1 DRAFT EPA/600/R-08/076A
2 DO NOT CITE OR QUOTE July 2008
3
4
5
6
7
8 Preliminary Steps towards Integrating Climate and Land Use: The
9 Development of Land-Use Scenarios Consistent with Climate
10 Change Emissions Storylines
11
12
13
14
15
16 NOTICE
17
18 THIS DOCUMENT IS A PRELIMINARY DRAFT. THIS INFORMATION IS
19 DISTRIBUTED SOLELY FOR THE PURPOSE OF PRE-DISSEMINA TION PEER REVIEW
20 UNDER APPLICABLE INFORMA TION Q UALITY GUIDELINES. IT HAS NOT BEEN
21 FORMALLY DISSEMINATED BY THE U.S. ENVIRONMENTAL PROTECTION AGENCY. IT
22 DOES NOT REPRESENT AND SHOULD NOT BE CONSTRUED TO REPRESENT ANY
23 AGENCY DETERMINA TION OR POLICY.
24
25
26
27 Global Change Research Program
28 National Center for Environmental Assessment
29 Office of Research and Development
30 U.S. Environmental Protection Agency
31 Washington, DC 20460
32
-------
1 DISCLAIMER
2
3
4 This document is an external draft for review purposes only. This information is
5 distributed solely for the purpose of pre-dissemination peer review under applicable information
6 quality guidelines. It has not been formally disseminated by the U.S. Environmental Protection
7 Agency. It does not represent and should not be construed to represent any agency determination
8 or policy. Mention of trade names or commercial products does not constitute endorsement or
9 recommendation for use.
10
11
12 ABSTRACT
13 Climate and land use change are major components of global environmental change with
14 feedbacks between these components. The consequences of these interactions also show that
15 land use may exacerbate or alleviate climate change effects. Based on these results it is important
16 to use land use scenarios that are consistent with the specific assumptions underlying the climate
17 change scenarios. The Integrated Climate and Land Use Scenarios (ICLUS) project developed
18 outputs that are based on the Intergovernmental Panel on Climate Change (IPCC) Special Report
19 on Emissions Scenarios (SRES) social, economic, and demographic storylines and downscaled
20 these to the United States. ICLUS outputs are derived from a demographic model and a spatial
21 allocation model that distributes the population as housing across the landscape for the four main
22 SRES storylines and a base case. The model is run for the conterminous United States and output
23 is available for each scenario by decade to 2100. In addition to maps of housing density across
24 the conterminous United States, this project also generated maps of impervious surface cover
25 based on the housing density projections. This report describes the modeling methodology for
26 the ICLUS project, some initial analyses using the ICLUS outputs, and recommendations for
27 further research.
28
29
30
31 Preferred Citation:
32 U.S. Environmental Protection Agency (EPA). (2008) Preliminary steps towards integrating climate and land use:
33 the development of integrated climate and land use scenarios. Global Change Research Program, National Center for
34 Environmental Assessment, Washington, DC; EPA/600/R-08/076A. Available from the National Technical
35 Information Service, Springfield, VA, and online at http://www.epa.gov/ncea.
36
-------
CONTENTS
1 INTRODUCTION 1
2 SRES DOWNSCALING TO THE UNITED STATES 2
2.1 OVERVIEW OF THE SRES STORYLINES 3
2.2 INTERPRETING AND DOWNSCALING THE SRES STORYLINES 6
2.2.1 Al Storyline Adapted for the United States 6
2.2.2 Bl Storyline Adapted for the United States 7
2.2.3 A2 Storyline Adapted for the United States 7
2.2.4 B2 Storyline Adapted for the United States 7
3 DEMOGRAPHIC PROJECTIONS 7
3.1 OVERVIEW 7
3.2 INITIAL POPULATION 9
3.3 FERTILITY AND MORTALITY 10
3.4 NET INTERNATIONAL MIGRATION 11
3.5 DOMESTIC MIGRATION AND GRAVITY MODELING 12
3.5.1 1995-2000 Migration Data 12
3.5.2 County Attribute Data 13
3.5.3 Distance Matrix 14
3.5.4 Model Specification 15
3.5.5 Stepwise Regression Results 16
3.5.6 Model Flow 18
3.5.7 Gravity Model and U.S.-Adapted SRES Scenarios 19
3.6 MODEL RESULTS 19
3.7 COMPARISON OF DEMOGRAPHIC MODEL WITH EXISTING PROJECTIONS 20
4 SPATIAL ALLOCATION MODEL 24
4.1 RATIONALE FOR THE SELECTION OF SERGOM 24
4.2 METHODOLOGY 25
4.3 INCORPORATING U.S.-ADAPTED SRES INTO SERGOM 28
4.4 INTEGRATION OF DEMOGRAPHIC, SERGOM, AND IMPERVIOUS MODELS 29
5 IMPACTS AND INDICATORS ANALYSIS 29
5.1 RATES OF GROWTH IN DIFFERENT REGIONS 29
5.2 HOUSING DENSITY TRENDS 37
5.3 IMPERVIOUS SURFACE CALCULATIONS 39
Draft Report-June 26, 2008 page i
-------
5.3.1 Percent of Watersheds over Stressed (>5%) Impervious Threshold 39
5.4 OPTIONS FOR FUTURE STUDY 51
6 DISCUSSION AND CONCLUSIONS 53
References 54
Appendix A - Maps for ICLUS Scenarios 58
Appendix B - Demographic Model Sensitivity Testing 73
Appendix C - Statistical Relationship between Housing Density and Impervious Surface Cover 77
Appendix D - Regional Population Growth Rates and Projections Based on U.S. EPA Regions 89
Appendix E - Component and Cohort Model Data 92
Draft Report-June 26, 2008 page i
-------
LIST OF TABLES
Table 2-1: Qualitative Demographic Assumptions in SRES Storylines 4
Table 3-1: Summary of Qualitative Adjustments to the Demographic Projections 9
Table 3-2: Gravity Model Results 17
Table 3 -3: Ranking of the contribution of independent variables to explanatory power of the models 18
Table 4-1: Summary of Adjustments to SERGoM v3 for SRES scenarios 29
Table 5-1: U.S. Census Regions 30
Table 5-2: Projected Regional Populations and Growth Rates 30
Table 5-3: Projected Urban and Suburban Area in Modeled Scenarios, 2000-2100 (km2) 38
Table 5-4: Projected area (km2) effects of Urban, Suburban, and Exurban housing densities on NLCD land cover
types in modeled scenarios for 2050 38
Table 5-5: Impervious surface estimates for SRES scenarios. "Stressed" level is defined as at least 5% impervious
surface 40
Table D-l: EPA Regions 89
Table D-2: Projected Population and Growth Rate by Scenario and EPA Region 90
Table E-l: Fertility Rates (Births per 1000 Women) 92
Table E-2: Mortality Rates (Lifespan-Equivalent) 95
Table E-3: Projected International Migration 96
Draft Report-June 26, 2008 page i
-------
1 LIST OF FIGURES
2
3 Figure 1-1: Model and information flow within the ICLUS project 2
4 Figure 3-1: Demographic model flow 8
5 Figure 3-2: Projected Net International Migrations under U.S. Census Bureau Scenarios 11
6 Figure 3-3: Total Population under Five ICLUS Scenarios 20
7 Figure 3-4: Comparison of California and ICLUS (base case) 2030 Projections 22
8 Figure 3-5: Comparison of Colorado and ICLUS (base case) 2030 Projections 22
9 Figure 3-6: Comparison of Florida and ICLUS (base case) 2030 Projections 23
10 Figure 3-7: Comparison of Minnesota and ICLUS (base case) 2030 Projections 23
11 Figure 3-8: Comparison of New Jersey and ICLUS (base case) 2025 Projections 24
12 Figure 4-1: SERGoM Functional Flow 27
13 Figure 5-1: Base Case Population by Census Region 31
14 Figure 5-2: Al Storyline Population by Census Region 31
15 Figure 5-3: A2 Storyline Population by Census Region 32
16 Figure 5-4: Bl Storyline Population by Census Region 32
17 Figure 5-5: B2 Storyline Population by Census Region 32
18 Figure 5-6: Base Case Annual Population Growth Rates by Region 33
19 Figure 5-7: Al Storyline Annual Population Growth Rates by Census Region 33
20 Figure 5-8: A2 Storyline Annual Population Growth Rates by Census Region 34
21 Figure 5-9: Bl Storyline Annual Population Growth Rates by Census Region 34
22 Figure 5-10: B2 Storyline Annual Population Growth Rates by Census Region 35
23 Figure 5-11: Northeast Region Population by Storyline 35
24 Figure 5-12: Midwest Region Population by Storyline 36
25 Figure 5-13: South Region Population by Storyline 36
26 Figure 5-14: West Region Population by Storyline 37
27 Figure 5-15: Urban/Suburban Housing Land-use Trends for ICLUS SRES Scenarios 38
28 Figure 5-16: Impervious surface area estimates, 2000-2100 40
29 Figure 5-17: 2050 Impervious Surface, Base Case 41
30 Figure 5-18: 2000-2050 Relative Change in Impervious Surface, Base Case 42
31 Figure 5-19: 2050 Impervious Surface, Al Storyline 43
32 Figure 5-20: 2000-2050 Relative Change in Impervious Surface, Al Storyline 44
33 Figure 5-21: 2050 Impervious Surface, A2 Storyline 45
34 Figure 5-22: 2000-2050 Relative Change in Impervious Surface, A2 Storyline 46
Draft Report-June 26, 2008 page iv
-------
1 Figure 5-23: 2050 Impervious Surface, Bl Storyline 47
2 Figure 5-24: 2000-2050 Relative Change in Impervious Surface, Bl Storyline 48
3 Figure 5-25: 2050 Impervious Surface, B2 Storyline 49
4 Figure 5-26: 2000-2050 Relative Change in Impervious Surface, B2 Storyline 50
5 Figure A-l: Base Case, Year 2010 Housing Density Map 58
6 Figure A-2: Base Case Storyline, Year 2050 Housing Density Map 59
7 Figure A-3: Base Case, Year 2100 Housing Density Map 60
8 Figure A-4: Al Storyline, Year 2010 Housing Density Map 61
9 Figure A-5: Al Storyline, Year 2050 Housing Density Map 62
10 Figure A-6: Al Storyline, Year 2100 Housing Density Map 63
11 Figure A-7: A2 Storyline, Year 2010 Housing Density Map 64
12 Figure A-8: A2 Story line, Year 2050 Housing Density Map 65
13 Figure A-9: A2 Storyline, Year 2100 Housing Density Map 66
14 Figure A-10: Bl Storyline, Year 2010 Housing Density Map 67
15 Figure A-ll: Bl Storyline, Year 2050 Housing Density Map 68
16 Figure A-12: Bl Storyline, Year 2100 Housing Density Map 69
17 Figure A-13:B2 Storyline, Year 2010 Housing Density Map 70
18 Figure A-14: B2 Storyline, Year 2050 Housing Density Map 71
19 Figure A-15: B2 Storyline, Year 2100 Housing Density Map 72
20 Figure B-l: Effect of fertility rate on national population 73
21 Figure B-2: Effect of international migration on national population 74
22 Figure B-3: Comparison of a broad range of scenarios 74
23 Figure B-4: Effect of mortality on national population 75
24 Figure C-l: Full regression tree backbone (58 terminal nodes) without labels 78
25 Figure C-2: Cross validation results for the full regression tree 79
26 Figure C-3: The relationship between percent impervious and housing density 79
27 Figure C-4: Top ten terminal nodes within full regression tree with housing density labels and percent impervious
28 estimates (terminal nodes) 80
29 Figure C-5: Estimated National Impervious Surface, 2000 82
30 Figure C-7: Difference in Impervious Surface, United States 84
31 Figure C-8: Difference in Impervious Surface, Colorado 85
32 Figure C-9: Difference in Impervious Surface, Mid-Atlantic Region 86
33 Figure C-10: Estimated Impervious Surface, 2030 88
34
Draft Report-June 26, 2008 pagev
-------
1 LIST OF ABBREVIATIONS AND ACRONYMS
2
3 Al The Al storyline in the Special Report on Emissions Scenarios
4 A2 The A2 storyline in the Special Report on Emissions Scenarios
5 B1 The B1 storyline in the Special Report on Emissions Scenarios
6 B2 The B2 storyline in the Special Report on Emissions Scenarios
7 GCM General Circulation Model
8 GCRP Global Change Research Program
9 HD Housing Density
10 HUC Hydrologic Unit Code
11 ICLUS Integrated Climate and Land Use Scenarios
12 IPCC Intergovernmental Panel on Climate Change
13 MIGPUMA Migration Public-Use Microdata Areas
14 MRLC Multi-Resolution Land Characteristics
15 NLCD National Land Cover Database
16 PUI Percent Urban Imperviousness
17 PUMA Public-Use Microdata Areas
18 PUMS Public-Use Microdata Samples
19 SERGoM Spatially Explicit Regional Growth Model
20 SRES Special Report on Emissions Scenarios
21
Draft Report - June 26, 2008
page vi
-------
1 PREFACE
2
3 This report was prepared jointly by ICF International, Colorado State University, and the
4 Global Change Research Program in the National Center for Environmental Assessment (NCEA)
5 of the Office of Research and Development at the U.S. Environmental Protection Agency (U.S.
6 EPA). The report describes the methodology used to develop and modify the models that
7 constitute the EPA Integrated Climate and Land Use Scenarios (ICLUS). The scenarios and
8 maps resulting from this effort are intended to be used as benchmarks of possible land use
9 futures that are consistent with socioeconomic storylines used in the climate change science
10 community. The two-way feedbacks that exist between climate and land use are not yet fully
11 understood and have consequences for air quality, human health, water quality, and ecosystems.
12 In this report we describe the first steps towards characterizing and assessing the effects of these
13 feedbacks and interactions by developing housing density and impervious surface cover
14 scenarios. These outputs facilitate future integrated assessments of climate and land-use changes
15 that make consistent assumptions about socioeconomic and emissions futures. EPA's intention is
16 to use the results of this first phase of modeling to inform and facilitate investigation of a broader
17 set of impacts scenarios and potential vulnerabilities in areas such as water quality, air quality,
18 human health, and ecosystems. More specifically, this research will enable more sophisticated
19 model runs that will evaluate the effects of projected climate changes on demographic and land
20 use patterns and the results of these changes on endpoints of concern.
21
Draft Report-June 26, 2008 pagevii
-------
1 AUTHORS AND REVIEWERS
2
3 The Global Change Research Program, within the National Center for Environmental
4 Assessment, Office of Research and Development, is responsible for publishing this report. This
5 document was prepared by ICF International under Contract No. GS-10F-0234J, U.S. EPA Order
6 No. 1101. Dr. Chris Pyke and Dr. Britta Bierwagen served as successive Technical Project
7 Officers. Drs. Pyke and Bierwagen provided overall direction and technical assistance, and Dr.
8 Bierwagen contributed as an author.
9
10 AUTHORS
11 ICF International Washington. DC
12 Anne Choate, Jonathan Cohen, Philip Groth
13
14 Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO
15 David M. Theobald
16
17 U.S. EPA
18 Britta Bierwagen, John Thomas, Chris Pyke*
19
20
21 REVIEWERS
22 U.S. EPA Reviewers
23 Cynthia Gage, Ellen Cooler, Sandy Bird, Laura Jackson, Matt Dalbey, Brooke Hemming, Henry
24 Lee
25 ACKNOWLEDGEMENTS
26
27
28
'Present affiliation: CTG Energetics, Washington, DC
Draft Report-June 26, 2008 page viii
-------
1 EXECUTIVE SUMMARY
2 Climate and land use change are major components of global environmental change.
3 Assessments of impacts associated with these changes often show interactions and two-way
4 feedbacks between climate and land use. The consequences of these interactions also show that
5 land use could exacerbate or alleviate climate-change effects. Based on these results it is
6 important to use land-use scenarios that are consistent with the specific assumptions underlying
7 recognized international climate-change scenarios.
8 The EPA Integrated Climate and Land Use Scenarios (ICLUS) project developed outputs
9 that are based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on
10 Emissions Scenarios (SRES) social, economic, and demographic storylines. We downscaled
11 these storylines to the United States and modified U.S. Census Bureau population and migration
12 projections to be consistent with these storylines. ICLUS outputs are derived from a
13 demographic model and a spatial allocation model that distributes the population projections
14 generated by the demographic model into housing units across the landscape. The demographic
15 model is at the county scale and the spatial allocation model is at the 1 ha pixel scale. Each
16 scenario is run for the conterminous U.S. to 2100.
17 The results of this first phase of this project are designed to provide a foundation for
18 evaluation of a broader set of impacts scenarios and potential vulnerabilities in areas such as
19 water quality, air quality, human health, and ecosystems. More specifically, these scenarios will
20 underlie more sophisticated model runs that will evaluate the effects of projected climate
21 changes (e.g., sea level rise) on demographic and land use patterns and the results of these
22 changes on endpoints of concern. The products generated in this first phase are consistent with
23 socio-economic storylines used by the climate change modeling community, but it does not
24 explicitly integrate climate change variables into the models.
25 The EPA-ICLUS project uses the SRES storylines because these storylines are used as
26 direct inputs into general circulation models used by the climate change science community. This
27 link facilitates integrated assessments of climate and land use, because the broad underlying
28 assumptions are the same. The SRES describe storylines along two major axes, economic vs.
29 environmentally-driven development (A-B) and global vs. regional development (1-2), which
30 make up the four combinations of storylines, Al, A2, Bl, and B2. We adapted these storylines
31 for the United States by changing fertility, domestic and international migration, household size,
32 and travel times from the urban core.
33 The demographic model is composed of five components, fertility, mortality, domestic
34 in-migration, domestic out-migration, and net international migration, which are calculated using
35 a cohort-component model and a gravity model. The population is divided into cohorts that are
36 age-, gender-, and race/ethnicity-specific. Changes due to these five components of change are
37 estimated over time as each cohort is tracked separately. The gravity model is used to track
38 domestic in- and out-migration by county. Components of the gravity model include certain
39 county amenities and functional distance that connects counties based on population locations
40 and transportation infrastructure. The resulting county-based population projections are the
41 inputs to the spatial allocation model.
42 The spatial allocation model distributes the population into housing units across the
43 country at a 1 ha pixel scale. The model used in this project is the Spatially Explicit Regional
44 Growth Model (SERGoM). SERGoM uses five main base datasets: housing units and population
Draft Report-June 26, 2008 page ix
-------
1 based on the 2000 census, undevelopable lands, road and groundwater well density,
2 commercial/industrial land use from the National Land Cover Database, and county population
3 projections from the demographic model described above. Household size and travel times are
4 adjusted to reflect the assumptions of the different SRES storylines. These modifications result in
5 different spatial allocations among the scenarios such that the B storylines show more compact
6 growth focused around urban areas and the A storylines show less compact growth overall and
7 more housing in suburban and exurban densities.
8 The scenarios result in a range of projected increases in urban and suburban area across
9 the United States. The smallest increase is 56% for the Bl scenario and the largest increase is
10 156% for A2. These increases in housing can be translated to changes in impervious surface
11 cover, which can be used to examine impacts on water quality, for example. Our results show
12 that there could be a doubling to nearly a tripling of watersheds (8-digit HUCs) that are likely to
13 be stressed from impervious surface coverage of at least of 5%. These changes will vary
14 regionally across the country.
15 This report describes the modeling methodology for the EPA-ICLUS project and some
16 initial analyses using the outputs. There are many additional modifications that are possible to
17 explore additional land use futures and there are many options for further research. Model
18 modifications can be made to further explore policy and planning alternatives such as Smart
19 Growth development patterns. The demographic and spatial outputs can be used in numerous
20 analyses examining potential future impacts on air quality, water quality, traffic and associated
21 emissions, and regional growth rates.
22
Draft Report-June 26, 2008 pagex
-------
1 1 INTRODUCTION
2
3 Climate change and land use change are global drivers of environmental change. Impact
4 assessments frequently show that interactions between climate and land use changes can create
5 serious challenges for aquatic ecosystems, water quality, and air quality. In many cases, it is
6 impossible to determine the impact of climate change without consideration of land use and land
7 cover dynamics. While land use can exacerbate climate impacts, land use planning, policy, and
8 management can also create important adaptation opportunities to increase the resilience of
9 sensitive socioeconomic or ecological systems.
10 Integrated assessments of both climate and land use changes currently are limited by fragmented
11 information on potential future land use. In many cases individual municipal areas have
12 conducted extensive analyses, but it is impossible to place results in regional or national
13 contexts. Moreover, the studies are often based on inconsistent or poorly documented
14 socioeconomic storylines. The motivation for the EPA-ICLUS project was derived from the
15 recognition of the complex relationships between land use change and climate change impacts
16 and the absence of an internally consistent set of land use scenarios that could be used to assess
17 climate change effects.
18 The EPA-ICLUS project developed scenarios for two important aspects of land use, housing
19 density and impervious surface cover, for the entire conterminous United States for each decade
20 through 2100. These scenarios are based on the Intergovernmental Panel on Climate Change
21 (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic
22 storylines (Nakicenovic 2000). These scenarios are rendered using a combination of models
23 representing demography, including domestic and international migration, and spatial allocation
24 of housing (Figure 1-1). The resulting scenarios (1) enable us, our partners, and our clients to
25 conduct assessments of both climate and land use change effects across the United States; (2)
26 provide consistent benchmarks for local and regional land use change studies; and (3) identify
27 areas where climate-land use interactions may exacerbate impacts or create adaptation
28 opportunities.
29
Draft Report-June 26, 2008 page 1
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Repeat
Initial county population
and SRES storylines
i
Demographic model
(cohort component & migration)
Population for next time step
Total population per
county per time step
1
Housing allocation
Figure 1-1: Model and information flow within the EPA-ICLUS project
The U.S. Environmental Protection Agency's Global Change Research Program (GCRP) in the
National Center for Environmental Assessment of the Office of Research and Development
began investigating the availability of state and county-level population projections in 2004.
Initial efforts evaluated the availability, sources, and extent of state and county-level population
projections with an emphasis on identifying projections for the time period from 2050 through
2100. These efforts yielded numerous datasets, but very few sources projected populations to
2050 and beyond. Demographic projections for these later years are particularly relevant when
considering the impacts of climate change on ecosystems, water infrastructure, transportation
infrastructure, and land protection efforts. Population and land use projections based on
economic factors such as regional income and employment growth can shift dramatically over
time. Projections based on fundamental demographic drivers such as fertility and mortality are
somewhat more stable, particularly over longer time frames. Therefore, the ICLUS project uses
demographic projections as the basis for modeling changes in housing density.
The modeling framework (Figure 1-1) presented in this report uses demography to drive the
number and migration of people, while a spatial allocation model governs the distribution of
people on the landscape in housing units. The demographic model has two parts, a cohort-
component model and a gravity model. The spatial allocation is conducted using an established
Geographic Information System (GlS)-based model, SERGoM (Theobald 2001, 2003, 2005).
The study was designed to provide county-level demography and housing density for the
conterminous United States with housing density allocations for each 1 ha pixel for each decade
through 2100.
26 2 SRES DOWNSCALING TO THE UNITED STATES
27 The socio-economic storylines in the SRES are derived from anticipated demographic,
28 economic, technological, and land-use changes data for the 21st century, and are highly
29 aggregated into four world regions (Nakicenovic et al. 2000). The SRES describe linkages
30 between physical changes in climate and socio-economic factors, because they link development
31 pathways with greenhouse gas (GHG) emissions levels used as inputs to general circulation
Draft Report - June 26, 2008
page 2
-------
1 models (GCMs) (Rounsevell et al. 2006). There have been other scenario-development exercises
2 to assess future impacts on a range of endpoints, including the Millennium Ecosystem
3 Assessment (MEA, 2005), which also describes economic and environmental conditions in the
4 future. The benefit of using the SRES storylines is their direct link to GCMs. This enables us to
5 link our land-use projections to emissions and future climate scenarios for integrated assessments
6 of the effects of land use change and climate change in a consistent way.
7 Modeling and projecting human activity into the future is a challenge for many reasons. Any
8 attempt to project demographic and economic changes over time must contend with large
9 degrees of uncertainty. Furthermore, taking discontinuous events into consideration—ones that
10 would have a profound effect on any anticipated trajectory—is even more difficult. Nevertheless,
11 a forward-looking approach to environmental and economic problems encourages us to look into
12 the future to attempt to better understand the challenges that lie ahead, and to better prepare our
13 society to confront those challenges.
14 By taking a scenario approach to such modeling efforts, we acknowledge this inherent
15 uncertainty and consider a variety of possible trajectories. This approach results in a range of
16 outputs. No single output may be the "right" one, but together they paint a picture of a likely
17 range of possible futures. The primary challenge to the scenario approach lies in developing a
18 reasonable range of scenarios that can be used in multiple modeling efforts.
19 The emissions scenarios in the SRES cover a wide range of possible paths for the primary social,
20 economic, and technological drivers of future emissions. These scenarios have since become the
21 standard input of socio-economic information for GCMs and other land-use change modeling
22 efforts (e.g., Solecki & Olivieri, 2004; Reginster & Rounsevelle, 2006; Rounsevelle et al. 2006)
23 providing a reasonable set of scenarios to bound the potential futures with respect to climate
24 change. By using the SRES storylines as the basis for the scenarios investigated by this project,
25 the results may be put into the context of widely available and peer-reviewed climate-change
26 model output (e.g., IPCC, 2001; IPCC, 2007).
27 2.1 OVERVIEW OF THE SRES STORYLINES
28 The development of SRES consisted of three main steps, beginning with qualitative "storylines"
29 that describe broad economic, environmental, technological, and social development patterns
30 that could unfold over the 21st century (Nakicenovic et al. 2000). Next, particular quantitative
31 paths for the fundamental driving forces of emissions, including population and gross domestic
32 product (GDP), were selected for each storyline. Finally, six different modeling teams produced
33 quantitative interpretations of the storylines, using the quantitative paths for driving forces as
34 inputs, resulting in 40 different scenarios for energy use, land use, and associated GHG
35 emissions over the next 100 years (Nakicenovic et al. 2000). The SRES describe storylines along
36 two major axes, economic vs. environmentally-driven development (A-B) and global vs. regional
37 development (1-2), which make up the four combinations of storylines, Al, A2, B1, and B2
38 (Figure 2-1). There are between 6 and 18 emissions scenarios within each of the four SRES
39 storylines. Table 2-1 provides a summary of the qualitative fertility, mortality, and migration
40 assumptions made by the SRES authors for each storyline for the industrialized country regions
41 and the developing country regions; these qualitative assumptions served as the framework for
42 the more quantitative inputs for the scenarios.
Draft Report-June 26, 2008 page 3
-------
SRES Scenarios
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Global
*^fP*
il ' *A~ B2
I
Environmental
Regional
r/V/ng FO*C&%
Figure 2-1: Illustration of the SRES scenario families along two dimensions that indicate
the relative orientation of the storylines along the axes of global or regional development
and economic or environmental concerns (reprinted from Nakicenovic et al. 2000).
Table 2-1: Qualitative Demographic Assumptions in global SRES Storylines
Storyline
A1/B1
A2
B2
Fertility
IND: medium
DEV: low
IND: high
DEV: high
IND: medium
DEV: medium
Mortality
IND: low
DEV: low
IND: high
DEV: high
IND: medium
DEV: medium
Migration
IND: medium
DEV: medium
IND: medium
DEV: medium
IND: medium
DEV: medium
Projection Source
HAS A, 1996
HAS A, 1996
UN, 1998
IND = "Industrialized country regions"; "DEV" = Developing country regions". The "high", "medium", and "low"
descriptions are interpreted as relative to the overall outlook within each region (i.e., high fertility in the IND region
means the high end of the plausible range for that region, but may in fact be lower than low fertility paths for the
DEV region, which occupy the low end of the plausible range for the DEV region).
Based on descriptions in the SRES report (primarily in sections 4.3 and 4.4 of the SRES report),
a summary of the reasoning for these assumptions is provided below:
• In the Al storyline, rapid economic development, associated with improved education and
reduced income disparities, is assumed to drive a relatively rapid fertility decline in the high
fertility regions. Global population is expected to rise until peaking in the middle of the
century, after which fertility is generally below replacement level. Fertility in industrialized
regions is assumed to follow a medium path at least in part so that, relative to the developing
Draft Report - June 26, 2008
page 4
-------
1 regions, the scenario is consistent with the assumption that social and economic convergence
2 will lead to demographic convergence as well. For mortality, it is assumed that the conditions
3 leading to low fertility are also consistent with relatively low mortality, so mortality is
4 assumed to be low in all regions. No explicit discussion of migration is provided, although the
5 projection eventually adopted assumes medium migration levels.
6 • In the A2 storyline, the regional orientation and slower rate of economic growth, limited flow
7 of people and ideas across regions, and orientation toward family and community values was
8 judged to be consistent with a relatively high fertility in all world regions. Mortality was
9 assumed to be high as well, based on the assumption that conditions leading to high fertility
10 would also lead to relatively high mortality in all regions. Although the storyline describes a
11 limited flow of people across regions, the storyline authors assumed medium migration flows,
12 as in all other storylines.
13 • The Bl storyline shares the same population projection as the Al storyline, although for
14 somewhat different reasons. Rapid social development, particularly for women, and an
15 emphasis on education drives a relatively rapid decline in fertility in developing country
16 regions (as opposed to the Al storyline, in which economic development is seen as the main
17 driver). Reasoning for fertility in industrialized countries, and for mortality and migration
18 assumptions, are the same as in Al.
19 • In the B2 storyline, economic development is moderate, particularly in the developing country
20 regions. However education and welfare programs are pursued widely and local inequity is
21 reduced through strong community support networks. The mix of moderate economic
22 development and strong but heterogeneous social development results in an assumption of
23 medium fertility and mortality paths. Migration is again assumed to be medium, with no
24 explicit discussion of this choice.
25 Demographic assumptions in SRES are intended to be consistent with storylines and with other
26 driving force assumptions. Consistent relationships among these factors mean that the
27 demographic assumptions occur in the context of other socio-economic trends in a way that is
28 not at odds with established theory, the weight of historical experience, or current thinking in the
29 literature on determinants of demographic trends. For example, a key factor differentiating
30 population assumptions across SRES scenarios is the assumed speed of the transition from high
31 to low fertility in regions with relatively high current fertility. The transition occurs faster in the
32 Al and Bl scenarios, and slowest in the A2 scenario. These choices are based on the rationale
33 that there are a range of conditions that contribute to fertility transitions, including economic
34 development, education, labor force opportunities for women, and the spread of ideas about
35 modern lifestyles (Lee, 2003). These factors are present, and stronger, in the Al and Bl
36 storylines (in different combinations) and absent, or weaker, in the A2 storyline. Thus, in this
37 case, there is a clear notion of consistency in which storyline elements can be said to favor
38 preferentially a particular demographic outcome.
39 However, consistency does not mean that the assumed demographic trends are the only possible
40 outcomes, or in some cases even the most likely outcomes, conditional on a particular storyline.
41 In some cases, storylines serve only as weak constraints on demographic futures, and a wide
42 range of demographic assumptions might all be consistent with the broader development trends.
43 For example, the demographic transition reasoning just described applies only to countries with
44 relatively high fertility (e.g., substantially above replacement level of about 2 births per woman).
45 These conditions occur for only about half the current population of the world, and for only the
Draft Report-June 26, 2008 page 5
-------
1 first half (or even less) of the 21st century, according to projections (Lee, 2003). Once the
2 transition to low fertility is complete, there is little theoretical basis for linking subsequent
3 fertility changes or cross-country differences to particular socio-economic trends; a wide range
4 of outcomes is possible (O'Neill, 2005). Yet SRES scenarios link the pace of economic growth
5 with fertility outcomes (i.e., demographic transition-type reasoning) for all regions of the world,
6 and for the entire century. It is certainly possible that rapid economic growth will be associated
7 with relatively low fertility (and slow growth with high fertility) in post-transition societies, and
8 it is not inconsistent in the sense of contradicting established theory (though this is at least partly
9 because there is no single established theory to contradict). Completely opposite associations
10 (e.g., low fertility with slow economic growth) are equally plausible (and equally consistent) for
11 post-transition societies.
12 Thus in considering the implications of SRES storylines for demographic outcomes at the
13 national and sub-national level, it must be kept in mind that it is possible that a wide range of
14 different assumptions could be judged to be consistent with SRES. Indeed other studies have
15 developed alternative demographic assumptions for SRES storylines, with different quantitative
16 outcomes (Hilderink, 2004), or have quantified a range of plausible outcomes associated with
17 scenario storylines (O'Neill 2004, 2005, 2005b).
18 2.2 INTERPRETING AND DOWNSCALING THE SRES STORYLINES
19 The SRES storylines do not provide a clear blueprint for downscaling to the local or even the
20 national level. In incorporating the SRES storylines into county-level projections for the United
21 States, an effort was made to be consistent in qualitative terms with the global SRES storylines.
22 Given the wide range of potential interpretations, this consistency was understood to imply that
23 the qualitative trends do not contradict established theory, historical precedent, or current
24 thinking. It was also a goal to model a wide a range of assumptions, while remaining consistent
25 with the SRES and U.S. demographic patterns. Rationales connected to SRES storylines are
26 discussed briefly for each scenario. For each of the storylines adapted to the United States, the
27 fertility assumptions are exactly consistent with the global assumptions, while domestic and
28 international migration patterns leave more room for interpretation and are more specifically
29 adapted to the United States. The low U.S. Census scenario for mortality was chosen for all
30 storylines used in the modeling (see 6Appendix B for more information).
31 2.2.1 Al Storyline Adapted for the United States
32 Al represents a world of fast economic development, low population growth, and high global
33 integration. In this storyline fertility is assumed to decline and remain low in a manner similar to
34 recent and current experience in many European countries (Sardon, 2004). A plausible rationale
35 would be that the rapid economic growth in this scenario leads to continuing high participation
36 of women in the workforce, but it becomes increasingly difficult to combine work with
37 childbearing due to inflexibilities in labor markets. At the same time, social changes in family
38 structures lead to increasing individuation, a rise in divorce rates, a further shift toward
39 cohabitation rather than marriage, later marriages and delayed childbearing, all of which
40 contribute to low fertility. Substantial aging resulting from the combination of low birth rates and
41 continued low death rates raises the demand for immigration. Meanwhile, economic growth
42 throughout the world and an increasingly unified global economy encourage the free movement
43 of people across borders. Domestic migration is anticipated to be relatively high as well, as
44 economic development encourages a flexible and mobile workforce.
Draft Report-June 26, 2008 page 6
-------
1 2.2.2 Bl Storyline Adapted for the United States
2 This storyline represents a globally-integrated world similar to Al, but with greater emphasis on
3 environmentally sustainable economic growth. Like Al, fertility is assumed to be low due to
4 higher incomes and economic development. International migration is expected be high due to
5 widespread economic development and freer global flows. Domestic migration however is lower
6 due to a combination of factors. First, an increased focus on sustainability leads to a reduction of
7 subsidies for development in previously rural counties with significant natural amenities.
8 Second, the information oriented economy increases demand for specialized labor pools and
9 increases the number of high paying j obs in traditional large urban centers.
10 2.2.3 A2 Storyline Adapted for the United States
11 The A2 storyline represents a world of continued economic development, yet with a more
12 regional focus and slower economic convergence between regions. Fertility is assumed to be
13 higher than in Al and Bl due to slower economic growth, and with it, a slower decline in
14 fertility rates. International migration is assumed to be low because a regionally-oriented world
15 would result in more restricted movements across borders. Domestic migration is high because,
16 like in Al, the continued focus on economic development is likely to encourage movement
17 within the United States.
18 2.2.4 B2 Storyline Adapted for the United States
19 The B2 represents a regionally-oriented world of moderate population growth and local solutions
20 to environmental and economic problems. Fertility rate is assumed to be medium, while
21 international and domestic migration are low due to the local emphasis, focus on sustainability,
22 and increasing number of jobs in urban centers. International migration is low due to the regional
23 orientation, as with A2. Domestic migration is low due to the more environmental orientation, as
24 withBl.
25 The SRES storylines adapted to the United States were integrated into our modeling framework.
26 We modified both the demographic and spatial allocation models for each of the four scenarios.
27 3 DEMOGRAPHIC PROJECTIONS
28 3.1 OVERVIEW
29 The ICLUS demographic model utilizes a cohort-component methodology. The cohort-
30 component methodology is a common technique for projecting population on the basis of five
31 independent variables or components of population change: fertility, mortality, domestic in- and
32 out-migration, and net international migration. The population is divided into cohorts that are
33 age-, gender-, and race/ethnicity-specific. Changes due to these five components of change are
34 estimated over time as each cohort is tracked separately, hence the term "cohort-component"
35 (Siegel and Swanson, 2004).
36 The methodology is flexible in that different assumptions can be applied to each component of
37 population change. For example, fertility—or the number of births—is estimated by multiplying
38 cohort-specific fertility rates (births per woman) times the population of each cohort of women.
39 Different rates can be applied to women of different ages and ethnicities. Furthermore, these
40 rates can change over time or between different scenarios. In all cases, the fundamental method
41 stays the same while changes in the rates can be used to simulate different SRES scenarios.
Draft Report-June 26, 2008 page 7
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
The population of a county in any year t as estimated by the model is determined using the
following equation:
Equation 3-1: Cohort-component model
P, = PM + B - D + NDM + MM
Where:
Pf = Population in year t
PM = Population in the previous year
B = Births in year t
D = Deaths in year t
NDM = Net Domestic Migration in year t
NIM = Net International Migration in year t
Beginning with an initial set of populations, annual components of change are applied in the
following process, which are repeated annually until the desired end year is reached.
1) Add births by cohort
2) Deduct deaths by cohort
3) Add net international migration
4) Add net domestic migration by combining domestic in-migration and out-migration
(Estimated every fifth year, as discussed below.)
5) Age population one year and repeat for the next year
This methodology is illustrated in Figure 3-1 below. The cycle begins with an initial Year 2005
population and is repeated until reaching Year 2100.
Initial Population in Year f
Apply Components of Change
-\.Add
Births
2. Subtract
Deaths
3. Add Net
International Migration
J
h
Artfi nonulation
4. Add Net Domestic
Migration
Population in Year t + 1
Figure 3-1: Demographic model flow
Draft Report - June 26, 2008
page 8
-------
1 Table 3-1 provides a qualitative description of how we modified the global storylines to the U.S.
2 As a general observation, note that the proposed fertility and mortality assumptions follow the
3 SRES assumptions, with the exception that the Al scenario assumes low fertility (rather than
4 medium as in SRES). In addition, the international migration assumptions assume high and low
5 immigration in scenarios Bl and A2, respectively (rather than medium as in SRES). These
6 choices are made in order to explore a fuller range of demographic trends for the United States
7 than in SRES, since the SRES storylines contained only a limited range of projections for the
8 North America region (O'Neill, 2005). It should also be noted that these assumptions are not
9 designed to cover the widest possible range of population size outcomes; e.g., the combinations
10 resulting in the highest and lowest population sizes are not explored here. In all cases the
11 mortality rate was kept constant across all scenarios. This was partly due to data availability—
12 the Census Bureau did not release alternate scenarios for mortality rates. Experiments with
13 adjusting these mortality rates (shown in Figure B-4 in 6Appendix B) demonstrated relatively
14 little change in total national population due to variations in the mortality rate.
15 Table 3-1: Summary of Qualitative Adjustments to the Demographic Projections
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Scenario
Al
Bl
A2
B2
Baseline
Demographic Model
Fertility
Low
Low
High
Medium
Medium
Domestic migration
High
Low
High
Low
Medium
Net international migration
High
High
Medium
Medium
Medium
In the following sections, the methods and data used for the initial population and each
component are discussed in greater detail.
3.2 INITIAL POPULATION
In order to use the rates for components of change provided by the U.S. Census Bureau (U.S.
Census Bureau, 2000) (discussed below), it was necessary to begin with an initial Year 2005
population dataset that was disaggregated using the same cohorts. These cohorts in the rates data
are divided into two genders (male and female), 100 age groups (0-99 in one year increments),
and five racial/ethnic—or "bridged race"—groups (Hispanic, non-Hispanic White, non-Hispanic
Black, non-Hispanic American Indian or Alaskan Native, and non-Hispanic Asian or Pacific
Islander).1 This represents 1000 distinct population cohorts (2 genders x 100 ages x 5 bridged
race groups).
County populations using bridged race and one-year age cohorts were most readily accessible
using the Bridged-Race Vintage 2006 dataset for July 1, 2005 provided by the National Center
for Health Statistics (NCHS, 2007). After downloading and parsing the data, two manipulations
1 In general, the U.S. Census Bureau considers the primary racial categories to be American Indian/Alaskan Native,
Asian/Pacific Islander, Black, and White. "Hispanic origin" is considered an ethnic category. The race and ethnicity categories
used by the Census have changed over time. In the 2000 Census, participants were allowed to identify with two or more racial
groups for the first time. This project utilized the "bridged race" categories listed above as a way of making data collected with
one set of categories consistent with data collected using another set of categories.
Draft Report - June 26, 2008
page 9
-------
1 were required. First, the dataset provided eight bridged race categories—the non-Hispanic
2 populations of the four race groups and the Hispanic populations of the four race groups. The
3 Hispanic populations were summed and combined into a single bridged race group. This group,
4 along with the four non-Hispanic race groups, comprised the five bridged race groups used
5 throughout the model.
6 Second, the census dataset provided one-year age groups from ages 0-84 and combined all others
7 in the 85+ age group. In order to extend the age distribution through 99 years, the 85+ population
8 data was disaggregated into ages 85-99. We used the national age 85-99 populations by race and
9 gender from the 2000 Census to allocate the 85+ age group from the NCHS data. We assumed an
10 identical race- and gender-specific age distribution in each county for the Year 2005 base
11 population. Although this step likely led to some distortions, the effects are not long-lasting in
12 the model as the initial 85-99 age groups "age out" of the model by 2015.
13 3.3 FERTILITY AND MORTALITY
14 Fertility and mortality followed a simple methodology. For fertility, the number of children born
15 equals the number of women in a given cohort times the average number of children born
16 annually to every 1000 women in that cohort divided by 1000. Because virtually all births occur
17 to women between the ages of 10 and 49, only those cohorts are considered in this model. These
18 births are summed by race and used to create a new age zero cohort. To allocate births between
19 males and females, we calculated a historic ratio of 1046 males born for every 1000 females born
20 and assumed that this ratio holds steady (Matthews and Hamilton, 2005).
21 Similarly, mortality is estimated by multiplying the number of people in a given cohort times the
22 cohort-specific mortality rates. The resulting number of deaths is then subtracted from the
23 cohort. Unlike fertility, all cohorts are subject to mortality. Therefore, mortality rates are applied
24 to each cohort. Although an increasing number of Americans is living to the age of 100 or more,
25 the model assumes 100% mortality after age 99 for the sake of computational efficiency. Even
26 with continued rates of survivorship past this age, the 100+ age group will remain a miniscule
27 portion of the population.
28 For fertility and mortality rates, the U.S. Census Bureau's "Component Assumptions of the
29 Resident Population by Age, Sex, Race, and Hispanic Origin" were used (U.S. Census Bureau,
30 2000). These are the same data used in Census projections. These components of change are
31 associated with the 1990 National Projections and are used in both the 1990 State Projections
32 (Campbell, 1996) and the 2000 National Projections (Hollman et al., 2000). While it would be
33 preferable to use more recent data, at this time components of change based on the 2000 Census
34 have not yet been released. While the rates are national averages, county differences that arise in
35 fertility and mortality are a reflection of each county's unique age, sex, and racial composition.
36 For both fertility and mortality, the so-called Middle Series of component information was used
37 as the baseline. Fertility rates are provided in a single file; mortality rates for each component are
38 provided in three different tables, for the years 1999-2010, 2015-2055, and 2060-2100. Projected
39 fertility rates are provided for each year to 2100, but beginning with 2010, mortality rates are
40 provided in five year increments only. We assumed that 2010 mortality rates held steady from
41 2010-2014, 2015 mortality rates held steady from 2015-2019, and so on.
42 The Census Bureau also provides a low and high scenario for fertility. Alternative scenarios for
43 mortality were not available. While the middle series was used in this "base case," the low and
44 high series were used when developing projections specific to the SRES storylines. The low data
Draft Report-June 26, 2008 page 10
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
set was used for the Al and B1 scenarios, the medium set was used for the B2 scenario, and the
high set was used for the A2 scenarios.
3.4 NET INTERNATIONAL MIGRATION
The projections for net international migration utilized a simple method based on the U.S.
Census Bureau's international migration projections for the entire country. These files contain
the projected net international migration for each gender, age, and race cohort for the years 2000-
2100. Like the fertility and morality rates, these data are provided by the Census (U.S. Census
Bureau, 2000).
Since the tables "Foreign-born Net Migration to the United States" contain only national level
data, it was necessary to allocate the national migrants to the counties. Using 2000 Census data
(U.S. Census Bureau, 2007, Summary File 3, Table P22), we determined each county's share of
the total population of recent immigrants (i.e. those who entered within the last five years). These
county shares were then used to allocate each cohort of immigrants among the nation's counties.
The estimated number of immigrants in each cohort was then added to the existing county
population of each cohort. This method assumes a constant distribution of recent immigrants
based on Year 2000 immigration patterns. While we anticipate that many of the current "gateway
counties" will continue to draw a large share of new immigrants, it is likely that new settlement
patterns will develop in the future.
The Census Bureau provides a low, medium, and high series for net international immigration. In
the base case, the middle series was used. The medium and high series were used when
developing SRES storyline-specific projections. The high series was used for the Al and Bl
storylines, while the medium series was used for the A2 and B2 storylines. The low set was not
used because the values projected under the U.S. Census Bureau's low set were so far below
current levels of immigration (Figure 3-2, Census Bureau, 2008) that they were considered to be
unrealistic for these purposes.
3.5
e
o
3.0 -
e
._§ 2.5 -|
e«
•£ 2.0 -
§ 1.5 -J
Low Projection
- Middle Projection
- High Projection
Estimated Historical Data
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Figure 3-2: Projected Net International Migrations under U.S. Census Bureau Scenarios
Draft Report - June 26, 2008
page 11
-------
1 3.5 DOMESTIC MIGRATION AND GRAVITY MODELING
2 Domestic migration is the most complex component of change included in this population
3 model. In contrast to straightforward processes like fertility and mortality, which may be
4 predicted in aggregate with reasonable confidence, domestic migration is much more difficult to
5 predict. People move within the United States with relative frequency—according to the 2000
6 Census, approximately 44 percent of Americans lived at a different address in 2000 as they did
7 in 1995 (Perry and Schachter, 2003) These migrations occur for a wide variety of personal and
8 economic reasons that are difficult to predict. Unlike international immigration, which can be
9 thought of as a single external source of immigrants, domestic migration works two ways—
10 migrants must choose to leave one place and resettle in another place.
11 To estimate domestic migration, this project utilized a "gravity model" approach. Gravity models
12 are a common type of spatial interaction model in which a dependent variable (in this case,
13 migration flow between a pair of counties) is estimated as a function of a series of independent
14 variables (Rodrigue et al. 2006). Common independent variables include population and distance
15 (Haynes and Fotheringham, 1984); natural amenities, such as climate variables and bodies of
16 water, have also been shown to affect migration patterns, particularly in rural areas
17 (McGranahan, 1999). Among these, temperate summers and warm winters have the strongest
18 correlation with population growth (R2=0.38 and R2=0.27, respectively), a finding that holds true
19 even when correcting for macro-trends such as the general movement from the Northeast and
20 Midwest to the South and West (McGranahan, 1999). Other work has also shown that amenity -
21 rich mountain and coastal areas are highly successful at attracting migrants, particularly those
22 who are affluent and/or retired (Manson and Groop, 2000). While a wide variety of independent
23 variables were tested, the final gravity model used in this effort included the following
24 independent variables: population of the origin and destination counties, selected climate
25 variables of the origin and destination counties, water surface area of the origin and destination
26 counties, ratio of 2000 county population to 1980 population of the destination county, and the
27 distance between counties.
28 The final model form and model coefficients were estimated by analyzing 1995 to 2000
29 migration data reported in Public Use Microdata Samples (PUMS) (Census Bureau, 2003). Using
30 county-to-county migration data as the dependent variable and the various county attributes as
31 dependent variables, we ran a series of stepwise regression analyses to select the most
32 statistically significant set of dependent variables and estimate the model coefficients. Below, we
33 discuss how each data source for the regression analyses was developed, the resulting model
34 form, and the way this model was implemented in the overall demographic model.
35 3.5.1 1995-2000 Migration Data
36 The 2000 Census Public-Use Microdata Samples (PUMS) data provides detailed records of
37 individual domestic migrations and characteristics of these migrants between 1995 and 2000
38 (Census Bureau, 2003). However, the raw data does not readily provide county-to-county
39 migration counts. Because the migrations are organized by the destination state, in-migrants to
40 any given state can be analyzed using that state's PUMS file, but analysis of out-migration
41 requires analyzing data from all 50 states, the District of Columbia, and Puerto Rico. This project
42 used a national migrant file created by the New York State Data Center from the individual state
43 and area files.2
The website for the New York State Data Center is http://www. empire. state.nv.us/nvsdc/default. asp.
Draft Report-June 26, 2008 page 12
-------
1 The second challenge with the PUMS data is that it is organized by Public-Use Microdata Areas
2 (PUMAs). Counties and PUMAs overlap in a non-systematic way. Large urban counties consist
3 of multiple PUMAs, while small rural counties are often combined into a single PUMA. To
4 further complicate the issue, the spatial area that PUMS uses to capture migration origins (where
5 respondents lived in 1995), the so-called Migration PUMA (MIGPUMA), is not the same spatial
6 area as the one used to capture where they lived in 2000 (the PUMA). The MIGPUMA is a
7 larger area, and there can be one or more PUMAs contained within the MIGPUMA.
8 Because we required a database that contains migration, attraction factors, production factors,
9 and distance information for pairs of areas, introducing the MIGPUMA into our spatial
10 framework would have required re-calculating all of the other variables at the MIGPUMA level,
11 and aggregating the migration data (destinations) to the MIGPUMA level, losing considerable
12 local detail. Instead, we elected to proportionately assign MIGPUMA origins to its constituent
13 PUMAs, on an average basis.
14 Furthermore, retaining a county-based framework is practical because the amenity data is
15 available only for counties and counties are a more popularly understood and conceptualized
16 units than PUMAs. For the sake of consistency and simplicity, we then proportionately assigned
17 PUMA origins and destinations into counties based on the proportion of each PUMA's
18 population belonging to one or more counties. In the case of large metropolitan counties, several
19 PUMAs were typically aggregated to a single county, while in suburban and rural areas, PUMAs
20 were often split among two or more counties. As a result of the transition from MIGPUMA to
21 PUMA to county, the current migration data set provides county-to-county flows.
22 These county-to-county flow data were then aggregated into various age groups. Since migration
23 behavior is hypothesized to vary for different age groups, different gravity models were
24 calibrated for different age cohorts. In initial runs, the population was divided into ten-year
25 cohorts, from 0-9 to 90 and up, and a different gravity model was estimated for each. We
26 observed some broad differences in behavior between the older (over 50) age groups and the
27 younger age groups, presumably due to differences in migration patterns due to retirement.
28 However, the differences between the ten individual models were not found to be significant
29 enough to warrant ten different age groups. As a result, the dataset was receded into just two age
30 groups: 0-49 and 50+.
31 3.5.2 County Attribute Data
32 After receding the migration data set from the original geographies to counties, we then added
33 the county data that would be included as independent variables in the analysis. These variables
34 include county environmental amenities, population, and 1980-2000 population growth, which
35 serves as a proxy for economic growth.
36 The source for the amenity information used in the regression was found in a database compiled
37 by the USD A (McGranahan, 1999), which utilizes data originally collected from the Center for
38 National Health Statistics, U.S. Department of Health and Human Services. The amenity index
39 includes a range of climatic and amenity factors that are thought to influence migration. The data
40 used from this set include:
41 • Mean temperature for January, 1941-70;
42 • Mean hours of sunlight for January, 1941-70;
43 • Mean temperature for July, 1941-70;
Draft Report-June 26, 2008 page 13
-------
1 • Mean relative humidity for July, 1941-70; and
2 • Percent water area.
3 County population data were drawn from the U. S. Census Bureau' s City and County Data Book.,
4 2000 edition. The population over age 50 — used in analyses of the 50-99 age group — was
5 determined from data downloaded from the 2000 U.S. Census (Summary File 1, Table P12). The
6 2000 county population data, combined with 1980 and 1990 county populations provided by the
7 Census Bureau (U.S. Census Bureau, 1992), were used to calculate the growth rate of counties
8 between 1980 and 2000. This term was added after initial model runs resulted in declining
9 populations in medium-sized counties that exhibited strong growth in the 1980s and 1990s.
10 3.5.3 Distance Matrix
1 1 A full county-to-county distance matrix was the final data input used in developing the gravity
12 model. Typical methods of migration movement between geographic locations (e.g., city to city
1 3 or county to county) assume that interaction can be estimated as straight-line county centroid-to-
14 centroid distance (Conley and Topa, 2002). Two main deficiencies are that: (1) often the
1 5 geographic centroid of a county poorly represents the population-weighted centroid of a county;
1 6 and (2) humans do not move around on the ground optimally using a straight-line strategy, rather
1 7 we are constrained to transportation infrastructure which in turn has evolved in response to major
1 8 topographic and water features.
1 9 Because of these deficiencies, we developed a "functional" estimate of county-to-county
20 interaction. The main assumptions incorporated into the county-to-county functional distance
21 calculations are that the amount of interaction, and more specifically migration, between counties
22 are better approximated by examining: (a) where in a county population is located, and (b) how
23 much ground- (and water-) based transportation infrastructure is in place.
24 The geographic centroids of each of 25, 1 50 U. S. Census Bureau-defined places (within
25 coterminous United States) were found and assigned to the county in which they were located.
26 The population (2000 Census) was then used to weight each point, and the population-weighted
27 centroid for each county was determined. For roughly a half-dozen counties, the population-
28 weighted centroid was calculated to be outside the boundaries of its respective county. For these,
29 the centroid was manually moved back into its respective county. We also generated a centroid
30 for a couple of counties in Nevada that did not have places in them (visually in the center).
31 We generated a cost- weight surface using major roads such that the weights were assigned the
32 assumed average speed limit assigned by road type. Cost distance was then computed using the
33 population-weighted centroids as the "seeds", computing minutes travel time T along the roads.
34 For each adjacent (first-order neighbor) county-county pair ij we adjusted the travel time to
35 account for k multiple roads (multiple connections between population centroids) to compute an
36 interaction weight W. The equation used to calculate the interaction weight fFis presented in
37 Equation 3-2 below.
38 Equation 3-2: Cost-Weight Surface for County Pairs
39
40 Where:
41 Wy = Interaction weight for the county-county pair /', j
Draft Report- June 26, 2008 page 14
-------
1 Tk = Travel time for k multiple roads
2 We then generated a network and used a network-based least-cost path algorithm to compute
3 effective distances along paths of pair-wise segments using the FunConn tools (Theobald et al.,
4 2006). We developed a distance matrix of pairwise functional distances (Wi}) computed in
5 minutes travel time. We exported this matrix from the GIS database in a list of from-county, to-
6 county, weight format (over 9 million records for 3109 counties times 3108 destination counties)
7 to the gravity model data table. We also manually added linkages between counties that are
8 served with regular ferry service as delineated in the U.S. Transportation Atlas 2006 (e.g., across
9 Lake Michigan and Nantucket Island).
10 3.5.4 Model Specification
11 After collecting the above data, it was reorganized into a single data table. Each record in the
12 data table contained the number of migrations from one county to another, the attributes of the
13 origin county, the attributes of the destination county, and the functional distance between them.
14 In keeping with the traditional functional form of gravity models, which states migration is
15 proportionate to attraction and production variables, and inversely proportional to distance, the
16 functional form of the model used was as follows:3
17 Equation 3-3: Gravity Model
18 Fij = a x Pi x JtlP3 x JSlP4 x Stlp5 x Sh* x Wlp? x P/P8 + P9 x PJ> x Jt/10 x Js/11 x St/12 x Sh/13
19 x Wjpl4 x Of5 x di/16
20 Where:
21 Fy = the migration of the population from county /' to county j from 1995 to 2000
22 P = the population of a county in 2000. For the 0-49 age group, total population was
23 used. For over-50 age group only the over-50 population was used.
24 Jt = the mean January temperature for a county
25 Js = the mean January hours of sunlight for a county
26 St = the mean July temperature for a county (S = Summer)
27 Sh = the mean July humidity for a county (S = Summer)
28 W = the percent water area for a county
29 G = county growth rate, expressed as the ratio of 2000 population to 1980 population
30 dy = the functional distance between /' andy
31 a, pi, ... P16 = parameters estimated by the regression model.
32 In order to estimate this equation using multiple regression, a logarithm of both sides is taken,
33 which yields the following linear equation.
3 The parameters estimated by the regression model (provided below) are positive for those variables that are directly
proportional to migration (e.g., population) and negative for those variables that are inversely proportional to migration (e.g.,
distance).
Draft Report-June 26, 2008 page 15
-------
1 Equation 3-4: Logarithmic Transformation of the Gravity Model Equation
2 Log(Fij) = log(a) + plxlog(PO + p2xp1xlog(P1) + p3xlog(JtO + p4xlog(JSl) + p5xl
3 p6xlog(Shi) + p7xlog(Wi) + p8xlog(Pj) + p9xpjXlog(Pj) + plOxlog(Jtj) + p
4 pl2xlog(Stj) + pl3xlog(Shj) + pl4xlog(Wj) + pl5xlog(Gj) + P16xlog(dy)
5 We also ran a variety of regionally-specific experimental runs to consider if different effects
6 were observed on a regional basis. The four Census-defined regions are the Northeast, South,
7 Midwest, and West. Although some differences were observed between the different regions, the
8 marginal improvement in model fit offered by regionally-specific models was outweighed by the
9 complexity of developing a separate model for each pair-wise combination of regions.
10 In the equations above, population is treated differently from other variables. Initial model runs
11 used the form:
12 log(migration) = a + b * log(population) + other terms
13 so that migration is proportional to (population)13. Under this model, log(migration) always
14 increases with log(population) at the same rate (assuming b > 0), all else being equal. When
15 running the model over many decades, this caused the model to predict that most of the
16 population of many small counties would migrate to nearby large counties. However, such a
17 scenario is unrealistic as large counties grow increasingly crowded and the differential between
18 land prices in the urban core and on the sub- and ex-urban edges grows. We then modified the
19 model so that the slope b is not constant but varies slowly with the size of the population. The
20 new model used b = c + d x population, providing the following form:
21 log(migration) = a + c x log(population) + d x population x log(population) + other terms
22 With this revised equation, migration is proportional to (population) (c + d x P°Pulatlon) AS expected,
23 the fitted c coefficient was positive while the d coefficient was small and negative, so that for
24 small populations, the new model looks like the old model, but for large populations,
25 log(migration) increases with log(population) at a slower rate. This model resulted in projections
26 more consistent with expectations: with more modest growth the largest populations did not
27 grow in extreme proportions, while the suburban and exurban counties (particularly those in
28 between two relatively close cities) grew the fastest.
29 3.5.5 Stepwise Regression Results
30 Table 3-2 below provides a summary of the stepwise regression results, including the model R2
31 and the individual parameters. The R2 indicates the overall goodness-of-fit of the regression
32 models, or what percentage of the variance of the logarithm of the flow is explained by the
33 independent variables. The estimates for each parameter in the models are also provided. The
34 regression modeling was performed using a stepwise approach, such that at each step a term was
35 added if the significance level was < 0.05 and terms were removed if the significance level was
36 >= 0.05. For these models, all the regression parameters except for one regression parameter in
37 the 50+ model were statistically significant at each step (p < 0.0001), i.e., the improvement in
38 goodness-of-fit was statistically significant. The relative explanatory power of each variable is
39 provided in Table 3-3 below. These results show that population and distance represent the
40 majority of the model's explanatory power. While certain amenity variables may be more
41 important in rural counties, when considered with much larger urban and suburban counties, the
42 additional contribution of these variables is smaller, though still significant.
Draft Report-June 26, 2008 page 16
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
We also ran a correlation on each of the independent variables to determine the prevalence of
collinearity among these variables. In nearly all cases, all pairs of variables were found to be
statistically collinear at the p<0.0001 level, although many of the estimated squared correlations
(R2) were small. With such an extremely large sample size (n=2,397,007, or the number of
migration records in PUMS), it is relatively easy for even very low R2 values to be statistically
significant. In this case, having a very significant p-value simply suggests that the data are
inconsistent with a true correlation of zero, but they could be consistent with some very small,
but non-zero, correlation.
Table 3-2: Gravity Model Results
Age Group*
Variable
Adj.R2
Intercept (log(a))
Production Population
(Production Population)productlon Populatlon
Production January Temp.
Production January Sun
Production July Temp.
Production July Humidity
Production Water Surface
Attraction Population
(Attraction Population)Attractlon Populatlon
Attraction January Temp.
Attraction January Sun
Attraction July Temp.
Attraction July Humidity
Attraction Water Surface
Attraction 1980-2000 Growth Rate
Distance
0-49 50+A
0.665 0.6591
5.74014 3.14215
0.7756 0.78429
-9.12E-09 -5.63E-08
0.07301 N/A
0.04666 -0.15858
-1.28061 -0.85029
-0.4482 -0.22306
0.01199 -0.00422
0.85084 0.84382
-1.58E-08 -8.83E-08
0.01362 0.09291
0.06784 0.09263
-0.78317 -0.78248
-0.38647 -0.28732
0.0192 0.01334
0.30131 0.54938
-0.98919 -0.83684
All parameters were significant at the pO.OOOl level, with the exception of Production January Temp, in the 50+
model, which was not significant.
* The variable result values correspond to the (3-values in Equations 3-3 and 3-4.
A Population over age 50 was used in place of total population in the Age 50+ model.
Draft Report - June 26, 2008
page 17
-------
1
2
Table 3-3: Ranking of the contribution of independent variables to explanatory power of
the models
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Ages 0-49
Attraction Population
Production Population
Distance
Production July Humidity
Attraction July Humidity
(Attraction
Population)Attractlonpopulatlon
Production July Temp.
Attraction 1980-2000
Growth Rate
Production Population A
Production Population
Attraction July Temp.
Attraction Water Surface
Production January
Temp.
Production Water Surface
Attraction January Sun
Production January Sun
Attraction January Temp.
Partial R-
Square
for Each
Variable
0.3150
0.2130
0.1231
0.0052
0.0030
0.0023
0.0010
0.0010
0.0007
0.0004
0.0001
0.0001
0.0001
0.0000
0.0000
0.0000
Cumulative
Model R-
Square
0.3150
0.5280
0.6511
0.6563
0.6593
0.6615
0.6625
0.6636
0.6643
0.6647
0.6648
0.6649
0.6650
0.6650
0.6650
0.6650
Ages 50+*
Attraction Population
Production Population
Distance
Attraction 1980-2000
Growth Rate
(Attraction
Population)Attractlonpopulatlon
Attraction July Humidity
(Production
Population)productlon p°Pulatlon
Production July Temp.
Production July Humidity
Production January Sun
Attraction July Temp.
Attraction January Temp.
Attraction Water Surface
Attraction January Sun
Production Water Surface
Partial R-
Square
for Each
Variable
0.3315
0.1976
0.1143
0.0062
0.0023
0.0025
0.0020
0.0016
0.0007
0.0002
0.0001
0.0001
0.0001
0.0001
0.0000
Cumulative
Model R-
Square
0.3315
0.5291
0.6433
0.6496
0.6518
0.6543
0.6563
0.6579
0.6586
0.6588
0.6589
0.6590
0.6591
0.6591
0.6591
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
* Population over age 50 was used in place of total population in the Age 50+ model.
3.5.6 Model Flow
Having estimated the gravity model parameters, the gravity model was incorporated into the
overall demographic model to estimate domestic migration. Because the underlying migration
data used in this analysis measured migration over a five-year period, the migration was
estimated at five year intervals.
Using a pair of nested loops, the model cycles through each pair of counties, estimating the
migration from each county to every other county. Using the same amenity and distance data
discussed in Section 3.5.1 - 3.5.3 and the current estimated county populations as calculated by
the model, it enters the terms into the gravity model equation provided above for each of sets of
model parameters for each of the two age groups.
Because the gravity model does not consider race, gender, or age beyond the two broad age
groups, every estimation of migration from county A to county B must be applied to the
individual age, gender, and race groups. The model assumes that all groups within one migration
model are equally likely to move, so it allocates the total migrants from one county to another
based on the relative populations of each group in the origin county. In reality, it is likely that
migration patterns vary by factors such as age, race, and immigrant status. However, the initial
data set used here did not provide the level of specificity needed to add greater detail to the
gravity model analysis.
Draft Report - June 26, 2008
page 18
-------
1 Once migrants from one county to another are estimated by age, race, and gender, these
2 migrations are then subtracted from the origin county and added to the destination county. This
3 process is then repeated for every pair of counties. One major exception is that for counties that
4 fall in the least populated quintile of counties (approximately 620 counties), with a maximum
5 population in 2000 of 9,350, the gravity model is not applied, and no domestic migration is
6 assumed for those counties. The counties were included in the regression modeling, and analyses
7 indicated that the smallest counties are less likely to conform to the modeled behavior. This
8 higher error rate, combined with the powerful attraction of large counties, caused many small
9 counties to decline precipitously. Although excluding them from the model likely leads to some
10 distortions in these counties, collectively they account for roughly 1% of the national population.
11 Therefore it is unlikely that their exclusion has much effect on the remainder of U.S. counties.
12 3.5.7 Gravity Model and U.S.-Adapted SRES Scenarios
13 The gravity model provides many potential levers for adjusting migration patterns to account for
14 various future scenarios. To model the high and low migration patterns needed for the SRES
15 storylines adapted to the United States, total migrations were scaled in order to estimate greater
16 flows of people. In the Al and A2 storylines, where domestic migration is assumed to be higher
17 than in the base case, all migrations were increased by 50 percent. In the B1 and B2 storylines,
18 where domestic migration is assumed to be lower than in the base case, all migrations were
19 reduced by 50 percent. Appendix B includes a discussion on testing other migration assumptions.
20 3.6 MODEL RESULTS
21 The demographic model was run for a base case (all parameters set to "medium") and for the
22 four SRES-compatible scenarios. A variety of sensitivity tests with a wider array of model inputs
23 were also carried out. These tests and the results are discussed in greater detail in Appendix A.
24 Model runs calculated population on an annual basis for 2005-2100, with county totals reported
25 for five-year intervals. These outputs were then used as inputs to the spatial allocation model
26 (Section 4). While the detailed model output is too large to present here, a summary discussion
27 of the demographic model results follows.
28 Figure 3-3 shows the total population for the conterminous U.S. for the years 2005-2100, as
29 modeled in the base case and four SRES-compatible scenarios. A2, with high fertility and high
30 net international migration represents the highest population scenario. The base case and
31 scenario B2 are the middle scenarios, with medium fertility and medium international migration.
32 The difference between these two scenarios lies in the domestic migration, where the base case
33 assumes middle-of-the-road migration flows, while B2 assumes low domestic migration flows.
34 As a result of this distinction, the county populations in urban and suburban areas generally grow
35 faster than in rural areas in the base case, but the experiences of individual counties vary. Al and
36 Bl, with low fertility and high international migration are the lowest of the population scenarios.
37 The primary difference between these scenarios occurs at the domestic migration level, with an
38 assumption of high domestic migration under Al and low domestic migration under B1. The
39 effect of different migration assumptions becomes evident in the spatial model when the
40 population is allocated into housing units across the landscape. A more extensive discussion of
41 the regional differences in population growth is included in Section 5.1.
42 In general, the need for additional development is directly proportional to population growth. As
43 shown in Table 5-3 below, the growth in the extent of urban and suburban areas is greatest in the
44 scenario with the highest project population growth (A2). The growth in urban and suburban
45 areas in the other scenarios is analogous to their population growth: B2 and the Base Case are in
Draft Report-June 26, 2008 page 19
-------
1
2
3
4
5
6
7
8
the middle, while Al and Bl are the lowest. With impervious surface, the results are somewhat
different (Table 5-5), indicating that the allocation and housing parameters adjusted in SERGoM
such as household size and commute travel time are a greater driver of changes in impervious
surface than population alone. In this case the Al and A2 scenarios have the highest growth in
impervious surface, while the Bl and B2 scenarios have the lowest. This suggests that decisions
about which lands are utilized are as important as actual population growth in driving land use
impacts. The impacts of the population scenarios on land use and the methodologies used to
model this are explored in the following chapters in greater detail.
onn nnn nnn
700,000,000 -
600,000,000 -
o
5 500,000,000 -
a.
o
Q.
/inn nnn nnn
300,000,000 -
onn nnn nnn
JD
&'
&'
,*'
&
sf
.<&'
jj **' uJk Q*J£~*lLt A A A A A
^^^^^
LOLOLOLOLOLOLOLOLOLO
Ot-C\ICO--d-LOCQh-OOa>
oooooooooo
C\IC\IC\IC\IC\IC\IC\IC\IC\IC\I
— • — Base Case
--B--A2
— A— B1
--D--B2
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Figure 3-3: Total Population under Five ICLUS Scenarios. Scenario B2 and the Base Case
have the same population trajectories, as do scenarios Al and Bl.
3.7 COMPARISON OF DEMOGRAPHIC MODEL WITH EXISTING PROJECTIONS
In order to substantiate the results of the demographic model, we compared the projected county
populations for five states with population projections developed by the states themselves. The
five states used were California, Colorado, Florida, Minnesota, and New Jersey. All state
projections were for the year 2030, except for New Jersey, which was for the year 2025. These
states were selected based on data availability and regional diversity.
Differences between the state-developed projections and the ICLUS projections were
anticipated. The ICLUS projections were developed using a single national model, while the
individual state methodologies, while not analyzed, were likely developed using state-specific
methods and information not available on the national scale.
The results of these comparisons are shown in Figure 3-4 through Figure 3-8. For each of the
five states, the graphs depict the difference between the ICLUS projections and the state
projections expressed as a percentage of the state projection on the Y-axis and the log of the
county population on the X-axis. Each data point represents one county. While the results are
Draft Report - June 26, 2008
page 20
-------
1 mixed, the position of each trend line below the X-axis indicates that ICLUS estimates were
2 lower than state estimates, on average. This is likely due to a combination of several factors:
3 • Higher estimated fertility, domestic in-migration, or international in-migration in the state
4 estimates than in the ICLUS estimates;
5 • Differences in the state methodology that likely do not fully estimate migration patterns
6 in other states; and
7 • Local knowledge about specific areas targeted for new development that was not
8 included in the ICLUS demographic model.
9 A second important observation is the slight positive slope in each trend line. This indicates that
10 the ICLUS model is likely to underestimate the population of smaller counties more than larger
11 counties. This result was anticipated because of the large power of population in the gravity
12 model—in original runs, large counties grew enormous and small counties were reduced to
13 nothing over several decades. As a result, the lowest quintile of counties were excluded from the
14 gravity model and a limiting term was added that helped slow growth in the largest counties. The
15 downside of this is that the ICLUS model is not able to predict population growth due to
16 migration in small rural counties with high natural amenities. McGranahan (1999) discusses the
17 role of natural amenities in driving rural population growth, and while the ICLUS model
18 included natural amenities so as to model this trend, the stronger predictive power of population
19 overwhelmed the impact of natural amenities.
20 In terms of overall population though, the trend line indicates that the larger the county, the more
21 likely it was to be closer to the X-axis. This indicates that the percentage difference between
22 state projections and ICLUS projections is smaller for large counties, where a majority of the
23 population lives. With the exception of Florida, where the difference between the total state
24 population projection and ICLUS was 11 percent, all of the other state projections discussed here
25 differed from ICLUS by less than 7 percent. Given the heterogeneity of data and methods
26 inherent in the state-specific projections as compared to the national approach applied at the
27 county level for ICLUS, we were satisfied with the performance of the ICLUS demographic
28 model.
29
Draft Report-June 26, 2008 page 21
-------
2
3
4
s
a
J
CD
O
~£
s
8
g
€
•a
s
80%
60% -
40% -
20% -
0% -
K
-20% -
-40% -
-60% -
-80% -
• *
• *
A£
' A • ' 4k *i^ *^3 • T i *~
o 1,00* 10*000 yg uftyiiTii y* i Jin umar 1 1 ioo,oc
4» ***** * *
*
0,000
Log of population (state estimates)
Figure 3-4: Comparison of California and ICLUS (base case) 2030 Projections
5
6
7
80%
Log of Population (state estimate)
Figure 3-5: Comparison of Colorado and ICLUS (base case) 2030 Projections
Draft Report - June 26, 2008
page 22
-------
30%
30%
i difference from state estimates
40%
20%
* *
0%
100
2Q A)
-40%
30%
-80%
Log of Population (state estimate)
10,000,
DOO
1
2 Figure 3-6: Comparison of Florida and ICLUS (base case) 2030 Projections
3
4
5
6
10,003,000
Log of Population (state estimate)
Figure 3-7: Comparison of Minnesota and ICLUS (base case) 2030 Projections
Draft Report - June 26, 2008
page 23
-------
2
3
80%
60% -
I 40% -
« 20% -
"S
£ o% -
Ǥ K
g -20% -
sS
=3 -40% -
-60% -
-80% -
/
* * j^*$
0 1,000 10,000 lOOggJlO^*""'"* ^,$0,000 10,00
*^*» *
0,000
Log of Population (state estimate)
Figure 3-8: Comparison of New Jersey and ICLUS (base case) 2025 Projections
4 4 SPATIAL ALLOCATION MODEL
5 We selected a spatial allocation model to distribute the population into housing units across the
6 country. SERGoM, or the Spatially Explicit Regional Growth Model (Theobald, 2005), was used
7 to develop the land use projections for this effort. This section begins with a discussion of the
8 reasons for selecting SERGoM, and continues with a discussion of SERGoM's methodology. We
9 then conclude this section with a discussion of how the demographic model outputs were
10 incorporated into SERGoM and how the model was adjusted for the SRES-compatible scenarios.
11 4.1 RATIONALE FOR THE SELECTION OF SERGOM
12 SERGoM, unlike the majority of land use change models, allocates a full continuum of housing
13 density, from urban to rural. This allows a more comprehensive examination of growth patterns,
14 since exurban/low-density development generally has a footprint 10 times as large as urban areas
15 and is growing at a faster rate than urban areas (Theobald 2005). Hence, it is an important aspect
16 of possible future growth scenarios. An advantage of modeling all types of housing density,
17 especially low density rural development, is that links to GHG emissions by housing density can
18 be made (Liu et al., 2003). In addition, SERGoM forecasts housing development by establishing
19 a statistical relationship between neighboring housing density, population growth rates, and
20 transportation infrastructure (Theobald, 2005). The model is dynamic in that as new urban core
21 areas emerge, the model re-calculates travel time from these areas. For this modeling effort the
22 expected changes in functional connectivity that would result from such emerging urbanization
23 were not fed back into the functional connectivity calculations used to calculate domestic
24 migration (Section 3.5.3 above). SERGoM also incorporates a detailed layer of developable/un-
25 developable areas that incorporates public protected lands as well as private protected (e.g.,
26 through conservation easements) lands. Finally, SERGoM was designed to forecast housing
27 density growth for large, broad (regional to national) extents. Population forecasts are a principal
Draft Report - June 26, 2008
page 24
-------
1 driver of SERGoM; in the model, population growth is converted to housing units, which are
2 spatially allocated in response to the spatial pattern of previous growth and transportation
3 infrastructure. An important technical advantage of this model is that it produces seamless,
4 nationwide maps at 1 ha resolution. The benefit of this approach is that there are fewer (internal
5 to coterminous United States) discrete differences across artificial analytical boundaries imposed
6 by "piecing" individual model runs into a nationwide map, although the allocation of new
7 housing units is restricted to counties. Growth rates and many model parameters are specified
8 spatially-explicitly, so different regions (even census tracts or neighborhoods) have different
9 parameters. Although not exercised in this project, some additional model parameters could be
10 made spatially-explicit so they too could vary regionally - these might include different housing
11 density thresholds for "urban" or "exurban" and relative changes in household size.
12 4.2 METHODOLOGY
13 The spatial database generated by SERGoM provides historical, current, and future estimates of
14 housing density for the coterminous United States. That is, it represents residential land uses,
15 which are the major types of development and intensification of land use related to urbanization
16 in the U. S. (note that we also recognize and map commercial/industrial land use - however, these
17 are static layers and we do not explicitly represent other "development" in the form of cropland
18 or forestry developments). Housing density (number of housing units per acres) was computed
19 for each 1 ha cell (100 m x 100 m raster; 2.47 acres). There are five main input spatial datasets
20 used to estimate housing density:
21 1. 2000 Census Bureau - Data were compiled from the 2000 census on the number of
22 housing units and population for each block and the geography or polygon boundary for
23 each census block using the 2000 Census geography (from the SF1 dataset). Block-
24 groups, which are a coarser-level aggregation of block polygons, and attributes of the
25 number of housing units built by decade were used to estimate the historical number of
26 housing units in each block. An operating assumption in estimating historical housing
27 units is that they have not declined over time, so that the number of housing units in any
28 past decade (back to 1940) did not exceed the number of units in any subsequent decade
29 (up to 2000). Reservoirs, lakes, and wide rivers that were identified as "water blocks"
30 were also removed, so that no housing units were placed in these undevelopable areas.
31 2. Undevelopable lands - Spatial data on land ownership were compiled from a variety of
32 sources to create the most current and comprehensive dataset - called the UPPT
33 (unprotected, private protected, public protected, and tribal/native lands). The UPPT
34 dataset was generated by starting from the Conservation Biology Institute's PAD v4
35 database (CBI2008). We updated the PAD dataset with more current data for 21 states.
36 The operating assumption is that housing units do not occur on publicly owned lands
37 (e-g-, national parks, forests, state wildlife areas, etc.) or on privately-owned, protected
38 lands. Some state lands in the western United States (the so-called "school lands"
39 sections, but not "stewardship" lands) were kept in the developable category because they
40 are in practice sold to generate revenue for state school systems. Also, tribal lands are
41 often considered federal (public), but here we included tribal lands as developable
42 (except for known tribal parks). The portions of blocks that overlapped with public (and
43 other non-developable lands) were deleted to create a modified or refined block. All
44 housing units associated with each block are then assumed to be located in the refined
45 (developable) portion of the blocks. Housing units were apportioned within the refined
46 block using a dasymetric mapping approach described below. The final product is a raster
Draft Report-June 26, 2008 page 25
-------
1 dataset that represents the developable and undevelopable lands for the SERGoM model,
2 which is called dev20080306 and is available through the ICLUS tools.
3 3. Road and groundwater well density - The existence of major roads (interstates, state
4 highways, county roads) was used to better allocate the location of housing units within a
5 block. In a previous SERGoM model (vl, v2), housing units were spread evenly
6 throughout the refined blocks. Here, in v3 of SERGoM, housing units were
7 disproportionately weighted to areas according to fine-grained land use/cover data from
8 NLCD 2001. Because major road infrastructure is included in the NLCD (actually burned
9 in as values 21, 22, and some 23), road density per se was not included. Also, in the
10 western US where the rural blocks are particularly large, groundwater well density was
11 included to refine the allocation of units. Also, the analytical hierarchy process (AHP;
12 Saaty 1980) was used to provide an estimate of logical consistency during the
13 development of the weights (the consistency index was 0.035, which is less that then 0.15
14 threshold, showing that these estimates were logically consistent). Note also that these
15 weights are applied in a relative, not absolute context. That is, the number of units that
16 will be distributed in a given area is specified by the census block and so units are
17 allocated in proportion to the weights found within a given block. This is robust in the
18 face of potential misclassification of land cover types, because all the known housing
19 units will be allocated to a given block, regardless of the land cover (but note that the
20 undevelopable - water and public lands - portions of the blocks are excluded).
21 4. County population projections - Population projections for each county, discussed in
22 Section 3, are used to drive the growth forecasts. Additional housing units were
23 computed by determining the number of new housing units needed to meet the needs of
24 the additional population, assuming the same (in 2000) population to housing unit ratio in
25 each tract, using 2000 U.S. Census of Housing data.
26 5. Commercial/industrial land use - We also mapped locations with land uses that would
27 typically preclude residential development (increased housing density), especially
28 commercial, industrial, as well as transportation land uses. Using urban/built-up
29 categories of NLCD 2001 (not open space developed), we identified locations (1 ha cells)
30 that had >25% urban/built-up land cover but that had also had lower than suburban levels
31 of housing density (because high-density residential areas would otherwise be included in
32 the urban/built-up land cover categories). Although some re-development of central
33 business districts ("gentrification") is occurring, SERGoM works from the operating
34 assumption that these are relatively smaller portions of the landscape and typically
35 brown-field settings.
36
37 These functional flow of the SERGoM model using data from these five sources is illustrated in
38 Figure 4-1.
39
Draft Report-June 26, 2008 page 26
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
Figure 4-1: SERGoM Functional Flow
Housing density for each decade from 2010 to 2100 was forecast using the Spatially-Explicit
Regional Growth Model (SERGoM) v3. SERGOM is a demand/allocation/supply model, where
the number of new housing units needed for the next decade is computed to meet the demands of
the projected population, computed here for each county (but could be other analytical unit
boundaries). The average growth rate for each state-housing density class is computed from the
previous to current time step (e.g., 1990 to 2000). These average growth rates are computed
using a moving neighborhood (radius =1.6 km, 500 m cell) for 16 development classes. These
classes are formed by overlaying four housing density classes - urban, suburban, exurban, and
rural - with four accessibility classes measured as travel time (minutes one way) from the nearest
urban core along (existing) major roads: 0-10, 10-30, 30-60, and >60 minutes. The resulting
combination creates a "surface" of raster values that reflect historical patterns of growth - called
allocation weights - and are used to allocate the new housing units for a given time step.
Based on the Census definition of urban areas, we defined urban housing densities as less than
0.1 ha per unit and suburban as 0.1 - 0.68 ha per unit. We defined exurban density as 0.68 -
16.18 ha per unit (to 40 acres) to capture residential land use beyond the urban/suburban fringe
that is composed of parcels or lots that are generally too small to be considered productive
agricultural land use (though some high-value crops such as orchards are a notable exception).
Rural is defined as greater than 16.18 ha per unit where the majority of housing units support
agricultural production.
The strength of the SERGoM model is that it provides a comprehensive, consistent, and
nationwide estimate of housing density. It uses the most fine-grained data set currently available,
has performed reasonably well in assessments (79 to 99% accuracy rates), and compares
favorably to parcel-level and aerial photography data during ad hoc analyses in a variety of
locations in the United States (Theobald 2005). It assumes that growth rates and patterns are
likely to be similar to recent times (1990s to 2000). The SERGoM outputs provide much more
Draft Report - June 26, 2008
page 27
-------
1 spatial detail as compared to the USDA Census of Agriculture4, which are county-based and
2 only provide data on land in farms. One other common datatset used to estimate the extent and
3 trend of urbanization in the United States is the NRCS's Natural Resources Inventory (NRI)5. It
4 is based on relatively fine-grained aerial photo analysis, but because they are sampled data, they
5 are aggregated up to coarse analysis units (either county, watershed 8-digit HUC, or Major Land
6 Resource Area). NRI also categorizes urban areas into only two classes - either as "small" or
7 "large" development - resolving housing densities at urban and roughly 1 per 10 acre densities.
8 4.3 INCORPORATING U.S.-ADAPTED SRES INTO SERGOM
9 In addition to changes in population that resulted from the various demographic assumptions
10 associated with each SRES-compatible scenario developed for the ICLUS project, the spatial
11 location of growth was modified using SERGoM in two ways, through household size and travel
12 time (Table 4-1). With SERGoM, household size is expected to reflect demographic changes due
13 to changes in fertility and socioeconomic changes that affect household formation. Travel time
14 from urban "central city" locations is used to help express how the evolution of the urban form
15 might by affected by changing priorities and increases in the cost of transportation. Table 4-1
16 also shows how travel times are translated into this urban form. Housing units are allocated to a
17 surface and the allocation is weighted by the accessibility to the transportation network, thereby
18 influencing urban form over time to create a more "compact" form of development when
19 allocations near urban centers are weighted more favorably.
20 The first SERGoM modification changed assumptions about households, particularly household
21 size (roughly family size), defined as the number of people living in a single housing unit.
22 Population projections from the U.S. Census assume that the ratio of population per unit,
23 computed at the tract level from the 2000 U.S. Census data, is static. We modified this ratio to
24 reflect assumptions in the SRES scenarios to adjust for assumed changes in demographic
25 characteristics. For example, SRES Al and Bl assume smaller household sizes (reduction by
26 15%), whereas scenarios B2 and baseline are not changed and A2 assumes a 15% increase in
27 household size (Jiang and O'Neill, 2007). The changes in household size correspond to changes
28 in fertility rates that are assumed under the different storylines. Under Al and B1, where fertility
29 is lowest, smaller average household sizes are also expected. Conversely, A2 has the highest
30 fertility rates, so an increase in household sizes is expected. In B2, which uses the medium
31 fertility rates, household sizes are not changed.
32 The second modification involves changing travel times by adjusting weighting values as a
33 function of distance away (travel time) from urban cores. Urban area (<5 minutes) weights can
34 be lowered by a given percentage to reflect a carrying capacity or saturation of an area, specified
35 by zoning perhaps; or raised to reflect increased desire for urban living (lofts, gentrification,
36 etc.). Exurban area weights (-30-60 minutes) can be lowered to reflect assumptions of lower
37 rates of development due to increased fuel prices or can be used as a surrogate for lower land
38 availability because of increased conservation purchases (or easements). It can also be raised for
39 exurban areas to reflect increased "urban flight" of baby-boomer retirees and rural amenities.
40 This weighting surface is re-computed at each time step. We modified the weights of travel times
41 for the Bl and B2 storylines to model a "compact" growth scenario (see Table 4-1). Given the
42 environmental orientation of the Bl and B2 storylines, we assumed that growth patterns in these
4 http://www.agcensus.usda.gov/
http://www.ncgc.nrcs.usda.gov/products/nri/
Draft Report-June 26, 2008 page 28
-------
1 scenarios would place a greater emphasis on promoting denser growth patterns closer to existing
2 urban centers.
3 We parameterized SERGoM to reflect the SRES scenarios in the following ways. First, the Al
4 and Bl scenarios were modeled to reflect a 15 percent decline in average household size. A2 was
5 modeled to show a 15 percent increase in average household size. B2 was modeled with no
6 change in household size. Thus, the household size for each census tract was modified by 0.85
7 for Al and Bl scenarios, and 1.15 for A2. Second, to model the "compact" growth scenarios for
8 the Bl and B2 scenarios, SERGoM was run with modifications to the spatial allocation of new
9 housing units as a function of travel time from urban cores (Table 4-1).
10 Table 4-1: Summary of Adjustments to SERGoM v3 for SRES scenarios
11
12
13
14
15
16
17
18
Scenario
Al
Bl
A2
B2
Baseline
Household size
Smaller (-15%)
Smaller (-15%)
Larger (+15%)
NC
NC
Travel time (minutes)
<5; 10; 20; 30; 45; >45
Weighting (in percent)
75; 75; 85; 90; 95; 100
90; 95; 85; 90; 95; 100
75; 75; 85; 90; 95; 100
90; 95; 85; 90; 95; 100
75; 75; 85; 90; 95; 100
Form
NC
Slight compact
NC
Slight compact
NC
NC = No change from the U.S. Census Bureau's estimates.
4.4 INTEGRATION OF DEMOGRAPHIC, SERGOM, AND IMPERVIOUS MODELS
The demographic inputs from each of the US-adapted SRES storylines were fed into the
SERGoM model, along with the SRES-specific adjustments in SERGoM for a given SRES
scenario. The outputs of the SERGoM models were then mapped for each decade from 2000 to
2100 (Appendix A). We also used the impervious surface model to convert the SRES housing
density estimates to the total percent impervious surface cover (Section 5.3). Section 5 discusses
some preliminary analyses and potential future developments of the ICLUS modeling
framework.
19 5 IMPACTS AND INDICATORS ANALYSIS
20 5.1 RATES OF GROWTH IN DIFFERENT REGIONS
21 The growth rates of the different regions of the United States under the various SRES storylines
22 provides some interesting insight into the potential relative growth patterns in the coming
23 decades. For this analysis of regional growth patterns, we used the U. S. Census regions (listed in
24 Table 5-1). A separate analysis using EPA regions is presented in Appendix D. The populations
25 of each of the Census regions and scenarios for 2005, 2030, 2060, and 2090, as well as the
26 growth rates for each intervening period are presented in Table 5-2. These data are then
27 displayed graphically to compare the different regions and scenarios (Figure 5-1 to Figure 5-14).
Draft Report - June 26, 2008
page 29
-------
1 Table 5-1; U.S. Census Regions
Census Region
Northeast
Midwest
South
West*
States
Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York,
Pennsylvania, Rhode Island, Vermont
Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota,
Ohio, South Dakota, Wisconsin
Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky,
Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina,
Tennessee, Texas, Virginia, West Virginia
Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah,
Washington, Wyoming
* Alaska and Hawaii were excluded from the West region in this analysis.
4 Table 5-2: Projected Regional Populations and Growth Rates
Census
Region
Population
2005
Growth Rate (%)
2030
2060
2090
2005-
2030
2030-
2060
2060-
2090
Base Case
Northeast
Midwest
South
West
54,679,292
65,936,398
108,981,468
64,973,375
63,384,211
71,250,888
134,649,231
81,705,117
70,279,618
73,287,983
158,547,450
96,926,417
79,600,361
76,745,425
187,417,301
114,073,784
16%
8%
24%
26%
11%
3%
18%
19%
13%
5%
18%
18%
Al Storyline
Northeast
Midwest
South
West
54,679,292
65,936,398
108,981,468
64,973,375
66,910,792
69,265,132
140,717,741
82,571,676
73,137,911
65,378,806
162,287,975
91,877,085
76,159,296
59,355,485
174,443,647
94,284,391
22%
5%
29%
27%
9%
-6%
15%
11%
4%
-9%
7%
3%
A2 Storyline
Northeast
Midwest
South
West
54,679,292
65,936,398
108,981,468
64,973,375
64,718,449
71,810,622
141,466,245
85,129,358
78,041,141
80,335,490
186,892,292
111,597,232
101,339,355
98,476,101
262,941,111
153,721,857
18%
9%
30%
31%
21%
12%
32%
31%
30%
23%
41%
38%
Bl Storyline
Northeast
Midwest
South
West
54,679,292
65,936,398
108,981,468
64,973,375
68,481,437
72,973,243
136,176,035
81,468,120
76,906,738
72,166,976
152,179,001
90,471,745
82,327,874
67,641,537
159,582,292
93,288,362
25%
11%
25%
25%
12%
-1%
12%
11%
7%
-6%
5%
3%
B2 Storyline
Northeast
Midwest
South
West
54,679,292
65,936,398
108,981,468
64,973,375
64,097,466
73,134,039
132,513,626
81,182,232
72,006,459
77,070,686
153,888,715
96,254,307
82,638,846
82,236,752
179,498,472
113,554,597
17%
11%
22%
25%
12%
5%
16%
19%
15%
7%
17%
18%
6 Figure 5-1 through Figure 5-5 compare the population in the four Census regions under each of
7 the four SRES storylines and the base case. In all five sets of population projections, the South
8 region remains the most populous region, while growing faster than most other regions in most
9 scenarios. The West region, which begins with approximately the same population as the
Draft Report - June 26, 2008
page 30
-------
1
2
3
4
5
6
7
8
9
Northeast and Midwest regions, outpaces those two regions in all scenarios except for B1.
Across the board, all regions experience growth in all scenarios, with the exception of the
Midwest region in scenarios Al and Bl.
350 -,
300
a 250
o
200
Northeast
-Midwest
- South
-West
2005 2010 2020 2030 2040
2050
Year
2060 2070 2080 2090 2100
Figure 5-1: Base Case Population by Census Region
350 -,
300 --
Northeast
-Midwest
- South
-West
2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
Figure 5-2: Al Storyline Population by Census Region
Draft Report - June 26, 2008
page 31
-------
1
2
3
350 -,
300
g 25°
5 200
-Northeast
•Midwest
- South
-West
2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
Figure 5-3: A2 Storyline Population by Census Region
4
5
6
350 -,
300
| 250
i
5 200
-•—Northeast
-A—Midwest
-•— South
-5R—West
2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
Figure 5-4: Bl Storyline Population by Census Region
7
8
9
350 -i
300
?250
I
5 200
Northeast
-Midwest
- South
-West
2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
Figure 5-5: B2 Storyline Population by Census Region
Draft Report - June 26, 2008
page 32
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
Figure 5-6 through Figure 5-10 compare the average annual growth rates during each modeled
decade under the different scenarios (e.g., a growth rate of 1.01 indicates growth of 1 percent).
Under the base case and B2 storyline, population growth rates are highest during the first time
period, then level off for the remaining two periods. The Al and Bl storylines, by comparison,
generally decline in growth rate throughout the 21st century. The A2 storyline, which has the
highest overall population growth, is the only one that exhibits steady increasing rates of
population growth over the next century.
1.015 -,
1.01 -
•S 1.005 -
0.995
2005-10 2010-20 2020-30 2030-40 2040-50 2050-60 2060-70 2070-80 2080-90 2090-
Time Range 210°
Figure 5-6: Base Case Annual Population Growth Rates by Region
1.015 -,
1.01 -
•S 1.005 -
0.995
2005-10 2010-20 2020-30 2030-40 2040-50 2050-60 2060-70 2070-80 2080-90 2090-
Time Range 210°
Figure 5-7: Al Storyline Annual Population Growth Rates by Census Region
Draft Report - June 26, 2008
page 33
-------
1
2
3
1.015 -,
1.01 -
•S 1.005 -
g
o
1 -
0.995
2005-10 2010-20 2020-30 2030-40 2040-50 2050-60 2060-70 2070-80 2080-90 2090-
Time Range 210°
Figure 5-8: A2 Storyline Annual Population Growth Rates by Census Region
4
5
Q
1.015 -i
1.01 -
•S 1.005 -
o
0.995
2005-10 2010-20 2020-30 2030-40 2040-50 2050-60 2060-70 2070-80 2080-90 2090-
Time Range 210°
Figure 5-9: Bl Storyline Annual Population Growth Rates by Census Region
Draft Report - June 26, 2008
page 34
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1.015 -,
1.01 -
<£
•5 1.005 -
1
O
1
0.995
Northeast
-Midwest
- South
-West
2005-10 2010-20 2020-30 2030-40 2040-50 2050-60 2060-70 2070-80 2080-90 2090-
Time Range 210°
Figure 5-10: B2 Storyline Annual Population Growth Rates by Census Region
Figure 5-11 through Figure 5-14 provide comparisons of the storylines for each of the four
Census regions. A2 produces the highest population in each region, confirming our expectations
based on our interpretation of the SRES storylines. Al produces the lowest population in the
Northeast and Midwest regions, while B1 produces the lowest population in the South and West
regions. This is because domestic migration, which is expected to continue to trend toward the
South and West (and away from the Midwest and Northeast), is set to "high" under Al and
"low" under Bl. Again, all regions grow under all scenarios, with the exception of the Midwest
region under Al and B1.
120 -,
Basecase
Al
--B--A2
Bl
-B2
2005 2010 2020 2030 2040 2050 2060 2070 2080 2090
Year
Figure 5-11: Northeast Region Population by Storyline
Draft Report - June 26, 2008
page 35
-------
1
2
3
120 -,
100
-H
Basecase
Al
--B--A2
Bl
- - B - - B2
2030 2040 2050
Year
2070
Figure 5-12: Midwest Region Population by Storyline
4
5
6
350 -i
300
Basecase
Al
--B--A2
Bl
- - O - - B2
2040 2050
Year
Figure 5-13: South Region Population by Storyline
Draft Report - June 26, 2008
page 36
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
2040 2050
Year
2100
Figure 5-14: West Region Population by Storyline
5.2 HOUSING DENSITY TRENDS
The projected growth in population and housing density is anticipated to lead to corresponding
impacts on environmental attributes such as water quality and air quality. Since the challenges
are expected to be greater in urban and suburban areas, we used the model outputs to estimate the
growth in these higher density areas. Under all modeled scenarios, urban and suburban areas are
expected to increase between 56 and 156%. For this analysis, urban and suburban areas are
defined as those areas with at least 0.6 units per acre (or less than 1.6 acres per unit). This land
class is expected to increase the most in the A2 scenario, adding over 185,000 km2 over the next
century, or 156% more than 2000 levels (about 120,000 km2) for a total of over 300,000 km2 of
urban/suburban area in 2100 (Table 5-3). Other increases are expected to be significant, but more
moderate than the A2 scenario, with Bl having the smallest increase (56%) (Table 5-3; Figure
5-15). The non-intuitive result that B2 has a higher amount of urban/suburban area as compared
to the base case may be the result of the net trade-off of negatively weighting growth in regions
beyond suburban areas (i.e., exurban and rural areas); this growth results in a greater extent of
the land surface containing urban/suburban densities, compared with the base case where those
housing units are more frequently in exurban or rural categories. Note that we do not include in
our estimates of urban/suburban housing densities the over 32,300 km2 of estimated
commercial/industrial land cover for 2000.
We also examined what broad land cover types were likely to be most affected by the projected
development patterns (Table 5-4). To do this, we quantified the spatial overlap of the urban,
suburban, and exurban housing densities (>1 unit per 40 acres) on the existing major land cover
type as characterized by NLCD 2001 Anderson Level I coding. The largest impacts for all
scenarios, both in terms of percentage and total area, are estimated to be on agricultural
(cropland) cover where approximately 33% of area is converted into housing in these scenarios.
Although wetlands cover less land area, our scenarios convert 30-36% of wetlands to housing.
Shrublands are similar in total area converted, although the scenarios present more of a range in
the amount converted (25-34%).
Draft Report - June 26, 2008
page 37
-------
1
2
Table 5-3: Projected Urban and Suburban Area Increases in Modeled Scenarios, 2000-2100
(km2)
Scenario
Base case
Al
A2
Bl
B2
2000
119,422
119,422
119,422
119,422
119,422
2050
181,897
182,425
188,824
170,856
172,972
2100
212,657
208,551
305,279
186,388
225,857
% Increase, 2000-2100
64%
75%
156%
56%
89%
4 Table 5-4: Projected area (km ) effects of Urban, Suburban, and Exurban Housing
5 Densities on NLCD Land Cover Types in Modeled Scenarios for 2050
Scenario
Current
Forest
400,118
Shrubland
47,943
Grassland
70,066
Agriculture
(cropland)
269,290
Wetland
44,007
Total area change between current and 2050
Base Case
Al
A2
Bl
B2
-40,477
-34,232
-36,628
-35,513
-39,833
-15,612
-12,234
-16,340
-14,743
-15,303
-16,289
-13,580
-15,441
-13,867
-14,411
-90,452
-88,890
-90,620
-89,443
-89,496
-15,721
-14,150
-15,140
-13,366
-14,687
Percent change between current and 2050
Base Case
Al
A2
Bl
B2
-10.1%
-8.6%
-9.2%
-8.9%
-10.0%
-32.6%
-25.5%
-34.1%
-30.8%
-31.9%
-23.2%
-19.4%
-22.0%
-19.8%
-20.6%
-33.6%
-33.0%
-33.7%
-33.2%
-33.2%
-35.7%
-32.2%
-34.4%
-30.4%
-33.4%
OCf|f|f|f|
onnnnn
?
-------
1 5.3 IMPERVIOUS SURFACE CALCULATIONS
2 Impervious surfaces such as pavement and roofs degrade water quality, and by collecting
3 pollutants, increasing run-off during storm events, and absorbing heat they also contribute to the
4 heat island effect (Frazer, 2005). To develop national estimates of likely future impervious
5 surface (IS), we developed a statistical relationship between 2000 housing density and the NLCD
6 2001 Percent Urban Imperviousness (ISPUi) data set (MRLC 2007). We aggregated the 30 m
7 ISpui data set to 900 m2 cells and then resampled using bilinear interpolation to 1 km2 resolution.
8 We aggregated the SERGoM 1 ha housing density up to 1 km2 as well. Based on a spatially-
9 balanced random sample of 200,000 points from across the coterminous United States, we
10 developed a relationship using the cv.tree function in S-Plus (Insightful Corporation, Seattle,
11 WA) between ISpui and HD20oo. To generate a map of IS based on the housing density (ISno), we
12 converted the tree into a set of if-then-else conditional statements in ArcGIS. A brief comparison
13 of our modeled IS to existing fine-grained (from high-resolution photography) validation
14 datasets resulted in an R2=0.69 (Elvidge et al. 2004) and R2=0.69 and R2=0.96 for Frederick
15 County, Maryland and Atlanta, Georgia (Exum et al. 2005). Because our estimates of IS are
16 produced on a per 1 km2 pixel basis, we were able to roughly compare our estimates to Exum et
17 al.'s (2005) by averaging pixels that fell within their 10-12 digit HUCs. It would be useful to
18 compare our results to other aerial-photography-based estimates, but to our knowledge the
19 spatial datasets of these estimates of IS are not readily obtainable. The detailed methods and
20 results are described in Theobald et al. (in press) and in 6Appendix C.
21 5.3.1 Percent of Watersheds over Stressed (>5%) Impervious Threshold
22 The total percent impervious surface (computed at 1 km2) for the United States in 2000 was
23 83,749 km2. We developed a regression model (described in Appendix C) that relates housing
24 density estimates in 2000 to estimates from the Percent Urban Impervious from the NLCD 2001
25 dataset. Based on that statistical relationship, we were able to forecast likely changes to
26 impervious surface for different future patterns of land use that reflect our SRES growth
27 scenarios (Table 5-5; Figure 5-16). We classified impervious surface estimates into 5 classes:
28 unstressed (0-0.9%), lightly stressed (1-4.9%), stressed (5-9.9%), impacted (10-24.9%), and
29 degraded (>25%), following Slonecker and Tilley (2004) and Elvidge et al. (2007) and Theobald
30 et al. (inpress). Note that our estimates here do not include impervious surface for known
31 commercial/industrial lands (in 2000) - that added an additional 13,430 km2 of impervious
32 surface area. All housing density classes were included when estimating the impervious surface.
33 In 2000, urban/suburban areas (< 1.6 acres per unit) comprised 49.6% of the total impervious
34 surface (accounting for different percent IS), exurban areas (1.6-40 ac per unit) comprise 34.1%,
35 and rural comprised 16.3%. We estimate that in 2000 there were 113 8-digit HUCs that were
36 stressed or higher (at least 5% IS), and this will likely increase to between 180 to 206 in 2050
37 and to between 197 and 290 in 2100 - a doubling to nearly a tripling.
38 In general, there are fairly large differences between the amount of impervious surface that likely
39 will result from different growth scenarios - from a 3.8% increase (Al) from base case to a 6.1%
40 decline (B2) from base case in 2050. Figure 5-17 to Figure 5-26 show the impervious surface
41 patterns for each U.S.-adapted SRES scenario and the relative differences between these
42 scenarios and the base case.
Draft Report-June 26, 2008 page 39
-------
1 Table 5-5: Impervious surface estimates for SRES scenarios. "Stressed" level is defined as
2 at least 5% impervious surface.
SRES
Base case
Al
A2
Bl
B2
Year 2050
Total impervious
surface (km2)
114,320
118,670
115,800
110,176
107,765
Number of stressed
8-digitHUCs (of
2127)
195
204
206
185
180
Year 2100
Total impervious
surface (km2)
124,190
129,322
156,100
116,344
123,255
Number of
stressed 8-digit
HUCs (of 2127)
216
227
290
197
217
4
5 I
Total Impervious Surface Area (sq
He
160 000
150 000
140 000
i ^o ooo
'E* 1 90 000
1 10 000
100 000
QO 000
80 000 -
_D
x'
s
s'
X
x'
•^-—-^^A
sd^^^— ~~~*
^^^^
J^^
tf^
2000 2050 2100
Decade
9 Base case
- - 0 - - A2
- - Q - - B2
ure 5-16: Impervious surface area estimates, 2000-2100
Draft Report - June 26, 2008
page 40
-------
Impervious Surface
BC2050
Figure 5-17: 2050 Impervious Surface, Base Case
Draft Report - June 26, 2008
page 41
-------
Impervious surface
BC 2050 increase
*
Figure 5-18: 2000-2050 Relative Change in Impervious Surface, Base Case
Draft Report - June 26, 2008
page 42
-------
Impervious Surface
A12050
Figure 5-19: 2050 Impervious Surface, Al Storyline
Draft Report - June 26, 2008
page 43
-------
Impervious surface
A1 2050 increase
Figure 5-20: 2000-2050 Relative Change in Impervious Surface, Al Storyline
Draft Report - June 26, 2008
page 44
-------
Impervious Surface
A22050
Figure 5-21: 2050 Impervious Surface, A2 Storyline
Draft Report - June 26, 2008
page 45
-------
Impervious surface
A2 2050 increase
Figure 5-22: 2000-2050 Relative Change in Impervious Surface, A2 Storyline
Draft Report - June 26, 2008
page 46
-------
Impervious Surface
B12050
Figure 5-23: 2050 Impervious Surface, Bl Storyline
Draft Report - June 26, 2008
page 47
-------
V ''."I:.-- •"•-
•^'Hfc^ ^\;
Impervious surface
B1 2050 increase
Figure 5-24: 2000-2050 Relative Change in Impervious Surface, Bl Storyline
Draft Report - June 26, 2008
page 48
-------
Impervious Surface
B22050
Figure 5-25: 2050 Impervious Surface, B2 Storyline
Draft Report - June 26, 2008
page 49
-------
Impervious surface
B2 2050 increase
Figure 5-26: 2000-2050 Relative Change in Impervious Surface, B2 Storyline
Draft Report - June 26, 2008
page 50
-------
1 5.4 OPTIONS FOR FUTURE STUDY
2 Impervious surface calculations and regional growth rates are just the first set of many possible
3 analyses using results from this project. The housing density and population projections can
4 inform modeling exercises that consider such diverse areas of research as traffic volumes, air
5 quality, and water quality. Additionally, the demographic and housing allocation models can be
6 further modified to incorporate climate change variables, incorporate additional factors affecting
7 population change and housing patterns, or consider specific policy responses such as an
8 emphasis on Smart Growth development patterns.
9 The current set of scenarios does not reflect the effects of climate change on development
10 patterns. Some climate change effects, e.g., sea level rise, are likely to have significant impacts
11 on development patterns. By integrating projected changes in sea level, projected population
12 growth, and land development patterns, the resulting scenarios would provide valuable
13 information to planners interested in coastal development, transportation infrastructure
14 vulnerability, housing density, water quality (e.g., salt water intrusion), and other endpoints of
15 concern. Such integration is a likely next step in the development of this project in order to begin
16 to incorporate climate change variables into the models to facilitate more comprehensive
17 assessments of the combined effects of climate change and land use change. Additional options
18 for incorporating climate change variables include modifications to the gravity model to allow
19 climate variables to change over time.
20 Another possible modification might examine changes in housing density regionally. Current
21 housing density class ranges are static and the same throughout the country. This can be easily
22 modified geographically, particularly west vs. east to explore different development patterns
23 regionally, such as larger ranches or farms in the western U.S. (e.g., 10-40 acres exurban vs. ~2-
24 10 acres exurban in the east). Also, the amount of developable land is currently assumed to be
25 static, but that could be modified to remove additional protected lands. However, an approach
26 would first need to be developed to identify where these specific lands may be protected and
27 what the relationship to new housing allocations and densities might be.
28 The driving factor in SERGoM is population, so that forecasts are exogenous variables that are
29 input to the model. Other population scenarios could also be modeled. In the current version of
30 SERGoM housing units do not move across boundaries if an analytical unit is saturated. One
31 reason for this is that it would require coupling the SERGoM model with the demographic model
32 (that is, at each decade SERGoM would have to pass back to the ICLUS demographic model the
33 actual number of people/households that were placed in a county). Rather, we endeavored to
34 make the ICLUS demographic model more responsive to broader-scale (county and up) trends
35 by using the amenity-based gravity model. A future refinement could be to couple the
36 demographic and SERGoM models to ensure explicit distribution of people/houses. Another way
37 to handle this challenge, if data were available and assumptions were reasonable, is to estimate
38 the "build-out" (or carrying capacity) of a county based on zoning, for example.
39 Housing density currently is spread from one location to another (even across county or state
40 analytical units) as a function of distance away (travel time) from urban core areas. Urban "core
41 areas" are identified by a high housing density level (e.g., urban) of a size/population/area to
42 approximate providing general services. In the ICLUS scenarios these parameters are specified
43 by two user-defined values that do not vary across the study area (e.g., nationwide model). These
44 could be easily changed so that they are spatially-explicit parameters, which would allow
Draft Report-June 26, 2008 page 51
-------
1 regional variation to occur. Also note that as growth continues through time, eventually some
2 new urban core areas will "emerge", creating a new "hot spots" or concentrations of growth.
3 Currently these new core areas do not feedback into the functional connectivity matrix that
4 influences domestic migration in the demographic model.
6 Some options for future research include:
7 • Identify new urban clusters. Currently, new urban core areas are modeled by SERGoM in the
8 spatial allocation process. An analysis of the detailed SERGoM outputs would help identify
9 new or expanded urban clusters. Such new urban clusters would likely experience dramatic
10 changes in air quality, water quality, and traffic. Identifying these growth areas could help
11 improve regional analyses and planning.
12 • Estimate traffic demands. Vehicle miles traveled (VMT) can be estimated for each grid cell
13 based on the number of housing units and available estimates on the number of automobile
14 trips generated per day that vary based on housing density class. When combined with
15 average trip lengths, these projections can be used to estimate fuel consumption, travel
16 demand, and other factors. Combining the housing density maps with the road network layer
17 of SERGoM can allow more sophisticated traffic analyses.
18 • Model air quality changes. The VMT outputs above, along with other layers of data about
19 stationary emissions sources, can be used with existing air quality models to estimate
20 projected air quality under the various scenarios.
21 • Analyze effect of impervious surface on water quality. The impervious surface analysis can be
22 used to consider the quality of stormwater runoff and its impact on water quality in key
23 watersheds. Groisman et al (2005) suggest that one potential impact of climate change is an
24 increase in the intensity of individual storm events. Since these events are responsible for the
25 majority of impacts to water quality from stormwater runoff, examining the possible extent of
26 impervious surfaces become even more important given the anticipated impacts of climate
27 change.
28 • Develop alternative Smart Growth scenarios. Alternative SERGoM runs could be used to
29 project housing density under alternative development patterns that reflect Smart Growth
30 goals. One example would be to model denser development along existing transportation
31 infrastructure. The results could also be combined with some of the other suggested analyses
32 to estimate the performance of such strategies. Another use would be to examine the amount
33 of growth that could be accommodated in brown/greyfield sites, vs. greenfield sites.
34 • Analyze effect of urban vs exurban growth on impervious surface thresholds. Another
35 interesting question to ask in the future would be the degree to which growth in
36 urban/suburban vs. exurban classes is the main cause for watersheds to cross over a threshold
37 (e-g-, >5%) into the stressed impervious surface classification. It would also be interesting to
38 explore the consequences of assumptions about how population per housing unit would vary
39 both between urban and rural areas, and through time.
40
Draft Report-June 26, 2008 page 52
-------
1 6 DISCUSSION AND CONCLUSIONS
2
3 The IPCC emissions storylines were adapted to the U.S. by modifying Census demographic
4 projections, by incorporating county-to-county connectivity and amenity variables, and by
5 modifying the spatial growth pattern of housing. The resulting scenarios provide benchmarks for
6 possible future housing density patterns. The comparison of our demographic projections at the
7 county level with selected state estimates shows that our model is in the range of other
8 projections, and therefore can be used to generate plausible population scenarios. The differences
9 also underscore that there are many approaches that can generate scenarios, and that these
10 approaches may use more detailed or finer-scale information. However, the EPA-ICLUS
11 methodology does produce demographic results within the range projected by other efforts and is
12 therefore useful in benchmarking scenarios within established GHG emissions scenarios.
13 The preliminary results presented in this report call attention to the expected spatial variability of
14 land-use effects and their possible intersection with regional climate changes. For examples,
15 population growth rates in the South and West may increase the vulnerability of these regions to
16 water quantity and quality issues if precipitation decreases. This is one way that these land-use
17 scenarios can facilitate integrated assessments of climate change and land-use change.
18 Our preliminary results also show differences in the impacts on various land-cover classes,
19 which, along with increases in impervious surface cover, will translate to effects on air quality,
20 human health, water quality, and ecosystems. Further assessments of these effects, including
21 more detailed spatial analyses of which watersheds and regions may be more vulnerable to these
22 changes; which wetland types and in which watersheds and regions may be more impacted by
23 land conversion; and which regions may benefit more from policies and planning that includes
24 Smart Growth development patters, will be important next steps. The explicit integration of
25 climate change into the next phase of modeling will further facilitate these assessments.
26 In conclusion, the U.S.-adapted SRES scenarios produce a range of outcomes both in the
27 demographic model of the EPA-ICLUS project and in the spatial allocation model. The range of
28 population projections, housing densities, and impervious surface cover allows for a broad
29 examination of trends and impacts to a variety of endpoints. The ICLUS methodology also
30 allows for future modifications that can incorporate more explicit climate-change information,
31 feedbacks to domestic migration patterns from emerging growth centers, and a variety of
32 regional changes to housing densities and allocation preferences. The current outputs from the
33 EPA-ICLUS project can be used in a variety of assessments that include effects on air quality,
34 water quality, and any other endpoints that are modeled using either population or land use as an
35 input.
Draft Report-June 26, 2008 page 53
-------
1 REFERENCES
2 Breiman, L., Friedman, J., Olshen, R. and Stone, C. 1984. Classification and Regression Trees.
3 Chapman and Hall, New York. 358 pp.
4 Campell, Paul. 1996. "Population Projections for States by Age, Sex, Race,
5 and Hispanic Origin: 1995 to 2025" U.S. Census Bureau. Available online at:
6 http://www.census.gov/population/www/projections/ppl47.html.
7 Chabaeva, A. A., D.L. Civco, and S. Prisloe. 2004. Development of a population density and land
8 use based regression model to calculate the amount of imperviousness. Proceedings of the
9 2004 ASPRS Annual Convention, Denver CO.
10 Conley, T.G. and G. Topa. 2002. Socio-economic distance and spatial patterns in unemployment.
11 Journal of Applied Econometrics 17: 303-327.
12 Conservation Biology Institute (CBI). 2008. Protected Areas Database v4. Corvallis, OR. URL:
13 http://www.consbio.org/cbi/projects/PAD/index.htm
14 Elvidge, C.D., C. Milesi, J.B. Dietz, B.T. Turtle, P.C. Sutton, R. Nemani, and I.E. Vogelmann.
15 2004. US constructed area approaches the size of Ohio. EOS Transactions, American
16 Geophysical Union 8 5 (24): 23 3 -240.
17 Elvidge, C.D., B.T. Turtle, P.C. Sutton, K.E. Baugh, A.T. Howard, C. Milesi, B.L. Bhaduri, and
18 R. Nemani. 2007. Global distribution and density of constructed impervious surfaces.
19 Sensor si: 1962-1979.
20 Exum, L.R., S.L. Bird, J. Harrison, and C.A. Perkins. 2005. Estimating and projecting
21 impervious cover in the southeastern United States. EPA report #EPA/600/R-05/061.
22 Frazer, Lance. 2005. Paving paradise: the peril of impervious surfaces. Environmental Health
23 Perspectives, 113(7):457-462.
24 Groisman P., R. Knight, D. Easterling, T Karl, G. Hegerl, V. Razuvaev. 2005. Trends in Intense
25 Precipitation in the Climate Record. Journal of Climate 18:1326-1350.
26 Manson, G. and Groop, R.E. 2000. U.S. intercounty migration in the 1990s: People and income
27 move down the urban hierarchy. The Professional Geographer, 52(3): 493-504.
28 Haynes, K.E. and Fotheringham, A.S. 1984. Gravity and Spatial Interaction Models. Beverly
29 Hills: Sage Publications.
30 Hilderink, H. 2004. Population and scenarios: Worlds to win? RIVMReport 550012001.
31 Bilthoven, The Netherlands.
32 Hollman, F., Mulder, T., and Kalian, J. "Methodology and Assumptions for the Population
33 Projections of the United States: 1999 to 2100." Population Division Working Paper No. 38.
34 Bureau of the Census. Washington, DC. 2000. Available online at:
35 http://www.census.gov/population/www/documentation/twps0038.html.
36 Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan. 2004. Development of a 2001 National
37 Landcover Database for the United States. Photogrammetric Engineering and Remote
38 Sensing, Vol. 70, No. 7, July 2004, pp. 829-840.
39 IPCC. 2001. Climate Change 2001: Impacts, Adaptation and Vulnerability. Contribution of
40 Working Group II to the Third Assessment Report of the Intergovernmental Panel on
Draft Report-June 26, 2008 page 54
-------
1 Climate Change, JJ. McCarthy, O.F. Canziani, N.A. Leary, DJ. Dokken and K.S. White,
2 Eds. Cambridge University Press, Cambridge, UK.
3 IPCC. 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of
4 Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on
5 Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E.
6 Hanson, Eds., Cambridge University Press, Cambridge, UK, 976pp.
7 Jiang, L. and O'Neill, B.C. 2005. Analysis of the impact of demographic events on household
8 compositional change. Draft manuscript.
9 Jiang, L. and O'Neill, B.C. 2007. "Impacts of Demographic Trends on US Household Size and
10 Structure" Population and Development Review 33(3): 567-591.
11 Lee, R., 2003. The demographic transition: Three centuries of fundamental change. Journal of
12 Economic Perspectives, 17: 167-190.
13 Liu, J., G. C. Daily, P. R. Ehrlich, and G. W. Luck. 2003. Effects of household dynamics on
14 resource consumption and biodiversity. Nature 421:530-533.
15 Lutz, W., Sanderson, W.C., Scherbov, S. and Goujon, A. 1996. World population scenarios for
16 the 21st century. In The Future Population of the World. What Can We Assume Today? W.
17 Lutz. Earthscan, London: 361-396.
18 Lutz, W., Sanderson, W.C., and Scherbov, S. 2001. The end of world population growth. Nature,
19 412:543-545.
20 Matthews, T.J., and Hamilton, Brady. "Trend Analysis of the Sex Ratio at Birth in the United
21 States." National Vital Statistics Reports 53(20), June 14, 2005.
22 McGranahan, David. 1999. "Natural Amenities Drive Rural Population Change," Agricultural
23 Economic Report No. (AER781). U.S. Department of Agriculture.
24 Millennium Ecosystem Assessment (MEA). Ecosystems and Human Well-being: Scenarios,
25 Volume 2. Washington: Island Press, 2005.
26 MRLC 2001. Multi-Resolution Land Characteristics consortium, www.mrlc.gov.
27 Nakicenovic, N. et al., 2000. Special Report on Emissions Scenarios. Intergovernmental Panel on
28 Climate Change. Cambridge University Press.
29 National Center for Health Statistics (NCHS). "Bridged-race Vintage 2006 postcensal population
30 estimates for July 1, 2000 - July 1, 2006, by year, county, single-year of age, bridged-race,
31 Hispanic origin, and sex." Released August 16, 2007. Available online at:
32 http ://www. cdc.gov/nchs/about/maj or/dvs/popbridge/datadoc.htm#vintage2006
33 O'Neill, B.C. 2004. Conditional probabilistic population projections: an application to climate
34 change, International Statistical Review 72(2), 167-184.
35 O'Neill, B.C. 2005. "US Socio-Economic Futures." Options for Future Climate Policy:
36 Transatlantic Perspectives. International Network to Advance Climate Talks, October 2005.
37 O'Neill, B.C. 2005b. Population scenarios based on probabilistic projections: An application for
38 the Millennium Ecosystem Assessment. Population & Environment 26(3), 229-254.
39 Perry, M. and Schachter, J. (2003) "Migration of Natives and the Foreign Born: 1995 to 2000."
40 Census 2000 Special Reports. U.S. Census Bureau.
41 Reginster, I. and Rounsevell, M. 2006. Scenarios of future urban land use in Europe.
42 Environmental and Planning B: Planning and Design 33: 619-636.
Draft Report-June 26, 2008 page 55
-------
1 Rodrigue, J.P., C. Comtois, and B. Slack. 2006. The geography of transport systems. New York:
2 Routledge.
3 Rogers, A. and Henning, S. 1999. The internal migration patterns of the foreign-born and native-
4 born populations in the United States: 1975-80 and 1985-90. International Migration
5 Review 33(2), 403-429.
6 Rounsevell, M. D. A., I. Reginster, M. B. Araujo, T. R. Carter, N. Dendoncker, F. Ewert, J. I.
7 House, S. Kankaanpaa, R. Leemans, and M. J. Metzger. 2006. A coherent set of future land
8 use change scenarios for Europe. Agriculture, Ecosystems & Environment 114:57-68.
9 Sardon, J.-P. 2004. Recent demographic trends in the developed countries. Population 59(2),
10 263-314.
11 Slonecker, E.T. and J.S. Tilley. 2004. An evaluation of the individual components and accuracies
12 associated with the determination of impervious area. GIScience and Remote Sensing 41(2):
13 165-184.
14 Siegel, J.S. and Swanson, D.A. (eds). 2004. The Methods and Materials of Demography, Second
15 Edition. San Diego: Elsevier Academic Press.
16 Solecki, W. D. and Olivieri, C. 2004: Downscaling climate change scenarios in an urban land use
17 change model . Journal of'EnvironmentalModeling 74, 105-115.
18 Theobald, D.M. and N.T. Hobbs. 1998. Forecasting rural land use change: A comparison of
19 regression- and spatial transition-based models. Geographical & Environmental Modelling
20 2(1): 65-82.
21 Theobald, D.M. 2001. Land use dynamics beyond the American urban fringe. Geographical
22 Review 91(3):544-564.
23 Theobald, D.M. 2003. Targeting conservation action through assessment of protection and
24 exurban threats. Conservation Biology 17(6): 1624-1637.
25 Theobald, D.M. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020.
26 Ecology and Society 10(1): 32. [online] URL:
27 http://www.ecologvandsociety.org/vollO/issl/art32/.
28 Theobald, D.M., J.B. Norman, and M.R. Sherburne. 2006. FunConn vl User's Manual: ArcGIS
29 tools for Functional Connectivity Modeling. Natural Resource Ecology Lab, Colorado State
30 University. April 17, 2006. 47 pages.
31 Theobald, D.M., D.L. Stevens, Jr., D. White, N.S. Urquhart, A.R. Olsen, and J.B. Norman. 2007.
32 Using GIS to generate spatially-balanced random survey designs for natural resource
33 applications. Environmental Management 40(1): 134-146.
34 Theobald, D.M., S.J. Goetz, J. Norman, and C. Jantz. (inpress). Watersheds at risk to increased
35 impervious surface in the conterminous US. Journal ofHydrologic Engineering.
36 United Nations (UN), 1998. World Population Projections to 2150. United Nations, New York.
37 United Nations (UN), 2004. World Population to 2300. United Nations, New York.
38 United States Census Bureau. 1992. "1980 to 1990 Demographic Components of Change file of
39 the U.S., States, and Counties." Available online at:
40 http://www.census.gov/popest/archives/1980s/.
41 United States Census Bureau. 2000. "Component Assumptions of the Resident Population by
42 Age, Sex, Race, and Hispanic Origin: Lowest, Middle, and Highest Series, 1999 to 2100."
43 Available online at http://www.census.gov/population/www/proj ections/natdet-D5 .html.
Draft Report-June 26, 2008 page 56
-------
1 United States Census Bureau. 2003. "5-Percent Public Use Microdata Sample (PUMS) Files."
2 Available online at http://www.census.gov/Press-Release/www/2003/PUMS5.html.
3 United States Census Bureau. 2008. "Population, Population change and estimated components
4 of population change: April 1, 2000 to July 1, 2007 (NST-EST2007-alldata)." Available
5 online at http://www.census.gov/popest/datasets.html.
6 United States Census Bureau. 2007. "Census 2000." Retrieved from http://factfmder.census.gov.
7 van Vuuren, D. and O'Neill, B.C. The consistency of IPCC's SRES scenarios to 1990-2000
8 trends and recent projections. Submitted to Climatic Change.
9
10
Draft Report-June 26, 2008 page 57
-------
Appendix A - MAPS FOR ICLUS SCENARIOS
Figure A-l: Base Case, Year 2010 Housing Density Map
Draft Report - June 26, 2008
page 58
-------
Figure A-2: Base Case Storyline, Year 2050 Housing Density Map
Draft Report - June 26, 2008
page 59
-------
Figure A-3: Base Case, Year 2100 Housing Density Map
Draft Report - June 26, 2008
page 60
-------
Figure A-4: Al Storyline, Year 2010 Housing Density Map
Draft Report - June 26, 2008
page 61
-------
Figure A-5: Al Storyline, Year 2050 Housing Density Map
Draft Report - June 26, 2008
page 62
-------
Figure A-6: Al Storyline, Year 2100 Housing Density Map
Draft Report - June 26, 2008
page 63
-------
Figure A-7: A2 Storyline, Year 2010 Housing Density Map
Draft Report - June 26, 2008
page 64
-------
Figure A-8: A2 Storyline, Year 2050 Housing Density Map
Draft Report - June 26, 2008
page 65
-------
Figure A-9: A2 Storyline, Year 2100 Housing Density Map
Draft Report - June 26, 2008
page 66
-------
Figure A-10: Bl Storyline, Year 2010 Housing Density Map
Draft Report - June 26, 2008
page 67
-------
Figure A-ll: Bl Storyline, Year 2050 Housing Density Map
Draft Report - June 26, 2008
page 68
-------
&vJ&.f 1
*S//
•«%
Figure A-12: Bl Storyline, Year 2100 Housing Density Map
Draft Report - June 26, 2008
page 69
-------
Figure A-13: B2 Storyline, Year 2010 Housing Density Map
Draft Report - June 26, 2008
page 70
-------
Figure A-14: B2 Storyline, Year 2050 Housing Density Map
Draft Report - June 26, 2008
page 71
-------
Figure A-15: B2 Storyline, Year 2100 Housing Density Map
Draft Report - June 26, 2008
page 72
-------
2 Appendix B - DEMOGRAPHIC MODEL SENSITIVITY TESTING
3
4
5
6
7
8
9
10
11
12
13
14
15
16
In order to explore the demographic model's response to changes to its inputs and a variety of
potential scenarios, we ran a series of sensitivity tests that paid particular attention to the
behavior of the gravity model. These tests were used to improve the model, as well as to develop
the downscaling approach to the SRES scenarios. We also compared the outputs for several
states to county-level projections produced by those states. While this project could not expect to
match the level of detail and state-specific methodology produced for each state's estimates, this
did provide us with a useful benchmark for comparison with the ICLUS outputs.
The first set of testing involved the fertility rate. International migration was held at medium,
while domestic migration was set to zero, since we were interested only in looking at total
national population at this time. The model was then run using the low, medium, and high
fertility rate scenarios provided by the U.S. Census Bureau. Figure B-l below compares the
impact of the fertility rate scenarios on national population. Under these settings, low fertility
predicts a mid-century peak in population followed by a small decline, with the medium and high
scenarios result in steadily rising population.
17
18
19
20
21
22
23
24
25
26
27
28
29
30
900,000,000
800,000,000 -
700,000,000 -
600,000,000 -
c
•B 500,000,000
_
g- 400,000,000
300,000,000 -
200,000,000 -
100,000,000
U5OU5OU5OU5OU5OU5OU5OU5OU5OU5O
8oo8888SS8888££8888?
CMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCM
Figure B-l: Effect of fertility rate on national population
Next, we considered the impact of international migration on total population. This time, fertility
was held constant at medium and domestic migration was set to zero. The model was run using
the low, medium, and high international migration scenarios provided by the U.S. Census
Bureau. Figure B-2 below displays the results of these scenarios. All scenarios result in steadily
rising national population, but since medium fertility produces a small steady increase in the
population, this increase is at least partially due to fertility. Figure B-3, which presents a wider
range of scenarios, shows that the combination of low fertility and low international migration
presents the lowest possible population trajectory given our current inputs. In Figure B-3, the
outputs for the base case and four SRES-compatible scenarios are presented, as well as the
maximum and minimum scenarios (calculated using high fertility and high immigration, and low
fertility and low immigration, respectively).
Draft Report - June 26, 2008
page 73
-------
2
3
4
Population
900,000,000 -
800,000,000 -
700,000,000 -
600,000,000 -
500,000,000 -
400,000,000 -
300,000,000 -
200,000,000 -
100,000,000 -
Comparison of International Migration Scenarios
^^^&
LOLOLOLOLOLOLOLOLOLO
-*- All Mid (Base)
-X- Intl Mig Low
->K- Intl Mig High
oooooooooo
C\IC\IC\IC\IC\IC\IC\IC\IC\IC\I
Figure B-2: Effect of international migration on national population
5
6
7
8
9
10
11
12
13
14
900,000,000
800,000,000 -
700,000,000 -
600,000,000 -
c
| 500,000,000
g- 400,000,000
300,000,000 -
200,000,000 -
100,000,000
—•—All Mid (Base)
—I—High Pert, High IM
—i— Low Pert, Low IM
—A— A1 (Low Pert, High IM)
—Q - A2 (High Pert, Low IM)
—*—B1 (Low Pert, High IM)
—G - B2 (Med Pert, Low IM)
CMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCvi
Figure B-3: Comparison of a broad range of scenarios
We also ran tests with the mortality rate, even though the only available data set for mortality
rates was the Census middle case, and we elected to use this set in all SRES storylines. To
explore the effect of mortality on national population, we used the Census middle case to create
additional sets of mortality rates by adjusting the Census medium scenario by +/-!%, +7-1.5%,
and +7-2% per decade so that by 2100, the high cases were 10, 15, and 20 percent higher than
middle and the low cases was 10, 15, and 20 percent lower than middle, respectively. Each was
run with fertility and international migration set to medium and domestic migration set to zero.
Draft Report - June 26, 2008
page 74
-------
1 As Figure B-4 shows, the effect of even the strongest change was relatively small compared to
2 changes in the other components of change.
3
4
500,000,000 -i
450,000,000
400000000
350,000,000
§ 300,000,000
| 250,000,000
£ 200,000,000
150,000,000
100,000,000
50,000,000
^^s^^^1
^^sm^ » "ll*""g'^
__^— *
*-—• '^
CMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCvi
-•- All Mid (Base)
-^ Mort High (+20)
-i— Mort High (+15)
Mort High (+10)
— Mort Low (-10)
Mort Low (-15)
Mort Low (-20)
Figure B-4: Effect of mortality on national population
6 Due to the complexity of the gravity model, there were many possible adjustments that could be
7 made to change the magnitude of domestic migration. The simplest change involved the
8 introduction of a scaling factor. Under this adjustment, the gravity model calculations would
9 proceed as normal, but all calculated migrations would be scaled upward or downward. For
10 example, if the normal model estimated 10 migrations from county A to county B, with a 50%
11 scaling factor to cut migrations in half, only 5 migrations would occur. Other approaches could
12 involve adjusting the model coefficients and/or y-intercept. These approaches would allow more
13 fine tuning by increasing the attraction of large cities or increasing the friction of distance, for
14 example.
15 In this analysis, we ran the gravity model with the following nine alternative scenarios:
16 1) Scaling all migrations by 50%. This has the practical effect of reducing migrations.
17 2) Scaling all migrations by 150%. This has the practical effect of increasing migrations.
18 3) Increasing the production population coefficient by 20%. Since the production population
19 coefficient is positive, this has the practical effect of increasing migrations.
20 4) Decreasing the production population coefficient by 20%. Since the production
21 population coefficient is positive, this has the practical effect of decreasing migrations.
22 5) Increasing the attraction population coefficient by 20%. Since the attraction population
23 coefficient is positive, this has the practical effect of increasing migrations.
24 6) Decreasing the attraction population coefficient by 20%. Since the attraction population
25 coefficient is positive, this has the practical effect of increasing migrations.
26 7) Increasing the distance coefficient by 20%. Since the distance coefficient is negative, this
27 has the practical effect of reducing migrations.
Draft Report - June 26, 2008
page 75
-------
1 8) Decreasing the distance coefficient by 20%. Since the distance coefficient is negative,
2 this has the practical effect of increasing migrations.
3 9) Increasing the 1980-2000 growth rate coefficient by 20%. Since the growth rate
4 coefficient is positive, this has the practical effect of increasing migrations. We did not
5 analyze a similar decrease because adding this coefficient was deemed to be a model
6 improvement and the areas of concern so far have been in the rapidly growing counties.
8 Because domestic migration can only be considered from a county perspective, we compared the
9 outputs from five states with state projections and the revised base case projections from
10 October. The five states—California, Colorado, Florida, Minnesota, and New Jersey—were
11 selected for their geographic diversity and availability of suitable county-based projections. We
12 compared the 2030 state projections (2025 for New Jersey) with the ICLUS base case projections
13 and with outputs from the scenario tests.
14 We used the outputs of these tests to help refine the demographic projections. For example, early
15 runs showed that urban counties were growing much faster in the ICLUS projections than
16 anticipated by state projections. This led to changes in how we modeled the attraction of
17 migrants to urban centers (see Section 3.5.4). We also found that counties currently identified by
18 the states as fast-growing areas did not grow as quickly in the ICLUS model as they did in the
19 state projections. Since the ICLUS model was designed to be a relatively simple national model,
20 it was not possible to include some of the specialized local details that the states included in their
21 projections. Therefore, divergences from the state projections were expected. This observation
22 did lead us to include 1980-2000 growth rates as a term in the migration model. As a result, those
23 fast-growing areas continued their relatively rapid growth rates in our projections.
24
Draft Report-June 26, 2008 page 76
-------
1 Appendix C - STATISTICAL RELATIONSHIP BETWEEN HOUSING DENSITY AND IMPERVIOUS
2 SURFACE COVER
4 BACKGROUND
5 The goal of this analysis was to develop a model to statistically relate housing density estimated
6 by SERGoM to impervious surface cover. To do this, we examined how impervious surface from
7 the National Land Cover Database (NLCD) (MRLC 2001) related to housing density and
8 ancillary variables including transportation (highways, secondary, local roads), and
9 neighborhood density of urban/built-up land uses. Statistical relationships were developed by
10 using regression tree models. We also investigated other regression-based approaches that
11 estimate imperviousness from land cover (e.g., NLCD) and/or population data. We felt these
12 were limited or not appropriate for our purposes primarily because they use population data
13 rather than housing data, and because population is tied to primary residence, they underestimate
14 the actual landscape effects of housing units. We also explored adding the locations of
15 commercial/industrial land use from NLCD 2001 to the impervious surface estimates for housing
16 density because they are likely to have very high impervious surface levels as well.
17 METHODS
18 We downloaded the Percent Urban Imperviousness (PUT) dataset from the MRLC Consortium
19 website.l The PUT dataset is produced using a Categorical Regression Tree that includes satellite
20 imagery and roads (Homer et al. 2004). We aggregated the 30 m resolution to roughly 1 km2
21 resolution (990 m) to compute the average PUT for each 1 km2 cell. We then resampled the
22 average PUT at 0.98 km2 cell to 1 km2 using bilinear interpolation. The 1 km2 resolution is a
23 commonly used resolution to develop national estimates of imperviousness (e.g., Elvidge et al.
24 2004).
25 We aggregated the housing density estimates for the year 2000 from the SERGoM model
26 (Theobald 2005) from 1 ha to 1 km2 resolution. This provided us with the average housing
27 density (HD) for each 1 km2. We generated a sample of 200,000 random points (generated in a
28 spatially-balanced way using the Reversed Recursive-Quadrant Randomized Raster algorithm;
29 Theobald et al. 2007) from across the coterminous US. We extracted the values of both the PUT
30 and HD at each point, and used a Categorical and Regression Tree model to develop a regression
31 equation to develop a relationship between PUT and HD.
32 We generated the percent impervious surface for current housing density using the tree function
33 in S-Plus (Insightful Corp, Seattle, Washington). See Brieman et al. (1984) for a review of
34 categorical and regression tree methods. The resulting regression tree (Figure C-l) was then
35 evaluated using a cross validation (cv.tree S-Plus function) technique to investigate if the tree
36 over-fitted the data. As the number of terminal nodes increase, the overall deviance decreases
37 (Figure C-2), indicating that the original tree is not over-fitting the data. If the deviance were to
38 start increasing after some point within the cross validation analysis, then the tree would need to
39 be pruned to a size that would minimize deviance. Because this was not the case, we decided to
40 develop the percent impervious surface with the original tree. The large tree size (58 nodes) can
www.mrlc.gov: accessed 12 February 2007
Draft Report-June 26, 2008 page 77
-------
1 be explained best by the relatively poor non-parametric relationship between PUT and HD (R2 =
2 0.38), meaning that there was not a simple, linear fit, as shown in Figure C-3.
3 The model resulted in a Residual Mean Deviance (which is the sum of the square differences
4 between the actual values and the predicted values divided by the sample size) of 4.671. This is
5 equivalent to the standard error in linear regression, which is the spread of error (in impervious
6 units) for a given observation. The distribution of the residuals (error associated with the
7 impervious surface observations) has a minimum value of-62.35, a mean of-1.132e-14 (about
8 0), and a max value of 89.5. This distribution was not unexpected because there are areas where
9 impervious values (independent variable) had values of 0 but have positive values of housing
10 density (dependent, response variable). Figure C-l shows the decision backbone of the full
11 regression tree with the length of a limb indicating deviance. The top ten nodes within the tree
12 minimized deviance the most, with the remaining nodes making small adjustments for non-
13 parametric small grain instances. Figure C-4 shows the top ten nodes within the full regression
14 tree and the housing density thresholds used to estimate percent impervious.
15
16 Figure C-l: Full regression tree backbone (58 terminal nodes) without labels
17
Draft Report - June 26, 2008
page 78
-------
3800000 15000
'-_• 10 0
iJC 0
190 0
-40 0
1
2
3
HJ
20
30
'IIZC
40
50
53
Figure C-2: Cross validation results for the full regression tree
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000
Housing Density (units/ha)
5 Figure C-3: The relationship between percent impervious and housing density
6
7
Draft Report - June 26, 2008
page 79
-------
bhd2000.us<1531
bhd2000.lus<336.5
bhd200ol.usc49.5|
0.2029 1.3430
bhd2000lus<7062
5.8370
bhd2000i
is<3292.;
153300
25.3300
40.8300
1
2
3
Figure C-4: Top ten terminal nodes within full regression tree with housing density labels
and percent impervious estimates (terminal nodes)
5 We then converted the tree into a "con" (conditional) statement that can be input into ArcGIS as
6 a Map Algebra statement. This statement then will then apply the regression model to generate a
7 spatially-explicit map (note that HD is in units of housing units x 1000 per hectare; and bhdis
8 the Block Housing Density raster dataset):
9 bhdjmperv = con(bhd < 1795.5, con(bhd < 354.5, con(bhd < 49.5, con(bhd < 1.5, con(bhd < 0.5,
10 0.1056, 0.2680), con(bhd < 12.5, 0.4103, con(bhd < 28.5, 0.5862, 0.7027))), con(bhd < 170.5, con(bhd <
11 107.5, con(bhd< 78.5, 0.9120, 1.1480), con(bhd < 147.5, 1.4070, 1.6870)), con(bhd < 292.5, con(bhd <
12 210.5, 2.0330, 2.4030), 3.0230))), con(bhd < 885.5, con(bhd < 542.5, con(bhd < 410.5, 3.6690, 4.3110),
13 con(bhd < 687.5, 5.5750, con(bhd < 688.5, 11.7400, con(bhd < 865.5, con(bhd < 844.5, 6.5670, 7.9770),
14 5.3150)))), con(bhd < 1219.5, con(bhd < 1095.5, 7.9370, 9.5950), con(bhd < 1528.5, con(bhd < 1225.5,
15 15.3900, con(bhd < 1516, con(bhd < 1499.5, 10.5000, 15.5000), 7.4670)), 12.6500)))), con(bhd < 5528,
16 con(bhd < 3109.5, con(bhd < 2501.5, con(bhd < 1799.5, 34.3900, con(bhd < 2084.5, 13.9300, con(bhd <
17 2117.5, 19.4400, 15.2300))), con(bhd < 2978.5, con(bhd < 2529.5, 20.6800, 17.2600), 19.5300)),
18 con(bhd < 4083, con(bhd < 3114.5, 32.2000, con(bhd < 3136.5, 16.3600, con(bhd < 3983, con(bhd <
19 3562.5, 20.9700, 22.8700), 19.5300))), con(bhd < 4462.5, 24.4400, con(bhd < 5518.5, con(bhd < 5248.5,
20 con(bhd < 4890, 28.0700, 25.0200), 28.6900), 17.8400)))), con(bhd < 11117, con(bhd < 6772.5, con(bhd
21 < 5551.5, 39.6500, con(bhd < 6744.5, con(bhd < 6715, con(bhd < 6679.5, con(bhd < 6553.5, 29.7700,
22 32.5200), 23.5500), 39.1900), 22.3900)), con(bhd < 9423.5, con(bhd < 7552, 34.8700, con(bhd < 9184.5,
23 con(bhd < 9146, 36.8200, 49.4300), 33.1500)), 39.6500)), con(bhd < 26946, con(bhd < 15496.5, 45.2100,
24 49.6600), 62.3500))))
25
Draft Report - June 26, 2008
page 80
-------
1 RESULTS AND DISCUSSION
2 Using 2000 SERGoM v2 housing density, we estimated 88,887 km2 of impervious surface
3 (Figure C-5). (When we fill in areas with no housing density with NLCD 2001 urban
4 imperviousness, the total impervious surface area increases to 92,639 km2. That is, in many parts
5 of central business districts within urban areas, there census data show no residential housing
6 density, although the land there is quite built-up comprised of buildings, parking lots, etc.).
Draft Report-June 26, 2008 page 81
-------
Estimated Impervious Surface 2000 Housing Density
Map created on 7 June 2007 for
EPA-ICLUS project by David
Theobald, Colorado State University.
Figure C-5: Estimated National Impervious Surface, 2000
Draft Report - June 26, 2008
page 82
-------
1 Using 2000 SERGoM v2 housing density, we estimated 88,887 km2 of impervious surface. Our
2 estimated extent of impervious surface (88,887 km2) is fairly similar to other nationwide
3 estimates. The NLCD 2001 Urban Imperviousness layer estimated an impervious surface of
4 95,746 km2. The impervious correlation coefficient against a ground-truth dataset was .83, .89,
5 .91 (Homer et al. 2004). Note that we were not able to develop "bracket" or "bookend" models
6 that incorporate under- and over- estimations based on deviations around published error
7 statistics because they were not provided for the NLCD 2001 impervious surface cover. Also, the
8 precision of estimated PUT below 20% is believed to diminish so that low % PUT estimates from
9 PUT were difficult to obtain (Homer et al. 2004).
10 Elvidge et al. (2004) estimated an impervious surface area of 113,260 km2 (they actually report
11 112,610; +7-12,725 km2) based on nighttime lights radiance, road density, and NLCD (1992)
12 urban land cover classes. Thus, we were within 7% of NLCD 2001. Because SERGoM has
13 NOD ATA values on public (non-developable) lands, there were some cases where impervious
14 surface occurs on non-developable lands, such as military bases, airports, developed portions
15 (visitor centers) of national parks, interstates, etc. When we filled in areas of the SERGoM-based
16 impervious surface that had no housing density (mostly public lands) with NLCD 2001 urban
17 imperviousness, the total impervious surface area increased slightly to 92,639 km2. Thus, we
18 recommend using this combination of datasets to better represent total impervious surface that
19 gets at roads and commercial/industrial areas as well.
20 We also conducted an additional validation step by developing a simple linear regression of the
21 SERGoM-based impervious surface against 80 data points generated from high-resolution aerial
22 photography of 1 km2 "chips" and used to generate the Elvidge et al. (2004) product. The
23 resulting R2 was 0.694. This result was better than expected, as the 80 data points were not
24 randomly selected, rather purposively targeted to capture a gradient of urbanization, and as a
25 result these points were selected to pick up much of the commercial and industrial land cover
26 types.
27 We also generated a difference map to compare the NLCD-derived estimates against the
28 SERGoM estimates (Figure C-6 through Figure C-8). In general, NLCD estimated higher
29 imperviousness in urban areas (shown in red), and under-represented imperviousness in lower-
30 density, suburban/exurban land use areas (shown in blue). Our estimates of imperviousness are
31 likely underestimated in urban areas because they do not include commercial and industrial land
32 uses. Because it is difficult for NLCD to identify low housing density land uses beyond the
33 suburban fringe, it is likely that NLCD PUT slightly underestimates impervious surface in
34 exurban and rural areas.
Draft Report-June 26, 2008 page 83
-------
Difference in Impervious Surface (NLCD - BHD)
^- -^i •' .- ••--. • •• - "*-,.***•• • ' .- . • i ••-•*.'••' •ST:---MBs^
'• .••'*' " ^aiS^lffiffillKS^i
— *"-•-'-.'•
:i':::.'••'-:>• *MS^S
,%;. • v:^ ;• ,...-* p.*3:^^
m:;c^. it' l*ip|||l^pl
Percent Impervious
^B-62--20
m -19 - -s
^^ -4. - -1
| | -0.9 - 1
I 11.1 -s
j^HJS.1 -20
^•20.1 -95
Map created on 7 June 2007 for
EPA-ICLUS project by David
Theobald, Colorado State Urwersity.
Figure C-6: Difference in Impervious Surface, United States
Draft Report - June 26, 2008
page 84
-------
Difference in Impervious Surface (NLCD-BHD)
. yi- .
•f *•••'».,
\
Percent Impervious
I I -4.9 - -1.0%
| |-0.9. 1.0 %
| | 1.1 • 10%
• 10.1 - 25%
H kilo meters
Map created on 7 June 2007 for
EPA-ICLUS project by David
Theobald. Colorado State Unrirersity.
Figure C-7: Difference in Impervious Surface, Colorado
Draft Report - June 26, 2008
page 85
-------
Difference in Impervious Surface (NLCD - BHD)
Percent Impervious
| | -4.9 - -1.0%
| | -0.9- 1.0 %
| | 1.1 - 10%
10.1 - 25%
ap created on 7 June 2007 for
EPA-ICLUS project by David
ribald, Colorado State Urwersity
Figure C-8: Difference in Impervious Surface, Mid-Atlantic Region
Draft Report - June 26, 2008
page 86
-------
1 Finally, in Figure C-9 we show the results of using the SERGoM housing density projections for
2 2030 - base case and the estimated impervious surface as a function of housing density. We will
3 need to consider how to incorporate commercial/industrial contributions to impervious surface
4 estimates for future projections. For now, we recommend reporting just housing density-based
5 impervious surface, realizing that it is a conservative estimate, and that future efforts should
6 better represent urban/industrial land use growth as a function of population/housing density
7 growth.
Draft Report-June 26, 2008 page 87
-------
Estimated Impervious Surface 2030 Housing Density
'.' - • •: j! i . ', • "" '-•
• -••,,••-. -
•
^ t
Ma p cr eate d o n 7 J u ne 2007 to r
EPA-ICLUS project by David
Theobald, Colorado Slate Unryersit
Figure C-9: Estimated Impervious Surface, 2030
Draft Report - June 26, 2008
page 88
-------
1 Appendix D - REGIONAL POPULATION GROWTH RATES AND PROJECTIONS BASED ON U.S.
2 EPA REGIONS
4
5
6
7
8
9
10
11
12
13
Table D-l below provides a list of the EPA regions that were used for the analysis of regional
differences in population growth. Table D-2 provides the projected populations for each of the
regions and each of the modeled storylines for 2005, 2030, 2060, and 2090.
Table D-l: EPA Regions
Region States
Region 1 Northeast Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont
Region 2 Mid-Atlantic New Jersey, New York*
Region 3 Mid-east Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West
Virginia
Region 4 Southeast Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South
Carolina, Tennessee
Region 5 Mid-west Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin
Region 6 Southwest Arkansas, Louisiana, New Mexico, Oklahoma, Texas,
Region 7 Cornbelt Iowa, Kansas, Missouri, Nebraska
Region 8 Mountain-west Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming
Region 9 Pacific-south Arizona, California, Nevada**
Region 10 Northwest Idaho, Oregon, Washington***
*Puerto Rico and the U.S. Virgin Islands were not included in Region 2 for this analysis
**Hawaii, American Samoa, Guam, Northern Mariana Islands, and Trust Territories were not included in Region 9
for this analysis.
***Alaska was not included in Region 10 for this analysis.
Draft Report - June 26, 2008
page 89
-------
1 Table D-2: Projected Population and Growth Rate by Scenario and EPA Region
Population
EPA
Region 2005 2030 2060
2090
Growth Rate
2005-
2030
2030-
2060
2060-
2090
Base Case
1
2
3
4
5
6
7
8
9
10
14,255,073
28,018,871
28,797,147
57,405,312
51,257,348
35,680,974
13,269,562
10,006,652
44,519,456
11,360,137
15,519,657
35,109,989
31,978,301
70,474,771
55,592,269
45,708,352
14,224,959
12,930,609
56,426,295
13,024,245
15,902,916
41,602,295
34,424,923
82,641,728
57,423,505
55,363,006
14,504,537
15,592,590
67,555,641
14,030,327
16,887,110
49,258,590
38,637,662
97,900,593
60,486,700
65,879,226
15,010,238
18,402,596
79,977,083
15,397,072
9%
25%
11%
23%
8%
28%
7%
29%
27%
15%
2%
18%
8%
17%
3%
21%
2%
21%
20%
8%
6%
18%
12%
18%
5%
19%
3%
18%
18%
10%
Storyline Al
1
2
o
J
4
5
6
7
8
9
10
14,255,073
28,018,871
28,797,147
57,405,312
51,257,348
35,680,974
13,269,562
10,006,652
44,519,456
11,360,137
15,141,889
39,117,688
32,682,229
73,800,430
54,146,181
47,647,066
13,780,804
13,473,510
56,021,194
13,654,349
14,141,988
46,902,172
34,693,103
85,111,046
51,539,278
55,559,004
12,800,038
15,807,016
61,616,885
14,511,247
12,659,207
51,792,819
36,115,978
92,041,933
47,200,686
59,071,696
11,445,472
16,909,652
62,219,659
14,785,715
6%
40%
13%
29%
6%
34%
4%
35%
26%
26%
-7%
20%
6%
15%
-5%
17%
-7%
17%
10%
10%
-10%
10%
4%
8%
-8%
6%
-11%
7%
1%
1%
Storyline A2
1
2
3
4
5
6
7
8
9
10
14,255,073
28,018,871
28,797,147
57,405,312
51,257,348
35,680,974
13,269,562
10,006,652
44,519,456
11,360,137
15,987,287
35,917,932
32,999,574
74,318,414
56,019,601
47,833,571
14,369,471
13,630,155
58,911,077
13,137,594
18,051,181
45,986,762
40,088,688
97,906,372
63,041,628
64,361,337
15,919,641
18,236,722
78,044,717
15,229,105
22,566,774
61,228,288
54,511,203
138,195,518
77,561,440
90,250,013
19,525,569
25,157,767
108,283,362
19,198,490
12%
28%
15%
29%
9%
34%
8%
36%
32%
16%
13%
28%
21%
32%
13%
35%
11%
34%
32%
16%
25%
33%
36%
41%
23%
40%
23%
38%
39%
26%
Storyline Bl
1
2
o
J
4
5
6
7
8
9
14,255,073
28,018,871
28,797,147
57,405,312
51,257,348
35,680,974
13,269,562
10,006,652
44,519,456
15,141,889
39,117,688
32,682,229
73,800,430
54,146,181
47,647,066
13,780,804
13,473,510
56,021,194
14,141,988
46,902,172
34,693,103
85,111,046
51,539,278
55,559,004
12,800,038
15,807,016
61,616,885
12,659,207
51,792,819
36,115,978
92,041,933
47,200,686
59,071,696
11,445,472
16,909,652
62,219,659
6%
40%
13%
29%
6%
34%
4%
35%
26%
-7%
20%
6%
15%
-5%
17%
-7%
17%
10%
-10%
10%
4%
8%
-8%
6%
-11%
7%
1%
Draft Report-June 26, 2008 page 90
-------
EPA
Region
10
Storyline
1
2
o
J
4
5
6
7
8
9
10
Population
Growth Rate
2005 2030 2060 2090
11,360,137
B2
14,255,073
28,018,871
28,797,147
57,405,312
51,257,348
35,680,974
13,269,562
10,006,652
44,519,456
11,360,137
13,654,349
15,543,670
35,422,606
32,086,103
69,018,096
57,089,003
45,225,069
14,552,312
12,703,387
55,960,255
13,326,862
14,511,247
15,997,050
42,541,498
34,216,490
79,590,169
60,336,151
54,516,450
15,227,145
15,085,697
66,985,991
14,723,525
14,785,715
17,077,923
51,216,096
37,647,265
92,831,550
64,684,730
64,720,598
16,041,584
17,639,212
79,576,422
16,493,287
2005-
2030
20%
9%
26%
11%
20%
11%
27%
10%
27%
26%
17%
2030-
2060
6%
3%
20%
7%
15%
6%
21%
5%
19%
20%
10%
2060-
2090
2%
7%
20%
10%
17%
7%
19%
5%
17%
19%
12%
1
2
Draft Report - June 26, 2008
page 91
-------
1
2
3
4
5
6
7
Appendix E - COMPONENT AND COHORT MODEL DATA
Through provide data used to calculate demographics for the component and cohort model. The
following tables provide summary values for the entire population; actual rates used in the model
vary by age, sex, and race/ethnicity.
Table E-l: Fertility Rates (Births per 1000 Women)
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
Low
2,036
2,032
2,026
2,021
2,015
2,009
2,002
1,996
1,990
1,984
1,978
1,971
1,964
1,958
1,951
1,944
1,937
1,931
1,924
1,917
1,910
1,903
1,896
1,888
1,881
1,873
1,866
1,864
1,862
1,860
1,857
1,855
1,852
1,849
1,846
1,843
Mid
2,048
2,055
2,063
2,070
2,077
2,083
2,090
2,097
2,103
2,110
2,117
2,123
2,129
2,135
2,140
2,146
2,152
2,157
2,163
2,169
2,175
2,180
2,186
2,191
2,197
2,202
2,207
2,208
2,210
2,211
2,212
2,213
2,213
2,213
2,214
2,214
High
2,059
2,080
2,100
2,120
2,140
2,161
2,181
2,201
2,221
2,241
2,261
2,280
2,299
2,318
2,337
2,355
2,374
2,392
2,411
2,430
2,448
2,467
2,485
2,503
2,522
2,540
2,558
2,563
2,567
2,572
2,576
2,580
2,584
2,588
2,591
2,594
Draft Report - June 26, 2008
page 92
-------
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
1,840
1,837
1,834
1,832
1,829
1,826
1,823
1,820
1,818
1,815
1,813
1,810
1,807
1,805
1,802
1,800
1,797
1,794
1,791
1,789
1,786
1,783
1,780
1,776
1,773
1,770
1,767
1,764
1,760
1,757
1,754
1,751
1,747
1,744
1,741
1,738
1,734
1,731
1,728
1,725
1,721
1,718
1,715
1,711
1,708
1,705
2,214
2,214
2,214
2,214
2,214
2,215
2,215
2,215
2,216
2,216
2,217
2,217
2,218
2,218
2,219
2,219
2,219
2,219
2,219
2,219
2,219
2,219
2,218
2,218
2,217
2,217
2,216
2,216
2,215
2,215
2,214
2,214
2,213
2,213
2,212
2,212
2,212
2,211
2,211
2,210
2,209
2,209
2,208
2,207
2,206
2,206
2,597
2,601
2,604
2,607
2,610
2,613
2,617
2,620
2,624
2,627
2,631
2,634
2,637
2,641
2,644
2,647
2,650
2,652
2,655
2,658
2,660
2,662
2,665
2,667
2,669
2,671
2,673
2,675
2,677
2,679
2,682
2,684
2,686
2,688
2,690
2,692
2,695
2,697
2,699
2,701
2,703
2,705
2,706
2,708
2,710
2,711
Draft Report - June 26, 2008
page 93
-------
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
1,701
1,698
1,694
1,690
1,687
1,683
1,680
1,676
1,672
1,669
1,665
1,661
1,658
1,654
1,651
1,647
1,643
1,640
1,636
1,632
2,205
2,204
2,203
2,202
2,201
2,199
2,198
2,197
2,196
2,195
2,194
2,193
2,191
2,190
2,189
2,188
2,187
2,185
2,184
2,183
2,713
2,714
2,716
2,717
2,719
2,720
2,721
2,723
2,724
2,725
2,726
2,728
2,729
2,730
2,731
2,733
2,734
2,735
2,736
2,737
1
2
Draft Report - June 26, 2008
page 94
-------
1
2
Table E-2: Mortality Rates (Lifespan-Equivalent)
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100
Low
79.74
80.01
80.16
80.32
80.47
80.62
80.78
80.94
81.09
81.24
81.39
81.66
82.41
83.26
83.88
84.60
85.20
85.90
86.51
87.21
87.80
88.47
89.05
89.70
90.24
90.86
91.39
92.00
92.51
93.00
Mid
79.74
79.9
80.05
80.2
80.36
80.51
80.67
80.82
80.97
81.13
81.28
81.43
82.19
82.93
83.56
84.17
84.78
85.4
86.01
86.62
87.22
87.81
88.4
88.97
89.53
90.09
90.64
91.18
91.71
92.24
High
79.68
79.95
80.10
80.25
80.40
80.54
80.69
80.85
80.99
81.14
81.29
81.54
82.26
83.07
83.65
84.33
84.88
85.53
86.08
86.71
87.22
87.82
88.30
88.85
89.28
89.80
90.21
90.69
91.06
91.42
3
4
Draft Report - June 26, 2008
page 95
-------
1
2
Table E-3: Projected International Migration
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
Low
670,138
593,255
531,919
462,200
404,294
355,173
312,734
275,252
241,908
211,938
184,668
179,040
174,268
170,255
166,833
163,809
161,032
158,577
156,347
154,457
152,592
166,085
179,067
191,267
202,980
214,147
224,882
235,272
245,307
255,086
264,891
257,533
250,901
244,888
239,382
234,661
230,311
226,341
222,757
219,520
216,643
213,996
Mid
1,020,435
1,030,425
1,030,293
995,679
961,750
928,453
895,833
863,540
831,875
800,663
769,797
774,125
778,360
782,327
786,288
790,010
793,650
797,129
800,507
803,815
806,979
840,341
873,156
905,230
936,857
967,891
998,465
1,028,622
1,058,406
1,087,913
1,117,043
1,110,301
1,104,092
1,098,310
1,092,915
1,088,005
1,083,384
1,079,014
1,074,903
1,071,081
1,067,573
1,064,300
High
1,432,695
1,570,973
1,671,881
1,704,589
1,722,833
1,729,993
1,728,678
1,720,305
1,706,158
1,687,319
1,664,477
1,699,910
1,733,468
1,765,441
1,795,997
1,825,332
1,853,500
1,880,682
1,907,047
1,932,527
1,957,368
2,041,510
2,125,426
2,208,839
2,292,232
2,375,342
2,458,340
2,541,223
2,624,090
2,706,829
2,789,455
2,797,150
2,804,858
2,812,504
2,820,177
2,827,787
2,835,422
2,842,719
2,850,066
2,857,333
2,864,468
2,871,496
Draft Report - June 26, 2008
page 96
-------
Year
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
Low
211,696
209,507
207,356
205,448
203,620
201,922
200,347
198,712
197,203
195,780
194,554
193,265
192,017
190,931
189,843
188,619
187,498
186,448
185,302
184,102
183,076
181,927
180,848
179,608
178,444
177,282
176,119
174,912
173,632
172,478
171,308
170,122
169,012
167,811
166,788
165,628
164,501
163,329
162,359
161,216
160,066
159,026
158,066
156,995
155,981
Mid
1,061,065
1,058,054
1,055,215
1,052,456
1,049,922
1,047,331
1,044,964
1,042,694
1,040,387
1,038,197
1,036,208
1,034,270
1,032,477
1,030,777
1,029,070
1,027,388
1,025,691
1,024,149
1,022,613
1,021,041
1,019,540
1,018,071
1,016,574
1,015,188
1,013,731
1,012,362
1,011,015
1,009,672
1,008,378
1,007,139
1,005,888
1,004,724
1,003,494
1,002,407
1,001,342
1,000,147
999,100
998,085
997,098
996,136
995,099
994,178
993,261
992,333
991,460
High
2,878,370
2,885,342
2,892,083
2,898,727
2,905,317
2,911,896
2,918,282
2,924,605
2,930,848
2,936,971
2,942,987
2,949,104
2,955,004
2,960,927
2,966,785
2,972,462
2,978,058
2,983,567
2,989,148
2,994,495
2,999,780
3,004,997
3,010,217
3,015,235
3,020,298
3,025,208
3,030,105
3,034,978
3,039,655
3,044,480
3,049,252
3,053,972
3,058,708
3,063,253
3,067,923
3,072,497
3,076,939
3,081,488
3,085,910
3,090,373
3,094,759
3,099,047
3,103,452
3,107,792
3,112,064
Draft Report - June 26, 2008
page 97
-------
Year
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
Low
154,984
154,078
153,015
152,160
151,203
150,290
149,360
148,521
147,543
146,699
145,840
145,004
144,195
143,407
Mid
990,630
989,894
989,122
988,353
987,576
986,934
986,237
985,542
984,815
984,223
983,650
983,071
982,546
982,038
High
3,116,245
3,120,601
3,124,829
3,129,030
3,133,229
3,137,378
3,141,620
3,145,634
3,149,846
3,153,959
3,158,109
3,162,141
3,166,233
3,170,286
Draft Report - June 26, 2008
page 98
------- |