Forecasts of Forest Conditions in

U.S. Regions Under Future Scenarios

A Technical Document Supporting

the Forest Service 2010 RPA Assessment

David N. Wear, Robert Huggett, Ruhong Li,

Benjamin Perryman, and Shan Liu
Product Disclaimer

The use of trade or firm names in this publication is for 

reader information and does not imply endorsement by the 

U.S. Department of Agriculture of any product or service.

The Authors:

David N. Wear, Project Leader and Research 

Forester, Forest Economics and Policy, Southern 

Research Station, Research Triangle Park, NC 27709; 

Robert Huggett, Research Assistant; Ruhong Li,

Research Associate; Benjamin Perryman, Research 

Associate; and Shan Liu, Research Assistant, 

Department of Forestry and Environmental 

Resources, North Carolina State University, 

Raleigh, NC 27695.

March 2013

Southern Research Station

200 W. T. Weaver Blvd.

Asheville, NC 28804

www.srs.fs.usda.gov
Forecasts of Forest Conditions in Regions 

of the United States under Future Scenarios: 

A Technical Document Supporting the 

Forest Service 2010 RPA Assessment

David N. Wear, Robert Huggett, Ruhong Li, Benjamin Perryman, and Shan Liu
ii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

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

chapter 1 Scenarios from the Resources Planning Act Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . 3

chapter 2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

chapter 3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

chapter 4 Forest Forecasts for the South . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

chapter 5 Forest Forecasts for the North . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

chapter 6 Forest Forecasts for the Rocky Mountains Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54

chapter 7 Forest Forecasts for the Pacific Coast Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

appendix Structure of the Forest Dynamics Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .92

Contents
iii

Figure 1—General schematic of the U.S. Forest Assessment System. The first (left) column describes the data sets and projections 

that initiate analysis and define scenarios. The second column describes the three modeling components of the system. The third and 

fourth columns describe primary outputs generated and secondary analysis that can be supported respectively . . . . . . . . . . . .1

Figure 2—Resources Planning Act (RPA) Assessment regions: South, North, Rocky Mountain, and Pacific Coast . . . . . . . . . .2

Figure 3—Total projected population in the United States to 2060 for the three Resources Planning Act (RPA) scenarios (A1B, A2, 

and B2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Figure 4—Projected per capita personal income (2006 = 100) for the United States to 2060 for the three Resources Planning Act 

(RPA) scenarios (A1B, A2, and B2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Figure 5—Projected change in population density (people per square mile), 1997–2060, for the Resources Planning Act (RPA) A1B 

scenario. Areas in blue would experience population increases under this scenario. Areas in green are forecasted to experience 

population declines over this period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Figure 6—Empirical distributions of monthly temperatures for the South Region for a historical decade (1991–2000) and projections 

of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60) . . . . . . . . . . . . . . . 12

Figure 7—Empirical distributions of monthly temperatures for the South Region for a historical decade (1991–2000) and for 6 

successive decades of climate projections (A) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) for the 

RPA B2-CSIRO-Mk2 combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 8—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Black areas indicate distributional disagreement between historical and projected temperature distributions (p value 

<0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 9—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) scenario A1B-CSIROMk2 combination. Black areas indicate distributional disagreement between historical and projected temperature distributions (p 

value <0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 10—Proportion of the nine Resources Planning Act (RPA) scenario-climate combinations yielding significant differences 

in decadal temperature distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the 

South Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 11—Empirical distributions of monthly precipitation values for the South Region for a historical decade (1991–2000) and 

projections of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60) . . . . . . . . 16

Figure 12—Empirical distributions of monthly precipitation for the South Region for a historical decade (1991–2000) and for 6 

successive decades (2010–60) of climate projections for the Resources Planning Act (RPA) A1B-MIROC3.2 combination . . . . . 16

Figure 13—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 14—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Figure 15—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) B2-HadCM3 

List of Figures
iv

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 16—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the South Region . . . . . 20

Figure 17—Total forest area for six RPA scenario-timber price combinations for the South Region. Forest area varies only by 

Resources Planning Act (RPA) scenario and by timber price futures but not by climate projections . . . . . . . . . . . . . . . . . . 20

Figure 18—Forecasted change in the proportion of county in forest land, 1997–2060, for the Resources Planning Act (RPA) B2 

scenario, which is characterized by high urbanization and low timber prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Figure 19—Forecasts of the area of forest types in the South Region for 20 Resources Planning Act (RPA) scenario-climate-timber 

price combinations, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 

general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIROMk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figure 20—Total biomass measured as growing stock inventory of forests in the South Region for 20 Resources Planning Act (RPA) 

scenario-climate-timber price combinations (see table 2 for labels), 2010–60. RPA A1B scenarios are represented by solid lines; 

RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 

and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines 

marked by triangles indicate scenarios with increasing prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . 24

Figure 21—Total softwood growing stock inventory in the South Region for 20 Resources Planning Act (RPA) scenario-climatetimber price combinations, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 

general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIROMk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figure 22—Total hardwood growing stock inventory in the South Region for 20 RPA scenario-climate-timber price combinations, 

2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model 

(GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and 

CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with increasing prices; circles 

indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 23—Softwood and hardwood growing stock inventories for the Southern United States, 1962–2060. Forecasts (2010–60) 

bracket the range of future softwood and hardwood inventories shown in figures 21 and 22, respectively . . . . . . . . . . . . . . . 25

Figure 24—Forecasts of total carbon stored in forests in the South Region, 2010–60, for 20 Resources Planning Act (RPA) scenarioclimate-timber price combinations. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 

general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIROMk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 25—Forecasts of carbon stored in the carbon within forests in the South Region, 2010–60, for 20 Resources Planning Act 

(RPA) scenario-climate-timber price combinations. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 

dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; 

CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate 

scenarios with increasing prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 26—Total harvest removals from inventory in the South Region for 20 Resources Planning Act (RPA) scenario-climatetimber price combinations, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 

general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIROMk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

List of Figures (continued)
v

Figure 27—Total removals by broad forest type in the South Region for 20 Resources Planning Act (RPA) scenario-climate-timber 

price combinations, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 

general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIROMk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Figure 28—Softwood and hardwood growing stock removals for the South Region, 1962–2060. Forecasts (2010–2060) bracket the 

range of future inventories shown in figure 27 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 29—Comparison of harvests predicted by U.S. Forest Products Model and the Forest Dynamics Model scenario associated 

with the U.S. Forest Products Model scenario for the South Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 30—(A) Age class distribution for planted pine forests in the South Region, 2010, and (B) the range of forecasted changes in 

age classes for planted pine forests, 2010–60, using forest forecasts for the 20 Resources Planning Act (RPA) scenario-climate-price 

combinations. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation 

model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and 

A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with increasing prices; 

circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 31—(A) Age class distribution for natural pine forests in the South Region, 2010 and (B) the range of forecasted changes in 

age classes for natural pine forests, 2010–60, using forest forecasts for the 20 Resources Planning Act (RPA) scenario-climate-price 

combinations. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation 

model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and 

A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with increasing prices; 

circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 32—(A) Age class distribution for upland hardwood forests in the South Region, 2010, and (B) the range of forecasted changes 

in age classes for upland hardwood forests, 2010–60, using forest forecasts for the 20 Resources Planning Act (RPA) scenarioclimate-price combinations. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general 

circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for 

A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with increasing 

prices; circles indicate scenarios with decreasing prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Figure 33—Empirical distributions of monthly temperatures for the North Region for a historical decade (1991–2000) and 

projections of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60) . . . . . . . . 35

Figure 34—Empirical distributions of monthly temperatures for the North Region for a historical decade (1991–2000) and for 6 

successive decades of climate projections for the (A) Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) RPA B2-

CSIRO-Mk2 combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 35—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Black areas indicate distributional disagreement between historical and projected temperature distributions (p value 

<0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 36—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 

combination. Black areas indicate distributional disagreement between historical and projected temperature distributions (p value 

<0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 37—Proportion of the nine scenario-climate combinations yielding significant differences in decadal temperature 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the North Region . . . . . 38

Figure 38—Empirical distributions of monthly precipitation values for the North Region for a historical decade (1991–2000) and 

projections of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60) . . . . . . . . 39

List of Figures (continued)
vi

Figure 39—Empirical distributions of monthly precipitation for the North Region for a historical decade (1991–2000) and for 6 

successive decades (2010–60) of climate projections for the Resources Planning Act (RPA) A1B- MIROC3.2 combination . . . . 39

Figure 40—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 41—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 42—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the North Region . . . . . 41

Figure 43—Total forest area for three Resources Planning Act (RPA) scenarios for the North Region. By model construction, forest 

area in the North varies only by RPA scenario and not by climate projection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 44—Forecasted change in the proportion of county in forest land, 1997–2060, for Resources Planning Act (RPA)A1B and B2 

scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Figure 45—Current and forecasts of forest type distributions for A) Resources Planning Act (RPA) A1B-MIROC3.2 and B) RPA B2-

HadCM3 scenario-climate combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Figure 46—Forecasts of the area of forest type groups in the North Region for nine Resources Planning Act (RPA) scenario-climate 

combinations, with harvesting based on historical behavior and three RPA scenario-climate combinations, with harvesting based 

on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. 

MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO 

(CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Figure 47—Forecasted total biomass measured as growing stock inventory of forests in the North Region for nine Resources 

Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate 

combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; 

RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with 

blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . 46

Figure 48—Forecasted hardwood biomass measured as growing stock inventory of forests in the North Region for nine Resources 

Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate 

combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; 

RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with 

blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . 47

Figure 49—Forecasted softwood biomass measured as growing stock inventory of forests in the North Region for nine Resources 

Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate 

combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; 

RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with 

blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . 47

Figure 50—Softwood and hardwood growing stock inventories for the North Region, 1962–2060. Forecasts (2010–60) bracket the 

range of future inventories shown in figures 48 and 49 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

List of Figures (continued)
vii

Figure 51—Forecasts of total carbon stored in forests in the North Region, 2010–60, for nine Resources Planning Act (RPA) 

scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with 

harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; 

RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) 

by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate 

the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Figure 52—Forecasts of carbon stored in the various pools within forests in the North Region, 2010–60, for nine Resources 

Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate 

combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; 

RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with 

blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . 49

Figure 53—Total removals from inventory in the North Region for nine Resources Planning Act (RPA) scenario-climate 

combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based 

on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. 

MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO 

(CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 54—Comparison of harvests predicted by the U.S. Forest Products Model and the Forest Dynamics Model scenario 

associated with the U.S. Forest Products Model scenario for the North Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Figure 55—Removals from inventory by forest type in the North Region for nine Resources Planning Act (RPA) scenario-climate 

combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based 

on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. 

MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO 

(CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Figure 56—(A) Age class distribution for hardwood forest types in the North Region in 2010 and (B) the range of forecasted changes 

in age classes, 2010–60,for nine Resources Planning Act (RPA) scenario-climate combinations with harvesting based on historical 

behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60. . . . . 52

Figure 57—(A) Age class distribution for softwood forest types in the North Region, in 2010 and (B) the range of forecasted changes 

in age classes, 2010–60, for nine Resources Planning Act (RPA) scenario-climate combinations with harvesting based on historical 

behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60 . . . . 52

Figure 58—Empirical distributions of monthly temperatures for the Rocky Mountains Region for a historical decade (1991–2000) 

and projections of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60) . . . . . . 54

Figure 59—Empirical distributions of monthly temperatures for the Rocky Mountains Region for a historical decade (1991–2000) 

and for 6 successive decades of climate projections (A) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) 

for the RPA B2-CSIRO-Mk2 combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Figure 60—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Black areas indicate distributional disagreement between historical and projected temperature distributions 

(p value <0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Figure 61—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 

combination. Black areas indicate distributional disagreement between historical and projected temperature distributions (p value 

<0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

List of Figures (continued)
viii

Figure 62—Proportion of the nine scenario-climate combinations yielding significant differences in decadal temperature 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the Rocky 

Mountain Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Figure 63—Empirical distributions of monthly precipitation values for the Rocky Mountains Region for a historical decade (1991–

2000) and projections of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–2060) . 58

Figure 64—Empirical distributions of monthly precipitation for the Rocky Mountains Region for a historical decade (1991–2000) 

and 6 successive decades (2010–60) of climate projections for the Resources Planning Act (RPA) A1B- MIROC3.2 combination . 58

Figure 65—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Figure 66—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions respectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60

Figure 67—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the Rocky 

Mountain Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Figure 68—Total forest area for three scenarios for the Rocky Mountains Region. Bymodel construction, forest area in the Rocky 

Mountains Region varies only by storyline (not by general circulation model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Figure 69—Forecasted changes in the proportion of non-Federal land within a county in forest, 1997–2060, for the A1B and B2 

storylines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Figure 70—Current and forecasts of forest type distributions for A) A1B-MIROC and B) B2-Hadley scenarios (this chart does not 

include forest area in the Great Plains States of North Dakota, South Dakota, Nebraska, and Kansas) . . . . . . . . . . . . . . . . . 63

Figure 71—Forecasts of the area of forest types in the Rocky Mountains Region for nine Resources Planning Act (RPA) scenarioclimate combinations with harvesting based on historical behavior, 2010–60. RPA A1B scenarios are represented by solid lines; 

RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with 

blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . 64

Figure 72—Forecasted biomass measured as growing stock inventory by forest type in the Rocky Mountains Region for nine 

Resources Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior, 2010–60. RPA 

A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is 

represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIROMk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using 

the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 73—Softwood and hardwood growing stock inventories for the Rocky Mountains Region, 1962–2060. Forecasts (2010–60) 

bracket the range of future inventories shown in figures 62 and 63 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 74—Forecasts of total carbon stored within forests in the Rocky Mountains Region for nine Resources Planning Act (RPA) 

scenario-climate combinations with harvesting based on historical behavior, 2010–60. RPA A1B scenarios are represented by solid 

lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for 

A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. 

Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . 66

List of Figures (continued)
ix

Figure 75—Forecasts of carbon stored in various pools within forests in the Rocky Mountains Region for nine Resources 

Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior, 2010–60. RPA A1B scenarios 

are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by 

red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by 

yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM 

GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Figure 76—(A) Age class distributions for softwood forest types in the Rocky Mountains Region in 2010 and for the B2-Hadley 

scenario in 2060, and (B) change in age class groups across all scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Figure 77—Empirical distributions of monthly temperatures for the Pacific Coast Region for a historical decade (1991–2000) and 

projections of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60) . . . . . . . . 70

Figure 78—Empirical distributions of monthly temperatures for the Pacific Coast Region for a historical decade (1991–2000) and 6 

successive decades of climate projections (A) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) for the 

RPA B2-CSIRO-Mk2 combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Figure 79—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Black areas indicate distributional disagreement between historical and projected temperature distributions 

(p value <0.05). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 80—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk2 

combination. Black areas indicate distributional disagreement between historical and projected temperature distributions (p value 

<0.05) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 81—Proportion of the nine scenario-climate combinations yielding significant differences in decadal temperature 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the Pacific Coast Region . 73

Figure 82—Empirical distributions of monthly precipitation values for the Pacific Coast Region for a historical decade (1991–2000) 

and projections of the nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60) . . . . . . 74

Figure 83—Empirical distributions of monthly precipitation for the Pacific Coast Region for a historical decade (1991–2000) and 6 

successive decades (2010–60) of climate projections for the Resources Planning Act (RPA) A1B-MIROC3.2 combination . . . . . 74

Figure 84—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 85—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical 

decade (1991–2000) and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 

combination. Grey and black areas on the maps indicate distributional disagreement between historical and forecasted distributions 

(p value <0.05) and indicate drier and wetter conditions, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 86—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the Pacific Coast Region . 76

Figure 87—Total forest area for three RPA scenarios for the Pacific Coast Region.. By model construction, forest area in the Pacific 

Coast Region varies only by Resources Planning Act (RPA) scenario (not by general circulation model) . . . . . . . . . . . . . . . 77

Figure 88—Forecasted changes in the proportion of non-Federal land within a county in forest, 1997–2060, for the A1B and B2 

storylines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Figure 89—Current and forecasts of forest type distributions for A) A1B-MIROC3.2 and B) B2-HadCM3 scenarios . . . . . . . . 79

List of Figures (continued)
x

Figure 90—Forecasts of the area of forest types in the Pacific Coast Region for nine Resources Planning Act (RPA) scenario-climate 

combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based 

on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. 

MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO 

(CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Figure 91—Forecasted biomass measured as growing stock inventory of forests in the Pacific Coast Region for nine Resources 

Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate 

combinations with harvesting based on the U.S. Forest Products Model, 2010-2060. RPA A1B scenarios are represented by solid 

lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for 

A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. 

Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM). . . . . . . . . . . . . 81

Figure 92—Softwood growing stock in the Pacific Coast Region, 1953–2060 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Figure 93—Forecasts of total carbon stored within forests in the Pacific Coast Region, 2010–60, for nine Resources Planning Act 

(RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations 

with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 

dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 

for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks 

indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . 83

Figure 94—Forecasts of carbon stored in various pools within forests in the Pacific Coast Region, 2010–60, for nine Resources 

Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate 

combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; 

RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with 

blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . 84

Figure 95—Removals from inventory in the Pacific Coast Region for nine Resources Planning Act (RPA) scenario-climate 

combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based 

on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. 

MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO 

(CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Figure 96—Softwood removals from growing stock for nine Resources Planning Act (RPA) scenario-climate combinations with 

harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest 

Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general 

circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 

for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the 

respective RPA scenario (all using the CGCM GCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Figure 97—Comparison of harvests predicted by U.S. Forest Products Model and the Forest Dynamics Model scenario associated 

with the U.S. Forest Products Model scenario for the Western United States (defined as the combination of the Rocky Mountain and 

Pacific Coast regions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Figure 98—(A) Age class distributions for all forests in the Pacific Coast Region, 2010 and 2060, for the B2-HadCM3 scenario and 

(B) the range of changes in forest age classes across all Resources Planning Act (RPA) scenarios . . . . . . . . . . . . . . . . . . . 87

Figure A1—Flow chart of the Forest Dynamics Model’s approach to projected forest inventories . . . . . . . . . . . . . . . . . . . 92

List of Figures (continued)
xi

Abstract

The 626 million acres of forests in the conterminous United States represent 

significant reserves of biodiversity and terrestrial carbon and provide substantial 

flows of highly valued ecosystem services, including timber products, watershed 

protection benefits, and recreation. This report describes forecasts of forest 

conditions for the conterminous United States in response to multiple scenarios 

evaluated by the 2010 Resources Planning Act Assessment. The results are based 

on the U.S. Forest Assessment System, a modeling system designed to forecast 

alternative futures for U.S. forests, and provide a forward-looking adjunct to the 

Forest Inventory and Analysis Program of the Forest Service U.S. Department of 

Agriculture. Scenarios address a range of future conditions regarding population 

growth, economic change, global forest product markets, and climate change. 

Forest forecasts are described for each of four Resources Planning Act regions 

(South, North, Rocky Mountain, and Pacific Coast) across the Resources Planning 

Act scenarios. In the Eastern United States, the range of socioeconomic futures 

leads to greater variation in future forest conditions than does the range of climate 

projections. Because of the dominance of public forests in the Western United 

States, socioeconomic changes hold less sway over forest futures there. Built 

directly from and as a forecasting adjunct to the monitoring system of the Forest 

Inventory and Analysis Program, the modeling components will be enhanced by 

additional forest inventory data as they become available. 

Keywords: Forecasts, forest inventory, integrated assessment.

Forests develop in response to their physical environment, 

biological dynamics, and decisions regarding their uses. Acting 

on different timeframes, these forest dynamics ultimately 

determine the services that flow from the Nation’s forests. The 

Forest and Rangeland Renewable Resources Planning Act 

(RPA) of 1974 mandates a careful analysis of forest conditions 

as part of a periodic assessment of the condition and trends 

of the Nation’s renewable resources. The RPA Assessment 

provides a snapshot of current U.S. forest and rangeland 

conditions and trends on all ownerships, identifies drivers 

of change, and projects 50 years into the future. Analyses of 

the status and trends for forest conditions, recreation, water, 

timber, wildlife and fish, biodiversity, and range resources as 

well as land-use change, climate change, and urban forestry are 

included (USDA Forest Service 2001).

This report describes forecasts of forest conditions for the 

conterminous United States in response to multiple scenarios 

evaluated by the 2010 RPA Assessment (fig. 1).1 The results 

1 Trends and current forest conditions in the United States are examined in 

Smith and others (2009).

are based on the U.S. Forest Assessment System, a modeling 

system designed to forecast alternative futures for U.S. forests 

(Wear 2010). Developed by the RPA Assessment program, this 

system is a forward-looking adjunct to the ongoing inventories 

of forest conditions conducted by the Forest Inventory and 

Analysis (FIA) Program of the Forest Service, U.S. Department 

of Agriculture (fig. 1). The FIA system provides nationwide 

monitoring through repeated inventories that measure forest 

conditions, while the Forest Assessment System addresses how 

biological, physical, and human factors could alter these forest 

inventories in the future and generates forecasts of detailed 

forest inventories along with forecasts of land use changes and 

forest product markets.

The Forest Assessment System represents a new technical 

approach and a shift in focus for the 2010 RPA. For previous 

RPA Assessments (Haynes 1990, Haynes and Adams 1985, 

Haynes and others 1995, Haynes and others 2007, USDA 

Forest Service 1982) and for Forest Service timber market 

assessments that predate the RPA (USDA Forest Service 

1920, 1933, 1958, 1965, 1974), analysis focused primarily on 

forecasts of timber production levels and prices. Forecasts 

Introduction

Figure 1—General schematic of the U.S. Forest Assessment System. The first (left) column describes the data sets and projections that initiate analysis and define 

scenarios. The second column describes the three modeling components of the system. The third and fourth columns describe primary outputs generated and 

secondary analysis that can be supported respectively.
2

Introduction

of timber supply required predictions of timber growth, 

mortality, and harvests for the various regions of the country 

and this gave rise to forest/timber inventory projection 

models based on aggregated forest inventory data (Mills 

and Kincaid 1992, Mills and Zhou 2007). Timber inventory/

supply modeling, conducted at regional scales, provides a 

useful tool for projecting timber market activities through 

time and also provided some insights into the future of forest 

conditions and potential effects on other resource values. 

While continuing to incorporate the analysis of timber 

markets, the Forest Assessment System puts the projection 

of forest conditions at the center of the RPA Assessment, 

addressing the effects of timber markets but also the effects 

of other factors such as climate change, natural disturbance, 

and land use dynamics in the evolution of future forest 

conditions. The resulting forecasts can support more detailed 

analysis of ecosystem conditions and services derived from 

forests in the United States in response to the RPA scenarios. 

Overall, the design of the Forest Assessment System aims to 

allow for integrated assessment of multiple resource issues 

across common scenarios.

The Forest Assessment System forecasts forest conditions by 

modeling the effects of changing climate, market-driven timber 

harvesting/management, and land use changes along with 

changes driven by disturbance and successional transitions in 

forest conditions. For the 2010 RPA Assessment, the future 

of these driving forces is defined through a set of scenarios 

that contain alternative projections of economic, climate, and 

population futures. The next sections of this report describe 

the structure of these scenarios, the modeling approach, and 

datasets. Subsequent sections describe the forecasts and 

implications for the future of forests in the four RPA regions of 

the United States: South, North, Rocky Mountain, and Pacific 

Coast (fig. 2). A technical appendix provides additional detail 

on the modeling approach.

Figure 2—Resources Planning Act (RPA) Assessment regions: South, North, Rocky Mountain, and Pacific Coast.
3

Scenarios from the Resources Planning Act Assessment

Future renewable resource conditions are influenced by a 

number of driving forces such as population change, economic 

growth, and land use change. Three scenarios in the Resources 

Planning Act (RPA) Assessment are used to characterize 

the global demographic, socioeconomic, and technological 

driving forces underlying changes in resource conditions 

and to evaluate the sensitivity of resource trends to a feasible 

future range of these driving forces. The use of scenarios 

links the underlying assumptions of the analyses of various 

resource conditions and uses and frames uncertainty in these 

driving forces within the integrated modeling and analysis 

framework of the 2010 RPA Assessment (see U.S. Department 

of Agriculture Forest Service, in press, for details on scenario 

development). RPA scenarios address change over 50 years, 

from 2010 to 2060.

Three RPA scenarios are linked to global storylines used 

in the Intergovernmental Panel on Climate Change (IPCC) 

Third and Fourth Assessments (TAR and AR4, respectively; 

Intergovernmental Panel on Climate Change 2007a, 2007b). 

The storylines are potential future states of the world that 

are largely qualitative, but also include assumptions about 

global population and economic growth. The storylines were 

transformed by the IPCC into quantitative scenarios through 

six integrated assessment models that had varying assumptions 

about technology change, energy use, and land use change. Of 

the 40 scenarios produced through this process, seven marker 

scenarios were subjected to more intensive review and tests of 

reproducibility by IPCC (Nakicenovic and others 2000).

The RPA selected marker scenarios for storylines A1B, A2, 

and B2 as the basis for the three RPA scenarios. The IPCC 

TAR and AR4 storyline labels (A1B, A2, and B2) have been 

maintained in the 2010 RPA Assessment documentation for 

continuity (hereafter referred to as RPA A1B, RPA A2, and 

RPA B2). The global data were scaled to the U.S. national level 

and sub-national levels for the 2010 RPA Assessment. U.S. 

gross domestic product and population projections used in the 

TAR and AR4 analyses were updated, and U.S. population and 

disposable personal income data were downscaled to the U.S. 

county level (Zarnoch and others 2010). Climate projections 

generated from three general circulation models (GCMs) for 

each of the three IPCC scenarios were also downscaled to the 

county scale (Joyce and others, in press). 

Population and personal income projections for the three RPA 

scenarios drive forecasts of urbanization and change in the 

area of forests using the All Land Use Model, as shown in 

figure 1 (Wear 2011). The RPA A1B population projections are 

based on 2004 Census projections for the entire United States, 

while RPA A2 and RPA B2 depart from these projections as 

described below. Zarnoch and others (2010) developed county 

scale projections for each scenario based on forecasts from 

Woods and Poole’s (2007) spatial econometric/demographic 

model—the Woods and Poole (2007) forecasts are generally 

consistent with the RPA A1B projection for 2000–30. Countylevel projections between 2030 and 2060 were disaggregated 

by extending historical patterns of growth from the Woods and 

Poole (2007) projections (Zarnoch and others 2010). Projections 

for RPA A2 and RPA B2 applied the same spatial pattern of 

population change, but were adjusted to yield county-level 

projections that add up to the national totals for the respective 

RPA scenarios.

As shown in figures 3 and 4, RPA A1B corresponds to midrange population growth and the highest per capita disposable 

personal income level of the three RPA scenarios. Under 

RPA A1B, by 2060, the projected population of the United 

States will be about 446 million, with real per capita personal 

income around $80,000. Scenario RPA A2 projects the highest 

population growth, more than 500 million people by 2060, 

and the lowest projected per capita personal income, around 

$56,000. Scenario RPA B2 projects the lowest population 

growth, with the population predicted around 

397 million people, and mid-level personal income, with 

per capita personal income predicted around $60,000.

Population is not forecast to grow across the entire United 

States. Rather, most projected growth occurs around a 

number of existing urban centers (fig. 5), e.g., around southern 

California and between Portland and Seattle in the West, 

around Minneapolis and Chicago in the Midwest, between 

Boston and Washington, DC, in the East, and along the 

Piedmont between Raleigh, NC, and Atlanta in the South. 

In addition, a large number of rural counties are expected to 

experience population declines (figure 5 shows these counties 

in green for RPA A1B). Population loss is forecasted to be 

especially high through much of the Great Plains and Corn 

Belt, in the southern Mississippi Alluvial Valley, and in a band 

from northern Indiana to upstate New York.

For the RPA Assessment, a consistent national database of 

downscaled climate data were critical to ensure consistency 

across RPA Assessment resource analyses, as well as 

consistency with the economic and demographic drivers from 

the RPA A1B, A2, and B2 scenarios (Joyce and others, in 

press). For the 2010 RPA analyses, downscaled climate datasets 

were developed using output from the GCMs used in the 

Chapter 1

SCENARIOS FROM THE RESOURCES PLANNING ACT ASSESSMENT
4

Scenarios from the Resources Planning Act Assessment

$0	
  

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$80,000	
  

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2006	
   2010	
   2015	
   2020	
   2025	
   2030	
   2035	
   2040	
   2045	
   2050	
   2055	
   2060	
  

Dollars	
  (2006=100)	
  

A1B	
  

A2	
  

B2	
  

Year	
  

0	
  

100	
  

200	
  

300	
  

400	
  

500	
  

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2006	
   2010	
   2015	
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Million	
  people	
  

A1B	
  

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Year	
  

Figure 4—Projected per capita personal income (2006 = 100) for the United States to 2060 for the three Resources Planning Act (RPA) scenarios (A1B, A2, and 

B2).

Figure 3—Total projected population in the United States to 2060 for the three Resources Planning Act (RPA) scenarios (A1B, A2, and B2).
5

Scenarios from the Resources Planning Act Assessment

IPCC AR4 for the A1B and A2 scenarios and for GCMs used 

in the IPCC TAR for the B2 emission scenario (table 1). The 

downscaling approach follows Price and others (2004). Data 

were downloaded from GCM data archival websites, extracted 

for the regions of the conterminous United States and Alaska 

(i.e., not including Hawaii) and “downscaled” using the “deltamethod” and a well-tested statistical interpolation technique 

(ANUSPLIN) (Joyce and others 2011). All scenario data were 

interpolated to a standard 5 arc-minute spatial resolution 

(0.0833 degree ≈ 10 km at mid-latitudes), using latitude and 

longitude as independent variables. These data were also reaggregated to generate records for each county (used for the 

forest forecasts). Details are described in Coulson and others 

(2010a) for the historical data, Coulson and others (2010b) for 

the A1B and A2 projections, and Coulson and Joyce (2010) for 

the B2 projections.

The simulated climate variables include monthly mean daily 

surface temperature, monthly mean daily maximum surface 

temperature, monthly mean daily minimum surface 

temperature, and monthly total precipitation. Potential 

evapotranspiration was computed from the downscaled 

temperature variables. These data were converted to “change 

factors.” In the case of temperature (monthly mean daily 

minimum and maximum, or monthly mean), the change factor 

is computed as the arithmetic difference between the monthly 

value and the mean for that month computed for 1961–90. For 

all other variables, the change factor is the ratio of the monthly 

value to the 1961–90 mean for that month.

The change factors were referenced to the 30-year monthly 

means for the simulated 1961–90 period and combined with 

gridded climate normals based on the Parameter-elevation 

Regression on Independent Slopes (PRISM) dataset for the 

same 30-year period to create the scenario data needed for 

the RPA 2010 Assessment (Coulson and Joyce 2010). The 

PRISM data over the 1961–90 period were used to develop 

the projections for the 2010 RPA Assessment based on these 

change factors from the downscaled GCM projections (Joyce 

and others, in press).

Figure 5—Projected change in population density (people per square mile), 1997–2060, for the Resources Planning Act (RPA) A1B scenario. Areas in blue would 

experience population increases under this scenario. Areas in green are forecasted to experience population declines over this period.
6

Scenarios from the Resources Planning Act Assessment

Table 1 describes the nine futures defined by three RPA 

scenarios linked to three sets of climate projections. Beyond 

the socioeconomic projections associated with the scenarios 

and the climate projections from the GCMs, forest futures are 

also influenced by forest management and timber harvesting 

driven by forest products markets. In the Forest Assessment 

System, the U.S. Forest Products Model simulates the future 

of domestic forest products markets in concert with global 

markets for each RPA scenario (Ince and others 2011). Initial 

analysis indicated that timber supply over the next 50 years did 

not vary substantially among the climate projections for a given 

RPA scenario. The market futures are driven by alternative 

projections of wood product demands linked to the RPA/

IPCC global economic outlook and projected socioeconomic 

conditions for the United States and are most strongly 

influenced by demands for biomass for bioenergy production—

overall, RPA A1B has the highest demand for bioenergy, 

RPA B2 has the least, and RPA A2 is intermediate. These 

futures are also influenced by demands for traditional products 

governed by population growth and levels of economic activity 

associated with the scenarios. The linkage between forest 

forecasts and market forecasts differs across the three Forest 

Products Model supply regions (South, North, and West, where 

West is defined by the combination of the Pacific Coast and 

Rocky Mountain RPA regions). 

This report describes forest forecasts for a large number of 

futures. In all regions, we simulate the future under the nine 

RPA scenario-climate combinations. In the South we also 

intersect these nine with market and management assumptions 

to define a total of 20 futures (table 2). In the other regions, we 

simulate the nine RPA combinations assuming a continuation 

of historical (~1995–2005) harvest patterns. We then link 

forest futures to the market forecasts for RPA scenarios 

from the Forest Products Model to provide estimates of how 

market futures would reshape forest conditions. In the South, 

we selected one future from among the twenty to represent 

each RPA scenario. In the North and Pacific Coast regions, 

we generated three separate futures by adjusting inventory 

removals to match the Forest Products Model outputs (table 3).

Table 1—Intergovernmental Panel on Climate Change scenarios and general circulation 

model climate models used for the 2010 Resources Planning Act Assessmenta

Scenario Integrated Assessment Model (IAM) General circulation model Model vintage

A1B AIM CGCM3.1(T47) MIROC3.2 

CSIRO-Mk3.5 AR4

A2 ASF CGCM3.1(T47) MIROC3.2 

CSIRO-Mk3.5 AR4

B2 MESSAGE

CGCM2 

CSIRO-Mk2 

UKMO-HadCM3

TAR

a The general circulation models are defined as follows: CGCM3.1 - Canadian Centre for Climate Modeling 

and Analysis Coupled Global Climate Model (CGCM3), Medium Resolution (T47). (Source: http://www.

cccma.bc.ec.gc.ca/models/cgcm3.shtml); CSIRO-Mk3.5 – Commonwealth Scientific and Industrial 

Research Organization (CSIRO) (Australia), CSIRO Mk3 Climate System Model. (Source: http://www.

cmar.csiro.au/e-print/open/gordon_2002a.pdf); MIROC3.2MR – Center for Climate System Research, 

University of Tokyo; National Institute for Environmental Studies and Frontier Research Center for Global 

Change (Japan), Model for Interdisciplinary Research on Climate (MIROC) Version 3.2 Medium Resolution. 

(Source: http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdf); CGCM2 - Coupled Global 

Climate Model, Medium Resolution (T47). Canadian Centre for Climate Modeling and Analysis. (Source: 

http://www.cccma.bc.ec.gc.ca/models/cgcm2.shtml); CSIRO-Mk2 - Australia’s Commonwealth Scientific 

and Industrial Research Organisation Australia (CSIRO). (Source: http://www.cmar.csiro.au/e-print/open/

hennessy_1998a.html#ccm); UKMO-HadCM3 - Hadley Centre for Climate Prediction and Research U.K. 

(Source: http://cera-www.dkrz.de/IPCC_DDC/IS92a/HadleyCM3/hadcm3.html).
7

Scenarios from the Resources Planning Act Assessment

Table 3—Labels for 12 futures evaluated in this report for the North, Rocky Mountain, and Pacific Coast 

regions defined by a combination of Resources Planning Act scenarios (A1B, A2, B2), general circulation 

models (MIROC, CSIRO, CGCM, and Hadley), and forest harvesting assumptions

Scenarios

General circulation 

model Forest harvesting A1B A2 B2

MIROC3.2 Historical A1_MI A2_MI

CGCMa Historical

 Market simulation

A1_CG

A1_CG_MKT

A2_CG

A2_CG_MKT

B2_CG

B2_CG_MKT

CSIROb Historical A1_CS A2_CS B2_CS

HadCM3 Historical B2_HA

a CGCM refers to CGCM3.1 for A1B and A2 and CGCM2 for B2.

b CSIRO refers to CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2. 

Table 2—Labels for 20 futures evaluated in this report for the South Region defined by 

a combination of Resources Planning Act scenarios (A1B, A2, B2), general circulation 

models (MIROC, CSIRO, CGCM, and Hadley), timber prices (high for increasing 

prices, low for decreasing prices), and alternative tree planting rates

Scenarios

General circulation 

model Timber prices A1B A2 B2

MIROC3.2 High

A1_MI_H

A1_MI_H_HPa

A2_MI_H

Low A1_MI_L A2_MI_L

CGCMb High A1_CG_H A2_CG_H B2_CG_H

Low A1_CG_L A2_CG_L B2_CG_L

CSIROc High A1_CS_H A2_CS_H B2_CS_H

Low A1_CS_L A2_CS_L B2_CS_L

HadCM3

High B2_HA_H

Low

B2_HA_L

B2_HA_L_LPd

a Scenario with elevated planting rates.

b CGCM refers to CGCM3.1 for A1B and A2 and CGCM2 for B2.

c CSIRO refers to CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2.

d Scenario with reduced planting rates.
8

Methods

Forest Dynamics Model

This report focuses on inputs to and forecasts from the Forest 

Dynamics Model, and the discussion of methods concentrates 

on this component of the U.S. Forest Assessment System (fig. 

1). Other components are described in separate reports [the 

U.S. Forest Products Model is described in Ince and others 

(2011), and the All Land Use Model is described in Wear 

(2011)]. The overview of methods that follows is augmented by 

a detailed description in the appendix to this report.

The primary objective of the Forest Dynamics Model is to 

forecast change in the forest inventories measured by the 

Forest Inventory and Analysis (FIA) Program of the Forest 

Service, U.S. Department of Agriculture. Thousands of plots 

comprise the FIA inventories, and the Forest Dynamics 

Model forecasts development of each observed forest plot 

in the inventories.1 Forecasting at this scale allows for 

re-aggregation of findings according to different schema 

(for example, by States, by survey units, or by ecological 

sections). For example, forecasts described in this report have 

been used to explore natural resource issues for the Southern 

Forest Futures Project (Wear and Greis, in press) and the 

Northern Forest Futures Project (http://nrs.fs.fed.us/futures/). 

Historical FIA plot data provide the information foundation 

for building forecast models, and results are expanded to 

broader scales using the area frame design of the forest 

survey. These methods generate full inventory datasets for 

each time step of the forecast.

To model and forecast changes in forest conditions, the Forest 

Assessment System forecasts the condition of each plot in the 

forest inventory in response to multiple vectors of change. Each 

plot record contains a set of measured and associated variables 

combined with an expansion factor that describes the area 

(portion of the sampled population of forests) that each plot 

represents. Expansion factors are reconciled at the county level 

within the FIA system. We define the information set for each 

plot I(p) as:

I(p) = {S, Z, C, E} (1)

1 The use of the term “plot” in this report refers to what is technically called a 

“condition” within a plot in the FIA system. Where plots can have more than 

one forest condition—e.g., they occur on a boundary between forest types—

then multiple conditions are recorded for each plot. Use of the word “plot” 

simplifies the language in this report without loss of precision.

where

S represents a vector of state variables

Z is a vector of condition variables

C is a vector of climate variables

E is a vector of expansion factors for the plot

S and Z together comprise the measured variables for each 

plot from phase II (field measurements) of the forest inventory 

while climate variables are derived from PRISM datasets 

(Coulson and Joyce 2010), and E is derived from phase I (aerial 

measurements) of the FIA inventory. We distinguish between 

state and condition variables to facilitate description of the 

modeling approach.

Forecasting plot conditions starts with projecting changes 

in the state variables (S) using transition models and climate 

variables (C) from downscaled climate projections for the 

Resources Planning Act (RPA) scenarios (Joyce and others, 

in press). For example, the future age of a forest plot is a state 

variable and is defined either by adding the time step of the 

simulation to the current age or resetting the age to zero due to 

a disturbance. Forest type may be forecasted based on current 

forest type and projected climate (C). Once the forecasted state 

and climate variables have been defined, then a historical plot 

record with comparable state and climate variable values is 

used to describe the future condition (including the Z variables). 

This historical plot record is randomly selected from a database 

of historical data according to an imputation or resampling 

algorithm. Changes in the expansion factors (E) for each plot are 

determined by the land use model that forecasts change in forest 

area as a function of urbanization and other land use choices that 

depend on the population and economic variables associated 

with the RPA scenarios (Wear 2011). Proportional changes in 

expansion factors were constructed at the county level.

Key decisions in developing the projection models include 

choosing which variables are state variables, how to project 

changes in state variables, and how to structure the imputation 

algorithm. These choices are based in part on the frequency 

of FIA inventories which varies between the regions of the 

country. In all regions however, the basic structure of the 

modeling approach is defined by three modules. The modeling 

starts with an algorithm that clusters similar plots according 

to a set of independent variables (the partitioning module). 

These partitions of plots define the groupings of plots for the 

imputation module and also define which variables are the 

state variables that need to be forecasted within the transition 

module. Changes in key state variables for each forest plot 

Chapter 2

Methods
9

Methods

are forecasted in response to projected forest aging, climate 

conditions, and human use choices (the transition module). 

After forecasting the state variables, a historical plot record 

with comparable conditions is selected to represent the 

simulated plot in the future inventory (the imputation module) 

based on clusters chosen in the partitioning module. 

In most cases models were developed for individual States 

because of disparities in inventory dates. In some cases, multiple 

adjacent States or portions of adjacent States (defined by FIA 

survey units) are modeled together either because a concurrence 

of inventory dates allowed for pooling of data—thereby 

enhancing the information content of the resulting models—or 

because a State was too small to support model estimation. 

For example, we pooled southeastern States (Virginia, North 

Carolina, South Carolina, and Georgia) to allow for more 

variation in climate conditions across latitudes and a deeper plot 

donor pool, and we aggregated the Delaware data with adjacent 

survey units from Pennsylvania due to its small sample size.

The transition and imputation modules define a probabilistic 

or Monte Carlo modeling framework that was run several 

times to simulate the variance associated with various model 

components. Simulation experiments indicated a convergence 

of variance estimates after about 20 simulations, and we chose 

to run the model for 26 iterations for each scenario (limits of 

computing time kept us from constructing more simulations). 

For the display of forecast results, we chose to use one complete 

simulated inventory rather than averages for variables because 

the latter would not be representative of the joint distribution 

of forest attributes. The iteration with the greatest “central 

tendency,” defined as minimum proportional distance of six 

variables from their means over the 50-year simulation period, 

was selected. These six variables were total growing stock 

volume (cubic feet), trees per acre, and sawtimber volume for 

hardwoods and softwoods.

Models were implemented for individual States, aggregates of 

States, or adjacent Survey Units across States, and results were 

summed up to display results for the RPA regions. The time 

step for each model in the East was defined by the time interval 

of the paired inventories used to build its transition module 

and in the West was defined as a fixed 10-year step because 

inventories could not be paired. Projections commenced from 

the date of the latest inventory available, so the years for 

which forecasts were generated varied across States. Regional 

aggregates for the reporting years (2020, 2030, 2040, 2050, 

and 2060) were constructed by assigning State inventories 

with most proximal dates to the reporting year, consistent with 

methods used to generate historical multi-State aggregates with 

FIA data (Smith and others 2009). See the appendix for details 

on all elements of the Forest Dynamics Model.

Data Analysis

Climate affects forest forecasts through its influence on 

transition and plot imputation modules. That is, changes in 

climate associated with a plot can yield changes in the plot 

partition from which future conditions are sampled. The 

specific variables affecting future forest conditions vary by 

State and by forest type across the RPA regions (table A1). 

However, average temperature and total precipitation are 

frequently influential in the resampling. To help interpret 

forecast results, we analyzed average temperatures and 

total precipitation from the climate projections using two 

approaches. For each region, we constructed the cumulative 

distribution of monthly values for all counties for decadal 

periods and examined the changes over time and between 

scenarios. This provides an aggregate measure of shifts in 

temperature and precipitation for the region. 

In addition, for each county, we constructed and 

compared distributions of monthly values of mean daily 

surface temperature and total annual precipitation using 

Kolmogorov-Smirnov (KS) tests, e.g., Press and others 

(1987), to test the null of no significant difference between 

decades. Tests were constructed for forecasted decades 

(2001–10, 2011–20, ..., 2051–60) versus the historical 

decade (1991–2000). The Kolmogorov-Smirnov test is a 

nonparametric test based on the distance between the two 

empirical distributions, where the null hypothesis is that 

the two samples are generated by the same distribution. We 

consider the KS test in this application to be a conservative 

test, in that it is nonparametric (makes no assumptions about 

the underlying distributions) and is most sensitive to changes 

at the center of the distribution (NIST/SEMATEC 2012) and 

not at the tails. Rejection of the null is therefore a strong signal 

of differences in the distributions and highlights especially 

strong climate change in these areas. Failure to reject the null 

should not be viewed as a rejection of change per se, especially 

given the conservative nature of the test (i.e., higher type II 

errors; failure to reject false nulls) and the stochastic nature of 

the climate projections. 

These tests define emphatic changes and support general 

discussion of climate projections as they influence our results. 

However, other variables are also potentially relevant to our 

analysis, including seasonal temperature averages, maximum 

and minimum temperature and precipitation values, or 

frequency of extreme events that can affect change in biological 

processes. Joyce and others (in press) indicate important shifts 

in several seasonal measures with these climate projections 

even where annual values are less variable. 
10

Data

For each State, we required plot records from the Forest 

Inventory and Analysis (FIA) Program of the Forest Service, 

U.S. Department of Agriculture, to construct the partitioning 

model, conduct the imputation, and build transition models. 

Analysis for the Eastern United States required two panels of 

matched plots to estimate transition models. The ideal case 

was where we had two panels from the national continuous 

inventory design. However, in many cases we faced a situation 

where only one continuous inventory was available and we 

used two older inventories (periodic inventories) to construct 

transition models for key variables. Because of changes in 

the plot measurement approach, we determined that it was 

important to use two inventories of comparable design to 

construct transition models. 

For each inventory that we used, we extracted raw data from 

publicly available plot records available at the FIA Web site. 

We were granted access to confidential plot location data 

which allowed us to conduct the plot matching for evaluating 

the transitions over time. FIA data are stored in tables, three 

of which are primary for our analysis (Miles and others 2001). 

The plot, condition, and tree tables provide information on 

the overall plot characteristics, discrete landscape features, 

and measures associated with individual trees larger than an 

inch in diameter, respectively. As a validation step we used the 

area frame to generate total values for States and survey units 

which could then be compared with online report generators 

and published reports to confirm the accuracy of the algorithms 

employed in this analysis.

Several other variables were summarized for the plots using 

plot, condition, and tree tables in the FIA database. In the 

South, forest type and stand origin were combined to create a 

broad forest type/management class variable coinciding with 

the definition in published FIA reports. The six broad classes; 

natural pine, planted pine, mixed pine-hardwood (further 

referred to as mixed pine), upland hardwood, lowland hardwood, 

and non-forest were recoded as binary variables (i.e., x=0 or 

x=1). In other regions, forest types were aggregated using 

standard forest type groups. The dynamics of a forest type group 

were modeled if the group comprised at least 2 percent of plots 

in the State’s inventory. Minor forest type groups were held 

constant throughout the forecast period (see the appendix for a 

list of forest type groupings used in each region).

Estimates of the carbon sequestered on a forest plot were 

attached to each historical plot using models developed and 

applied by FIA using the U.S. Forest Carbon Calculation Tool 

(Smith and others 2007). This tool incorporates estimates 

derived from field measures and uses the FORCARB2 model 

to provide carbon inventories that are consistent with standards 

developed by the Intergovernmental Panel on Climate Change 

(Penman and others 2003). Carbon estimates for historical plot 

records contained in the FIA database were attached to future 

plots through the imputation algorithm and this provided plotlevel forecasts of carbon stored for each of eight forest pools 

under each of the evaluated scenarios. 

Climate data records were attached to each forested plot in 

the inventory as described in the section on future scenarios. 

Historical data were derived from the PRISM dataset and 

provided monthly values spanning a 55-year period (1950-

2005) at the county level [see Coulson and others (2010a) for 

details on aggregation of 2.5 arc-minute grid data to define 

county aggregates]. Using three general circulation models 

associated with each of the three Resources Planning Act 

(RPA) scenarios, the nine downscaled climate projections were 

also attached to each forest plot record based on the county 

location of the plot. For the analysis of forest conditions in 

the East, we averaged the climate variables for the life of the 

forest plot, e.g., if the plot was assigned an age of 25 years, we 

applied 25-year averages for each of the climate variables used 

in the partitioning module. In the West, we used a fixed-length 

average (20 years) to link climate conditions to forest plots.

Fitting the timber harvest models for the South required estimates of potential revenues from alternative treatments of each 

plot (full harvest, partial harvest, or deferred harvest) based on 

volume measures derived from the FIA records coupled with 

timber product prices. Product prices were defined as the average of stumpage prices recorded during the State’s inventory 

period by Timber Mart South, a region-wide price reporting 

service. Prices for hardwood sawtimber, hardwood pulpwood, 

softwood sawtimber, and softwood pulpwood were recorded 

for the Timber Mart South’s sub-State regions and assigned 

to plots on a county basis. Potential revenues further required 

forecasted volumes, which we estimated using a series of regression equations for each major forest type based on unharvested plots as explained in Polyakov and others (2010).

Chapter 3

DATA
11

Forest Forecasts for the South

For the South, forest forecasts were constructed for each of the 

three Resources Planning Act (RPA) scenarios linked to the 

general circulation models (GCMs) described in table 2. To 

explore a range of potential futures for forest conditions we also 

considered a range of market conditions defined by alternative 

price paths for timber products: real prices increasing at a 

compounded 1 percent per year and real prices decreasing at a 

compounded 1 percent per year from a base year of 2006. 

Another key element in the development of forests in the 

South over the past 50 years and likely for the future is the 

rate of tree planting. Tree planting is forecasted based on the 

observed rates of tree planting following a harvest by forest 

type in the South based on historical inventories by the Forest 

Inventory and Analysis (FIA) Program of the Forest Service, 

U.S. Department of Agriculture. Future planting rates could be 

altered by various changes in landowner preferences including 

the effects of an increasingly dynamic forest ownership 

base (Butler and Wear, in press). We therefore consider two 

additional forecasts, one which increased the base planting 

rates by 50 percent for an RPA A1B storyline (strong economic 

growth) with high prices and one which reduced the planting 

rate by 50 percent for the RPA B2 storyline (low economic 

growth) with low prices.1

Table 2 provides a summary of the resulting 20 alternative futures 

(three RPA scenarios by three climate projections, by two price 

alternatives plus two alternative planting scenarios) evaluated 

for the South. After considering the range of forest forecasts for 

these 20 futures, we examine the potential implications of the 

timber market simulations simulated by the U.S Forest Products 

Model (Ince and others 2011), by linking one of these 20 futures 

to represent associated changes in forest conditions. 

Climate

To frame the discussion of the forest forecasts, we compare 

climate projections from the nine RPA scenario-climate 

combinations with historical conditions for the South. 

Distributions of monthly values for a projected decade (2010 

refers to the monthly values between 2001 and 2010, 2020 

1 A more ideal model would allow planting decisions to be tied to current and 

anticipated timber market conditions, i.e., allowing planting and other forest 

investments to be determined as endogenous variables. These relationships 

proved elusive to our modeling efforts as recent tree planting patterns have 

proved counterintuitive to economic logic—tree planting expanded by 

about 30 percent between the late 1990s and 2010, when prices for softwood 

products declined and remained suppressed.

refers to monthly values between 2011 and 2020, and so on) are 

compared with a historical decade (2000 for the monthly values 

between 1991 and 2000). We start by plotting the cumulative 

distributions of temperatures/precipitation for a graphical view 

of regional change over time. We then map where significant 

differences arise between projected and historical distributions 

for counties in the region.

Figure 6 shows a comparison of cumulative temperature 

distributions in the 2060 decade for the nine RPA scenarioclimate scenarios and for the historical decade (2000). 

Projected regional temperature distributions shift to the right 

of the historical distribution reflecting increasing temperatures. 

RPA A1B-MIROC3.2 and RPA A2-MIROC3.2 scenarios show 

the greatest increase in temperatures in 2060, with a 3 °C 

increase in temperature at the 50th percentile of the distribution 

and a 4 °C increase at the 90th percentile. The other RPA 

scenario-climate combinations all show temperature increases 

that are somewhat less than for the RPA A1B-MIROC3.2 

combination (about a 2 °C increase at the 50th percentile and a 

2.5 °C increase at the 90th percentile).

A graph of temperature distributions for each of the six decades 

for the RPA A1B-MIROC3.2 combination, shows temperatures 

increasing in every decade, with the largest increase between 

the final two decades compared (2050 and 2060; fig. 7A). A 

similar pattern holds for the RPA B2-CSIRO-Mk2 scenario 

(fig. 7B) and for all other RPA scenario-climate combinations 

(not shown here).

Spatial patterns of change vary across the RPA scenarioclimate combinations. Figure 8 shows spatial patterns of 

change based on the Kolmogorov-Smirnov test (p=0.05) at the 

county level applied to the RPA A1B-MIROC3.2 combination 

(the scenario-climate combination with the greatest overall 

increase in temperature by 2060). The black areas indicate 

counties where there is significant distributional disagreement 

between historical and projected data for the specified decade. 

Figure 9 provides the same set of maps for the RPA A1BCSIRO-Mk3.5 scenario. For the RPA A1B-MIROC3.2 scenario 

(fig. 8), temperature differences are discernible in the Florida 

Peninsula in 2020, spreading north into Georgia and Alabama 

and emerging in Oklahoma and northern Arkansas in 2030. 

By 2040, most other States, except Tennessee, North Carolina, 

Kentucky, and Virginia, exhibit significantly different 

temperatures. By 2060, the entire map is black, indicating a 

significant outward shift in temperature distributions for all 

counties under this RPA scenario-climate combination. 

Chapter 4

FOREST FORECASTS FOR THE SOUTH
12

Forest Forecasts for the South

Average monthly temperature ( ̊C)

Percentile

Figure 6—Empirical distributions of monthly temperatures for the South Region for a historical decade (1991–2000) and projections of the nine Resources 

Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60).

Average monthly temperature ( ̊C)

Percentile

Figure 7—Empirical distributions of monthly temperatures for the South Region for a historical decade (1991–2000) and for 6 successive decades of climate 

projections (A) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) for the RPA B2-CSIRO-Mk2 combination. Percentile

Average monthly temperature ( ̊C)Average monthly temperature ( ̊C)

Percentile

A1B-MIROC3.2 B2-CSIRO-Mk2
13

Forest Forecasts for the South

Figure 8—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) and 

6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p value <0.05).

fail to reject null: p > 0.05

reject null: p ≤ 0.05

The change pattern is quite different for RPA A1B-CSIROMk3.5 (fig. 9). As with RPA A1B-MIROC3.2, temperature 

increases are first detected in the peninsula of Florida, but 

“spread” at a slower rate. In 2040, the entire Florida peninsula 

has significantly higher temperatures. In 2060, significant 

shifts in temperature distributions are found in Florida, the 

remainder of the Gulf Coast region and through southern and 

western Texas.

Figure 10 maps the proportion of the nine RPA scenarioclimate combinations that lead to rejection of equivalent 

temperature distributions between the 2000 and 2060 decades 

(KS test, p=0.05). Temperature distributions are significantly 

warmer in at least four scenario-climate combinations for a 

majority of counties. Counties in roughly the southern third of 

the region have significantly different temperatures in seven or 

more of the nine climate realizations. Two small sub-regions 
14

Forest Forecasts for the South

have less than four significantly different temperature 

outcomes: the Piedmont and Coastal Plain areas of North 

Carolina and Virginia and an area that spans north-central 

Texas and most of Oklahoma.

Projections of precipitation are more variable than temperature 

projections across the RPA scenario-climate combinations 

and across the region (Joyce and others, in press). Under some 

combinations, total regional precipitation declines; under 

others, total regional precipitation increases. Cumulative 

distributions of monthly precipitation values show an inward 

shifting of precipitation (drying) for the RPA A1B-MIROC3.2 

and RPA A2 -MIROC3.2 scenarios (fig. 11). For the other 

scenario-climate combinations, the distributions pivot, 

indicating that some areas become wetter while others become 

drier than the historical decade. For the RPA A1B-MIROC3.2 

and RPA A2-MIROC3.2 scenarios, the precipitation 

distribution shifts inward (becomes drier) at every time step, 

with the biggest shift occurring between 2050 and 2060 (fig. 

12).

The spatial pattern of precipitation change is also variable. 

For RPA A1B-MIROC3.2 (fig. 13), significant declines in 

precipitation occur across a large area of the South including 

Figure 9—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) and 

6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) scenario A1B-CSIRO-Mk2 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p value <0.05).

fail to reject null: p > 0.05

reject null: p ≤ 0.05
15

Forest Forecasts for the South

Figure 10—Proportion of the nine Resources Planning Act (RPA) scenario-climate combinations yielding significant differences in decadal temperature 

distributions between the historical decade (1991–2000) and the 2060 decade (2051–60) for counties in the South Region.

up to 0.3333

0.3334 – 0.6666

0.6667 – 1

the Coastal Plain and much of Texas by 2030. The area of 

significant drying increases through 2060 to include Oklahoma, 

Arkansas, and Louisiana. Change in precipitation is different 

and more sporadic under RPA A1B-CSIRO-Mk3.5 (fig. 14), 

shifting between drier and wetter conditions between decades 

in some areas. In contrast, under RPA B2-HadCM3 (fig. 15), 

little difference in precipitation distributions can be detected 

across most of the South. Figure 16 maps the proportion of the 

nine RPA scenario-climate combinations leading to rejection 

of equivalent monthly precipitation distributions between the 

1991–2000 decade and the 2051–60 decade (KS test, p=0.05). 

A large majority of the region shows significant differences in 

precipitation distributions for one to three of the scenario-climate 

combinations. Four to six scenario-climate combinations yield 

significant change in precipitation distributions for counties in 

the peninsula of Florida, southern and eastern Texas, and central 

Tennessee. These models generally show drying in the Florida 

sub-region and wetter conditions in the Tennessee sub-region, 

but either drier or wetter conditions are predicted for eastern 

Texas depending on the model.

In sum, the RPA scenario-climate combinations project 

significant warming for the South across most models but 

yield projections of precipitation that vary between models 

and across time and space within models. However, even in 

the case of constant precipitation, water availability for plant 

metabolism would be reduced by rising temperatures. Maps 

of significant changes highlight areas with emphatic change in 

these two variables (i.e., these are conservative tests) and other 

climate variables may also have important influence on future 

forest conditions but are not examined here.
16

Forest Forecasts for the South

Figure 11—Empirical distributions of monthly precipitation values for the South Region for a historical decade (1991–2000) and projections of the nine Resources 

Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60). Percentile

Monthly precipitation (mm)

Figure 12—Empirical distributions of monthly precipitation for the South Region for a historical decade (1991–2000) and for 6 successive decades (2010–60) 

of climate projections for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Percentile

Monthly precipitation (mm)
17

Forest Forecasts for the South

Forest Area

Forecasts of forest area change derive from the land use 

analysis contained in Wear (2011).2 All 20 alternative futures 

predict declines in forest area, with losses ranging from 4–21 

million acres (2–10 percent) between 2010 and 2060, the result 

of population- and income-driven urbanization and of changes 

in the relative price of timber products (fig. 17). The smallest 

loss of forest area is forecasted for scenario RPA B2, with high 

timber prices, which has the lowest population growth and the 

lowest income growth resulting in the lowest urbanization, and 

increasing timber prices resulting in shifts of some rural land 

toward forest uses. The largest loss of forest area is forecasted 

2 The total forest area changes reported in this report differ from those in Wear 

(2011) because (1) the analysis in Wear (2011) is based on National Resource 

Inventory (NRI) data benchmarked in 1997, while this chapter translates 

those projections into FIA data benchmarked in 2010; and, (2) while the NRI 

measures only non-Federal land uses, the FIA data address all ownerships.

for RPA A1B and low timber prices, where population growth 

is moderate but income growth is strong (resulting in high 

urbanization), and timber prices are falling (resulting in shifts 

of forest land to agricultural uses). Figure 17 also shows that 

price effects dominate the projection of forest area, with 

the highest forest loss associated with scenarios that have 

decreasing prices; the three scenarios with the lowest forest loss 

have increasing prices.

Forest losses are especially high in a few areas of the South 

(fig. 18). For all alternative futures, forest losses are 

concentrated in the Southern Appalachian Piedmont region 

from northern Georgia through North Carolina and parts of 

Virginia, the Atlantic and Gulf of Mexico coastal areas, and 

the area surrounding Houston, TX. The population and 

income-fueled urbanization in RPA A1B spreads low intensity 

forest losses across a broader area of the South (Wear 2011). For 

all alternative futures, the number of acres lost is largest in the 

Coastal Plain and smallest in the Mississippi Alluvial Valley 

Figure 13—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) and 

6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Grey and black areas on the maps indicate 

distributional disagreement between historical and forecasted distributions (p value <0.05) and indicate drier and wetter conditions, respectively. 

fail to reject null

reject null: drier

reject null: wetter
18

Forest Forecasts for the South

and the Mid-South. The percent of acres lost is largest in the 

Piedmont followed by the Appalachian-Cumberland highlands 

and the Coastal Plain.

Forest Types

Forest area change also differs among the five forest 

management types: planted pine, natural pine, mixed pinehardwood, upland hardwood, and lowland hardwood. The 

upland and lowland hardwood types are forecasted to comprise 

between 51 and 53 percent of all forests in 2060, a decline 

from about 54 percent in 2010. The greatest changes however 

are found among the softwood types — as planted pine area 

increases while natural pine and mixed pine-hardwood area 

declines. These forest dynamics are heavily influenced by the 

interaction of land use changes and management for forest 

products, which in turn is driven by timber market conditions, 

and by the rate of forest planting.

Planted pine—The area of planted pine forests in the South, 

currently at about 39 million acres or 19 percent of forest area, 

is forecasted to increase by 2060 (fig. 19A). Forecasts that apply 

the historical rate of tree planting for harvested area by forest 

type and State yield a range of 53.8 million to 59.9 million 

acres of planted pine by 2060 or an increase of 38–54 percent. 

Among this group of alternative futures, high prices (lines with 

triangles in fig. 19A) yield more acres of planted pine than do low 

prices (lines with circles in fig. 19A). The high and low planting 

scenarios expand this range to 46.8 million to 67.2 million acres 

or an increase of 20–72 percent. Even with the high planting 

rates, planted pine area would grow at rates much lower than 

those observed between the 1980s and 2010 (e.g., planted pine 

area expanded by about 30 percent between 1999 and 2010).

Natural pine—Forecasted losses in the area of naturally 

regenerated pine forest types mirror the gains in planted pine 

forests and are therefore inversely related to the condition of 

forest products markets (fig. 19B). The largest decrease in 

natural pines—a loss of 58 percent from 31.5 million acres 

in 2010 to 13.5 million acres in 2060—is associated with 

the highest planting rates. The smallest decline occurs with 

lower timber prices and planting rates, but losses are still 

Figure 14—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) and 

6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 combination. Grey and black areas on the maps indicate 

distributional disagreement between historical and forecasted distributions (p value <0.05) and indicate drier and wetter conditions, respectively.

fail to reject null

reject null: drier

reject null: wetter
19

Forest Forecasts for the South

substantial—7.6 million acres or 25 percent from 2010 to 2060. 

Regardless of the future evaluated, naturally regenerated pine 

types are forecasted to decline, continuing a trend that has 

dominated forest type dynamics since the 1960s. A strong 

propensity to harvest mature natural pine forests combined 

with a strong propensity to use planting as a regeneration 

technique following harvests in this type of forest drives the 

changes shown in figure 19B.

Mixed pine-hardwood—The area of the mixed pinehardwood forest management type also declines for all futures, 

with a similar pattern of change but smaller acreage and 

percent changes than is forecasted for natural pines (fig. 19C). 

As with natural pine, mixed pine-hardwood is more heavily 

influenced by timber market conditions than by urbanization 

rates. Mixed pine-hardwood declines range from 8.5 million 

acres (38 percent) to 3.9 million acres (17 percent) by 2060. 

Upland hardwood—At more than 80 million acres in 2010, 

upland hardwoods are the predominant forest type in the 

South, more than double the area of the next largest forest type. 

Upland hardwoods are forecasted to decline for all futures 

(fig. 19D), and variations in forecasts are associated more with 

the rate of urbanization than with timber market futures. The 

three futures with the lowest upland forest loss are associated 

with lower urbanization (RPA B2 scenario) and high timber 

prices, and the three futures with the highest loss are associated 

with the higher urbanization forecasts (RPA A1B scenario) 

and low timber prices. Loss of upland hardwood forests ranges 

from 5.9 million acres (about 8 percent) for RPA B2 with high 

prices to 11.2 million acres (14 percent) for RPA A1B with low 

prices. Figure 19D shows little to no separation of forecasts for 

different GCMs applied to the same future. The set of forecasts 

demonstrates therefore that prices interact with urbanization 

forecasts to determine future upland hardwood forest area. 

Lowland hardwood—The area of lowland hardwood forests 

is also more sensitive to the rate of urbanization and less 

sensitive to forest products markets than the softwood types. 

For this forest management type (fig. 19E), forecasts indicate 

Figure 15—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) and 

6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) B2-HadCM3 combination. Grey and black areas on the maps indicate 

distributional disagreement between historical and forecasted distributions (p value <0.05) and indicate drier and wetter conditions, respectively.

fail to reject null

reject null: drier

reject null: wetter
20

Forest Forecasts for the South

Figure 17—Total forest area for six Resources Planning Act (RPA) scenario-timber price combinations for the South Region. Forest area varies only by RPA 

scenario and by timber price futures but not by climate projections.

Figure 16—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation distributions between the historical 

decade (1991–2000) and the 2060 decade (2051–60) for counties in the South Region.

0

up to 0.3333

0.3334 – 0.6666
21

Forest Forecasts for the SouthFigure 18—Forecasted change in the proportion of county in forest land, 1997—2060, for the Resources Planning Act (RPA) B2 scenario, which is characterized by high urbanization and low timber prices.

Forest < -0.12

-0.12 to -0.07-0.07 to -0.02-0.02 to 0.020.02 to 0.070.07 to 0.12> 0.12

Scenario B2
22

Forest Forecasts for the South

Figure 19—Forecasts of the area of forest types in the South Region for 20 

Resources Planning Act (RPA) scenario-climate-timber price combinations, 

2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; 

RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented 

by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO 

(CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 

by black. Lines marked by triangles indicate scenarios with increasing prices; 

circles indicate scenarios with decreasing prices.
23

Forest Forecasts for the South

losses ranging from 1.7 million acres (5 percent) to 4 million 

acres (12 percent) by 2060 from a base of 32 million acres in 

2010. The relative ranking of change across futures is broadly 

similar to the forecasts for upland hardwood types. Lowland 

forests lose proportionally less area than the other forest types 

that experience losses.

Standing Biomass

Of the various metrics available for measuring biomass 

changes for a site in a forest inventory, we opted to focus on the 

volume of growing stock because it is a useful index both for 

timber analysis and for measuring other ecosystem services. 

Total growing stock volumes are forecasted to change in 

response to both land use changes and timber harvesting levels 

(fig. 20). From a base of about 292 billion cubic feet in 2010, 

inventories increase at most by about 11 percent in 2060 under 

the low-urbanization (RPA B2)/low-timber-price future. The 

smallest increase in total growing stock inventories is found 

with the high-urbanization (RPA A1B)/high-timber-price 

future, with an increase in volume to 2030 and then a decline 

over the remainder of the forecast period. For these futures, the 

volume in 2060 is about 1 percent higher than values observed 

in 2010. While at no time during the forecast period does 

inventory fall below 2010 levels, 16 of 20 futures are trending 

downward by the end of the forecast period. Growing stock 

forecasts are more strongly affected by timber price futures 

than by RPA scenario (population and income), with some 

separation between GCM projections appearing toward the end 

of the forecast period.

Patterns of change differ between hardwood and softwood 

components of the inventory and generate generally 

countervailing changes. Under all futures, softwood growing 

stock inventories increase (fig. 21). For the low-urbanization 

(RPA B2)/low-timber-price future, softwood inventories 

increase from a base of about 121 billion cubic feet in 2010 to 

as much as 148 billion cubic feet (37 billion cubic feet or 

22 percent). The smallest increase is 15 percent (18 billion cubic 

feet) for the high-urbanization (RPA A1B)/high-timber-prices 

future. With the exception of one future, all forecasts indicate 

at least a slight upward trend in volumes of softwood growing 

stock between 2050 and 2060.

Hardwood growing stock inventories reveal different patterns 

of change. Starting from about 171 billion cubic feet in 2010, 

hardwood growing stock volumes peak somewhere between 

2020 and 2040 for all futures and then decline to 2060 (fig. 22). 

The most pronounced declines are for futures with high rates of 

urbanization (RPA A1B) and high timber prices. Declines for 

these futures are in the range of 15 billion cubic feet (9 percent) 

from 2010 to 2060. Futures with low urbanization (RPA B2) and 

low timber prices result in increases of about 3 billion cubic feet 

(2 percent) for the same period. All forecasts show downward 

trends in the volume of hardwood growing stock for 2050–60.

These changes in growing stock volume depart from historical 

patterns of volume accumulation in the South. Between 1963 

and 2010 southern forests accumulated about 2.5 billion 

cubic feet per year or roughly 70 percent. Hardwood forests 

accounted for most of this biomass accumulation (61 percent). 

Although growth is projected to continue over at least the next 

10 years, growing stock volume reaches a maximum and then 

declines somewhat over the following 40 years (fig. 23), with 

hardwood growing stocks declining, especially in response to 

urbanization, while softwood volumes increase only slightly.

Forest Carbon

Carbon contained in the forests of the South amounts to about 

12.4 billion tons at the beginning of forecasts with a majority 

of the carbon in soil organic matter and living trees (fig. 24). 

Accordingly, forecasts of forest carbon would be affected by 

changes in the area of forests and in the amount of biomass 

as described earlier. Figure 24 shows forecasts of future 

forest carbon in the South and generally shows a peaking of 

forest carbon in either 2020 or 2030 followed by a downward 

trajectory. Figure 24 reveals three clusters of futures. The 

greatest loss of forest carbon is recorded for high urbanization 

(RPA A1B)/low price scenarios, with the least area of forest 

land in the future (see fig. 24). The least loss of forest carbon 

is recorded for the seven futures with the least urbanization 

(RPA B2). The middle cluster of futures is occupied by middle 

urbanization (RPA A2) and high urbanization (RPA A1B) 

futures with high timber prices. In all cases, within a future, 

higher prices yield somewhat higher levels of carbon stored in 

forests when compared to the low price futures.

With only two exceptions, these futures predict carbon stocks in 

2060 that are reduced from 2010 values. The two exceptions are 

for low urbanization (RPA B2) futures with high timber prices. 

However, the magnitude of overall change is relatively small. For 

example, the maximum loss of forest carbon between 2010 and 

2060 is roughly 5 percent or a loss of 0.6 billion tons.

Estimates of the total carbon stored in forests derive from 

summing estimates of the carbon stored in eight different 

compartments or pools in forested ecosystems. Aboveground 

pools include live trees, understory plants, down and standing 

dead trees, and litter. Belowground pools include root material 

for live trees, understory plants, and soil organic matter. 

The sum of soil organic matter, aboveground live trees, and 
24

Forest Forecasts for the South

Figure 20—Total biomass measured as growing stock inventory of forests in the South Region for 20 Resources Planning Act (RPA) scenario-climate-timber price 

combinations (see table 2 for labels), 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation 

model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by 

yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with increasing prices; circles indicate scenarios with decreasing prices.

Figure 21—Total softwood growing stock inventory in the South Region for 20 Resources Planning Act (RPA) scenario-climate-timber price combinations, 

2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; 

CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines 

marked by triangles indicate scenarios with increasing prices; circles indicate scenarios with decreasing prices. Billion cubic feet Billion cubic feet
25

Forest Forecasts for the South

Figure 22—Total hardwood growing stock inventory in the South Region for 20 RPA scenario-climate-timber price combinations, 2010–60. RPA A1B scenarios 

are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 

and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate 

scenarios with increasing prices; circles indicate scenarios with decreasing prices.

Figure 23—Softwood and hardwood growing stock inventories for the Southern United States, 1962–2060. Forecasts (2010–60) bracket the range of future 

softwood and hardwood inventories shown in figures 21 and 22, respectively.
26

Forest Forecasts for the South

belowground live trees comprises nearly 80 percent of the 

total, and forecasts of these three pools are shown in fig. 

25. The pattern of change for soil organic carbon (fig. 25A) 

mirrors forest area changes shown in fig. 17. The forecasts are 

dominated by price futures, with high (low) prices yielding 

more forest land and higher (lower) soil organic carbon. The 

forecasts are then organized by future with low urbanization 

(RPA B2) futures having higher carbon than high urbanization 

futures (RPA A1B). Live tree above- and belowground carbon 

pools (figs. 25B and 25C, respectively) have very similar 

patterns of change. As with soil carbon pools, prices dominate 

the differences among scenarios except that carbon in these 

pools is highest with low prices and lowest with high prices.

Removals 

Harvest choice models used in the Forest Dynamics Model 

generate a set of predicted removals associated with each of the 

20 futures evaluated here. Total harvest removals displayed in 

figure 26 are charted for each period of the simulation. Note 

that the time step associated with the removals is different from 

that associated with measures of the inventory; removals are 

labeled with midpoint values (2015, 2025, etc.) because these 

values represent the average annual removals for the decade, 

rather than an accumulated value. Figure 26 shows that price 

futures dominate future harvesting with increasing prices 

(lines with triangles in fig. 26) leading to increases in total 

removals (average increase of about 36 percent). Declining 

prices are not, however, associated with declines in harvests. 

Rather, these futures (lines with circles in fig. 26) show a slight 

increase to 2025, and then harvest levels stabilize.

Additional information can be derived from examining 

removals forecasts for softwoods and hardwoods separately. 

Figure 27A shows that softwood harvests are more variable 

than total harvests. Increased prices yield as much as a 

45 percent increase in harvest output with the high planting 

future yielding the most timber. For declining prices, harvests 

also increase by as much as 15 percent—the lowest removals 

occur with the lower planting rates. This strong harvest 

response reflects the strong growth in planted pine area 

experienced in the 1990s and 2000s and forecasts of additional 

growth in planted pine area. In addition, it reflects a financial 

maturation of naturally regenerated pine and mixed forests.

The story for hardwoods is quite different (fig. 27B). Increases 

in prices yield expanded removals (generally about an 18 

percent increase). Decreases in prices yield a decline in 

harvests of roughly the same magnitude. This suggests that 

supply is relatively stable over time for hardwoods, likely 

reflecting an offsetting of the losses of hardwood forest area 

with the financial maturation of the remaining forests.

Figure 24—Forecasts of total carbon stored in forests in the South Region, 2010–60, for 20 Resources Planning Act (RPA) scenario-climate-timber price 

combinations. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented 

by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. 

Lines marked by triangles indicate scenarios with increasing prices; circles indicate scenarios with decreasing prices. Million metric tons
27

Forest Forecasts for the South

Figure 25—Forecasts of carbon stored in the carbon within forests in the South 

Region, 2010–60, for 20 Resources Planning Act (RPA) scenario-climatetimber price combinations. RPA A1B scenarios are represented by solid 

lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model 

(GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) 

by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by 

yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices.

Removal forecasts can be further examined in light of a 

longer historical record. Figure 28 shows estimates of timber 

removals for the South between 1962 and 2060 and indicates 

that softwood and hardwood removals were roughly equivalent 

in 1962 at about 2.8 billion cubic feet. Softwood removals 

expanded by more than 132 percent between 1962 and 1998, 

and hardwood removals expanded by about 37 percent. As 

a result, softwood output was about 75 percent higher than 

hardwood output in 1996. Harvest outputs declined after 1996 

and have not recovered to date (the data labeled as 2010 may 

actually overstate most recent removal levels). Forecasts of 

timber removals are based on price projections that start with 

prices in 2006 and then move upward or downward by about 1 

percent per year (high and low price scenarios, respectively). 

All of these forecasts show an increase in harvests and a return 

to output growth. These increases are driven by two factors: 

(1) a strong accumulation of biomass over the past 15 years, 

partially explained by the decline in harvests, i.e., wood has 

been accumulating on the stump in lieu of harvests, and (2) a 

strong growth in timber investments through the planting of 

pine, i.e., reflecting the 30 percent expansion between the late 

1990s and 2010. Forecasts of softwood removals span a range 

between a return to 1980s levels of growth and a leveling of 

output about 38 percent above current prices.

The forest forecasts described for the South are driven by futures 

defined with exogenous price paths and this range of price paths, 

along with climate, population, and income projections, show a 

range of plausible futures for forests in the region. The removals 

forecasts from these and other runs of the Forest Dynamics 

Models were used to generate the supply functions for four 

specific products incorporated in the U.S. Forest Products Model 

as described by Ince and others (2011). Set within a model of 

global forest products markets called the Global Forest Products 

Model (Buongiorno and others 2003, 2012), the Forest Products 

Model provides detailed forecasts of market outcomes to 

simulate the effects of the global economic forecasts and energy 

forecasts (notably, forest biomass used to generate energy) 

associated with each of the three RPA scenarios. The latter 

involves downscaling continental-scale demands for wood in 

Million metric tons Million metric tonsMillion metric tons
28

Forest Forecasts for the South

Figure 26—Total harvest removals from inventory in the South Region for 20 Resources Planning Act (RPA) scenario-climate-timber price combinations, 

2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; 

CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines 

marked by triangles indicate scenarios with increasing prices; circles indicate scenarios with decreasing prices.

Figure 27—Total removals by broad forest type in the South Region for 20 Resources Planning Act (RPA) scenario-climate-timber price combinations, 2010–60. 

RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM 

(CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by 

triangles indicate scenarios with increasing prices; circles indicate scenarios with decreasing prices.
29

Forest Forecasts for the South

bioenergy applications to individual countries and simulating the 

effects on fiber markets and trade. In the United States, the RPA 

A1B scenario is associated with high bioenergy demands, RPA 

B2 is associated with much lower bioenergy demands, and RPA 

A2 is intermediate to RPA A1B and B2. 

Within the Forest Products Model then, timber supply from 

the South is derived from the land use and forest dynamics 

components of the U.S. Forest Assessment System, and so is 

consistent with each RPA scenario’s projections of population 

growth and income growth (at a county scale) and with shifts 

in forest conditions affected by harvesting, successional 

dynamics, climate change, and land use changes. However, 

differences in removal forecasts are not readily discernable 

between GCMs (figs. 26 and 27). 

To examine the resource implications of the market outcomes 

associated with each of the three RPA scenarios, we assign a 

forest simulation to each RPA scenario defined by the Forest 

Products Model projections. Due to the stochastic nature of 

the models and the inherent variability of removals estimates 

in the FIA inventory, we did not expect to find an exact match 

for harvests; instead we sought a simulation with a close 

approximation of the harvest pattern and harvest totals for the 

model run.3

 Figure 29 compares the South’s timber harvest 

forecasts from the Forest Products Model with the implied 

harvests from the selected runs of the Forest Dynamics Model, 

and shows that total harvest forecasts are largely consistent 

between the models for the specific futures. This includes 

the productivity enhancement for pine plantations under the 

RPA A1B-high price future. Forest Dynamics Model harvest 

removals are within 10 percent of harvest removals from the 

Forest Products Model for all periods and scenarios, with 

one exception. For the last 2 decades, the RPA A1B Forest 

Products Model projection is roughly 20 percent higher than 

the Forest Dynamics Model projections. For 2055, RPA A2 

3 As with any sampling scheme, FIA inventories are designed to provide 

estimates with a certain level of precision. Forest area projections require 

a sampling error of 3 percent per 1 million acres of timberland, and FIA 

guidelines target an error rate of 5 percent in the East and 10 percent in the 

West for volume, removals, and net annual growth (Woudenberg and others 

2010). Removals forecasts across the 26 runs for each FDM simulation 

reflect this level of variability, and the translation of removals into harvest 

estimates introduces another source of error. Overall, we sought to produce 

Forest Dynamics Model simulations that were within 10 percent of the Forest 

Products Model simulations as representative of forest conditions under a 

particular scenario. An evaluation of total harvest levels over the length of the 

simulation provided a means for evaluating bias, i.e., whether the differences 

between the two models were consistently greater or lesser.

Figure 28—Softwood and hardwood growing stock removals for the South Region, 1962–2060. Forecasts (2010–2060) bracket the range of future inventories 

shown in figure 27. Million cubic feet
30

Forest Forecasts for the South

Figure 29—Comparison of harvests predicted by U.S. Forest Products Model and the Forest Dynamics Model scenario associated with the U.S. Forest Products 

Model scenario for the South Region.

Forest Products Model projections are about 11 percent lower 

than the Forest Dynamics Model projections. Total removals 

match fairly closely for the entire simulation period for the 

RPA B2 scenario. The Forest Dynamics Model projects 93, 

103, and 95 percent of removals forecast across all decades by 

RPA A1B, A2, and B2 scenarios, respectively. Based on these 

comparisons, we use RPA A1B-MIROC3.2 with high prices, 

RPA A2-CSIRO-Mk3.5 with high prices and RPA B2-CGCM2 

with low prices to represent futures under these RPA scenarios 

consistent with the market analysis conducted by the Forest 

Products Model.

Age Structure/Habitat

Simulated inventories can be used to evaluate various aspects 

of future forest conditions. One aspect of significance to 

wildlife habitat conditions in the South is the distribution of 

forests by age class, and the implications for the provision 

of early, middle, and late age forest conditions. We evaluate 

changes in the age class distributions of forests of various type 

using two charts. One displays the aerial age class distribution 

by decade for the most recent inventories of the 13 Southern 

States. We then summarize changes in age class resulting 

from the 20 futures between 2010 and 2060 using box plots. 

Figure 30 shows the current age class distribution for planted 

pine forests in the South by 10-year age classes. Each box plot 

describes the distributions of changes in age classes. 

The bottom of the box represents the 25th percentile of the 

predicted changes, the top of the box represents the 75th percentile 

of the predicted changes, the line in the box indicates the median 

forecast, and the plus in the box indicates the mean forecast. The 

“whiskers” of the box show the extreme values of the forecasts.

Not surprisingly, a large majority of planted pine forests are 

between the ages of 0 and 30, with the largest share between 

the ages of 11 and 20 (fig. 30). By 2060, all age classes are 

forecasted to gain area except the 11–20 class, which would 

decline but still be comparable in area to the 0–10 and 21–30 

age classes. Increases in older planted pine forests indicate that, 

while the probabilities of harvest are high, forecasted planting 

is higher than that demanded for additional harvesting over the 

next 50 years. However, only a tiny proportion of planted pine 

forests is expected to exceed 60 years of age by 2060.

Age dynamics differ strongly for natural pine (fig. 31). 

Recalling that this forest type is forecasted to lose substantial 

area by 2060 irrespective of future, forecasted declines in 

nearly every age class are consistent with overall trends 

(fig. 31). For this forest type, the area of forest in the 0–10 age 

class remains relatively constant—harvests not followed by 

planting essentially “recharge” this age class. However the area 

of forests in age classes between 11 and 70 years are forecast 

to decline substantially. The only age class forecasted to gain 

substantial area is the 81-plus age class. Early age habitats 

Million cubic meters
31

Forest Forecasts for the South

Figure 30—(A) Age class distribution for planted pine forests in the South Region, 2010, and (B) the range of forecasted changes in age classes for planted pine 

forests, 2010–60, using forest forecasts for the 20 Resources Planning Act (RPA) scenario-climate-price combinations. RPA A1B scenarios are represented by 

solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for 

B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices.

Million acres

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81+

Age class

(B) Range of forecasted changes in age classes

0	
  

2	
  

4	
  

6	
  

8	
  

10	
  

12	
  

14	
  

0-­‐10	
   11-­‐20	
   21-­‐30	
   31-­‐40	
   41-­‐50	
   51-­‐60	
   61-­‐70	
   71-­‐80	
   81+	
  

Million	
  acres	
  

	
  Age	
  class	
  

(A)	
  Age	
  class	
  distribu4on	
  

0	
  

1	
  

2	
  

3	
  

4	
  

5	
  

6	
  

0-­‐10	
   11-­‐20	
   21-­‐30	
   31-­‐40	
   41-­‐50	
   51-­‐60	
   61-­‐70	
   71-­‐80	
   81+	
  

Million	
  acres	
  

	
  Age	
  class	
  

(A)	
  Age	
  class	
  distribu4on	
  

Figure 31—(A) Age class distribution for natural pine forests in the South Region, 2010 and (B) the range of forecasted changes in age classes for natural pine 

forests, 2010–60, using forest forecasts for the 20 Resources Planning Act (RPA) scenario-climate-price combinations. RPA A1B scenarios are represented by 

solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for 

B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate scenarios with 

increasing prices; circles indicate scenarios with decreasing prices.

Million acres

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81+

Age class

(B) Range of forecasted changes in age classes
32

Forest Forecasts for the South

are relatively constant, while middle age classes decline 

substantially and old age classes increase somewhat.

Mixed pine-hardwood forests are forecast to experience 

declines in the earliest age classes, smaller declines in the 

middle age classes, and slight gains in the oldest age classes 

(not shown). The current age class distribution of upland 

hardwoods shows a U-shape with a large area in the 0–10 age 

class, much lower area in the 11–40 age classes, and majority of 

area in the 41–70 age classes (fig. 32). Forecasts show a strong 

downward shift in early and middle age classes, with a strong 

growth in the older age classes (>70 years). The early age class 

grouping would decline by about 50 percent, and the middle 

age classes would decline by about 30 percent from a currently 

low value. While the area of upland hardwood forests may 

decline only slightly, their age class composition is forecasted 

to be substantially different by 2060 (fig. 32).

The pattern of the current age class distribution of lowland 

hardwoods is similar to the pattern for upland hardwoods (not 

shown). Patterns of change are also comparable, but with a 

lower magnitude. Early and middle age classes decline in area 

while the area in the oldest age class increases substantially by 

2060 (from about 2.7 million acres to about 7 million acres). 

Discussion 

The 20 futures considered for the South Region provide a range 

of future forest conditions that reflects uncertainty inherent in 

projecting the future as well as the state of knowledge regarding 

the response of forests and land uses to future conditions. In 

many cases, consistent trends are revealed but perhaps more 

importantly, variation across futures suggests which variables 

are most important in determining future conditions.

Climate futures reveal a gradual change in climate conditions 

across the South between 2010 and 2060 with the greatest 

changes associated with the RPA A1B-MIROC3.2 and 

A2-MIROC3.2 combinations. Significant changes in temperature 

and precipitation values begin to arise over substantial areas 

beginning in 2030. Given the ways in which climate factors into 

the forest forecasts, the biggest impacts of climate are realized 

in regenerated forests. For unharvested forests, these types of 

gradual changes are unlikely to generate substantial changes in 

forest conditions in the 50 years of the forecasts. This is reflected 

in the clustering of biomass and removals forecasts around price 

and scenario forecasts and with little separation in forecasts 

across GCMs for most variables at the regional level.

Future forest area is most strongly determined by the future 

price of timber products but also almost as strongly influenced 

by the population and income forecasts associated with the 

RPA scenarios. Urbanization, driven by the latter, would shift 

away from forests and toward agricultural lands as timber 

prices increase. For example, RPA A1B has the highest level of 

urbanization with a forest loss of about 21 million acres between 

2010 and 2060 under the low timber price scenario. With the 

high timber price scenario, forest loss is reduced to about 

11 million acres.

Timber prices also influence the condition of forests, especially 

through forest planting. Across the 20 futures, the middle 

forecast is for about 56 million acres of planted pine in 2060 from 

a base of about 39 million acres in 2010. With high prices, the 

future area of planted pine area would range from 58 to as much 

as 67 million acres. Higher timber prices therefore suppress 

overall forest losses but shift the condition of forests toward more 

planted pine—at least 50 percent of the urbanization offset (and 

as much as 100 percent) described earlier is accounted for by 

increased planted pine area. Currently, planted pine represents 

about 19 percent of forest area in the South. By 2060, planted 

pine could represent between 24 and 34 percent of the region’s 

forests, depending on market conditions.

Biomass accumulated substantially in the South over the past 

50 years: total growing stock inventory expanded by 67 percent 

between 1962 and 2010. However, forecasts for the next 

50 years suggest a change in this pattern. Due largely to the aging 

of forests, but also because of land use and forest type changes, 

the accumulation of biomass would slow substantially, with net 

increases in total biomass amounting to 1–10 percent between 

2010 and 2060. This trend in total inventory volume is based 

on forecasted increases in softwood volume with simultaneous 

decreases in hardwood volume. As a result the share of softwood 

in the inventory shifts from about 41 percent in 2010 to about 46 

percent in 2060, with little variation across the futures.

Forest carbon pools are determined by the area of forests 

and the distribution of forest conditions. The net impact on 

forest carbon pools of land use changes, forest type changes, 

and forecasted removals is essentially a stasis. Forest growth 

largely offsets losses accruing to forest area losses so that in 

2060 the forest carbon pool ranges from 99–105 percent of its 

value in 2010. This is not a complete accounting for all landbased carbon pools—forests converted to agricultural or urban 

uses will retain some amount of stored carbon—nor does it 

account for the amount of carbon stored for long periods in 

solid wood products. These findings do suggest that there is 

little opportunity to expand standing forest carbon pools except 

where land use conversions can be prevented. It should also be 

emphasized that these scenarios include no policy incentive to 

retain or sequester additional carbon on forest lands.

Forecasts of removals based on disaggregate harvest choice 

models indicates the potential for a substantial expansion 

in the harvest of timber products from the South. Even with 

steadily declining prices, removals are forecast to increase, 
33

Forest Forecasts for the South

reflecting the substantial expansion in supply from the area of 

planted pine forests in the region over the past 20 years as well 

as a “backlog” of unharvested forests stored “on the stump.” 

These forecasts also reveal that forecasted forest losses do 

not suppress removals due largely to the location and types 

of forests affected by urbanization. Supply expansion can be 

viewed as an ongoing transition to agricultural-style forestry 

in the South. This also indicates changes in the removals to 

inventory ratio, which increases substantially between 2010 

and 2060 as harvests increase by as much as 90 percent while 

inventories of standing biomass grow by only 1–10 percent.

Actual harvests will be determined, not only by the supply 

relationships simulated here, but also by forecasted demand 

for various forest products. The market future associated 

with each of the three RPA scenarios has been taken up by 

Ince and others (2011) for the 2010 RPA Assessment. Their 

analysis, using supply estimates derived from the analysis 

described here, indicates widely varying market futures for the 

three RPA scenarios. RPA B2, with its low economic growth, 

lower population growth, and low demands for wood-based 

bioenergy yields market futures that are most similar to the 

RPA B2-low price futures explored in this report. Under these 

futures, we would expect a moderate loss of forest land (about 

12 million acres), forecasts of planted pine area at the lower 

end (47–55 million acres in 2060), expanding standing biomass 

inventories through 2050 followed by a leveling, and very 

moderate long run growth in removals.

RPA A2 yields a price future that is quite comparable to RPA 

B2 through 2040, after which, timber prices rise somewhat. 

Through 2040 then the RPA A2 low price future would hold, 

but with price levels reaching the area of RPA A2 high price 

future by 2060. For RPA A2 then, forest area losses would be 

somewhat higher than for the RPA B2 case, and inventories 

would level off about 20 years earlier (but would be at 2010 

levels in 2060). For both the RPA B2 and A2 scenarios, 

changes between 2010 and 2060 would be orderly, with paths 

of change quite comparable to the paths of change experienced 

over the past 50 years.

The RPA A1B scenario suggests structural changes in timber 

markets and forest conditions by the end of the forecast 

period. Prices for harvested products are projected to increase 

substantially beginning in 2025 and reach beyond the price 

forecasts of the 20 futures by 2040. These price signals would 

provide strong incentives for forest landowners to intensify 

investment in planted forests suggesting that the RPA A1Bhigh price and high planting scenario would provide a start 

for examining the effects of this future. Under this future, the 

area of planted pine forests would increase to about 67 million 

acres and planted pine would represent about 34 percent of 

forest area in the region. Harvests would begin to expand 

substantially, and inventories would begin to trend downward 

in about 2030. Forest carbon stocks under this future would 

trend down slightly but would remain at 97 percent of 2010 

values in 2060.

Figure 32—(A) Age class distribution for upland hardwood forests in the South Region, 2010, and (B) the range of forecasted changes in age classes for upland 

hardwood forests, 2010–60, using forest forecasts for the 20 Resources Planning Act (RPA) scenario-climate-price combinations. RPA A1B scenarios are 

represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines marked by triangles indicate 

scenarios with increasing prices; circles indicate scenarios with decreasing prices.

5	
  

6	
  

7	
  

8	
  

9	
  

10	
  

11	
  

12	
  

13	
  

0-­‐10	
   11-­‐20	
   21-­‐30	
   31-­‐40	
   41-­‐50	
   51-­‐60	
   61-­‐70	
   71-­‐80	
   81+	
  

Million	
  acres	
  

Age	
  class	
  

(A)	
  Age	
  class	
  distribu4on	
  

Million acres

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81+

Age class

(B) Range of forecasted changes in age classes
34

Forest Forecasts for the South

The future captured by the RPA A1B scenario involves price 

increases beyond 2040 that are far outside the historical data 

upon which this analysis is based, implying structural changes 

that would not be captured by the models. Such price changes 

would encourage changes within the forest sector including 

more planting and more intense management on planted 

forests. Recent research in the South (McKeand and others 

2003) suggest that a doubling of the fiber production from 

planted pine forests is plausible given new methods of genetic 

selection, fertilization, and other intensive silvicuture. Beyond 

the forest sector, other changes are possible including priceinduced switching to non-wood feedstocks for bioenergy or 

movement toward other energy technologies such as wind or 

solar. While the RPA B2 and RPA A2 scenarios are consistent 

with orderly changes given the condition and management of 

southern forests, demands at the level anticipated by RPA A1B 

would involve substantial structural changes.
35

Forest Forecasts for the North

For the North, forest forecasts were generated for each of 

the nine Resources Planning Act (RPA) scenario-climate 

combinations. For these nine combinations, we apply timber 

harvest models that reflect historical harvest relationships—

that is, probabilities of harvest are functions of plot attributes 

based on observed harvesting over the previous inventory 

interval. In addition, we generate separate forest forecasts 

that simulate timber market projections generated by the U.S. 

Forest Products Model for the economic futures attached 

to the three RPA scenarios. These three forecasts use the 

climate projections from the CGCM3.1 GCM for RPA A1B 

and A2 and the CGCM2 GCM for RPA B2. To match the 

harvests from the Forest Products Model, we adjust a scaling 

parameter on the set of harvest probability models (see 

appendix, equation 4) to reflect Forest Products Model runs 

for the respective scenario. Harvest outcomes in the Forest 

Products Model are partially determined by an aggregate 

model of timber supply for the region. Table 3 defines labels 

for the 12 futures evaluated in this section.

Climate

To frame a discussion of the forest forecasts, we compare climate 

projections from the nine RPA scenario-climate combinations 

with historical conditions for the North. Distributions of monthly 

values for a projected decade (2010 refers to the monthly values 

between 2001 and 2010, 2020 refers to monthly values between 

2011 and 2020, and so on) are compared with a historical decade 

(2000 for the monthly values between 1991 and 2000). We 

start by plotting the cumulative distributions of temperatures/

precipitation for a graphical view of change across time. We then 

map where significant differences arise between projected and 

historical data for counties in the region.

Figure 33 shows a comparison of empirical temperature 

distributions for the 2060 decade for the nine RPA scenarioclimate combinations with the historical period (2000). For all 

forecasts, the distributions shift outward over time indicating 

temperature increases. The RPA A1B-MIROC3.2 combination 

shows the greatest increase in temperatures between the 2010 

and 2060 decades, with a 4 °C increase in temperature at the 

Chapter 5

FOREST FORECASTS FOR THE NORTH

Figure 33—Empirical distributions of monthly temperatures for the North Region for a historical decade (1991–2000) and projections of the nine Resources 

Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60).

Average monthly temperature ( ̊C)

Percentile
36

Forest Forecasts for the North

50th percentile of the distribution and a 4.5 °C increase at the 

90th percentile. The other combinations yield roughly half the 

temperature increase shown for RPA A1B-MIROC3.2 (about a 

2 °C increase at the 50th percentile and a 2.5 °C increase at the 

90th percentile). 

Comparing forecasts by decade shows that under RPA A1BMIROC3.2 (the warmest scenario in the North), temperatures 

increase in every decade, with the greatest increase between 

the final 2 decades compared (2050 and 2060; fig. 34A). For 

the RPA CSIRO-Mk2 combination (fig. 34B) temperature 

distributions also shift outward between decades, but greater 

increases are shown for the earlier decades. The other seven 

RPA scenario-climate combinations (not shown here) also show 

outward temperature shifts in every decade. 

Spatial patterns of change vary across the scenario-climate 

combinations. Figure 35 shows spatial patterns of change based 

on the Kolmogorov-Smirnov test for monthly temperatures at the 

county level applied to RPA A1B-MIROC3.2 (the combination 

with the greatest overall increase in temperature by 2060). 

The black areas indicate counties where there is distributional 

disagreement between historical and forecasted temperatures for 

the specified decade. Figure 36 provides the same set of maps 

for RPA A1B CSIRO-Mk3.5. For RPA A1B-MIROC3.2 (fig. 

35), discernible temperature differences appear beginning in 

2030 in small portions of Iowa, Missouri, and Illinois. By 2040 

temperature increases consolidate across these three States and 

in peripheral States. By 2050, significant temperature increases 

are indicated for much of the Corn Belt, the lower peninsula of 

Michigan, and parts of the mid-Atlantic States of Pennsylvania, 

West Virginia, and Maryland. By 2060, the entire map is 

black for this scenario, indicating a significant outward shift in 

temperature distributions for all but two counties in the North. 

The change pattern is quite different for RPA A1B-CSIROMk3.5 (fig. 36). Unlike with MIROC3.2, significant 

temperature increases do not appear throughout the region until 

2060. In 2060, the northwestern corner of the region, including 

Minnesota, Wisconsin, Iowa, northern Missouri, and much of 

Michigan has significantly higher temperature distributions. 

Figure 37 maps the proportion of the nine RPA scenario-climate 

combinations leading to rejection of equivalent temperature 

distributions in the 2000 and 2060 decades (KS test, p=0.05). 

Four or more of the nine climate realizations have significantly 

different temperatures across roughly the western half of the 

region. Seven or more of the nine climate realizations have 

significantly different temperatures in counties in a sub-region to 

the west of Lake Michigan in Illinois, Iowa, and Wisconsin, and 

in some counties in the upper peninsula and the upper part of the 

lower peninsula of Michigan. 

Projections of precipitation are much more variable than 

temperature projections across the nine RPA scenario-climate 

combinations and across the region. Under some scenarioclimate combinations, overall precipitation declines by 2060; 

under others precipitation increases by 2060. Cumulative 

distributions of monthly precipitation for the nine climate 

projections for the 2060 decade compared with the historical 

decade (2000) show an inward shifting of precipitation 

(drying) for RPA A1B-MIROC3.2 and RPA A2-MIROC3.2 

combinations and a strong outward shift (increase) in 

precipitation for RPA A2-CSIRO-Mk3.5 and RPA A1BCGCM combinations (fig. 38). For the other scenario-climate 

ombinations, the distributions pivot, indicating that some areas 

are wetter while others are drier in 2060 than in 2000. For the 

various combinations (including RPA A1B-MIROC3.2 which 

is drier in 2060), the precipitation distribution shifts vary 

between time steps, in some cases alternating between wetter 

and drier decades (fig. 39).

The spatial pattern of precipitation change also varies between 

scenario-climate combinations and across the region. For 

RPA A1B-MIROC3.2 (fig. 40) significant differences in the 

precipitation distribution arise largely in the northeast, with a 

drying trend between Massachusetts and Maine in the 2010 and 

2020 decades. While these effects shrink or expand over the 

remaining decades, the northeast is generally drier under this 

scenario-climate combination. Additional drying is evident by 

2060 in parts of Missouri, Illinois, and Michigan. Precipitation 

change follows a different pattern and is more sporadic under 

RPA A1B-CSIRO-Mk3.5 (fig. 41) and under RPA B2-HadCM3 

(not shown). 

Figure 42 maps the proportion of the nine climate realizations 

leading to rejection of equivalent monthly precipitation 

distributions between the historical (2000) decade and the 

2060 decade (KS test, p=0.05). Very few counties show no 

significant difference across all models, with northwestern 

Minnesota being an exception. For much of New England, 

at least four model runs show significantly different 

precipitation between 2000 and 2060, with the highest degree 

of concurrence along the coast. Models generally show a 

drying trend for the New England States.

Maps of significant shifts in mean temperature and total 

precipitation highlight areas with emphatic change in these 

two variables, i.e., these are conservative tests. Other climate 

variables may also have important influence on future forest 

conditions but are not examined here.
37

Forest Forecasts for the North

Figure 35—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p value <0.05).

fail to reject null: p > 0.05

reject null: p ≤ 0.05

Figure 34—Empirical distributions of monthly temperatures for the North Region for a historical decade (1991–2000) and for 6 successive decades of climate 

projections for the (A) Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) RPA B2-CSIRO-Mk2 combination.

Average monthly temperature ( ̊C)

Percentile

Average monthly temperature ( ̊C)

Percentile

Average monthly temperature ( ̊C)

Percentile

A1B-MIROC3.2 B2-CSIRO-Mk2
38

Forest Forecasts for the North

Figure 36—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p value <0.05).

fail to reject null: p > 0.05

reject null: p ≤ 0.05

Figure 37—Proportion of the nine scenario-climate combinations yielding significant differences in decadal temperature distributions between the historical 

decade (1991–2000) and the 2060 decade (2051–60) for counties in the North Region.

up to 0.3333

0.3334 – 0.6666

0.6667 – 1

0
39

Forest Forecasts for the North

Figure 38—Empirical distributions of monthly precipitation values for the North Region for a historical decade (1991–2000) and projections of the nine Resources 

Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60).

Monthly precipitation (mm)

Percentile

Figure 39—Empirical distributions of monthly precipitation for the North Region for a historical decade (1991–2000) and for 6 successive decades (2010–60) 

of climate projections for the Resources Planning Act (RPA) A1B-MIROC3.2 combination.

Monthly precipitation (mm)

Percentile
40

Forest Forecasts for the North

Forest Area

Forecasts of forest area change derive from the land use 

analysis contained in Wear (2011).1 All RPA scenarios predict 

declines in forest area with losses ranging from 6 million to 

11 million acres (3–6 percent) between 2010 and 2060, the 

result of population- and income-driven urbanization 

(fig. 43). The smallest loss of forest area is forecasted for 

scenario RPA B2 (6 million acres), which has the lowest 

1 The total forest area changes reported in this report differ from those in Wear 

(2011) because (1) the analysis in Wear (2011) is based on National Resource 

Inventory (NRI) data benchmarked in 1997, while this chapter translates 

those projections into FIA data benchmarked in 2010; and, (2) while the NRI 

measures only non-Federal land uses, the FIA data address all ownerships.

population growth and the lowest income growth resulting 

in the lowest urbanization. The largest loss of forest area is 

forecasted for RPA A1B (11 million acres), where population 

growth is moderate but income growth is strong—this 

combination results in higher rates of urbanization. Under 

scenario RPA A2, where population growth is highest but 

income growth is lowest, forest loss is intermediate between 

RPA A1B and B2 (9 million acres).

Forest losses are especially high in a few areas of the North 

(fig. 44). Under the RPA A1B scenario (with the highest 

rate of urbanization), the largest contiguous area of forest 

losses is along the eastern seaboard stretching from Boston 

to Washington, DC, and extending inland to include all of 

Figure 40—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Grey and black areas on the maps indicate 

distributional disagreement between historical and forecasted distributions (p value <0.05) and indicate drier and wetter conditions, respectively.

fail to reject null

reject null: drier

reject null: wetter
41

Forest Forecasts for the North

Figure 42—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation distributions between the historical 

decade (1991–2000) and the 2060 decade (2051–60) for counties in the North Region.

Figure 41—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 combination. Grey and black areas on the maps indicate 

distributional disagreement between historical and forecasted distributions (p value <0.05) and indicate drier and wetter conditions, respectively.

fail to reject null

reject null: drier

reject null: wetter

up to 0.3333

0.3334 – 0.6666

0.6667 – 1

0
42

Forest Forecasts for the North

Maryland, parts of West Virginia, and much of southeastern 

Pennsylvania. In addition, parts of New Hampshire-Maine 

and northern Michigan have areas of high forest losses. Areas 

with lesser but still substantial losses are found in central 

Missouri and in an area stretching northwest and southeast of 

Minneapolis. In contrast to the population and income-fueled 

urbanization in RPA A1B, scenario RPA B2 has less forest 

losses, and losses are more concentrated around the metro areas 

that anchor growth under RPA A1B.

Forest Types

Hardwoods dominate forest types in the North, accounting for 

about 83 percent of forest area. Among hardwood forest types, 

maple-beech-birch and oak-hickory groups are the largest, 

accounting for 26 and 36 percent of total forest area in 2010. 

Among softwood types, spruce-fir (9 percent) and white-redjack pine (5 percent) account for the largest shares of total 

forest area, and these forest types are concentrated in northern 

parts of the region. For the all forecasts, there is little change 

in the overall shares represented by hardwoods and softwoods. 

However, different shifts among forest types arise within the 

hardwood and softwood groups (fig. 45).

The area of oak-hickory, the largest forest type in the North, 

declines between 2010 and 2060 for all scenario-climate 

combinations (fig. 46A). Declines range between 3 million and 

6 million acres (5–10 percent) over this period with the highest 

losses for the RPA A1B scenario-climate combinations and 

the least losses for the RPA B2 scenarios. The clumping of 

projections around scenarios indicates that land use changes 

(driven by the socioeconomic components of the scenarios) 

dominate forecasts of change for oak-hickory forests. There is 

little variation among projections of this forest type based on 

different GCMs for a given scenario; even the market-based 

scenarios do not significantly alter the area of oak-hickory.

In contrast, the area of maple-beech-birch increases between 

2010 and 2060 for all scenario-climate combinations, with 

increases ranging between 1 million and 3 million acres 

(3–7 percent) (fig. 46B). Results are similar across all of the 

RPA scenario-climate combinations, with no clear separation 

between the scenarios. Among the hardwood types, elm-ashcottonwood forests decline by the greatest percentage across all 

RPA scenario-climate combinations, falling by 13–20 percent 

(1–2 million acres) (fig. 46C) between 2010 and 2060. Aspenbirch forecasts are the most variable among hardwood types 

and all scenario-climate combinations lead to losses in 

aspen-birch area. Minimum losses are 1.4 million acres 

Figure 43—Total forest area for three Resources Planning Act (RPA) scenarios for the North Region. By model construction, forest area in the North varies only 

by RPA scenario and not by climate projection.
43

Forest Forecasts for the North

Scenario A1

Forest < -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.7

0.07 to 0.12

> 0.12

0.7 or 0.07

< -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.07

0.07 to 0.12

> 0.12

Scenario B2

Forest

< -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.07

0.07 to 0.12

> 0.12

Scenario B2

Forest

Scenario B2

Figure 44—Forecasted change in the proportion of county in forest land, 1997–2060, for Resources Planning Act (RPA) A1B and B2 scenarios.

Scenario A1B
44

Forest Forecasts for the North

(7.7 percent) and maximum losses are 3.0 million acres (16.9 

percent) (fig.46D). For aspen-birch, the three market forecasts 

lead to the lowest amount of loss.

The two dominant softwood types show very different patterns 

of change across the scenario-climate combinations. Sprucefir area decreases for all scenarios. While forecasts for the 

nine RPA scenario-climate combinations clump together and 

amount to 8–10 percent of area, the three market scenarios 

result in losses of 12–17 percent (fig. 46F). In contrast, the area 

of white-red-jackpine increases by 5–10 percent for the nine 

combinations but declines by between 3 and 16 percent for the 

three market forecasts (fig. 46E).

Standing Biomass

Of the various metrics available for measuring biomass changes 

for a site in a forest inventory, we opted to focus on the volume 

of growing stock because it is a useful index both for timber 

analysis and for measuring other ecosystem services. Total 

growing stock volumes are forecasted to change in response 

to both land use changes and timber harvesting levels. Total 

growing stock volumes increase from a base of 263 billion cubic 

feet to between 277 billion cubic feet and 290 billion cubic feet in 

2060 for the nine RPA scenario-climate combinations (fig. 47). 

These nine combinations reflect a continuation of harvest activity 

observed over the past decade. In contrast, the three market 

forecasts show a peaking of inventory between 2020 (for RPA 

A1B-CGCM3.1-BIO) to 2030 (for RPA A2-CGCM3.1-BIO and 

RPA B2-CGCM2-BIO) and then declines in total inventories. In 

2060, total growing stock inventories would be 1 percent lower 

than 2010 levels for RPA B2, 8 percent lower for RPA A2, and 

20 percent lower for RPA A1B. Because hardwoods account 

for about 80 percent of total growing stock volumes, the pattern 

of change for hardwoods is nearly identical to the pattern for 

the total (fig. 48). The pattern for softwoods is similar though 

the percentage declines for the market scenarios are somewhat 

higher: softwood inventories would be 2 percent lower than 2010 

levels for RPA B2, 13 percent lower for RPA A2, and 24 percent 

lower for RPA A1B (fig. 49).

All these changes in growing stock volumes depart from 

historical patterns of volume accumulation in the North. Over 

the past 50 years, total growing stock volumes roughly doubled 

from 128 billion cubic feet in 1962 to 263 billion cubic feet in 

2010 (fig. 50). Between 2010 and 2060, growing stock volume 

would either level off at around 285 billion cubic feet under 

the nine RPA scenario-climate combinations or peak and then 

decline somewhat under the three market scenarios. Under 

the RPA A1B scenario, the scenario with the highest rate of 

timber harvests, growing stock volumes would decline to levels 

observed in the 1990s by 2060.

Forest Carbon

Carbon contained in the forests of the North amounts to about 

14.3 billion tons at the beginning of forecasts (fig. 51) with a 

majority of the carbon in soil organic matter and living trees 

(fig. 52). Accordingly, forecasts of forest carbon would be 

affected by changes in the area of forests and in the amount 

of biomass as described above. Figure 51 shows forecasts 

of future forest carbon in the North and generally shows a 

peaking of forest carbon in either 2020 or 2040 followed by 

a downward trajectory and reveals three clusters of the nine 

RPA scenario-climate combinations. The greatest loss of forest 

carbon is recorded for high urbanization (RPA A1B) scenarios 

with the least area of forest land in the future. The least loss 

of forest carbon is recorded for the scenarios with the least 

Figure 45—Current and forecasts of forest type distributions for A) Resources Planning Act (RPA) A1B-MIROC3.2 and B) RPA B2-HadCM3 scenario-climate 

combinations.

0	
  

20	
  

40	
  

60	
  

80	
  

100	
  

120	
  

140	
  

180	
  

200	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

(B)	
  RPA	
  B2-­‐HadCM3	
  	
  

Aspen	
  birch	
   Elm	
  ash	
  co9onwood	
   Maple	
  beech	
  birch	
   Mixed	
  pine	
  

Oak	
  hickory	
   Spruce	
  fir	
   White	
  red	
  jack	
  pine	
   other	
  

0	
  

20	
  

40	
  

60	
  

80	
  

100	
  

120	
  

140	
  

160	
  

180	
  

200	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

(A)	
  RPA	
  A1B-­‐MIROC3.2	
  

Aspen	
  birch	
   Elm	
  ash	
  co9onwood	
   Maple	
  beech	
  birch	
   Mixed	
  pine	
  

Oak	
  hickory	
   Spruce	
  fir	
   White	
  red	
  jack	
  pine	
   other	
  

0	
  

20	
  

40	
  

60	
  

80	
  

100	
  

120	
  

140	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

Aspen	
  birch	
   Elm	
  ash	
  co9onwood	
   Maple	
  beech	
  birch	
   Mixed	
  pine	
  

Oak	
  hickory	
   Spruce	
  fir	
   White	
  red	
  jack	
  pine	
   other	
  

0	
  

20	
  

40	
  

60	
  

80	
  

100	
  

120	
  

140	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

Aspen	
  birch	
   Elm	
  ash	
  co9onwood	
   Maple	
  beech	
  birch	
   Mixed	
  pine	
  

Oak	
  hickory	
   Spruce	
  fir	
   White	
  red	
  jack	
  pine	
   other	
  
45

Forest Forecasts for the North

Figure 46—Forecasts of the area of forest type groups in the North Region for nine Resources Planning Act (RPA) scenario-climate combinations, with harvesting 

based on historical behavior and three RPA scenario-climate combinations, with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios 

are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM).
46

Forest Forecasts for the North

Figure 48—Forecasted hardwood biomass measured as growing stock inventory of forests in the North Region for nine Resources Planning Act (RPA) scenarioclimate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest 

Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is 

represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 

by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM).

Figure 47—Forecasted total biomass measured as growing stock inventory of forests in the North Region for nine Resources Planning Act (RPA) scenario-climate 

combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products 

Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented 

by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. 

Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM).
47

Forest Forecasts for the North

Figure 49—Forecasted softwood biomass measured as growing stock inventory of forests in the North Region for nine Resources Planning Act (RPA) scenarioclimate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest 

Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is 

represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 

by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM).

Figure 50—Softwood and hardwood growing stock inventories for the North Region, 1962–2060. Forecasts (2010–60) bracket the range of future inventories 

shown in figures 48 and 49.
48

Forest Forecasts for the North

urbanization (RPA B2). The middle cluster of scenarios is 

occupied by middle urbanization (RPA A2) scenarios. 

The three market scenarios generate higher levels of carbon 

losses from the forests of the North (fig. 51). Under the RPA 

B2 market scenario, with the lowest increase in harvesting, 

carbon trajectories are similar to those forecast for the high 

urbanization (RPA A1B) scenarios with a loss of 3 percent of 

the stored carbon by 2060. For the RPA A2 market scenario 

(intermediate forest harvesting), stored carbon in 2060 would 

be about 7 percent lower than 2010 values and for the RPA 

A1B market scenario (highest levels of forest harvesting), 

carbon stocks in 2060 would be 11 percent lower than 

2010 values.

Estimates of the total carbon stored in forests derive from 

summing estimates of the carbon stored in eight different 

compartments or pools in forested ecosystems. Aboveground 

pools include live trees, understory plants, down and standing 

dead trees, and litter. Belowground pools include root material 

for live trees, understory plants, and soil organic matter. 

The sum of soil organic matter, aboveground live trees, and 

belowground live trees comprises nearly 80 percent of the 

total, and forecasts of these three pools are shown in fig. 

52. The pattern of change for soil organic carbon (fig. 52A) 

mirrors forest area changes shown in figure 43. The forecasts 

are organized by scenario with low urbanization (RPA B2) 

combinations having higher carbon than high urbanization 

combinations (RPA A1B). Live tree above- and belowground 

carbon pools (figs. 52B and 52C respectively) have patterns 

of change that are very similar to forecasts for growing stock 

inventories. 

Removals

Removals from growing stock show very little variation across 

the nine RPA scenario-climate combinations reflecting the 

structure of the harvest choice model. Recalling that harvest 

probability estimates are associated with forest conditions on 

each plot (i.e., growth, site attributes, and diversity), and are 

not directly connected to market scenarios, these forecasts 

show harvest responses resulting only from the evolution of the 

inventory and assuming an unchanged market condition. For 

these nine combinations, removals move upward slightly for both 

hardwoods and softwoods between 2010 and 2060 (figs. 53 and 

55). Hardwood removals grow by about 2 percent and softwood 

removals grow by about 10 percent between 2010 and 2060.

The three market scenarios generate removal patterns that are 

substantially different from the nine RPA scenario-climate 

combinations. Implemented by adjusting harvest probabilities 

to reflect the harvests predicted by the U.S. Forest Products 

Model, these scenarios show substantial growth in harvesting 

reflecting the expansion in demands for forest biomass in 

bioenergy production associated with each scenario. For RPA 

B2 (lowest harvest increases) harvests depart from the nine 

combinations beginning in 2030 and are nearly double their 

2010 levels in 2060. For RPA A2 (intermediate harvests) 

harvests expand beginning in 2020, double by 2050, and are 

roughly 2.5 times 2010 harvest levels by 2060. For RPA A1B 

(highest harvest growth) total harvest also begins to grow 

substantially after 2020, but doubles by 2040 and is roughly 

three times 2010 levels by 2060. The percentage changes in 

removals are comparable for softwoods and hardwoods; rates 

of growth in hardwood removals are only slightly higher than 

those for hardwoods.

Figure 54 compares the North’s timber harvest forecasts from 

the Forest Products Model with the implied harvests from 

the Forest Dynamics Model, and shows that harvest forecasts 

are largely consistent between the models for the specific 

scenarios. Forest Dynamics Model harvest removals are within 

10 percent of harvest removals from the Forest Products Model 

for all periods and scenarios with two exceptions. For the last 

two decades, the RPA A1B Forest Products Model projection 

is roughly 28 percent higher than the Forest Dynamics Model 

projections. As discussed previously, this scenario generates 

harvest demands unlikely to be met by traditional forest 

production techniques, implying structural changes beyond 

the scope of the model. The other exception is the last decade 

for RPA A2, where Forest Dynamics Model harvests fall 18 

percent below Forest Products Model forecasts. Overall, the 

Forest Dynamics Model generates total harvests that are 79, 93, 

and 100 percent of Forest Products Model forecasts for A1B, 

A2, and B2 scenarios, respectively. 

Age Structure/Habitat

Age class distributions are affected by the starting 

distributions and by disturbance regimes forecast for forests 

in the future. Simulated inventories can be used to evaluate 

various aspects of future forest conditions. One aspect of 

significance to wildlife habitat conditions in the North is the 

distribution of forests by age class, and the implications for 

the provision of early, middle, and late age forest conditions. 

We evaluate changes in the age class distributions of 

softwood and hardwood forest types using two charts. One 

displays the aerial age class distribution by decade for the 

most recent inventories of the northern States. We then 

summarize changes in age class resulting from the nine RPA 

scenario-climate combinations between 2010 and 2060 using 

box plots. Figure 56 shows the current age class distribution 

for hardwood forests in the North by 10-year age classes. 

Each box plot describes the distributions of changes in age 

classes. The bottom of the box represents the 25th percentile 

of the predicted changes, the top of the box represents the 

75th percentile of the predicted changes, the line in the 

box indicates the median forecast, and the plus in the box 
49

Forest Forecasts for the North

Figure 52—Forecasts of carbon stored in the various pools within forests in 

the North Region, 2010–60, for nine Resources Planning Act (RPA) scenarioclimate combinations with harvesting based on historical behavior and three 

RPA scenario-climate combinations with harvesting based on the U.S. Forest 

Products Model, 2010–60. RPA A1B scenarios are represented by solid 

lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model 

(GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) 

by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by 

yellow; HadCM3 by black. Lines with blocks indicate the market scenario for 

the respective RPA scenario (all using the CGCM GCM).

Figure 51—Forecasts of total carbon stored in forests in the North Region, 2010–60, for nine Resources Planning Act (RPA) scenario-climate combinations 

with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60. 

RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM 

(CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks 

indicate the market scenario for the respective RPA scenario (all using the CGCM GCM). Million metric tons Million metric tons Million metric tons Million metric tons
50

Forest Forecasts for the North

indicates the mean forecast. The “whiskers” of the box show 

the extreme values of the forecasts. Figure 57 contains the 

same results for softwood forest types in the North.

Figure 56 implies two overall results for hardwood forests 

in the North. The pattern of disturbances causes an overall 

flattening of the age classes between ages of 0 and 70. The 

area of the youngest ages (0–10, 11–20, and 21–30 years old) 

remains relatively constant at 5–7 million acres each from 2010 

to 2060, while the area of the middle age classes (31–40, 41–50, 

and 51–60 years old) declines substantially and approaches the 

same level by 2060. In contrast, the area of older forests 

(> 80 years old) more than doubles by 2060. A similar pattern, 

relatively stable disturbance patterns leading to a flattening 

of the age distribution and a strong increase in older forest 

conditions, applies to softwood forests as well (fig. 57).

Discussion

The 12 futures modeled for the North provide a range of 

plausible futures for forests. The nine RPA scenario-climate 

combinations define an evolution of forest inventory with 

harvests at levels observed over the present past but also 

affected by projected changes in climate and changes in land 

use driven by population and income changes. The three 

market scenarios examine how these futures might be altered 

by increased demands for woody biomass to provide various 

forms of bioenergy.

Climate projections affect changes in forest conditions 

through the imputation module which determines the 

subpopulation of forest plots that represent future inventories. 

The nine climate realizations used here project increasing 

temperatures throughout the region. As measured here, 

widespread significant changes in temperature distributions 

do not arise until late in the projection period (2050–60). The 

exception is the RPA A1B-MIROC3.2 combination where 

significant changes arise in 2040. Precipitation projections vary 

substantially—in some cases wetter, in others drier. In general, 

models suggest drier conditions in the New England States.

In the nine combinations, land use plays a dominant role in 

determining future forest area. Driven by a strong urbanization 

dynamic along the eastern seaboard and around some cities 

in the North Central States, forest losses amount to between 6 

million and 11 million acres. While the pattern of forest losses 

remains constant, the amount varies across the three RPA 

scenarios based on both forecasted population and forecasted 

personal income. Urbanization affects dominate climate effects 

across the nine combinations. 

Figure 53—Total removals from inventory in the North Region for nine Resources Planning Act (RPA) scenario-climate combinations with harvesting based on 

historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are 

represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM).
51

Forest Forecasts for the North

Figure 55—Removals from inventory by forest type in the North Region for nine Resources Planning Act (RPA) scenario-climate combinations with harvesting 

based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B 

scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 

for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate 

the market scenario for the respective RPA scenario (all using the CGCM GCM).

Figure 54—Comparison of harvests predicted by the U.S. Forest Products Model and the Forest Dynamics Model scenario associated with the U.S. Forest 

Products Model scenario for the North Region. Million cubic meters
52

Forest Forecasts for the North

Figure 57—(A) Age class distribution for softwood forest types in the North Region, in 2010 and (B) the range of forecasted changes in age classes, 2010–60, for 

nine Resources Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations 

with harvesting based on the U.S. Forest Products Model, 2010–60.

0	
  

1	
  

2	
  

3	
  

4	
  

5	
  

6	
  

7	
  

8	
  

1-­‐10	
   11-­‐20	
   21-­‐30	
   31-­‐40	
   41-­‐50	
   51-­‐60	
   61-­‐70	
   71-­‐80	
   81+	
  

Million	
  acres	
  

Age	
  class	
  

(A)	
  Age	
  class	
  distribu4on	
  for	
  so6wood	
  forest	
  types	
  in	
  the	
  North	
  in	
  2010	
  

Million acres

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81+

Age class

(B) Range of forecasted changes in age classes

Figure 56—(A) Age class distribution for hardwood forest types in the North Region in 2010 and (B) the range of forecasted changes in age classes, 2010–60,for 

nine Resources Planning Act (RPA) scenario-climate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations 

with harvesting based on the U.S. Forest Products Model, 2010–60. 

Million acres

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81+

Age class

(B) Range of forecasted changes in age classes
53

Forest Forecasts for the North

The areas of forest types change in response to land use 

changes but also in response to timber market futures. Forest 

type changes reflect a successional dynamic revealed by recent 

forest inventories and are not directly affected by climate 

changes. Within forest types, species compositions can shift 

in response to shifts in the donor pools used to populate future 

inventories. While the distribution between hardwood and 

softwood types holds relatively constant over the forecast 

period, changes within these large groups arise. For hardwoods, 

oak-hickory and elm-ash-cottonwood areas decline while 

maple-beech-birch area increases. For softwoods, spruce-fir 

area declines while white-red-jack pine area increases. As 

harvesting increases across the three market scenarios, so does 

the area of maple-beech-birch and aspen-birch while the area of 

softwood types declines.

The area of the elm-ash-cottonwood forest type declines by the 

greatest percentage across scenarios (13–20 percent) between 

2010 and 2060. While these scenarios do not explicitly address 

the spread of emerald ash borer and resulting mortality of ash 

in northern forests, the forest type observations used to forecast 

changes in types may reflect the early impacts of this epidemic, 

i.e., a retyping of forest plots influenced by ash mortality. 

While capturing some of this “signal” in the inventory data, the 

forecasts do not explicitly account for any anticipated changes 

in pest or epidemic dynamics.

The nine RPA scenario-climate combinations show a leveling 

of standing biomass volumes in the North. This approach 

to an asymptotic biomass reflects the aging of forests in the 

region coupled with a fairly constant amount of future timber 

harvests for these scenarios. With the three market scenarios 

which reflect substantial expansion in harvests, growing stock 

inventories reach a peak between 2020 and 2030 and then 

decline. By 2060, these scenarios show a net drop from 2010 

inventory levels (3 percent for RPA B2, 7 percent for RPA 

A2, and 11 percent for RPA A1B), reflecting the persistence of 

carbon in forest soils as aboveground biomass is removed.

The forecasted declines of standing biomass and carbon 

stocks in the North for the three market scenarios need to 

be considered in light of some modeling assumptions. Most 

importantly, the model takes no account of the potential for 

shifts in management strategies associated with increased 

market demands in the North. Because these scenarios 

represent what could be viewed as unprecedented market 

conditions, they could also affect structural changes in 

forest management practices, including increased planting, 

shifts in tree species, and even land use changes out of 

agriculture and into actively managed forests. We should 

expect some type of management response under these 

conditions, but our data are limited for incorporating 

these types of impacts within the inventory forecasts. The 

forecasts do provide insights into the circumstances under 

which management changes should be anticipated, namely 

under production scenarios comparable to RPA A1B and 

RPA A2.
54

Forest Forecasts for the Rocky Mountains Region

For the Rocky Mountains Region, forest forecasts were 

generated for each of the nine Resources Planning Act (RPA) 

scenario-climate combinations. For these nine combinations, 

we apply disturbance models that reflect historical harvest 

and other disturbance relationships—that is, we model the 

probability of mortality forest-replacing disturbance as a 

function of plot attributes recorded for each plot. Forest 

replacing disturbance includes timber harvesting but harvest 

records are so infrequent for inventory plots in the Rocky 

Mountain States as to preclude direct modeling of timber 

harvests. Because paired inventories were not available, future 

forest types could not be modeled using transition models and 

so instead were addressed using a forest type classification 

algorithm linked to climate projections and site attributes. This 

region spans a great variety of conditions between the northern 

and southern Rocky Mountains and also includes the Great 

Plain States of North and South Dakota, Nebraska, and Kansas. 

Table 3 defines labels used to describe the futures evaluated in 

this section.

Climate

To frame a discussion of the forest forecasts, we compare 

climate projections from the nine RPA scenario-climate 

combinations with historical conditions for the Rocky 

Mountains Region. Distributions of monthly values for a 

projected decade (2010 refers to the monthly values between 

2001 and 2010, 2020 refers to monthly values between 2011 

and 2020, and so on) are compared with a historical decade 

(2000 for the monthly values between 1991 and 2000). We 

start by plotting the cumulative distributions of temperatures/

precipitation for a graphical view of change across time. We 

then map where significant differences arise between projected 

and historical distributions for counties in the region.

Figure 58 compares cumulative temperature distributions 

between the historical decade and the 2060 decade for the nine 

RPA scenario-climate combinations. For all projections, the 

distributions shift outward over time as temperature increases. 

Chapter 6

Forest Forecasts for the Rocky Mountains Region

Figure 58—Empirical distributions of monthly temperatures for the Rocky Mountains Region for a historical decade (1991–2000) and projections of the nine 

Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60).

Average monthly temperature ( ̊C)

Percentile
55

Forest Forecasts for the Rocky Mountains Region

The RPA A1B-MIROC3.2 combination shows the greatest 

overall increase in temperatures between 2010 and 2060; with 

a 4 °C increase in temperature at the 50th percentile of the 

distribution and a 5 °C increase at the 90th percentile (the RPA 

A2-MIROC3.2 combination has the second largest increases). 

The other scenario-climate combinations also show outward 

shifts in temperature distributions between 2000 and 2060 but 

with roughly half the temperature increase shown for RPA 

A1B-MIROC3.2 (about a 2 °C increase at the 50th percentile). 

Comparing projections by decade shows that under RPA A1BMIROC3.2 (the warmest combination), temperatures increase 

in every decade (fig. 59A).1 A similar pattern holds for RPA B2-

CSIRO-Mk2 (fig. 59B) and for the other seven combinations 

not shown here. 

Spatial patterns of change vary across the scenario-climate 

combinations and across time and space. Figure 60 shows 

spatial patterns of change based on the Kolmogorov-Smirnov 

comparison of 2060 and 2000 distributions (p=0.05) for the 

temperature distributions for RPA A1B-MIROC3.2. The 

black areas indicate counties where there is distributional 

disagreement between historical and projected data for the 

specified decade. Figure 61 provides the same set of maps for 

RPA A1B-CSIRO-Mk3.5. For RPA A1B-MIROC3.2 (fig. 60), 

discernible temperature differences appear beginning in 2030 

in eastern and southern Colorado, southwestern Arizona, and 

central and western Montana. By 2040, temperature increases 

consolidate across much of Arizona, Nevada, New Mexico, 

and Colorado. By 2050, nearly all of the Rocky Mountains 

Region shows significant shifts in temperature (the exception is 

1 In contrast, for the A1B-MIROC scenario in the North and South, 

temperatures increase more in the second half of the forecast.

eastern Montana). By 2060, the entire map is black indicating 

a significant increase in temperature distributions beyond 2000 

levels for all counties for RPA A1B-MIROC3.2. 

The change pattern is different for RPA A1B-CSIRO-Mk3.5 

(fig. 61) where significant temperature increases arise only after 

2040. In 2050, a small number of counties show significant 

differences, especially in southern Arizona and New Mexico. 

In 2060, temperature increases are found west of the Rocky 

Mountain front from New Mexico to Montana and throughout 

southern Arizona and New Mexico. 

Figure 62 maps the proportion of the nine climate realizations 

leading to rejection of equivalent temperature distributions in 

2000 and 2060 (KS test, p=0.05). By 2060, four or more of the 

nine RPA scenario-climate combinations show significantly 

different temperature distributions across nearly the entire 

Rocky Mountains Region. Counties in most of New Mexico 

and a majority of Arizona, along with counties in the Colorado 

Rocky Mountains region, western Wyoming, western Montana, 

and northern Idaho demonstrate the greatest degree of model 

concurrence with significantly different temperatures in seven 

or more of the nine climate scenarios. A large part of the Great 

Plains sub-region, including all of Kansas, and portions of 

Nebraska and South Dakota, shows less convergence across the 

temperature forecasts. 

Projections of precipitation are more variable than temperature 

projections across the RPA scenario-climate combinations 

and across the region (Joyce and others, in press). Under some 

projections, the precipitation distribution shifts inward between 

2000 and 2060; under others precipitation increases by 2060. 

Cumulative distributions of monthly precipitation values for the 

historical decade (2000) and for the nine climate projections 

Figure 59—Empirical distributions of monthly temperatures for the Rocky Mountains Region for a historical decade (1991–2000) and for 6 successive decades of 

climate projections (A) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) for the RPA B2-CSIRO-Mk2 combination.

Average monthly temperature ( ̊C)

Percentile

(A) RPA A1B-MIROC3.2

Average monthly temperature ( ̊C)

Percentile

(B) RPA B2-CSIRO-Mk2

Average monthly temperature ( ̊C)

Percentile

(A) RPA A1B-MIROC3.2

A1B-MIROC3.2 B2-CSIRO-Mk2
56

Forest Forecasts for the Rocky Mountains Region

in 2060 show an inward shifting of precipitation (drying) for 

RPA A1B-MIROC3.2 and RPA A2 MIROC3.2-A1B and a 

strong outward shift (increase) in precipitation for RPA A2-

CSIRO-Mk3.5 (fig. 63). For the other climate projections, the 

distributions pivot, indicating that some areas become wetter 

while others become drier. For the various combinations 

(including RPA A1B-MIROC3.2, which is drier in 2060), the 

precipitation projections vary across time steps, in some cases 

alternating between wetter and drier decades (fig. 64).

The spatial pattern of precipitation change is highly variable. 

For RPA A1B-MIROC3.2 (fig. 65) the greatest number of 

significant differences in precipitation distributions are shown 

for the 2010 decade. In subsequent decades, the effects shrink 

and shift around the region, and only northern Montana shows 

a consistent drying across the decades. A similar pattern of 

change is shown for RPA B2-HadCM3 (not shown)—drying 

generally focused on north central Montana. RPA A1BCSIRO-Mk3.5 (fig. 66) gives rise to much less significant 

change in precipitation patterns across the decades. 

Figure 67 maps the proportion of the nine climate 

realizations leading to rejection of equivalent monthly 

precipitation distributions between the 1991–2000 decade 

and the 2051–60 decade (KS test, p=0.05). The nine climate 

projections generate no significantly different precipitation 

projections between 2000 and 2060 for much of the plains 

(most of the Dakotas, much of Nebraska and nearly all of 

Wyoming). For most of Nevada, Utah, and Arizona, one to 

three of the nine climate projections generate significantly 

different precipitation forecasts. North central Montana is 

the only large area with seven to nine significantly different 

precipitation projections reflecting the projections of drying 

for this area in figures 65 and 66.

Maps of significant shifts in mean temperature and total 

precipitation highlight areas with emphatic change in these 

two variables (i.e., these are conservative tests). Other climate 

variables may also have important influence on future forest 

conditions but are not examined here.

Figure 60—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p value <0.05).

fail to reject null: p > 0.05

reject null: p ≤ 0.05
57

Forest Forecasts for the Rocky Mountains Region

Figure 61—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p value <0.05).

fail to reject null: p > 0.05

reject null: p ≤ 0.05

Figure 62—Proportion of the nine scenario-climate combinations yielding significant differences in decadal temperature distributions between the historical 

decade (1991–2000) and the 2060 decade (2051–60) for counties in the Rocky Mountains Region.

up to 0.3333

0.3334 – 0.6666

0.6667 – 1
58

Forest Forecasts for the Rocky Mountains Region

Figure 63—Empirical distributions of monthly precipitation values for the Rocky Mountains Region for a historical decade (1991–2000) and projections of the 

nine Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–2060). Percentile

Monthly precipitation (mm)

Figure 64—Empirical distributions of monthly precipitation for the Rocky Mountains Region for a historical decade (1991–2000) and 6 successive decades 

(2010–60) of climate projections for the Resources Planning Act (RPA) A1B- MIROC3.2 combination. Percentile

Monthly precipitation (mm)
59

Forest Forecasts for the Rocky Mountains Region

Forest Area

Forecasts of forest area change derive from the land use 

analysis contained in Wear (2011).2 Unlike forecasts for the 

North, South, and the Pacific Coast regions, forecasts of forest 

losses are very small in absolute and percentage terms for 

the Rocky Mountains Region. The preponderance of public 

forests and the concentration of forests in a small portion of 

the region largely explain this result. All three RPA scenarios, 

driven by population and income forecasts, predict increased 

developed area on the private lands of the region. However, 

because a large portion of forests are held in public ownership 

[approximately 75 percent, according to Smith and others 

(2007)], development is generally concentrated on other types 

2 The total forest area changes reported in this report differ from those in Wear 

(2011) because: (1) the analysis in Wear (2011) is based on National Resource 

Inventory (NRI) data benchmarked in 1997, while this chapter translates 

those projections into FIA data benchmarked in 2010; and, (2) while the NRI 

measures only non-Federal land uses the FIA data address all ownerships.

of lands. Of the 7–10 million acres forecast to be developed 

between 1997 and 2060 in the Rocky Mountains Region, most 

involve conversion from an agricultural or rangeland use. 

About 1–1.5 million acres of forest is forecast to be developed 

in this region between 2010 and 2060 (fig. 68).

Forest losses are concentrated in only a few counties in the 

Rocky Mountains Region (fig. 69). The largest areas of loss are 

found in Colorado, along the Interstate 25 corridor between 

Fort Collins and Colorado Springs, and extending west of 

Denver along Interstate 70. Losses of forest land are also 

concentrated in a few counties in southwestern Colorado and 

around Albuquerque and Santa Fe, NM, and St. George and 

Salt Lake City, UT. Northwestern Montana and northern Idaho 

also show some losses of non-Federal forests. 

Figure 65—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Grey and black areas on the maps indicate 

distributional disagreement between historical and forecasted distributions (p value <0.05) and indicate drier and wetter conditions, respectively.

fail to reject null

reject null: drier

reject null: wetter
60

Forest Forecasts for the Rocky Mountains Region

Figure 66—Results of county-level Kolmogorov-Smirnov tests comparing distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning Act (RPA) A1B-CSIRO-Mk3.5 combination. Grey and black areas on the maps indicate 

distributional disagreement between historical and forecasted distributions (p value <0.05) and indicate drier and wetter conditions respectively.

fail to reject null

reject null: drier

reject null: wetter

Figure 67—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation distributions between the historical 

decade (1991–2000) and the 2060 decade (2051–60) for counties in the Rocky Mountains Region.

up to 0.3333

0.3334 – 0.6666

0.6667 – 1

0
61

Forest Forecasts for the Rocky Mountains Region

Forest Types

While the total area of forests varies little across the 50-year 

forecasts by scenarios, the areas of individual forest types do 

change in response to shifts in projected climates associated 

with the scenarios. Recall that western models of forest type 

transitions derive from a single cross-sectional dataset and focus 

on predicting forest type probabilities as functions of site and 

climate variables. General circulation model (GCM) projections 

for a given time period and scenario defines the set of forest type 

probabilities and random draws are used to select forest type for 

each plot in the inventory. In this way, shifts in climate give rise 

to predicted changes in forest type distributions.

The Rocky Mountains Region contains numerous forest 

type groupings, but six types comprise about 85 percent of 

total forest area in 2010 (fig. 70): pinyon juniper (34 percent), 

fir-spruce-hemlock (15 percent), Douglas fir (13 percent), 

lodgepole pine (8 percent), ponderosa pine (9 percent), and 

aspen-birch (5 percent). Pinyon juniper is a woodland type that 

occupies arid sites. The remaining six forest types occupy a 

wide range of conditions and generally contain overlapping 

mixes of tree species, e.g., fir-spruce-hemlock types may 

contain Douglas fir trees and Douglas fir types may contain 

grand fir trees. The classification algorithm therefore projects 

a shift in the dominance of certain tree species in response to 

climate and other variables.

Forecasts show changes in forest type distributions over time. 

The largest forest type group in the Rocky Mountains Region is 

pinyon juniper (combined here with other western softwoods). 

Starting at a little more than 50 million acres in 2010, across 

the nine RPA scenario-climate combinations, forecasts range 

from a loss of about 4 million acres (8 percent) for RPA B2-

HadCM to a very slight gain in acreage (about 1 million acres 

or 2 percent) for RPA A1B-CGCM3.1 (fig. 71A). There is no 

clear separation of pinyon juniper forecasts across the scenarios 

or climate projections.

At 23 million acres, the fir-spruce-hemlock forest type group 

is the second largest in the region. The area of this forest type 

is forecasted to increase across all nine combinations, though 

it dips for the first 2 decades under RPA B2-CSIRO-Mk2 (fig. 

71B). Area expands between 2010 and 2060 by half a million 

acres to 3 million acres (up to about 15 percent of 2010 area). The 

greatest increase occurs for RPA A1B MIROC3.2 and RPA A2-

MIROC3.2; the smallest increase is for RPA B2 CSIRO-Mk2.

In contrast, the area of the Douglas fir forest type group falls the 

most in percentage terms between the nine combinations 

Figure 68—Total forest area for three scenarios for the Rocky Mountains Region. By model construction, forest area in the Rocky Mountains Region 

varies only by storyline (not by general circulation model).
62

Forest Forecasts for the Rocky Mountains Region

Figure 69—Forecasted changes in the proportion of non-Federal land within a county in forest, 1997–2060, for the A1B and B2 storylines.

Scenario A1

Forest < -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.07

0.07 to 0.12

> 0.12

< -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.7

0.07 to 0.12

> 0.12

Scenario B2

Forest

0.7 or 0.07?

Scenario A1

Forest < -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.07

0.07 to 0.12

> 0.12

Scenario B2

Forest

Scenario A1B

Forest
63

Forest Forecasts for the Rocky Mountains Region

(fig. 71C). Declines range from 3 million to 6 million acres 

(16–33 percent), with unclear separation among forecasts for 

the three scenarios, but clearly evident separation between the 

GCMs applied to the scenarios. Scenarios paired with MIROC3.2 

and HadCM3 GCMs yield the greatest declines while scenarios 

paired with the CGCM3.1 and CGCM2 GCMs yield the lowest 

declines. Scenarios paired with the CSIRO-Mk3.5 and CSIROMk2 GCMs yield intermediate results. The area of the lodgepole 

pine forest type group also declines across all RPA scenarioclimate combinations with the ranking of forecasts across the 

combinations similar to that for the Douglas fir group (fig. 71E). 

Declines in the area of this forest type group are also large and 

range from 1 million to 3.5 million acres (8–32 percent).

All combinations yield increases in the area of ponderosa 

pine forest type group—between 0.5 million and 4 million 

acres. Increases in the Ponderosa pine type group mirror the 

decline in the Douglas fir and lodgepole pine type groups 

across scenarios (fig. 71D). The greatest increases in ponderosa 

pine occur with the MIROC3.2 and HadCM3 GCMs and the 

smallest increases occur with the CGCM3.1 and CGCM2 

GCMs. The dominant net shifts in forest types for the Rocky 

Mountains Region are from Douglas fir and lodgepole forest 

type groups to ponderosa pine and fir-spruce-hemlock forest 

type groups. In contrast, the area of aspen-birch forests changes 

relatively little between 2010 and 2060, ranging from slight 

increase to a slight decrease over the forecast period (fig. 71F). 

Standing Biomass

Projections of growing stock inventories (figs. 72A and 72B) 

show that future biomass varies little over current levels 

across the nine RPA scenario-climate combinations. Softwood 

growing stock inventory (roughly 90 percent of biomass) 

stands at about 125 billion cubic feet in 2010 and is forecasted 

to be between 123 billion cubic feet and 128 billion cubic feet 

in 2060—maximum growth of 3 percent over this 50-year 

period. Hardwood inventory (about 10 percent of biomass) 

stands at about 12 billion cubic feet in 2010 and would remain 

at this level through 2060. These forecasts suggest that 

mortality and removals from inventory would be just offset 

by inventory growth. This type of balance occurred during 

the 1960s and 1970s followed by strong increases in growing 

stock inventories in the 1980s and 1990s (fig. 73). Inventory 

data since the 1990s have indicated a slowing of inventory 

expansion and the forecasts show a continuation of these 

trends, i.e., further slowing.

Forest Carbon

Carbon contained in the forests of the Rocky Mountains Region 

amounts to about 7.2 billion metric tons at the beginning of 

forecasts (fig. 74) with a majority of the carbon in soil organic 

matter and living trees (fig. 75). Accordingly, forecasts of forest 

carbon would be affected by changes in the area of forests and 

in the amount of biomass as described above. Figure 74 shows 

forecasts of future forest carbon totals for the Rocky Mountains 

Region and shows an essentially constant level of carbon in 

forest pools between 2010 and 2060. 

Estimates of the total carbon stored in forests derive from 

summing estimates of the carbon stored in eight different 

compartments or pools within forested ecosystems. 

Aboveground pools include live trees, understory plants, down 

and standing dead trees, and litter. Belowground pools include 

root material for live trees, understory plants, and soil organic 

matter. The sum of soil organic matter, aboveground live trees, 

and belowground live trees comprises nearly 80 percent of the 

total and forecasts of these three pools are shown in figure 74. 

The pattern of change for soil organic carbon (fig. 75A) mirrors 

forest area changes shown in figure 68. The forecasts reflect the 

slight downward trend in total forest area in the region. Live 

tree above- and belowground carbon pools (figs. 75B and 75C, 

respectively) have patterns of change that are very similar to 

forecasts for growing stock inventories and reflect a very slight 

Figure 70—Current and forecasts of forest type distributions for A) A1B-MIROC and B) B2-Hadley scenarios (this chart does not include forest area in the Great 

Plains States of North Dakota, South Dakota, Nebraska, and Kansas).

0	
  

20	
  

40	
  

60	
  

80	
  

100	
  

120	
  

140	
  

160	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

Million	
  acres	
  

(A)	
  A1B-­‐MIROC	
  

Aspen	
  Birch	
   Douglas	
  fir	
   Fir-­‐spruce-­‐hemlock	
   Lodgepole	
  pine	
   Pinon	
  juniper	
   Ponderosa	
  pine	
   Other	
  

0	
  

20	
  

40	
  

60	
  

80	
  

100	
  

120	
  

140	
  

160	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

Million	
  acres	
  

(B)	
  B2-­‐Hadley	
  

Aspen	
  Birch	
   Douglas	
  fir	
   Fir-­‐spruce-­‐hemlock	
   Lodgepole	
  Pine	
   Pinon	
  juniper	
   Ponderosa	
  pine	
   Other	
  

0	
  

20	
  

40	
  

60	
  

80	
  

100	
  

120	
  

140	
  

160	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

Million	
  acres	
  

(A)	
  A1B-­‐MIROC	
  

Aspen	
  Birch	
   Douglas	
  fir	
   Fir-­‐spruce-­‐hemlock	
   Lodgepole	
  pine	
   Pinon	
  juniper	
   Ponderosa	
  pine	
   Other	
  
64

Forest Forecasts for the Rocky Mountains Region

Figure 71—Forecasts of the area of forest types in the Rocky Mountains Region for nine Resources Planning Act (RPA) scenario-climate combinations with 

harvesting based on historical behavior, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general 

circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 

for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM).
65

Forest Forecasts for the Rocky Mountains Region

Figure 72—Forecasted biomass measured as growing stock inventory by forest type in the Rocky Mountains Region for nine Resources Planning Act (RPA) scenarioclimate combinations with harvesting based on historical behavior, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. 

MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 

and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM).

Figure 73—Softwood and hardwood growing stock inventories for the Rocky Mountains Region, 1952–2060. Forecasts (2010–60) bracket the range of future 

inventories shown in figures 62 and 63.
66

Forest Forecasts for the Rocky Mountains Region

upward trend in these pools. Slight decreases in soil carbon just 

offset by increases in carbon in overstory and understory pools 

yield stable carbon pools through time.

Removals

Due to limited data, harvests are not directly modeled for 

the Rocky Mountains Region. Harvests are infrequent when 

compared to the area or volume of forests in this region—about 

0.5 percent of growing stock inventory per year (Smith and 

others 2007)—so harvested plots are observed very rarely in 

data from the Forest Inventory and Analysis (FIA) Program of 

the Forest Service, U.S. Department of Agriculture, making 

the direct modeling of harvest activities implausible. Instead 

we focus on modeling stand replacing events generally. 

Accordingly there is no direct estimate of removals available 

from the model. We can infer from age class projections that 

the rate of removals is roughly consistent with the average 

values observed between the mid-1990s and the mid-2000s plus 

or minus the FIA sampling error for removals. We discuss total 

removals forecasts for the West in the section of this report on 

the Pacific Coast Region.

Age Structure/Habitat

Age class distributions are affected by the starting distributions 

and by disturbance regimes forecast for forests in the future. 

Simulated inventories can be used to evaluate various aspects 

of future forest conditions. One aspect of significance to 

wildlife habitat conditions in the Rocky Mountains Region is 

the distribution of forests by age class, and the implications for 

the provision of early, middle, and late age forest conditions. 

We evaluate changes in the age class distributions of softwood 

forest types using two charts. One displays the aerial age class 

distribution by decade for the most recent inventories of the 

Rocky Mountains Region. We then summarize changes 

in age class resulting from the nine RPA scenario-climate 

combinations between 2010 and 2060 using box plots. Figure 

76 shows the current age class distribution for hardwood 

forests in the Rocky Mountains Region by 20-year age classes. 

Each box plot describes the distributions of changes in age 

classes. The bottom of the box represents the 25th percentile 

of the predicted changes, the top of the box represents the 75th 

percentile of the predicted changes, the line in the box indicates 

Figure 74—Forecasts of total carbon stored within forests in the Rocky Mountains Region for nine Resources Planning Act (RPA) scenario-climate combinations 

with harvesting based on historical behavior, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general 

circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 

for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM). Million metric tons
67

Forest Forecasts for the Rocky Mountains Region

Figure 75—Forecasts of carbon stored in various pools within forests in the Rocky Mountains Region for nine Resources Planning Act (RPA) scenario-climate 

combinations with harvesting based on historical behavior, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. 

MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 

and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM). Million metric tons Million metric tons Million metric tons
68

Forest Forecasts for the Rocky Mountains Region

the median forecast, and the plus in the box indicates the mean 

forecast. The “whiskers” of the box show the extreme values of 

the forecasts. 

The age class distribution in 2010 is peaked in the 81–100 and 

101–120 age class groupings, reflecting disturbance history 

including early logging and fires (fig. 76). Forecasts emphasize 

an aging of this cohort and an expansion in the area of forests 

in the 201+ age classes. Stand replacement models yield a 

steady level of the youngest aged forests and models indicate 

a substantial reduction in the area of the middle age classes, 

especially for the 81–100 and 101–120 classes. 

Discussion

The nine RPA scenario-climate combinations considered for 

the Rocky Mountains Region provide a range of plausible 

futures affecting forests in the region. Population and income 

changes affect an increase in developed land, but the effects 

on forest area in the Rocky Mountains Region is small 

in terms of area and proportion—a loss of 1–1.5 million 

acres amounting to < 1 percent of total forest area. More of 

the forecasted development is focused on agricultural and 

rangelands in this region.

Inventories of forest biomass are forecast to remain relatively 

constant in the region as forests age and stand replacement 

rates are essentially constant. These forecasts imply that 

removals and mortality just offset forest growth and growth is 

expected to decline as forest aging progresses.

What is variable across the forecasts is change in forest types 

driven by projected changes in climate variables between 

2010 and 2060. The direction of these changes is generally 

consistent across RPA scenario-climate combinations but the 

magnitude of change is variable. The greatest changes arise for 

lodgepole pine, ponderosa pine, and Douglas fir forest types. 

Across all combinations, both lodgepole pine and Douglas fir 

types are forecasted to decline in area (8–32 percent and 

16–33 percent, respectively) while the area of the ponderosa 

pine type is forecasted to increase (up to 29 percent). A 

moderate increase in spruce-fir forests type area is also 

projected. Two studies addressing the potential influence of 

climate change on forest species distributions in the Rocky 

Mountains Region, while not congruent with our approach 

to modeling forest type associations, provide points of 

comparison. Our forecasts of increasing ponderosa pine 

types are generally consistent with increased P. ponderosa

distributions predicted by Shafer (2001) and by Rehfeldt and 

others (2006). Increases in spruce-fir types conflicts with 

the prediction by Rehfeldt and others (2006) of decline but 

is consistent with Shafer’s prediction of an increase in P. 

engelmannii in the region. 

Rates of forest type changes are generally ordered by the 

GCMs paired with the RPA scenarios. The greatest change 

in forest types occur under the MIROC3.2 (both A1B and A2 

scenarios) and HadCM3 (B2) GCMs, the warmest and driest 

scenario-climate combinations. Conversely the least change is 

associated with the CGCM3.1 and CGCM2 GCMs. The RPA 

scenarios paired with the MIROC3.2 GCM (A1B and A2) 

produce the greatest increases in temperature between 2010 

Figure 76—(A) Age class distributions for softwood forest types in the Rocky Mountains Region in 2010 and for the B2-Hadley scenario in 2060, and (B) change 

in age class groups across all scenarios. 

Million acres

0-20 21-40 41-60 61-80 81-100 101-120 121-140 141-160 161-180

Age class

(B) Change in age class groups across all scenarios

181-200 201+
69

Forest Forecasts for the Rocky Mountains Region

and 2060 and by 2030, and temperature changes are largely 

coincident with the forested portions of the Rocky Mountains 

Region. HadCM3 temperatures are coincident with MIROC3.2 

beyond the 75th percentile of the temperature distribution, 

i.e., in the warmest 25 percent of the region. MIROC3.2 and 

HadCM3 generate the greatest reductions in total precipitation 

between 2010 and 2060 among the GCMs evaluated. Forecast 

models for forests in the West (Rocky Mountains Region and 

Pacific Coast) differ from those developed for the eastern 

regions in some fundamental ways. Inventory data are 

relatively scarce in the West with survey internals exceeding 

10 years for most States and the transition to the newer annual 

inventory approach taking longer. In all cases, the inventory 

design changed substantially between the most recent two 

inventories, effectively precluding the use of matched plots to 

estimate change models. As a result, transition models could 

not be fit and models of forest type change relied on crosssectional analysis (classification) of forest type occurrences 

relative to current conditions. While these models allow for a 

high degree of detail regarding the relationship between site 

variables, climate variables, and forest conditions, they cannot 

incorporate the relative inertia or the speed of change of type 

condition on forest plots. We might expect these types of 

models to overestimate the rates of resulting changes in forest 

conditions. This suggests placing most focus on the longer term 

projections where disturbance, mortality, and recruitment have 

time to play out in generating the changes.
70

Forest Forecasts for the Pacific Coast Region

For the Pacific Coast Region, forest forecasts were generated 

for each of the nine Resources Planning Act (RPA) scenarioclimate combinations with harvest models that reflect 

historical harvest relationships. In addition, we simulate 

inventory development under three futures (based on the 

RPA A1B-CGCM3.1, A2- CGCM3.1, and RPA B2-CGCM2 

combinations); with adjusted harvest probabilities to simulate 

removals predicted by the U.S. Forest Products Model (Ince 

and others 2011) for the economic futures attached to the RPA 

scenarios. To match the harvests from the Forest Products 

Model, we adjust the scaling parameter in equation 4 to reflect 

Forest Product Model runs for the respective scenario. Because 

paired inventories are not available, future forest types could 

not be modeled using transition models but were modeled 

using a forest type classification algorithm linked to climate 

forecasts and site attributes. This region spans a great variety of 

conditions between the Eastside and Westside of the Cascades, 

so separate models were developed for these two sub-regions 

for both forest type transitions and for harvest probabilities by 

ownership class. Data for Oregon and Washington were pooled 

to estimate models and a separate set of California models 

were estimated. Table 3 defines the labels for the 12 futures 

evaluated in this section. 

Climate

To frame a discussion of the forest forecasts, we compare 

climate projections from the nine RPA scenario-climate 

combinations with historical conditions for the Pacific Coast 

Region. Distributions of monthly values for a projected decade 

(2010 refers to the monthly values between 2001 and 2010, 

2020 refers to monthly values between 2011 and 2020, and 

so on) are compared with a historical decade (2000 for the 

monthly values between 1991 and 2000). We start by plotting 

the cumulative distributions of temperatures/precipitation for 

a graphical view of change across time. We then map where 

significant differences in these distributions arise between 

projected and historical data for counties in the region.

Figure 77 shows a comparison of empirical temperature 

distributions between the historical decade and the 2060 

decade for the nine climate projections. For all projections, 

the distributions shift outward over time as temperatures are 

projected to rise. The RPA A1B-MIROC3.2 combination 

generally shows the greatest increase in temperatures between 

2010 and 2060, with a 3 °C increase in temperature at the 50th 

percentile of the distribution and a 4 °C increase at the 90th 

Chapter 7

Forest Forecasts for the Pacific Coast Region

Figure 77—Empirical distributions of monthly temperatures for the Pacific Coast Region for a historical decade (1991–2000) and projections of the nine Resources 

Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60).

Average monthly temperature ( ̊C)

Percentile
71

Forest Forecasts for the Pacific Coast Region

percentile. Through the 50th percentile, the other scenario-climate 

combinations yield outward shifts in temperature distributions of 

about 75 percent of the temperature increase shown for RPA A1BMIROC3.2 (about a 2.5–3 °C increase at the 50th percentile). 

However, beyond the 65th percentile the RPA B2-HadCM3 

combination yields larger temperature increases (+5 °C 

at the 90th percentile). This combination generates the greatest 

increase in temperature at the warm end of the distribution and 

the least increase at the cool end of the distribution. All scenarioclimate combinations show a fairly regular outward shift in 

distributions over the decades (figs. 77 and 78).

Spatial patterns of change vary across the scenario-climate 

combinations. Figure 79 shows spatial patterns of change 

based on the Kolmogorov-Smirnov test (p=0.05) for monthly 

temperatures at the county level applied to the RPA A1BMIROC3.2 combination (one of the two warmest scenarioclimate combinations). The black areas indicate counties 

where there is distributional disagreement between historical 

and projected temperature data across the decades. Figure 80 

provides the same set of maps for RPA A1B- CSIRO-Mk3.5. 

For RPA A1B-MIROC3.2 (fig. 79), discernible temperature 

differences appear beginning in 2030 largely along the coastal 

portions of the Pacific Coast Region. By 2050 temperature 

increases consolidate along the coast and include inland areas 

of southern and central California, and eastern Washington and 

Oregon. Beginning with the 2050 decade, many more inland 

counties show significant change. By 2060, the entire map is 

black for this combination, indicating a significant difference in 

temperature distributions over 2000 distributions for all counties. 

For the Pacific Coast Region, the change pattern is similar for 

RPA A1B-MIRO3.2 and RPA A1B-CSIRO-Mk3.5 combination 

(fig. 80). Significant temperature increases start along the 

coastal areas of the Pacific Coast Region in 2030 and spread 

inland over the decades. By 2060, temperature distributions 

are significantly different for most of the region (exceptions are 

eastern Washington and south central California). Figure 81 

shows a strong degree of concurrence among the RPA 

scenario-climate combinations for temperature projections

in the Pacific Coast Region. Along the coast, and for a strong 

majority of counties in the region, 7 to 9 of the 9 climate 

projections show significant differences in temperature 

(KS test, p=0.05). This region shows the greatest concurrence 

across climate projections among RPA regions.

Projections of precipitation are more variable than temperature 

projections across the RPA scenario-climate combinations 

and across the region (Joyce and others, in press). Under some 

combinations, overall precipitation is lower tha historical values 

in the 2060 decade; under others precipitation is higher in the 

2060 decade (fig. 82). Cumulative distributions of monthly 

precipitation values for the historical decade (2000) and for 

the nine climate projections in 2060 show an inward shifting 

of precipitation (drying) for RPA A2-MIROC3.2 and RPA B2- 

HadCM3 and a strong outward shift (increase) in precipitation 

for RPA A1B-CGCM3.1 and RPA B2-CGCM2 (figs. 82 

and 83). For the other combinations, the distributions pivot, 

indicating that some areas become wetter while others become 

drier. For the various scenarios, the precipitation distribution 

shifts vary between time steps, in some cases alternating 

between wetter and drier decades (figs. 84 and 85). 

Figure 86 indicates very few counties with significantly 

different precipitation distributions in 2060 for four or more 

scenarios. These are focused in an area from the Bay area of 

California to the south.

Maps of significant shifts in mean temperature and total 

precipitation highlight areas with emphatic change in these 

two variables, i.e., these are conservative tests. Other climate 

variables may also have important influence on future forest 

conditions but are not examined here.

Figure 78—Empirical distributions of monthly temperatures for the Pacific Coast Region for a historical decade (1991–2000) and 6 successive decades of climate 

projections (A) for the Resources Planning Act (RPA) A1B-MIROC3.2 combination and (B) for the RPA B2-CSIRO-Mk2 combination.

Average monthly temperature ( ̊C)

Percentile

(A) A1B-MIROC3.2

Average monthly temperature ( ̊C)

Percentile

(A) A1B-MIROC3.2

Average monthly temperature ( ̊C)

Percentile

(B) RPA B2-CSIRO-Mk2 A1B-MIROC3.2 B2-CSIRO-Mk2
72

Forest Forecasts for the Pacific Coast Region

Figure 79—Results of county-level Kolmogorov-Smirnov tests comparing 

distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning 

Act (RPA) A1B-MIROC3.2 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p 

value <0.05). 

fail to reject null: p > 0.05

reject null: p ≤ 0.05

o reject null: p > 0.05

t null: p ≤ 0.05

fail to reject null: p > 0.05

reject null: p ≤ 0.05

fail to reject null: p > 0.05

reject null: p ≤ 0.05

Figure 80—Results of county-level Kolmogorov-Smirnov tests comparing 

distributions of monthly temperatures for a historical decade (1991–2000) and 

6 successive forecasted decades (2010–60) for the Resources Planning Act 

(RPA) A1B-CSIRO-Mk3.5 combination. Black areas indicate distributional 

disagreement between historical and projected temperature distributions (p 

value <0.05).
73

Forest Forecasts for the Pacific Coast Region

Figure 81—Proportion of the nine scenario-climate combinations yielding significant differences in decadal temperature distributions between the historical 

decade (1991–2000) and the 2060 decade (2051–60) for counties in the Pacific Coast Region.

up to 0.3333

0.3334 – 0.666

0.6667 – 1

up to 0.3333

0.3334 – 0.6666

0.6667 – 1
74

Forest Forecasts for the Pacific Coast Region

Figure 82—Empirical distributions of monthly precipitation values for the Pacific Coast Region for a historical decade (1991–2000) and projections of the nine 

Resources Planning Act (RPA) scenario-climate combinations in the 2060 decade (2051–60). Percentile

Monthly precipitation (mm)

Figure 83—Empirical distributions of monthly precipitation for the Pacific Coast Region for a historical decade (1991–2000) and 6 successive decades (2010–60) 

of climate projections for the Resources Planning Act (RPA) A1B-MIROC3.2 combination. Percentile

Monthly precipitation (mm)
75

Forest Forecasts for the Pacific Coast Region

Figure 84—Results of county-level Kolmogorov-Smirnov tests comparing 

distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning 

Act (RPA) A1B-MIROC3.2 combination. Grey and black areas on the maps 

indicate distributional disagreement between historical and forecasted 

distributions (p value <0.05) and indicate drier and wetter conditions, 

respectively. 

fail to reject null

reject null: drier

reject null: wetter

o reject null

t null: drier

t null: wetter

fail to reject null

reject null: drier

reject null: wetter

fail to reject null

reject null: drier

reject null: wetter

Figure 85—Results of county-level Kolmogorov-Smirnov tests comparing 

distributions of monthly temperatures for a historical decade (1991–2000) 

and 6 successive forecasted decades (2010–60) for the Resources Planning 

Act (RPA) A1B-CSIRO-Mk3.5 combination. Grey and black areas on the 

maps indicate distributional disagreement between historical and forecasted 

distributions (p value <0.05) and indicate drier and wetter conditions, 

respectively. 
76

Forest Forecasts for the Pacific Coast Region

up to 0.3333

0.3334 – 0.6666

0.6667 – 1

0

Figure 86—Proportion of the nine scenario-climate combinations yielding significant differences in decadal precipitation distributions between the historical 

decade (1991–2000) and the 2060 decade (2051–60) for counties in the Pacific Coast Region.

up to 0.3333

0.3334 – 0.6666

0.6667 – 1

0
77

Forest Forecasts for the Pacific Coast Region

Forest Area

Across the three RPA scenarios, the Pacific Coast Region is 

forecast to lose between 1.7 million and 2.7 million acres of 

forest in response to ongoing population-fueled development 

between 2010 and 2060 (fig. 87). The highest loss is for 

the RPA A2 and A1B scenarios (unlike other regions, the 

Pacific has nearly identical forest loss trajectories for these 

two scenarios). The least area of loss accrues to the RPA B2 

scenario. Due especially to patterns of land use in the region, 

land use changes and forest losses are not spread evenly 

across the Pacific Coast Region. Instead there are several large 

blocks of changes within the region (fig. 88). The largest area 

of projected forest losses is forecasted for the west side of the 

Cascade mountain range in Washington, in an area centered 

on Seattle but extending between the Canadian border and the 

Oregon border. In Oregon, forest losses concentrate in two 

areas, the northwestern corner centered on Portland and the 

southwest corner centered on Medford. In California, forest 

losses are forecasted for the San Francisco Bay area and along 

the Interstate 80 corridor to the Nevada border at Reno. 

Forest Types

While the 50-year forecast of forest area varies little across 

the various three RPA scenarios, the area of individual forest 

types does change in response to shifts in projected climates 

associated with the scenario-climate combinations. Western 

models of forest transitions derive from a single cross-sectional 

dataset and focus on predicting forest type probabilities as 

a function of site and various climate variables. General 

circulation model projections for a given year and scenario 

define the set of forest type probabilities, and random draws 

are used to select forest type for each plot within the inventory. 

In this way, shifts in climate can give rise to changes in overall 

forest type distributions. 

The broad diversity of forest types in the Pacific Northwest 

reflects the variety of growing conditions from the Coast to the 

Inland West. Six forest type groups—Douglas fir, fir-sprucehemlock, ponderosa pine, hemlock-sitka spruce, white oak, and 

California mixed conifer (CMC)—combine to make up about 

73 percent of forests in the region in 2010 (fig. 89). Douglas fir and 

Figure 87—Total forest area for three RPA scenarios for the Pacific Coast Region. By model construction, forest area in the Pacific Coast Region varies only by 

Resources Planning Act (RPA) scenario (not by general circulation model).
78

Forest Forecasts for the Pacific Coast Region

hemlock-Sitka spruce dominate the Westside forests, but Douglas 

fir is also a dominant type in the drier and less productive forests 

of the east side, reflecting its very broad ecological amplitude 

(Rehfeldt and others 2008). Forecasts suggest some changes in 

type composition over the next 50 years.

In 2060, the area of Douglas fir ranges from its 2010 value 

(20 million acres) to an increase of about 15 percent across the 

scenario-climate combinations (fig. 90A). The relative stability of 

Douglas fir in the Pacific Coast Region is the result of offsetting 

trends in Eastside and Westside forests. On the Westside, 

Scenario A1

Forest < -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.7

0.07 to 0.12

> 0.12

0.7 or 0.07 ??

Scenari

Forest 

Scenario B2

Forest < -0.12

-0.12 to -0.07

-0.07 to -0.02

-0.02 to 0.02

0.02 to 0.07

0.07 to 0.12

> 0.12

Figure 88—Forecasted changes in the proportion of non-Federal land within a county in forest, 1997–2060, for the A1B and B2 storylines

Scenario A1B

Forest Scenario B2

Forest
79

Forest Forecasts for the Pacific Coast Region

Douglas fir increases and mirrors strong declines in hemlockSitka spruce (from 4 million acres in 2010 to between half a 

million and 2 million acres in 2060; fig 90D). On the east side, 

Douglas fir declines and mirrors increases in ponderosa pine.

The area of fir-spruce-hemlock declines for all scenario-climate 

combinations (fig. 90B). Starting at roughly 9 million acres in 

2010, this forest type group which is predominantly on the East 

side of the Cascades, falls to between 3.5 million and 7 million 

acres (a decline of 20–60 percent) by 2060. The ordering of 

combinations shows no clear differences between scenarios or 

GCMs and change in the area of fir-spruce-hemlock.

Ponderosa pine, in contrast, shows strong gains across all 

scenario-climate combinations (fig. 90C). By 2060, ponderosa 

pine area is forecasted to grow by substantial amounts from 

9.2 million acres in 2010 to between 12 million and 18 million 

acres (gains of 30–96 percent). This gain in ponderosa pine 

forests in Eastside forests is qualitatively similar to forecasts of 

expanding ponderosa pine for the Rocky Mountains Region.

Hemlock-Sitka spruce has the greatest percentage loss of 

area among forest type groups across the forecasts (fig. 90D). 

Starting at 4.3 million acres in 2010 this forest type group is 

forecast to occupy between half a million and 2.3 million acres 

in 2060 (losses of between 55 and 88 percent). 

The CMC forest type shows some variation over time but in 

2060 all forecasts are very close to the 8 million acres observed 

in 2010 (fig. 90E). In contrast, the white oak forest type group 

is forecasted to expand across all scenarios from about 10 

million acres in 2010 to between 12.4 million and 14.9 million 

acres (gains of 24–49 percent) in 2060 (fig. 90F). Most of this 

forest type and all the gains forecast for the type are found in 

California.

Standing Biomass

Of the various metrics available for measuring biomass 

changes for a site in a forest inventory, we opted to focus 

on the volume of growing stock because it is a useful index 

both for timber analysis and for measuring other ecosystem 

services. Total growing stock volumes are forecasted to change 

in response to both land use changes and timber harvesting 

levels. In the Pacific Coast Region, nearly 90 percent of total 

growing stock inventory is softwood. Standing at 204 billion 

cubic feet in 2010, softwood inventories are forecast to expand 

slightly between 2010 and 2030 and then decline gradually to 

2060 (fig. 91A). In 2060, softwood inventories range between 

175 billion and 197 billion cubic feet (net declines of 2–12 

percent from 2010 values; fig. 91A). Hardwood inventories, 

about 10 percent of total inventory, would fall by about 

20 percent for all nine of the scenarios (fig. 91B).

Figure 92 puts changes in softwood growing stock in the 

context of a longer history of changes. Peak values forecast for 

2020–30 would be roughly equivalent to growing stock values 

estimated for the early 1950s. Relatively high harvest levels 

from the 1960s to the 1990s were accompanied by a decline in 

inventories. Harvest reductions beginning in the early 1990s 

were followed by a net accumulation of inventory in the region. 

Figure 89—Current and forecasts of forest type distributions for A) A1B-MIROC3.2 and B) B2-HadCM3 scenarios.

0	
  

10	
  

20	
  

30	
  

40	
  

50	
  

60	
  

70	
  

80	
  

90	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

???	
  

(A)	
  A1B-­‐MIROC3.2	
  

Douglas	
  fir	
   Fir	
  spruce	
  hemlock	
   Ponderosa	
  pine	
   Hemlock-­‐Sitka	
  spruce	
   White	
  oak	
   Cmc	
   Other	
  

0	
  

10	
  

20	
  

30	
  

40	
  

50	
  

60	
  

70	
  

80	
  

90	
  

2010	
   2020	
   2030	
   2040	
   2050	
   2060	
  

???	
  

(B)	
  B2-­‐HadCM3	
  

Douglas	
  fir	
   Fir	
  spruce	
  hemlock	
   Ponderosa	
  pine	
   Hemlock-­‐Sitka	
  spruce	
   White	
  oak	
   Cmc	
   Other	
  

Douglas fir Fir-spruce-hemlock Ponderosa pine Hemlock-Sitka spruce White oak CMC Other

Million acres

Million acres
80

Forest Forecasts for the Pacific Coast Region

Figure 90—Forecasts of the area of forest types in the Pacific Coast Region for nine Resources Planning Act (RPA) scenario-climate combinations with 

harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA 

A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM 

(CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks 

indicate the market scenario for the respective RPA scenario (all using the CGCM GCM).
81

Forest Forecasts for the Pacific Coast Region

Softwood growing stock inventories in 2060 would be roughly 

equivalent to the range of values observed between the 1990s 

and the 2000s.

Forest Carbon

Carbon contained in the forests of the Pacific Coast Region 

amounts to about 7.8 billion metric tons at the beginning 

of the forecasts (fig. 93), with a majority of the carbon in 

soil organic matter and living trees (fig. 94A). Accordingly, 

forecasts of forest carbon would be affected by changes in 

the area of forests and in the amount of biomass as described 

earlier. Figure 93 shows forecasts of future forest carbon in 

the Pacific Coast Region and a gradual decline in the amount 

of carbon in forest pools between 2010 and 2060. Total forest 

carbon declines to between 7.1 billion and 7.5 billion tons 

(or a loss of between 4 and 9 percent) over this period. 

Patterns of loss are dominated by the change in growing stock 

volumes (forest area loss is relatively small in this region over 

the simulation period). 

Estimates of the total carbon stored in forests derive from 

summing estimates of the carbon stored in eight different 

compartments or pools in forested ecosystems. Aboveground 

pools include live trees, understory plants, down and standing 

dead trees, and litter. Belowground pools include root material 

for live trees, understory plants, and soil organic matter. 

The sum of soil organic matter, aboveground live trees, and 

belowground live trees comprises nearly 80 percent of the 

total and forecasts for these three pools are shown in figs. 94A 

through C. Losses of soil organic carbon are greater than the 

slight downward trend in total forest area in the region (5–8 

percent versus 1–3 percent). We posit that they also reflect 

changes in forest types associated with the climate futures. 

Live tree above- and belowground carbon pools show patterns 

of change that are very similar to forecasts for growing 

stock inventories, e.g., a decline of 6–13 percent for live tree 

aboveground pools.

Removals

Forecasts of removals for the nine scenario-climate combinations 

without links to the Forest Product Model outputs are driven 

by harvest probabilities assigned to plots based on ownership 

types, forest type, and age class and adjusted to reflect recent 

historical regional removal patterns. Applying these probabilities 

to forecasted forest inventories (i.e., accounting for climate, 

forest type shifts, and aging), yields a set of removals forecasts 

that extends historical patterns to future conditions. For eight 

of the nine combinations, forecasted softwood removals (about 

83 percent of total removals) increase over the first 20 years 

Figure 91—Forecasted biomass measured as growing stock inventory of forests in the Pacific Coast Region for nine Resources Planning Act (RPA) scenarioclimate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest 

Products Model, 2010-2060. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is 

represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 

by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM). 
82

Forest Forecasts for the Pacific Coast Region

and then decline slightly (fig. 95A). In 2060, softwood removals 

would be between 77 and 97 percent of 2010 values.

Softwood removals forecast for the three market scenarios 

are based on the CGCM3.1 GCM for scenarios RPA A1B 

and RPA A2 and the CGCM2 GCM for scenario RPA 

B2. The 2060 removals reflect increases over the 2010 

removals level by roughly 3, 29, and 40 percent for the RPA 

B2, A2, and A1B scenarios, respectively and A1B and A2 

removals are substantially larger than forecasts for the nine 

RPA scenario-climate combinations (fig. 95A). Hardwood 

removals, which are a very small portion of total removals in 

the Pacific Coast Region, do not differ substantially between 

the market scenarios nor among the nine RPA scenarioclimate combinations (fig. 95B). Hardwood removals, which 

comprised a small portion of harvests in 2010 (17 percent), 

decline throughout the forecasts. All nine combinations yield 

substantial declines in hardwood removals—2060 removals 

levels amount to about 50 percent of their 2010 values. A 

comparison of forecasted harvest removals under even the A1B 

market scenario would not begin to approach peak harvest 

levels achieved in the early 1990s (fig. 96). 

Figure 97 compares the West’s (Rocky Mountain plus Pacific 

Coast regions) timber harvest forecasts from the Forest 

Products Model with the implied harvests from the Forest 

Dynamics Model, and shows that harvest forecasts are largely 

consistent between the models for the specific scenarios. Forest 

Dynamics Model harvest removals are within 10 percent 

of harvest removals from the Forest Products Model for all 

periods and scenarios, with one exception. As is the case in the 

other regions, the RPA A1B Forest Products Model projection 

is higher than the Forest Dynamics Model projections in the 

last 2 decades of the simulation—by roughly 20 percent in 

the West. Total removals match fairly closely for the entire 

simulation period. The Forest Dynamics Model projects 94, 

Figure 92—Softwood growing stock in the Pacific Coast Region, 1953–2060. Billion cubic feet
83

Forest Forecasts for the Pacific Coast Region

Figure 93—Forecasts of total carbon stored within forests in the Pacific Coast Region, 2010–60, for nine Resources Planning Act (RPA) scenario-climate 

combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products 

Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented 

by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. 

Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM).

101, and 101 percent of total removals forecast by RPA A1B, 

A2, and B2 scenarios, respectively. 

Age Structure/Habitat

Age class distributions are affected by the starting distributions 

and by disturbance regimes forecast for forests in the future. 

Simulated inventories can be used to evaluate various aspects 

of future forest conditions. One aspect of significance to 

wildlife habitat conditions in the Pacific Coast Region is the 

distribution of forests by age class, and the implications for 

the provision of early, middle, and late age forest conditions. 

We evaluate changes in the age class distributions of softwood 

forest types using two charts. One displays the aerial age class 

distribution by decade for the most recent inventories of the 

Pacific Coast Region. We then summarize changes in age class 

resulting from the nine RPA scenario-climate combinations 

between 2010 and 2060 using box plots. Figure 98 shows the 

current age class distribution for hardwood forests in the Pacific 

Coast Region by 20-year age classes. Each box plot describes 

the distributions of changes in age classes. The bottom of the 

box represents the 25th percentile of the predicted changes, the 

top of the box represents the 75th percentile of the predicted 

changes, the line in the box indicates the median forecast, and 

the plus in the box indicates the mean forecast. The “whiskers” 

of the box show the extreme values of the forecasts. 

The 2010 age class distribution of forests in the Pacific Coast 

Region is strongly peaked in the 61–80 age category, reflecting 

harvest history in the region (fig. 98). As harvests and other 

disturbances are applied, 50-year forecasts shift this peak 

out and flatten the age class distribution. The oldest age class 

remains relatively constant in this area, reflecting an offsetting 

of mortality and recruitment and the area of the youngest age 

classes (0–20 and 21–40) remains relatively constant between 

2010 and 2060 across the scenarios.

Million metric tons
84

Forest Forecasts for the Pacific Coast Region

Figure 94—Forecasts of carbon stored in various pools within forests in the Pacific Coast Region, 2010–60, for nine Resources Planning Act (RPA) scenarioclimate combinations with harvesting based on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest 

Products Model, 2010–60. RPA A1B scenarios are represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is 

represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 

by black. Lines with blocks indicate the market scenario for the respective RPA scenario (all using the CGCM GCM). Million metric tons Million metric tons Million metric tons
85

Forest Forecasts for the Pacific Coast Region

Figure 96—Softwood removals from growing stock for nine Resources Planning Act (RPA) scenario-climate combinations with harvesting based on historical 

behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are represented 

by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and CGCM2 for 

B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market scenario for the 

respective RPA scenario (all using the CGCM GCM).

Figure 95—Removals from inventory in the Pacific Coast Region for nine Resources Planning Act (RPA) scenario-climate combinations with harvesting based 

on historical behavior and three RPA scenario-climate combinations with harvesting based on the U.S. Forest Products Model, 2010–60. RPA A1B scenarios are 

represented by solid lines; RPA A2 dashed; RPA B2 dotted. MIROC3.2 general circulation model (GCM) is represented by red; CGCM (CGCM3.2 for A2 and 

CGCM2 for B2) by blue; CSIRO (CSIRO-Mk3.5 for A1B and A2 and CSIRO-Mk2 for B2) by yellow; HadCM3 by black. Lines with blocks indicate the market 

scenario for the respective RPA scenario (all using the CGCM GCM). Million cubic feet

Million cubic feet

Million cubic feet
86

Forest Forecasts for the Pacific Coast Region

Figure 97—Comparison of harvests predicted by U.S. Forest Products Model and the Forest Dynamics Model scenario associated with the U.S. Forest Products 

Model scenario for the Western United States (defined as the combination of the Rocky Mountain and Pacific Coast regions).

Figure 98—(A) Age class distributions for all forests in the Pacific Coast Region, 2010 and 2060, for the B2-HadCM3 scenario and (B) the range of changes in 

forest age classes across all Resources Planning Act (RPA) scenarios.

Million acres

0-20 21-40 41-60 61-80 81-100 101-120 121-140 141-160 161-180

Age class

(B) Range of changes in forest age classes across all RPA scenarios

181-200 201+
87

Forest Forecasts for the Pacific Coast Region

Discussion

Forecasts of forest conditions in the Pacific Coast Region 

indicate a loss of 2–3 percent of forest area, amounting to 

1.7–2.7 million acres between 2010 and 2060, with most of this 

loss concentrated in the highly productive Westside forests of 

Oregon and Washington. This forest loss has implications for 

the standing biomass in the region with a decline in softwood 

and hardwood growing stock volumes between 2010 and 2060. 

Due to land area and growing stock declines overall, the total 

amount of carbon stored in Pacific Coast Region forest is also 

forecast to decline, falling between 4 and 9 percent between 

2010 and 2060.

While forecasts of overall forest area, biomass, and carbon 

attributes change gradually over time, change in forest types 

is more substantial. For the Westside of the Cascades, the area 

of the hemlock-Sitka spruce forest type group is forecast to 

decline substantially (50–85 percent) while the Douglas fir 

forest type group increases. While we caution against overinterpreting forecasts for small forest area (at 4 million acres 

hemlock-Sitka spruce represents about 5 percent of forest area 

in 2010) our forecasts indicate especially important shifts in 

the habitat suitability for this forest type over all the climate 

scenarios considered here. This is consistent with Hansen and 

others’ (2001) finding that habitats for rainforest conifers in the 

Pacific Northwest are likely to decline in area as a response to 

anticipated climate changes.

The dominant forest type dynamic for inland forests in the 

Pacific Coast Region are declines in the subalpine sprucefir-hemlock forest type group (20–60 percent) and a strong 

increase in ponderosa pine forest type group (30–96 percent) 

between 2010 and 2060. Similar patterns of change have been 

forecast by Rehfeldt and others (2006), who simulate loss of 

suitable habitat for Picea engelmannii (47 percent across the 

Rocky Mountain and Pacific Coast regions by 2060), and 

gains in suitable habitat for Pinus ponderosa (11 percent by 

2060). Both are consistent with overall declines in subalpine 

vegetation simulated by Rehfeldt and others (2008) and by 

Bachelet and others (2001) under various climate futures.

While the trends are generally consistent across scenarios, 

the broad range of the forecasts for forest type areas indicates 

that these forecasts are sensitive to climate futures. Futures 

generated using the CGCM3.2 and CGCM2 produced the least 

change across most variables; the HadCM3 and MIROC3.2 

GCMs, with the warmest and driest futures, generally 

produced the most change in key variables from forest type 

areas to carbon storage.
88

Conclusions

In this report, we describe 50-year forecasts of forest conditions 

for several scenarios regarding future demographic, economic, 

and climate conditions. Forecasts define a range of plausible 

futures for forests reflecting the breadth of the Resources 

Planning Act (RPA) scenarios, as interpreted by a set of models 

and the design of the U.S. Forest Assessment System. The 

Forest Dynamics Model defines forecasts that directly simulate 

change in the forest inventories conducted by the Forest 

Inventory and Analysis (FIA) Program of the Forest Service, 

U.S. Department of Agriculture. Forecast variables therefore 

correspond directly to the measures used to monitor the 

Nation’s forest conditions, and establish a means of evaluating 

the performance of forecasts using subsequent inventories. 

In a sense, this defines the Forest Assessment System as 

a “hypothesis generator” regarding the future of forest 

inventories in the United States, with hypotheses defined using 

metrics consistent with the ongoing monitoring system.

The Forest Dynamics Model attempts to draw maximum 

information from the data contained in the FIA system to 

forecast future inventories. Future measured inventories 

should provide additional information for improving the 

various component elements of the model and for validating/

invalidating forecast performance. Additional panels of data for 

the Western United States, where it is not yet possible to match 

plots between successive inventories, should prove especially 

useful, but new inventories will allow the refinement of 

models in all regions, e.g., in the South where data are already 

relatively plentiful but where harvesting is the most frequent 

and land use dynamics are the most rapid in the United States.

Any modeling approach has strengths and weaknesses. In 

the case of the Forest Dynamics Model, use of historical 

plot imputations allows for forecasts of forest conditions that 

preserve the joint distributions of the many plot measures 

contained within the FIA system (Reams and McCullum 2000). 

This coherence depends on the assumption that the constituent 

elements of the future inventory (plots) will be consistent with 

the universe of historical plot observations—for example, that 

a forest plot of forest type X at age Y with average temperature 

Z in the future can be represented by a historical forest plot 

with X, Y, and Z attributes. In effect we assume that “novel” or 

“no-analog” conditions are unlikely to arise over the 50-year 

timeframe evaluated with these models. 

Several potential sources of novelty might be anticipated. 

Climate novelty (Williams and Jackson 2007) occurs where 

future climate conditions are unprecedented within a region. 

While the extent of novelty may be much greater over a 

100-year forecast period, our analysis indicates relatively 

low occurrences of significant shifts in temperature and 

precipitation distributions in the first three decades of the five 

decade period considered by the RPA Assessment. Novelty 

could also occur where the intersection of climate and site 

variables give rise to favorable conditions for an unprecedented 

combination of tree species (Iverson and others 2008, Rehfeldt 

and others 2006). Atmospheric composition defines another 

potential source of novelty, e.g., CO2

 fertilization has enhanced 

tree growth in controlled experiments (Norby and others 2005). 

Our approach to imputing future forest plots by resampling 

from plots of forest type groups allows for shifts in species 

composition both within and between type groups. For 

example, while a plot might retain its assignment to the oak/

hickory type group (500), its resampled type could shift from 

yellow poplar/white oak (506) to white oak/red oak/hickory 

(503) in response to climate changes and forest aging. Still all 

species combinations are restricted to those observed within 

the universe of measured FIA plots contained in the donor 

pool. Iverson and others (2008) in the East and Rehfeldt and 

others (2006) in the West indicate a potential for structural 

shifts in species mixes over a 100-year timeframe with the 

extent of change depending on the severity of the projected 

climate changes. Our decision to retain historical forest type 

associations is fundamental to the use of the imputation 

approach—without it, the species structure of novel plots would 

need to be constructed introducing new sources of forecast 

error—and might be justified based on the 50-year timeframe 

of the forecasts and the inertia of forest ecosystems. The 

degree to which this assumption could be violated is expected 

to be directly proportional to the timeframe of the forecasts. 

Uncertainty regarding the plausibility of predicted forest 

composition increases over time.

The inertia of existing forest ecosystems likewise depends 

on the future of forest disturbance regimes across the regions 

of the United States (Turner 2008). Our modeling approach 

forecasts forest conditions based on disturbance dynamics 

reflected in historical inventories, e.g., a proportion of southern 

pine forests reflect damage from southern pine beetle in 

the South consistent with historical insect dynamics, and 

a proportion of lodgepole pine forests in the West reflects 

historical mountain pine beetle dynamics. In the East and the 

Pacific Coast regions, we explicitly forecast timber harvesting 

but leave other disturbance activities as “background” 

disturbances—not explicitly modeled but influencing forest 

conditions through donor pools. In the Rocky Mountains 

Region, we explicitly model probabilities of fire and insect 

Conclusions
89

Conclusions/Acknowledgments/Literature Cited

damage (as measured on the FIA plots and as a function of 

climate and site variables) and then the probability of a stand 

replacement contingent on these events. Unprecedented 

disturbance vectors, for example those generated by new 

nonnative pests, have not been addressed. Incorporating their 

effects defines a potentially useful line of future inquiry, e.g., 

in ongoing work, we are addressing the spread of emerald ash 

borer in the North Region.

Carbon accounting allows for a comprehensive evaluation 

of forest carbon pools consistent with inventory measures 

of carbon and potentially policy relevant information from 

the forecasts. This accounts for the temporal dynamics of 

forests and how they may be influenced by human uses, 

disturbances, and climate-driven productivity changes. Our 

approach is to monitor net carbon change consistent with all 

of these dynamics. Our findings highlight different carbon 

trajectories in the various regions of the United States. In the 

South, carbon stocks reach a peak and then either level off 

or decline somewhat depending on the scenario. In general, 

losses due to removals are offset by strong growth and 

management responses. In western regions, where removals 

are small portions of standing inventories, carbon stocks are 

flat to declining as forests reach ages where mortality begins 

to outweigh removals. The greatest potential decrease in 

carbon forecast is in the North where, under RPA scenario 

A1B, removals could increase substantially but countervailing 

investment in forest management is not simulated. This 

type of unprecedented demand futures would likely lead to 

unprecedented management responses in the North, but this 

was not addressed by the models. Nationwide, the greatest 

influence on future carbon is forecasted change in forest area 

related to urbanization. Some portion of the projected loss of 

carbon from forest pools would therefore be transferred to 

carbon stored in other land uses, e.g., suburban or crop uses, 

and would not be emitted to the atmosphere. Ongoing analysis 

addresses these carbon transfers among land uses.

The Forest Assessment System is designed to support strategic 

analysis of forest conditions in the United States as a part of 

the RPA assessment and in support of various forest futuring 

efforts. The results presented in this paper describe the first 

implementation of this modeling system and we anticipate 

refining the methods and updating models as new data become 

available from FIA. Future work on the Forest Dynamics 

Model will focus on refining the algorithms used to transition 

forest plots through time, including forest type transitions 

and forest harvesting. The latter is an essential element of 

the linkage between the Forest Dynamics Model and Forest 

Products Model. As currently formulated, the model allows 

for economic modeling of harvest choices on plots only for 

the South, where data are the most plentiful. In other regions, 

we rely on aggregate supply equations to simulate markets 

in the Forest Products Model and downscale harvests in the 

Forest Dynamics Model to plots by adjusting relative harvest 

probabilities for various plot types. Regional differences 

among forest type transition models reflect differences in 

data availability. Future models might further consider the 

timeframe of potential transitions and differentiate models to 

ask short-run and long-run questions.

Additional work is needed to more precisely address the 

potential for and implications of novel conditions and to explore 

methods for examining unprecedented forest conditions in 

the future. In addition to exploring potential climate novelty, 

models might examine the influence of highly invasive pest 

species, e.g., emerald ash borer, expanded forest management 

(especially in the North Region), and genetically modified 

production forests (especially in the South). All elements of 

the modeling should be improved as additional panels of data 

are provided by the ongoing FIA forest inventories across the 

United States.

ACKNOWLEDGMENTS

The authors acknowledge helpful reviews and technical advice 

from Robert C. Abt, North Carolina State University and Linda 

Joyce, U.S. Department of Agriculture Forest Service, Rocky 

Mountain Research Station.

LITERATURE CITED

Alig, R.J.; Zheng, D.; Spies, T.A.; Butler, B.J. 2000. Forest cover dynamics 

in the Pacific Northwest west side: regional trends and projections. Res. 

Pap. PNW-RP-522. Portland, OR: U.S. Department of Agriculture Forest 

Service, Pacific Northwest Research Station. 22 p.

Bachelet, R.R.; Neilson, P.; Lenihan, J.M.; Drapek, R.J. 2001. Climate 

change effects on vegetation distribution and carbon budget in the United 

States. Ecosystems. 4(1): 161-185.

Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. 1984. 

Classification and regression trees. Belmont, CA: Wadsworth International 

Group. 358 p.

Buongiorno, J.; Zhu, S.; Runikar, R.; Prestemon, J. 2012. Outlook to 2060 

for world forests and forest industries: a technical document supporting the 

Forest Service 2010 RPA Assessment. Gen. Tech. Rep. SRS-151. Asheville, 

NC: U.S. Department of Agriculture Forest Service, Southern Research 

Station. 119 p.

Buongiorno, J.; Zhu, S.; Zhang, D. [and others]. 2003. The Global Forest 

Products Model: structure, estimation, and applications. San Diego: 

Academic Press/Elsevier. 301 p..

Butler, B.; Wear, D.N. [In press]. Forest ownership. In: Wear, D.N.; Greis, 

J.G., eds. The southern forest futures project: technical report. Gen. Tech. 

Rep. Asheville, NC: U.S. Department of Agriculture Forest Service, 

Southern Research Station.
90

Literature Cited

Cortes, C.; Vapnik, C. 1995. Support-vector networks. Machine Learning. 

20: 273-297.

Coulson, D.P.; Joyce, L.A.; Price, D.T. [and others]. 2010a. 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://www.fs.fed.us/rm/data_archive/dataaccess/

US_ClimateScenarios_county_A1B_A2_PRISM.shtml. [Date accessed: 

August 3, 2010].

Coulson, D.P.; Joyce, L.A.; Price, D.T.; McKenney, D.W. 2010b. 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://www.fs.fed.us/rm/data_archive/dataaccess/

US_ClimateScenarios_county_B2_PRISM.shtml. [Date accessed: August 

3, 2010].

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://www.fs.fed.us/rm/

data_archive/dataaccess/US_HistClimateScenarios_county_PRISM.shtml. 

[Date accessed: August 2, 2010].

De’ath, G.; Fabricius, K.E. 2000. Classification and regression trees: a 

powerful yet simple technique for ecological data analysis. Ecology. 81(11): 

3178-3192.

Ford, B.L. 1983. An overview of hot-deck procedures. In: Madow, W.G.; 

Olkin, I.; Rubin, D.B., eds. Incomplete Data in Sample Surveys vol. 2, Part 

IV. New York: Academic Press Inc: 185-207.

Gosz, J.R.; Sharpe, P.J.H. 1989. Broad scale concepts for interactions of 

climate, topography, and biota at biome transitions. Landscape Ecology. 

3(3/4): 229-243.

Hansen, A.J.; Nielson, R.P.; Dale, V.H. [and others]. Global change in 

forests: responses of species, communities, and biomes. BioScience. 51(9): 

765-779.

Haynes, R.W.; Adams, D.M. 1985. Simulations of the effects of alternative 

assumptions on demand-supply determinants on the timber situation in the 

United States. Washington, DC: U.S. Department of Agriculture Forest 

Service, Forest Resources Economics Research. 113 p.

Haynes, R.W.; Adams, D.M.; Alig, R.J. [and others]. 2007. The 2005 RPA 

timber assessment update. Gen. Tech. Rep. PNW-GTR-699. Portland, OR: 

U.S. Department of Agriculture Forest Service, Pacific Northwest Research 

Station. 212 p.

Haynes, R.W., coord. 1990. An analysis of the timber situation in the United 

States 1989–2040. Gen. Tech. Rep. PNW-GTR-199. Fort Collins, CO: U.S. 

Department of Agriculture Forest Service, Rocky Mountain Forest and 

Range Experiment Station. 286 p.

Haynes, R.W.; Adams, D.M.; Mills, J.R. 1995. The 1993 RPA timber 

assessment update. Gen. Tech. Rep. RM-GTR-259. Fort Collins, CO: U.S. 

Department of Agriculture Forest Service, Rocky Mountain Forest and 

Range Experiment Station. 66 p.

Ince, P.J.; Kramp, A.D.; Skog, K.E. [and others]. 2011. U.S. Forest 

Products Module for RPA forest assessment. Research Paper FPL-RP-662. 

Asheville, NC: U.S. Department of Agriculture Forest Service, Southern 

Research Station. 61 p.

Intergovernmental Panel on Climate Change (IPCC). 2007a. Climate 

change 2007, the fourth IPCC assessment report. http://www.ipcc.ch/

ipccreports/index.htm. [Date accessed: December 2008].

IPCC. 2007b. General guidelines on the use of scenario data for climate 

impact and adaptation assessment. http://www.ipcc-data.org/guidelines/

TGICA_guidance_sdciaa_v2_final.pdf. [Date accessed: January 27, 2010].

Iverson, L.R.; Prasad, A.M. 1998. Predicting abundance of 80 tree species 

following climate change in the Eastern United States. Ecological 

Monographs. 68(4): 465-485.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, M. 2008. Estimating 

potential habitat for 134 Eastern U.S. tree species under six climate 

scenarios. Forest Ecology and Management. 254: 390-406.

Joyce, L.A.; Price, D.T.; McKenney, D.W. [and others]. 2011. High 

resolution interpolation of climate scenarios for the conterminous United 

States and Alaska derived from general circulation model simulations. 

Gen. Tech. Rep. RMRS-GTR-263. Fort Collins, CO: U.S. Department of 

Agriculture Forest Service, Rocky Mountain Research Station. 87 p.

Joyce, L.A.; Price, D.T.; Coulson, D.P. [and others]. [In press]. Projecting 

climate change in the United States: a technical document supporting the 

Forest Service RPA 2010 assessment. Gen. Tech. Rep. Fort Collins, CO: 

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

Station. 

McKeand, S.; Mullin, T.; Byram, T.; White, T. 2003. Deployment of 

genetically improved loblolly and slash pines in the South. Journal of 

Forestry. 101(3): 32-37.

Miles, P.D.; Brand, G.J.; Alerich, C.L. [ and others]. 2001. The Forest 

Inventory and Analysis database: database description and users manual 

version 1.0. Gen. Tech. Rep. NC-218. St. Paul, MN: U.S. Department of 

Agriculture Forest Service, North Central Research Station. 

Mills, J.R.; Kincaid, J.C. 1992. The aggregate timberland assessment 

system—ATLAS: a comprehensive timber projection model. Gen. Tech. 

Rep. PNW-GTR-281. Portland, OR: U.S. Department of Agriculture Forest 

Service, Pacific Northwest Research Station. 160 p.

Mills, J.R.; Zhou, X. 2003. Projecting national forest inventories for the 2000 

RPA timber assessment. Gen Tech. Rep. PNW-GTR-568. Portland OR: 

U.S. Department of Agriculture Forest Service, Pacific Northwest Research 

Station. 58 p.

Moisen, G.G.; Frescino, T.S. 2002. Comparing five modeling techniques for 

predicting forest characteristics. Ecological Modeling. 157: 209-225.

Nakicenovic, N.; Swart, R., eds. 2000. Special report on emissions 

Scenarios. A special report of working group iii of the Intergovernmental 

Panel on Climate Change. Cambridge, United Kingdom, and New York, 

NY, USA. Cambridge University Press. 599 p. http://www.ipcc.ch/

ipccreports/sres/emission/index.htm [Date accessed: May 22, 2012].

NIST/SEMATECH. 2012. e-Handbook of Statistical Methods. http://www.

itl.nist.gov/div898/handbook/. [Date accessed: May 22, 2012].

Norby, R.J.; DeLucia, E.H.; Gielen, B. [and others]. 2005. Forest response 

to elevated CO2

 is conserved across a broad range of productivity. 

Proceedings of the National Academy of Sciences. 102: 18,052–18,056.

Penman, J.; Gytarsky, M.; Hiraishi, T. [and others], eds. 2003. Good 

practice guidance for land use, land use change, and forestry. Hayama, 

Kanagawa, Japan: Institute for Global Environmental Strategies for the 

Intergovernmental Panel on Climate Change. 502 p. http://www.ipcc-nggip.

iges.or.jp/public/gpglulucf/gpglulucf.htm. [Date accessed: April 13, 2007].

Polyakov, M.; Wear, D.N.; Huggett, R. 2010. Harvest choice and timber 

supply models for forest forecasting. Forest Science. 56(4): 344-355.

Press, W.H.; Flannery, B.P.; Teukolsky, S.A.; Vetterling, W.T. 1987. 

Numerical recipes: the art of scientific computing. New York: Cambridge 

University Press. 818 p.

Price, D.T.; McKenney, D.W.; Papadopol, P. [and others]. 2004. High 

resolution future scenario climate data for North America. In: Proceedings 

of the 26th Conference on Agricultural and Forest Meteorology. Boston, 

MA: American Meteorological Society: 1-13. Vol. 7.7.
91

Literature Cited

Reams, G.A.; McCollum, J.M. 2000. The use of multiple imputation in 

the southern annual forest inventory system. In: Hansen, M.H.; Burk, T., 

eds. Integrated tools for natural resource inventories in the 21st century: 

an international conference on the inventory and monitoring of forested 

ecosystems. Gen. Tech. Rep. NC-212. Boise, ID: U.S. Department of 

Agriculture Forest Service, North Central Research Station: 228-233.

Rehfeldt, G.E.; Crookston, N.L.; Warwell, M.V.; Evans, J.S. 2006. 

Empirical analyses of plant-climate relationships for the Western United 

States. International Journal of Plant Science. 167(6): 1123-1150.

Rehfeldt, G.E.; Ferguson, D.E.; Crookston, N.L. 2008. Quantifying the 

abundance of co-occurring conifers along Inland Northwest (USA) climate 

gradients. Ecology. 89(8): 2127-2139.

Shafer, S.L.; Bartlein, P.J.; Thompson, R.S. 2001. Potential changes in the 

distributions of western North America tree and shrub taxa under future 

climate scenarios. Ecosystems. 4(1): 200-215.

Smith, J.E.; Heath, L.S.; Nichols, M.C. 2007. U.S. forest carbon calculation 

tool: forest-land carbon stocks and net annual stock change. Gen Tech. Rep. 

NRS-13. Newtown Square, PA: U.S. Department of Agriculture Forest 

Service, Northern Research Station. 28 p.

Smith, W.B., tech. coord.; Miles, P.D., data coord.; Perry, C.H., map 

coord.; Pugh, S.A., data CD coord. 2009. Forest Resources of the United 

States, 2007. Gen. Tech. Rep. WO-78. Washington, DC: U.S. Department 

of Agriculture Forest Service, Washington Office. 336 p.

Turner, M.G. 2008. Disturbance and landscape dynamics in a changing 

world. Ecology. 91(10): 2833-2849.

U.S. Department of Agriculture (USDA) Forest Service. 1920. Timber 

depletion, lumber prices, lumber exports, and concentration of timber 

ownership. Report on Senate Res. 311, 66th Congress, 2d session. [The 

Caper Report]. Washington, DC: U.S. Government Printing Office. 71 p.

USDA Forest Service. 1933. A national plan for American forestry. S. Doc. 

12, 73rd Congress, 1st session. [The Copeland Report.]. 2 vols. Washington, 

DC: U.S. Government Printing Office. 1677 p.

USDA Forest Service. 1958. Timber resources for America’s future. [The 

Timber Resources Review Report.] For. Resour. Rep. 14. Washington, DC: 

U.S. Government Printing Office. 713 p.

USDA Forest Service. 1965. Timber trends in the United States. For. Resour. 

Rep. 17. Washington, DC: U.S. Government Printing Office. 235 p.

USDA Forest Service. 1974. The outlook for timber in the United States. For. 

Resour. Rep. 20. Washington, DC: U.S. Government Printing Office. 367 p.

USDA Forest Service. 1982. An analysis of the timber situation in the United 

States: 1952-2030. For. Resour. Rep. 23. Washington, DC: U.S. Government 

Printing Office. 499 p.

USDA Forest Service. 2001. 2000 RPA assessment of forest and range lands. 

FS-687. Washington DC: U.S. Department of Agriculture, Forest Service. 

78 p.

USDA Forest Service. 2005. Projections of timber harvest in western 

Oregon and Washington by county, owner, forest type, and age class. Gen.

Tech. Rep. PNW-GTR-633. Portland, OR: U.S. Department of Agriculture 

Forest Service, Pacific Northwest Research Station. 30 p. 

USDA Forest Service. [In press]. Future scenarios: a technical document 

supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 

USDA Natural Resources Conservation Service. 2000. Summary report 

1997 National Resources Inventory (revised December 2000). Ames, IA: 

Iowa State University, Statistical Laboratory. 94 p. http://www.nrcs.usda.

gov/Internet/FSE_DOCUMENTS/nrcs143_012094.pdf. [Date accessed: 

October 13, 2011].

Van Deusen, P.C. 1997. Annual forest inventory statistical concepts with 

emphasis on multiple imputation. Canadian Journal of Forest Research. 27: 

379-384. 

Van Deusen, P.C. 2006. Projecting biomass in Georgia with inventory plot 

matching and harvest scheduling. Canadian Journal of Forest Research. 99: 

1-12.

Vokoun, M.; Wear, D.N.; Abt, R.C. 2009. Testing for change in structural 

elements of forest inventories. Forest Science. 55(5): 455-466.

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.

Wear, D.N.; Greis, J.G., eds. [In press]. The southern forest futures project: 

technical report. Gen. Tech. Rep. Asheville, NC: U.S. Department of 

Agriculture Forest Service, Southern Research Station.

Williams, J.W.; Jackson, S.T. 2007. Novel climates, no-analog communities, 

and ecological surprises. Frontiers in Ecology and the Environment. 5(9): 

475-482.

Woods and Poole Economics. 2007. Complete Economic Data Source 

(CEDDS) technical documentation. Washington, DC: Woods and Poole 

Economics, Inc. 101 p.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M. [and others]. 2010. 

The forest inventory and analysis database: database description and 

user’s manual version 4.0 for phase 2. Gen. Tech. Rep. RMS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture Forest Service, Rocky 

Mountain Research Station. 336 p.

Zarnoch, S.J.; Cordell, H.K.; Betz, C.J.; Langner, L. 2010. Projecting 

county-level populations under three future scenarios: a technical 

document supporting the Forest Service 2010 RPA assessment. e-Gen. 

Tech. Rep. SRS–128. Asheville, NC: U.S. Department of Agriculture Forest 

Service, Southern Research Station. 8 p.
92

Appendix: Structure of the Forest Dynamics Model

This report focuses on the forecasts of forest conditions 

provided by the Forest Dynamics Model (fig. A1). Discussion 

of methods concentrates on this component of the U.S. Forest 

Assessment System (fig. 1), while the U.S. Forest Products 

Model is described in Ince and others (2011) and the All Land 

Use Model is described in Wear (2011).

Within the Forest Dynamics Model, forecasts of the development 

of forest conditions in the United States are constructed for each 

observed forest plot in the inventories.1 This is the constituent 

scale of the inventory of the Forest Inventory and Analysis (FIA) 

Program of the Forest Service, U.S. Department of Agriculture, 

and forecasting at this level allows for re-aggregation of findings 

at different scales of resolution, though not at levels finer than 

appropriate for reporting historical inventory data. For example, 

forecasts described in this report have also been used as a 

foundation of the Southern Forest Futures Project (Wear and 

Greis, in press) and the Northern Forest Futures Project (http://

nrs.fs.fed.us/futures/). To the extent possible, simulations start 

with direct evaluation of FIA plot data and results are expanded 

to broader scales using the area frame design of the forest survey. 

These methods generate full inventory datasets for each time 

step of the forecast.

To model and forecast changes in forest conditions, the U.S. 

Forest Assessment System forecasts the condition of each 

plot in the forest inventory in response to multiple vectors of 

change. Each plot record contains a set of measured variables 

combined with an expansion factor which describe the area 

(portion of the sampled population of forests) that each plot 

represents. Expansion factors are reconciled at the county level 

in the FIA system. The information set for each plot I(p) is 

defined as:

I (p)={S, Z, C, E} (1)

where 

S represents a vector of state variables

Z = a vector of condition variables

C = a vector of climate variables

E = a vector of expansion factors for the plot 

S and Z together comprise the measured variables for each 

plot from phase 2 (field measurements) of the FIA inventory 

1 The use of the term “plot” in this report refers to what is technically called a 

“condition” within a plot in the FIA system. Where plots can have more than 

one forest condition, e.g., they occur on a boundary between forest types, 

then multiple conditions are recorded for each plot. Use of the word “plot” 

simplifies the language in this report without loss of precision.

while climate variables are derived from ancillary datasets and 

E is derived from phase 1 (aerial measurements) of the FIA 

inventory. Our definition of state and conditional variables 

facilitates descriptions of modeling approaches.

Forecasting plot conditions starts with projecting changes in 

the state variables (S) using transition models. For example, the 

future age of a forest plot is determined by adding the time step 

of the simulation to the current age unless a forest disturbance 

causes the age to be reset to another age (typically to zero). Once 

the forecasted state variables have been established, then a plot 

record is assigned based on an imputation or resampling of the 

historical database of plots with comparable values for the state 

variables. Changes in the expansion factors (E) for each plot are 

Appendix: Structure of the Forest Dynamics Model

Figure A1—Flow chart of the Forest Dynamics Model’s approach to projected 

forest inventories.
93

Appendix: Structure of the Forest Dynamics Model

determined by a land use model that forecasts urbanization and 

other land use choices driven by the population and economic 

variables contained in the RPA scenarios (Wear 2011).

Key decisions in developing the projection models involve 

choosing which variables are state variables, how to project 

changes in state variables, and the structure of the imputation 

algorithm. These choices are based in part on the frequency of 

FIA inventories which varies among the regions of the country. 

In all regions however, the basic structure of the modeling 

approach is defined by three modules. Changes in key state 

variables for each forest plot are forecasted in response to 

projected forest aging, climate conditions, and human use 

choices (the transition module). After forecasting the state 

variables, a historical plot record with comparable conditions is 

selected to represent the simulated plot in the future inventory 

(the imputation module). The modeling starts with an algorithm 

that clusters similar plots according to a set of independent 

variables (the partitioning module). These partitions of plots 

define the groupings of plots for the imputation module and 

also define which variables are the state variables that need to 

be forecasted within the transition module. 

In most cases, models are developed on a State-by-State 

basis because of disparities in inventory dates. In some 

cases, multiple adjacent States are modeled together either 

because a concurrence of inventory dates allowed for pooling 

of dates—thereby enhancing the information content of the 

resulting models—or because a State was too small to support 

model estimation. For example, we pooled southeastern States 

(Virginia, North Carolina, South Carolina, and Georgia) and 

aggregated the Delaware data with adjacent survey units from 

Pennsylvania.

The Partitioning Module

The objective of the partitioning model is to define aggregates 

of plots that share common distributions of variables of interest, 

in this case the biomass contained on each plot (softwood 

and hardwood growing stock or live tree volumes). Plots are 

clustered according to several state variables and a clustering 

rule provides a compact description of the relationship 

between the state variables and the biomass distributions. 

Applying the clustering rule to an inventory places each plot 

into a partition with a similar joint distribution of biomass 

variables. Multivariate regression trees are used to generate 

the partitioning model, i.e., to sort plots into clusters for the 

imputation module. A detailed description of the partitioning 

approach can be found in Vokoun and others (2009).

Regression tree analysis is a nonparametric method for 

describing the relationship between independent state variables 

and the dependent variables (Breiman and others 1984). 

Univariate regression tree techniques split a dataset, referred to 

as the root node, into a set of nodes (or bins) where the values 

of a single index or response variable are similar (De’ath and 

Fabricus 2000). Variation of the dependent variable within 

each node (called impurity) is measured by the sum of squared 

differences from the node mean. The technique begins by 

splitting the root node into two subsets, or daughter nodes, 

based on the value of a state variable. The best split is defined 

by the partition of a single state variable that minimizes the 

total of the two sums of squares across the daughter nodes or 

equivalently, maximizes the reduction in the sum of squares in 

the aggregate dataset (De’ath and Fabricus 2000). Subsequent 

splits are performed until a stopping criterion is met, usually a 

minimum threshold for reductions in the sums of squares. The 

terminal nodes have the least impurity or variation about the 

mean of the index variable. The partitions based on values of 

the state variables define the clustering rule for a tree. 

Univariate or multivariate regression trees extend the univariate 

case by allowing more than one index or response variable. 

Node impurity in the multivariate case is the total of the sum of 

squared differences from the node mean across all dependent 

variables (De’ath and Fabricus 2000). Splitting is accomplished 

by choosing the partitions in the state variables that maximize 

the reduction in the multivariate sum of squares. We included 

various specifications of climate variables and FIA plot attribute 

variables as candidates for constructing the regression trees.

The R statistical software package with the MVPART 

(MultiVariate PARTitioning) package was used to generate 

state-level multivariate regression trees and the corresponding 

clustering rules to describe the relationships between the 

state variables and dependent variables for FIA plots. The 

regression tree algorithm sorts among several plot level 

variables and climate variables in determining the final tree. 

For each of the modeled States or State aggregates, we generate 

a multivariate regression tree for each of several forest type 

groups. Accordingly, the forest type is one state variable for 

our partitioning of plots into clusters. Other state variables are 

defined by the splits in each regression tree which vary by State 

and forest type, and include forest age, various climate variables, 

and site attributes such as ownership, aspect, and slope.

The specification of candidate variables for constructing 

regression trees greatly influences the outcome. For measured 

plot records, average values of variables were applied, e.g., 

average age, average diameter. For climate records, multiple 

formulations and variables were possible. We studied Iverson 

and Prasad’s (1998) classification trees of individual tree 

species for guidance on useful climate variables, e.g., average 

temperatures in January and July, average annual precipitation, 

and potential evapotranspiration. In addition, we experimented 

with various approaches to averaging the climate data and 

settled on different approaches for the East and the West based 
94

Appendix: Structure of the Forest Dynamics Model

on the fit of these models. In the East, climate variables were 

averaged over the life of a forest—i.e., a 25-year-old plot would 

have climate conditions averaged over the previous 25 years—

for the West, climate variables were averaged over a fixed time 

period—generally 20 years—for constructing the regression 

trees. In all cases, a large number of variables were provided 

for regression tree construction and the algorithm selected 

state variables for partitioning the FIA plots. Table A1 shows 

the number of regression trees in each region that contained 

specific climate variables.

The Transition Module

Regression trees separate inventory plots into partitions based 

on two types of state variables: (1) time invariant variables 

associated with plot conditions, for example slope or aspect, 

and (2) those that may change over time, for example forest 

age or climate. Forecasted changes in the latter variables 

predict changes in the plot cluster each plot location would be 

associated with in the future. Time variant variables are either 

exogenous climate variables—projected by general circulation 

models for the modeled scenarios (Joyce and others, in press)—

or are driven by human choices and plot dynamics which 

we model with a set of transition probabilities or rules. We 

develop models to yield three interlinked transitions: (1) forest 

harvesting, (2) forest aging, and (3) forest type changes.

Harvesting

Conditional harvest probabilities simulate the harvesting 

of plots in the inventory in response to changes in forest 

conditions and economic factors. The specification of these 

models varies between the regions and most importantly 

depends on the frequency of inventory measurements. For 

example, in the South Region, where the inventory interval 

is shortest, models derive from an explicit economic choice 

model that accounts for the effects of timber price levels on the 

likelihood of harvesting forest plots. In the North Region, the 

interval between forest inventories is longer and while harvest 

models show significant relationships between harvesting and 

biophysical attributes of the plot, timber prices did not yield 

significant effects. In the Western United States, the inventories 

are too infrequent to deduce harvesting relationships between 

matched inventories, so the distribution of harvest activity 

was inferred from a single inventory coupled with ancillary 

harvesting data. Resulting empirical models were calibrated to 

reflect historical patterns of timber removals.

In all cases, harvest probabilities can be adjusted to simulate 

future market outcomes. In the South, harvest probabilities are 

explicit functions of timber prices. In the other regions of the 

country, where harvest probabilities are not explicitly linked to 

prices, regional harvest levels can be shifted up or down using 

a scalar adjustment factor applied to the harvest probabilities. 

Market simulations for the U.S. Forest Assessment System, using 

the Forest Products Model, are based on information regarding 

Table A1—Occurrence of averaged climate variables in regression trees for 

each of the Resources Planning Act (RPA) regions (the Rocky Mountains 

Region is separated into its Great Plains [Plains] and Mountain State [RM] 

components)

RPA Region

Climate variable South North Plains RM Pacific Coast

number of regression trees

Annual average temperature 4 8 3 2 5

Average temperature in January 5 6 1 2 2

Average temperature in July 5 3 1 1 3

Average temperature May-September 2 2 1 0 1

Total annual precipitation 3 8 1 5 5

Average potential evapotranspiration 5 4 0 3 2

Average aridity July-August 4 7 1 3 4
95

Appendix: Structure of the Forest Dynamics Model

timber supply derived from the Forest Dynamics Model for the 

South and from aggregate supply models for the North and West. 

Because forest inventory dynamics alter supply futures, the 

Forest Products Model (with aggregate supply models) and the 

Forest Dynamics Model (with plot level harvest choice models) 

need to be reconciled.

In the South, harvesting models have the following general 

form and are described in detail in Polyakov and others (2010):

Pr (Harvest) = f {BH, BD} (2)

Harvest probabilities are modeled as a function of the 

differences between the marginal benefits of delaying harvest 

(BD) and the marginal benefits of harvesting (BH). These 

benefits can accrue not only to timber revenues but also to 

nonmonetary returns to holding the forest. Conditional logit 

models define the probability of observing harvests of two 

intensities (partial harvest and complete harvest) as functions 

of revenue and forest attribute variables which could proxy 

for nonmarket returns as well as site-specific costs. The logit 

harvest model has the following form:

Pr (havest | p, q) = ———————— (3)

where 

h = one of three harvest options: (1) no harvest, (2) partial 

harvest, and (3) complete harvest 

μ proxies for the difference in returns to harvesting and 

not harvesting as a function of revenue and forest attribute 

variables (p and q respectively) for each harvest option (h). 

Harvest simulations therefore depend on conditions of the the 

plot and timber prices which together define the revenue terms 

of equation 3.

Harvest choice models described by equation 3 were developed 

for each of the States in the South.2

 Separate harvest models 

were estimated for the five broad forest management types 

(planted pine, natural pine, mixed pine-hardwood, upland 

hardwood, and lowland hardwood) and for public and private 

ownership groups. Ten States or State aggregates, five forest 

management types, and two ownership groups define 100 

separate harvest choice models.

Models similar to those estimated for the South were attempted 

in the North, but estimation results did not support their use. 

Correlations between harvest probability and revenue variables 

were generally inconsistent and lacked significance, perhaps 

reflecting the large variety of hardwood products harvested 

2 In some cases, small sample sizes precluded estimating separate models for a 

State. For example, we applied the harvest choice model for Tennessee to FIA 

plots in Kentucky and used Arkansas models for harvest choices in Oklahoma.

from forest stands and less frequent FIA records in this region. 

An alternative, two-step empirical harvest probability model 

was estimated for each forest type in each State of the North. 

In the first step, harvest probability was modeled as a function 

of a set of biophysical attributes in the inventory, including 

sawtimber volumes observed at the beginning and predicted 

for the end of the period, stand age, slope, ownership class, 

average stand diameter, and an index that gauges the diversity 

of the tree species on the plot. In the second step, harvests 

were defined as full or partial based on the frequency of these 

harvest types observed for the paired inventories for each Stateforest type permutation.

Models for the North can apply observed harvest patterns from 

the historical period (generally, the mid-1990s to the mid2000s) to future forest inventories to project future harvests. 

Alternatively, aggregate timber harvests can be adjusted up or 

down using the following equation:

Pr (Harvest) = δ f {Z} (4)

where

d = a scaling parameter used to increase or decrease harvest

f(Z) defines the harvest probability as a function of the set of 

attributes (Z) that are described above. 

Adjusted harvest levels therefore preserve the ordering of 

harvest probability across plot attributes observed in inventory 

records while allowing simulations of increasing or decreasing 

harvest levels. We use this approach to describe the effects of 

market futures on forest conditions.

In the West, inventories and especially harvests are even 

less frequent than in the North and harvests are modeled in 

different fashion with separate modeling approaches applied 

in the Rocky Mountain and the Pacific Coast regions. For 

the Rocky Mountains Region, instead of modeling harvests 

separately, we model the probability of a stand replacement 

event (indicated by a plot with forest age of between 0 and 10 

years). In this region, plot records indicate harvests of very 

low frequency—too low to estimate with empirical models. 

Stand replacement is therefore modeled using sequential logit 

models that (1) estimate the probability of fire occurrence, (2) 

estimate the probability of insect occurrence, and (3) estimate 

the probability of stand replacement as a function of forest 

type, forest age, and the insect and fire occurrences. Stand 

replacement therefore accounts for harvest effects but is more 

strongly influenced by other types of forest disturbances.

In the Pacific Northwest, especially on the west side of the 

Cascade Mountains, timber harvesting dominates forest 

disturbance but the infrequency of FIA inventories precluded 

estimating harvest probabilities using paired inventory 

exp (μt (h, p, q))

∑ exp (μt (k, p, q)) k∈H
96

Appendix: Structure of the Forest Dynamics Model

observations. Instead we developed a set of net harvest 

probabilities that accounted for natural disturbance and 

reproduced historical proportions of partial and full harvests. 

Removal intensities were adjusted to simulate historical 

removal totals. We used inventory records from reserved public 

land to infer the mortality rates for forests associated with 

natural disturbances in all forests—assuming that plots aged 

0–10 had experienced mortality in the previous 10 years. 

mj = ——————— (5)

where

F≤ 10, R, j = the area of forest from age 0 to 10 years in reserved 

areas

F> 10, R, j = the area of reserved forest with age > 10 years

mj = the derived mortality rate for forest type j.

The area of clearcutting was estimated as the area of forests 

with ages of 0–10 years that was not explained by the 

background mortality rates. The literature on forest harvesting 

in the Pacific Northwest (Alig and others 2000, Zhou and 

others 2005) suggests that private landowners on the westside 

of the Cascades typically conduct final harvests at between 45 

and 65 years for both Douglas fir and hemlock forest types. We 

used the estimated clearcut area along with the pool of eligible 

acres—defined for example, for Douglas fir, as forests in the 

40–80 year age range plus the harvested acres—to estimate the 

previous period’s rates of final harvest by ownership and forest 

type for the eligible age class. Separate models were developed 

for the east and west sides of the Cascades to account for 

differences in forest conditions.

Pr (clearcut|owner i, forest type = j)= ———————— (6)

where

Feligible, NR, i, j = the area of nonreserved forest of ownership i and 

forest type j eligible for clearcuting (defined as 40–80 years old 

for Douglas fir) and other variables are defined above.

For public forests, this approach generated little evidence of standreplacing harvests over the last decade, consistent with Forest 

Service management reports. As a result, nearly all harvesting on 

this ownership is expected to be from partial cutting. 

Ancillary data on the proportion of harvested area accounted 

for by partial cutting and clearcutting (State of Oregon) 

was then used to assign an acreage estimate for the area of 

partially harvested plots for both west and east sides. As a 

final step, removal rates for partial cuts and clearcuts (as a 

portion of total growing stock) were used to estimate total 

harvest removals, and removal rates were adjusted to simulate 

historical removal levels for the region [based on Smith and 

others (2009) and Haynes and others (2007)]. This algorithm 

provides sets of harvest probabilities for clearcutting and 

partial harvesting by major forest type groups, ownership 

types, and age classes. The resulting harvest probabilities 

can be scaled or adjusted up or down to reflect increasing or 

decreasing harvest levels in the region as predicted by market 

forecasts for various scenarios from the Forest Products 

Model (Ince and others 2011).

Aging

The second stage of the transition module addresses changes 

in the attributes of unharvested plots. In addition to several 

climate variables, the partitioning module identified forest age 

as an important state variable. We predict change in the average 

forest age (Age) either using a fixed time step or a regression 

equation where

Aget+1 = f {Aget , FT} (7)

Within the inventory framework, the relationship between 

age and the time step of the simulation may not always be 

additive. Because forests are often collections of trees of 

multiple ages, plot level age is based on the average age of 

individual trees measured for the plot, and the rate of age 

change may vary with the age of the plot. For example, in 

a case where older cohorts of trees are replaced by younger 

trees in a multi-aged plot, the rate of aging for the overall 

plot could decline with time, suggesting an approach toward 

an age asymptote or even an eventual decline in age at some 

point in forest development due to mortality.

Forest Type Change

Forest type transition models vary between regions and model 

choice reflects data availability and model performance. For 

the South, models use a combination of rules and empirical 

probabilities to forecast forest type changes. In the North, 

empirical transition frequencies based on matched inventories 

define conditional transition probabilities. For the West, where 

only one useable forest inventory was available and forest type 

transitions could not be explicitly modeled, a classification 

approach was applied where the probability of observing each 

forest type was modeled as a function of plot and climate 

variables. Each approach is described in turn below. Our 

definition of forest type for modeling is technically the forest type 

group or aggregates of these groups defined by FIA (table A2).

For the South, after experimenting with a variety of forest 

type transition models we adopted a combination of rules and 

F≤ 10, R, j

F≤ 10, R, j + F> 10, R, j

F≤ 10, NR, i, j (1-mj)

Feligible, NR, i, j +F≤ 10, NR, i, j
97

Appendix: Structure of the Forest Dynamics Model

Table A2—Forest type groups as defined in the database of the Forest Inventory and Analysis (FIA) Program of the Forest 

Service, U.S. Department of Agriculture

100 White / red / jack pine group

101 Jack pine

102 Red pine

103 Eastern white pine

104 Eastern white pine / eastern hemlock

105 Eastern hemlock

120 Spruce / fir group

121 Balsam fir

122 White spruce

123 Red spruce

124 Red spruce / balsam fir

125 Black spruce

126 Tamarack

127 Northern white-cedar

128 Fraser fir

129 Red spruce / Fraser fir

140 Longleaf / slash pine group

141 Longleaf pine

142 Slash pine

150 Tropical pine group

151 Tropical pines

160 Loblolly / shortleaf pine group

161 Loblolly pine

162 Shortleaf pine

163 Virginia pine

164 Sand pine

165 Table mountain pine

166 Pond pine

167 Pitch pine

168 Spruce pine

170 Other eastern softwoods group

171 Eastern redcedar

172 Florida softwoods

180 Pinyon / juniper group

182 Rocky Mountain juniper

184 Juniper woodland

185 Pinyon / juniper woodland

200 Douglas-fir group

201 Douglas-fir

202 Port-Orford-cedar

203 Bigcone Douglas-fir

220 Ponderosa pine group

221 Ponderosa pine

222 Incense-cedar

224 Sugar pine

225 Jeffrey pine

226 Coulter pine

240 Western white pine group

241 Western white pine

260 Fir / spruce / mountain hemlock group

261 White fir

262 Red fir

263 Noble fir

264 Pacific silver fir

265 Engelmann spruce

266 Engelmann spruce / subalpine fir

267 Grand fir

268 Subalpine fir

269 Blue spruce

270 Mountain hemlock

271 Alaska-yellow-cedar

280 Lodgepole pine group

281 Lodgepole pine

300 Hemlock / Sitka spruce group

301 Western hemlock

304 Western redcedar

305 Sitka spruce

320 Western larch group

321 Western larch

340 Redwood group

341 Redwood

342 Giant sequoia

360 Other western softwoods group

361 Knobcone pine

362 Southwestern white pine

363 Bishop pine

364 Monterey pine

365 Foxtail pine / bristlecone pine

366 Limber pine

367 Whitebark pine

368 Miscellaneous western softwoods

369 Western juniper

370 California mixed conifer group

371 California mixed conifer

380 Exotic softwoods group

381 Scotch pine

383 Other exotic softwoods

384 Norway spruce

385 Introduced larch

390 Other softwoods group

391 Other softwoods 

400 Oak / pine group

401 East wh pine/ northern red oak /white ash

402 Eastern redcedar / hardwood

403 Longleaf pine / oak

404 Shortleaf pine / oak

405 Virginia pine / southern red oak

406 Loblolly pine / hardwood

407 Slash pine / hardwood

409 Other pine / hardwood

500 Oak / hickory group

501 Post oak / blackjack oak

502 Chestnut oak

503 White oak / red oak / hickory

504 White oak

505 Northern red oak

506 Yellow-poplar /white oak /northern red oak

507 Sassafras / persimmon

508 Sweetgum / yellow-poplar

509 Bur oak

510 Scarlet oak

511 Yellow-poplar

512 Black walnut

513 Black locust

514 Southern scrub oak

515 Chestnut oak / black oak / scarlet oak

516 Cherry / white ash / yellow-poplar

517 Elm / ash / black locust

519 Red maple / oak

520 Mixed upland hardwoods

600 Oak / gum / cypress group

601 Swamp chestnut oak / cherrybark oak

602 Sweetgum / Nuttall oak / willow oak

605 Overcup oak / water hickory

606 Atlantic white-cedar

607 Baldcypress / water tupelo

608 Sweetbay / swamp tupelo / red maple

609 Baldcypress / pondcypress

700 Elm / ash / cottonwood group

701 Black ash / American elm / red maple

702 River birch / sycamore

703 Cottonwood

704 Willow705 Sycamore / pecan / American elm

706 Sugarberry / hackberry / elm / green ash

707 Silver maple / American elm

708 Red maple / lowland

709 Cottonwood / willow

722 Oregon ash 

800 Maple / beech / birch group

801 Sugar maple / beech / yellow birch

802 Black cherry

805 Hard maple / basswood

809 Red maple / upland

900 Aspen / birch group

901 Aspen

902 Paper birch

903 Gray birch

904 Balsam poplar

905 Pin cherry

910 Alder / maple group

911 Red alder

912 Bigleaf maple

920 Western oak group

921 Gray pine

922 California black oak

923 Oregon white oak

924 Blue oak

931 Coast live oak

933 Canyon live oak

934 Interior live oak

935 California white oak (valley oak)

940 Tanoak / laurel group

941 Tanoak

942 California laurel

943 Giant chinkapin

960 Other hardwoods group

961 Pacific madrone

962 Other hardwoods

970 Woodland hardwoods group

971 Deciduous oak woodland

972 Evergreen oak woodland

973 Mesquite woodland

974 Cercocarpus (mountain brush) woodland

975 Intermountain maple woodland

976 Miscellaneous woodland hardwoods

980 Tropical hardwoods group

982 Mangrove

983 Palms

989 Other tropical hardwoods

990 Exotic hardwoods group

991 Paulownia

992 Melaleuca

993 Eucalyptus

995 Other exotic hardwoods

999 Nonstocked

Note: The forest type names used by FIA do not come from a single published reference. The current list of forest type names has been developed 

over time using sources such as historical FIA lists, lists from the Society of American Foresters, and FIA analysts who developed names to meet 

current analysis and reporting needs. (Source: Woudenberg and others 2011).
98

Appendix: Structure of the Forest Dynamics Model

empirical probabilities to forecast forest types. Inventory plots 

reveal few transitions between five broad forest management 

types in the South—planted pine, natural pine, mixed pinehardwood, upland hardwood, and lowland hardwood—likely 

reflecting the physiographic basis for these broad forest 

management types. Instead we observe strong associations 

between forest age and the occurrence of fine scale forest types 

within the broad type group (e.g., poplar/sweetgum on young 

upland hardwood plots and red oaks predominately on older 

upland hardwood plots). Accordingly, for this set of simulations 

we hold the broad forest management type of the plot constant 

over time except when the plot is predicted to be harvested. 

Transitions among forest types within each forest management 

type are simulated through the imputation algorithm described 

below. When harvest occurs, the plot returns to its historical forest 

management type or is transitioned to a planted pine management 

type. The probability of being planted derives from the observed 

planting frequency (PF) for each forest management type within 

the modeled State and adjusted to reflect market conditions.

pr (planting|harvest, FT ) = f {γ, PF [FT ]} (8)

where

γ = a scaling factor which is used to adjust the expansion in 

planted pine area consistent with the economic conditions of 

the scenario

FT = the observed forest type. 

Forest type transitions in the South rely on these rules 

and empirical probabilities and are not directly influenced 

by changes in climate conditions. As explained later, the 

imputation module allows the species composition within 

each of these forest type groups to shift in response to climate 

variables.

Forest type transitions in the North are modeled based on 

empirical transition matrices derived from matched plots. A 

separate model was derived for each State or State-aggregate 

in the North based on the most recent pair of inventories. In 

each State, forest types representing more than 2 percent of the 

forest area in the most recent survey were selected for inclusion 

in the transition matrix (table A3).

pr (FTt+1 = 1|FTt = j) = fi,j (9)

where

fi,j = the observed frequency of transitions between inventories. 

Equation 9 defines a transition matrix for all modeled forest 

types. Plots of all other forest types (those with < 2 percent of 

forest area) have fixed forest types throughout the simulation. 

Forest type transitions in the North therefore rely strictly on 

transition propensities observed across recent inventories and 

are not directly influenced by changes in climate conditions. 

As explained later, the imputation module allows the species 

composition within each of these forest type groups to shift in 

response to climate variables.

Table A3—Modeled forest type groups [Forest Inventory and Analysis (FIA) codes] and modeling units (States 

or State aggregates) for the North Resources Planning Act Region

State or State/Survey unit-aggregatea

Forest type group

(FIA code)b MN MI WI MO ME IA VT-NH

MACT-RI

East PAEast MDNJ-DE

East 

OH-West 

PA-West 

MD-WV

IL-IN-West 

OH NY

Aspen-birch (900) X X X X X X X

Elm-ash-cottonwood (700) X X X X X X X X X X X X

Maple-beech-birch (800) X X X X X X X X X X X

Mixed pine hardwood (400) X X X X X X X X X X X X

Oak-hickory (500) X X X X X X X X X X X X

Spruce fir (120) X X X X X X

White/red/jack pine (100) X X X X X X X X

Loblolly/shortleaf pine (160) X X

Oak-gum-cypress (600) X

Eastern red cedar (171) X

a States are named by their postal abbreviations.

b See table A2 for explanations of FIA codes.
99

Appendix: Structure of the Forest Dynamics Model

For the West, matched pairs of inventory plots were unavailable 

for modeling. Instead, a classification model for forest types 

in the West defines the occurrence probability of each forest 

type (rather than the transition between types) as a function 

of various site and climate variables. After experimenting 

with various approaches including naïve Bayes and clustering 

models, we chose a quadratic programming approach to 

derive conditional probabilities of observing a forest type 

conditioned on site and climate variables (Cortes and Vapnik 

1995). For each forest type, the model generates a “supporting 

hyperplane” as a function of climate and site variables that 

separates the referenced forest type from all other observed 

forest types. 

The quadratic programming approach defines a classification 

model. Given k forest plots and a set of m explanatory variables 

(which could include interaction terms), the quadratic program 

is specified to minimize the classification noise, i.e., to 

maximize the match between actual and predicted forest types. 

The measure of model fitness (an L2 norm or Euclidean error 

norm) is based on the classification rates for the modeled forest 

type as a relative measure of accuracy. 

Given the following variables as vectors:

Xi = Predicted occurrence of forest type i for each observation 

using the model (kx1 matrix)

Actuali = Actual forest type i for each observation, a vector 

of binary variables with 1 indicating the plot has the modeled 

forest type (kx1 matrix)

αi = estimated coefficients from the quadratic model (mx1 

matrix)

B = m explanatory variables for the k observations (k x m

matrix)

The objective function is to minimize L2:

L2 (Xi) = (Xi – Actuali)2 (10.1)

Subject to:

B x αi = Xi (10.2)

Separate models are estimated for each forest type and result in 

probabilities of observing the specified forest type conditioned 

on the climate and site variables (B) observed for the plot. 

For each plot the probabilities of observing all forest types 

are normalized so that they sum to one. As climate variables 

are shifted by the scenarios, the QP approach generates new 

probabilities of observing each forest type for each forest plot. 

In each State or State aggregate, forest types representing more 

than 2 percent of forest area in the most recent survey were 

modeled (tables A4 and A5).

This classification approach to modeling future forest types 

for the Western United States is structurally similar to several 

studies that address future ranges of either broad biome types 

or individual tree species using nonparametric models. For 

example, Iverson and others (2008) in the Eastern United 

States and Rehfeldt and others (2006) in the West use similar 

approaches to constructing classification models—e.g., 

Random Forests models—to predict suitable habitat for 

individual tree species as functions of similar sets of climate 

and site variables. A longer history of data envelopment 

studies also describe (classify) boundaries between broad 

biome types—such as between prairie and forest (Gosz and 

Sharpe 1989). Applying climate projections to these models 

allows for inferences regarding the potential responses of 

species/biome ranges to future climates. A related body of 

work predicts forest inventory characteristics based on site 

attributes and ancillary data from satellite imagery (Moisen 

and Frescino 2002), not to produce temporal forecasts but 

to develop spatial interpolation of inventory data to maps of 

forest conditions.

In contrast to the transition models developed for eastern 

regions and explicitly linked to existing conditions, these 

classification models for western forests allow for more rapid 

transitions in type in response to projections of future climates. 

This suggests placing most emphasis in the longer term 

projections, e.g., focusing mostly on 30- to 50-year projections. 

Still, projections are provided for 10-year time steps to allow 

for insights into model behavior. 

Tree species-climate envelope models correspond most 

directly to our forest type classification models. However, our 

approach—predicting forest type groups to support whole 

plot imputation modeling—does not allow for novel species 

assemblages that can be implied by projections from speciesclimate envelope models (Williams and Jackson 2007). By 

construction, our approach constrains future assemblages to 

be consistent with historical occurrences, an assumption that 

seems reasonable given the 50-year timeframe of forecasts, 

the inertia of persistent vegetation, and uncertainty about 

mechanisms of these changes.

The Imputation Module

The implementation of the forest harvesting, aging, and 

planting equations are set within a Monte Carlo modeling 

framework. Projections of exogenous variables for the various 

scenarios (e.g., climate variables and timber prices) and fixed 

variables for plots (e.g., slope) are used to assign forest type, 

harvest and planting probabilities, and ages based on equations 

2–10. Implementation of the probabilities using random number 

generators defined predicted outcomes for the state variables. 
100

Appendix: Structure of the Forest Dynamics Model

Table A5—Modeled forest type groups [by Forest Inventory and 

Analysis (FIA) codes] and modeling units (States or State aggregates) 

for the Pacific Coast Resources Planning Act Region 

State or State-Aggregatea

Forest type group

(FIA Code)b CA

OR-WA 

West side

OR-WA 

East side

California mixed conifer (370) X

Douglas fir (200) X X X

Fir-spruce-mountain hemlock (260) X X X

Lodgepole pine (280) X X

Other western hardwoods (920) X

Piñon juniper plus (180+) X

Ponderosa pine (220) X X

Tanoak-laurel (940) X

Redwood (340) X

Western oak (920) X

Alder-maple (910) X

Hemlock-Sitka spruce X

Other western softwoods (360) X

a States are named by their postal abbreviations.

b See table A2 for explanations of FIA codes.

Table A4—Modeled forest type groups [by Forest Inventory and Analysis (FIA) codes] and 

modeling units (States or State aggregates) for the Rocky Mountain Resources Planning Act 

Region 

State or State-Aggregatea

Forest type group

(FIA Code)b CO-UT MT WY ID NV AZ NM ND-SD KS-NE

Aspen birch (900) X X X X X X

Douglas fir (200) X X X X X X

Deciduous oak woodland (970) X

Fir-spruce-mountain hemlock (260) X X X X X X

Lodgepole pine (280) X X X X

Piñon juniper (180) X X X X X X

Ponderosa pine (220) X X X X X X X X

Other western softwoods (360) X

Fir-spruce-hemlock (260) X X

Piñon juniper plus (180+) X X

Western larch (320) X X

Other western hardwoods (920) X X X

Elm-ash-cottonwood (700) X X

Oak-hickory (500) X X

Eastern redcedar (171) X

Mixed pine hardwood (400) X

a States are named by their postal abbreviations.

b See table A2 for explanations of FIA codes.
101

Appendix: Structure of the Forest Dynamics Model

Forecasts of all state variables using climate projections, price 

forecasts, and the transition module, place each plot into a plot 

cluster defined by the partitioning module. To construct the 

future inventory, a historic plot is drawn at random and with 

replacement from the plot cluster. Plot draws for all plot locations 

define a complete set of plot records for the future inventory. 

This set of resampled historic plot records provides estimates of 

all forest attributes measured by the forest inventory.

Using whole plot records to populate the inventory defines 

this imputation procedure as a “hot-decking” technique from 

the literature on the analysis of incomplete data or missing 

observations in survey processing. Here we extend the 

structure of hot-deck imputation from missing data to provide 

the entire forecasted dataset using historical plot records as 

proposed by Van Deusen (1997, 2006). 

Our partitioning model using regression trees defines what 

Ford (1983) calls a classification process for the hot-decking 

model, whereby observations within each partition are as 

homogenous as possible. Our objective in applying this hotdecking approach is to produce forecasts that have reasonable 

joint and marginal distributions for measured forest inventory 

attributes. We should expect that the distributions within 

each partition are unbiased. Reams and McCullum (2000) 

demonstrate that this type of whole plot imputation procedure 

is equivalent to using regression equations to estimate average 

biomass volumes for historical simulations but that it preserves 

the joint distribution attributes between multiple independent 

variables. What’s more, the hot-decking approach preserves 

the variability of the data generation process inherent in the 

inventory (Reams and McCullum 2000) and contributes to 

estimates of the variance of the forecasts.

Simulation Structure

Recalling the definition of the information set for each plot in 

the inventory: 

I (p) = {S, Z, C, E} (1)

The Partitioning, Transition, and Imputation models provide a 

mechanism for predicting the plot attributes (S, Z) for the entire 

inventory conditioned on the climate (C). Linking the forecasted 

plot record to the scaling framework defined by the survey design, 

or the expansion factor (E), then completes the projection. To 

account for changes in the area frame of the survey we employed 

a land use model (Wear 2011) estimated with county-level data 

derived from the National Resource Inventories (U.S. Department 

of Agriculture Natural Resources Conservation Service 2000). 

This model provides estimates of the distribution of non-Federal 

land uses, including cropland, pasture, range, developed, 

forest, and other uses, in response to population, economic, and 

biophysical variables while Federal land area is held constant. 

Non-Federal FIA forest area within a county is assumed to 

change in direct proportion to the forest area change forecasted 

by the land use model. The expansion factors for all non-Federal 

plots in the county are adjusted to reflect these forecasts of 

changes in county forest area over time. These land use forecasts 

are driven by the population and income forecasts of the RPA 

scenario and, in the South, by timber price forecasts associated 

with the model run.

We seek to generate aggregate estimates of inventory values for 

reporting units such as ecological sections, State survey units, and 

State totals, consistent with the sampling design of the inventory. 

To test the performance of the modeling framework we conducted 

validations at two different levels: aggregate forecasts and the 

distribution of attributes within each of the partitions defined 

by the partitioning model. During the design phase for several 

States in the East, we simulated the development of the inventory 

between two measured inventories and compared actual values 

with those forecasted by the model. For aggregate validation, 

we compared a forecasted 95-percent confidence interval for six 

variables using 26 iterations of the model: trees per acre, total 

biomass (measured as growing stock volume), and sawtimber 

biomass (volume in trees > 9 inches in diameter) for both 

hardwood trees and softwood trees. Validation tests proved useful 

for identifying specification and programming errors.

The structure of the Monte Carlo simulation algorithm is 

described in figure A1. Simulation experiments indicated a 

convergence of variance estimates after about 20 simulations 

and we chose to run the model for 26 iterations for each scenario 

(limits of computing time kept us from constructing more 

simulations). For the display of forecast results, we selected one of 

the 26 model runs as a “representative” realization of the future 

inventory based on the minimum proportional distance of six 

variables from the means for the run over the 50-year simulation 

period. These six variables were total growing stock volume 

(cubic feet), trees per acre, and sawtimber volume for hardwoods 

and softwoods for the State or State-aggregate being modeled.

Models were implemented for individual States or aggregates 

of two to four States or adjacent survey units across States and 

results were summed up to display results for the RPA regions. 

The time step for each model in the East was defined by the time 

interval of the paired inventories used to build the transition 

module and in the West was defined as a fixed 10-year step. 

Inventories commenced from the date of the latest inventory 

available, so the years for which forecasts were generated 

varied across States. To construct regional aggregates for the 

reporting years (2020, 2030, 2040, 2050, and 2060), we assigned 

the inventory with the age closest to the reporting year to the 

aggregate, consistent with methods used to generate historical 

aggregates with FIA data (Smith and others 2009).



Wear, David N.; Huggett, Robert; Li, Ruhong; Perryman, Benjamin; and Liu, Shan. 2013.

Forecasts of forest conditions in regions of the United States under future scenarios: a technical 

document supporting the Forest Service 2012 RPA Assessment. Gen. Tech. Rep. SRS-170. 

Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 101 p.

The 626 million acres of forests in the conterminous United States represent significant reserves 

of biodiversity and terrestrial carbon and provide substantial flows of highly valued ecosystem 

services, including timber products, watershed protection benefits, and recreation. This report 

describes forecasts of forest conditions for the conterminous United States in response to multiple 

scenarios evaluated by the 2010 Resources Planning Act Assessment. The results are based on the 

U.S. Forest Assessment System, a modeling system designed to forecast alternative futures for U.S. 

forests, and provide a forward-looking adjunct to the Forest Inventory and Analysis Program of 

the Forest Service, U.S. Department of Agriculture. Scenarios address a range of future conditions 

regarding population growth, economic change, global forest product markets, and climate change. 

Forest forecasts are described for each of four Resources Planning Act regions (South, North, Rocky 

Mountain, and Pacific Coast) across the Resources Planning Act scenarios. In the Eastern United 

States, the range of socioeconomic futures leads to greater variation in future forest conditions than 

does the range of climate projections. Because of the dominance of public forests in the Western 

United States, socioeconomic changes hold less sway over forest futures there. Built directly from and 

as a forecasting adjunct to the monitoring system of the Forest Inventory and Analysis Program, the 

modeling components will be enhanced by additional forest inventory data as they become available. 

Keywords: Forecasts, forest inventory, integrated assessment.

The Forest Service, United States Department of Agriculture 

(USDA), is dedicated to the principle of multiple use management 

of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, 

cooperation with the States and private forest owners, and 

management of the National Forests and National Grasslands, it strives—as 

directed by Congress—to provide increasingly greater service to a growing Nation.

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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, SW, Washington, D.C. 20250-9410, or call 

(800) 795-3272 (voice) or (202) 720-6382 ( TDD). USDA is an equal opportunity 

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