolina 0.51

South Dakota 0.29

Tennessee 0.67

Texas 0.30

Utah 0.21

Vermont 0.77

Virginia 0.59

Washington 0.97

West Virginia 0.66

Wisconsin 0.53

Wyoming 0.23

Conterminous U.S.

e 0.49

Table 5 (continued).

0.47 0.14 0.52 0.09 0.54 0.07

0.09 0.01 0.08 0.02 0.12 -0.02

0.44 0.10 0.40 0.14 0.42 0.12

0.35 0.05 0.28 0.12 0.27 0.13

0.20 0.04 0.19 0.05 0.22 0.02

0.65 0.13 0.65 0.13 0.67 0.11

0.48 0.15 0.52 0.11 0.54 0.09

0.43 0.14 0.48 0.09 0.50 0.07

0.44 0.10 0.47 0.07 0.50 0.04

0.42 0.11 0.48 0.05 0.51 0.02

0.31 0.04 0.32 0.03 0.28 0.07

0.38 0.15 0.43 0.10 0.43 0.10

0.42 0.18 0.48 0.12 0.48 0.12

0.37 0.09 0.35 0.11 0.35 0.11

0.23 0.09 0.22 0.10 0.25 0.07

0.42 0.21 0.52 0.11 0.54 0.09

0.56 0.05 0.50 0.11 0.49 0.12

0.71 0.07 0.67 0.11 0.68 0.10

0.46 0.16 0.51 0.11 0.53 0.09

0.66 0.12 0.65 0.13 0.68 0.10

0.46 0.11 0.43 0.14 0.45 0.12

0.36 0.07 0.29 0.14 0.32 0.11

0.50 0.12 0.50 0.12 0.51 0.11

0.35 0.16 0.37 0.14 0.39 0.12

0.24 0.03 0.24 0.03 0.23 0.04

0.23 0.06 0.23 0.06 0.22 0.07

0.12 0.01 0.11 0.02 0.12 0.01

0.66 0.11 0.64 0.13 0.65 0.12

0.55 0.12 0.58 0.09 0.60 0.07

0.11 0.03 0.11 0.03 0.14 0.00

0.57 0.14 0.58 0.13 0.58 0.13

0.47 0.14 0.54 0.07 0.58 0.03

0.23 0.05 0.19 0.09 0.20 0.08

0.41 0.19 0.48 0.12 0.49 0.11

0.29 0.08 0.24 0.13 0.28 0.09

0.77 0.09 0.64 0.22 0.54 0.32

0.52 0.17 0.57 0.12 0.58 0.11

0.67 0.12 0.66 0.13 0.70 0.09

0.41 0.10 0.48 0.03 0.51 0.00

0.23 0.06 0.20 0.09 0.20 0.09

0.46 0.21 0.54 0.13 0.57 0.10

0.26 0.04 0.22 0.08 0.23 0.07

0.18 0.03 0.19 0.02 0.21 0.00

0.66 0.11 0.65 0.12 0.63 0.14

0.42 0.17 0.49 0.10 0.52 0.07

0.85 0.12 0.70 0.27 0.58 0.39

0.44 0.22 0.53 0.13 0.57 0.09

0.44 0.09 0.39 0.14 0.40 0.13

0.21 0.02 0.20 0.03 0.20 0.03

0.38 0.11 0.39 0.10 0.40 0.09

2060b

B2

2000a CGCM2 CSIRO-Mk2 HADCM3

State aridityc aridityc changed aridityc changed aridityc changed

a 2000 is the modeled base year (annual values from decadal average of 1995 to 2004)

b 2060 is the modeled year for projections (annual values from decadal average of 2055 to 2064)

c Aridity is the ratio of precipitation (P) to potential evapotranspiration (PET); P/PE (Middleton and Thomas, 1997) 

d Change = aridity 2000 – aridity 2060

e Summary for lower 48 states including Washington, DC
16 Tree Cover and Aridity Projections to 2060

Table 6.—Land area distribution of categories of aridity for the conterminous United States, 2000 to 2060

2060

A1B

2000 CGCM3.1 CSIRO-Mk3.5 MIROC3.2

Aridity Class percenta percenta changeb percenta changeb percenta changeb

Hyperarid 0.5

Arid 26.3

Semi-arid 35.3

Dry subhumid 25.2

Humid 12.8

1.7 1.2 1.4 0.9 2.5 2.0

26.8 0.5 32.1 5.8 39.0 12.7

45.8 10.5 41.5 6.2 49.7 14.4

19.1 -6.1 19.5 -5.7 5.1 -20.1

6.6 -6.2 5.6 -7.2 3.7 -9.1

2060

A2

2000 CGCM3.1 CSIRO-Mk3.5 MIROC3.2

Aridity Class percenta percenta changeb percenta changeb percenta changeb

Hyperarid 0.5

Arid 26.3

Semi-arid 35.3

Dry subhumid 25.2

Humid 12.8

2.4 1.9 0.5 0.0 2.5 2.0

30.0 3.7 25.6 -0.7 38.0 11.7

50.3 15.0 46.6 11.3 49.8 14.5

11.9 -13.3 21.6 -3.6 5.0 -20.2

5.4 -7.4 5.7 -7.1 4.6 -8.2

2060

B2

2000 CGCM2 CSIRO-Mk2 HADCM3

Aridity Class percenta percenta changeb percenta changeb percenta changeb

Hyperarid 0.5

Arid 26.3

Semi-arid 35.3

Dry subhumid 25.2

Humid 12.8

1.4 0.9 1.7 1.2 0.3 -0.2

31.5 5.2 35.4 9.1 35.5 9.2

53.0 17.7 45.0 9.7 42.9 7.6

9.1 -16.1 13.3 -11.9 16.9 -8.3

4.9 -7.9 4.6 -8.2 4.3 -8.5

a Values calculated for conterminous U.S. including Washington, D.C. from counties within each aridity ratio category b Change = year 2060 values – year 2000 values

a Values calculated for conterminous U.S. including Washington, D.C. from counties within each aridity ratio category b Change = year 2060 values – year 2000 values

a Values calculated for conterminous U.S. including Washington, D.C. from counties within each aridity ratio category b Change = year 2060 values – year 2000 values
A Technical Document Supporting the Forest Service 2010 RPA Assessment 17

Figure 5.—2000 aridity by county

Scenario RPA A1B

Across all three climate projection models in RPA 

A1B, aridity of the conterminous United States 

is projected to increase between 2000 and 2060. 

The projected aridity (P/PE) ranges from 0.30 (an 

increase of 0.19) from the MIROC3.2 model to 0.44 

(an increase of 0.05) from the CGCM3.1 model 

(Table 5). Despite the change in aridity ratio for the 

conterminous United States, the aridity class remains 

the same: semi-arid. Regionally, the changes indicated 

by aridity class conversion occur in the pattern 

described below with less conversion in CGCM3.1 to 

the most conversion in MIROC3.2 projections (Fig. 6). 

Generally, aridity classes convert to more arid classes 

in a west to east pattern, with hyperarid conversions 

from arid in the southwest, arid conversions from 

semi-arid in the Great Plains and Intermountain states, 

semi-arid conversion from dry subhumid in the North 

Central to the South Central states, and dry subhumid 

conversion from humid in the East. 

In 2060, the area of hyperarid land of the conterminous 

United States is projected to increase in all three 

models ranging from a 2.0 percent increase in the 

MIROC3.2 results to a 0.9 percent increase in the 

CSIRO-Mk3.5 model (Table 6). Arid land increases 

in all three projections, ranging from a 12.7 percent 

rise in the MIROC3.2 results to a 0.5 percent increase 

in the CGCM3.1 projection. Semi-arid land grows 

in all three models, ranging from a 14.4 percent 

increase with the MIROC3.2 projection to 6.2 percent 

growth in the CSIRO-Mk3.5 results. Dry subhumid 

land area decreases among all models, ranging from 

a 20.1 percent reduction in the MIROC3.2 results 

to a 5.7 percent reduction in the CSIRO-Mk3.5 

projection. Humid land area also decreases in the three 

projections, ranging from a 9.1 percent decline in the 

MIROC3.2 results to a 6.2 percent decrease in the 

CGCM3.1 model. 
1

8 Tree Cover and Aridity Projections to 2060

Figure 6.—2060 aridity by 

county for RPA A1B-CGCM3.1, 

A1B-CSIRO-Mk3.5, and 

A1B-MIROC3.2
A Technical Document Supporting the Forest Service 2010 RPA Assessment 19

Arizona is projected to remain the most arid state in all 

three models with no change from 2000 in the CSIROMk3.5 results to a 0.03 increase in aridity in the 

MIROC3.2 projection, but with no change in aridity 

class (Table 5). Washington is projected to continue 

as the least arid state with no change in aridity class, 

but all models project increasing aridity in that state 

ranging from a 0.21 change in the CSIRO-Mk3.5 

model to a 0.10 change in the CGCM3.1 results. 

Imperial County, California, continues to be the most 

arid county projected in all three 2060 projections 

with an aridity ratio of 0.02 (no change from 2000) 

(Fig. 6). The least arid county among all three models 

in 2060 continues to be Jefferson County, Washington, 

but its aridity is increasing from 2000, ranging from a 

change of 0.62 in the CSIRO-Mk3.5 results to a 

0.26 difference in the CGCM3.1 projection. With these 

changes the county does not change its aridity class. 

Increases of aridity from 2000 to 2060 are of greater 

magnitude and frequency than decreases of aridity 

and were variable across the United States (Fig. 7). In 

model CGCM3.1, aridity changes between -0.04 to 

0.48, and the greatest change (increase of aridity) is 

found in Pacific Northwest. The results from CSIROMk3.5 have the greatest range of the three models; 

-0.08 to 0.66 with the greatest change (increase of 

aridity) also found in the Pacific Northwest. The 

MIROC3.2 projection has a range of 0.00 to 0.58 

with no counties of decreasing aridity. In addition to 

the greatest aridity increases projected in the Pacific 

Northwest, the model projects a substantial increase of 

aridity extending from the Gulf of Mexico coast to the 

north through the South and North Central states. 

The counties with the greatest increase of aridity 

are Tillamook County, Oregon, in the CGCM3.1 

projection and Skamania County, Washington, in both 

CSIRO-Mk3.5 and MIROC3.2 models, with increases 

of 0.66 and 0.58, respectively. In both cases, aridity 

class does not change. In the CGCM3.1 model, the 

county with the greatest decrease in aridity is Nance 

County, Nebraska, with a change of -0.04 (no change 

in aridity class). In the CSIRO-Mk3.5 projection, 

the greatest decrease in aridity is Volusia County, 

Florida, a change of -0.08 (no change in aridity class). 

In the MIROC3.2 model, there are no counties with 

decreases in aridity. 

Scenario RPA A2

Aridity in the conterminous United States is projected 

to increase between 2000 and 2060 across all three 

climate projections in RPA A2. The projected aridity 

ratio (P/PE) in 2060 ranges from 0.32 (an increase of 

0.17) from the MIROC3.2 model to 0.44 (an increase 

of 0.05) from the CSIRO-Mk3.5 results (Table 5). The 

aridity class of the conterminous United States remains 

the same: semi-arid. The pattern of aridity class change 

is similar to RPA scenario A1B (Fig. 8). The least 

amount of change occurs in the CSIRO-Mk3.5 model 

and the most in the MIROC 3.2 model. 

In 2060, the area of hyperarid land of the conterminous 

United States is projected to increase in two models 

ranging from a 2.0 percent increase in the MIROC3.2 

forecast to a 1.9 percent increase in the CGCM3.1 

model (Table 6). In the CSIRO-Mk3.5 results, there 

is no change from 2000. Arid land increases in two 

projections, ranging from an 11.7 percent rise in the 

MIROC3.2 results to a 3.7 percent increase in the 

CGCM3.1 model. In the CSIRO-Mk3.5 projection, 

arid land decreases 0.7 percent. Semi-arid land area 

increases in all three models, ranging from a 15.0 

percent increase with the CGCM3.1 projection to 

11.3 percent growth in the CSIRO-Mk3.5 results. Dry 

subhumid land area decreases in all models, ranging 

from a 20.2 percent reduction in the MIROC3.2 

model to a 3.6 percent reduction in the CSIRO-Mk3.5 

projection. Humid land area also decreases in the three 

projections, ranging from an 8.2 percent decline in 

the MIROC3.2 results to a 7.1 percent decrease in the 

CSIRO-Mk3.5 model. 

In two projections, Arizona continues to be the most 

arid state, and in the CGCM3.1 model it is tied with 

Nevada as the most arid (Table 5). In the CSIROMk3.5 results, Arizona is projected to become slightly 

less arid, a 0.02 decrease, but in the other two models 

the state becomes more arid, with a 0.01 increase in 

the CGCM3.1 projection results and a 0.03 increase in 

the MIROC3.2 model. Nevada’s change is projected 
20 Tree Cover and Aridity Projections to 2060

Figure 7.—Difference in 

aridity ratio by county for RPA 

A1B-CGCM3.1, A1B-CSIROMk3.5, and A1B-MIROC3.2 

from 2000 to 2060. Maximum 

and minimum values are 

represented in the legends
A Technical Document Supporting the Forest Service 2010 RPA Assessment 21

Figure 8.—2060 aridity by 

county for RPA A2-CGCM3.1, 

A2-CSIRO-Mk3.5, and 

A2-MIROC3.2
22 Tree Cover and Aridity Projections to 2060

to be a 0.04 increase in the CGCM3.1 model. These 

changes do not change the aridity class of either state. 

Washington is projected to remain the least arid state 

in 2060 in all three models, but it is also becoming 

more arid with a range of increases from 0.20 in 

the CSIRO-Mk3.5 results to 0.12 in the CGCM3.1 

projection. Despite this substantial increase, the aridity 

class for Washington does not change. 

Imperial County, California, continues to be the most 

arid county in all three 2060 projections with an aridity 

ratio of 0.02 in the CSIRO-Mk3.5 results (no change 

from 2000) and a ratio of 0.01 (an aridity increase of 

0.01) in the other two models (Fig. 8). The most humid 

county among all three models in 2060 continues 

to be Jefferson County, Washington, but its aridity 

increases from 2000, ranging from a change of 0.52 

in the CSIRO-Mk3.5 and MIROC3.2 results to a 0.30 

difference in the CGCM3.1 projection. None of the 

counties listed above change aridity class.

Like RPA scenario A1B, the frequency and magnitude 

of aridity increases are greater than decreases and 

variable across the United States (Fig. 9). In model 

CGCM3.1, the range of change is -0.03 to 0.58 with 

the greatest change (increase of aridity) found along 

the Pacific coast. The CSIRO-Mk3.5 projection has 

the greatest range of change: -0.07 to 0.67; but it 

has the greatest area of no change or a decrease in 

aridity, notably in the Southwest. Of note is the area of 

northwestern Texas that is projected to shift from arid 

to semi-arid. The MIROC3.2 model illustrates only 

increases in aridity, with a range of 0.01 to 0.60. The 

greatest change is found in the Pacific Northwest and 

in the South, especially along the southern Mississippi 

River basin.

The counties with the greatest projected increase 

of aridity are Tillamook County, Oregon, in both 

CGCM3.1 and MIROC3.2 models (changes of 

0.58 and 0.60 respectively) and Skamania County, 

Washington (a change of 0.67) in the CSIRO-Mk3.5 

projection. In both cases aridity class does not change. 

In the CGCM3.1 model, the county with the greatest 

projected decrease in aridity is Big Horn County, 

Montana, with a change of -0.03. In addition, Big 

Horn County converts from an arid classification 

to a semi-arid classification. In the CSIRO-Mk3.5 

projection, the greatest decrease in aridity is Tulare 

County, California, a change of -0.07, but it has no 

change in aridity class. In the MIROC3.2 model, 

the smallest increase of aridity is Imperial County, 

California, with an increase of 0.01 but no change in 

aridity class.

Scenario RPA B2

Across all three climate projections in RPA B2, aridity 

of the conterminous United States is projected to 

increase between 2000 and 2060. The projected aridity 

ratio (P/PE) in 2060 ranges from 0.38 (an increase of 

0.11) from the CGCM2 model to 0.40 (an increase 

of 0.09) from the HADCM3 results (Table 5). The 

aridity class remains the same as 2000: semi-arid. The 

pattern of conversion in RPA scenario B2 is similar to 

the previous two scenarios with the CGCM2 model 

with the greatest change followed by CSIRO-Mk2 and 

HADCM3 projections. 

In 2060, hyperarid land area of the conterminous 

United States is projected to increase in two models 

ranging from a 1.2 percent increase in the CSIRO-Mk2 

projection to a 0.9 percent increase in the CGCM2 

model (Table 6). In the HADCM3 results, there is a 

decrease in hyperarid land area of 0.2 percent from 

2000. Arid land area increases in all three projections, 

ranging from a 9.2 percent rise in the HADCM3 

results to a 5.2 percent increase in the CGCM2 model. 

Semi-arid land area increases in all three projections, 

ranging from a 17.7 percent increase with the CGCM2 

projection to 7.6 percent growth in the HADCM3 

results. Dry subhumid land decreases among all 

models, ranging from a 16.1 percent reduction in the 

CGCM2 results to an 8.3 percent reduction in the 

HADCM3 projection. Humid land area also decreases 

in the three projections, ranging from an 8.5 percent 

decline in the HADCM3 results to a 7.9 percent 

decrease in the CGCM2 model. 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 23

Figure 9.—Difference in 

aridity ratio by county for RPA 

A2-CGCM3.1, A2-CSIROMk3.5, and A2-MIROC3.2 

from 2000 to 2060. Maximum 

and minimum values are 

represented in the legends
24 Tree Cover and Aridity Projections to 2060

In all three models, Arizona continues to be the 

most arid state. In the HADCM3 projection, Arizona 

does become slightly less arid, a 0.02 decrease, but 

otherwise the state becomes more arid, with a 0.01 

increase in the CGCM2 projection results and a 0.02 

increase in the CSIRO-Mk2 model. Arizona does 

not change aridity class. Washington is projected to 

continue as the least arid state in two models, but it has 

increasing aridity ranging from a 0.27 change in the 

CSIRO-Mk2 results to a 0.12 change in the CGCM2 

model. Neither change affects Washington’s aridity 

class. In the HADCM3 projection, Rhode Island is the 

least arid state but with increasing aridity of 0.09. The 

change has no effect on the aridity class for Rhode 

Island.

Imperial County, California, continues to be the most 

arid county projected in all three 2060 results with an 

aridity ratio of 0.02 in the CGCM2 and CSIRO-Mk2 

results (no change from 2000) and a ratio of 0.04 (a 

decrease of aridity of 0.02, but no change in aridity 

class) in the HADCM3 model (Fig. 10). The least 

arid county among all three models in 2060 continues 

to be Jefferson County, Washington, but its aridity 

is projected to increase from 2000, ranging from a 

change of 1.21 in the HADCM3 results to a 0.24 

difference in the CGCM2 projection. Neither change 

affects the county aridity class. 

Like the previous two RPA scenarios, the frequency 

and magnitude of increasing aridity are greater than 

the decreases and variable across the United States 

(Fig. 11). The range of change in model CGCM2 

is -0.03 to 0.49, and the greatest projected changes 

are located in the Pacific Northwest and in the 

Appalachian Mountains. In projection CSIRO-Mk2 

the range of change is -0.02 to 0.95 with the greatest 

change found along the northern Pacific Coast from 

northern California to Washington. The greatest range 

of change is found in the HADCM3 results: -0.05 to 

1.21. Like the previous model, the greatest projected 

aridity increase is found along the northern Pacific 

coast. 

The counties with the greatest increase of aridity 

are Tillamook County, Oregon, in both CGCM2 

and CSIRO-Mk2 models (changes of 0.49 and 0.95 

respectively), and Jefferson County, Washington (a 

change of 1.21), in the HADCM3 projection. The 

changes in aridity ratio do not affect the aridity class in 

either county. In the CGCM2 model, the county with 

the greatest projected decrease in aridity is Calhoun 

County, Texas, with a change of -0.03. The change 

in ratio does not affect aridity class. In the CSIROMk2 projection, the greatest increase of humidity is 

Butte County, Idaho, a change of -0.02 with no effect 

on county aridity class. In the HADCM3 results, the 

largest decrease of aridity is Yavapai County, Arizona, 

with a difference of 0.05. The change in ratio does not 

affect the county aridity class. 

Aridity Discussion

In all nine scenarios, the aridity of the conterminous 

United States is projected to increase by 2060, ranging 

from an increase of 0.05 (from 0.49 in 2000 to 0.44 in 

2060) in RPA A1B-CGCM 3.1 and RPA A2-CSIROMk3.5 to an increase of 0.19 (from 0.49 in 2000 to 

0.30 in 2060) in RPA A1B-MIROC3.2 (Table 5). 

Despite these changes, the aridity class for the United 

States does not change―it remains semi-arid for 

all projections. Among the three climate model sets 

across the conterminous United States, the most arid 

projections come from the MIROC3.2 (A1B and A2) 

and HADCM3 (B2) models with an average aridity 

in 2060 of 0.24 (with the two MIROC3.2 scenarios 

substantially more arid than the HADCM3), followed 

by the CGCM model set (0.31), and CSIRO model set 

(0.32). Among the three RPA scenarios, RPA A1B is 

the most arid (2060 aridity at 0.38), followed by RPA 

A2 and B2, each at 0.39.

Changes in the amount of hyperarid areas (0.5 percent 

of the conterminous United States in 2000) range 

from a 2.0 percent increase in RPA A1B-MIROC 3.2 

and RPA A2-MIROC 3.2 to a decrease of 0.2 percent 

in 2060 in RPA B2-HADCM3 (Table 6, Fig. 12). For 

arid regions (26.3 of the conterminous United States 

in 2000), the projection varies from a 12.7 percent 

increase in 2060 RPA A1B-MIROC3.2 to a decrease of 

0.7 percent in 2060 RPA A2-CSIRO-Mk3.5. Semi-arid 

areas (35.3 percent in 2000) are projected to increase 

by 6.2 percent in 2060 RPA A1B-CSIRO-Mk3.5 to as 

much as a 17.7 percent in 2060 RPA B2-CGCM2. Dry 

subhumid areas (25.2 percent in 2000) are projected 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 25

Figure 10.—2060 aridity by 

county for RPA B2-CGCM2, 

B2-CSIRO-Mk2, and B2-

HADCM3
26 Tree Cover and Aridity Projections to 2060

Figure 11.—Difference in 

aridity ratio by county for RPA 

B2-CGCM2, B2-CSIRO-Mk2, 

and B2-HADCM3 from 2000 to 

2060. Maximum and minimum 

values are represented in the 

legends
A Technical Document Supporting the Forest Service 2010 RPA Assessment 27

Figure 12.—Distribution of categories of aridity for the conterminous United States from 2000 to 2060 across all nine climate 

change projections

to decrease by as much as 20.2 percent in 2060 RPA 

A2-MIROC3.2 to as little as 3.6 percent in 2060 RPA 

A2-CSIRO-Mk3.5. Humid areas (12.8 percent in 

2000) are also projected to decrease with the greatest 

decrease of 9.1 percent in 2060 RPA A1B-MIROC3.2 

and the smallest decrease of 6.2 percent in 2060 RPA 

A1B-CGCM3.1. 

The frequency and magnitude of increasing aridity 

across the conterminous United States is greater 

than decreases in aridity. While the patterns vary 

among the nine scenario-climate combinations, the 

greatest changes exist among the areas classified 

as dry subhumid and humid in 2000. For the most 

part, substantial increases in aridity occur along the 

northern Pacific coast of northern California through 

Washington, and from the Great Plains states to the 

South Central and Southeastern states, and to the 

Northeast states. As previously illustrated, the general 

trend is a conversion of areas of dry subhumid to areas 

categorized as semi-arid. 

Overall, the aridity change for the entire conterminous 

United States is not large, but this analysis illustrates 

the variability of aridity changes at the county scale 

and demonstrates that some areas, like the counties of 

the northern Pacific Coast, may have larger increases 

in aridity and greater potential impacts to those 

ecosystems. As reported by the RPA Assessment 

(U.S. Forest Service 2012a), climate change will 

impact ecosystems and their derived benefits, and 

ecosystems will have diverse responses to stress 

caused by climate change. In particular, the assessment 

emphasizes that climate change may have greater 

influence on fragile ecosystems and ecosystems found 

in the transition zones between major biomes, like the 

grassland-forest land transition in the central United 

States and areas of high topographic relief, like those 

found in the Intermountain west. These influences can 

cause habitat stress and ecosystem change that may 

result in decreased capacity for sustained regeneration, 

species decline, and species mix changes, such as 

the projected general decline of lodgepole pine and 
28 Tree Cover and Aridity Projections to 2060

Douglas fir throughout the Rocky Mountains, or the 

decline of hemlock-Sitka spruce along the northern 

Pacific coast, which has some of the greatest increases 

in aridity. Climate change can also lead to increased 

stress in ecosystems making them more susceptible 

to invasive species and wildfire, such as the increased 

threat of bark beetle infestation and wildfire in the 

western states (U.S. Forest Service 2012a). 

The RPA Assessment (U.S. Forest Service 2012a) 

projects that climate change will increase the areas 

vulnerable to water shortage because of decreasing 

tree cover, decreased precipitation, and increased 

demand for water resources. The most vulnerable 

area of overlapping increased aridity and increased 

water shortage vulnerability is the agricultural center 

of the conterminous United States just west of the 

Mississippi River.

Throughout the conterminous United States, the ability 

to sustain tree cover and related species mix may 

be compromised by climate change and increasing 

aridity. Tree cover loss from land use change may 

further impact those ecosystems stressed by climate 

change. The next section examines the regions of the 

conterminous United States that may be at risk by both 

increasing aridity and land use derived tree cover loss. 

Aridity and Tree Cover Change Index

Scenario: RPA A1B

In the 2000 to 2060 aridity and tree cover change 

index for RPA scenario A1B-CGCM3.1 (Fig. 13), 

the greatest projected impact from tree cover loss 

and/or increasing aridity is found along the Pacific 

Coast, urbanizing counties, developing counties on 

the edges of protected lands, and highlands east of the 

Mississippi River. Many of these regions were within 

humid, dry subhumid and semi-arid areas and had 

a relatively high percent tree cover in 2000. Five of 

the bottom 10 counties with the lowest index scores 

are in the Pacific Northwest near the coast, which 

have high tree cover and low aridity. Generally, the 

areas demonstrating positive values of the change 

index are in low tree cover areas in the arid and 

semi-arid regions of the Intermountain and Great 

Plains states, including most of Nebraska. Much of 

the Southwest has little change. Dare County, North 

Carolina, is projected to have the greatest impact from 

both increased aridity and/or decreased tree cover. In 

Dare County, the aridity increases 0.04, and percent 

tree cover decreases 44.2 percent. Lincoln County, 

Washington, has the greatest index value with aridity 

increasing 0.02 and tree cover percent increasing 

9.2 percent.

In the 2000 to 2060 aridity and tree cover change 

index for RPA scenario A1B-CSIRO-Mk3.5, the 

greatest impact from projected tree cover loss and/or 

increasing aridity is similar to the pattern from the 

CGCM3.1 model above, with expanded impacts in the 

Midwest, and less severity overall. Like CGCM3.1, 

the lowest index scores are found in the Pacific 

Northwest. The areas demonstrating minor positive 

values of the change index are in more sparsely-treed 

areas in the arid and semi-arid counties of west Texas, 

Nebraska, and scattered throughout the Intermountain 

states. The exception is Florida and South Carolina 

where tree cover is moderate and where the aridity 

is mostly in the dry subhumid category. The region 

of little or no change covers of the remainder of the 

Great Plains and Intermountain region. Snohomish 

County, Washington, has the greatest impact from both 

increased aridity and decreased percent tree cover. In 

Snohomish County, the aridity increases 0.58, and tree 

cover decreases 11.3 percent. Lincoln County, Idaho, 

has the most positive value with aridity increasing 0.01 

and tree cover increasing 7.2 percent.

In the 2000 to 2060 aridity and tree cover change 

index for RPA scenario A1B-MIROC3.2, the greatest 

impact from tree cover loss and/or increasing aridity 

is found in most of the states east of the Great Plains, 

and like the previous patterns, also in the Pacific 

Northwest and scattered urbanizing and developing 

counties. This climate model has the broadest impact 

of the three models in the RPA A1B scenario. Five of 

the ten lowest index scores are found in Georgia with 

most of those five in the Atlanta region. There are 

very few counties with positive index scores found 

in Idaho and eastern Washington. The areas with 

little to no change are found scattered throughout the 

Intermountain region. Dare County, North Carolina, 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 29

Figure 13.—Aridity and tree 

cover change index for RPA 

A1B-CGCM3.1, A1B-CSIROMk3.5, and A1B-MIROC3.2 

from 2000 to 2060
30 Tree Cover and Aridity Projections to 2060

has the greatest impact from both increased aridity 

and decreased percent tree cover. In Dare County, 

the aridity increases 0.21, and tree cover decreases 

44.2 percent. Lincoln County, Washington, has the 

most positive index value with aridity increasing 

0.05 and tree cover increasing 9.2 percent.

Scenario: RPA A2

In the 2000 to 2060 aridity and tree cover change 

index for RPA scenario A2-CGCM3.1 (Fig. 14), the 

greatest impact from tree cover loss and/or increasing 

aridity is found in the Pacific Coast States and in 

urbanizing counties, developing counties on the edges 

of protected lands scattered throughout the states of the 

east, with heavier concentrations in the South. Many 

of these regions were within humid, dry subhumid and 

semi-arid areas and had high tree cover in 2000. The 

counties with the lowest index scores are in the Pacific 

Northwest, Georgia, Virginia, California, Florida, and 

North Carolina. Generally, the regions with positive 

index values are found in the northern Intermountain 

(much of eastern Montana), Great Plains states, and 

in west Texas, areas categorized as arid and semi-arid 

and with low tree cover. Little or no change occurs in 

scattered counties among the Intermountain and Great 

Plains states. Dare County, North Carolina, has the 

greatest projected impact from both increased aridity 

and decreased percent tree cover. In Dare County, 

the aridity increases 0.08, and tree cover decreases 

44.2 percent. Val Verde County, Texas, has the most 

positive index value with aridity increasing 0.04 and 

tree cover increasing 15.1 percent.

In the 2000 to 2060 aridity and tree cover change 

index for RPA scenario A2-CSIRO-Mk3.5, the greatest 

impact from tree cover loss and/or increasing aridity 

is found in the Pacific Northwest states, Northeastern 

states, scattered throughout the Southeast, and in 

urbanizing and developing counties, similar to the 

patterns previously described. Most of the counties 

with the 10 lowest index scores are found in the 

Pacific Northwest. Generally, the regions with 

positive index values are found in the Southwest 

region extending from west Texas to California, to 

the north through the Intermountain states, and within 

Nebraska, Iowa, Illinois, and Kansas. The areas of 

little to no change are found throughout the center of 

the conterminous United States. Dare County, North 

Carolina, has the greatest impact from both increased 

aridity and decreased percent tree cover. In Dare 

County, the aridity increases 0.09, and tree cover 

decreases 44.2 percent. Val Verde County, Texas, has 

the most positive value with aridity increasing 0.02 

and tree cover increasing 15.1 percent.

In the 2000 to 2060 aridity and tree cover change 

index for RPA scenario A2-MIROC3.2, the greatest 

projected impact from tree cover loss and/or increasing 

aridity is found in the Pacific Northwest states, in a 

region extending from the Midwest and east Texas 

to the Atlantic coast, and within the urbanizing and 

developing counties, similar to the patterns found 

in the previous models. The pattern of counties 

with the lowest index scores is similar to those 

described from the A2-CGCM3.1 model above. Only 

a few counties have positive index values found in 

Washington, Idaho, and Texas. Little or no change is 

found scattered throughout the Intermountain states. 

Dare County, North Carolina, has the greatest impact 

from both increased aridity and decreased percent 

tree cover. In Dare County, the aridity increases 0.18, 

and tree cover decreases 44.2 percent. Val Verde 

County, Texas, has the most positive value with aridity 

increasing 0.09 and tree cover increasing 15.1 percent.

Scenario: RPA B2

In the 2000 to 2060 aridity and tree cover change 

index for RPA scenario B2-CGCM2 (Fig. 15), the 

greatest impact from tree cover loss and/or increasing 

aridity is found in the Pacific Northwest states, 

urbanizing counties, developing counties on the 

edges of protected lands, and in a region extending 

from Missouri to the east through the Midwest and 

South Central states to the Mid-Atlantic. Many of 

these regions were within humid, dry subhumid and 

semi-arid areas and had high tree cover in 2000. 

Most of the counties with the lowest 10 index scores 

are in the Pacific Northwest and in the Atlanta, 

Georgia, metropolitan region. Positive index values 

are scattered throughout the Intermountain region 

and in southeast coastal Texas. Little or no change 

in index values are found in Texas and through the 
A Technical Document Supporting the Forest Service 2010 RPA Assessment 31

Figure 14.—Aridity and tree 

cover change index for RPA 

A2-CGCM3.1, A2-CSIROMk3.5, and A2-MIROC3.2 

from 2000 to 2060
3

2 Tree Cover and Aridity Projections to 2060

Figure 15.—Aridity and tree 

cover change index for RPA 

B2-CGCM2, B2-CSIRO-Mk2, 

and B2-HADCM3 from 2000 to 

2060
A Technical Document Supporting the Forest Service 2010 RPA Assessment 33

Intermountain states. Dare County, North Carolina, 

has the greatest impact from both increased aridity 

and decreased percent tree cover. In Dare County, 

the aridity increases 0.12 and tree cover decreases 

44.2 percent. Lincoln County, Washington, has the 

most positive value with aridity increasing 0.04 and 

tree cover increasing 8.1 percent.

In the 2000 to 2060 climate and tree cover change 

index for RPA scenario B2-CSIRO-Mk2, the greatest 

projected impact from tree cover loss and/or increasing 

aridity is found along the Pacific Coast and scattered 

throughout the conterminous United States, but with 

less severity than the B2-CGCM2 scenario. Most of 

the counties with the lowest index scores are found 

in the Pacific Northwest. The counties with positive 

index values are found mostly in Idaho, areas that 

generally had low tree cover and that were more arid 

in 2000. The area of little or no change is found in the 

Intermountain and Great Plains states. Dare County, 

North Carolina, has the greatest impact from both 

increased aridity and decreased percent tree cover. In 

Dare County, the aridity increases 0.07, and tree cover 

decreases 44.2 percent. Lincoln County, Idaho, has the 

most positive value with aridity increasing 0.01 and 

tree cover increasing 8.1 percent.

In the 2000 to 2060 climate and tree cover change 

index for RPA scenario B2-HADCM3, the greatest 

projected impact from tree cover loss and/or increasing 

aridity is found along the Pacific Coast and with a 

very similar pattern to the previous scenario. Most of 

the counties with the lowest 10 index scores are in the 

Pacific Northwest. The regions with positive index 

values are found in the Southwest, including most of 

Arizona. The area of little or no change is found in a 

pattern similar to the previous scenario. Dare County, 

North Carolina, has the greatest impact from both 

increased aridity and decreased percent tree cover. In 

Dare County, the aridity increases 0.05, and tree cover 

decreases 44.2 percent. Lincoln County, Washington, 

has the most positive value on the other side of the 

spectrum with aridity increasing 0.09 and tree cover 

increasing 8.1 percent.

Index Discussion

When the aridity and tree cover projections are 

combined across the conterminous United States, the 

scenario with the least tree cover loss and smallest 

increase in aridity is 2060 RPA B2-HADCM3. The 

scenario with the greatest percent tree cover loss 

and greatest increase in aridity in 2060 is RPA A1BMIROC3.2. Consistent among all scenarios is that 

the tree cover losses and aridity increases, illustrated 

by the combination of both indicators, are of greater 

frequency and magnitude throughout most of the 

conterminous United States. The regions of the 

greatest impact from both increased aridity and/or 

decreased tree cover are relatively consistent among 

the scenarios with counties of the Pacific Northwest 

and Southeast having some of the lowest index scores. 

In addition, scattered urbanizing and developing 

counties, including some that border protected lands 

also tend to have low index scores. The Southwest and 

Intermountain regions are generally areas of little or 

no aridity increase and/or tree cover increase among 

all scenarios. The areas of greatest change tend to be 

the least arid regions with high tree cover. Much of 

this same area overlaps regions of established and 

expanding human settlement, such as the growing 

metropolitan region in the Pacific Northwest states 

(between the Cascade and Coast Ranges), and 

around Atlanta, Georgia. Human population growth, 

urbanization, and low density development is the 

primary driver of tree cover loss, and while increasing 

aridity may not be caused by local impacts of human 

expansion, many of the same areas of the lowest 

aridity and impacted by aridity increases are also areas 

of established and expanding human settlement. 

As summarized in the previous sections of tree cover 

and aridity changes, tree cover loss will decrease forest 

inventories and carbon stocks, degrade habitat, and 

impact water availability while the demands for the 

goods and services from ecosystems increase. Aridity 

increases will also impact ecosystems and cause 

diverse ecosystem responses to the stress caused by 

climate change. Those changes could be particularly 

acute in more sensitive ecosystems. Ultimately, the 
34 Tree Cover and Aridity Projections to 2060

combination of tree loss and aridity increase could 

impair the stability of existing ecosystems and their 

ongoing sustainability. The stress to these systems 

could lead to impaired regeneration, species decline, 

species mix change, and increase the vulnerability to 

other threats such as invasive species and wildfire. 

Finally, increasing aridity will increase water supply 

vulnerability, and many of the areas projected to 

gain tree cover may not increase in cover because 

of increasing demands on a shrinking water supply. 

Future development and climate change will have 

significant and varying effects on tree cover and 

ecosystems in the United States with most areas 

projected to lose tree cover and become more arid 

by 2060. Aridity is projected to increase because of 

continued global climate change. Both decreases and 

increases of tree cover are driven by human population 

growth, urbanization, and low density development, 

with decreases in tree cover found in the forested areas 

and increases in tree cover in sparsely treed land uses 

that convert to developed land uses. These developed 

land uses tend to remove trees in forest regions and 

bring in trees and increase tree cover in low tree cover 

regions. Thus, the tree and forest landscape of the 

United States are likely to undergo substantial changes 

in the coming century. Managers and planners need to 

understand these changes to help sustain healthy and 

functioning landscapes to meet the needs of changing 

society.

Conclusion

This report provides further detail in support of the 

2010 RPA Assessment by illustrating the variability 

of tree cover and aridity changes throughout the 

conterminous United States. We found that in all 

projections that the conterminous United States loses 

tree cover in 2060, ranging from 1.1 to 1.6 percent; 

and that the conterminous United States is becoming 

more arid in 2060, ranging from a 0.05 to a 0.19 

increase in aridity ratio. Overall, the frequency and 

magnitude of percent tree cover losses and aridity 

increases among the counties of the conterminous 

United States are greater than percent tree cover 

gains and decreases in aridity. Mapping these areas 

of change illustrates the areas at greatest risk of 

ecological change from tree loss and increased aridity. 

These higher risk areas are generally in rapidly 

urbanizing regions with high tree cover and low 

aridity, such as those found in the metropolitan regions 

of the Pacific Northwest, Southeast, and Northeast. 

Despite this generalization, tree cover and aridity 

change is highly variable throughout the conterminous 

United States. 

Land development will continue to threaten the 

integrity of natural ecosystems driven by projected 

population growth, urbanization, and low density 

development including agricultural uses at the expense 

of natural landscapes. This development will cause 

natural habitat loss, but also increase the amount of 

urban land in the United States. Trees and forests 

in urban areas will become increasingly important 

in providing benefits to both humans and nature. 

Climate change will alter natural ecosystems and 

affect their ability to provide goods and services. In 

particular aridity changes may have a greater impact 

on more sensitive and vulnerable ecosystems. As 

population increases and natural land cover decreases, 

the competition for goods and services from natural 

ecosystems will increase, especially at the wildlandurban interface. The impacts are geographically 

variable, and resource responses to land development 

and climate change drivers will require national, 

regional, and local strategies to address future resource 

management issues. 

Literature Cited

Coulson, D.P.; Joyce, L.A. 2010. Historical climate 

data (1940-2006) for the conterminous United 

States at the county spatial scale based on 

PRISM climatology. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. http://dx.doi.

org/10.2737/RDS-2010-0010

Coulson, D.P.; Joyce, L.A.; Price, D.T.; McKenney, 

D.W. 2010a. Climate scenarios for the 

conterminous United States at the county spatial 

scale using SRES scenario B2 and PRISM 

climatology. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Research Station. http://dx.doi.org/10.2737/RDS2010-0009
A Technical Document Supporting the Forest Service 2010 RPA Assessment 35

Coulson, D.P.; Joyce, L.A.; Price, D.T.; McKenney, 

D.W.; Siltanen, R.M.; Papadopol, P.; Lawrence, K. 

2010b. Climate scenarios for the conterminous 

United States at the county spatial scale using 

SRES scenarios A1B and A2 and PRISM 

climatology. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Research Station. http://dx.doi.org/10.2737/RDS2010-0008 

Forest and Rangeland Renewable Resources Planning 

Act of 1974 [RPA]; 16 U.S.C. 1601 (note).

Hardy, S.D.; Koontz, T.M. 2010. Collaborative 

watershed partnerships in urban and rural 

areas: Different pathways to success? Landscape 

and Urban Planning. 95(3): 79-90.

Intergovernmental Panel on Climate Change [IPCC]. 

2007. Climate Change 2007: Synthesis report.

Contribution of Working Groups I, II and III to 

the Fourth Assessment. Geneva, Switzerland: 

Intergovernmental Panel on Climate Change. 104 p.

Joyce, L.A.; Price, D.T.; Coulson, D.P. [et al.]. [In 

review]. Projecting climate change in the United 

States: a technical document supporting the 

Forest Service 2010 RPA Assessment. Gen. Tech. 

Rep. RMRS. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Research Station.

Kuo, F.E.; Sullivan, W.E. 2001. Environment and 

crime in the inner city: does vegetation reduce 

crime? Environmental Behavior. 33(3): 343-365.

Middleton, N.; Thomas, D., eds. 1997. World atlas of 

desertification 2nd ed. London, UK: Routledge. 

192 p.

Nakicenovic, N.; Swart, R., eds. 2000. Special 

report on emissions scenarios. Prepared for the 

Intergovernmental Panel on Climate Change. 

Cambridge, UK: Cambridge University Press. 

570 p. Available: http://www.ipcc.ch/ipccreports/

sres/emission/index.htm. 

Nowak, D.J.; Dwyer, J.F. 2007. Understanding the 

benefits and costs of urban forest ecosystems. In: 

Kuser, J., ed. Urban and community forestry in the 

northeast. New York, NY: Springer: 25-46.

Nowak, D.J.; Greenfield, E.J. 2010. Evaluating 

the national land cover database tree cover 

and impervious cover estimates across the 

conterminous United States: a comparison with 

photo-interpreted estimates. Environmental 

Management. 46(3): 378-390.

Ostrom, E. 1990. Governing the commons: the 

evolution of institutions for collective actions.

Cambridge, UK: Cambridge University Press. 

298 p.

Wear, D.N. 2011. Forecasts of county-level land 

uses under three future scenarios: A technical 

document supporting the Forest Service 2010 

RPA Assessment. Gen. Tech. Rep. SRS-141. 

Asheville, NC: U.S. Department of Agriculture 

Forest Service, Southern Research Station. 41 p.

Westphal, L.M. 2003. Urban greening and social 

benefits: a study of empowerment outcomes.

Journal of Arboriculture. 29(3): 137-147.

Wolf, K.M. 2003. Public response to the urban 

forest in inner-city business districts. Journal of 

Arboriculture. 29(3): 117-126.

Ulrich, R.S. 1984. View through a window may 

influence recovery from surgery. Science. 224: 

420-421.

U.S. Forest Service. 2012a. Future of America’s 

forests and rangelands: Forest Service 2010 

Resources Planning Act Assessment. Gen. Tech. 

Rept. WO-87. Washington, DC: U.S. Department of 

Agriculture, Forest Service, Washington Office. 

198 p. 

U.S. Forest Service. 2012b. Future scenarios: a 

technical document supporting the Forest 

Service 2010 RPA Assessment. Gen. Tech. Rep. 

RMRS-272. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Research Station. 34 p.

U.S. Geologic Survey [USGS]. 2008. Multiresolution land characteristics consortium.

Washington, DC: U.S. Department of Interior, 

Geologic Survey. www.mrlc.gov. (2008 August 1).

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and 

activities on the basis of race, color, national origin, age, disability, and where applicable, sex, 

marital status, familial status, parental status, religion, sexual orientation, genetic information, 

political beliefs, reprisal, or because all or part of an individual’s income is derived from any public 

assistance program. (Not all prohibited bases apply to all programs.) Persons with disabilities who 

require alternate means for communication of program information (Braille, large print, audiotape, 

etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a 

complaint of discrimination, write to USDA, Director, Office of Civil Rights, 1400 Independence 

Avenue, S.W., Washington, DC 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 

(TDD). USDA is an equal opportunity provider and employer.

Printed on recycled paper

Greenfield, Eric J.; Nowak, David J. 2013. Tree cover and aridity projections 

to 2060: a technical document supporting the Forest Service 2010 RPA 

assessment. Gen. Tech. Rep. NRS-125. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research Station. 35 p.

Future projections of tree cover and climate change are useful to natural resource 

managers as they illustrate potential changes to our natural resources and the 

ecosystem services they provide. This report a) details three projections of tree 

cover change across the conterminous United States based on predicted land-use 

changes from 2000 to 2060; b) evaluates nine climate projections for the same 

period to assess which areas of the country may become more or less arid; and 

c) provides an index of combined tree-cover and aridity change for nine modeled 

projections to illustrate which areas of the U.S. are projected to experience the 

greatest impact from tree-cover loss and increasing aridity. The index illustrates 

a new approach to highlight areas of ecological vulnerability or concern that may 

develop at the nexus of projected land use and climate change. We found that in 

all projections the conterminous U.S. loses tree cover by 2060, ranging from a 

1.1 to 1.6 percent decline; and that the conterminous United States is becoming 

more arid by 2060, ranging from a 0.05 to 0.19 decrease in the aridity ratio. Overall, 

the frequency and magnitude of percent tree cover losses and aridity increases 

among the counties of the conterminous U.S. are greater than percent tree cover 

gains and decreases in aridity. The index illustrates that the areas at greatest risk 

of ecological change from tree loss and increased aridity generally are rapidly 

urbanizing regions of high tree cover and low aridity such as those found in the 

metropolitan regions of the Pacific Northwest, Southeast, and Northeast.

KEY WORDS: forecasting, land use change, climate change