DRAFT EPA/600/R-14/324
DO NOT CITE OR QUOTE April 2016
External Review Draft
www.epa.gov/iclus
Updates to the Demographic and Spatial Allocation
Models to Produce Integrated Climate and Land Use
Scenarios (ICLUS) Version 2
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally
released by the U.S. Environmental Protection Agency and should not at this
stage be construed to represent Agency policy. It is being circulated for comment
on its technical accuracy and policy implications.
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document is distributed solely for the purpose of predissemination peer review under
applicable information quality guidelines. It has not been formally disseminated by EPA. It
does not represent and should not be construed to represent any Agency determination or policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
ABSTRACT
The first version of the Integrated Climate and Land Use Scenarios (ICLUS) project
modeled population, residential development, and impervious surface cover changes by decade
to the year 2100 consistent with four Intergovernmental Panel on Climate Change (IPCC)
emissions scenarios and a baseline. This report discusses improvements to the underlying
demographic and spatial allocation models of the ICLUS that result in version 2 (v2) consistent
with two of the new Shared Socioeconomic Pathways (SSPs) and two Representative
Concentration Pathways (RCPs). Improvements include the use of updated data sets, integration
of changing climate variables within the migration model, inclusion of transportation network
capacity and its increase over time, growth in commercial and industrial land uses, and the use of
population density-driven demands for residential housing, commercial development, and
industry. This report demonstrates the effect of these improvements by comparing national and
regional results among the SSP and RCP combinations and the two climate models selected.
ICLUS v2 shows differences in population migration patterns by including climate variables that
change over time rather than ones that are static. Additionally, changing commercial and
industrial land uses can drive patterns of new urban growth with consequences for many
environmental endpoints.
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CONTENTS
LIST OF TABLES v
LIST OF FIGURES vi
LIST OF ABBREVIATIONS AND ACRONYMS viii
PREFACE ix
AL I I IORS AM) REVIEWERS x
ACKNOWLEDGEMENTS x
EXECUTIVE SUMMARY xi
1. INTRODUCTION 1
2. UPDATES TO THE MIGRATION MODEL 2
2.1. UPDATING THE MIGRATION MODEL 3
2.1.1. Domestic Migration 3
2.1.2. Functional Connectivity 5
2.1.3. Historic Climate Amenities 6
2.1.4. Future Climate Amenities 8
2.1.5. Redesign and Recalibration of the Migration Model 9
2.2. MIGRATION MODEL INTERPRETATION 12
3. UPDATES TO THE SPATIAL ALLOCATION MODEL 13
3.1. OVERVIEW OF THE UPDATED SPATIAL ALLOCATION MODEL 14
3.2. ICLUS V2 LAND USE CLASSES 17
3.3. QUANTIFYING LAND USE CHANGES, 2000-2010 18
3.4. TRANSITION-PROBABILITY MODEL 21
3.4.1. Empirical Estimation of Transition Probabilities 24
3.5. ACCESSIBILITY-CAPACITY SURFACE 27
3.5.1. Creating the Initial Accessibility-Capacity Surface 27
3.5.2. Updating the Accessibility-Capacity Surface 28
3.6. LAND USE AND CAPACITY DEMAND MODELS 29
3.7. LAND USE PATCH ALLOCATION PROCESS 31
4. RESULTS 34
4.1. POPULATION PROJECTIONS 34
4.1.1. National Projections 34
4.1.2. Regional Projections 35
4.1.3. The Effect of Changing Climate Amenities 36
4.1.4. Subregional Projections 40
4.2. LAND USE PROJECTIONS 47
4.2.1. National Projections 47
4.2.2. Regional Projections 49
4.2.3. Subregional Projections 51
5. CONCLUSION 58
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CONTENTS (continued)
6. REFERENCES 61
APPENDIX A. REGIONAL LAND-USE CHANGES FOR 2000-2010 A-l
APPENDIX B. REGIONAL TRANSITION PROBABILITY MODELS B-l
APPENDIX C. LAND USE CLASS (LUC) AND CAPACITY DEMAND MODELS .C-l
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LIST OF TABLES
1. Migration model results 13
2. LUCs used in the ICLUS v2 model 18
3. Goodness-of-fit test results comparing LUCs in 2000 and 2010, nationally 20
4. LUCs transitions from 2000 (rows) to 2010 (columns) incorporated into ICLUS
v2 23
5. GLS model results 46
6. Cumulative change in developed LUCs for 2050 (top row) and 2100 (bottom row)
by Shared Socioeconomic Pathways (SSPs), Representative Concentration
Pathways (RCPs) and climate model (in parentheses) 51
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LIST OF FIGURES
1. Comparison of ICLUS vl and ICLUS v2 2
2. ICLUS v2 geographic units include Metropolitan Statistical Areas (MSAs),
Micropolitan Statistical Areas, and stand-alone counties 4
3. Biplots used to select climate projections used in this report 9
4. Proportion of total migration between MSAs, Micropolitan Statistical Areas, and
rural counties 10
5. ICLUS v2 spatial allocation flow diagram 15
6. Regions used in ICLUS version 2 16
7. Land use comparisons between 2000 and 2010, nationally 21
8. Predicted transition probability by capacity class into LUCs in 2010 26
9. Predicted log transformed pixel or capacity density (km-2) ( ± 2 SE) by log
transformed population density (km-2) 33
10. Total population for the conterminous United States to 2100 showing projections
for ICLUS v2 35
11. Total population for each ICLUS region to 2100 under four scenario
assumptions 36
12. The effect of climate change-induced domestic migration 38
13. The effect of climate change-induced domestic migration 39
14. Differences in population and climate change projections driven by FIO-ESM and
HadGEM2-AO under SSP5 and RCP8.5 assumptions for 2050 and 2100 41
15. Differences in population and climate projections driven by FIO-ESM and
HadGEM2-AO under SSP1 and RCP4.5 assumptions for 2050 and 2100 42
16. Average ICLUS GU 10-year population change by (A) starting population density
and ICLUS region from 2010-2050, (B) starting population density and ICLUS
region from 2060-2100, (C) starting population density and SSP from
2060-2100. P1-P5: <5.0; 5.1-1 45
17. National land use projections from ICLUS v2 to 2100 47
18. Relative increases in the area of developed LUCs nationally at 2050 (top row) and
2100 (bottom row) 49
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LIST OF FIGURES (continued)
19. Land use change in the vicinity of the Portland, OR-Vancouver, WA Metro Area
under the SSP1-RCP4.5 (FIO-ESM) scenario: 2010, 2050, and 2100 53
20. Land use change in the vicinity of the Portland, OR-Vancouver, WA Metro Area
under the SSP5-RCP8.5 (HadGEM2-AO) scenario: 2010, 2050, and 2100 54
21. Land use change in the vicinity of the Springfield, MO Metro Area under the
SSP1-RCP4.5 (FIO-ESM) scenario: 2010, 2050, and 2100 55
22. Land use change in the vicinity of the Springfield, MO Metro Area under the
SSP5-RCP8.5 (HadGEM2-AO) scenario: 2010, 2050, and 2100 56
23. Land use change in the vicinity of the Washington-Arlington-Alexandria, DC-VA
Metro Area under the SSP1-RCP4.5 (FIO-ESM) scenario: 2010, 2050, and
2100 57
24, Land use change in the vicinity of the Washington-Arlington-Alexandria, DC-VA
Metro Area under the SSP5-RCP8.5 (HadGEM2-AO) scenario: 2010, 2050, and
2100 58
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LIST OF ABBREVIATIONS AND ACRONYMS
BCSD
bias-correcting and spatial-downscaling
CI
confidence interval
CMIP
Coupled Model Intercomparison Project
df
degrees of freedom
DUA
dwelling units per acre
EPA
U.S. Environmental Protection Agency
FIO-ESM
First Institute of Oceanography-Earth System Model
GAM
generalized additive model
GCM
general circulation model
GU
geographic unit
HadGEM2-AO
Hadley Global Environment Model 2 Atmosphere-Ocean
HUC
hydrologic unit code
ICLUS
Integrated Climate and Land Use Scenarios
IIASA
International Institute for Applied Systems Analysis
IPCC
Intergovernmental Panel on Climate Change
IRS
Internal Revenue Service
LUC
land use class
MIGPUMA
Migration Public-Use Microdata Area
MSA
Metropolitan Statistical Area
NCEA
National Center for Environmental Assessment
NLCD
National Land Cover Database
US-NLUD
National Land Use Dataset
OR
odds ratio
ORD
Office of Research and Development
P
population density, pixel
PUMA
Public Use Microdata Area
PUMS
Public Use Microdata Sample
RCP
Representative Concentration Pathway
SERGoM
Spatially Explicit Regional Growth Model
SRES
Special Report on Emissions Scenarios
SSP
Shared Socioeconomic Pathway
USGS
U.S. Geological Survey
vl
version 1
v2
version 2
WCRP
World Climate Research Programme
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PREFACE
This report was prepared jointly by the Office of Research and Development (ORD) at
the U.S. Environmental Protection Agency (EPA), ICF International, Colorado State University,
and Conservation Science Partners. The report describes the updates to data sets and models that
constitute ICLUS version 2 (v2). Because this is an update to ICLUS version 1 (vl), many of the
concepts and models build on the original report (U.S. EPA, 2009). Users familiar with ICLUS
vl can use this report as a reference guide to understand what changes have been made and the
implications for the resulting data sets and maps. Output data sets and maps are intended to be
used in a scenario context to assess the risks, vulnerabilities, impacts, and adaptation options of
climate change.
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AUTHORS AND REVIEWERS
The Global Change Assessment Staff within the National Center for Environmental
Assessment, Office of Research and Development, is responsible for publishing this report. This
document was prepared by ICF International under Contract No. GS-10F-0124J, EPA Order No.
EP09C000208. Dr. Britta Bierwagen served as Technical Project Officer and provided overall
direction and technical assistance.
AUTHORS
U.S. EPA
Britta Bierwagen, Philip Morefield
Oak Ridge Institute for Science and Engineering (ORISE) Fellow at U.S. EPA
Jonathan Witt1
ICF International, Washington, DC
Anne Choate, Jonathan Cohen, Philip Groth, Dana Spindler
Conservation Science Partners; Department of Fish, Wildlife and Conservation Biology,
Colorado State University, Fort Collins, CO
David M. Theobald
REVIEWERS
U.S. EPA Reviewers
William Kepner (ORD/NERL), James Wickham (ORD/NERL)
ACKNOWLEDGEMENTS
The authors would like to thank Alicia Barnash, Angelica Murdukhayeva, and Jennifer
James for their assistance visualizing data sets and outputs. The thoughtful comments from
reviewers substantially improved this report.
1 Current affiliation: Fairfax County Stormwater Planning Division, VA
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EXECUTIVE SUMMARY
The Integrated Climate and Land Use Scenarios (ICLUS) version 1 (vl) furthered land
change modeling by providing nationwide housing development scenarios to 2100. ICLUS
version 2 (v2) builds on this modeling approach by updating population and land use data sets
and addressing two sets of limitations identified in ICLUS vl. This report documents the
changes made to the underlying data sets used for model parameterization and to the
demographic and spatial allocation models. The purpose is to address limitations encountered in
ICLUS vl and identified, in part, by the ICLUS user community.
The first limitation is with the ICLUS vl migration component of the demographic
model, which incorporated a limited time frame of human migration data, road-based
connectivity only among counties, and a static climate variable. To address these limitations,
ICLUS v2 uses a data set from the Internal Revenue Service (IRS) of county-to-county migration
from 1990-2000 to parameterize the migration model. Intercounty connectivity calculations
include fixed mass transit as well as roads.
The final update to the migration model is the inclusion of changing climate variables as
part of the amenity parameters. ICLUS vl used static amenity variables, including county-level
historical climate data. ICLUS v2 now parameterizes the model with updated historical climate
data (1980-2009) and includes projected climate variables for each time step to 2100. Analyses
in this report use two different climate models: (1) the First Institute of Oceanography-Earth
System Model (FIO-ESM) and (2) the Hadley Global Environment Model 2 Atmosphere-Ocean
(HadGEM2-AO) to illustrate the effect of changing climate variables on migration patterns.
Specific climate variables include January and July humidity-adjusted temperature and summer
(June, July, August) and winter (December, January, February) precipitation incorporated as the
running average of the previous 5 years of climate model output. Comparisons of the results
with and without projected climate variables show that differences in regional migration patterns
occur when changing climate variables are included. Results between the climate models and
results using constant climate are more similar to each other than are differences in migration
patterns between combinations emissions and demographic scenarios. Different fertility and
migration rates in the scenarios exert much larger influences on the overall migration patterns
than changes in climate amenities.
Several additional changes in ICLUS v2 resulted from the updates of data sets used in the
demographic model. The use of the 2010 U.S. Census Bureau's data in the demographic model
results in new national population projections for each of the scenarios documented in this
report. Because the IRS database does not contain demographic information, the migration
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model in ICLUS v2 combines all age groups into a single population, whereas ICLUS vl
contained migration information for populations under and over 50 years old separately.
The second set of limitations identified was within the ICLUS vl spatial allocation
model, which used population to calculate housing density based on household size, while all
other land use classes remained static. ICLUS v2 uses statistical relationships between
population density, road capacity, and land use classes to allocate new land uses at the next time
step based on the demands of the growing population. Demand calculations are done nationally
for each developed land use class and transition probabilities from one land use to another
incorporate differences in growth patterns for each of seven regions of the conterminous United
States, similar to U.S. Census Bureau regions. In addition to residential housing classes,
commercial and industrial land uses also change at each time step.
The spatial allocation model also incorporates updated data sets for land use (a new U.S.
National Land Use Dataset [US-NLUD] based on the 2011 National Land Cover Database
[NLCD] and many other detailed data on land use), transportation (roads and fixed mass transit),
and developable area derived from the 2012 U.S. Geological Survey (USGS) Protected Areas
Database. The model uses land use transitions from 2000 to 2010 as the basis for all future land
use changes. The spatial allocation model projects transitions for five residential housing classes
and commercial and industrial land uses. The sequence of land use class changes are based on
the theory that the highest and best use prevails, generally as determined by land value. The
spatial allocation model uses output from the demographic model to calculate demand for each
land use class in relation to population density. New land uses are allocated as patches based on
the existing distribution of patch shapes and sizes for each land use class. The probability of
placement of a patch depends on the antecedent land use class (to determine allowable
transitions) and a capacity surface (based on the transportation matrix and capacity of different
types of roads to transport varying numbers of people).
The resulting land use allocation replaces low-density residential development by higher
density land uses as a population grows within ICLUS geographic units. Low-density
development generally expands outward. The development of higher density residential,
commercial, and industrial classes levels off in terms of demand at high population densities,
exhibiting a threshold effect. This threshold shows that these land use classes are not rapidly
replaced once developed, and that there are observed limitations in the density of particular land
use classes in dense metropolitan areas. Similarly, transportation capacity also reaches a
threshold. Dense cities add new road capacity more slowly than do smaller cities.
The emergence of new socioeconomic and emissions scenarios (e.g., Shared
Socioeconomic Pathways [SSPs] and reference concentration pathways [RCPs]) utilized in
ICLUS v2, as opposed to the previous emissions storylines used in ICLUS vl, limits the
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usefulness of direct comparisons between outputs from both ICLUS versions. This report
discusses results as comparisons between the SSP-RCP combinations implemented in ICLUS v2.
Improvements in ICLUS v2 allow discussions of results in terms of national changes, as well as
regional and subregional changes over time.
The output of the demographic model is similar to globally based population estimates
for the United States that are consistent with the SSPs. ICLUS v2 population estimates for the
United States by 2100 are slightly higher for both SSP1 and SSP5 than those derived by KC and
Lutz (2014) for SSP1 and SSP5 from the global estimates for the United States, although the
relative difference in population between the scenarios in 2100 is similar. This range in
population estimates allows for an interpretation of the differences in impacts between the two
scenarios that is consistent with the global community. Regionally, differences in population
growth are greater between the SSPs than differences between climate models used with the
same SSP. However, comparisons between model runs with changing climate variables and
static climate, show regional differences in population of up to 4%. Subregionally, there are
additional differences that are reinforced by the choice of climate model used in the migration
model. These differences are more distinct at higher population densities and during the last half
of the century, especially using SSP5, which has higher fertility rates than SSP1.
The national-scale land use projections show nearly identical trends when comparing
outcomes under the same SSP assumption; the choice of climate model has no discernible effect
on the overall amount of projected development. However, there are differences in amount and
allocation of land uses when comparing between SSPs and examining changes regionally.
Regional allocation patterns reflect existing differences across the conterminous United States
that continue to shape patterns into the future. While nearly all developed land use classes
increase in nearly all regions, the magnitude of changes reflect current trends, such that
low-density residential classes continue to increase in the Intermountain West more so than in
other regions, and regions with higher densities continue to increase their urban land uses.
Overall, ICLUS v2 provides users with the ability to model population and land use
changes consistent with SSP and RCP scenarios and specific climate models to improve
integrated climate and land use assessments. While this report only uses SSP1-RCP4.5 and
SSP5-RCP8.5 in conjunction with FIO-ESM and HadGEM2-AO to illustrate ICLUS v2
improvements, the model structure allows users the flexibility to change SSP, RCP, and climate
model. The use of statistically based transition and demand models also allows users to change
parameters for further scenario explorations that alter development pathways from current
trajectories. Therefore, ICLUS v2 is better suited to explore scenarios of climate change
impacts, vulnerability, and adaptation options, including the use of ICLUS v2 outputs in models
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projecting emissions from developed land uses and consequences for water and air quality
endpoints, as well as human health.
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1. INTRODUCTION
Changes in climate and land use are global drivers of environmental impacts. The
interactions between climate and land use changes are complex and can result in challenges for
ecosystems and environmental health. The motivation for the U.S. Environmental Protection
Agency (EPA) Integrated Climate and Land Use Scenarios (ICLUS) project originated with the
recognition of this complex relationship and the absence of an internally consistent set of land
use scenarios that support national assessments of climate change effects. This report describes
updates to the ICLUS model data, methods, and outputs described in Land-Use Scenarios:
National-Scale Housing-Density Scenarios Consistent with Climate Change Storylines
(U.S. EPA, 2009).
ICLUS version 1 (vl) developed future scenarios of population, housing density, and
impervious surfaces that were consistent with the Intergovernmental Panel on Climate Change
(IPCC) Special Report on Emissions Scenarios (SRES) storylines
(Nakicenovic and Swart, 2000). ICLUS vl integrated two main components: a demographic
model and a spatial allocation model (see Figure 1). ICLUS vl helped advance land change
modeling by providing nationwide development scenarios to 2100. ICLUS version 2 (v2) builds
on this modeling approach by addressing two sets of limitations. First, the demographic model
in vl incorporated a limited timeframe of movement data, road-based connectivity among
counties, and a static climate variable in its migration model. Second, the spatial allocation
model used population to calculate housing density based on household size, while all other land
use types remained static, including commercial and industrial uses. ICLUS v2 also incorporates
updated data sets of population, land use, and land cover. The addition of dynamic future
climate variables draws on the most recent climate data which use Representative Concentration
Pathways (RCPs) rather than SRES. The RCPs are targets of greenhouse gas concentrations that
general circulation models reach by the year 2100 to depict a range of climate change outcomes.
Thus, ICLUS v2 is now consistent with the most recent suite of climate change scenarios, linking
RCP-driven climate model output with Shared Socioeconomic Pathways (SSPs).
This report covers the updates to the demographic model in Section 2 and the spatial
allocation model in Section 3. Section 4 focuses on model outputs and compares these outputs
from ICLUS v2 with those from vl. Descriptions of the updates and analyses of v2 outputs are
intended to assist users of the ICLUS data sets and maps to understand which changes were
made, why, and what the consequences for the outputs are.
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County Population:
SRES Storylines
2000
Fertility, Immigration
1
Migration
1^1
County Population
I
Housing Density
I
Impervious Surface
SSP Storylines
County Population:
2010
RCP Climate
Fertility, Immigration
Migration
MSA & County
Population
Land Use
y. ,
Impervious Surface \
Figure 1. Comparison of ICLUS vl and ICLUS v2. The two model versions
are conceptually identical. ICLUS v2 reflects substantial updates to key inputs as
well as a modified geographic framework. Estimates of percentage impervious
surface change for ICLUS v2 will be produced subsequent to this report.
2. UPDATES TO Till MIGRATION MODEL
The ICLUS demographic model projects county4evel population for the conterminous
United States on an annual basis from 2010 to 2100 for a number of socioeconomic scenarios
and climate projections. The demographic model uses a cohort-component methodology to
project fertility, mortality, and international migration. The model also includes a submodel to
project county-to-county domestic migration (U.S. EPA, 2009). Population variables in ICLUS
v2 use the most recent 2010 U.S. Census Bureau data (NCHS, 2011) but use the same fertility
and migration rates as ICLUS vl. The combinations of demographic variables in ICLUS v2
differ from vl to better align with recent interpretations of RCP and SSP combinations (Samir
and Lutz, 2014; van Vuuren and Carter, 2014). Model updates discussed in this report use
combinations of RCPs and SSPs that succinctly demonstrate a range of possible ICLUS v2
projections with respect to climatic changes and population growth. We used a peer-reviewed
crosswalk of the SSPss and Representative Concentration Pathways (RCPs) to the SRES
scenario framework to identify combinations of SSPs and RCPs (van Vuuren and Carter, 2014)
that resemble the bounding scenarios used to demonstrate the range of impacts explored with
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ICLUS vl (e.g., Bierwagen et al., 2010; Voorhees et al., 2011; Georgescu et al., 2014). We
selected the combination of SSP5 and RCP8.5 to represent a high emissions, high population-
growth scenario, and the combination of SSP1 and RCP4.5 as a lower emissions, lower
population-growth scenario. Like ICLUS vl, the population-growth scenarios were generated
using projections of immigration, fertility, and mortality produced by the U.S. Census Bureau
(2000). Specifically, the SSP5-RCP8.5 scenario uses the U.S. Census Bureau's high fertility,
high domestic migration, and medium immigration rates; SSP1-RCP4.5 uses medium fertility,
high domestic migration, and medium immigration. These combinations are qualitatively
consistent with rates for high-income countries globally (Samir and Lutz, 2014) and generally
correspond to the SRES AlFi and B1 SRES scenarios, respectively (van Vuuren and Carter,
2014).
The focus of the remainder of Section 2 is on implementing the migration model within
the cohort-component model. The following subsections describe changes to the migration
component of the ICLUS vl demographic model, including updates to domestic movements and
the incorporation of climate change projections. Section 4.1 shows the results of the updated
model and compares these to ICLUS vl outputs.
2.1. UPDATING HI! MIGRATION MODEL
The demographic component in ICLUS vl included a migration model that simulated
domestic migration by estimating flows between pairs of counties. ICLUS v2 updates the
underlying data used to parameterize the migration model, adds new independent variables,
incorporates a county-to-county migration data set that covers a longer historical time period
than the data set in ICLUS vl, and aggregates some counties into Metropolitan and Micropolitan
Statistical Areas, defined as 50,000 people or more in an urban area and at least 10,000 but less
than 50,000 people, respectively. Finally, amenity variables use recent climate data for model
calibration and update these data each decade with model output of future climate variables.
2.1.1. Domestic Migration
ICLUS v2 incorporates definitions of both Metropolitan and Micropolitan Statistical
Areas (OMB, 2010), and aggregates counties into geographic units accordingly. This change
effectively reduces the number of migration origin and destination locations and simplifies
analysis of the historic migration information by excluding many short-distance moves. In
addition, a small number of independent cities that were not absorbed into Metropolitan or
Micropolitan areas were merged with their surrounding counties. The resulting geographic
framework consists of 2,256 units, composed of Metropolitan and Micropolitan Statistical Areas
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and stand-alone rural counties, referred to hereafter as ICLUS geographic units (GUs)
(see Figure 2).
mSISI| Mr • n »'¦ ^ '
Metropolitan Statistical Area
Micropolitan Statistical Area
Figure 2. ICLUS v2 geographic units include Metropolitan Statistical Areas
(MSAs), Micropolitan Statistical Areas, and stand-alone counties.
The ICLUS vl migration model used a temporally limited data set to parameterize
county-to-county movements across the conterminous United States, specifically the 1995 to
2000 Public Use Microdata Samples (PUMSs) (U.S. Census Bureau, 2003a). Although this data
set includes millions of migration records {n = 2,397,007), it covers just a single 5-year time
span. ICLUS v2 uses 10 years (1991 to 2000) of the IRS (2014) county-to-county migration data
to update the migration model.2 The values in the migration data set, combined with specific
county-level information, such as population size, growth rates, climate, and connectivity to
other counties, are used to parameterize the updated migration model.
The IRS data set provides a full count of all income tax filers based on year-to-year
address changes reported on individual income tax returns. Data are expressed in terms of
inflows (the number of new residents who moved to a county or state and where they originated)
1 These data are available for public download: h1tp://www.irs.gov/uac/SOI-Tax-Stats-Migration-Data.
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and outflows (the number of residents leaving a county or state and where they went). The data
set covers all counties in the United States, but only reports county-to-county migrations when
10 or more such migrations occurred. Additionally, this data set quantifies international
immigration for each county on an annual basis, an important component of net migration within
the demographic model.
The IRS data present multiple advantages. First, unlike the PUMS migration data used in
ICLUS vl, the IRS migration data are true county-to-county records. The PUMS migration data
represent migrations between Migration Public-Use Microdata Areas (MIGPUMAs). This
required a two-stage conversion, from MIGPUMA to Public Use Microdata Areas (PUMAs),
and then from PUMAs to counties. Second, the IRS data represent full counts of all income tax
filers, while the PUMS data are based on a statistical sample. Third, and most importantly, the
IRS data used in this analysis are annual data for the years 1991-2000, compared with a single
5-year period of PUMS data.
However, the IRS data has a different set of limitations not present in the PUMS data.
First, age is not included in the IRS data. The ICLUS vl migration model consisted of two age
groups (ages 0-49 years and ages 50 years and older). ICLUS v2, therefore, does not separate
the model into different age groups. Second, the IRS data are based on the number of income tax
filers and exemptions, not the number of people. The number of exemptions, however, closely
matches the number of people (IRS, 2014). Also, people who did not file income tax returns are
excluded from the IRS data. Thirdly, in cases where fewer than 10 migrations were recorded
between any county pair, migration flows are aggregated in the IRS data. These flows represent
about 7% of total migrations but were not included in the analysis due to lack of specific
origin/destination pairing.
From the IRS data set, we extracted two key variables used in this analysis: (1) total
outflow expressed as a percentage of the county population and (2) individual county-to-county
migration records.
2.1.2. Functional Connectivity
ICLUS v2 also includes updated measures of connectivity. Like ICLUS vl,
population-weighted centroids were generated for each of the 2,256 geographic units. Centroids
for a few units were manually moved inside of the respective geographic boundaries. To
evaluate the connectedness of each geographic unit, a network-based travel time was calculated
for every possible origin-destination combination. Travel times were estimated using StreetMap
North America3 and the Network Analyst extension for ArcGIS 10.3. The population-weighted
3 http://resources.arcgis.eom/en/help/main/10.l/index.html#/About_StreetMap_North_America/001z00000039000000/.
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centroids were snapped to the nearest network feature, including regular ferry routes where
applicable.
2.1.3. Historic Climate Amenities
Linkages between climate variables and human migrations are evident in the literature
(e.g., Alonso, 1971; Cragg and Kahn, 1996; Rappaport, 2007; Feng et al., 2010; Maxwell and
Soule, 2011; Sinha and Cropper, 2013) and form the basis for our exploration of the inclusion of
changing climate variables in the migration model. ICLUS vl used a static set of 30-year
average climate data based on 1941-1970 records (McGranahan, 1999). ICLUS v2 improves on
the inclusion of a climate amenity value in two ways. First, the historic climate data were
updated to cover the 1980-1999 time period and coincide with the IRS migration data. Second,
future projections of climate change are used to update these amenity values at each time step of
the migration model. Together, these improvements allow the ICLUS v2 migration model to
better reflect the current and future amenity value of climate.
In order to incorporate both observed and projected climate amenity values in the
migration model, data covering the observed historical period and future time period need to be
consistent. ICLUS vl used January temperature, January sunlight, July temperature, and July
humidity as the climate amenity variables. However, sunlight variables generally are not
available as output from general circulation models (GCM) used to model climate change.
Furthermore, results from Sussman et al. (2014) suggest that precipitation is a key climate
amenity driving housing prices and should not be omitted in a migration model.
Climate variables also need to be resolved at the spatial scale of ICLUS geographic units
(or smaller) for consistency with the migration model. While raw GCM output covers much
larger geographic areas, downscaled products reduces the spatial resolution. Historical and
projected climate data are available for download from the World Climate Research
Programme's (WCRP's) Coupled Model Intercomparison Project Phase 5 (CMIP5) multimodel
data set with bias-correcting and spatial-downscaling (BCSD) methodology applied
(Wood et al., 2004; Maurer et al., 2007).4 The BCSD methodology uses statistical bias
correction to interpret GCM output over a large spatial domain based on current observations.
The principal potential weakness of this approach is an assumption of stationarity. That is, the
assumption is made that the relationship between large-scale precipitation and temperature and
local precipitation and temperature in the future will be the same as in the past. Thus, the
method can successfully account for orographic effects that are observed in current data, but not
for impacts that might result from the interaction of changed wind direction and orographic
4 Bureau of Reclamation/Santa Clara University /Lawrence Livermore archive of downscaled IPCC model runs available at
http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/.
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1 effects. A second assumption included in the bias-correction step is that any biases exhibited by
2 a GCM for the historical period will also be exhibited in simulations of future periods.
3 The variables selected for use in the migration model were average monthly
4 humidity-adjusted temperature (January and July) and average seasonal precipitation (December
5 through February, or "winter," and June through August, or "summer"), although a number of
6 permutations were tested to maximize model fit. These included:
7 • comparing the role of absolute temperature versus changes in temperature relative to
8 the mean;
9 • comparing the role of absolute precipitation versus changes in precipitation relative to
10 the mean;
11 • considering the impact of including temperature-squared and precipitation-squared
12 terms as quadratic terms;
13 • comparing temperature versus humidity-adjusted temperature (a function of
14 temperature and humidity); and
15 • considering alternative specifications of precipitation (monthly, seasonal, annual,
16 etc.).
17 The precipitation variables used in ICLUS v2 were calculated from climate model output
18 downscaled using the BCSD methodology. Humidity-adjusted temperature is generally not
19 available as a downscaled climate model output. Instead, this variable was calculated using a
20 polynomial equation (Eq. 1) relating humidity-adjusted temperature to absolute temperature and
21 relative humidity (Rothfusz, 1990):
22 Humidity-adjusted temperature is calculated by:
23 Th = -42.379 + (2.04901523 X T) + (10.1433127 X RH) - (0.22475541 xTxRH)
24 - (0.00683783 X T2) - (0.05481717 X RH2) + (0.00122874 X T2 X RH)
25 + (0.00085282 X T X RH2) - (0.00000199 X T2 X RH2)
26 (1)
27 Where:
28 Th = average monthly humidity-adjusted temperature
29 T = average monthly air temperature in degrees Fahrenheit
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RH = average monthly relative humidity
Humidity-adjusted temperature (TH) was calculated only when absolute temperature (T) was
greater than 80°F and relative humidity was greater than 40%. When either of those conditions
was not met, unadjusted T was used.
2.1.4. Future Climate Amenities
The selection of climate data for the migration model is another opportunity for
consistency with the SSP and RCP scenarios. For each of the RCP8.5 and RCP4.5 emission
scenarios, we identified two climate change projections that generally capture the range of
potential climate change for the contiguous United States. We constructed scatterplots of all
climate projections in the BCSD CMIP climate projection archive using climate amenity
descriptions to form axes of "summer" and "winter" scatterplots and duplicated those scatterplots
for both emissions scenarios. As shown in Figure 3 below, the scatterplots provide a simple,
visual heuristic device to identify climate projections that bracket a broad range of future climate
change uncertainty. Using the plots in Figure 3, we selected projections from the HadGEM2-AO
and FIO-ESM climate models for the analyses included in this report.
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34 realizations of climate change from BCSD-CMIP5
Emissions Scenario: RCP45 Study area: conus
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A Mean July Temperature (°C): 2051 to 2080
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Figure 3. Biplots used to select climate projections used in this report.
Dashed lines show median values. The HadGEM2-AO and FIO-ESM climate
projections circled in red were selected for this report because they generally
spanned the range of climate outcomes regardless of emissions scenario (RCP4.5
and RCP8.5) or season (winter precipitation/January temperature and summer
precipitation/July temperature).
1 2.1.5. Redesign and Recalibration of the Migration Model
2 Each of the updated data sources required some modification to the migration model. In
3 order to accommodate the IRS data, the two age groups (under or over 50) used in ICLUS vl
4 were combined into a single population for ICLUS v2. The migration model also calculates
5 migrations annually because the IRS data are based on single-year records. ICLUS vl was based
6 on 5-year migration records.
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In addition, an important constraint was introduced to the updated migration model that
gives more reasonable projections across the ICLUS v2 geographic framework. The IRS
migration records for 1991-2000 were grouped such that total migration between and among
Metropolitan Statistical Areas (MSAs), Micropolitan Statistical Areas, and stand-alone (rural)
counties could be quantified. The relative proportions shown in Figure 4 are used at each annual
time step to adjust the raw migrations calculated by the migration model.
Proportion of migration by flow type
IRS files, 1991-2000
3.2%
1.6%
Figure 4. Proportion of total migration between MSAs, Micropolitan
Statistical Areas, and rural counties.
The migratory flows shown in Figure 4 are consistent with U.S. Census Bureau data
reported for the 1995-2000 time period (U.S. Census Bureau, 2003). Incorporating these values
into the ICLUS v2 migration model provides two important advantages. First, we are able to
capture import macro-level trends, such as a net migration deficit for MSAs. Second, these
values serve as useful parameters for scenario exploration in future phases of ICLUS
development.
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2.1.5.1. Revised functional form and model statistics
The historical migration records and historical climate amenities discussed above were
combined so that a record in the data table contained the number of migrations from one ICLUS
GU to another, the attributes of the origin unit, the attributes of the destination unit, and the
functional distance between them. Equation 2 shows the variables used in the migration model.
To estimate the number of migrants we used negative binomial regression with a natural log link.
Predictor variables were transformed as needed to control for skewness or heavy tails and were
standardized (Schielzeth, 2010). Because we expected that the amenity values associated with
temperature would depend on precipitation, we included interactions between those terms for
both summer and winter origin and destination units. As suggested by Dormann et al. (2013)
and to avoid the effects of collinearity, we used only predictor variables with absolute
correlations less than 0.70 (all correlations except those for summer and winter temperature were
less than 0.40).
The migration model calculation is:
ln(F;y) = Po + Pi X In (Ay) + [Pi X In (P;) +ftx In (Py)] +
[/?4 x Gj 4 + /?5 x Gj 4] + [/?6 x ln(i4j) + /?7 x ln(i4y)] +
\P8 x SH, +f]9x SHj] + [p10 x WHl + x WHj\ +
[Pi2 x SPi + 013 x SPj] + [/?14 x WPl'2 + p15 x WPl'2\ +
[Pie x SHt x SPt + f]l7 x SHj x SPj] +
[f]l8 x WHi x WPL1/2 + f]l9 x WHj x WPj1'2]
(2)
Where:
i = origin
j = destination
Fy = people migrating from unit i to unit j between year n and n 1
fik = intercept or slopes quantifying the relationship between the parameters and number of
migrants
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Dy = functional distance between unit i and j
P = population density
G = population growth rate, previous time step
A = developable land area
SH = mean summer (July) apparent temperature, 10 year running average
SP = mean summer (June, July, August) precipitation, 10 year running average
WH = mean winter (January) apparent temperature, 10 year running average
WP = mean winter (December, January, February) precipitation, 10 year running average
2.2. MIGRATION MODEL INTERPRETATION
The migration model parameters are derived from a generalized linear modeling
approach, so common measures of model performance are not available. However,
Nagelkerke's R2 was equal to 0.62 for the final model specification (Faraway, 2006).
Interpretation of the role of climate variables in the model is difficult, largely because
both origin and destination locations are affected simultaneously. Furthermore, migration is
calculated between all possible origin-destination pairs, meaning the observed net migration is
the difference between two opposing flows. Despite this complexity, the effects of variables in
the migration model may be characterized three ways.
First, the sign and magnitude of the coefficient indicates whether a variable will tend to
generally increase or decrease migrations. For example, winter temperature (WH) has a positive
coefficient for both the origin (WHi = 0.141) and destination (WH, = 0.207) locations. If all other
variables were held constant, more total migrations would occur between places with warm
winters, relative to places with cold winters. The magnitude of this influence is less than that of
population density (Pi = 0.530 and Pj = 0.430), which exerts the largest influence on migration
(see Table 1).
Second, comparing the origin and destination coefficients indicates the net directional
influence of that variable. For example, if all other factors are equal, the net flow of migrants
will be to locations with warmer winter temperatures (WH, < WHj) and less winter precipitation
(WP: > WPj) (see Table 1).
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Table 1. Migration model results. Parameters are sorted by whether they
applied to origin or destination county (z or /), and matching pairs of parameters
share a row. Differences in slope estimates between matching pairs of parameters
are provided in the last column. Variables are defined in Equation 2. refers to
the estimate of the variable.
Parameter
Pk
P
Parameter
Pk
P
\Pki - Pkj
Intercept
4.472
<0.0001
Fy
-1.048
<0.0001
Pr
0.530
<0.0001
Pj
0.430
<0.0001
0.100
Gi
0.027
<0.0001
Gj
-0.051
<0.0001
0.078
Ai
0.385
<0.0001
Aj
0.352
<0.0001
0.033
SH
-0.080
<0.0001
SHj
-0.042
<0.0001
0.038
WH\
0.141
<0.0001
WH,
0.207
<0.0001
0.066
SPi
-0.088
<0.0001
SPj
-0.082
<0.0001
0.006
WP,
-0.077
<0.0001
WPj
-0.101
<0.0001
0.024
SHi x SP,
0.022
<0.0001
SHj x SPj
0.019
<0.0001
WHt x WP,
0.002
0.3040
WHj x WPj
0.040
<0.0001
Lastly, the relative contribution of each climate variable to net migration patterns is also
related to the absolute difference between the origin and destination coefficients (the last column
in Table 1). For example, winter temperature is the most influential climate variable in the
ICLUS v2 migration model, given both the relative size of the absolute difference between the
origin and destination coefficients and the size of the coefficients relative to other climate
variables. Summer temperature, winter precipitation, and summer precipitation variables follow
winter temperature in influence on net migration.
3. UPDATES TO Till SPATIAL ALLOCATION MODEL
ICLUS vl used the Spatially Explicit Regional Growth Model (SERGoM) to project
future increases of housing density at a relatively fine spatial resolution (Theobald, 2005;
Bierwagen et al., 2010). Aside from the dynamic growth of this single (residential) land use, no
other land use types were modeled, although commercial and industrial lands were identified and
held constant through time. ICLUS v2 uses a deterministic demand-allocation approach, similar
to SERGoM, that assumes many aspects of future growth will resemble the recent past
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(i.e., 2000 to 2010). However, this new model sequentially allocates pixels from seven discrete
land use classes (LUC) that include five levels of residential density, commercial, and industrial
uses.
This update to the spatial allocation model addresses review comments of ICLUS vl and
incorporates advances in the literature on land use change modeling. The new literature suggests
that land use models should (1) incorporate spatial dynamics5 and multiple sources of spatial
heterogeneity, (2) explicitly describe transitional dynamics of urban land use, (3) incorporate
direct effects of market adjustments, (4) use local-scale heterogeneity to determine urban spatial
dynamics (Irwin, 2010), and (5) integrate top-down and bottom-up methods that incorporate the
effects of national and global drivers of change while also accounting for local drivers of change
and feedbacks (Sohl et al., 2010). For ICLUS vl, SERGoM met the conditions for (1), (4), and
partially (5). The revised allocation model in ICLUS v2 addresses (2) by using a transition
probability model, partially addresses (3) by incorporating an assumption of maximum utility of
land use (Alonso, 1964), and strengthens (5) by using a finer spatial and thematic resolution.
3.1. OVERVIEW OF THE UPDATED SPATIAL ALLOCATION MODEL
The updated spatial allocation model incorporates information from multiple spatial
scales. At the national scale, all 2,256 ICLUS GUs (see Figure 2) in the conterminous United
States were used to construct a statistical model that generates local demands for new pixels of
land use based on changes in population density. This approach is consistent with a unified
theory of city growth that represents multiple land uses across spatial scales (Bettencourt et al.,
2007; Bettencourt, 2013; Batty, 2013). ICLUS v2 land use changes are based on population
inputs from the migration model; informed at the pixel scale by allowable transitions and
accessibility to transportation; allocated as patches at the subcounty scale, with residential patch
allocation also based on accessibility to commercial areas; and consistent with land use-specific
patch-size distributions and densities on a regional basis (see Figure 5). Figure 5 illustrates the
spatial allocation process and references-specific sections for each step in the flow diagram. The
areas used to calculate regionally specific distributions and demands are similar to U.S. Census
Bureau regions (see Figure 6).
5 A spatially dependent dynamic process is one in which a change over time at one location is dependent on the state or changes
in the state at other locations.
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Capacity
surface,^
(Section 3.4.1)
Land Uset_j
Land use,
(Section 3.1)
I
Population,
(Chapter 2)
Demand for new
transportation
capacity
(Section 3.3)
Capacity
surface^
Cost distance from
commercial corest
(Section 3.6;
Residential classes only)
Land Use
Transition
Probability
r*
(Section 3.5)
i
Commercial
pixels,
Demand for
new pixels
(Section 3.3)
Repeat for each land use class
Repeat for each time step
Figure 5. ICLUS v2 spatial allocation flow diagram. Land use change at time
step I, as well as expanded capacity of transportation networks, is driven by
population growth. ICLUS v2 uses a decadal time step, so that when / is 2050,
for example, /-lis 2040 and / - 2 is 2030.
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Figure 6. Regions used in ICLUS v2.
The application of regions within ICLUS v2 maintains differences in land use patterns
across the country and over time. At this regional scale (see Figure 6), patterns of change
between 2000 and 2010 are summarized to form a land use transition matrix that captures the
likelihood of a given pixel converting to a specific land use given (1) the antecedent LUC and (2)
the accessibility of the pixel as defined by transportation capacity classes (based on the
transportation matrix and capacity of different types of roads to transport varying numbers of
people). These likelihoods, or more precisely, probabilities, are not used as such in a statistical
sense. Instead, ICLUS v2 prioritizes pixels to convert in order from highest probability to
lowest. We similarly allocate new land use pixels beginning with highest value land uses (e.g.,
Industrial, Commercial) and continuing in order to the lowest value land uses (i.e., exurban-low).
The process of allocating new land use pixels to the most likely remaining locati on continues
until demand for each LUC has been satisfied. While somewhat simplistic, this approach
nevertheless reflects classic land use theory, that is, a pattern of transition to the highest and best
use for a given location (Chisolm, 1962). New land uses are then allocated as patches based on
the existing distribution of patch shapes and sizes for each LUC within each region. All new
land use patches that appeared between 2000 and 2010 are compiled into a patch library. The
probability of placement of a patch depends on the antecedent LUC and the current
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transportation capacity surface. These patches are reused in ICLUS v2 at each time step such
that the size, shape, and frequency of new patches within a region reflect existing distributions
observed for each LUC.
Finally, at the subcounty level, the allocation of residential land use patches also
considers accessibility to commercial areas. This holds to the precept that people will generally
prefer to live close to areas that offer employment opportunities, as well as the goods, services,
and other amenities associated with commercial development. A similar concept was used in
ICLUS vl and yields a modeling framework that is responsive to emergent urban areas.
3.2. ICLUS V2 LAND USE CLASSES
In ICLUS v2 land use is represented by 19 discrete categories delineated in the U.S.
National Land Use Dataset (US-NLUD; Theobald, 2014). The US-NLUD contains
high-resolution (90-meter pixels) land use information for the years 2000 and 2010 and provides
the statistical underpinnings for ICLUS v2 land use change probabilities. The US-NLUD
synthesizes data from multiple sources, including remotely sensed data, to map the primary land
use at a given location.
From the US-NLUD, we retained four nonresidential land use categories (commercial,
industrial, institutional, and transportation) within the developed land use group, and further
subdivided the residential: urban and residential: rural subgroups to form five categories of
residential intensity. Urban residential uses are defined at the 1.6-dwelling units per acre (DUA;
3.95 units per hectare) threshold based on the U.S. Census Bureau definition of urban population
of 1,000 people per square mile (Theobald, 2001). The urban high category is greater than 10
DUA based on typical densities at which public transportation is viable (Ewing and Cervero,
2010). Suburban areas have residential densities below the urban low threshold but greater than
the 0.4 DUA threshold, which is commonly the density at which services such as municipal
sewer and water supply are provided. Lower densities are split into two additional categories
with exurban high as 0.1-0.4 DUA and exurban low as 0.02-0.1 DUA. We also included nine
other land use/land cover categories that can be converted into developed land uses, such as
cropland, grazing, and timber. The complete list of LUCs used in ICLUS v2 is shown in Table
2. Further detail on the entire US-NLUD can be found in Theobald (2014).
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Table 2. LUCs used in the ICLUS v2 model
Code
Group
Class Name
0
Water
Natural water
1
Reservoirs, canals
2
Wetlands
3
Protected
Recreation, conservation
4
Working/production
Timber
5
Grazing
6
Pasture
7
Cropland
8
Mining, barren land
9
Developed
Parks, golf courses
10
Exurban, low density
11
Exurban, high density
12
Suburban
13
Urban, low density
14
Urban, high density
15
Commercial
16
Industrial
17
Institutional
18
Transportation
1 3.3. QUANTIFYING LAND USE CHANGES, 2000-2010
2 To examine relative changes in land use between 2000 and 2010, we estimated the
3 number of 1-km2 units of land assigned to each of the seven developed LUCs and used these as
4 counts in chi-square goodness-of-fit tests. First, both nationally and in each ICLUS region, we
5 tested whether the percentage of land in developed LUCs increased from 2000 to 2010. Then,
6 we tested whether or not the percentage of developed land assigned to the seven individual
7 developed LUCs changed between 2000 and 2010. Only allowable transitions (see Table 3)
8 were considered. The results of these statistical tests show whether development increased
9 significantly (p <0.05) between 2000 and 2010 and whether development patterns changed
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significantly over the same period (p <0.05). Appendix A presents results for each of the seven
ICLUS regions.
If development patterns changed significantly, we examined the changes among the
seven developed classes for which ICLUS v2 models transitions. We first compared the odds
that a unit of land was assigned to a particular developed LUC in 2010 and 2000, compared to all
other developed LUCs. If the confidence interval (CI) of the calculated odds ratio (OR) spanned
zero, the percentage of developed land assigned to that particular class did not change
significantly between the two time periods. If the OR was statistically significantly greater or
less than zero, then the percentage of developed land assigned to that particular class increased or
decreased in 2010, respectively.
We compared the odds that a unit of land was assigned to particular residential class in
2010 and 2000 with its neighboring residential class. The five residential classes are only
allowed to transition in one direction progressively from exurban low to urban high. This
resulted in four comparisons: (1) exurban high versus exurban low, (2) suburban versus exurban
high, (3) urban low versus suburban, (4) urban high versus urban low. If the CI of the calculated
OR spanned zero, the relative amount of land assigned to the two residential classes did not
differ between 2000 and 2010 (i.e., the two residential classes were not distinguishable). If the
OR was significantly greater or less than zero, relatively more or less land, respectively, was
assigned to the higher density residential class in 2010. To correct for multiple comparisons,
confidence intervals of 98.3% were considered. Furthermore, because the data were aggregated
at a 1-km2 resolution rather than at 8,100 m2 (the native resolution of the model), our results
should be considered conservative.
Combining the data from all ICLUS regions, both the percentage of land assigned to
developed use classes increased between 2000 and 2010 (x2 = 34,501.40, df = \,p <0.0001;
Table 3, A; Figure 7, C), and the relative amount of land assigned to each of the seven developed
LUCs changed over the same period (x2 = 276.07, df = 8, p <0.0001; Table 3, B). Among the
developed classes, the proportion of developed land in the urban low, commercial, and industrial
LUC decreased, while the proportion of developed land in the exurban low, suburban, and urban
high LUCs increased between 2000 and 2010 (see Figure 7, A). The relative amount of
developed land in the exurban high LUC did not change significantly between 2000 and 2010
(see Figure 7, A). Relative growth in the urban high LUC was larger than the urban low LUC
(see Figure 7, B). Conversely, relative growth in the urban low LUC was less than the suburban
LUC. The relative growth in the suburban LUC was not significantly different than exurban
high LUC, and growth in the exurban high LUC was not statistically significantly different than
the exurban low LUC (see Figure 7, B).
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Table 3. Goodness-of-fit test results comparing LUCs in 2000 and 2010,
nationally. Values are limited to developable area and LUCs that transition in the
model. (A) Land assigned to developed and undeveloped LUCs. (B) Percentage
developed land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
12.60%
16.61%
Undeveloped
87.40%
83.39%
X2 = 34,501.40
df: 1
/j-value: <0.0001
(B) Developed LUC
2000
2010
Exurban low
53.04%
53.33%
Exurban high
26.00%
26.14%
Suburban
8.93%
9.11%
Urban low
7.81%
7.48%
Urban high
0.44%
0.52%
Commercial
2.38%
2.22%
Industrial
1.40%
1.20%
X2 = 276.07
df: 8
/j-value: <0.0001
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1.40
1 20
1.00
¦o
¦o
O
0.80
0.60
(A)
•
~
* i
I
Exurban Low Exurban High Suburban Urban Low Urban High Commercial
Industrial
1.60
1.40
o 1.20
at
1.00
0.80
0.60
(B)
(C)
<
~
m
i
Exurban High vs.
Exurban Low
Suburban vs. Exurban
High
Urban Low vs.
Suburban
Urban High vs. Urban
Low
Developed vs.
Undeveloped
Figure 7. Land use comparisons between 2000 and 2010, nationally. (A)
Odds ratios (ORs) and confidence intervals comparing allocations among the
seven developed LUCs; (B) ORs and confidence intervals comparing adjacent
residential LUCs (high density vs. low density); and (C) OR comparing developed
and undeveloped LUCs.
1 3.4. TRANSITION-PROBABILITY MODEL
2 We calculated the transition probabilities between LUCs empirically from the baseline
3 change layers (i.e., 2000 and 2010 land use layers). We applied general logic (i.e., we identified
4 transitions that were plausible and then further identified transitions that were plausible but could
5 not be supported by the underlying data [see Table 4]) to correct for spurious changes that
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1 resulted from artifacts in the various data sets. For example, the institutional land use data set
2 does not contain information about the year that land use first appeared; therefore, we could not
3 infer any change in the institutional category. Furthermore, as in ICLUS vl, land uses transition
4 to increasing intensity and, therefore, "backwards" transitions are excluded (e.g., urban to
5 suburban). Note that this also requires generation of a modified land use data set for 2000, such
6 that the classes are consistent logically with 2010. In ICLUS v2, 2010 is the base year for future
7 projections; thus, the 2000 data set needed to be consistent with 2010 information.
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Table 4. LUCs transitions from 2000 (rows) to 2010 (columns) incorporated into ICLUS v2. Filled circles (•)
denote transitions that were included in the model; shading added for emphasis. Empty circles (o) denote plausible
transitions that were excluded for the purpose of model simplification. Hatches (x) denote plausible transitions that
were excluded from the model because temporal data were not available. Unmarked transitions were excluded from
the model because they were considered unlikely or infrequent and temporal data were not available.
Water
Wetland
Rec Con
Timber
Graze
Pasture
Crop
Mining
Parks
Exurb L
Exurb H
Suburb
Urban L
Urban H
Conun
Indust
Inst
Trans
Water
X
Wetland
X
X
X
X
X
X
X
•
•
•
•
•
•
•
X
X
Recreation and Conservation
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Timber
X
X
o
o
o
X
X
•
•
•
•
•
•
•
X
X
Grazing
X
X
o
o
o
X
X
•
•
•
•
•
•
•
X
X
Pasture
X
o
o
o
X
X
•
•
•
•
•
•
•
X
X
Cropland
X
o
o
X
X
•
•
•
•
•
•
•
X
X
Mining
X
X
X
X
X
X
X
X
X
X
X
Parks and Open Space
X
X
X
X
X
X
X
X
X
Exurban Low
o
o
o
o
X
X
•
•
•
•
•
•
X
X
Exurban High
X
X
o
•
•
•
•
•
X
X
Suburban
X
X
X
o
o
•
•
•
o
X
X
Urban Low
X
X
o
o
o
•
•
X
X
Urban High
X
X
o
o
o
o
•
X
X
Commercial
X
X
o
o
o
o
o
•
X
X
Industrial and Utility
X
X
X
o
o
o
o
o
o
X
X
Institutional
X
X
X
X
X
X
X
X
X
Transportation
X
X
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3.4.1. Empirical Estimation of Transition Probabilities
A series of multinomial generalized additive models (GAMs) model LUC transitions
using the VGAM package in R (Yee, 2010; R Core Team, 2015). The GAMs predict the
probability that a pixel transitioned from one LUC to another between 2000 and 2010 by
transportation capacity class. Capacity class here is determined by binning raw capacity values
into eight ordinal values, 1-8, where lower values represent higher transportation capacity.
Because there are a total of 53 possible transitions between LUCs, transition probabilities were
modeled in two stages. For each ICLUS region, we modeled the probability that a pixel
transitioned into each LUC,/>(LUCj), by capacity class, where subscript j is the LUC in 2010.
These seven regional, "marginal" models had capacity class as their predictor variable and LUCj
as a categorical response variable (seven levels: exurban low, exurban high, suburban, urban
low, urban high, commercial, and industrial) (see Figure 8). Seven "conditional" models for
each region and LUCj model the probability that a pixel transitioned from a LUC in 2000
(represented as subscript z) if it transitioned into LUCj in 2010, /;(LUCi j), by capacity class.
Each of these models had capacity class as its predictor variable and LUCiy as a categorical
response variable (up to ten levels depending on the region and LUCj: wetland, timber, grazing,
pasture, cropland, exurban low, exurban high, suburban, urban low, urban high, and
commercial). These sets of models are the basis for probability calculations that a pixel
transitioned from one LUC to another by multiplying the corresponding two model predictions
together, that is, for a given capacity class and region, the probability that a LUC transitioned
from LUCi to LUCj isp{LUCij) = /XLUCj) x /;(LUCi j). Pixels that did not transition and
response categories with zero pixels were not included in the analysis and were given transition
probabilities of zero. The many transitions containing a small number of pixels required limiting
the degrees of freedom used in the smoothing function to three. Each multinomial GAM used a
logit link, and the LUC category with the largest number of pixels was set as the reference.
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Region 1 I Transitions into LUC2010 Region 2 / Transitions into LUC2010
Exurban Low Urban Low Industrial
Exurban High Urban High
— Suburban — Commercial
Exurban Low — Urban Low Industrial
Exurban High Urban High
Suburban Commercial
Exurban Low Urban Low Industrial
Exurban High — Urban High
— Suburban — Commercial
Exurban Low Urban Low Industrial
Exurban High Urban High
Suburban Commercial
Exurban Low Urban Low Industrial
Exurban High Urban High
Suburban Commercial
Exurban Low — Urban Low Industrial
Exurban High Urban High
Suburban Commercial
1 2 3 4 5 6 7 8
Capacity Class
Region 3 / Transitions into LUC2010
i i i i i i i r
1 2 3 4 5 6 7 8
Capacity Class
Region 5 / Transitions into LUC2010
T 1 I 1 I I I T
1 2 3 4 5 6 7 8
Capacity Class
Region 4 I Transitions into LUC2010
1 I I I I I I r
1 2 3 4 5 6 7 8
Capacity Class
Region 6 / Transitions into LUC2010
T I I I I I I r
1 2 3 4 5 6 7 8
Capacity Class
Capacity Class
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Region 7 /Transitions into LUC2010
o
Exurban Low
Exurban High
Suburban
Urban Lew
Urban High
Commercial
o
o
1 2 3 4 5 6 7 8
Capacity Class
Figure 8. Predicted transition probability by capacity class into LUCs in
2010. Each panel shows transitions for each of the seven regions.
Due to the large number of models, Tables B-l to B-7 (see Appendix B) contain only
model outputs that include the significance of the individual smoothing terms and global tests.
Global test results, which compare models with capacity class as a predictor variable to intercept
only models using the difference in the deviance and residual df between models, showed that
capacity class was a highly significant predictor of transition probability overall (p <0.0001 in all
cases, Tables B-l to B-7). Figure 8 shows the relationships between the probability of
transitioning into LUCj in 2010 and capacity class for the seven regional, marginal models.
Figures B-l to B-7 (see Appendix B) also show the full regional transitional probabilities,
created by multiplying the marginal and conditional models together. Generally, pixels were
more likely to transition into higher density residential classes at lower capacity class values and
vice versa with some regional variability on that overall pattern. The intermediate density
residential LUC showed unimodal responses, while the probability of transitioning into urban
high and exurban low monotonically decreased and increased in higher capacity classes,
respectively. Transitions into the commercial LUC displayed more regional variability, with
some monotonically decreasing with capacity class or displaying unimodal behavior. Industrial
transitions, however, were relatively low overall. Although the general pattern of transitions into
the seven LUC held across the regions, we expect regional variability to produce different
growth patterns over the 80-year projection period.
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3.5. ACCESSIBILITY-CAPACITY SURFACE
When allocating new housing units, ICLUS vl used a nationwide surface of travel time
to preferentially weight new growth in areas most accessible to existing development. A key
limitation of the ICLUS vl model was that this travel time surface was static at each time step,
and not updated to reflect improvements to transportation networks. ICLUS v2 uses a more
sophisticated surface of accessibility that incorporates the capacity of roads and fixed mass
transit, and is also updated at each time step.
3.5.1. Creating the Initial Accessibility-Capacity Surface
The spatial allocation model is initialized with a capacity surface for the year 2010. To
generate the capacity surface, we followed methods outlined in Theobald (2008), which are
summarized here. Conceptually, this followed three steps. First, we identified urban cores (e.g.,
central business districts) at multiple resolutions. Second, we calculated the travel time at each
location of the road infrastructure, assuming travel speeds occur at typical speed limits for
different road types, including fixed mass transit, and using walking speeds at off-road pixels.
We then calculated the travel time from the centroid of each urban core through the
transportation infrastructure using cost-distance analysis as determined by distance and travel
time. Finally, we incorporated the different capacity of roads by increasing accessibility linearly
by the number of highway lanes.
Urban cores are built directly on the LUCs by converting developed LUCs to the
following weights: exurban high = 1; suburban and institutional (only where NLCD is developed
with values of 23 or 24) = 5; urban low and transportation = 8; and urban high and
commercial =10. These values are then aggregated by summing their values to 270-m
resolution. We then found the upper half of values (greater than mean of 136) and calculated a
kernel density on these cells with a radius of 1 mile. Then, we identified the cells resulting from
the kernel density operation that have values in the upper half of values. To get at urban areas as
a multiscale phenomenon, we generated urban core areas at six spatial scales using the natural
log of the number of cells. These areas of urban clusters range from: 1.2, 3.2, 8.8, 23.9, 64.7,
and 175.0 km2. We then found the centroid of each of these clusters at the six different scales
and used these as the starting location from which to calculate travel time. The benefit to this
approach is that the centroid of the urban area is defined by the land use pattern.
The next step was to create the cost weights that reflect the assumed travel speeds
through the transportation infrastructure. We assumed the same travel speeds as in ICLUS vl
but updated the transportation infrastructure to the U.S. Census Bureau Topologically Integrated
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Geographic Encoding and Referencing (TIGER) 2010 roads.6 For each of the six urban cluster
starting locations, we generated a cost-distance layer that reflected the travel time from the urban
core through the infrastructure. We then combined the six time travel surfaces by averaging
them to generate a travel time surface.
The accessibility surface provides a platform on which to allocate new growth, but it does
not yet account for differences and changes in the capacity of the infrastructure. That is, most
infrastructure changes are simply to widen or increase the number of lanes on a given road,
rather than to generate a brand new highway through a roadless area. To account for capacity
(measured as passenger cars per hour per lane), we calculated the number of cars that could be
handled by converting travel time to units of hours, then multiplying by the number of lanes of
road. State and U.S. highways and interstates that had information on the number of lanes in the
National Transportation Atlas Database7 were used, otherwise we assumed only a single lane
(each way). We also accounted for fixed mass transit (i.e., light rail). We assumed that a light
rail system added the equivalent in capacity as a single lane of interstate highway (roughly 2,000
passenger cars per lane)8 because we did not have individual transit information on the number
of cars, number of passengers carried in each car, and other pertinent data.
We converted the continuous capacity surface into a series of eight capacity classes. We
used these classes to compute the transition probabilities of growth as a function of the broader
neighborhood location of change, rather than the more local scale that the strict LUC transitions
provided. That is, for each land use type, we found the transition probabilities for each capacity
class independently (or jointly). To identify the class thresholds, we calculated class breaks
using the "Natural Breaks (Jenks)" method in ArcGIS and then modified class breaks slightly
using visual analysis of five "representative" urban areas: San Francisco, Portland, Denver,
Atlanta, and New York City. The classes are at breaks of: 1 > 1,300; 2 = 900-1,200;
3 = 600-900; 4 = 300-600; 5 = 200-300; 6 = 150-200; 7 = 100-150; and 8 < 100. The capacity
class values for time step t - 1 are combined with the land use surface from t~ 1 to yield a
transition probability surface at time step t (see Figure 5).
3.5.2. Updating the Accessibility-Capacity Surface
As shown in Figure 5, the surface of continuous capacity values at time step t - 2 is
updated and used to form a surface of land use transition probabilities at time step t. To
complete this update, we treat the capacity values as unitless quantities and construct a statistical
model to generate demands for new capacity units based on changes in population density. New
6 ftp://ftp2.census.gov/geo/tiger/TIGER2010/ROADS/.
7 http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html.
8 http://www.fhwa.dot.gov/ohim/hpmsmanl/appn2.cfm.
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1
2
3
capacity units are allocated using proportional weights specific to each combination of LUC i
and region k. First we calculated the sum of capacity units C by land use and region, averaged
across 2000 and 2010:
£2000 i,k + ^2010 i,k
Li,k n
(3)
6 Next, we calculated a relative weight Wfor each LUC, where CMAX is the maximum
7 result from Equation 3 for region k\
Ci k
W,jc = '
Cmax
(4)
10 Equation 4 yields the final weights used to allocate new capacity units through time.
11 Each time the capacity update function is called, new capacity units Ufor pixel P are given as:
12
13
WP
Up - WtxDt
(5)
14 where Wp is the weight value from Equation 4; Wr is the sum of pixels weights for the entire
15 county being processed; and Dt is the countywide demand for new capacity units. Equation 5
16 thus represents the culmination of the capacity update function.
17 3.6. LAND USE AND CAPACITY DEMAND MODELS
18 To estimate LUC demands and changes in capacity, we created eight GAMs to predict
19 LUC density from population density within each ICLUS GU (see Figure 2 for representation of
20 ICLUS GUs). Each of the seven developed LUCs and capacity has its own GAM, created using
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the mgcv package in R (Wood, 2004; R Core Team, 2015). Each model includes population
density, ln((people +1) km-2), as its primary predictor variable and either LUC pixel density,
ln((pixels + 1) km-2), or capacity density per km-2, Incapacity km-2), as its response variable.
Density calculations use both 2000 and 2010 population data within each ICLUS GU and the
developable area for each ICLUS GU, estimated from the 2010 U.S. census and USGS Protected
Areas Database of the United States (USGS, 2012), respectively.
Comparison of the difference in estimated number of pixels for each LUC or capacity
between adjacent time periods is the basis of the demand calculation for each decade from 2020
to 2100. For example, 2050 demands were calculated by subtracting modeled 2040 from 2050
pixel counts or capacity. The pixel counts and capacity for each ICLUS GU and decade were
calculated by back transforming the valueyu + £i,2010, where is the modeled response for a
specified ICLUS GU and decade, and $¦,2010 is the raw residual associated with the 2010
measurement for that GAM and ICLUS GU. Adding the raw residual for 2010 ensured that all
ICLUS GU densities were scaled to their actual densities in 2010, and that each GU followed a
course parallel to the estimated density curve over time on the log scale. In effect, this can be
thought of as estimating proportional changes in LUC density or capacity from proportional
changes in population density. ICLUS v2 does not generate LUC or capacity demands for
counties that are projected to lose population, meaning land use patterns in these counties do not
change.
Table C-l (see Appendix C) presents summary results of the GAMs with a brief
overview presented here. Smoothing terms of the eight models are highly significant (p <0.0001
for all cases) and the adjusted R2 of the curves ranges from 0.550 for the exurban low model to
0.889 for the suburban model. Relationships between In population density and In pixel density
are displayed in Figure 9. For all exurban low and high classes, the relationship between
population and pixel density was unimodal, and monotonically increasing for all others. This
matched our expectation regarding urban land use succession (i.e., higher density pixels should
displace lower density pixels at high accessibility locations, while low-density pixels displace
nonurban land uses at the urban fringe). Urban high, the highest density class, continues to
increase rapidly with population density, while the rates of increase for other classes level off.
Generally, the persistence of high-density residential classes at high population densities
suggests urban areas are either better mixed (less likely to be replaced with growth) or that
expansion and replacement rates of these classes balance as cities expand outward. Similar to
high density residential classes, commercial and industrial classes tend to level off in counties
with high population densities. This leveling off indicates that these classes are not rapidly
replaced or that growth and replacement rates balance as counties grow. Similar to the urban
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high residential class, transportation capacity initially increases approximately exponentially and
then linearly at higher population densities.
3.7. LAND USE PATCH ALLOCATION PROCESS
At each time step, the allocation of new land use pixels occurs on a county-by-county
basis. Industrial pixels are allocated first, based on the reasoning that fundamental services such
as water and electric utilities have the least flexibility in terms of location siting. Commercial is
allocated next, and the process continues iteratively through the urban, suburban, and exurban
residential classes following the highest-to-lowest order of land use intensity and value. After
the allocation of commercial patches, the model calculates a cost-distance surface such that each
pixel in the county is assigned a functional distance from commercial areas. All five residential
LUCs include this cost-distance surface as a spatial allocation weight for new patches.
ICLUS v2 uses the observed set of land use patches as an analog for future development
patterns. That is, for each LUC-region combination, a patch is drawn at random from the set of
patches that appeared between 2000 and 2010. That patch is compared against the transition
probability surface and placed at the location of the highest median probability, with the
constraint that all probabilities considered must be greater than zero. In the case of ties between
two or more locations, one location is selected at random. This process is repeated until the
demand for each land use is satisfied. As in ICLUS vl, we assume that the vast majority of land
use changes will be to a higher intensity or value, and thus restrict new patches of land use from
replacing pixels of a higher use. There is no "undevelopment" in either ICLUS vl or ICLUS v2,
although we recognize that in a few urban areas (e.g., Detroit, Michigan) recent and
unprecedented economic conditions have resulted in conversion of higher density areas to less
developed land uses.
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F»Low
«W|
Exurban High |LUC11|
SubiNtan(LUC12)
:
J
I
Ifrtwi Mink fHJ£MJ
l(i.UC15l
*:p.> #t* ert %'f*w2)
-2 i 2 * •
»*t p c«w*r fta»»-2}
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Industrial |LUC1S|
Capacity
4N —
-> —
_
i,
f f-
-2 i 2 4 6 -2 0 2 *6
_M Feci, at'-SF! D STiS.tjr (tm-2) LH PupiiMMn Eternity »m-2i
Figure 9. Predicted log transformed pixel or capacity density (km-2) ( ± 2
SE) by log transformed population density (km-2). Each panel shows a smooth
curve for a different LUC or for mean capacity nationally.
The patch allocation process uses morphological functions from the Python programming
language,9 specifically the SciPy10 package (Jones et al., 2001). An important change in this
new version of the ICLUS model is the use of pseudorandom numbers at two stages of the patch
allocation process: (1) patch selection and (2) choosing between locations of equal probability.
It is not the goal of the ICLUS project to generate probabilistic forecasts of land use change;
therefore, stochastic processes were not incorporated into any phase of the model. Instead,
Python's random number generator was "seeded" at the start of the initial patch allocation
process for each county. For this, we used the integer version of the five-digit county Federal
Information Procession Standard (FIPS) code. This step ensures that, holding all other
parameters constant, consecutive runs of the model will yield identical results.11
9 www.python.org.
10 The binary hit or miss function from scipy.ndimage is used to identify valid locations for a new patch. The median Jilter
function is then used to identify the valid location(s) of the highest median transition probability.
11 Results shown in this report were generated on a computer using Windows 7 (64-bit) and Python version 2.7.10 and SciPy
version 0.16. Executing the ICLUS v2 model on computers with different software will yield different random number draws,
despite the "seeding" process described above.
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4. RESULTS
This section discusses the consequences of the data set and model updates for ICLUS v2.
Similar to the overall model flow, Section 4.1 provides results for the demographic model and
Section 4.2 describes land use changes.
The discussion of the demographic model begins at the national level, then examines
regional population trends including the effect of changing climate variables in the migration
model. This subsection delves into further detail on the influence of climate on domestic
migration by ICLUS geographic units in relation to climate variables. These maps demonstrate
the absolute and relative influence that climate change has on domestic migration in the
ICLUS v2 modeling approach. The discussion on migration concludes with an analysis of the
relative contribution of the different scenarios, climate models, and regions on migration
patterns.
The discussion of land use changes initially focuses on the addition of commercial and
industrial classes to the set of transitioning land uses. This section also examines growth in all
developed LUCs by region over time. Finally, comparisons of standardized LUCs between
ICLUS vl and v2 show the overall differences in output that result from all of the data set and
model updates.
4.1. POPULATION PROJECTIONS
4.1.1. National Projections
Figure 10 shows the total population for the conterminous United States. Nationally, the
ICLUS SSP5 scenario results in the highest total population because of higher fertility rates than
the ICLUS SSP1 scenario. The relative difference in population in 2100 between ICLUS SSP1
and 5 (229 million) is similar to the relative difference between the IIASA SSP1 and 5 scenarios
(247 million), allowing qualitative comparisons and exploration of differences in impacts
between scenarios. Both SSP scenarios fall within the range of the U.S. Census Bureau's 2000
projections (see Figure 10).
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u)
c
0
1
c
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Q.
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D.
2000 2020 2040 2060 2080 2100
1
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7
National Population Projections
1,182
- Historic Census
Census (2000), Highest Series
- - Census (2000), Lowest Series
- - Census (2014)
-X— NASA SSP5
IIASASSP1
ICLUS SSP5
ICLUS SSP1
Figure 10. Total population for the conterminous United States to 2100
showing projections for ICLUS v2. For comparison, historic and projected
population from the U.S. Census Bureau, and projected population from the
International Institute for Applied Systems Analysis (HASA)12 are shown. The
most recent census projection (2014) aligns well with the SSP1 projection used in
this report through 2060.
4.1.2. Regional Projections
By region, ICLUS v2 total population projections are similar within the same SSP-RCP
combination but using different climate model output in the migration model (see Figure 11).
Even when climate change projections are selected to maximize differences, regional population
projections will largely reflect demographic parameters such as fertility rates, net immigration
assumptions, and so forth. Section 4.1.4 discusses differences between scenarios at the
subregional scale that arise from the spatial allocation model.
12 These population projections are available at https://secure.iiasa.ac.at/web-apps/ene/SspDb.
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SSP1, RCP4.5 (FIO-ESM) SSP1, RCP4.5 (HadGEM2-AO)
250
200
150
100
SSP5, RCP8.5 (FIO-ESM)
SSP5, RCP8.5 (HadGEM2-AO)
2020 2040 2060 2080 2100 2020 2040 2060 2080 2100
Region 1 — Region 3 Region 5 Region 7
— Region 2 Region 4 Region 6
Figure 11. Total population for each ICLUS region to 2100 under four
scenario assumptions.
4.1.3. The Effect of Changing Climate Amenities
A key feature introduced in ICLUS v2 is the integration of climate change as an amenity
(or ifo-amenity) in the migration model equation used to simulate domestic migration at each
annual time step (see Section 2.1.4). Using this additional information means that a wider range
of spatial patterns are theoretically possible with respect to population distribution because each
unique climate change projection should produce a unique pattern of domestic migration.
Moreover, small differences between two similar climate change projections could yield
pronounced differences in migration patterns as the cumulative effect of simultaneously
adjusting amenity values for each geographic unit at each annual time step plays out over time.
Figure 12 shows the effect of climate change-induced migration by ICLUS region and
scenario relative to a migration model that, like ICLUS vl, holds climate amenity variables
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constant over time for all scenarios. There were no entirely consistent patterns with respect to
population differences, as five regions (1, 3, 4, 6, and 7) had either higher or lower total
populations by 2100 depending on the scenario. The total population of Region 5 (Great Lakes)
was higher relative to the no climate change model regardless of scenario, while in Region 2
(Intermountain West) the opposite was true, especially under the ICLUS SSP5-RCP8.5
scenarios. Across all scenarios explored in this report, the effect of climate change-induced
migration on total population for any ICLUS Region was no more than about ± 2,500,000 people
(see Figure 12).
This diversity of outcomes is not surprising given the complexity of the modeling
involved. Each climate change projection presents a unique spatiotemporal pattern of migration
model inputs. These patterns in turn alter the spatial distribution of population over time and
across the modeling domain, which enhance or diminish migration feedbacks via other variables
in the migration model equation (i.e., population density or growth rate). While the relative net
effect of these interactions may total millions of people for a given region, we note that these
differences are a small fraction of total population. Figure 13 shows that, in relative terms, the
effect of climate change-induced migration is no more than -4% of the regional population, as
seen in Region 2 under SSP5-RCP8.5 using the HadGEM2-AO climate data. Most differences
are between ± 2% of the regional population regardless of scenario and climate model.
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X SSP1, RCP4.5 (HadGEM2-AO)
X SSP5, RCP8.5 (HacfGEM2-AO)
2020 2040 2060 2080 2100
Figure 12. The effect of climate change-induced domestic migration
expressed as differences in millions of people. Differences in regional
population projection by emissions scenario and climate model are shown.
Values are expressed as the difference from a "no climate change" version of the
migration model.
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Region 1
Region 2
2
0
-2
4
4
2
*-S
0s
Q ffl IB ^
( V X
w © C
9 b !
3 e &
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« ^ « w. ~ ^ « O SSP5, RCP8.5 (FIO-ESM)
0 -M—Q—¥—5?—^—S—^—v—S-
SSP1, RCP4.5 (HadGEM2-AO)
2 X SSP5, RCP8.5 (HadGEM2-AO)
4
2020 2040 2060 2080 2100
Figure 13. The effect of climate change-induced domestic migration
expressed as percentage differences. Relative differences in regional population
projection by emissions scenario and climate model are shown. Values are
expressed as the percentage difference from a "no climate change" version of the
migration model.
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4.1.4. Subregional Projections
The effect of climate change on ICLUS v2 population projections can be further
illustrated with difference maps comparing climate variables derived from the FIO-ESM and
HadGEM2-AO climate projections and their respective population projections. An examination
and interpretation of the migration model is provided in Section 2.2; however, some general
spatial relationships between climate variable differences and population differences are
apparent.
For example, under the SSP5-RCP8.5 scenario assumptions, total population in Region 6
(Southeast) is generally higher when the migration model is driven by the HadGEM2-AO
climate projection (see Figures 14, A and B, green areas). In this comparison, all parameters and
assumptions are identical except for the annual climate amenity values; therefore, differences in
the spatial pattern of population are the cumulative result of migration differences where and
when the climate projections diverge.
The difference between the two climate models in terms of winter precipitation seems to
play an important role in this particular spatial pattern. While relatively warmer winter
temperatures are projected by the HadGEM2-AO model over most of the country, the
southeastern United States is one of the few areas to show relatively dryer winters by
HadGEM2-AO. The effect of markedly warmer winters (which would attract more migrants)
projected by HadGEM2-AO across the northern plains is difficult to discern because of generally
smaller, fewer, and more distant high-population areas relative to the southeastern United States.
In addition, relatively more winter precipitation would also work to slow migration into and
within the northern plains area.
A comparison of population difference maps in Figures 14 and 15 shows somewhat
larger migration differences under RCP4.5—the lower emissions scenario of the two. This
somewhat counterintuitive result is explained by the relatively larger difference between the two
climate models as shown in the climate maps. The spatial extent and magnitude of divergent
projections is clear for virtually all combinations of variables and years. The cumulative effect
of these comparatively larger differences in climate variables results in comparatively larger
migration differences.
These maps demonstrate some implications of the ICLUS v2 modeling approach;
however, care should be taken to avoid over-simplifying the apparent spatial relationship
between climate variables and population shown in Figures 14 and 15. The suite of interactions
and feedbacks present in the migration model extends beyond the figures presented here, and
cannot be exhaustively characterized by examples presented in this report.
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(A)
HADGEM2-A0 (SSP5, RCP85) minus FIO-ESM (S5P5, RCP85)
(B)
HADGEM2-A0 (SSP5, RCP85) minus FIO-ESM (S5P5, RCP85)
(C)
HADGEM2-AO(RCP85) minus FIO-ESM(RCP85): Winter temperature
mam
(E)
HADGEM2-AO(RCP85) minus FIO-ESM(RCP85): Summer temperature
(G)
(I)
HADGEM2-AO(RCP85) minus FIO-ESM(RCP85): Winter precipitation
2050
2.0 to -3.1 mm/day
10 to -2.0
0.0 to -10
¦ 0.0 to 1.0
2.0 to 3.1 mm/day
HADGEM2-AO(RCP85) minus FIO-ESM(RCP85): Summer precipitation
2050
3 mm/day
d o to -l.o
0.0 to 1.0
1.0 to 2 0
2 0 to 4 7 mm/day
8faji
(D)
HADGEM2-AO(RCP85) minus FIO-ESM(RCP85): Winter temperature
(F)
HADGEM2-AQ(RCP85) minus F1Q-ESM(RCP85): Summer temperature
(H)
(J)
HADGEM2-AO(RCP85) minus FIO-ESM(RCP85): Winter precipitation
2100
1.0 to-1.9
0 0 to -1.0
0.0 to 1.0
¦ 1
2.0 to 3.9 mm/day
HADGEM2-AO(RCP85) minus FIO-ESM(RCP85): Summer precipitation
2100
-2.0 to -5.4 mm/day
-1.0 to -2.0
0.0 to 10
10 to 2 0
2.0 to 4 5 mmfday
Figure 14. Differences in population and climate change projections driven
by FIO-ESM and HadGEM2-AO under SSP5 and RCP8.5 assumptions for
2050 and 2100. (A) Population differences by ICLUS GU in 2050 and (B) in 2100;
(C) differences in change in winter temperature in 2050 and (D) in 2100; (E) differences
in change in summer temperature in 2050 and (F) in 2100; (G) differences in change in
winter precipitation in 2050 and (H) in 2100; (I) differences in summer change in
precipitation in 2050 and (J) in 2100.
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(A)
HADGEM2-AO (SSP1. RCP45) minus FIO-ESM (S5P1, RCP45)
(C)
(E)
(G)
(I)
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Winter temperature
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Summer temperature
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Winter precipitation
2050
2.0 to -3.4 mm/day
0.0 to-1.0
0.0 to 1.0
1.0 to 2.0
2 0 to 2 9 mm/day
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Summer precipitation
(B)
HADGEM2-AO (SSP1, RCP45) minus FIO-ESM (SSP1, RCP45)
2050
•2.0 to -3.3 mm/day
-1.0 to -2.0
0.0 to -1.0
0.0 to 1.0
1.0 to 2 0
2.0 to 4 4 mmfday
(D)
(F)
(J)
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Winter temperature
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Summer temperature
(H)
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Winter precipitation
2100
2.5 mm/day
1-0 to -2.0
0.0 to 1.0
L0 to 2.0
2.0 to 3.4 mm/day
HADGEM2-AO(RCP45) minus FIO-ESM(RCP45): Summer precipitation
2100
2.0 to -4.2 mnvday
L0 to -2.0
10 to 2.0
2.0 to 5.2 mmyday
Figure 15. Differences in population and climate projections driven by
FIO-ESM and HadGEM2-AO under SSP1 and RCP4.5 assumptions for 2050
and 2100. (A) Population differences by ICLUS GU in 2050 and (B) in 2100; (C)
differences in change in winter temperature in 2050 and (D) in 2100; (E) differences in
change in summer temperature in 2050 and (F) in 2100; (G) differences in change in
winter precipitation in 2050 and (H) in 2100; (I) differences in change in summer
precipitation in 2050 and (J) in 2100.
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To further investigate the differences among scenario, climate model, and region, we
developed a factorial generalized least squares model to test the influence of these variables and
their interactions on mean 10-year changes in population density. The categorical variables
include ICLUS GU initial population density (people per km2 in five size bins, P1-P5: <5.0;
5.1-15.0; 15.1-45.0; 45.1-135.0; >135.1; see Figure 16), ICLUS region (seven regions;
see Figure 6), SSP (two scenarios: SSP1, SSP5), and climate model (three levels: no climate
change, FIO-ESM, HadGEM2-AO). The model also includes all possible 2-way, 3-way, and
4-way interactions among the variables. To meet the assumption of homogeneity, we allowed
each county size class to have its own residual variance (Zuur et al., 2009). We ran separate
models to look at population differences between 2010-2050 and 2050-2100 because results
suggest higher divergence in populations by the end of the century (see Figures 14 and 15).
During the initial decades modeled, 2010-2050, the magnitude of population change
depends on the initial population density, which varies by region (single 2-way interaction;
see Table 5, A). ICLUS GUs with higher initial population densities have larger increases in
population density overall and show the most distinct regional differences (see Figure 16, A). In
the second half of the century, 2060-2100, the magnitude of population change still depends on
the initial population density, but vary by both region and SSP (two 2-way interactions;
Table 5, B). As in the initial decades, ICLUS GUs with higher initial population densities have
larger increases overall and show the most distinct regional differences (see Figure 16, B).
Similarly, differences between SSPs are more distinct at higher population densities (see
Figure 16, C), in part because SSP5 uses a higher fertility rate and therefore has more people to
distribute across ICLUS GUs. The addition of the SSP variables in the late-century model shows
that the pathways diverge during this time period, but are similar during the first half of the
century.
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400
300
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100
I I I I I I
R1R2R3R4 R5 R6 R7
PI
R1R2R3 R4R5R6R7
P2
R1R2R3R4R5R6 R7
P3
R1R2R3 R4 R5R6R7
P4
R1R2R3R4R5R6 R7
P5
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900
800
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JO
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400
3
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C 300
135.1.
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Table 5. GLS model results. Model output includes degrees of freedom (dj), F-
statistic, and significance (p)
Change in population
density:
2010-2050
df
F
P
Change in population
density:
2050-2100
df
F
P
Initial population density
4
97.706
<0.0001
Initial population density
4
119.594
<0.0001
ICLUS region
6
20.639
<0.0001
ICLUS region
6
34.111
<0.0001
Socioeconomic pathway
1
0.183
0.6686
Socioeconomic pathway
1
1.534
0.2156
Climate model
2
0.055
0.9462
Climate model
2
0.024
0.9761
PxR
24
7.240
<0.0001
PyR
24
13.929
<0.0001
P*S
4
0.343
0.8493
P y S
4
3.750
0.0047
R y S
6
0.053
0.9994
R y S
6
1.441
0.1944
Py-M
8
0.071
0.9998
PyM
8
0.084
0.9996
RxM
12
0.094
1.0000
RyM
12
0.246
0.9959
S y M
2
0.008
0.9919
S y M
2
0.007
0.9929
PxRxS
24
0.050
1.0000
PyRyS
24
0.501
0.9797
P X RxM
48
0.034
1.0000
P x RyM
48
0.059
1.0000
Py-Sy-M
8
0.020
1.0000
PySyM
8
0.007
1.0000
Ry-Sy-M
12
0.017
1.0000
RySyM
12
0.028
1.0000
P y R y S y M
48
0.008
1.0000
P y Ry S y M
48
0.009
1.0000
P: Initial population density
R: ICLUS Region
S: Socioeconomic pathway
M: Climate model
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4.2. LAND USE PROJECTIONS
4.2.1. National Projections
The national-scale land use projections show nearly identical trends when comparing
outcomes under the same SSP assumption; the choice of climate model has no discernible effect
on the overall amount of projected development at the national level (see Figure 17). Relative to
SSP1, the larger national population under SSP5 drives more development overall, particularly
with respect to exurban residential density (yellow wedge in all panels in Figure 17). By 2100,
the area of developed land in the conterminous United States increases by more than 80% of the
2010 value, yielding a total of more than 1.6 million square kilometers under the SSP5 scenario.
Under the SSP1 scenario, the increase is nearly 50%, and yields more than 1.3 million square
kilometers of developed land by 2100 (see Figure 17).
SSP1, RCP4.5 (FIO-ESM)
SSP1, RCP4.5 (HadGEM2-AO)
1600
CO
"33
L
_o
ra
O"
CO
1200
800
400
SSP5, RCP8.5 (FIO-ESM)
SSP5, RCP8.5 (HadGEM2-AO)
1600
1200
800
400
2020
2040
2060
2080
2020
2040
2060
2080
EXURBAN
SUBURBAN
URBAN
COMMERCIAL
INDUSTRIAL
Figure 17. National land use projections from ICLUS v2 to 2100. Trends in
total area of exurban (exurban low + exurban high), suburban, urban (urban
low + urban high), commercial, and industrial lands are shown under four
scenarios.
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Differences in the percentage changes in each of the LUCs emerge nationally when
comparing SSP1-RCP4.5 and SSP5-RCP8.5 (see Figure 18). The SSP1-RCP4.5 projection using
FIO-ESM climate data has the smallest increases over time, as compared to the SSP5-RCP8.5
projection using HadGEM2-AO climate data. These two scenario combinations represent the
extremes explored in ICLUS v2 in terms of demographic and climatic change rates. For SSP1-
RCP4.5, only the combined urban category increases by more than 100% in 2100 and
commercial land uses increase nearly that much. This scenario consists of a relatively lower
national population (SSP1) and lower anthropogenic perturbation of the climate system (RCP4.5)
modeled with a demonstrably less sensitive climate model (FIO-ESM).
Conversely, the SSP5-RCP8.5 (HadGEM2-AO) projection models more than a 100%
increase in the extent of all developed LUCs already by 2050. The extent of urban land increases
by more than 200% by 2050 under this scenario, and more than quadruples by 2100. This
projection uses a very high population scenario (SSP5) and climate scenario of high
anthropogenic forcing (RCP8.5) modeled with a demonstrably more sensitive climate model
(HadGEM2-AO). This combination of model variables leads to greater changes in the extent of
developed lands than the SSP1-RCP4.5 (FIO-ESM) combination, even though the initial land use
demands and transition probabilities are the same. Changes in land use demands and transition
probabilities represent a future pathway to explore further differences among ICLUS v2
scenarios.
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15
SSP1, RCP4.5 {FIO-ESM)
SSP5, RCP8.5 (HadGEM2-AO)
Figure 18. Relative increases in the area of developed LUCs nationally at
2050 (top row) and 2100 (bottom row). The left column shows results for
SSP1-RCP4.5 using FIO-ESM climate data; the right column shows results for
SSP5-RCP8.5 using HadGEM2-AO climate data. The SSP x RCP x climate
model combinations shown at the top of the columns bracket the range of national
population projections, emissions scenarios, and climate model sensitivity,
respectively, of all combinations considered in this report.
4.2.2. Regional Projections
Summarizing the ICLUS v2 land use projections by region illustrates substantial
differences between the SSP1-RCP4.5 and SSP5-RCP8.5 scenarios. In almost every case,
developed land use categories in all regions increase (see Table 6). The magnitudes of those
increases vary based on the SSP (i.e., population) assumption being considered.
ICLUS v2 projects a net decrease in the lowest residential density class (exurban-low) in
Region 7 (Northeast) by 2100 under the SSP5-RCP8.5 (HadGEM2-AO) scenario (see Table 6).
This singular instance of an extent decrease reflects the relatively high population density of the
northeastern United States, and the concomitant demand for higher-density residential
development. In that case, the conversion of exurban-low pixels to other developed uses has
outpaced the demand for low-density residential pixels.
The urban-high LUC shows the greatest percentage increase by 2050 in both SSPs
considered, although smaller increases occur in Region 7 (Northeast) and, under SSP1-RCP4.5
(FIO-ESM), in Region 5 (Great Lakes). Substantial increases in commercial and industrial land
uses occur in Regions 2-4 under both SSPs by 2050, with more moderate increases in the
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1 remaining regions. Region 2 (Intermountain West), which is currently less densely developed
2 than most other regions, also has greater percentage increases in both exurban classes under both
3 SSPs by 2050. In 2100, this remains true for SSP1-RCP4.5 (FIO-ESM), although Regions 3 and
4 4 have the next highest percentage increases compared to the other regions, while the increases
5 in Regions 3 and 4 under SSP5-RCP8.5 (HadGEM2-AO) are more similar to Region 2 and larger
6 than the other regions (see Table 6). The overall regional pattern across both SSPs is that urban-
7 high increases sooner than lower density land uses, and that generally the pattern of increases
8 follows the density classes from urban-high to exurban-low.
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Table 6. Cumulative change in developed LUCs for 2050 (top row) and 2100
(bottom row) by Shared Socioeconomic Pathways (SSPs), Representative
Concentration Pathways (RCPs) and climate model (in parentheses). Values
shown represent the change in square kilometers for each LUC since 2010.
Shading is used to describe that change as a percentage, with the darkest gray
indicating a >100% change, medium gray 50-100% change, light gray 0-50%
change, and peach <0% change.
5SPL RCPJ5jFIO-ESM}
2050
SSP5, RCP35( HadG EM 2-AO(
2060
ICLUS
REGION
EXURBAN
LOW
EXURBAN
UGH
SUBURBAN
URBAN
LOW
URBAN
HGH
COMMERCIAL
INDUSTRIAL
1
36
321
245
584
112
101
58
2
1,531
791
305
488
45
133
68
3
1,344
804
306
492
36
157
91
4
2,859
1,254
595
778
66
226
128
5
262
769
441
813
64
150
75
6
2.027
2,412
1,472
1,347
122
298
144
7
147
223
19D
230
87
42
15
2100
ICLUS
REGION
EXURBAN
LOW
EXURBAN
UGH
SUBURBAN
URBAN
LOW
URBAN
hi Gfl
COMILERCIAL
INDUSTRIAL
1
131
497
437
1294
333
224
111
2
3,799
1,605
712
1,153
130
292
139
3
3265
1,720
712
1,125
99
341
195
4
7.053
2,619
1242
1,735
184
484
252
5
751
1,254
859
1J066
170
301
138
6
4,6+4
4,169
2,804
2,787
348
607
261
7
51
262
395
497
257
94
32
EXURBAN
EXURBAN
SUBURBAN
URBAN
URBAN
COMMERCIAL
INDUSTRIAL
LOW
HIGH
LOW
HIGH
55
393
310
764
152
132
73
2,013
993
383
642
62
171
86
1590
933
338
588
44
182
105
3,881
1,053
767
1,031
92
296
164
300
887
518
982
83
181
89
2.608
2,788
1,717
1572
152
353
166
99
213
221
272
113
50
17
2100
EXURBAN
EXURBAN
SUBURBAN
URBAN
URBAN
COMMERCIAL
INDUSTRIAL
LOW
HIGH
LOW
HIGH
£49
728
651
1,889
645
3W
161
6,657
2,433
1,082
1,885
251
461
209
5,615
2,592
1,072
1,812
183
525
293
12,348
4,082
2,027
2,978
381
813
393
1,761
1,719
1292
2.619
331
470
205
8J619
6,3ffi)
4515
4.612
705
1,039
411
-21
270
530
851
506
164
EC
Percent Change
< 0%
O- 50%
50- 100%
>100%
1 4.2.3. Subregional Projections
2 Using the new outputs from the demographic model (see Section 2), which includes
3 projected climate data (see Section 2.1.5.1), updated land use data (see Section 3.2), transition
4 probabilities (see Section 3.4), changes in capacity (see Section 3.5.2), and distribution of
5 patches for each developed LUC (see Section 3.6) (see Figure 5 for model flow overview), the
6 spatial allocation model for ICLUS v2 projects commercial, industrial, and five residential LUCs
7 to the year 2100 by decade for each ICLUS GU for a specified scenario. The resulting maps
8 show changes in these land uses for three selected metropolitan areas (see Figures 19-24).
9 Changes in other land uses (e.g., agriculture, recreation) are only negative and only result from
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1 transitions into developed classes. For example, in the Portland, OR-Vancouver, WA
2 metropolitan area most of the growth in low-density urban land uses results from conversion of
3 suburban and exurban areas, although more conversions of cropland to urban low occur in the
4 decades from 2050-2100 than the earlier time period under both SSPs (see Figures 19 and 20).
5 Similar trends also occur in cities in other regions (e.g., Springfield, MO; see Figures 21 and 22).
6 In contrast, some metropolitan areas that already have multiple high-density urban centers
7 throughout the area (e.g., Washington, DC metropolitan area) and have high population growth
8 convert more of the existing residential land uses to additional high-density urban areas under
9 both SSPs (see Figures 23 and 24). These three metropolitan areas exemplify changes nationally
10 in such areas and illustrate the spatial patterns produced using ICLUS v2.
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Natural water
Reservoirs, canals
Wetlands
Conservation
|{ Timber
Grazing
~ Pasture
1 Cropland
Mining, barren
Parks, open space
Exurban. low
Exurban. high
Suburban
Urban, low
Urban, high
Commercial
Industrial
Institutional
Transportation
Figure 19. Land use change in the vicinity of the Portland, OR-Vancouver,
WA Metro Area under the SSP1-RCP4.5 (FIO-ESM) scenario: 2010, 2050,
and 2100.
K 10 V
ft •Jl-n
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r.;
"¦ k-
^ rUJ5$"|vv r• -.. ,..ir • " • .f .¦ y; "A.•*. . - •. . _ ¦
¦Ah? /¦¦¦* . yr; ;¦ %\.,/ -'. r* k*13/n*
's-,# <• / • •• ...¦% ¦ ¦> -
t ftr1* * ' " ' ">
t'Jte'. - ti,. .¦*"" T~ ^ - . 'V" a" - *>*¦ ¦
Natural water
Reservoirs, canals
Wetlands
Conservation
Timber
Grazing
~ Pasture
1 Cropland
Mining, barren
Parks, open space
Exurban. low
Exurban. high
Suburban
Urban, low
Urban, high
Commercial
Industrial
Institutional
Transportation
V;,. %> ;v:-: L • *•
: r /m JfQi
S§|& ;* •, ¦
¦
' . - *V f ifi
Figure 20. Land use change in the vicinity of the Portland, OR-Vancouver,
WA Metro Area under the SSP5-RCP8.5 (HadGEM2-AO) scenario: 2010,
2050, and 2100.
This document is a draft for review purposes onlv and does not constitute Agencv policy.
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2010
/ ii
m.
>¦
.-f i
^ w^r>>V%.U-
,'•> ' , 1
¦¦¦##- '
/.V; >-'
•a.
'*: 1 t '
¦
Natural water
Reservoirs, canals
Wetlands
Conservation
Timber
Grazing
~ Pasture
| Cropland
Mining, barren
Parks, open space
Exurban, low
Exurban, high
Suburban
Urban, low
Urban, high
Commercial
Industrial
Institutional
Transportation
2050
/ *
j'
ir/
*
> ( 4>*l
,-f- I
" . -:u ¦ S^!:J
W £ tofcit*- .» . . : -'*¦ ¦ L"
«-%3k •• frifc i¦
/•.V, :y.
>v;. ¦¦
•fU: *
f S'V* ' 1
• r -- f
. u
2100
1V
•'1
* *\ 'A
'J*
^ ' 1
-f*'
, V*
gr
J'
\
rj
_W ,: ' J 1
u ?¦. , •.•
' ~* V to I
I I » iMilp..
Figure 21. Land use change in the vicinity of the Springfield, MO Metro
Area under the SSP1-RCP4.5 (FIO-ESM) scenario: 2010, 2050, and 2100.
This document is a draft for review purposes onlv and does not constitute Agencv policy.
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2010
i «£
¦W *>!f-
r i .\r
i - u.
J-
s-f I
¦
;' ¦£&.}.¦ ,'feH
¦- L. JCUfc 4 •
,.! i
<•*
,-V-
jr'
w I **¦
_ i
/if -V;
Mi
¦fe ¦f:",
. T ' 1. t 1
' I • 1
¦n>.
Natural water
Reservoirs, canals
Wetlands
Conservation
Timber
Grazing
~ Pasture
| Cropland
Mining, barren
Parks, open space
| | Exurban, low
J Exurban, high
] Suburban
I I Urban, low
| Urban, high
Commercial
Industrial
Institutional
Transportation
2050
% y
jp
% < iVa
».S '.j'r
--
-r, mf
; ~" A*
mm
">*i
¦*>-.
_[1
2100
.«' **
r V ' ' \ '•»
1®^" :
\
n
w
-III T #1. vrr :>£V..
' i
:fc-
*;.
;; . T -W ¦[. /
,/TV;
c
3 Miles
Figure 22. Land use change in the vicinity of the Springfield, MO Metro
Area under the SSP5-RCP8.5 (HadGEM2-AO) scenario: 2010, 2050, and
2100.
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Figure 23. Land use change in the vicinity of the Washington-Arlington-
Alexandria, DC-VA Metro Area under the SSP1-RCP4.5 (FIO-ESM)
scenario: 2010, 2050, and 2100.
Natural Water
Reservoirs, canals
Wetlands
Conservation
Timber
Grazing
~ Pasture
Cropland
Mining, barren
Parks, open space
| | Exurban, low
J Exurban, high
] Suburban
I I Urban, low
| Urban, high
Industrial
Commercial
Institutional
Transportation
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MWW.!
Figure 24, Land use change in the vicinity of the Washington-Arlington-
Alexandria, DC-VA Metro Area under the SSP5-RCP8.5 (HadGEM2-AO)
scenario: 2010, 2050, and 2100.
J Natural Water
| Reservoirs, canals
1 Wetlands
Conservation
Timber
Grazing
~ Pasture
| Cropland
Mining, barren
Parks, open space
| | Exurban, low
J Exurban, high
] Suburban
I I Urban, low
| Urban, high
Industrial
Commercial
Institutional
Transportation
1 5. CONCLUSION
2 The updated data sets and underlying statistical and spatial methods result in realizations
3 of future land use changes that are substantially different from ICLUS vl. The improvements
4 made in ICLUS v2 have many advantages, particularly for assessments of future climate change
5 impacts, vulnerabilities, and adaptation options. These advantages include the ability to (1)
6 develop future scenarios that include changes in commercial and industrial land uses, (2)
7 examine the effect of changes in transportation capacity through additional lane miles or added
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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
32
33
34
35
36
fixed mass transit, (3) examine trends in land use changes regionally, and (4) assess differences
among scenarios consistent with current socioeconomic and emissions storylines (i.e., SSPs and
RCPs). However, some of the updates have disadvantages. For example, the use of the IRS
migration data set requires collapsing all age classes from the cohort component model into one
population, compared with the two age groups sustained in ICLUS vl. The loss of this
resolution theoretically results in less useful demographic model outputs because the assessment
of future health impacts related to climate change typically is improved by using segmented age
groups. This limitation is somewhat mitigated by the fact that ICLUS vl only retained two
broad population segments, over 50 and under 50. Methods to add more detailed demographic
information back into the migration model would make the data more useful for the health
impacts communities, research on vulnerable populations, and examinations of potential
environmental justice issues.
ICLUS v2 represents significant progress in the development of land use change
scenarios that are consistent with emissions story lines and has the flexibility to adapt to other
emerging story lines from the climate change modeling community. For example, land use
transitions can be altered by changing the population density and land use demand relationships.
The current transitions follow an expected development path from low to high densities,
generally expanding outwards from population centers. Higher density residential classes,
commercial, and industrial development exhibit a threshold effect at high population densities,
such that these land uses generally are not replaced once they are developed. This has
implications in terms of the continuity of urban form, redevelopment patterns, creation of park
and recreation areas, and other "undevelopment" (e.g., moving from higher use classes to lower
ones), which in turn influences subsequent development patterns. The current model structure
does allow for the future exploration of these phenomena through scenarios.
ICLUS v2 also makes significant progress in providing future estimates of commercial
and industrial land use changes. These estimates serve as inputs to a variety of environmentally
relevant models that project changes in emissions and other air quality factors. Additional
research into the emergence of new commercial areas and densities and occurrences of mixed
commercial and residential buildings in urban areas would be useful inputs into future ICLUS
updates and land use change scenarios. These data are critical for modeling future changes in a
variety of air and water quality endpoints.
Another important advancement of ICLUS v2 is the inclusion of future climate change
variables in the migration model. While climate variables represent a relatively small
instantaneous influence on migration, the cumulative effect of this influence through time on a
process as complex as human migration results in meaningful spatial variability of population
projections across the ICLUS GUs. The strength of this influence also can be explored through
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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
32
33
34
scenarios that alter migration responses to climate change over time. Additionally, differences in
migration patterns can be explored as other climate model data are incorporated.
The use of changing climate variables in the migration model does produce some
differences in population distribution. Differences in regional populations between static and
dynamic climate variables are no more than approximately 4%. Most differences are ±2% of the
regional population, regardless of scenario and climate model combination. Nationally, the
choice of climate model has little effect on the overall development pattern. However, this report
only used two different climate models as an example to implement the changes in the ICLUS v2
models. Other climate change models may have more extreme temperature or precipitation
values in certain regions that may exert larger influences on population migration. ICLUS v2
users can explore impacts of other climate change model values as part of scenario and
sensitivity analyses.
The results presented in this report cover only two of the many possible GCMs and two
emissions scenario. Data from other climate change models can be incorporated easily into the
migration model. Additional emissions scenarios also can be explored. Transition probabilities
and land use and capacity class relationships can be modified to create land use patterns
consistent with SSP and RCP combinations not explored in this report.
As in ICLUS vl, this version focuses on developed land uses. It would be useful to
integrate this model with models using similar principles that change other land uses, such as
agriculture and forestry—particularly for more comprehensive assessments of impacts,
vulnerabilities, and adaptation options related to climate change. The composition of
agricultural, forest, and natural landscapes has changed and will continue to change over time in
response to human, climatic, and other factors. A large body of research exists that models
changes in various species distributions under the SRES storylines (e.g., Thomas et al., 2012).
These types of analyses can make use of the changing development patterns from the ICLUS
output, and provide feedbacks from changes in the undeveloped landscape that can be
incorporated into the ICLUS modeling structure. Several models exist that can easily integrate
ICLUS data and vice versa. For example, the FOREcasting SCEnarios of Land-use Change
model (Sohl et al., 2007) also uses scenario assumptions to examine changes in forest
composition in the future, while Forestry and Agricultural Sector Optimization Model can
integrate changes in the available agricultural and forest land area to develop projections of
future markets based on population demands (Zhang et al., 2014). These types of feedbacks and
interactions among changes in land use and land cover are an active area of research that are
likely to improve future version of ICLUS output.
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6. REFERENCES
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projecting land use change using scenario-based modeling. J Land Use Sci 2(2): 103-126.
Sohl, TL; Loveland, TR; Sleeter, BM; Sayler, KL; Barnes, CA. (2010) Addressing foundational elements of regional
land-use change forecasting. Landscape Ecology 25(2):233-247.
Sussman, F; Saha, B; Bierwagen, BG; Weaver, C; Morefield, P; Thomas, J. (2014) Estimates of changes in county-
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Theobald, DM. (2001) Land-use dynamics beyond the American urban fringe. Geogr Rev 91(3):544-564.
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Theobald, DM. (2008) Network and accessibility methods to estimate the human use of ecosystems. Proceedings of
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Theobald, DM. (2014) Development and applications of a comprehensive land use classification and map for the
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Thomas, KA; Guertin, PP; Gass, L. (2012) Plant distributions in the southwestern United States; a scenario
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File Report 2012-1020, 83 p. and 166-page appendix], Washington, DC: U.S. Department of Interior, U.S.
Geological Survey, http://pubs.usgs. gov/of/2012/1020/
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U.S. Census Bureau. (2000) Assumptions for the components of change. In Methodology and assumptions for the
population projections of the United States: 1999-2100. Washington, DC: U.S. Census Bureau,
Department of Commerce, http://www.census.gov/population/proiections/data/national/natproi2000.html
U.S. Census Bureau. (2003) Census 2000, Public Use Microdata Sample (PUMS). Washington, DC: U.S. Census
Bureau, Department of Commerce https://www.census.gov/census2000/PUMS5.html.
U.S. EPA (Environmental Protection Agency). (2009) Land-use scenarios: National-scale housing-density scenarios
consistent with climate change storylines (Final Report). [EPA/600/R-08/076F], Washington, DC: National
Center for Environmental Assessment.
http://cfpub.cpa. gov/ncea/risk/recordisplav.cfm?deid=203458&CFID=42769880&CFTOKEN=75743611
USGS (Geological Survey) (2012). Protected areas database of the United States (PADUS) version 1.3. National
Gap Analysis Program.
http://gapanalvsis.usgs. gov/padus/?s=+Protected+Areas+Database+of+the+United+States+PADUS+versio
n+13&submit=Go
van Vuuren, DP; Carter, TR. (2014) Climate and socio-economic scenarios for climate change research and
assessment: Reconciling the new with the old. Climatic Change 122:415-429.
Voorhees, AS; Fann, N; Fulcher, C; Dolwick, P; Hubbell, B; Bierwagen, B; Morefield. (2011) Climate change-
related temperature impacts on warm season heat mortality: a proof-of-concept methodology using
BenMAP. Environ Sci Technol 45(1450-1457.
Wood, AW; Leung, LR; Sridhar, V; Lettenmaier. DP. (2004) Hydrologic implications of dynamical and statistical
approaches to downscaling climate model outputs. Climatic Change 62:189-216.
Wood, SN. (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J
Am Stat Assoc 99:673-686.
Yee, TW. (2010) The VGAM package for categorical data analysis. J Stat Softw 32:1-34.
Zhang, Y-Q; Cai, Y-X; Beach, RH; McCarl, BA. (2014) Modeling climate change impacts on the US agricultural
exports. J Integr Agricul 13(4):666-676.
Zuur, A; Ieno, EN; Walker, N; Saveliev, AA; Smith, GM. (2009) Mixed effects models and extensions in ecology
withR. New York, NY: Springer.
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13
APPENDIX A. REGIONAL LAND-USE CHANGES FOR 2000-2010
A.l. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 1
(PACIFIC) LAND USE CHANGES
In the Pacific region (Integrated Climate and Land Use Scenarios [ICLUS] Region 1), the
percentage of land assigned to developed use classes increased between 2000 and 2010 (see
Table A-l, A, Figure A-l, C). Over the same period, the relative amount of land assigned to
each of the seven developed land use classes (LUCs) also changed (see Table A-l, B). Among
the developed classes, the proportion of developed land in the urban low LUC decreased, while
the proportion of land in the urban high LUC increased between 2000 and 2010 (see Figure A-l,
A). The relative amount of developed land in the exurban low, exurban high, suburban,
commercial, and industrial LUCs did not change statistically significantly between 2000 and
2010. Relative growth in the urban high LUC was larger than in the urban low LUC (see
Figure A-l, B). The relative amount of growth in paired comparisons of exurban high with
exurban low, suburban with exurban high, and urban low with suburban LUCs show no
statistically significant differences.
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Table A-l. Goodness-of-fit test results comparing LUCs in 2000 and 2010 in
Integrated Climate and Land Use Scenarios (ICLUS) Region 1 (Pacific).
Values are limited to developable area and LUCs that transition in the model. (A)
Land assigned to developed and undeveloped LUCs. (B) Percentage developed
land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
13.33%
15.72%
Undeveloped
86.67%
84.28%
X2: 873.48
DF: 1
/j-value: <0.0001
(B) Developed LUC
2000
2010
Exurban low
39.28%
39.11%
Exurban high
25.67%
26.44%
Suburban
11.10%
10.70%
Urban low
16.86%
16.27%
Urban high
1.23%
1.60%
Commercial
3.58%
3.76%
Industrial
2.28%
2.12%
X2: 47.74
DF: 8
/j-value: <0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
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(A)
i
~
i
1
~
t i
<
t
Exurban Low Exurban High Suburban Urban Low Urban High Commercial Industrial
(B)
(C)
i
>
i
* I
i-*—i
i—<
Exurban High vs. Suburban vs. Exurban Urban Low vs. Urban High vs. Urban Developed vs.
Exurban Low High Suburban Low Undeveloped
Figure A-l. Land use comparisons between 2000 and 2010 in Integrated
Climate and Land Use Scenarios (ICLUS) Region 1 (Pacific). (A) Odds ratios
(ORs) and confidence intervals comparing allocations among the seven developed
LUCs; (B) ORs and confidence intervals comparing adjacent residential LUCs
(high density versus low density); and (C) OR comparing developed and
undeveloped LUCs.
A.2. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 2
(INTERMOUNTAIN WEST) LAND USE CHANGES
1 In the Intermountain West region (ICLUS Region 2), the percentage of land assigned to
2 developed use classes increased between 2000 and 2010 (see Table A-2, A, Figure A-2, C).
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1 Over the same period, the relative amount of land assigned to each of the seven developed LUCs
2 also changed (see Table A-2, B). Among the developed classes, the proportion of developed
3 land in the exurban high, urban low, and industrial LUC decreased, while the proportion of
4 developed land in the exurban low and urban high LUCs increased between 2000 and 2010
5 (see Figure A-2, A). The relative amount of developed land in the suburban and commercial
6 LUCs did not change significantly between 2000 and 2010. Relative growth in the urban high
7 LUC was larger than the urban low LUC (see Figure A-2, B). However, relative growth in the
8 exurban high LUC was less than the exurban low LUC. The relative amount of growth in the
9 suburban LUC was not significantly different from the exurban high LUC, and the relative
10 amount of growth in the urban low LUC was not significantly different from the suburban LUC.
Table A-2. Goodness-of-fit test results comparing LUCs in 2000 and 2010 in
Integrated Climate and Land Use Scenarios (ICLUS) Region 2
(Intermountain West). Values are limited to developable area and LUCs that
transition in the model. (A) Land assigned to developed and undeveloped LUCs.
(B) Percentage developed land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
3.41%
4.53%
Undeveloped
96.59%
95.47%
X2: 1,557.17
DF: 1
/j-value: <0.0001
(B) Developed LUC
2000
2010
Exurban low
37.39%
40.62%
Exurban high
29.45%
27.92%
Suburban
12.05%
11.67%
Urban low
13.86%
13.01%
Urban high
0.58%
0.77%
Commercial
4.39%
4.07%
Industrial
2.28%
1.94%
X2: 99.84
DF: 8
/j-value: <0.0001
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1 so
1.60
1.40
o
CD
en
(/)
-o
-o
O
1 20
1.00
0 80
0.60
(A)
i
~
i
i t i
-L <
t
Exurban Low Exurban High Suburban Urban Low Urban High Commercial Industrial
2.00
1.80
1.60
I 140
1.20
1.00
0.80
0.60
(B)
(C)
f
£
1
Exurban High vs. Suburban vs. Exurban Urban Low vs. Urban High vs. Urban Developed vs.
Exurban Low High Suburban Low Undeveloped
Figure A-2. Land use comparisons between 2000 and 2010 in ICLUS Region
2 (Intermountain West). (A) Odds ratios (ORs) and confidence intervals
comparing allocations among the seven developed LUCs; (B) ORs and
confidence intervals comparing adjacent residential LUCs (high density versus
low density); and (C) OR comparing developed and undeveloped LUCs.
A.3. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 3
(NORTH CENTRAL) LAND USE CHANGES
1 In the North Central region (ICLUS Region 3), the percentage of land assigned to
2 developed use classes increased between 2000 and 2010 (x2 = 1,507.45, DF = 1 ,p< 0.0001;
3 see Table A-3, A, Figure A-3, C). Over the same period, the relative amount of land assigned to
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1 each of the seven developed LUCs also changed (x2 = 149.09, DF = 8 ,p< 0.0001;
2 see Table A-3, B). Among the developed classes, the proportion of developed land in the
3 exurban high, suburban, urban low, and industrial LUC decreased, while the proportion of
4 developed land in the exurban low and urban high LUCs increased between 2000 and 2010
5 (see Figure A-3, A). The relative amount of developed land in the commercial LUC did not
6 change significantly for the same period. Relative growth in the urban high LUC was larger than
7 the urban low LUC (see Figure A-3, B). Conversely, relative growth in the exurban high LUC
8 was less than the exurban low LUC. The relative amount of growth in the suburban LUC was
9 not significantly different than the exurban high LUC, and the relative amount of growth in the
10 urban low LUC was not significantly different than the suburban LUC.
Table A-3. Goodness-of-fit test results comparing LUCs in 2000 and 2010 in
Integrated Climate and Land Use Scenarios (ICLUS) Region 3 (North
Central). Values are limited to developable area and LUCs that transition in the
model. (A) Land assigned to developed and undeveloped LUCs. (B) Percentage
developed land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
4.05%
5.10%
Undeveloped
95.95%
94.90%
X2: 1,507.45
DF: 1
/j-value: <0.0001
(B) Developed LUC
2000
2010
Exurban low
47.01%
50.55%
Exurban high
27.72%
25.51%
Suburban
9.17%
8.61%
Urban low
9.93%
9.49%
Urban high
0.24%
0.32%
Commercial
3.62%
3.47%
Industrial
2.31%
2.05%
X2: 149.09
DF: 8
/j-value: <0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
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2.00
1 SO
1.60
1 1.40
§ 1.20
1.00
0 80
0.60
(A)
i
~
it* 1
~
)
t
Exurban Low Exurban High Suburban Urban Low Urban High Commercial Industrial
2.00
1.80
1.60
I 140
1.20
1.00
0.80
0.60
(B)
(C)
i
I 1
i
Exurban High vs. Suburban vs. Exurban Urban Low vs. Urban High vs. Urban Developed vs.
Exurban Low High Suburban Low Undeveloped
Figure A-3. Land use comparisons between 2000 and 2010 in ICLUS Region
3 (North Central). (A) Odds ratios (ORs) and confidence intervals comparing
allocations among the seven developed LUCs; (B) ORs and confidence intervals
comparing adjacent residential LUCs (high density versus low density); and (C)
OR comparing developed and undeveloped LUCs.
A.4. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 4
(SOUTH CENTRAL) LAND USE CHANGES
In the South Central region (ICLUS Region 4), the percentage of land assigned to
developed use classes increased between 2000 and 2010 (see Table A-4, A, Figure A-4, C).
Over the same period, the relative amount of land assigned to each of the seven developed LUCs
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
also changed (see Table A-4, B). In this particular region, the amount of developed land
allocated to the exurban low and exurban high LUCs was lower in 2000 than expected
(see Table A-4, B), and a large number of grazing land use pixels transitioned into these LUCs in
2010. However, values for the exurban low and exurban high LUCs were comparable to other
regions in 2010, which suggests the model had difficulty distinguishing between exurban and
agricultural classes in 2000. As a result, comparisons among the LUCs below are not
particularly meaningful, but are presented for completeness. Among the developed classes, the
proportion of developed land in the exurban high, suburban, urban low, urban high, commercial,
and industrial LUCs decreased, while the proportion of developed land in the exurban low LUC
increased between 2000 and 2010 (see Figure A-4, A). Relative growth in the exurban high
LUC was less than the exurban low LUC, relative growth in the suburban LUC was less than the
exurban high LUC, and relative growth in the urban low LUC was less than the suburban LUC
(see Figure A-4, B). The relative amount of growth in the urban high LUC was not significantly
different than the urban low LUC.
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Table A-4. Goodness-of-fit test results comparing LUCs in 2000 and 2010 in
Integrated Climate and Land Use Scenarios (ICLUS) Region 4 (South
Central). Values are limited to developable area and LUCs that transition in the
model. (A) Land assigned to developed and undeveloped LUCs. (B) Percentage
developed land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
3.90%
11.52%
Undeveloped
96.10%
88.48%
X2: 41,129.98
DF: 1
/j-value: <0.0001
(B) Developed LUC
2000
2010
Exurban low
19.28%
54.43%
Exurban high
31.26%
25.62%
Suburban
16.43%
8.07%
Urban low
19.47%
7.19%
Urban high
0.95%
0.34%
Commercial
8.04%
2.78%
Industrial
4.56%
1.57%
X2: 17,949.23
DF: 8
/j-value: <0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
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6.00
5.00
4.00
3.00
2 00
1.00
0.00
Exurban Low Exurban High Suburban Urban Low Urban High Commercial Industrial
3.50
3.00
(B)
(C)
2.50
I
en
w
| 1.50
1.00
0.50
0.00
Exurban High vs.
Exurban Low
Suburban vs. Exurban
High
Urban Low vs.
Suburban
Urban High vs. Urban
Low
Developed vs.
Undeveloped
Figure A-4. Land use comparisons between 2000 and 2010 in Integrated
Climate and Land Use Scenarios (ICLUS) Region 4 (South Central).
(A) Odds ratios (ORs) and confidence intervals comparing allocations among the
seven developed LUCs; (B) ORs and confidence intervals comparing adjacent
residential LUCs (high density versus low density); and (C) OR comparing
developed and undeveloped LUCs.
A.5. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 5
(GREAT LAKES) LAND USE CHANGES
1 In the Great Lakes region (ICLUS Region 5), the percentage of land assigned to
2 developed use classes increased between 2000 and 2010 (see Table A-5, A, Figure A-5, C).
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1 Over the same period, the relative amount of land assigned to each of the seven developed LUCs
2 also changed (see Table A-5, B). Among the developed classes, the proportion of developed
3 land in the exurban high and urban high LUCs increased, while the proportion of developed land
4 in the exurban low LUC decreased between 2000 and 2010 (see Figure A-5, A). The relative
5 amount of developed land in the suburban, urban low, commercial, and industrial LUCs did not
6 change significantly. Relative growth in the exurban high LUC was larger than in the exurban
7 low LUC, and relative growth in the urban high LUC was larger than the urban low LUC (see
8 Figure A-5, B). The relative amount of growth in the suburban LUC was not significantly
9 different than the exurban high LUC, and the relative amount of growth in the urban low LUC
10 was not significantly different than the suburban LUC.
Table A-5. Goodness-of-fit test results comparing LUCs in 2000 and 2010 in
Integrated Climate and Land Use Scenarios (ICLUS) Region 5 (Great
Lakes). Values are limited to developable area and LUCs that transition in the
model. (A) Land assigned to developed and undeveloped LUCs. (B) Percentage
developed land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
20.12%
23.99%
Undeveloped
79.88%
76.01%
X2: 2,329.40
DF: 1
/j-value:<0.0001
(B) Developed LUC
2000
2010
Exurban low
53.02%
52.07%
Exurban high
25.52%
26.40%
Suburban
8.30%
8.31%
Urban low
9.04%
9.02%
Urban high
0.35%
0.47%
Commercial
2.22%
2.28%
Industrial
1.55%
1.44%
X2: 55.17
DF: 8
/j-value: <0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
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1 so
1.60
(A)
1.40
o
"+J
CD
a:
w
~o
~o
O
1.20
1.00
5 5
0 80
0.60
Exurban Low Exurban High Suburban Urban Low Urban High Commercial Industrial
1.80
1.60
1.40
¦
i T
i I
o
'IS
1.20
1.00
0.80
0.60
Exurban High vs. Suburban vs. Exurban Urban Low vs. Urban High vs. Urban Developed vs.
Exurban Low High Suburban Low Undeveloped
Figure A-5. Land use comparisons between 2000 and 2010 in ICLUS Region
5 (Great Lakes). (A) Odds ratios (ORs) and confidence intervals comparing
allocations among the seven developed LUCs; (B) ORs and confidence intervals
comparing adjacent residential LUCs (high density versus low density); and (C)
OR comparing developed and undeveloped LUCs.
A.6. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 6
(SOUTHEAST) LAND USE CHANGES
In the Southeast region (ICLUS Region 6), the percentage of land assigned to developed
use classes increased between 2000 and 2010 (see Table A-6, A, Figure A-6, C). Over the same
period, the relative amount of land assigned to each of the seven developed LUCs also changed
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1 (see Table A-6, B). Among the developed classes, the proportion of developed land in the
2 exurban low LUC decreased, while the proportion of developed land in the exurban high,
3 suburban and urban low, urban high and commercial LUCs increased between 2000 and 2010
4 (see Figure A-6, A). The relative amount of developed land in the industrial LUC did not change
5 significantly. Relative growth in all of the LUC comparisons were greater in 2010 than in 2000
6 (see Figure A-6, B).
Table A-6. Goodness-of-fit test results comparing LUCs in 2000 and 2010 in
Integrated Climate and Land Use Scenarios (ICLUS) Region 6 (Southeast).
Values are limited to developable area and LUCs that transition in the model. (A)
Land assigned to developed and undeveloped LUCs. (B) Percentage developed
land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
27.38%
34.17%
Undeveloped
72.62%
65.83%
X2: 10,532.23
DF: 1
/j-value: <0.0001
(B) Developed LUC
2000
2010
Exurban low
61.97%
57.74%
Exurban high
24.18%
25.28%
Suburban
7.55%
9.22%
Urban low
3.87%
5.04%
Urban high
0.17%
0.28%
Commercial
1.47%
1.64%
Industrial
0.79%
0.81%
X2: 1,562.88
DF: 8
/j-value: <0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
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2.00
1.80
o
= 1 40
(0
en
•
Exurban Low Exurban High Suburban Urban Low Urban High Commercial Industrial
1.60
1.40
o 1.20
'13
at
1.00
0.80
0.60
(B)
(C)
i
~
•
* 1
Exurban High vs.
Exurban Low
Suburban vs. Exurban
High
Urban Low vs. Urban High vs. Urban Developed vs.
Suburban Low Undeveloped
Figure A-6. Land use comparisons between 2000 and 2010 in Integrated
Climate and Land Use Scenarios (ICLUS) Region 6 (Southeast). (A) Odds
ratios (ORs) and confidence intervals comparing allocations among the seven
developed LUCs; (B) ORs and confidence intervals comparing adjacent
residential LUCs (high density versus low density); and (C) OR comparing
developed and undeveloped LUCs.
A.7. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 7
(NORTHEAST) LAND USE CHANGES
1 In the Northeast region (ICLUS Region 7), the percentage of land assigned to developed
2 use classes increased between 2000 and 2010 (see Table A-7, A, Figure A-7, C). Over the same
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1 period, the relative amount of land assigned to each of the seven developed LUCs also changed
2 (see Table A-7, B). Among the developed classes, the proportion of developed land in the
3 exurban low LUC decreased, while the proportion of developed land in the exurban high,
4 suburban, urban low, urban high, and commercial LUCs increased (see Figure 13, A). The
5 relative amount of developed land in the industrial LUC did not change significantly between
6 2000 and 2010 (see Figure A-7, A). Relative growth in all of the LUC comparisons was greater
7 in 2010 than in 2000, except for the urban low LUC, which was not significantly different from
8 the suburban LUC (see Figure A-7, B).
Table A-7. Goodness-of-fit test results comparing LUCs in 2000 and 2010 in
Integrated Climate and Land Use Scenarios (ICLUS) Region 7 (Northeast).
Values are limited to developable area and LUCs that transition in the model. (A)
Land assigned to developed and undeveloped LUCs. (B) Percentage developed
land assigned to the seven developed LUCs.
(A) Land Use Type
2000
2010
Developed
41.02%
46.97%
Undeveloped
58.98%
53.03%
X2: 2,248.51
DF: 1
/j-value: <0.0001
(B) Developed LUC
2000
2010
Exurban low
56.44%
54.21%
Exurban high
27.18%
27.90%
Suburban
8.33%
9.16%
Urban low
5.54%
5.89%
Urban high
0.66%
0.81%
Commercial
1.22%
1.37%
Industrial
0.64%
0.65%
X2: 178.10
DF: 8
/j-value: <0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
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1.60
1.40
o 1 20
-o
o 1 00
0 80
0.60
(A)
<
\ T
. } i 1
o
<
\
i
Exurban Low Exurban High Suburban Urban Low Urban High Commercial Industrial
1.40
1.20
1.00
0.80
0.60
(B)
(C)
$
i
• i
~
it
Exurban High vs.
Exurban Low
Suburban vs. Exurban
High
Urban Low vs.
Suburban
Urban High vs. Urban
Low
Developed vs.
Undeveloped
Figure A-7. Land use comparisons between 2000 and 2010 in Integrated
Climate and Land Use Scenarios (ICLUS) Region 7 (Northeast). (A) Odds
ratios (ORs) and confidence intervals comparing allocations among the seven
developed LUCs; (B) ORs and confidence intervals comparing adjacent
residential LUCs (high density versus low density); and (C) OR comparing
developed and undeveloped LUCs.
This document is a draft for re\'iew purposes only and does not constitute Agency policy.
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APPENDIX B. REGIONAL TRANSITION PROBABILITY MODELS
B.l. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 1
(PACIFIC) TRANSITION PROBABILITY MODELS
Table B-l. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial Generalized Additive Models
(GAMs). The top marginal model predicts the probability of transitioning into
each land use class (LUC)j in 2010,/>(LUCj), by capacity class, while the bottom
conditional models predict the probability of transitioning from each LUCi in
2000 given that they transitioned into a particular LUCj in 2010, /;(LUCi j), by
capacity class. RefLevel is the reference level land use from which transitions are
calculated.
Smoothing Terms
edf
x2
P
For Transitions into LUC2010 by Capacity Class (RefLevel: Exurban Low)
Capacity class (exurban high)
1.87
10,633.33
<0.0001
Capacity class (suburban)
1.73
5,543.97
<0.0001
Capacity class (urban low)
1.66
9,450.96
<0.0001
Capacity class (urban high)
1.48
855.19
<0.0001
Capacity class (commercial)
1.83
29,894.84
<0.0001
Capacity class (industrial)
1.97
604.42
<0.0001
Global test
16.55
1,463,202
<0.0001
From LUC2000 for Transitions into Exurban Low by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
2.03
411.02
<0.0001
Capacity class (pasture)
2.01
2,039.43
<0.0001
Capacity class (cropland)
2.02
510.69
<0.0001
Global test
9.07
9,242.02
<0.0001
From LUC2000 for Transitions into Exurban High by Capacity Class (RefLevel: Exurban
Low)
Capacity class (timber)
2.00
221.42
<0.0001
Capacity class (grazing)
1.98
10,906.72
<0.0001
Capacity class (pasture)
2.03
215.47
<0.0001
Capacity class (cropland)
2.08
93.19
<0.0001
Global test
12.09
17,273.73
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-l DRAFT—DO NOT CITE OR QUOTE
-------
Table B-l. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUCj in 2010, /;(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUCi in 2000 given that they transitioned into a
particular LUCj in 2010, /XLUCij), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Suburban by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
1.94
11.08
0.0036
Capacity class (timber)
2.19
76.11
<0.0001
Capacity class (grazing)
2.12
85.46
<0.0001
Capacity class (pasture)
2.23
85.63
<0.0001
Capacity class (cropland)
2.37
363.75
<0.0001
Capacity class (exurban low)
2.07
594.51
<0.0001
Global test
18.91
15,207.84
<0.0001
From LUC2000 for transitions into Urban Low by Capacity Class (RefLevel: Suburban)
Capacity class (wetlands)
1.74
5.59
0.0469
Capacity class (timber)
2.24
86.71
<0.0001
Capacity class (grazing)
2.05
221.43
<0.0001
Capacity class (pasture)
2.39
44.67
<0.0001
Capacity class (cropland)
2.46
278.04
<0.0001
Capacity class (exurban low)
2.18
490.91
<0.0001
Capacity class (exurban high)
1.98
1,315.07
<0.0001
Global test
22.03
10,766.11
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-2 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-l. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUCj in 2010, /;(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUCi in 2000 given that they transitioned into a
particular LUCj in 2010, /XLUCij), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Urban High by Capacity Class (RefLevel: Urban
Low)
Capacity class (wetlands)
1.42
2.36
0.1966
Capacity class (timber)
1.81
5.66
0.0486
Capacity class (grazing)
1.78
11.73
0.0021
Capacity class (pasture)
1.1
4.43
0.0409
Capacity class (cropland)
1.95
8.8
0.0115
Capacity class (exurban low)
1.98
25.38
<0.0001
Capacity class (exurban high)
1.83
103.49
<0.0001
Capacity class (suburban)
2.00
119.96
<0.0001
Global test
21.84
2,022.44
<0.0001
From LUC2000 for Transitions into Commercial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
2.07
270.23
<0.0001
Capacity class (suburban)
1.74
272.1
<0.0001
Capacity class (urban low)
1.73
378.23
<0.0001
Capacity class (urban high)
1.61
6.07
0.0317
Global test
11.14
20,068.51
<0.0001
From LUC2000 for Transitions into Industrial by Capacity Class (RefLevel: Exurban
High)
Capacity class (grazing)
0.72
5.79
0.0096
Capacity class (exurban low)
2.06
16.7
3.00 x 10 4
Global test
4.79
1,078.55
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-3 DRAFT—DO NOT CITE OR QUOTE
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Sfrg or 1J ComSsln&d Moo tic-
Traitltort from ILUC2DDQ la Exurbam Law
vwrtxrq
I
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Region 11 Combined Mode c
•n-anEltlorw from LUC2 DDD to Ex urban Hlgft
o
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04
o
o
o
I-soac Claess
Capacity CJai
Rtg ion 1 > Combined Woe»lc
Trantltiant from LUC2D00 1a Sub jrban
Region 11 Combined Mode X
~rartltori from LUC2DD*] ta Urban Low
£
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O
o
o
o
I-aoac ?y Class
C^apadtj" C ass
=5 to on 1J Combined M :«c<
Trancltlonc from LUC20a§to Urh-sn High
o
o
o
o
o
i
i
E
Region 1 ' Combined Models
Trancfttor* from LUCiOOD to Commas lal
o
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o
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O
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rulU^LJ* '
rulU 'iHgr ¦
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Capacity GJas
TTz/'s document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-4 DRAFT—DO NOT CITE OR QUOTE
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Region 1 / Combined Models
Transitions from LUC2000 to Industrial
.O
£
Q.
c
O
c
,f5
CO
O
(O
o
^r
o
<\l
O
Exurban High
Capacity Class
Figure B-l. Predicted transition probabilities by capacity class from LUCs
in 2000 to LUCs in 2010 in Integrated Climate and Land Use Scenarios
(ICLUS) Region 1 (Pacific). Each panel shows transitions into a particular LUC
in 2010. These combined probabilities are the product of corresponding marginal
and conditional models (i.e., for a given capacity class the probability of
transitioning from LUCi into LUCj is P(LUCij) = P(LUCj) X P(LUCi|j).
This document is a draft for re\'iew purposes only and does not constitute Agency policy.
3/7/16 B-5 DRAFT—DO NOT CITE OR QUOTE
-------
B.2. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 2
(INTERMOUNTAIN WEST) TRANSITION PROBABILITY MODELS
Table B-2. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010,/>(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a particular
LUC in 2010,/?(LUCi[j), by capacity class. RefLevel is the reference level land
use from which transitions are calculated.
Smoothing Terms
edf
x2
P
For Transitions into LUC2010 by Capacity Class (RefLevel: Exurban Low)
Capacity class (exurban high)
1.89
3,226.33
<0.0001
Capacity class (suburban)
1.8
11,531.99
<0.0001
Capacity class (urban low)
1.7
14,389.11
<0.0001
Capacity class (urban high)
1.78
3,477.46
<0.0001
Capacity class (commercial)
1.9
35,289.11
<0.0001
Capacity class (industrial)
1.91
1,313.18
<0.0001
Global test
16.98
1,050,212
<0.0001
From LUC2000 for Transitions into Exurban Low by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
1.94
1,030.15
<0.0001
Capacity class (pasture)
2
2,895.73
<0.0001
Capacity class (cropland)
2.01
1,152.97
<0.0001
Global test
8.95
5,582.88
<0.0001
From LUC2000 for Transitions into Exurban High by Capacity Class (RefLevel: Exurban
Low)
Capacity class (timber)
2.13
492.9
<0.0001
Capacity class (grazing)
1.96
3,483.53
<0.0001
Capacity class (pasture)
2
94.44
<0.0001
Capacity class (cropland)
2.04
180.76
<0.0001
Global test
12.13
21,938.08
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-6 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-2. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Suburban by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
1.91
12.11
0.0021
Capacity class (timber)
2.29
139.88
<0.0001
Capacity class (grazing)
2.02
39.53
<0.0001
Capacity class (pasture)
2.24
55.39
<0.0001
Capacity class (cropland)
2.33
168.36
<0.0001
Capacity class (exurban low)
2.02
481.68
<0.0001
Global test
18.81
16,786.93
<0.0001
From LUC2000 for Transitions into Urban Low by Capacity Class (RefLevel: Suburban)
Capacity class (wetlands)
1.85
11.91
0.0021
Capacity class (timber)
2.22
46.77
<0.0001
Capacity class (grazing)
2.12
912.79
<0.0001
Capacity class (pasture)
2.27
87.06
<0.0001
Capacity class (cropland)
2.29
454.97
<0.0001
Capacity class (exurban low)
2.21
619.65
<0.0001
Capacity class (exurban high)
1.99
1,638.98
<0.0001
Global test
21.96
12,595.07
<0.0001
From LUC2000 for Transitions into Urban High by Capacity Class (RefLevel: Urban
Low)
Capacity class (wetlands)
1.05
3.04
0.0874
Capacity class (timber)
1.26
6.91
0.0109
Capacity class (grazing)
2.07
219.32
<0.0001
Capacity class (pasture)
2.05
9.09
0.0113
Capacity class (cropland)
1.51
11.57
0.0016
Capacity class (exurban low)
2.23
56.06
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-7 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-2. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
Capacity class (exurban high)
2.04
200.89
<0.0001
Capacity class (suburban)
1.88
83.19
<0.0001
Global test
22.09
1,850.01
<0.0001
From LUC2000 for Transitions into Commercial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
2.01
195.61
<0.0001
Capacity class (suburban)
1.95
521.53
<0.0001
Capacity class (urban low)
1.96
272.46
<0.0001
Capacity class (urban high)
1.70
5.78
0.013
Global test
11.62
11,500.45
<0.0001
From LUC2000 for Transitions into Industrial by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
1.15
24.53
<0.0001
Capacity class (grazing)
1.48
20.35
<0.0001
Capacity class (pasture)
0.64
1.39
0.143
Capacity class (exurban low)
2.08
36.75
<0.0001
Global test
9.36
993.61
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-8 DRAFT—DO NOT CITE OR QUOTE
-------
Re>Uton£ 1 Com&lr »d Wocelc
Trancltior t from LUC2DDQ 1a E< urb:n Law
Region 1! Combined Modelc
Translttor* from LUC2D0D to Exurtun H ftgh
o
-------
Region 2 / Combined Models
Transitions from LUC2000 to Industrial
o
£
J5
CO
2
o
2
Cl
C
o
P
CNI
O
O
O
Grazing
Exurban Lew
Exurban High
Capacity Class
Figure B-2. Predicted transition probabilities by capacity class from LUCs
in 2000 to LUCs in 2010 in Integrated Climate and Land Use Scenarios
(ICLUS) Region 2 (Intermountain West). Each panel shows transitions into a
particular LUC in 2010. These combined probabilities are the product of
corresponding marginal and conditional models, i.e., for a given capacity class the
probability of transitioning from LUCi into LUCj is P(LUCij) = P(LUCj) X
P(LUCi[j).
This document is a draft for re\'iew purposes only and does not constitute Agency policy.
3/7/16 B-10 DRAFT—DO NOT CITE OR QUOTE
-------
B.3. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 3
(NORTH CENTRAL) TRANSITION PROBABILITY MODELS
Table B-3. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010,/>(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a particular
LUC in 2010,/?(LUCi[j), by capacity class. RefLevel is the reference level land
use from which transitions are calculated.
Smoothing Terms
edf
x2
P
For Transitions into LUC2010 by Capacity Class (RefLevel: Exurban Low)
Capacity class (exurban high)
1.86
4,754.47
<0.0001
Capacity class (suburban)
1.8
16,711.47
<0.0001
Capacity class (urban low)
1.74
13,319.25
<0.0001
Capacity class (urban high)
2.18
22,346.04
<0.0001
Capacity class (commercial)
1.85
9,804.43
<0.0001
Capacity class (industrial)
1.94
1,802.87
<0.0001
Global test
17.37
1,058,696
<0.0001
From LUC2000 for Transitions into Exurban Low by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
2.02
22.43
<0.0001
Capacity class (pasture)
2.01
3,718.37
<0.0001
Capacity class (cropland)
2.01
237.31
<0.0001
Global test
9.04
24,725.45
<0.0001
From LUC2000 for Transitions into Exurban High by Capacity Class (RefLevel: Exurban
Low)
Capacity class (timber)
2.08
157.92
<0.0001
Capacity class (grazing)
1.96
4,738.75
<0.0001
Capacity class (pasture)
1.99
225.36
<0.0001
Capacity class (cropland)
1.99
801.72
<0.0001
Global test
12.02
9,249.58
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-l 1 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-3. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Suburban by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
1.97
123.14
<0.0001
Capacity class (timber)
2.05
14.99
0.0006
Capacity class (grazing)
2.04
101.77
<0.0001
Capacity class (pasture)
2.11
216.86
<0.0001
Capacity class (cropland)
2.16
262
<0.0001
Capacity class (exurban low)
2.1
495.46
<0.0001
Global test
18.43
19,567.29
<0.0001
From LUC2000 for Transitions into Urban Low by Ca
jacity Class (RefLevel: Suburban)
Capacity class (wetlands)
1.74
22.39
<0.0001
Capacity class (timber)
1.87
3
0.2027
Capacity class (grazing)
2.07
214.66
<0.0001
Capacity class (pasture)
2.07
160.27
<0.0001
Capacity class (cropland)
2.2
36.16
<0.0001
Capacity class (exurban low)
2.2
282.27
<0.0001
Capacity class (exurban high)
2.03
955.3
<0.0001
Global test
21.17
8,447.26
<0.0001
From LUC2000 for Transitions into Urban High by Capacity Class (RefLevel: Urban
Low)
Capacity class (wetlands)
1.15
4.86
0.0343
Capacity class (grazing)
2.29
40.23
<0.0001
Capacity class (pasture)
1.74
11.91
0.0018
Capacity class (cropland)
0.97
5.85
0.0149
Capacity class (exurban low)
2.02
39.67
<0.0001
Capacity class (exurban high)
2.03
139.47
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-12 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-3. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
Capacity class (suburban)
1.85
163.45
<0.0001
Global test
19.05
541.05
<0.0001
From LUC2000 for Transitions into Commercial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
1.91
319.34
<0.0001
Capacity class (suburban)
1.91
1,006.82
<0.0001
Capacity class (urban low)
2.12
1,607.49
<0.0001
Capacity class (urban high)
2.19
674.84
<0.0001
Global test
12.13
9,102.24
<0.0001
From LUC2000 for Transitions into Industrial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
2.21
31.74
<0.0001
Global test
3.21
37.65
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-13 DRAFT—DO NOT CITE OR QUOTE
-------
Sso &ri i • Combined Wocslc-
TranclttonB from LUC2D00 1a s,»urt:n Low
Region t! Combined Mode t
Trantltions from LUC2DDD to Ex urban Htgh
I
6
CapacSj Class
G^padt/ Cia;i
Keg Ion £ J Comalr ed Wocelt
Trancrtinric from LUC2D0G Id Suburban
Region 4,' Combined Mode t
TranclttorK. frooi LUC2DD0 to Urban Low
'v3P3C Tj> ^ .43 S
Capactty Class
ft*q on 31 Combined Hodtlc
Tr&nGltlanc from LUC2QOO to Urban High
oo
o
•O
O
CM
O
O
O
Gapacly Class
Region 3-." Combined Module
Transit loot from LUC2DDD to Commercial
to
-
o
o
o
Capacity '--ass
3/7/16
This document is a draft for review purposes only and does not constitute Agency policy.
B-14 DRAFT—DO NOT CITE OR QUOTE
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Region 3 / Combined Models
Transitions from LUC2000 to Industrial
o
£
J5
CO
2
o
2
Cl
C
o
P
CNI
O
O
O
Exurban High
Capacity Class
Figure B-3. Predicted transition probabilities by capacity class from LUCs
in 2000 to LUCs in 2010 in Integrated Climate and Land Use Scenarios
(ICLUS) Region 3 (North Central). Each panel shows transitions into a
particular LUC in 2010. These combined probabilities are the product of
corresponding marginal and conditional models, i.e., for a given capacity class the
probability of transitioning from LUCi into LUCj is P(LUCij) = P(LUCj) X
P(LUCi[j).
This document is a draft for re\'iew purposes only and does not constitute Agency policy.
3/7/16 B-15 DRAFT—DO NOT CITE OR QUOTE
-------
B.4. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 4
(SOUTH CENTRAL) TRANSITION PROBABILITY MODELS
Table B-4. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010,/>(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a particular
LUC in 2010,/?(LUCi[j), by capacity class. RefLevel is the reference level land
use from which transitions are calculated.
Smoothing Terms
edf
x2
P
For Transitions into LUC2010 by Capacity Class (RefLevel: Exurban Low)
Capacity class (exurban high)
1.93
1,729.42
<0.0001
Capacity class (suburban)
1.95
12,232.72
<0.0001
Capacity class (urban low)
1.89
7,131.93
<0.0001
Capacity class (urban high)
2.06
498.58
<0.0001
Capacity class (commercial)
1.90
3,650.58
<0.0001
Capacity class (industrial)
1.87
543.53
<0.0001
Global test
17.60
1,912,574
<0.0001
From LUC2000 for Transitions into Exurban Low by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
1.99
341.64
<0.0001
Capacity class (pasture)
2.01
6,979.23
<0.0001
Capacity class (cropland)
2.00
277.28
<0.0001
Global test
9.00
50,282.78
<0.0001
From LUC2000 for Transitions into Exurban High by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
2.03
499.96
<0.0001
Capacity class (pasture)
2
1,659.84
<0.0001
Capacity class (cropland)
2
261.75
<0.0001
Capacity class (exurban low)
1.99
806.3
<0.0001
Global test
12.03
19,077.08
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-16 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-4. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Suburban by Capacity Class (RefLevel: Grazing)
Capacity class (wetlands)
2.07
190.8
<0.0001
Capacity class (timber)
2.2
236.43
<0.0001
Capacity class (pasture)
2.07
462.93
<0.0001
Capacity class (cropland)
2.1
224.46
<0.0001
Capacity class (exurban low)
2.04
22.22
<0.0001
Capacity class (exurban high)
1.91
302.98
<0.0001
Global test
18.39
5,044.57
<0.0001
From LUC2000 for Transitions into Urban Low by Capacity Class (RefLevel: Grazing)
Capacity class (wetlands)
1.91
3.03
0.2059
Capacity class (timber)
2.15
20.35
<0.0001
Capacity class (pasture)
2.18
20.45
<0.0001
Capacity class (cropland)
2.28
21.35
<0.0001
Capacity class (exurban low)
2.1
5.58
0.0676
Capacity class (exurban high)
1.94
19.43
0.0001
Capacity class (suburban)
1.95
545.75
<0.0001
Global test
21.51
5,170.4
<0.0001
From LUC2000 for Transitions into Urban High by Capacity Class (RefLevel: Urban
Low)
Capacity class (wetlands)
1.09
1.23
0.2935
Capacity class (timber)
1.98
1.1
0.572
Capacity class (grazing)
1.85
26.12
<0.0001
Capacity class (pasture)
2.37
5.1
0.107
Capacity class (cropland)
2.04
6.98
0.0317
Capacity class (exurban low)
1.68
2.26
0.2599
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-17 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-4. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
Capacity class (exurban high)
1.84
3.09
0.1888
Capacity class (suburban)
1.85
1.45
0.4467
Global test
22.7
352.8
<0.0001
From LUC2000 for Transitions into Commercial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
2.15
32.86
<0.0001
Capacity class (suburban)
1.99
147.97
<0.0001
Capacity class (urban low)
2.06
60.21
<0.0001
Capacity class (urban high)
0.68
2.48
0.0706
Global test
10.88
1,823.53
<0.0001
From LUC2000 for Transitions into Industrial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
1.99
25.82
<0.0001
Global test
2.99
185.08
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-18 DRAFT—DO NOT CITE OR QUOTE
-------
Rtglori-* • OombloKl Wocslt
Tra-itltore- from LUC2DD0 1a £j.iirt-in Low
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TTz/'s document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-19 DRAFT—DO NOT CITE OR QUOTE
-------
Region 4 / Combined Models
Transitions from LUC2000 to Industrial
.O
£
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Capacity Class
Figure B-4. Predicted transition probabilities by capacity class from LUCs
in 2000 to LUCs in 2010 in Integrated Climate and Land Use Scenarios
(ICLUS) Region 4 (South Central). Each panel shows transitions into a
particular LUC in 2010. These combined probabilities are the product of
corresponding marginal and conditional models, i.e., for a given capacity class the
probability of transitioning from LUCi into LUCj is P(LUCij) = P(LUCj) X
P(LUCi[j).
This document is a draft for re\'iew purposes only and does not constitute Agency policy.
3/7/16 B-20 DRAFT—DO NOT CITE OR QUOTE
-------
B.5. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 5
(GREAT LAKES) TRANSITION PROBABILITY MODELS
Table B-5. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010,/>(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a particular
LUC in 2010,/?(LUCi[j), by capacity class. RefLevel is the reference level land
use from which transitions are calculated.
Smoothing Terms
edf
x2
P
For Transitions into LUC2010 by Capacity Class (RefLevel: Exurban Low)
Capacity class (exurban high)
1.82
5,643.06
<0.0001
Capacity class (suburban)
1.74
3,016.31
<0.0001
Capacity class (urban low)
1.71
3,105.97
<0.0001
Capacity class (urban high)
1.72
568.65
<0.0001
Capacity class (commercial)
1.77
6,819.62
<0.0001
Capacity class (industrial)
1.98
125.28
<0.0001
Global test
16.74
1,646,923
<0.0001
From LUC2000 for Transitions into Exurban Low by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
2.01
110.82
<0.0001
Capacity class (pasture)
2.02
5,294.72
<0.0001
Capacity class (cropland)
2.02
2,428.31
<0.0001
Global test
9.05
16,604.58
<0.0001
From LUC2000 for Transitions into Exurban High by Capacity Class (RefLevel: Exurban
Low)
Capacity class (timber)
2.22
909.1
<0.0001
Capacity class (grazing)
1.96
4,938
<0.0001
Capacity class (pasture)
2.03
99.87
<0.0001
Capacity class (cropland)
2.04
195.63
<0.0001
Global test
12.24
7,392.31
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-21 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-5. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Suburban by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
1.93
168.63
<0.0001
Capacity class (timber)
2.21
126.68
<0.0001
Capacity class (grazing)
1.98
112.65
<0.0001
Capacity class (pasture)
2.14
136.16
<0.0001
Capacity class (cropland)
2.3
299.09
<0.0001
Capacity class (exurban low)
2.14
324.83
<0.0001
Global test
18.7
14,336.52
<0.0001
From LUC2000 for Transitions into Urban Low by Ca
jacity Class (RefLevel: Suburban)
Capacity class (wetlands)
1.88
47.52
<0.0001
Capacity class (timber)
1.97
36.46
<0.0001
Capacity class (grazing)
1.95
108.77
<0.0001
Capacity class (pasture)
2.21
75.13
<0.0001
Capacity class (cropland)
2.29
77.92
<0.0001
Capacity class (exurban low)
2.1
348.34
<0.0001
Capacity class (exurban high)
1.99
741.13
<0.0001
Global test
21.4
8,664.14
<0.0001
From LUC2000 for Transitions into Urban High by Capacity Class (RefLevel: Urban
Low)
Capacity class (wetlands)
1.95
0
1
Capacity class (grazing)
2.15
8.44
0.0172
Capacity class (pasture)
1.50
3.13
0.1373
Capacity class (cropland)
1.79
5.13
0.0629
Capacity class (exurban low)
2.00
16.35
0.0003
Capacity class (exurban high)
2.05
20.07
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-22 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-5. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
Capacity class (suburban)
1.94
104.79
<0.0001
Global test
20.37
1,324.68
<0.0001
From LUC2000 for Transitions into Commercial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
2.06
90.98
<0.0001
Capacity class (suburban)
1.76
36.55
<0.0001
Capacity class (urban low)
1.73
12.29
0.0015
Capacity class (urban high)
1.46
2.26
0.2156
Global test
11.01
12,256.77
<0.0001
From LUC2000 for Transitions into Industrial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
2.01
5.08
0.0799
Global test
3.01
278.67
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-23 DRAFT—DO NOT CITE OR QUOTE
-------
R*q or.&) Combined MocbIc
"franclttonG from _U C2DDQ 1o E.*. urban Law
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TTz/'s document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-24 DRAFT—DO NOT CITE OR QUOTE
-------
Region 5 / Combined Models
Transitions from LUC2000 to Industrial
.O
£
Q.
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Capacity Class
Figure B-5. Predicted transition probabilities by capacity class from LUCs
in 2000 to LUCs in 2010 in Integrated Climate and Land Use Scenarios
(ICLUS) Region 5 (Great Lakes). Each panel shows transitions into a particular
LUC in 2010. These combined probabilities are the product of corresponding
marginal and conditional models, i.e., for a given capacity class the probability of
transitioning from LUCi into LUCj is P(LUCij) = P(LUCj) X P(LUCi|j).
This document is a draft for re\'iew purposes only and does not constitute Agency policy.
3/7/16 B-25 DRAFT—DO NOT CITE OR QUOTE
-------
B.6. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 6
(SOUTHEAST) TRANSITION PROBABILITY MODELS
Table B-6. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010,/>(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a particular
LUC in 2010,/?(LUCi[j), by capacity class. RefLevel is the reference level land
use from which transitions are calculated.
Smoothing Terms
edf
x2
P
For Transitions into LUC2010 by Capacity Class (RefLevel: Exurban Low)
Capacity class (exurban high)
1.8
19,946.39
<0.0001
Capacity class (suburban)
1.63
40,462.82
<0.0001
Capacity class (urban low)
1.64
64,034.18
<0.0001
Capacity class (urban high)
1.78
27,887.66
<0.0001
Capacity class (commercial)
1.83
61,257.16
<0.0001
Capacity class (industrial)
1.93
11,358.44
<0.0001
Global test
16.62
8,232,880
<0.0001
From LUC2000 for Transitions into Exurban Low by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
2.01
102.93
<0.0001
Capacity class (pasture)
2.02
2,185.1
<0.0001
Capacity class (cropland)
2.03
995.05
<0.0001
Global test
9.06
183,239.3
<0.0001
From LUC2000 for Transitions into Exurban High by Capacity Class (RefLevel: Exurban
Low)
Capacity class (timber)
2.02
325.96
<0.0001
Capacity class (grazing)
1.95
35,257.99
<0.0001
Capacity class (pasture)
2.04
676.23
<0.0001
Capacity class (cropland)
2.05
1,016.18
<0.0001
Global test
12.06
51,509.93
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-26 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-6. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Suburban by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
1.99
105.92
<0.0001
Capacity class (timber)
2.1
118.7
<0.0001
Capacity class (grazing)
1.96
3,812.02
<0.0001
Capacity class (pasture)
2.11
821.79
<0.0001
Capacity class (cropland)
2.17
931.83
<0.0001
Capacity class (exurban low)
1.99
4,745.53
<0.0001
Global test
18.32
94,724.94
<0.0001
From LUC2000 for Transitions into Urban Low by Capacity Class (RefLevel: Suburban)
Capacity class (wetlands)
1.97
151.68
<0.0001
Capacity class (timber)
1.99
202.48
<0.0001
Capacity class (grazing)
2.00
1,279.8
<0.0001
Capacity class (pasture)
2.13
1,002.82
<0.0001
Capacity class (cropland)
2.21
756.81
<0.0001
Capacity class (exurban low)
2.07
6,374.45
<0.0001
Capacity class (exurban high)
1.97
7,737.2
<0.0001
Global test
21.34
56,526.73
<0.0001
From LUC2000 for Transitions into Urban High by Capacity Class (RefLevel: Urban
Low)
Capacity class (wetlands)
1.58
14.89
0.0003
Capacity class (timber)
1.29
8.3
0.0064
Capacity class (grazing)
2.01
219.79
<0.0001
Capacity class (pasture)
2.06
37.33
<0.0001
Capacity class (cropland)
1.87
36.2
<0.0001
Capacity class (exurban low)
1.99
342.86
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-27 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-6. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class. RefLevel is the reference
level land use from which transitions are calculated, (continued)
Smoothing Terms
edf
x2
P
Capacity class (exurban high)
2.01
692.26
<0.0001
Capacity class (suburban)
2.00
357.40
<0.0001
Global test
22.81
3,211.57
<0.0001
From LUC2000 for Transitions into Commercial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
1.98
241.71
<0.0001
Capacity class (suburban)
1.90
2,772.21
<0.0001
Capacity class (urban low)
1.95
2,106.21
<0.0001
Capacity class (urban high)
1.85
257.98
<0.0001
Global test
11.68
34,302
<0.0001
From LUC2000 for Transitions into Industrial by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
0.78
8.62
0.0022
Capacity class (grazing)
2.39
252.5
<0.0001
Capacity class (pasture)
1.89
59.64
<0.0001
Capacity class (cropland)
1.44
12.58
0.0008
Capacity class (exurban low)
2.11
129.01
<0.0001
Global test
13.61
2,721.24
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-28 DRAFT—DO NOT CITE OR QUOTE
-------
RfrQkm ft .< Comlslr&d Mode>lc
Traitrtorc from LUC2DD"] in E .(.urban Law
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This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-29 DRAFT—DO NOT CITE OR QUOTE
-------
Region 6 / Combined Models
Transitions from LUC2000 to Industrial
.O
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Exurban Lew
Exurban High
Capacity Class
Figure B-6. Predicted transition probabilities by capacity class from LUCs
in 2000 to LUCs in 2010 in Integrated Climate and Land Use Scenarios
(ICLUS) Region 6 (Southeast). Each panel shows transitions into a particular
LUC in 2010. These combined probabilities are the product of corresponding
marginal and conditional models, i.e., for a given capacity class the probability of
transitioning from LUCi into LUCj is P(LUCij) = P(LUCj) X P(LUCi|j).
This document is a draft for re\'iew purposes only and does not constitute Agency policy.
3/7/16 B-30 DRAFT—DO NOT CITE OR QUOTE
-------
B.7. INTEGRATED CLIMATE AND LAND USE SCENARIOS (ICLUS) REGION 7
(NORTHEAST) TRANSITION PROBABILITY MODELS
Table B-7. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010,/>(LUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a particular
LUC in 2010,/?(LUCi[j), by capacity class.
Smoothing Terms
edf
x2
P
For Transitions into LUC2010 by Capacity Class (RefLevel: Exurban Low)
Capacity class (exurban high)
1.72
22,867.66
<0.0001
Capacity class (suburban)
1.61
5,812.7
<0.0001
Capacity class (urban low)
1.62
5,180.12
<0.0001
Capacity class (urban high)
1.63
1,221.28
<0.0001
Capacity class (commercial)
1.88
12,214.7
<0.0001
Capacity class (industrial)
1.93
1,653.35
<0.0001
Global test
16.39
2,190,093
<0.0001
From LUC2000 for Transitions into Exurban Low by Capacity Class (RefLevel: Grazing)
Capacity class (timber)
2.1
68.99
<0.0001
Capacity class (pasture)
2.01
13,104.18
<0.0001
Capacity class (cropland)
2.07
4,203.43
<0.0001
Global test
9.17
72,913.49
<0.0001
From LUC2000 for Transitions into Exurban High by Capacity Class (RefLevel: Exurban
Low)
Capacity class (timber)
2.01
327.83
<0.0001
Capacity class (grazing)
1.99
16,657.91
<0.0001
Capacity class (pasture)
2.04
263.96
<0.0001
Capacity class (cropland)
2.16
1,047.29
<0.0001
Global test
12.2
31,983.46
<0.0001
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 B-31 DRAFT—DO NOT CITE OR QUOTE
-------
Table B-7. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class, (continued)
Smoothing Terms
edf
x2
P
From LUC2000 for Transitions into Suburban by Capacity Class (RefLevel: Exurban
High)
Capacity class (wetlands)
1.98
600.77
<0.0001
Capacity class (timber)
1.95
51.98
<0.0001
Capacity class (grazing)
1.94
604.6
<0.0001
Capacity class (pasture)
2.07
32.79
1.00 x 10-4
Capacity class (cropland)
2.2
225.2
<0.0001
Capacity class (exurban low)
2
205.86
<0.0001
Global test
18.14
18,896.3
<0.0001
From LUC2000 for Transitions into Urban Low by Ca
jacity Class (RefLevel: Suburban)
Capacity class (wetlands)
1.83
14.17
0.0007
Capacity class (timber)
1.76
8.68
0.0099
Capacity class (grazing)
1.93
103.01
<0.0001
Capacity class (pasture)
2.19
47.36
<0.0001
Capacity class (cropland)
2.15
115.43
<0.0001
Capacity class (exurban low)
2.04
173.29
<0.0001
Capacity class (exurban high)
1.97
713.07
<0.0001
Global test
20.88
9,717.48
<0.0001
From LUC2000 for Transitions into Urban High by Capacity Class (RefLevel: Urban
Low)
Capacity class (wetlands)
0.97
3.08
0.0757
Capacity class (timber)
1.2
1.35
0.3004
Capacity class (grazing)
1.88
32.85
<0.0001
Capacity class (pasture)
1.55
3.21
0.1377
Capacity class (cropland)
1.21
16.2
0.0001
Capacity class (exurban low)
1.9
33.15
<0.0001
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Table B-7. Estimated nonlinear degrees of freedom (edf) and significance of
the smoothing terms for the multinomial GAMs. The top marginal model
predicts the probability of transitioning into each LUC in 2010, /XLUCj), by
capacity class, while the bottom conditional models predict the probability of
transitioning from each LUC in 2000 given that they transitioned into a
particular LUC in 2010, /;(LUCi j), by capacity class, (continued)
Smoothing Terms
edf
x2
P
Capacity class (exurban high)
2.05
44.44
<0.0001
Capacity class (suburban)
1.98
73.51
<0.0001
Global test
20.74
1,673.8
<0.0001
From LUC2000 for Transitions into Commercial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
1.97
576.07
<0.0001
Capacity class (suburban)
1.82
80.49
<0.0001
Capacity class (urban low)
1.78
15.94
0.0003
Capacity class (urban high)
1.68
9.73
0.0052
Global test
11.25
11,010.72
<0.0001
From LUC2000 for Transitions into Industrial by Capacity Class (RefLevel: Exurban
High)
Capacity class (exurban low)
2.13
330.36
<0.0001
Global test
3.13
469.32
<0.0001
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3/7/16 B-33 DRAFT—DO NOT CITE OR QUOTE
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Reg Ion 7J Combined Uodtlc.
Tranertionc from LUC2DD0 1a Ejuirban Law
Region 7«' Combined Models
Trancrttont from LUC2DD0 to Ex urban High
o
<0
o
©
O
O
I
6
Class
TT?/^ document is a draft for review purposes only and does not constitute Agency policy.
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Region 71 Combined Models
Transitions from LUC2000 to Industrial
.O
£
Q.
c
O
c
,f5
CO
O
(O
o
^r
o
<\l
O
Exurban High
Capacity Class
Figure B-7. Predicted transition probabilities by capacity class from LUCs
in 2000 to LUCs in 2010 in Integrated Climate and Land Use Scenarios
(ICLUS) Region 7 (Northeast). Each panel shows transitions into a particular
LUC in 2010. These combined probabilities are the product of corresponding
marginal and conditional models, i.e., for a given capacity class the probability of
transitioning from LUCi into LUCj is P(LUCij) = P(LUCj) X P(LUCi|j).
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APPENDIX C. LAND USE CLASS (LUC) AND CAPACITY DEMAND MODELS
Table C-l. Generalized additive model (GAM) model output relating
natural log (In) transformed LUC density and capacity to In transformed
population density. Output includes an estimate of the intercept, estimated
degrees of freedom (edf) for the smoothing term, the adjusted R2 associated with
the model, the standard error (SE) associated with the estimate of the intercept and
T and F statistics associated with the significance of the intercept and smoothing
terms, respectively.
GAM Relating ln(Exurban Low Pixel Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
0.744
0.027
22.77
<0.0001
Smoothing Terms
edf
F
P
Population density
5.899
773.2
<0.0001
Adjusted R2
0.550
GAM Relating ln(Exurban High Pixel Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
0.455
0.011
41.63
<0.0001
Smoothing Terms
edf
F
P
Population density
8.177
2,214
<0.0001
Adjusted R2
0.812
GAM Relating ln(Suburban Pixel Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
-0.745
0.007
-100.1
<0.0001
Smoothing Terms
edf
F
p
Population density
7.021
4,453
<0.0001
Adjusted R2
0.889
GAM Relating ln(Urban Low Pixel Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
-1.365
0.010
-132.1
<0.0001
Smoothing Terms
edf
F
P
Population density
7.005
2,218
<0.0001
Adjusted R2
0.800
This document is a draft for review purposes only and does not constitute Agency policy.
3/7/16 C-l DRAFT—DO NOT CITE OR QUOTE
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Table C-l. Generalized additive model (GAM) model output relating
natural log (In) transformed LUC density and capacity to In transformed
population density. Output includes an estimate of the intercept, estimated
degrees of freedom (edf) for the smoothing term, the adjusted R2 associated with
the model, the standard error (SE) associated with the estimate of the intercept
and T and F statistics associated with the significance of the intercept and
smoothing terms, respectively, (continued)
GAM Relating ln(Urban High Pixel Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
-5.559
0.013
-413.4
<0.0001
Smoothing Terms
edf
F
P
Population density
6.730
1,819
<0.0001
Adjusted R2
0.761
GAM Relating ln(Commercial Pixel Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
-2.540
0.015
-175.1
<0.0001
Smoothing Terms
edf
F
P
Population density
5.479
1,675
<0.0001
Adjusted R2
0.713
GAM Relating ln(Industrial Pixel Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
-23.182
0.018
-177.0
<0.0001
Smoothing Terms
edf
F
P
Population density
6.056
1,051
<0.0001
Adjusted R2
0.629
GAM Relating ln(Capacity Density) to ln(Population Density)
Parametric Terms
Estimate
SE
T
p
Intercept
9.505
0.004
2,292
<0.0001
Smoothing Terms
edf
F
P
Population density
7.939
1,109
<0.0001
Adjusted R2
0.682
This document is a draft for review purposes only and does not constitute Agency policy.
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