Mid-Atlantic Regional Assessment (MARA)
Draft Preliminary Report
on Impacts from Climate Change
April 1999
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Mid-Atlantic Regional Assessment (MARA)
Draft Preliminary Report
on Impacts from Climate Change
April 1999
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draft
Mid-Atlantic Regional Assessment (MARA)
Draft Preliminary Report
on Impacts from Climate Change
April 1999
(Short Title: MARA Draft Preliminary Report)
Compiled by Penn State's core MARA Team:
Ann Fisher
David Abler
Eric Barren
Richard Bord
Robert Crane
David De Walle
William Easterling
Greg Knight
Ray Najjar
Egide Nizeyimana
Robert O'Connor
Adam Rose
James Shortle
Brent Yaraal
and the research associates, graduate and undergraduate research assistants, and external
collaborators listed in Appendix A
Sponsored by the US Environmental Protection Agency, Office of Research and
Development, Global Change Research Program (Cooperative Agreement No. CR
826554-01)
and the following Pennsylvania State University units: Earth System Science Center and
Center for Integrated Regional Assessment within the College of Earth and Mineral
Sciences, Department of Agricultural Economics & Rural Sociology and the College of
Agricultural Sciences, and the Environmental Resources Research Institute.
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Table of Contents
[To be written.]
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Preface
The Global Change Research Act of 1990 requires the US Global Change Research
Program (USGCRP) to submit a report to Congress (by January, 2000) on the potential
national impacts of global climate change. Input for that report is being provided by
simultaneous assessments for regions within the United States and for national cross-
cutting issues such as water resources and human health. These assessments are unique
because of their reliance on multi-disciplinary integrated approaches and substantial
stakeholder participation. These challenges are compounded by the uncertainties in
projecting both climate change and how our society will evolve - with or without climate
change.
Aided by financial support from the US Environmental Protection Agency (EPA), Penn
State here provides a draft preliminary report on the progress made toward these
challenges for the Mid-Atlantic region (MAR). The full Mid-Atlantic Regional
Assessment (MARA) team (i.e., the core faculty, research associates and assistants, and
external collaborators, as listed in Appendix A) has accomplished an immense amount of
work through their initial analysis of the region's current stresses, how those stresses
might be affected by climate change, what actions could be taken soon to capitalize on
opportunities or reduce vulnerabilities from climate change, and what information is most
needed to improve the region's decisions related to climate change. Yet much work
remains to be done before the MARA team will be satisfied to produce the Report on the
First Mid-Atlantic Regional Assessment of Impacts from Climate Change. This draft
is being circulated now to get feedback on the work done so far and to provide
preliminary input for the national synthesis.
This draft is truly the product of a team effort - again, see Appendix A. Meetings among
the researchers and with stakeholders often have seemed to sparkle when people from
different disciplines and perspectives suddenly realize how diverse components fit
together into a whole that is greater than the sum of its parts. My thanks to each team
member for his or her contributions to this draft. On behalf of the core faculty, thanks to
the research associates and assistants, and to the external collaborators for expanding our
expertise with their input. On behalf of the full MARA team, thanks to the Advisory
Committee members for their insights and thoughtful responses to our requests for
information. On behalf of all of these groups, I extend sincere appreciation to Ron Smart
for compiling the diverse components into a readable draft preliminary report.
All of us look forward to your feedback, which can be directed to any team member or to
me (e-mail: fisherann@psu.edu; phone: 814-865-3143; fax: 814-865-3746; mail:
PSU/AERS, 107 Armsby Building, University Park, PA 16802).
Ann Fisher
April 6,1999
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Part 1: Introduction
(Fisher)
1.1 National needs
The impacts of climate change will differ across regions, and people will experience
these impacts where they live. Some (but not all) of the processes regulating vulnerability
to climate change operate at local scales and could be missed in aggregate national and
global studies. Recognizing this, the US Global Change Research Program (USGCRP)
has been collaborating with federal agencies (represented in the National Assessment
Working Group, NAWG) to sponsor 18 regional assessments. These organizations also
are sponsoring 6 nation-wide assessments of the following "sectors": coastlines, fresh
water, agriculture, forests, health, and Native Peoples. The over-arching goal is to
provide scientific information useful to society by identifying how people and their
surroundings will be affected by climate change, how individuals and communities can
take advantage of opportunities and reduce vulnerabilities resulting from climate change,
and what additional information and research are needed to improve decisions related to
impacts from climate change. These assessments will allow the interdisciplinary
National Assessment Synthesis Team (NAST) (whose members represent academia,
government, and business) to convey important differences across regions and sectors in
its synthesis report. The NAST report is due to Congress by January 2000. Appendix D
has additional information about the national assessment process.
1.2 Regional assessment approach
An interdisciplinary Pennsylvania State University (Perm State) team is leading the first
Mid-Atlantic Regional Assessment (MARA) of Climate Change Impacts. Appendix A
lists MARA's many partners and participants. As the report cover shows, the Mid-
Atlantic region (MAR) includes all or parts of eight states (NY, NJ, PA, DE, MD, WV,
VA, and NC) and the District of Columbia. Four questions guide MARA:
1. What are the region's current stresses and issues?
2. How would climate change and variability affect these stressors, or create new
ones?
3. What actions would increase the region's resiliency to climate variability,
reducing negative impacts and taking advantage of opportunities created by
climate change?
4. What new information is needed to better answer questions 1) and 2) and to
evaluate adaptation options?
Perm State's approach is based on a framework developed by its Center for Integrated
Regional Assessment (CIRA) (Knight et al., 1999), and models such as the one they
developed for the Susquehanna River Basin assessment. NAST and NAWG have
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recommended specific global climate models (GCMs) and socioeconomic projections, to
enable aggregation across the assessments. In addition, Perm State is using its own
empirical downscaling and nested GCM/mesoscale models, which provide finer
resolution as appropriate for regional assessment. The CIRA framework accommodates
an iterative approach, with increasingly complex quantitative analysis for important
components. Appendix B includes more information on the assessment approaches used
for MARA.
1.3 Regional assessment process
Perm State's early steps included a September 9-11,1997 Mid-Atlantic Workshop
focusing on the watersheds for the Chesapeake and Delaware Bays. The 92 participants,
representing federal, state and local government, industry, academia, and public interest
groups, reported learning more about climate change and its potential for regional impacts.
They were enthusiastic about education and information dissemination, especially for
reducing uncertainties about climate variability - at scales fine enough to help water
managers and farmers with their planning. They expressed strong concerns about
potential impacts from sea-level rise on ecosystems and recreation, and about human
health impacts. For more about this workshop, see http://www.essc.psu.edu/ccimar/ or
Fisher etal. 1999.
A June 8-9,1998 researchers' meeting explored questions raised during the September
1997 workshop and identified available data bases and current research useful for
MARA. This open process showed the need to address five topics being emphasized in
the national synthesis—forests, agriculture, water, coasts, and human health - as well as
cross-cutting issues such as ecosystems. MARA's working groups for these topics
include about 20 collaborators from other research organizations.
To maximize the assessment's usefulness, the MARA team has been relying on frequent
input from the MARA Advisory Committee. This diverse group represents potentially
interested or affected parties, i.e., stakeholders. Its 85 members, listed in Appendix A,
include 9 researchers, 20 public interest group representatives, 24 from industry and 32
from local, state, and federal government. At meetings, by mail, phone, fax, and e-mail,
they are providing feedback to:
1) indicate what information stakeholders need to make more informed decisions related
to regional impacts of climate change,
2) ensure the assessment is responsive to climate-related concerns most important to the
people who live and work in the region,
3) identify relevant data not otherwise available to the assessment team, and
4) help prioritize options for building resilience and flexibility within the region, based
on their knowledge of regional and local social, cultural, and political institutions.
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1.4 Reporting on MARA
Reporting on MARA can provide an overview of baseline conditions and how human and
natural systems might be affected by climate change, using an integrated assessment
framework and in-depth case studies to illustrate important impacts. The report brings
together information about diverse beneficial and detrimental impacts into a picture of the
effects on the region as a whole.
Because MARA is an interactive, iterative process, this Draft Preliminary Report is being
circulated in April 1999 even though much assessment work is not yet complete. One
reason for the timing of this draft is to help NAST with its strict schedule. The other
reason is to get early stakeholder feedback on preliminary findings. At a May 2-3,1999
meeting, the Advisory Committee will review the draft assessment results and
recommend strategies to display and disseminate the findings. Based on additional
assessment and Advisory Committee feedback, this draft and its supporting documents
will be revised and circulated for additional review (Summer, 1999). A revised Final
Preliminary Report is planned for distribution by the end of 1999. Concise versions of
the report's main sections will be submitted for a special issue of the journal, Climate
Research.
Assessment work will continue during the coming year. (Periodic updates will be posted
at http://lumen.deasy.psu.edu/mara/.) Additional assessment results will be combined
with the results here into a Report on the First Mid-Atlantic Regional Assessment of
Impacts from Climate Change. Throughout the assessment, Team members will present
results at professional meetings for peer review. The final products will serve as a
baseline for future assessments, expected to be conducted on a 4-5 year cycle.
During the coming year strategies for communication and outreach will be emphasized,
especially strategies that establish new and enhance existing linkages for decision making
within and among communities and organizations across the region. MARA and its
interactive, substantive stakeholder participation process can be a key to helping
communities move toward sustainability. Appendix C describes this important
component of MARA.
1.5 Guide to this report
Part 2 describes the Mid-Atlantic region's physical and economic features, and the
region's historical climate. This serves as a baseline for "what-if' scenarios of the
region's future, in terms of both socio-demographics and climate. Note that time and
budget constraints precluded printing in color for this draft. Color versions of several
maps and figures are available on the MARA web site; throughout the draft, the specific
address is given with the version printed here.
Part 3 summarizes the consequences, challenges, and opportunities facing the region. To
make it manageable, Part 3 is segmented into discussions for agriculture, forestry, water,
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coasts, ecosystems, and human health. Then the economic impacts are summarized
across these topics.
Part 4 summarizes the key findings from Part 3, and available strategies for adapting to
both opportunities and challenges posed by climate change. Part 4 also discusses how to
make sure the assessment results get used now and updated for future use. The
continuing process requires maintaining and enhancing mechanisms for public
involvement as well as setting priorities for research and information needs.
The Appendices contain additional information about the regional and national
assessment process and its participants. They also explain more about the assessment
data, methods and analysis, and how uncertainty is factored into the results. Appendix E
includes a glossary of terms and identifies acronyms, so that the reader does not have to
search for a definition. Perhaps most important, Appendix C summarizes the broader
stakeholder engagement process, including initial plans for the coming year.
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Part 2. The Mid-Atlantic Region: Past, Present and Future
2.1 Past and Present (Polsky)
2.1.1 Introduction
An assessment of likely regional impacts from climate change begins with the baseline
setting, including the region's climate and natural and human environments. For the
Mid-Atlantic Region (hereafter MAR; Figure 2.1.1), historical climate is outlined by
Yarnal in section 2.3. The region's natural and human environments and their evolutions
over the past three decades are summarized here, providing context for the sector-specific
analyses that follow.
The MAR as defined for this assessment includes all of five states (Delaware, Maryland,
Pennsylvania, Virginia, West Virginia) parts of three states (south-central New York,
western and southern New Jersey, northeastern North Carolina), and the District of
Columbia. The region contains 358 counties intersecting four principal physiographic
regions (Figure 2.1.1 and Appendix E), or areas of similar landforms (Tarbuck and
Lutgens, 1996; Miller, 1995). Table 2.1.1 shows the size of these regions relative to the
greater MAR. In all, the MAR covers about 5 percent of the land area in the 48
contiguous United States (US Bureau of Census, 1997).
Sub Regions
Plateau
Ridge and Valley
Piedmont
Coastal
Figure 2.1.1. Mid-Atlantic Region Counties and Physiographic Regions
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Table 2.1.1. The Mid-Atlantic Region at a Glance
Coastal Plain
Piedmont
Ridge & Valley
Appalachian Plateau
Mid-Atlantic
Region
Counties
98
76
75
109
358
Surface Area
(square miles)
31,005
32,851
29,857
69,237
162,950
(NPA, 1998;USBOC, 1998a)
2.1.2 The Natural Environment
The Physiographic Regions
The relatively flat coastal plain is composed mostly of sedimentary rock and extends
inland from the oceans and estuaries. This zone traverses all of Delaware and parts of
New Jersey, Maryland, Virginia and North Carolina. The piedmont plateau is the
foothills region covering the eastern, lower portion of the Appalachian Mountain range.
The piedmont plateau, composed mostly of metamorphic and igneous rock, covers north-
central New Jersey, southeastern Pennsylvania and in the central portions of Virginia,
Maryland and North Carolina.
The MAR ridge and valley zone has folded terrain with a series of parallel, eroded
mountains. This strip of land extends from the northwest corner of New Jersey to the
southwest, passing through Pennsylvania, Maryland and Virginia. A notable part of this
zone is the so-called Blue Ridge region. These extended, thin ridges of the Appalachian
Mountains - including the highest points of that range - are found primarily in Virginia
and Maryland, with smaller representations in Pennsylvania and New Jersey. The
Appalachian plateau is a swath of land from the New York portion of the MAR through
northern and western Pennsylvania, the western edge of Maryland and most of West
Virginia. This region is composed mostly of relatively flat sedimentary rock, dissected in
many places by meandering waterways (Tarbuck and Lutgens, 1996; Miller, 1995).
Land Cover
The MAR is largely covered by forest and agriculture (US EPA, 1997). As shown in
Table 2.1.2, these two categories account for about 90 percent of land cover in the MAR
(65 percent forest, 25 percent agricultural). The highest concentrations of forest area are
in and around West Virginia and north-central Pennsylvania; agriculture is the
predominant land use in the lowlands to the east.
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Table 2.1.2. Land Use in the Mid-Atlantic Region, 1992
T j TT /-. * Percentage of
Land Use Category _, . . T j .
& J Total Land Area
Forest 64.5
Agriculture 25.0
Wetlands 4.1
Commercial, Industrial, and Residential 3.6
Open Water 1.6
All Other Land Uses L2
All MAR watersheds have at least half of their total stream length in forest cover,
providing some buffering against agricultural runoff and other pollution to water sources
(US EPA, 1997). However, all MAR watersheds also have some agriculture operations
and many have roads located near the streams, indicating a potential for considerable, if
diffuse, risk of water pollution (US EPA, 1997). For instance, two-thirds of the
Chesapeake Bay's nutrient loading comes from upstream sources, and the same
proportion of its sediment loading comes from non-point sources. Pollution in this water
body is long-lived: the Chesapeake Bay flushing rate, at about 350 days, is one of the
slowest rates for a water body in the United States (NOAA, 1998b). The Chesapeake
Bay and Albermarle/Pamlico Sound are the nation's two largest estuaries - or zones of
mixing between fresh and ocean waters - and are thus of particular ecological and
economic importance both to the MAR and the US (NOAA, 1998b). The chapter on
ecosystem impacts contains more information about the plants and animals in the MAR's
natural environment.
2.1.3 The Human Environment
The People
The natural environment must be viewed alongside the MAR's human population to
define the relative sensitivities of the region to climate change. From a demographic
perspective, the Mid-Atlantic Region has a growing and aging population. In economic
terms, the region has grown wealthier over the past few decades, both on regional
aggregate and per capita bases. The MAR economy is diversified and well connected to
the rest of the country and the international marketplace. Of all the sectors and
geographic locations, the Coastal Region may in the medium- and long-run prove to be
the area most sensitive to the impacts of climate change and most vulnerable to these
changes. In contrast, for a relatively small economic sector such as agriculture, whatever
threat climate change poses to agricultural employment and production, these impacts
should not profoundly affect the region as a whole.
Tables 2.1.3 and 2.1.4 and the subsequent sections in this document expand upon the
general trends noted above. Approximately 35.2 million people live in the MAR (1995
estimate; Table 2.1.3), representing close to 15 percent of the entire US population (NPA,
1998; US Bureau of the Census, 1998c). Nearly ninety percent of the MAR population is
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under the age of 65. The western half of the region houses about one-third of the MAR
working-age population, and one-half of the farm employment. At least 60 percent of all
MAR population, income and jobs are concentrated in the coastal plain and piedmont,
where the urban agglomerations are found. These concentrations translate into a
markedly higher per capita income for these two sub-regions compared to the Ridge &
Valley and Appalachian Plateau.
While large cities do not cover much of the MAR surface area, collectively they
constitute one of the more important population concentrations in the country. Of the
MAR urban areas, Philadelphia, Pittsburgh, Baltimore, Washington, D.C., Richmond and
Norfolk are among the largest cities in the US (US Bureau of the Census, 1998a). These
cities have accounted for about one-half of the total MAR population since 1980 (data for
earlier years unavailable) (NPA, 1998).
Table 2.1.3. Geographic Distribution of Key Socio-Economic Variables
for the Mid-Atlantic Region: 1995 Percent and Value
Population
(million people)
Age 0-1 9
Age 20-64
Age 65+
Income
(billion 1992$)
Income per Capita
(1992$)
Total Employment
(million jobs)
Total Farm
Employment
(thousand jobs)
Coastal Plain
36% 12.7
37% 3.5
37% 7.6
33% 1.6
38% $301
$23,747
38% 7.5
19% 48
Piedmont
29% 10.1
28% 2.7
29% 6,1
27% 1.3
22% $255
$25,272
30% 5.9
30% 77
Ridge &
Valley
10% 3.5
9% 0.9
10% 2.0
10% 0.5
8% $64
$18,277
10% 1.9
21% 53
Appalachian
Plateau
25% 8.9
25% 2.4
25% 5.1
29% 1.4
22% $171
$19,160
23% 4.5
30% 76
Totals
100% 35.2
100% 9.5
100% 20.8
100% 4.8
100% $791
$22,479
100% 19,7
100% ' 254
(NPA, 1998) ''..,.
The MAR population increased by approximately 20 percent during the past three
decades, about 0.7 percent per year on average (Table 2.1.4). This trend resembles that
for the nation as a whole, which grew by 33 percent at a rate of 1 percent per year (US
Bureau of the Census, 1998c). The MAR has experienced a steady increase (1.2 percent
per year) of working-age residents since the late-1960s, and a steady decrease (about 0.6
percent per year) of people under the age of twenty; in recent years this latter trend has
reversed. In contrast, the elderly population in the MAR grew by about 70 percent during
the same period.
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Total regional personal income more than doubled over this period, and per capita
income grew by 82 percent (Table 2.1.4). Income in the services sector recorded the
largest growth over the period, over 300 percent. The sectors of services, government
and manufacturing presently account for nearly one-half of all regional income (NPA,
1998). Total employment has increased by more than half since 1967, fueled largely by
non-farm industries such as services, which more than doubled over the period. By
contrast, farm-related employment declined by almost one-half. As such, agriculture in
the MAR is becoming progressively less important over time from an economic
perspective, reflecting the broader national trend (Shane, Roe and Gopinath, 1998).
Table 2.1.4. Changes in Key Socio-Economic Variables
for the Mid-Atlantic Region: 1967-1995
Total Population
Age 0-19
Age 20-64
Age 65+
Income
Income per Capita
Total Employment
Total Farm
Employment
Total Change
22.5%
-13.4%
39.1%
79.1%
129.9%
92.3%
65.0%
-43.5%
Average Annual Change
0.7%
-0.5%
1.1%
2.0%
2.9%
2.3%
1.8%
-1.9%
(NPA, 1998)
The Coming Decades
There are many ways to project future values of these economic and demographic
variables using the historical trends noted above. The resulting projections can differ
substantially depending on the projection methodology employed. One set of projections
for three key variables (population, income, employment) to the year 2025 is presented in
Figure 2.1.2. This figure reflects a 'baseline' scenario (i.e., where current trends are
assumed to persist), and 'low' and 'high' scenarios (i.e., where current trends diminish
and increase, respectively, in the coming decades). These projections are driven
primarily by assumptions regarding the net effect of birth, death, immigration and
internal (US) migration rates, per capita income, and overall economic activity. For more
details on the underlying assumptions, see Section 2.2.2 and NPA (1998).
The MAR Economy in Detail (Rose)
MAR's employment of nearly 20 million in 1995 represented 13% of the US total, and its
income of $915 billion in that year represented 15% of the US total. Another measure of
economic activity is gross output (sales revenue), which includes both intermediate goods
(goods used to produce other goods and services) and final (consumer) goods, amounting
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to nearly $1.7 trillion. Of this, $547 billion, or 32.7% of gross output, was exported (see
Table 4). Imports of $597 billion comprised 35.7% of inputs to the regional economy as
well. Thus the MAR is not self sufficient, but it has a reasonable trade balance. Still, the
sizeable export and import flows do have broader ramifications. They mean that major
impacts of climate change in MAR will affect other parts of the US economy; even if the
MAR region has only minimal direct effects, its economy could be affected significantly
by economic changes elsewhere in the US and the world. These conditions would affect
the cost of imports to the MAR, as well as the demand and price for its exports.
MAR Employment: 1967 - 2025
1967 1971 1975 1979 1983 1967 1991 1995 1999 2003 2007 2011 2015 2019 2023
Year
Figure 2.1.2a. Employment projection.
MAR Income: 1967-2025
1967 1971 1975 1979 1983 I9B7 1991 1995 1999 2003 2X7 2011 2015 2019 2023
Year
Figure 2.1.2b. Income projection.
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MAR Population: 1967 - 2025
0)
a.
I 35
i
1967 1971 1975 1979 1963 1967 1991 1995 1999 2C03 2007 2011 2015 2019 2023
Year
Figure 2.1.2c. Population projection.
Table 2.1.5. Structure of the Mid-Atlantic Region Economy, 1995 (billions of dollars)
Agriculture
Forestry
Mining
Construction
Manufacturing
Food/Textiles
Wood/Paper
Chemical/Materials
Primary/Fabricated Metals
Equipment
Other Manufacturing
Transport & Communication
Utilities
Trade
Services
Health & Education
Government Enterprises
Total
13
5
14
105
444
97
64
114
55
105
9
86
46
201
440
142
174
1671
(Data from IMPLAN, 1998)
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Table 2.1.6. Sectoral Production in the Mid-Atlantic Region, 1995 (billions of dollars)
Coastal Plain
Total Employment
Income
Total Gross Output
Structure of Output
Agriculture
Forestry
Mining
Construction
Manufacturing
Food/Textiles
Wood/Paper
Chemical/Materials
Primary/Fabricated Metals
Equipment
Other Manufacturing
Transport & Communication
Utilities
Trade
Services
Health & Education
Government Enterprises
Trade Balance
Commodity Exports
Commodity Imports
6.8
359.1
610.5
4.3
1.7
1.2
32.5
134.3
31.5
20.9
44.7
8.0
26.9
2.2
31.8
13.4
71.0
171.5
49.8
98.8
172.3
210.2
Piedmont
5.8
274.6
511.1
3.8
1.5
0.9
34.5
157.7
43.6
21.0
34.7
17.7
39.1
1.6
23.8
12.3
61.1
139.7
39.1
37.0
174.5
184.9
Ridge &
Valley
2.8
97.1
189.5
2.4
0.6
2.0
15.0
48.5
12.3
9.7
7.9
5.0
12.8
0.8
12.3
4.7
23.1
50.9
18.0
12.1
79.8
64.6
Plateau
4.5
184.4
360.0
2.9
0.8
10.2
22.8
104.0
9.5
12.5
27.1
24.3
26.3
4.2
18.4
15.7
45.7
78.1
35.4
25.8
120.6
137.0
Total
19.8
915.2
1671.1
13.4
4.7
14.3
104.8
444.4
96.9
64.1
114.4
55.1
105.2
8.8
86.3
46.1
200.9
440.3
142.3
173.6
547.3
596.8
(MPLAN, 1998)
Table 2.1.5 presents a structural disaggregation of the MAR economy. On a direct output
basis, the primary sectors of Agriculture, Forestry, and Mining together comprise only
$32 billion, or about 2% of the Region's economy. On the other hand, Manufacturing
output of $444 billion and Service sector output of $440 billion, comprise 26.6% and
26.3%, respectively, of the economy. On the surface it would appear that the MAR
economy is not very vulnerable to climate change, but this can be misleading. All sectors
of an economy are linked directly and indirectly, and a shock to any one of them will
ripple through the economy so that the total effect of climate change would be some
multiple of the original stimulus. These "multiplier" effects would typically be on the
order of 2.0 to 3.0 for a region of the size and structure of the MAR. An example of this
interdependence can be illustrated by referring again to Table 2.1.5. Damage to trees in
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the MAR affects not only the Forestry sector of $5 billion, but also the Wood/Paper
industry of $64 billion. Furthermore, any decrease in output in the latter sector would
touch off a decrease in orders for goods and services to direct and indirect suppliers.
Also, decreased profits and worker layoffs would result in reduced income, which sets off
its own multiplier effects.
The overall impact from a change to one (or several) of these small sectors is likely to
have a modest impact on the overall regional economy. Of course, there still could be
substantial direct impacts on these small sectors themselves, and substantial indirect
impacts in sub-regions where these account for a large share of economic activity. So
distribution impacts may be more pronounced than the impact on the MAR economy as a
whole.
The MAR Physiographic Regional Economies in Detail
The structure of the economies of the four physiographic sub-regions is summarized in
Table 2.1.6. The Ridge and Valley sub-region stands out in terms of its relatively small
size (see rows 1 and 2), but is the only sub-region with a positive trade balance (see the
last 2 rows). A structural comparison of sectors can best be depicted by Figure 2.1.3, in
which each bar represents the proportion of a sub-region's gross output devoted to an
individual sector. Interestingly, there is little variation for most sectors across the sub-
regions. Exceptions on the high side include Food/Textiles in the Piedmont and Metal
Manufacturing in the Ridge and Valley and Plateau sub-regions. These same sub-regions
have relatively less Food/Textiles. Services stand out as dominant. These sub-regions
are highly interconnected, even more so than the MAR is with the rest of the US.
30.0
v
0.
10.0
5.0
0.0
Coastal Plain
Piedmont
Regions
Ridge and Valley
Plateau
BAgriculture 0 Food/Textiles HWood/Paper QChemical/Materials 0Primary/Fabricated Metals SServices
Figure 2.1.3. The Economic Structure of the Mid-Atlantic Region
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2.2 Future Baseline Socioeconomic Scenarios
2.2.1 General Considerations in Constructing Scenarios (Abler and Shortle)
By definition, socioeconomic impact analysis involves comparing "with" and "without"
states of the socioeconomic systems under study. At least three kinds of scenarios are
required for socioeconomic impact assessment of climate change. These are 1) climate
scenarios that describe future climates, 2) baseline scenarios that describe what the
economy and society would be like without climate change, and 3) climate response
scenarios that describe how society might respond to climate change.
Climate Change Scenarios
In climate impact research, the typical approach is to generate climate scenarios based on
assumptions about growth in emissions of greenhouse gases and other driving forces
behind climate change. The climate scenarios then are the basis for analyzing
socioeconomic impacts. This approach is flawed if the economy is important to
greenhouse gas emissions and other drivers behind climate change. Climate and
economic scenarios are necessarily inseparable and dynamic at the global or large-scale
regional level. However, economic activity in a region such as the MAR is likely to have
minimal effects on the global climate, making it more plausible to use separable climate
and socioeconomic scenarios.
Baseline Socioeconomic Scenarios
Because climate change is a long-term phenomenon, potential societal conditions far into
the future must be considered to generate baseline socioeconomic scenarios. Global,
national, and regional economies and societies have changed radically over the last
century, and there is no reason to expect the rapid pace of socioeconomic change to slow
down. Economic growth from the beginning of civilization, and especially since the
industrial revolution, has involved substitution of human capital and physical capital for
natural capital, greatly reducing the degree to which human conditions are affected by
and dependent on the natural environment (Solow, 1992; Ruttan, 1992). Most people live
and work in structures that protect them from the elements, often with sophisticated
climate control systems. Unlike preindustrial times, even in many developing countries
only a small proportion of the population is directly engaged in producing food and fuel.
Substitution of human and physical capital for natural capital (through, e.g.,
mechanization, specialization of production, irrigation systems, pest management
systems, transportation systems to move food and inputs) has tremendously increased the
productivity of agricultural systems in developed countries and many developing
countries (Hayami and Ruttan, 1985). The prevention and treatment of disease has been
revolutionized since the 1850s (Patz et al.). Mining, forestry, agriculture, and
manufacturing were the largest components of the economy at the turn of the century, but
today they are much diminished in importance. Similarly, the economy and society of
the MAR will undoubtedly be substantially different in the future than they are today in
terms of structure, producer and consumer technologies, the range of available goods and
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services, and public and private institutions. This in turn means that the MAR may be
significantly different in terms of its sensitivity to climate change and its potential for
response and adaptation.
Climate Change Response Scenarios
Climate change, as well as expectations of climate change, will stimulate socioeconomic
responses to reduce risks and exploit opportunities. These responses differ from, but
have the potential to shape, final impacts. Studies of the economic impacts of climate
change differ substantially in the assumptions they make about the ability of economic
actors to respond and adapt to a changing climate (Tol and Fankhauser, 1998). Tol et al.
(1998) distinguish among four approaches to characterizing adaptation in the economic
literature: no adaptation; arbitrarily imposed types or levels of adaptation (arbitrary
adaptation); adaptation based on observed responses by economic actors to different
climatic conditions in other regions or at previous points in time (observed adaptation);
and adaptation based on simulation models of the behavior of economic actors (modeled
adaptation). Simulation models typically presume that economic actors make choices so
as to maximize their own objective functions, subject to limits on the physical and
financial resources at their disposal and to constraints imposed by the economic, political,
and natural environment.
The extreme characterization of no adaptation is sometimes referred to as "business as
usual." No adaptation is not a plausible assumption for market goods and services.
Insofar as changes in climate lead to changes in prices of market goods and services, or
prices of inputs into production, producers and consumers will have direct and obvious
incentives to respond. Simulations in Yohe and Schlesinger (1998) suggest that
adaptation can significantly reduce the economic costs of sea level rise along the US
coastline. Studies of agricultural impacts also indicate that adaptation by farmers can
significantly reduce economic costs or increase economic gains (see Tol et al., 1998). On
the other hand, the situation could be much different for nonmarket goods and services,
because there are no direct price signals to guide producers and consumers.
Of the three remaining approaches to characterizing adaptation, modeled adaptation may
be most appropriate for assessing responses to climate change. Modeled adaptation can
be impeded by computational considerations, which often require the aggregation of
goods, services, production inputs, and economic actors into a relatively small number of
categories, and which often necessitate strong simplifying assumptions about the
behavior of economic actors. Nonetheless, unlike arbitrary adaptation, which is typically
confined to a few alternative adaptation possibilities, modeled adaptation can permit a
wide range of types and levels of responses. These can include responses that a modeler
attempting to construct a list of "plausible" alternatives for an arbitrary adaptation
exercise would have never foreseen. Unlike observed adaptation, modeled adaptation in
principle can examine responses under scenarios with future climates, technologies, and
economic and political institutions that have no contemporary or historic analogues.
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Within the category of modeled adaptation, two modeling frameworks may be
distinguished: static and dynamic. By "static," we mean that production technologies for
market and nonmarket goods used within the region, the region's stock of human capital,
its stock of physical capital, its stock of natural capital, and its economic and political
institutions are all assumed to be exogenous. Stocks of physical, human, and natural
capital may change as a direct consequence of climate change (e.g., losses in physical
capital due to hurricanes), but they do not change in response to decisions made by
economic actors within or outside of the region. Technologies, capital stocks, and/or
institutions are endogenous within a dynamic framework, and as such can change in
response to decisions by economic actors.
Socioeconomic Scenario Design
The goal in constructing socioeconomic scenarios should not be to assemble an
exhaustive list of all possible futures, or even "probable" futures. Even if future
scenarios are defined with respect to a small number of variables, and only a few values
for these variables are considered, the number of possible combinations quickly becomes
unmanageable. For example, suppose that socioeconomic futures are defined with
respect to k variables and that a alternative values are considered for each variable. For
instance, when a = 3, one could think in terms of a "high," a "medium," and a "low"
value for each variable. The number of possible combinations of values in this case is ak,
which is large even for moderate values of a and k. For example, if a = 3 and k = 5, the
number of possible combinations is 35 = 243. If k = 10, the number of possible
combinations is 310 = 59,049.
At the same time, the goal in constructing socioeconomic scenarios should not be limited
to making point forecasts of future socioeconomic conditions. Economic and
technological forecasting accuracy diminishes rapidly with forecast length. Point
forecasts of socioeconomic conditions for the year 2030, to say nothing of the year 2100,
would be far more likely to be wrong and misleading than to be useful. In this respect,
economic modeling is well behind climate modeling - though the challenges involved in
long-term economic modeling are arguably much greater than those involved in long-
term climate modeling.
This inability to forecast is more acute at a regional level than at a national level. Many
socioeconomic processes and interrelationships are less stable over time and thus less
predictable at a regional level. This is because large shifts in the production of many
goods and services can occur from one region to another within a country based on
regional differentials in labor costs, government fiscal and regulatory policies, or other
factors. These shifts can lead to significant changes in income, employment, and
socioeconomic structure at the regional level even while these variables are relatively
stable for the country as a whole. For example, population cannot be predictgd_accuratejy
at a regional level, because at this level the key*determinants oi population growth are not
birth and death rates (which can be predicted with some confidence) but rather migration
inflows and outflows (which are essentially impossible to predict on a long-term basis).
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It is more fruitful to construct socioeconomic scenarios with the objectives of identifying
and bounding major potential threats and opportunities, and identifying critical research
and adaptation policy issues. Increased vulnerability clearly emerges in scenarios that
combine greater future baseline socioeconomic or ecosystem sensitivity with increased
climate stresses on socioeconomic or ecological systems and little ecological and/or
socioeconomic adaptation (see Table 2.2.1). Upper bounds would be in this category.
Similarly, reduced risks clearly emerge in scenarios that combine reduced baseline
socioeconomic or ecosystem vulnerability with reduced climate stresses. Lower bounds
would fall in this category. Combinations of climate and socioeconomic scenarios that
combine offsetting effects may have greater or smaller risks. The intervals between the
upper and lower bounds could be viewed as confidence intervals. This is much different
from placing upper and lower bounds on each of the variables hypothesized to affect
future baseline socioeconomic conditions. Out of the large number of possible
combinations of alternative values for the baseline variables, a much smaller number of
combinations of these values may suffice to establish upper and lower bounds on climate
change impacts.
Table 2.2.1. Future Baseline Socioeconomic/Ecological Sensitivity
Future
Climate
Stress
Greater
Smaller
Future Baseline Socioeconomic/Ecological Sensitivity
Greater Sensitivity
Societal/Ecological Adaptation
Responses
Low
Increased
Vulnerability
?
High
?
Reduced
Vulnerability ?
Smaller Sensitivity
Societal/Ecological Adaptation
Responses
Low
9
Reduced
Vulnerability
High
? Moderate
Vulnerability
Reduced
Vulnerability
In selecting scenarios to identify and bound risks, attention should be given to errors
analogous to the Type I and Type II errors in statistical hypothesis testing. A Type I error
would be to accept a false positive on either a threat or opportunity from climate change.
A Type II error would be to accept a false negative about either a threat or opportunity
from climate change. In the former case, the error is to falsely conclude that climate
change has a significant impact (positive or negative), while in the latter, the error is to
falsely conclude that climate change is benign. There is generally a tradeoff between
these errors. In classical statistical hypothesis testing with a random sample of
observations of the system, the chance of a Type I error decreases while that of a Type II
error increases with the stringency of the test for accepting the hypothesis of a significant
impact. In climate impact analysis, researchers do not typically have random samples of
observations for use in testing. The choice of scenarios and their analysis are key
determinants of the errors.
In constructing and analyzing scenarios, it should be borne in mind that the ultimate goal
is to inform present-day public and private decision-making. Rather than an abstract
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exercise in futurism, the assessment should have concrete implications for choices and
decisions facing us today. It should indicate when climate change impacts are likely to be
large enough to justify action today and what types of actions may be desirable in order
to exploit opportunities and reduce threats.
Key Socioeconomic Variables for the Mid-Atlantic Region
Key socioeconomic variables for assessing impacts include major indicators of the status
of the economy and human welfare, and indicators of socioeconomic drivers that affect
climate, ecosystems, socioeconomic systems. Figure 2.2.1 illustrates linkages between
the global climate, the MAR economy, and the economy of the rest of the world (ROW)
(which encompasses other regions of the US and other countries). (This discussion is
based on Abler et al, 1999.) Climate potentially can have strong impacts on both the
MAR economy and the ROW economy. Activities in the ROW economy also can have
strong feedback effects on climate. But activities in the regional economy are less likely
to have significant feedback effects on climate, at least when regions are defined at a
scale that is small in economic terms. However, it might still be possible for a region to
be small in economic terms but important as a source or sink of greenhouse gases or other
climate-altering activities. Thus, even though the ROW economy can exert a strong
influence on the regional economy, the MAR economy is unlikely to affect the ROW
economy because MAR is small in economic terms. Of course, the sum total of regions
comprises the global economy, and regional analysis is valuable as a bottom-up
determination of aggregates.
Global
Climate
Economy
In Rest of
World
Regional
Economy
Figure 2.2.1. Climate-Regional Economic Interactions
Broadly speaking, climate change can have four types of economic impacts at a regional
level. First, it can affect production of market goods and services within the region.
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Market goods and services are those sold by an economic actor (a company, institution,
household, or individual) to another economic actor, with payment being in cash, in kind
(barter transactions), or both. Second, climate change can affect the supply of nonmarket
goods and services within the region. Nonmarket goods are those that are not traded, and
in which property rights are not well defined. Unlike a market good, the value of a
nonmarket good (what people would be willing to pay for it if it were bought and sold) is
unaccounted for by the market. Third, climate change can have indirect effects on
economic sectors within the region that are not directly affected by climate change.
Fourth, climate change can have indirect effects on the region through effects on other
regions of the same country or other countries.
Table 2.2.2, from the 1995 IPCC report (IPCC, 1996b), provides an overview of several
potential market and nonmarket impacts of climate change, as well as the state of the
literature on estimating these impacts at the time the report was written. The table is
helpful in providing some examples of market and nonmarket goods. For instance,
agricultural products, forest products, water, and energy are all market goods, since they
are all bought and sold, at least in most countries. On the other hand, ecosystems,
species, human health, and human life are all nonmarket goods because there are no
markets where these goods are bought and sold. Even though there are no markets to
assign prices to them, these goods have value to society. Excluded from the nonmarket
category in Table 2.2.2 are more traditional public goods such as education, national
defense, police protection, and information services. Although these goods also are very
important and potentially could be affected by climate change, we confine the discussion
here to human health and environmental services.
The distinction between market and nonmarket goods is critical in analyzing the impacts
of climate change. Those who have property rights in market goods stand to reap the
rewards or suffer the consequences of direct and indirect effects that climate change may
have on the value of their assets. This gives them incentives to anticipate climate change
and respond to its impacts. In contrast, while nonmarket goods are essential to human
welfare and of great economic importance (Costanza et al., 1997), markets do not provide
meaningful incentives or mechanisms to reduce risks or exploit opportunities.
Referring to Figure 2.2.1, effects on market and nonmarket goods and services within the
region are represented by the arrow running directly from global climate to the regional
economy. Effects operating indirectly through other regions are represented by the two
arrows running first from global climate to the rest-of-world economy and then from the
rest-of-world economy to the regional economy.
This framework suggests the following types of socioeconomic variables:
il. Standard socioeconomic indicators, such as population, age distribution, per capita
income, employment, and health status.
2. Key variables describing conditions in regional and international markets for goods
and services produced in the MAR and exported to ROW, especially in those
sectors that are highly climate dependent such as agriculture, forests, and water.
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3. Key variables describing conditions in markets for goods and services purchased
from ROW by consumers and producers in the MAR region.
4. Technologies available to producers and consumers in the MAR region.
5. Indicators of the use and status of nonmarket goods.
6. Characterizations of public policies and institutions
Note three caveats for the following discussion: 1) We emphasize the period between
now and 2030, because projections become so much more speculative as the time horizon
is extended. 2) We exclude consideration of major surprises in the rest of the world, such
as multi-country uprisings or widespread epidemics. 3) At this stage, the interest is in
general trends that will occur even without climate change. Part 3 will add climate
change to the assessment.
Summary charts based on NPA point projections are described earlier for most variables
in item 1). We present this data as one look at the future. The caveats we noted in our
earlier discussion of point forecasts apply to these data. At the national level, NPA
assumes rising labor force participation rates (particularly for older people), rising birth
rates, and more immigration. NPA expects aging of the population to reduce the ratio of
employment to population after 2015. Yet earnings per job and per capita income will
continue to grow (although more slowly than recently) because of new capital investment
and improvements in productivity.
NPA uses a "regional growth accounting model" and disaggregates the national forecast
to counties by using relative growth rate differentials (e.g., for employment by industry)
and multiplier analyses. The regional growth rate differentials for each industry are
assumed to decay over time. Similarly, the differences in regional earnings-per-job
multipliers are assumed to decay over time. Aside from near-term adjustments for
closings and relocations of military bases, the NPA projections to not account for future
changes in institutional structure, the types of commodities people might want to buy
(including entirely new goods), or technologies for producing them. Nor does NPA
account for changes in institutions that might affect the demand for or profitability of
different goods.
Migration was estimated from the difference between the open population projected by
NPA's REPS economic model and their closed population projections based on births
and deaths in each area. NPA adjusted domestic migration rates to reflect college,
correctional, and active duty military populations.
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Table 2.2.2. A. Taxonomy of Market and Nonmarket Impacts of Climate Change
State of
Literature
Fully Estimated,
Based on
Willingness to
Pay
Fully Estimated,
Using
Approximations
Partially
Estimated
Not Yet Estimated
Market Impacts
Primary Other
Economic Economic Property Loss
Sectors Sectors
Agriculture Dryland loss
Coastal
Protection
Forestry Water Supply
Fisheries Energy Urban
Demand Infrastructure
Leisure
Activity
Insurance
Construction
Transport
Energy Supply
Damage from
Extreme Events
Hurricane
Damage
Damage from
Droughts
Nontropical
Storms
River Floods
Hot/Cold Spells
Other
Catastrophes
Nonmarket Impacts
Ecosystem
Damage
Wetland loss
Forest Loss
Species Loss
Other
Ecosystem
Loss
Human Impacts
Human Life
Air Pollution
Water Pollution
Migration
Morbidity
Physical
Comfort
Political
Stability
Human
Hardship
Damage from
Extreme Events
Hurricane
Damage
Nontropical
Storms
River Floods
Hot/Cold Spells
Other
Catastrophes
(IPCC, 1996b, Chapter 6)
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Because of uncertainties about any baseline projection, NPA also prepared high and low
growth projections by using substantially higher or lower assumed values of these
variables: birth rates, survival/death rates, international immigration, labor force
participation rates of population groups age 55 and older, and the growth rates of national
productivity (output per person employed). The values are intended to represent
plausible bounds on the wide range of uncertain growth outcomes.
Thus the NPA projections presume a continuation of recent trends. Factors for which the
MAR counties currently differ from national trends are assumed to be moderated so they
become more like the national pattern. The NPA projections do not account for surprises,
such as major changes in tastes and preferences, technology, or institutions.
2.2.3 A View of the Mid-Atlantic Region in 2030 (Fisher)
Because of the many uncertainties in forecasting, this view is intended not as a point
estimate but as an informed yet impressionistic view of the future. It provides context for
deciding whether the NPA projections (summarized above) provide appropriate bounds,
and can serve as a rough baseline for constructing the upper and lower bound scenarios
for each sectoral analysis in Part 3.
The Mid-Atlantic region is likely to continue having a diverse economic base, although
with changes in relative importance among sectors. Population is likely to grow in the
VA, DE, MD and NC portions of the MAR, but less so in the PA, NY, NJ, WV and DC
portions. Much of this growth is expected to occur in the eastern portions, particularly in
coastal counties. The age distribution in the MAR is likely to become older, partly
reflecting the national trend and partly because more people decide to retire in a region
with changing seasons, diverse geography, and cultural amenities in a range of community
sizes. The aging is likely to increase demand for health services. Despite expected
growth in managed health care, improvements in medical technology, and increased self-
care and use of alternative therapies, medical care is likely to become more costly.
Agriculture is expected to continue the trend toward a smaller economic role in the MAR,
because of factors such as trade liberalization and higher values for land in alternative
uses. Pressure for development of farmland will continue, especially in those sub-regions
that we expect to have continued population growth. Concerns about 1) preserving our
agricultural heritage, 2) cultural groups that rely heavily on agriculture, and 3) the
amenity aspects of agricultural landscapes may increase the amount of farmland in land
conservation programs within MAR. Growing urban/suburban areas are likely to
continue converting nearby agricultural and forest lands for residential and commercial
development. Such conversion is likely to exacerbate concerns about fragmentation and
the implications for wildlife habitat. At the same time, marginal agricultural lands in
more rural areas are likely to revert to forest cover. The harvest of hardwoods is
expected to increase, but probably will not exceed forest growth in the MAR.
If trends of the recent past hold, rising per capita incomes are expected to increase the
demand for outdoor recreation as well as other forms. Rising per capita incomes also
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increase the demand for environmental quality. These non-market components range
from landscape and other aesthetics to wildlife habitat to air and water quality. For
example, the increase in impervious surfaces accompanying residential and commercial
development can increase impacts from storm water runoff, such as localized flooding,
erosion, and eutrophication of downstream areas because of nutrient enrichment. Rising
per capita incomes may increase the demand for infrastructure to moderate these impacts
and recharge groundwater. Although the MAR is water-rich overall, areas with growing
populations can expect corresponding pressure on water supplies, infrastructure, and land
use planning at a time when many government responsibilities are being shifted from the
national level to state and local levels. Some areas could experience substantial
competition for water supplies. For example, although the ski industry is small in the
MAR, snow making accounts for the second largest withdrawals (behind power
generation) from the Susquehanna River Basin.
The MAR will have more interconnectedness with the rest of the nation and the rest of
the world as communication technology continues to improve and transportation costs
continue to decline. The resulting increase in external competition makes it more
important to identify and capitalize on those activities for which the region has a
comparative advantage, as a way to strengthen its economic base. At the same time, the
interconnectedness will reduce prices faced by MAR residents for externally produced
inputs and outputs.
After writing baseline descriptions of current conditions for their sector, each working
group developed an initial set of key socioeconomic variables and boundary scenarios.
The intent was to identify broad trends likely to be affected by climate change, and
account for uncertainties such as potential thresholds that might substantially affect one
or more trends. The sets of scenarios were reviewed by the MARA team and the
Advisory Committee, and revised for use in assessing the Mid-Atlantic region's potential
impacts from climate change. The variables that are key for one sector are not always the
ones that are key for another sector. Thus each sectoral assessment in Part 3 describes its
key variables and boundary scenarios. Yet all of these are consistent with the general
scenario described above. After the challenging task of projecting how these key
variables might change in the future, Part 3 identifies (for each sector) those likely to be
affected by changes in the MAR's climate so that the climate impacts can be assessed.
2.3 Mid-Atlantic Region's Climate (Yarnal)
2.3.1 Introduction
One way to estimate the regional impacts of future global climate change is to study how
the region has responded to past climate variations. First, however, it is important to
clarify some basic definitions and facts about climate, climate change, and climate
variation.
Climate is much more than average weather. Weather is the hour-to-hour and day-to-day
state of the atmosphere. In contrast, climate encompasses the longer-term condition of
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the atmosphere, including the frequency and intensity of weather at a time and place,
including storms, cold outbreaks, and heat waves.
The climate system varies and changes because of the interaction of the atmosphere with
the total Earth system of the water, rock, soil and life. This variation and change includes
commonplace daily and seasonal cycles and short-term extreme events such as floods and
droughts. It also includes long-term episodes such as wet and dry decades, cool or warm
centuries, and glacial-interglacial cycles.
There is much confusion over the terms climate variation and climate change. For the
United Nations Convention on Climate Change, scientists, decision-makers, and other
stakeholders have resolved that climate variation refers to natural variation in climate,
while climate change pertains to those variations in climate attributable to human
activity. Relevant human activities are those that influence the planetary energy balance.
These activities - such as industrial operations or land transformations - emit or lead to
the creation of the radiatively active gases carbon dioxide, methane, nitrous oxide, and
the halon family, or inject microscopic particles into the atmosphere. Radiatively active
gases enhance the natural greenhouse effect and warm the planet, while microscopic
particles screen out incoming sunlight and cool the surface.
Climate change arises from human activity, but climate variation results from natural
forces operating at different time scales. The two most fundamental variations in climate
are daily cycle and the seasonal cycle, which result from relationships between the sun
and Earth on time scales of days and years, respectively. The third most important
variation in the climate system is El Nino-Southern Oscillation (ENSO) events, which
perturb the global oceanic and atmospheric circulations and can produce droughts and
floods in certain regions. For example, during warm phases of ENSO (known as El
Nino), the southern United States tends to be very wet and may suffer floods; during
ENSO cold phases (known as La Nina), this region is prone to drought. ENSO extremes
usually occur roughly every three to five years and last for about a year and a half.
Scientists are developing the ability to predict these events and their regional patterns of
flood and drought many months in advance. Besides ENSO, other large-scale
perturbations of the ocean-atmosphere system influence regional climates. One example
is the North Atlantic Oscillation, which affects the climate of the North Atlantic basin
and which may be particularly important to the Mid-Atlantic Region. Many other
seasonal to interannual causes of variation, such as volcanic eruptions, are difficult to
predict.
Climate also varies on time scales ranging from decades to millions of years. Decade-
long variations result from interactions among the different components of the Earth
system: atmosphere, ocean, land, biosphere, and ice. Because each of these components
is characterized by different response times, their interactions produce climate variations
on many time scales. Decade-long variations can also result from variations in solar
output tied to sunspot cycles. On century time scales, planetary warming and cooling can
be caused by long-term oscillations in solar output. In the last two to three million years
and on time scales often thousand years or more, variations in Earth's orbit around the
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Sun are related to more than 20 glacial-interglacial cycles. Even longer-term changes in
the configuration of the continents and associated mountain-building episodes result in
climate variations over ten million years or more. Examples include the ice-age
conditions of the last 35 million years and the generally warm Earth inhabited by the
dinosaurs.
Climate varies over space as well as time. In any given year or decade, the climate in one
region may be unusually warm or cool, or wet or dry, while temperature and precipitation
in an adjacent region may be close to average.
In the United States, storms, floods, heat waves, droughts, and cold outbreaks are
regional phenomena, although their impacts may be serious enough to spread throughout
the nation and the world. For example, the 1988 drought so devastated the Midwest that
it had a measurable influence on the national and world economies.
Although there is considerable uncertainty about how climate change will influence
specific regions, scientists expect changes in the frequency and intensity of storms,
floods, droughts, heat waves, and cold outbreaks. Some regions may suffer more
frequent and intense droughts, while others may have fewer and weaker droughts. In any
case, because regional variations in climate are normal, it may be difficult to distinguish
anthropogenic climate change from natural climate variation.
Thus, an important first step in determining the potential impacts of climate change is to
understand how climate variation affects regional climate today. The next subsection
summarizes climate variations observed in the Mid-Atlantic region during the past
century and then during the last 1000 years. The section concludes by summarizing what
we know about climate variation in the Mid-Atlantic Region and what we need to learn.
2.3.2 Recent Climate Variation in the Mid-Atlantic Region
There is a wealth of observed climate data - that is, data recorded by weather instruments
- for the Mid-Atlantic Region. The relatively short period of these data, however, only
provides insight on recent climate variations of years and decades. Longer time-scale
variations can be reconstructed by interpreting natural phenomena and human artifacts
containing climate information, as described in the next subsection.
Data for precipitation and temperature extend back to 1895 and are shown in Figure 2.3.1
for a Mid-Atlantic region defined by the watershed boundaries of the Chesapeake and
Delaware Bays. The figure shows that the precipitation and temperature of the Mid-
Atlantic Region have varied substantially on annual and decadal time scales over the last
century.
i
Relationship to atmospheric circulation variations
The global models used to project climate change rely heavily on atmospheric circulation
inputs. Thus linking surface climate variations to variations in the region's atmospheric
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.circulation is an important step in assessing changes in the region's climate (as discussed
further in Section 2.4).
There are clear relationships among the Mid-Atlantic Region's precipitation and
temperature variations of the last century. In general, from the beginning of the record to
about 1930, the climate was cool and dry. The early 1930s saw a couple of exceedingly
hot, dry years that match the timing of the midwestern Dust Bowl. This short, sharp
drought was replaced by nearly three decades of relatively warm, moist climate. This
period was replaced by a cool and very dry climate in the 1960s. In contrast, the 1970s
were very wet, but varied between warm and cold. Since the late 1970s-early 1980s,
precipitation and temperature have varied above and below normal.
0)
u
1695 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995
49
1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995
Figure, 2.3.1. Departures from long-term (1895-1996) average monthly precipitation (inches, in top figure)
and annual temperature (° Fahrenheit, in bottom figure) in the Mid-Atlantic Region. The dashed line
denotes average annual values, while the bold, solid line is a five-year running average.
These variations in regional climate since World War II can be explained by changes in
the atmospheric circulation. (Jet stream-level data needed to make the following
generalizations are not available for earlier periods.) A zonal regime dominated the
atmospheric flow over North America through the late 1940s and early 1950s. (A zonal
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flow regime means that the jet stream over the United States tends to flow from west to
east, with few of the north-south excursions that produce ridges of warm air and troughs
of cool air. In contrast, during periods of meridional/low, the jet stream has a much
greater north-south amplitude, often producing a big ridge of warm sub-tropical air over
the western United States and a deep trough of polar air over the eastern half of the
nation.) The zonal regime produced normal to slightly above-normal temperatures and
variable precipitation over the Mid-Atlantic Region. Then in the mid- to late 1950s, the
circulation changed from zonal to meridional flow. During the 1960s, the Mid-Atlantic
Region tended to be influenced by a deep trough of continental polar air, that pushed the
storm track southeast of its long-term average position so that precipitation often fell off
the Atlantic coast. This regime promoted a relatively cool, dry climate. The early 1970s
saw the continuation of this meridional regime, but the trough migrated westward, putting
the average position of the storm track over the Mid-Atlantic Region. This change
increased precipitation in the region. Finally, the mid-to-late 1970s brought a large
change in the atmospheric circulation. Since then, there have been unusually large
variations in the shape and positioning of the month-to-month and year-to-year jet stream
flow over North America. Such major variations in circulation have produced a highly
variable surface climate.
2.3.3 Short-term extreme events
Because of the potential harm from extreme events such as severe storms, heat waves,
droughts, and cold winters, a regional climate impact analysis needs to determine whether
extreme events are increasing or decreasing. Finding trends in the historical climate
records, however, is difficult because data on extreme events still are sparse. Still, a few
trends are emerging.
Current trends suggest a change toward fewer extreme temperatures in the Mid-Atlantic
Region. The last frost of spring tends to come progressively earlier, and there are fewer
very cold winter days. Although the region's winters are warming, the number of very
hot summer days appears to be decreasing (although there is some question about the
reliability of these data).
Extreme precipitation, expressed as those rainfall events exceeding two inches in 24
hours, has increased dramatically in the Mid-Atlantic Region during the 20th Century
(and is reflected in the region's observed overall increase in precipitation). One
implication is that severe thunderstorms are on the increase over the Mid-Atlantic
Region. In addition, winter coastal storms affecting the region appear to be increasing in
their power, with seven of the eight most destructive storms of the past half-century
occurring in the last 25 years (Davis and Dolan, 1993).
The region also appears to be experiencing significant interannual and intra-annual
swings in the climate. For example, the three coldest winters in the record happened in
succession in 1976-77, 1977-78, and 1978-79, while some of the warmest winters ever
occurred in 1982-83, 1994-95, and 1997-98. The region has experienced several severe
droughts in the last two decades, but the wettest year in more than a century was 1996
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and the second wettest year was 1972 (Figure 2.3.1). In most of the Mid-Atlantic
Region, the four snowiest winters were 1977-78, 1992-93,1993-94, and 1995-1996,
while 1994-1995 and 1997-98 were two of the least snowy winters on record.
Within individual seasons and years, climate has oscillated, too. For instance, December
1989, the coldest December on record, was followed by the warmest January-February in
the books. Interestingly, the extreme opposite signs of these months canceled each other
so that winter 1989-1990 was an average winter statistically. Precipitation has behaved
similarly. The first half of 1998 was the wettest on record in many areas in the Mid-
Atlantic Region and it appeared that calendar year 1998 was going to beat 1996 easily for
the all-time wettest year. In spite of that, drought gripped the region during the second
half of 1998, making it an average year statistically.
2.3.4 The climate of the last 1000 years
Long records of climate variations would allow scientists to compare the natural variation
of the period before the Industrial Revolution with the mixed natural and anthropogenic
climate signal observed since then. Unfortunately, comprehensive climate observations
are restricted to the last century and a half, and to even shorter periods in many regions.
As a result, pre-instrument (paleoclimate) climate variations must be deduced from proxy
data for climate-sensitive phenomena, such as tree rings, corals, glacier ice cores, and
chronologies of alpine glacier advance and retreat. Quantitative data can also be culled
from historical records, such as journals and tax and mercantile records. Data from the
last 1000 years have the advantage of being relatively abundant and fresh. In addition,
the variations they portray are most relevant to the current climate.
Tree-ring data are the most plentiful of the natural proxy data, but they are still too sparse
to provide a complete global picture over time. Ice-core, coral, and glacier advance-
retreat data are even less common. Thus, the majority of the data for the last millennium
may reflect regional, rather than global climate signals.
Despite these data problems, paleoclimate reconstructions show that globally the 20th
century has been warmer than any century since 1400 AD, and is probably the warmest
century in the last 1000 years. Some evidence suggests that this century is as warm as
any comparable period in the last 10,000 years - that is, since the retreat of the
continental glaciers from North America and Eurasia. The estimated variation of global
average temperature over the last millennium is less than ± 0.9°F, but century-scale
climate data suggest that natural variation can be abrupt and large.
Reconstructions of Mid-Atlantic Region climate using tree rings and historical data from
diaries, newspapers, and periodicals suggest that between the mid-17th century and the
late 19th century, the region was cool and somewhat wet, but was affected by individual
years of intense drought and occasional decades of prolonged drought. Overall, the year-
to-year and decade-to-decade climate was highly variable and not unlike that observed in
the latter 20th century. After about 1880, the area warmed gradually. Climate variation
decreased until the latter 20th century. For the entire record, the longest drought
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extended from about 1850-1873, although the most severe prolonged drought occurred in
the 1960s. Throughout the entire period of reconstruction, the (admittedly limited)
evidence suggests that ENSO events have a significant effect on the hydrology of the
Mid-Atlantic Region.
2.3.5 Conclusions
Knowing how the climate of a region has varied in the past provides a baseline for
assessing future regional climate change.
Climate variations in the Mid-Atlantic Region have been considerable. For example,
each year in the 1960s was cool and very dry, while the 1970s possessed a more variable
temperature regime and was very wet. Other decades, such as the 1980s and first half of
the 1990s, were distinguished by relatively extreme year-to-year variation in climate.
These interannual and decadal climatic variations were associated closely with the
atmospheric circulation over the region and were related to United States and global
variations. Longer time scales of climate variation for the Mid-Atlantic Region are only
crudely documented and understood, but some facts are known. For instance, the 1960s
appear to have been the driest decade of the last few centuries, while the large interannual
climate variation of the 1980s and 1990s are not unprecedented.
2.4 MARA Region Climate Change Scenarios (Crane)
The Mid-Atlantic Region (MAR) climate change scenarios are derived from a numerical
model of the global climate system developed by the Hadley Center for Climate
Prediction and Research. The model projects climate change as an experiment including
the effects of both sulfate aerosols (which tend to reduce temperatures) and atmospheric
greenhouse gases, which increase global temperatures. Starting with 1990 values, this
experiment increases the greenhouse gas (carbon dioxide [CC^]) content of the
atmosphere by 1% per year, to the year 2099. (Additional information about climate
modeling appears in Appendix B.)
The net effect of the projected global changes in sulfate aerosols and greenhouse gases is
to produce an increase in maximum and minimum temperatures for the MAR of
approximately 4° F and 5° F respectively over the 100 year period (Figure 2.4.1). The
annual average temperature change is on the order of 4.5° F - slightly less than the global
average of approximately 5.5° F. By the decade 2025-2034, minimum and maximum
temperatures both increase by approximately 2° F. Note, however, that there is
considerable year-to-year and decade-to-decade variability. The decade 2035-2044
actually cools slightly before increasing again from 2050 onward. Average monthly
precipitation increases by approximately 0.25 inches per month between the present and
2025-2034, and by about one inch per month by 2090-2099 (Figure 2.4.2).
Figure 2.4.3 shows the distribution of modeled January and July average maximum
temperatures over the MARA region for the present (1984-93) and for 2025-2034. It
suggests that much of the temperature change will occur in the summer, with the greatest
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changes being in the southeast. Minimum temperatures show a somewhat different
pattern (Figure 2.4.4). Higher summer temperatures again occur in the southeast, but
there are lower winter temperatures in the central and northern parts of the region.
Much of the increase in precipitation also takes place in the summer. Figure 2.4.5 shows
the modeled distribution of precipitation for 1984-1993 and for 2025-2034. A
comparison of the modeled and observed data for 1984-1993 indicates that in January the
model produces slightly more precipitation than observed over much of the region except
for the Carolina coastline, which receives slightly less. The net effect is to slightly
reduce the east-west precipitation gradients. In July, the model is too dry over most of
the region. In both seasons, however, the model does produce very realistic patterns of
the geographic distribution of precipitation. Comparing the model results for 1984-1993
with 2025-2034 we see a slight and fairly uniform increase in January. Precipitation also
increases across the whole region in July, but with the greatest increases being in the
southeast.
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53 r—
51
49
47
45
43
41
39
37
35-k
\ \ \ \ \ \
Year
\
Figure 2.4.1. Average annual maximum (top) and minimum (bottom) temperatures
for the MARA region from the Hadley Center global climate model.
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5.5
5
4.5
4
3.5
3
2.5
2 4rmrrm
draft
,..,.., .rrr TTT- n—r_rrrr
Year
Figure 2.4.2. Monthly precipitation for the MARA region from the Hadley
Center global climate model.
Modeled |1BW -1983)
Modeled (2025 - 2034)
,v,--^|S#
Mnpirui'trC;
•/*•
Modeled (1984 -1993)
Modeled (2026 - 2034)
Figure 2.4.3. Average January (top) and July (bottom) maximum temperatures
from the Hadley Center model for 1984-1993 (left) and 2025-2034 (right).
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-19931
Mod E led! 3025 203-1)
Figure 2.4.4. Average January (top) and July (bottom) minimum temperatures
from the Hadley Center model for 1984-1993 (left) and 2025-2034 (right).
Modeled (1984 -1993)
Modeled (2025 -20M)
I'Ci
>^L4
-7.H
-14 . .1.1
-IA D.O
JjJ-1.8
Modeled (1984-1993)
Modaled (2025-2034)
Figure 2.4.5. Average January (top) and July (bottom) precipitation from the
Hadley Center model for 1984-1993 (left) and 2025-2034 (right).
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These results are broadly consistent with earlier studies of the MARA region using a
different global climate model (GENESIS). The GENESIS climate change experiment
was for a doubling of atmospheric CC^ above late 20th century values, and did not
include the effects of sulfates. A study that used a regional climate model in conjunction
with GENESIS global model produced a similar increase in annual precipitation,
although with a greater increase in winter rather than summer. A second study,
employing a later version of GENESIS and using observed relationships between the
regional climate and local precipitation, also found a large increase in precipitation. In
this case the increase was concentrated in the summer months, with the largest changes
occurring to the south, but over the mountains.
In sum, these models suggest a) modest warming (2°F) by 2030, and substantial warming
(4.5°F) by 2100, with more of the warming during the summer and in the southeast
portion of the MAR, and b) modest increases in precipitation (0.25 inches/month) by
2030 and substantial increases (1 inch/month) by 2100. The precipitation increase is
likely to be mostly in the summer, but there is substantial uncertainty about its typical
distribution within the year and within the region. .
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Part 3: Impacts, Challenges and Opportunities
The background summaries in Part 2 provide context about the Mid-Atlantic region's
physical and socio-demographic environment within which its people live, its historical
climate, and likely changes in its future climate. The next step is to assess a) the impacts
on the MAR from climate change, b) how the region's citizens can take advantage of
opportunities and increase resilience to vulnerabilities from climate change, and c)
additional information needed to improve individual and organizational decisions related
to impacts from climate change.
The MARA team is committed to an integrated assessment approach. But few studies
have taken an integrated approach at the scale of a region such as the Mid-Atlantic. Thus
it initially appeared desirable to demonstrate the MARA approach on a small number of
sectors especially likely to be affected by climate change. However, available research
suggested a broad range of potential impacts, with none of those overwhelming the others
for this region. Meetings with the Advisory Committee demonstrated substantial interest
in many types of potential impacts. This convergence of scientific implications and
stakeholder interests resulted in a decision to assess impacts for each of the following:
agriculture, forestry, water resources, coastal zones, ecosystems and human health.
These include the sectors to be covered in the national assessment. Their order of
coverage here reflects the fact that linkages among the first four tend to flow
downstream; the ecological and human health impacts are more cross-cutting. The
assessment focuses on the year 2030, discussion of impacts for 2100 is necessarily more
speculative.
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3.1 Agriculture (Abler, Shortle, Nizeyimana, and Corradini)
Mid-Atlantic agriculture, like agriculture worldwide, has an intrinsic relationship with
climate. Climate variability has strong impacts on Mid-Atlantic agriculture. Climate
change potentially also could have significant impacts. This section reviews the current
status and stresses on Mid-Atlantic agriculture and how climate variability affects
agriculture in the region. It goes on to consider how climate change might affect future
Mid-Atlantic agriculture, bearing in mind that Mid-Atlantic agriculture is likely to change
dramatically independent of climate change. This section then considers some
management and adaptation options for farmers, agribusinesses, and governments. It
concludes with priorities for research and information that should be addressed in future
assessments.
3.1 A Current Status and Stresses
Compared to many other parts of the US, Mid-Atlantic agriculture is characterized by
smaller farms and a wider range of crops and livestock products. Average farm size in
the Mid-Atlantic is about 180 acres, compared with over 500 acres for the rest of the US
(USDA National Agricultural Statistics Service, 1999a). However, poultry and hog
operations within the Mid-Atlantic tend to be quite large as measured by the number of
livestock per farm and quite intensive as measured by the number of livestock per acre.
'The single largest source of cash receipts in most of Pennsylvania, upstate New York,
and much of Maryland is dairy production. Mushrooms and other vegetables and nursery
products are important in New Jersey, parts of Maryland, and parts of eastern
Pennsylvania. Chicken and eggs tend to dominate in the Delmarva Peninsula and in parts
of Virginia and southern Pennsylvania. Significant production of apples, peaches, and
other tree fruits occurs in certain areas of Maryland, New Jersey, Pennsylvania, and West
Virginia. In western Virginia and West Virginia, cattle farming is the most important
agricultural activity. Tobacco production tends to predominate in southern Virginia and
northern North Carolina.
Due to historically adequate supplies of rainfall in most years, crop production in the
Mid-Atlantic region is overwhelmingly rainfed. Less than 3% of crop acreage in the
Mid-Atlantic is irrigated, compared with about 13% in the rest of the US. (USDA
National Agricultural Statistics Service, 1999a).
Present-day Mid-Atlantic agriculture can be illustrated using data foonaigr land resource
areas (MLRAs) within the region. MLRAs are areas characterized by common patterns
of soil, climate, water resources, and land uses. MLRAs for the Mid-Atlantic region were
obtained using geographic information systems (GIS) boundaries assembled by the US
Geological Survey (1999). Figure 3.1.1 shows MLRAs for the Mid-Atlantic region.
Table 3.1.1 presents statistics for Mid-Atlantic agriculture at the MLRA level.
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Figure 3.1.1. MLRA Map of the Mid-Atlantic Region
Agricultural land use and sales data in Table S.l.l^e from coumy=ievel data in the 1992
Census of Agriculture (Government Information Sharing Project, 1999)1 Employment
data, which include full-time as well as part-time farrners, u6e43Q6-ceunty-level data
from the US Bureau of Labor Statistics (1999). In cases where a county spans two or
more MLRAs, county data are apportioned among MLRAs according to the proportion of
total county area in each MLRA.
Table 3.1.1 indicates that agriculture accounts for about one-fourth of total land area in
the Mid-Atlantic region. Among MLRAs, this proportion varies from over one-half in
the Mid-Atlantic Coastal Plain (153C) to less than 4% in the Cumberland Plateau and
Mountains (125)._Hay and pastureland are the predominant uses of agricultural land,
accounting for nearly three-fourths of total agricultural land in the Mid-Atlantic region.
Thq remainder, about one-fourth, is accounted for by cropland. Hay and pastureland are
also1 the predominant uses of agricultural land in most MLRAs. Exceptions include the
Atlantic Coastal Flatwoods (153A) and the Tidewater Area (153B) along the southern
Virginia and northern North Carolina coasts, where producers grow a mixture of crops.
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Table 3.1.1. Mid-Atlantic Agriculture at the MLRA Level
Percentage of Total Land in MLRA, 1992
Percentage of Total
Farm Sales, 1992
MLRA
100
101
124
125
126
127
128
130
133A
136
139
140
144A
147
148
149A
153A
153B
153C
MLRA Area
Lying in
Mid-Atlantic
Region
MLRA Name (1000 Acres)
Erie Fruit and Truck Area
Ontario Plain and Finger Lakes
Region
Western Allegheny Plateau
Cumberland Plateau and
Mountains
Central Allegheny Plateau
Eastern Allegheny Plateau and
Mountains
Southern Appalachian Ridges
and Valleys
Blue Ridge
Southern Coastal Plain
Southern Piedmont
Eastern Ohio Till Plain
Glaciated Allegheny Plateau
and Catskill Mountains
New England and Eastern New
York Upland, Southern Part
Northern Appalachian Ridges
and Valleys
Northern Piedmont
Northern Coastal Plain
Atlantic Coastal Flatwoods
Tidewater Area
Mid-Atlantic Coastal Plain
426
835
7
9,196
25,855
25,935
16,105
7,106
10,888
27,707
311
30,017
874
29,605
16,586
11,557
5,994
9,082
6,458
Hay and
Pastureland
25.0
29.6
13.5
2.4
18.6
8.3
18.2
20.9
12.7
15.8
26.5
19.6
16.8
26.6
37.9
13.4
10.4
12.0
30.0
Cropland
9.3
6.9
2.5
1.5
3.2
1.7
2.9
2.2
12.8
5.8
9.9
3.7
2.6
7.6
10.8
6.7
18.9
19.5
21.8
All
Agricultural
Land
34.3
36.6
16.0
3.9
21.8
10.0
21.1
23.1
25.5
21.5
36.4
23.3
19.5
34.2
48.7
20.0
29.3
31.5
51.8
Farm Labor
Force as
Percentage of
Total Labor
Force, 1996
1.1
3.7
1.3
2.8
4.1
3.2
10.9
10.1
4.9
7.6
2.4
3.6
1.7
4.6
2.6
1.1
6.3
6.7
3.4
Livestock
and
Livestock
Products
45
89
31
27
56
78
79
78
14
34
74
67
45
68
43
5
32
8
35
Crops
55
11
69
73
44
22
21
22
86
66
26
33
55
32
57
95
68
92
65
Entire Mid-Atlantic Region
234,545
18.4
6.6
25.0
4.0
26
74
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About thregjjbiirths fHPlotal farm sales in the Mid-Atlantic region are accounted for by
crons_rather than livestock or livestock products. Among MLRAs, crop sales as a
proportion of total farm sales vary from about one-tenth in the Ontario Plain and Finger
Lakes Region (101) to over 90% in the Northern Coastal Plain (149A) and the Tidewater
Area(153B).
Agriculture accounts for about 4% of the total labor force in the Mid-Atlantic region
(including both full-time and part-time farmers). This proportion ranges from over 10%
in the Southern Appalachian Ridges and Valleys (128) and Blue Ridge (130) to less than
1% in the heavily urbanized Erie Fruit and Truck Area (100) and Northern Coastal Plain
(149A).
Agriculture's importance in the Mid-Atlantic region extends well beyond its role as a
source of income and employment. Rural and urban populations within and outside the
region value the region's agricultural and rural land as open space and as a source of
countryside amenities. Fishing, boating, hunting, sightseeing, and other recreational
activities are important in rural areas throughout the Mid-Atlantic. Agricultural land is an
important habitat for some wildlife species within the region. These values are reflected
in public programs to protect farmland from development and preserve agricultural
landscapes in all eight states within the region (American Farmland Trust, 1997).
Programs in place within the region include agricultural protection zoning, differential
property assessment, and conservation easements.
Agriculture in the Mid-Atlantic is also a source of negative environmental impacts,
particularly water pollution from nutrients, eroded soils, and pesticides. Of 2,105
,,- ^., - watersheds (defined at the 8-digit hydrologic unit code level) in the 48 contiguous states,
f^ \ p' watersheds in southern New York, northern Pennsylvania, southeastern Pennsylvania,
A western Maryland, and western Virginia rank in the top 10% in terms of manure nitrogen
AT ' runoff, manure nitrogen leaching, manure nitrogen loadings from confined livestock
' operations, and soil loss due to water erosion (Kellogg et al., 1997). Watersheds in
southeastern Pennsylvania and along the southern Virginia/northern North Carolina
coasts also rank in the top 10% in terms of nitrogen loadings from commercial fertilizer
applications (Kellogg et al., 1997). Watersheds in the tobacco-growing areas of southern
Virginia and northern North Carolina rank near the top as measured by potential threats
to human drinking water supplies, fish, and other aquatic life from pesticide leaching and
runoff (Kellogg et al., 1999).
Environmental side effects of agricultural production in the Mid-Atlantic are of concern
for many reasons, but perhaps the most important is because of their impact on the
Chesapeake Bay. Human activity within the Chesapeake Bay watershed during the last
three; centuries has had serious impacts on this ecologically rich area, including
significant declines in highly valued fish and shellfish populations. Soil erosion and
nutrient runoff from crop and livestock production have played major roles in the decline
of the Chesapeake Bay.
\j
\
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3.1.2 Climate Variability and Mid-Atlantic Agriculture
Crop production in the Mid-Atlantic region has historically been sensitive to climatic
variations. Extreme events such as heat waves, droughts, freezes, floods, hailstorms, and
hurricanes have had strong impacts on crop yields over the years. For example, a drought
and heat wave in the summer of 1980, as well as in the summer of 1991, significantly
reduced crop yields in Maryland, Pennsylvania, Virginia, and other parts of the Mid-
Atlantic.
Among major crops within the Mid-Atlantic, yields of corn are perhaps the most climate-
sensitive because virtually all corn is rainfed and water is a limiting input into corn
production in many years. This is confirmed by statistical analysis of crop yield data for
1980-1998 from the USDA National Agricultural Statistics Service (1999b) for states in
the Mid-Atlantic. This analysis indicates that corn yields tend to deviate significantly
around their trend rates of growth - often more than 30% above or below trend. On the
other hand, yields of crops such as hay and tobacco show much smaller deviations -
generally on the order of 5-10% - around their trend rates of growth.
In general, livestock production tends to be less sensitive to climate variability than crop
production. This is particularly true for poultry productidn in the Mid-Atlantic, the vast
majority of which occurs indoors under controlled climatic conditions. For outdoor
livestock production, heat waves can lead to increased livestock mortality, lower
livestock yields, and lower reproductive capacity (Klinedinst et al., 1993). Especially
cold weather during the winter can also increase livestock mortality.
3.1.3 Future Agricultural Baseline Scenarios
Mid-Atlantic agriculture, like US agriculture as a whole, has changed radically during the
last century. With the notable exception of some Amish, tractors and other farm
machinery have virtually eliminated the use of draft animals and have made it possible
for a single farmer to cultivate tracts of land orders of magnitude larger than a century
ago. The introduction of synthetic organic pesticides in the 1940s revolutionized the
control of weeds and insects. Similarly, there has been tremendous growth in the use of
manufactured fertilizers and hybrid seeds. Farmers have become highly specialized in
the livestock products and crops they produce, and they have become much more
dependent on purchased inputs. Crops that were virtually unheard of 100 years ago, such
as soybeans, are of major importance today. As agricultural productivity has risen, and
as real (inflation-adjusted) prices of farm commodities have fallen, substantial acreage in
the Mid-Atlantic has been taken out of agriculture and either returned to forest or
converted to urban uses.
For reasons discussed in more detail in Appendix B, there are few reasons to expect this
rapid pace of change to slow down during the coming century. Biotechnology is already
having significant impacts on agricultural production, and could lead to revolutionary
changes in the types of crops and livestock produced and in the way that they are
produced. Precision agriculture and improved climate forecasts may give farmers much
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greater understanding of, and control over, growing conditions. Biotechnology and
precision agriculture could also lead to substantial reductions in the negative
environmental side effects of agricultural production. At the same time, economic
conditions facing Mid- Atlantic agriculture can be expected to continue changing for
many other reasons. Real prices for agricultural commodities are likely to continue their
long-term downward trend, the opportunity cost of labor facing Mid- Atlantic agriculture
^whaTfarmers and farm workers could earn in other occupations) is likely to continue its
long-term upward trend, and pressures to convert agricultural land to urban uses are
likely to continue, but perhaps at a slower pace due to farmland protection efforts. These
trends, when taken as a whole, suggest that Mid- Atlantic agriculture will be smaller but
more productive on a per farm basis.
.an eye toward establishing plausible upper and lower bounds on potential climate
change impacts on Mid- Atlantic agriculture, along the lines discussed in Part 2 of this
report, two baseline scenarios are considered here for the year 2030. These two
scenarios, continuation of the status quo (SQ) and a smaller, more "environmentally
friendly" agriculture (SEF), are detailed in Table 3.1.2. The SEF scenario is much more
probable than any scenario approximating a continuation of the status quo, but both
scenarios are needed to establish bounds on climate change impacts. As discussed in
Appendix B, the SEF scenario helps establish lower bounds on any negative impacts on
agricultural production due to climate change, and upper bounds on any positive impacts
on production. It also helps establish lower bounds on positive or negative impacts of
how climate change might affect the environmental side effects of agricultural
production. The SQ scenario is the opposite of the SEF scenario, in that it helps establish
upper bounds on negative production impacts, lower bounds on positive production
impacts, and upper bounds on positive or negative environmental impacts.
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Scenario
Table 3.1.2. Baseline Agricultural Scenarios for the Year 2030
Scenario Details
Smaller, More "Environmentally
Friendly" Agriculture (SEF)
Major decline in field crop production in region
Smaller but still significant decline in livestock production
Significant decrease in number of farms in region
Substantial increase in agricultural productivity due to
biotechnology and precision agriculture
Major increase in agricultural production per farm on the
remaining farms
Significant decrease in agriculture's sensitivity to climate
variability due to biotechnology, precision agriculture, and
improved climate forecasts
Some conversion of agricultural land to urban uses, with
conversion slowed by farmland protection programs
Some reforestation of existing, economically marginal agricultural
lands
Significant decrease in commercial fertilizer and pesticide usage
due to biotechnology
Less runoff and leaching of agricultural nutrients and pesticides
due to precision agriculture
Stricter environmental regulations facing agriculture, especially
intensive livestock operations
Status Quo (SQ)
Agriculture as it exists today in the Mid-Atlantic region
For the year 2100, the uncertainties are so overwhelming that it is very difficult to think
about baseline agricultural scenarios. To illustrate this point, it would have been
exceedingly difficult if not impossible for someone in 1900 to foresee the dramatic
changes that would occur in Mid-Atlantic agriculture during the 20th century. It is
probable that Mid-Atlantic agriculture in 2100 will bear only a faint resemblance to the
region's agriculture today, but it is not possible to say with any confidence what the
major changes between now and then might be.
3.1.4 Potential Climate Change Impacts on Mid-Atlantic Agriculture
This section assesses potential climate change impacts on four types of crops (com,
soybeans, tobacco, and tree fruits) and two types of livestock (dairy and poultry) that are
currently important to Mid-Atlantic agriculture. The region's major tree fruits are apples,
cherries, peaches, and pears. This section also assesses potential climate change impacts
on environmental side effects of agricultural production within the region. Our
assessment draws in part on previous assessments for US and world agriculture (Adams
et al., 1999; Adams et al., 1998; Darwin et al., 1995; IPCC, 1996; Lewandrowski and
Schimmelpfennig, 1999; Rosenzweig and Hillel, 1998; Schimmelpfennig et al., 1996).
Carbon dioxide (CC^) accumulation and climate change are expected to have direct
effects on the region's agriculture (Adams et al., 1999; Rosenzweig and Hillel, 1998).
47
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Elevated levels of COa may lead to an increase in photosynthesis and thus crop yields, a
phenomenon known as the CO2 fertilizalinn effect. They may also lead to a decrease in
transpiration (evaporation from plant foliage), which would reduce water stress during
periods with little or no rainfall. Projected increases in summer temperatures and
summer precipitation within the region (see Part 2 of this report) could also have
significant effects, particularly on climate-sensitive crops such as corn.
Beyond these direct effects, climate change may have indirect effects on Mid-Atlantic
agriculture (Adams et al, 1999; Schimmelpfennig et al., 1996). Climate change in other
regions and countries may affect agricultural production in those areas. As national and
global agricultural commodity markets adjust to these changes in production, commodity
prices facing Mid-Atlantic farmers could change. Climate change may also have impacts
on nonagricultural sectors of the Mid-Atlantic economy or economies of other regions
and countries. These changes, which we refer to as economywide effects, might manifest
themselves as changes in prices of purchased inputs used by Mid-Atlantic farmers,
competing demands for land within the region, or alternative employment opportunities
available to Mid-Atlantic farmers.
Potential climate change impacts on Mid-Atlantic agricultural production in the year
2030 are shown in Table 3.1.3. Impacts are reported under our two alternative baseline
scenarios - a smaller, more environmentally friendly agriculture (SEF), or a continuation
of the status quo (SQ). Each impact in Table 3.1.3 is classified as either a significant
increase (+), significant decrease (-), no significant impact in either direction (0), or
unknown (?) based on currently available knowledge. Table 3.1.3 also reports our
assessment of overall effects for each of the four crops and two livestock products.
Overall, the impacts of climate change on crop production within the Mid-Atlantic may
be beneficial. Soybean and tree fruit production within the region may increase under
both baseline scenarios due to COa fertilization effects, increased summer precipitation,
and reduced transpiration (for soybeans). Corn production will probably either not
change significantly (SQ scenario) or may even increase (SEF scenario). Of the four
crops, tobacco appears to have the highest probability of suffering production losses
because of climate change. Even here, the direct effects of climate change on production
within the region may on the whole be beneficial. However, similar direct effects are
also expected to be operating in other regions and countries, leading to increases in global
tobacco production and that would depress world tobacco prices and act as a disincentive
to tobacco production within the Mid-Atlantic.
48
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Table 3.1.3. Potential Climate Change Impacts on Agricultural Production in 2030
Impact Accounting for Adaptation by Producers*
(Significant Positive +, Significant Negative -, No Significant Impact 0, or Unknown ?)
Direct Effects**
Increased Photosynthesis
Reduced Transpiration
Higher Summer Temperatures
Increased Summer Precipitation
Changes in Extreme Weather
Events
Changes in Weeds, Insects, and
Diseases
Corn Soybeans
0 +
+ +
OSEF 0
-SQ
+ +
? ?
0 SEF 0 SEF
? SQ ? SQ
Tobacco
9
+
OSEF
-SQ
+
9
OSEF
?SQ
Tree Fruits
+
0
OSEF
-SQ
+
?
OSEF
?SQ
Dairy
0
0
0
0
0
OSEF
?SQ
Poultry
0
0
0
0
0
OSEF
?SQ
Indirect Effects**
Changes in Farm Commodity
Prices
Economywide Effects
OVERALL EFFECTS:
SEF Scenario
SQ Scenario
0
0
0
0
0
0
0
0
+
0
0
0
0
0
0
0
0
* Accounting for actions taken by producers to minimize negative climate change impacts on production and exploit positive impacts on production.
** Unless otherwise noted, the effect (+, -, 0 or ?) is the same in the SEF and SQ scenarios.
49
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We do not anticipate the effects of climate change on livestock production within the
Mid-Atlantic to be significant in either a positive or negative direction. For example,
increases in summer temperatures projected for the region will probably not be large
enough to be a major detriment to livestock production. This is particularly true for
confined livestock operations, where producers have a number of low-cost ways to adapt
to higher temperatures, including fans and improved ventilation. Confined livestock
operations already account for a significant proportion of total livestock production
within the region, and we anticipate that this proportion will increase. In principle,
climate change can also affect livestock production through changes in the quality and
availability of forage, or through changes in prices of purchased feeds. Here too,
however, available evidence suggests that there may be no significant impacts one way or
another.
The impacts in Table 3.1.3 take into account adaptation by farmers to climate change.
Farmers have a wide array of options at their disposal for minimizing negative impacts on
production and exploiting positive impacts. For crops these options including changes in
crop acreages, the types or varieties of crops grown, planting and harvesting dates, crop
rotations, tillage practices, fertilization practices, and pest management practices. For
livestock these options include changes in herd sizes, livestock types or breeds, feeding
rations, and heating and cooling systems.
Potential climate change impacts for the year 2030 on the environmental side effects of
agricultural production are shown in Table 3.1.4. Impacts are reported in Table 3.1.4
under the two alternative baseline scenarios and are classified as either a significant
increase (+), significant decrease (-), no significant impact in either direction (0), or
unknown (?) based on currently available knowledge. An increase implies a worsening
of an environmental problem, while a decrease implies an environmental improvement.
Table 3.1.4 first reports impacts assuming that farmers do not adapt in any way to climate
change, and then brings potential environmental side effects of farmer adaptation into the
picture. Impacts assuming no farmer adaptation are based on existing studies (e.g.,
Favis-Mortlock and Savabi, 1996; Follett, 1995; Phillips et al., 1993; and Rosenzweig
and Hillel, 1998). Environmental side effects of farmer adaptation are based on changes
in crop acreages, crop management practices, and other factors that we anticipate might
occur as a result of the production impacts reported in Table 3.1.3.
Many of the impacts of climate change on environmental side effects of agricultural
production are very difficult to assess given current evidence. In particular, changes in
extreme weather events such as floods or heavy downpours could easily overwhelm the
other effects in Table 3.1.4, but we lack good evidence on how these extreme events
might change. Leaving aside extreme events, nutrient leaching and runoff from livestock
may increase in the SQ scenario, primarily due to an increase in summer precipitation. In
the. SEF scenario, no such increase occurs because in this scenario livestock producers are
subject to stricter environmental regulations that limit nutrient losses. In the SEF
scenario, livestock production is also much smaller than in the SQ scenario and has fewer
50
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Table 3.1.4. Potential Climate Change Impacts on Environmental Side Effects from Agriculture in 2030
Impact
(Significant Positive +, Significant Negative -, No Significant Impact 0, or Unknown ?)
Nutrient Leaching Nutrient Leaching
and Runoff from and Runoff from Pesticide Leaching
Crops Livestock and Runoff
Water Erosion
Effects Assuming Farmers Do Not Adapt
to Climate Change*
Increased Photosynthesis
Reduced Transpiration
Higher Summer Temperatures
Increased Summer Precipitation
Changes in Extreme Weather Events
Changes in Weeds, Insects, and
Diseases
Effects of Farmer Adaptations to
Climate Change*
OVERALL EFFECTS:**
SEF Scenario
SQ Scenario
—
-
0
+
?
0
OSEF
?SQ
0
0
0
0
0
OSEF
+ SQ
9
0
0
0
+
OSEF
?SQ
OSEF
?SQ
0
OSEF
+ SQ
?
0
OSEF
-SQ
0
0
?
9
0
+
?
0
OSEF
?SQ
0
0
* Fanner adaptation is discussed in the text. Unless otherwise noted, the effect (+, -, 0 or ?) is the same in the SEF and SQ scenarios.
** Effects assuming no significant changes in extreme weather events.
51
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environmental side effects because of biotechnology that reduces the nutrient content of
animal wastes. Beyond this, based on the existing evidence we do not anticipate major
changes in either direction in environmental side effects.
For the year 2100, the same overwhelming uncertainties that make it impossible to
construct baseline scenarios also make it impossible for us to assess potential climate
change impacts on agricultural production or its environmental side effects.
3.1.5 Management and Adaptation Options
In their review of the literature on climate change and US agriculture, Lewandrowski and
Schimmelpfennig (1999) conclude that costly adaptation strategies are not warranted on
the basis of available evidence. Our assessment for the Mid-Atlantic leads to the same
conclusion. The impacts of climate change on Mid-Atlantic crop production may on the
whole be beneficial, while impacts on Mid-Atlantic livestock production will probably
not be large one way or the other.
Many adaptations to exploit opportunities created by climate change and minimize
climate-related risks will occur more or less autonomously as farmers and agribusinesses
react to experiences with climate change and evolving climate expectations. Agriculture
is an industry already very familiar with continual, rapid, and often tumultuous change.
Nevertheless, there are actions that can be taken to facilitate adaptation. Our assessment
of agriculture's adaptive abilities hinges in large part on the development and adoption of
new technologies, particularly biotechnology, precision agriculture, and improved
climate forecasting. Farmers will need to have the education and skills to understand and
exploit these technologies. Public- and private-sector agricultural and meteorological
research organizations will need employees with the scientific skills to build on today's
technologies. This poses a challenge for educational institutions within the region,
particularly the region's land-grant institutions.
One potential threat to adaptation identified in previous assessments for other regions of
the US is access to additional irrigation water, particularly in the face of growing
demands for water from other sectors (Lewandrowski and Schimmelpfennig, 1999).
Based on available evidence, this would not appear to be a major concern for the Mid-
Atlantic because less than 3% of its crop acreage is irrigated. Irrigation is currently
uneconomic for most crops in most parts of the Mid-Atlantic, and projections suggest that
regional precipitation may increase under climate change.
3.1.6 Priorities for Research and Information
Several types of additional research and information would be useful. However, three
stand out as priorities for Mid-Atlantic agriculture and perhaps other regions of the US as
well:
Climate Change and Weeds, Insects, and Diseases
52
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Climate change is likely to affect pest-crop and pest-livestock relationships, but we have
very little evidence on how these relationships are likely to change (Rosenzweig and
Hillel, 1998). Consequently, we were forced to largely ignore them for purposes of this
assessment, even though they could be more important than many of the impacts we did
consider. Additional research on these relationships is needed at all levels - from the
level of individual weed, insect, crop, and livestock species to the aggregate ecosystem
level.
Extreme Weather Events and Agriculture
Additional research is needed on the effects of climate change on extreme weather events
and in turn on agricultural production and environmental side effects from agricultural
production. We currently lack good evidence on how the timing, frequency, and intensity
of extreme events might change, particularly at a regional level. We also lack good
evidence on how changes in extreme events might affect agricultural production and its
environmental side effects.
Climate Change and Environmental Side Effects from Agriculture
The vast majority of research to date on climate change and agriculture has focused on
agricultural production impacts. Very little work has been done on how climate change
might lead to changes in the environmental side effects of agricultural production. To
our knowledge, no research has been done at all that considers how responses by farmers
to climate change might mitigate or exacerbate environmental side effects. Given the
magnitudes of environmental side effects in many areas, including the Chesapeake Bay,
this should be a high priority for research.
53
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3.2 Forestry (DeWalle, Easterling, Iverson, Prasad, Rose, Buda, Cao)
3.2.1 Current Status and Stresses
3.2.1.1 Forests of the Mid-Atlantic Region
Forests are the dominant land cover in the Mid-Atlantic Region with a rich mix of species
diversity from the pine and coastal wetlands regions in the south to the northern upland
hardwoods (Figure 3.2.1). In terms of current volumes of growing stock, dominant
hardwood species are red oaks, white oak, yellow-poplar, red maple, sugar maple, black
cherry, beech and sweetgum. Softwood forests are dominated by loblolly, shortleaf, and
white pines and hemlock. Many other species are abundant only in localized parts of the
region. The dominant forest types in the region are oak-hickory (46% of area) and
maple-beech-birch (37% of area), with pine and mixed pine-hardwood forests
representing about 8% of the forest area.
No data
I Longleaf-Slash Pine
Loblolly-Shortleaf Pine
I Oak-Pine
I Oak-Hickory
I Oak-Gum-Cypress
Elm-Ash-Cottonwood
Maple-Beech-Birch
Figure 3.2.1. Distribution of Major Forest Types in the Mid-Atlantic Region. (Based upon Forest
Inventory and Analysis data collected by USDA, Forest Service and compiled by Iverson (1996).)
Forests in the region were extensively cut for wood products in the early 1900's. Active
management and protection from fire since then has resulted in second-growth forests
that are rapidly approaching maturity. Forests in the region as a whole are primarily in
10-12 inch (25-30 cm) diameter classes, however substantial volumes of sawtimber exist
in the larger diameter classes (Powell et al. 1994).
Forested area in the Mid-Atlantic states is relatively stable, decreasing very slowly by
about 1% per decade (Powell et al. 1994). Most of the forests (88%) in the region are
privately owned and management decisions rest largely with non-industrial private
landowners.
54
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Net volume of forest growing stock in the Mid-Atlantic Region states for hardwoods is
steadily increasing, but growth rates are slowing as the forests approach maturity (Figure
3.2.2). Softwood growing stock volumes have leveled off somewhat and are expanding
only very slowly. Net growth of wood (growth minus mortality) currently exceeds
removals for wood products by about two to one for the region; softwood=1.25 and
hardwood=2.18. Mortality is only about 0.6 to 0.8% of growing stock annually.
Net Volume Growing Stock: Hardwood vs. Softwood
120000
100000
80000
60000
2
et
B
1
O
i
I 40000
20000
1952
1962
1977
Year
1987
1992
Figure 3.2.2. Trends in the Net Volume of Forest Growing Stock
for States in the Mid-Atlantic Region (Powell et al., 1996)
Forest products produced in this region are primarily sawlogs, pulpwood, fuelwood, and
veneer logs, and other products such as maple syrup, nuts and edible plants. Forests in
this region are also highly valued for the recreational, watershed and riparian buffer,
wildlife, biodiversity and other ecosystem benefits they provide.
3.2.1.2 Role of Mid-Atlantic Forests in the Regional Economy
This analysis examined forest-related economic activity in 9 sectors: Forest Products,
Forestry Products, Forestry/Services, Logging Camps & Contractors, Sawmills, Millwork
& Plywood, Other Woodproducts, Wood Furniture & Fixtures, and Paper and Paper
Products. The combined total gross output (sales revenue) of these sectors in 1995 was
$41.9 billion, or 2.5% of the $1,671.2 billion total gross output in the Mid-Atlantic
Region. This small percentage, however, understates the economic role of forest-related
sectors. First, these sectors stimulate additional production and employment in supplier
and customer sectors through backward and forward linkages, respectively. Second,
55
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forests provide a base for hunting, camping, hiking, birdwatching and fishing, which
contribute to the service and other sectors of the economy. In addition, forests provide a
range of non-market services such as carbon sequestration and wildlife habitat.
An input-output economic analysis for the forest-related sectors in the Mid-Atlantic
Region yields insight into the interconnections among the individual forest-related sectors
and their role in the regional economy (Table 3.2.1). The rows labeled Rl to R9 depict
the sales of each of these products by businesses within the Region for intermediate
demand, for consumer demand, and for export. Analogously, the rows labeled Ml to M9
depict imports of each of these products by each of the demand categories. Despite the
Region's extensive forest resources, more than half of each of the nine forest-related
sector products is imported. At the same time, the Region exports $27.0 billion of its
$41.8 billion production of forest-related products, or well over 50%.
Economic interdependency with the rest of the US stems from the fact that the Mid-
Atlantic region borders regions with extensive forest resources as well, and hence many
Mid-Atlantic businesses may be closer to suppliers and customers in other regions than to
suppliers and customers within the region itself. It also stems from the uniqueness of
some resources (e.g., hardwoods) that have a broad export market both domestically and
internationally. Finally, the relatively high level of aggregation in Table 3.2.1 obscures
the production of specialty products (e.g., wood furniture and printing) that are typically
not self-contained within any one region. The implications are that climate change
effects on forests in the Mid-Atlantic region will have an economic ripple effect on other
regions and vice versa.
Despite economic linkages to other regions, forest sectors within the Mid-Atlantic Region
are also highly interdependent, as is evidenced by the large numbers in the sub-matrix of
rows Rl to R9 and columns 1 to 9 in Table 3.2.1. For example, the inputs of Millwork &
Plywood, Other Woodproducts, Wood Furniture & Fixtures, and Paper & Paper Products
are mainly from Sawmills as indicated by the transaction from Sawmills to those four
sectors of $260.4 million, $322.5 million, $137.9 million, and $233.3 million,
respectively. Also, Regional Logging Camps & Contractors supplied the majority of
inputs to Sawmills and Pulpmills in the region.
3.2.1.3 Current Stresses on the Mid-Atlantic Forests
Forests in the Mid-Atlantic Region are currently stressed by factors that are natural or
linked to human activities: loss of forest land to urban/suburban development, insects and
diseases (especially gypsy moths), atmospheric pollution and wildfire. Although overall
forested area currently is declining only slightly, increased urban/suburban development
could contribute to decreased forest land areas in localized areas. Fragmentation of
forests into smaller areas due to development, which may limit the ability of plant and
animal species to survive and migrate, is a related stress.
56
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Table 3.2.1. Forest-related Sector Flows in the Mid-Atlantic Region Input-Output Table, 1995:
Intra-Regional and Import Flows (in millions of 1995 dollars)
Forest-Related Subtotal Regional Inputs:
Rl. Forest Products
R2. Forestry Products
R3. Agricultural, Forestry, Fishing Services
R4. Logging Camps & Contractors
R5. Sawmills
R6. Millwork & Plywood
R7. Other Woodproducts
R8. Wood Furniture & Fixtures
R9. Paper & Paper Products
Total Regional Intermediate Inputs
Total Imported Inputs
Forest-Related Subtotal Imports:
Ml. Forest Products
M2. Forestry Products
M3. Agricultural, Forestry, Fishing Services
M4. Logging Camps & Contractors
M5. Sawmills
M6. Millwork & Plywood
M7. Other Woodproducts
M8. Wood Furniture & Fixtures
M9. Paper & Paper Products
Total Value Added
Total Gross Outlay
Intermediate Sector Demand
1
6.2
0
0
5.6
0
0
0
0.6
0
0
148.2
81.5
15.3
0
0
14.5
0
0
0
0.8
0
0
2
107.8
0
1.9
105.9
0
0
0
0
0
0
316.7
450.6
347.9
0
73.1
274.6
0
0
0
0
0
0.2
3
1.5
0
0
1.4
0
0
0
0
0
0.1
211.1
26.9
10.6
0
1.8
3.7
0
0
0
0
0
5.1
4
77.8
0
10.8
0
65.7
1.3
0
0
0
0
246.0
556.8
464.2
0
409.6
0.1
52.8
1.3
0
0
0
0.4
5
768.0
0
8.0
0.2
563.1
193.3
0
3.4
0
0
1465.5
1087.5
958.3
0
301.5
0.6
452.6
196.2
0
5
0
2.4
6
516.6
0
2.3
0.2
88.6
260.4
121.6
43.2
0
0.3
1074.7
743.7
543.5
0
85.2
0.5
71.2
264.3
43.3
62.4
0
16.6
7
607.5
0
0
0.3
64.5
322.5
53.9
147.7
18.4
0.2
1438.6
1018.5
635.5
0
0
0.7
51.8
327.3
19.2
213.3
10.3
12.9
8
359.5
0
0
0.4
0
137.9
47.7
48.6
123.7
1.2
1397.3
1205.0
371.0
0
0.9
1.1
0
140.0
17.0
70.1
69.1
72.8
Total F-R
Interned
Sales
9
637.4
0
0
1.8
317.8
233.3
0
8.2
0
76.3
6641.0
7107.2
5024.2
0
0
4.6
255.4
236.8
0
11.9
0
4515.5
3082.3
0
23.0
115.8
1099.7
1148.7
223.2
251.7
142.1
78.1
12939.1
12277.7
8370.5
0
872.1
300.4
883.8
1165.9
79.5
363.5
79.4
4625.9
Personal
Consump-
tion
1972.0
1.1
6.5
31.3
0
2.3
10.6
168.1
1709.5
42.6
483426.4
177705.2
4127.3
75.5
245.7
81.2
0
2.4
3.8
242.7
954.2
2521.8
Exports
29942.1
291.2
866.7
2478.6
85.4
1726.6
183.2
2384.7
1271
20654.7
686481.3
0
Other
Final
Demand
1202.9
0
0.2
96.7
15.0
10.7
34.4
217.7
809.5
18.7
430147.2
56389.1
2170.2
3.0
6.3
250.7
12.1
10.9
12.2
314.3
451.9
1108.8
Total Gross
Output
41826.1
292.2
906.3
3453.8
1217.9
3720.0
3206.0
3846.1
4212.5
20971.3
1131387.7
502838.8
32822.4
78.6
1385.8
2512.4
905.1
2115.3
1083.2
2092.3
1702.2
20947.5
62.4 138.9 3130.6 415.5 1167.1 1387.6 1389.0 1610.2 7223.0 16524.3 20904.7 13556.8 2468.4 36929.9
292.2 906.3 3453.8 1217.9 3720.0 3206.0 3846.1 4212.5 20971.3 41826.1 682036.3 349204.5 489004.7 1671156.4
Each entry in the main body of the table represents a sale from the sector (in the MARA Region) indicated by the corresponding row label to the MARA Region sector indicated by the
corresponding column label.
(Computed from IMPLAN, 1997)
57
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Forests in the Mid-Atlantic Region periodically experience problems due to insects and
diseases. In particular, gypsy moth larvae have caused extensive and locally heavy
defoliation of hardwood (especially oak) forests over the past 1-2 decades in all Mid-
Atlantic Region states except North Carolina. Successive years of defoliation have led to
tree mortality in localized areas. Fortunately, forests generally survive isolated
defoliation events even though trees are undoubtedly weakened.
Atmospheric pollution leading to high levels of deposition of acidic compounds and high
ground levels of ozone also are stressing forests in the region. Acidic atmospheric
deposition of nitrate and sulfate can accelerate leaching losses of base cations from forest
soils. Base cations such as calcium and magnesium are needed to maintain forest health
and growth.
Wildfires are not a serious problem currently within the Mid-Atlantic Region. However,
occasionally in dry years wildfires damage large acreages of forest land, especially in the
south of the region.
3.2.2 Effects of Current Climate Variability
Because little specific information was available on how climate currently affects forestry
activities in the Mid-Atlantic Region, we developed a questionnaire to investigate how
extreme weather affects day-to-day forestry operations. The questionnaire was targeted
to government agencies (federal and state), private firms (consulting foresters, loggers,
and industrial foresters), and urban and municipal foresters within the Mid-Atlantic
region. The questions were designed to obtain responses about effects of extreme
weather on specific aspects of forestry operations, coping mechanisms currently being
employed or contemplated, and effects on costs of operation and income. Respondents
were identified via a random sample. A total of 592 surveys were mailed in late
November 1998 followed by a second mailing to non-respondents in January 1999. A
total of 322 surveys were returned, yielding an overall response rate of 57% after
correction for erroneous addresses.
Respondents primarily represented private forestry firms (159 consulting foresters,
logging companies, and industrial foresters) and public forestry agencies (114 state and
federal agencies/offices). Of the total respondents, 66 percent operated in the upland
hardwood forest type, while 22 percent operated only within the pine types. Watershed
protection (60%), harvesting sawtimber (59%) and maintenance of forest aesthetics
(38%;) were the three management objectives most commonly cited by respondents.
58
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25
20
oo
1
10-
o4
High Winds High Rainfall Low Rainfall Heavy
Snowfall
Ice Storms High Air Low Air
Temperatures Temperatures
Figure 3.2.3. Effects of Extreme Weather Events on Forestry Activities in the Mid-Atlantic Region.
Percentage of 322 Respondents Indicating Major Impacts Due to Each Type of Extreme Weather Event.
Several types of extreme weather events currently have major impacts on forestry
activities in the Mid-Atlantic Region (Figure 3.2.3). Over 15% of respondents
report major impacts (5 point scale from l=no impact to 5=major impact)from high
winds, high rainfall, low rainfall, heavy snowfall and ice storms on their forestry
operations. Fewer than 5% of respondents perceived major problems from extremely
high or low air temperatures. Extreme rainfall, snow and ice events cause problems by
reducing access to forest lands and increasing the cost of road maintenance. Dry periods
had the opposite effect, improving access to normally wet areas. Respondents associated
wind and ice events with direct damage to trees. As a result of these extreme weather
events, respondents indicated that they had to modify their management activities and
their costs of operation generally increased. It is anticipated that the magnitude of cost
increases will be depend on the management objectives and type of forestry operation
(private firms should be affected more directly than government agencies) and the
specific geographic location of the respondent. Further analysis will explore these and
other hypotheses.
3.2.3 Future Effects of Climate Change
Currently, Mid-Atlantic forests exist under a climatic regime that is relatively hospitable
to tree growth and survival. Trees grow in parts of the United States that are significantly
hotter or colder, wetter or drier than the Mid-Atlantic region. However, individual tree
species could have specific temperature and moisture requirements that will make them
vulnerable to climatic change. Or some other factor critical to a species survival (e.g. a
particular soil type) may no longer occur where climate is acceptable. In addition,
species may be unable to migrate to keep pace with a changing climate. Secondary
59
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I 0 ^ impacts of climate change, such as changes in pests, fire frequency, and climate-sensitive
OP \ \VY soil processes, will also be important.
^ V*1
"X\ u The IPCC 2nd Assessment report (1996) concluded that, although changes in the potential
VI y area of temperate forests (which includes the Mid- Atlantic) are projected to be less than
for other latitudinal zones, they are likely to undergo significant changes in species
composition. Forests will be directly impacted by changes in temperature, solar
radiation, soil moisture, and concentrations of atmospheric COi. Possible indirect
impacts include changes in climate-sensitive soil processes (such as erosion and nutrient
leaching) and changes in weeds, insects and disease that are themselves affected by
changes in climate and atmospheric constituents. Impacts may be positive or negative,
and will likely vary among species. Higher temperatures could increase forest primary
production if more moisture is available.
Iverson and Prasad (1998) examined potential changes in the distribution and abundance
of 80 tree species in response to climate change in the eastern US using a statistical
approach. Predictive models were developed for each of 80 tree species, using regression
tree analysis (RTA) combined with data in a geographic information system (GIS) format
to relate current species' distributions to environmental factors, including climate.
Models were applied to climate change data predicted using two global climate models
GCMs (GISS and GFDL) to predict potential changes in tree species' distributions in
response to climate changes associated with a doubling of atmospheric
As part of the Mid- Atlantic Regional Assessment, Iverson and Prasad extracted results
for the Mid- Atlantic region from those of their previous analysis and added prediction
results for three additional GCMs~the United Kingdom Meteorological Office (UKMO),
Hadley Centre (HAD), and Canadian Climate Centre (CCC) models. As before, the
climate scenarios were based on a doubling of CC«2. For comparison with the transient
GCM scenarios used in other portions of the Mid- Atlantic study, if greenhouse gases
increase at a rate of 1 percent per year (as assumed in developing the transient climate
scenarios), a doubling will occur in 71 years, or by year 2064. Of course, because of tree
longevity and remnant refugia, it could take centuries for these shifts to be fully realized.
Under all five GCM scenarios, maple-beech-birch forest is no longer a dominant type in
the Mid- Atlantic region (Table 3.2.2). Predictions suggest that maple-beech-birch forest
would be replaced by oak-hickory forest across the northern regions. Southern pine and
mixed oak-pine forest also moves northwest to some degree under each of the scenarios.
Results for the UKMO scenario are the most extreme, showing mixed oak-pine forest
becoming important as far north as western PA and southern NY. Results under the
Hadley scenario show the least areas of pine and mixed pine forest. Overall the predicted
diversity of forest types is reduced under all of the climate change scenarios.
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Table 3.2.2. Current and Predicted Potential Areas (%) of Selected Forest Types
in the Mid-Atlantic Region (Predicted by Iverson and Prasad)
Forest Type
Maple-beech-birch
Oak-hickory
Oak-pine
Loblolly-shortleaf pine
Current
Area (%)
37
46
4
4
Future Areas (%)
Predicted with Several Global Climate Models
GISS
2
66
6
18
GFDL
0
68
28
4
UKMO
0
46
50
2
HAD
0
82
16
1
CCC
0
72
23
6
The above analysis represents one equilibrium approach to modeling the potential effects
of climate change on forests due to a doubling of COa. However, it does not provide
estimates of changes in forest productivity or carbon storage, nor does it incorporate
direct effects of elevated C02 on plant growth and water use. Model outputs also assume
that species would be able to migrate unencumbered into the new habitat. Results must
also be extended to the response of individual species, rather than just forest types. Loss
of a forest type does not necessarily mean that all species within a type will be lost.
Forest succession models, also known as gap models, have also been applied to simulate
the temporal dynamics of changing forest species composition and carbon storage due to
climate change, but extensive data requirements prevent application for the Mid-Atlantic
region at this time. Earlier VEMAP research (VEMAP members, 1995) compared
potential large scale vegetation shifts due to a doubling of COa to show that cool-
temperate forests, such as maple-beech-birch in Iverson and Prasad's results, could be
displaced by warm temperate forests. VEMAP results thus support findings in the Mid-
Atlantic regional studies.
3.2.4 Management/Adaptation Options
To help manage problems caused by gradual tree species shifts to more pine and oak-
hickory forests, long-term management and silvicultural plans should encourage species
best suited to oak-hickory and oak-pine types and cutting schemes likely to minimize
wind and ice damage problems that may occur along with climate change. Increased
wildfire and insect/disease problems will also likely occur, which would increase overall
forest land management costs.
Land use planning could minimize forest fragmentation. This would make it more likely
that tree species and their accompanying wildlife could migrate as climate conditions
change.
Possible mitigation to counter potential impacts of climate change that can be adapted to
the Mid-Atlantic Region could be sequestering more CO2 by encouraging more tree
planting on urban and marginal agricultural lands or reducing CO2 emissions by recycling
more paper and burning biomass rather than fossil fuels (EPA 1995).
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3.2.5 Priorities for Research and Information
To understand the impact of climate change on forestry in the Mid-Atlantic region we
need further information and analysis. To complete a comprehensive analysis, we need
,to compile all county forest inventory data at the county level rather than relying on state-
level data such as that included in this report. A more complete statistical analysis of our
questionnaire data is also needed to help predict differences in the effects of severe
weather on forests managed by private or public agencies and on geographically disparate
forests. Finally, the predicted response of individual forest tree species to climate change
must be included.
Answers to some of the above research questions also can shed light on how climate
change might affect diverse functions provided by forests in the Mid-Atlantic region. For
example, little currently is known about how changes in the dominant tree species will
affect a forest's capacity to filter water, or the timing of water flows through the forest to
groundwater or streams. Similarly, changes in dominant tree species will affect other
components of the forest ecosystem in an area. Research on how the forest ecosystem
might change could be an input into additional study of how important those changes are.
There is a significant need for analyzing economic and policy responses using integrated
ecological and economic modeling approaches. This type of modeling can be used to
gain insight into economic consequences of changes in species mix and primary
productivity. It can also be used to analyze and evaluate adaptation policy options, and
the interaction between biological and economic adaptation. Finally, because of the
central role of forests as a source of non-market goods, research on non-market impacts
of climate induced change in the regions' forests is crucial.
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3.3 Water Resources (Yarnal)
Overall, the Mid-Atlantic Region has abundant fresh water resources. The average
annual rainfall total of approximately 43 inches is distributed somewhat evenly
throughout the year, suggesting that there are ample quantities of water available in all
seasons for public, domestic, commercial, industrial, and other uses.
Fresh-water is withdrawn from both surface and ground water (Solley et al., 1998). In
1995, approximately 92% of all Mid-Atlantic Region withdrawals were from surface
water (i.e., lakes, reservoirs, and streams) and 8% were from ground water (i.e., wells).
Delaware and Maryland used proportionally more ground water than other Mid-Atlantic
states (15% and 17% of their withdrawals, respectively). In contrast, West Virginia and
the District of Columbia used much less ground water than other political units in the
region (3% and 5% of their respective total withdrawals), relying more heavily on surface
water.
As the MAR state with the largest population, Pennsylvania used more fresh water in
1995 than any other state in the Mid-Atlantic Region - 44% of total regional
withdrawals. However, Pennsylvania's per capita use was moderate. Virginia (25%),
West Virginia (21%), Maryland (7%), and Delaware (3%) followed Pennsylvania in
order. To achieve its large withdrawals with its relatively small population, West
Virginia had per capita water use that was 2.5 to nearly 9 times that of the other states in
the region-primarily because of industry and thermoelectric cooling.
Three categories dominated 1995 fresh-water use in the Mid-Atlantic Region:
thermoelectric power generation (62%), industry (17%), and public supply (16%).
Domestic supplies only used 2% of the region's fresh water supply. A large proportion
of the domestic water supply in the region comes from privately owned wells. For
instance, in the Susquehanna River Basin, over one third of all households derive their
water from such wells. Irrigation accounts for less than 1% of total fresh-water use.
Despite the fact that water is generally available in the Mid-Atlantic Region, certain
stresses affect the quantity and quality of water available to the population. One set of
stressors relates to the people of the region, their activities, and the environmental
impacts of these activities. Another important stressor is climate variability. The
following subsections discuss these.
3.3.1 Human Activity and Environmental Impacts on Water Resources
Before European settlement the native peoples of the Mid-Atlantic Region had little
impact on water resources (e.g., Cooper and Brush, 1993). Early European settlers also
had minimal influence, but by the late 18th century rapid population growth and
associated land clearing, agriculture, and construction produced severe sedimentation of
the water bodies of the region. Although land clearing, agriculture, and therefore
sedimentation declined rapidly after the Civil War, industrialization and continued
population growth resulted in other forms of water pollution. Many of the most severe
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problems have been cleaned up in the last few decades, although effects linger from acid
mine drainage. Nevertheless, since World War II, extensive development around and
between urban centers, increased use of fertilizers, and increased atmospheric nitrogen
deposition further degraded the waters of the region.
Human activities have affected the nutrient loading of the region's water resources in
many ways (Walker et al., 1999a). For example, increased nutrient loading is associated
with increased occurrence of hypoxic and anoxic conditions, excessive algal growth,
blooms of undesirable algae, and changes in submerged aquatic vegetation. On the one
hand, estimated phosphorus loading from the land, through the rivers, and into the
, estuaries and bays increased slowly between 1900 and 1945, then began a sharp increase
that lasted until 1971. The majority of this increase was due to wastewater inputs. Since
1971, a ban on phosphorus in detergents and improvements in wastewater treatment have
led to a substantial decline in phosphorus inputs from wastewater. On the other hand,
agricultural inputs of nitrogen to the Mid-Atlantic Region's estuaries and bays have risen
steadily since World War II. Estimated nitrogen inputs attributable to atmospheric
deposition rose steadily until 1970 but have fallen somewhat since then. Direct
wastewater contributions of nitrogen have risen systematically throughout the century.
The net effect has been a dramatic increase in the nitrogen flowing from the land.
3.3.2 Impact of Climate Variability on Water Resources
Climate variation has a large impact on the water resources of the Mid-Atlantic Region.
Figure 3.3.1 shows the close relationship between precipitation and stream flow from the
Potomac River Basin (adjusted R2 of 41% for the unsmoothed values) for a recent 13-
year period. Note that stream flow values naturally lag precipitation, as water moves
over the land or through the soil to streams. The monthly average curves in the figure
show the close association between high stream flow and basin-wide weather and climate
events, such as the March 1993 "superstorm," the spring melt after the record snow year
of 1993-94, the January 1996 flood (Yarnal et al., 1997), and the 1996 record wet year
(Yarnal et al., 1999a). In contrast, drought periods, such as the 1991 and 1995 droughts
are readily apparent only in the smoothed curves.
Climate variation affects the quality of the region's waters as well as the quantity of
water available. For example, Walker et al. (1999b) and Yamal et al. (1999b) have
demonstrated a chain of associations linking the global-scale atmospheric circulation,
MidrAtlantic Region climate, regional stream flow, nutrient flows to the region's
estuaries and bays, and oxygen conditions in these water bodies. Cronin and
collaborators (personal communication) have found similar associations between the
global-scale El Nino-Southern Oscillation, regional climate, stream flow, and
sedimentation in the Chesapeake Bay. This is in part due to the role of dilution as a
solution to pollution. Low stream flow years typically continue to receive approximately
the same waste load discharges from industry and municipal sources. Drought periods
then lead directly to water quality impacts. Climate variation also can affect water
quality if severe storms cause higher rates of nutrient, pesticide, and sediment runoff into
streams than moderate storms. Such impacts could occur even without a change in total
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expected precipitation. These linkages have clear implications for potential water quality
impacts from climate change.
3.3.3 Potential Impact of Climate Change on Water Resources
Climate change projections from the transient Canadian Climate Centre and Hadley
Centre GCMs show the Mid-Atlantic region receiving much more precipitation (getting
much wetter) in all seasons, with greater increase in the warmer portion of the year.
Empirical downscaling by Crane and Hewitson (1998) using the GENESIS GCM in a
2XCO2 experiment agrees with these findings. Suggesting that climate change may
already be underway, Karl et al., (1996) observed that the region has had a strong
increase in precipitation over the past century, including a significant upward trend in the
number of intense precipitation events. Models and empirical work show little support
for overall drying in the region from climate change.
Because of the close association between water resources and climate variation
demonstrated above, it seems likely that water resources management in the future
generally will need to address issues associated with increased moisture, rather than
decreased moisture. For example, Walker et al. (1999b) suggest that if stream flow rises
in the future, then more nitrogen will go to the region's estuaries and bays and general
ecological health of these water bodies will decrease. To meet goals of reducing nitrogen
inflows and increasing ecological health, it will be necessary to strengthen controls on
sources of nitrogen, such as fertilizer use and atmospheric deposition.
Care must be taken, however, not to assume that dry periods will not occur - recent
experience suggests that severe drought can be embedded in overall moist regimes.
Indeed, future dry periods expected with normal climate variation could be intensified by
the higher temperatures projected for the region. Thus, we expect overall wetter
conditions punctuated by sharp drought in the future.
Research is underway to project future hydrologic regimes associated with regional
climate change projections. Based on the relationships established between regional
stream flow and precipitation (e.g., Figure 3.3.1), it is possible to model future stream
flow by using the GCM precipitation projections and observed stream flow (Figure
3.3.2). Similar techniques can be used to project future regional ground water trends
(Figure 3.3.3).
Each of these relationships must carefully consider use patterns, trends, and projections in
order to accurately assess potential scarcity. For example, groundwater levels are
affected by groundwater withdrawals. Therefore, projected future groundwater
withdrawal will strongly affect future groundwater availability. Use projections should
be considered in conjunction with hydrologic supply projections for this purpose. The
seasonal timing of use and availability is particularly important.
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2500 T
Stream Flow = 442.08 + 110.04*Precip - 8.99*Temp
Adj. R2 = .41
0
01/1985
Mi 1 i '' "5I
mrnm
01/1987 01/1989 01/1991 01/1993
Date (month/year)
01/1995
01/1997
•Monthly Average Flow
- Precipitation
"Precipitation Trend
"Flow Trend
Figure 3.3.1. Monthly stream flow and precipitation for the Potomac River Basin, 1985-97.
2500 T
01/1985 01/1987 01/1989 01/1991 01/1993
Date (month/year)
01/1995
01/1997
•Observed Stream Flow
Predicted Stream Flow
3.3.2. Observed vs. predicted stream flow, Potomac River Basin, 1985-1997.
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0)
en
I
£
ra
a
-2.54
1970
1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996
Year
-Predicted Groundwater Levels —Observed Groundwater Levels
3.3.3. Observed vs. predicted ground water levels for central Pennsylvania.
3.3.4 Management and Adaptation Options
As noted above, water resource management and adaptation strategies should focus on
problems resulting from increased moisture, including more severe precipitation events
(and associated phenomena, such as wind and lightning) and perhaps more frequent
floods. Although emergency management efforts for water resources at the state and
inter-state levels have tended to focus on drought contingency plans, greater emphasis
should be given to designing storm and flood contingency plans. Commerce, industry,
agriculture, and public utilities especially should develop management schemes and
adaptation options to deal with wetter conditions, including higher ground water tables,
stream levels, and reservoir levels. Stricter enforcement of the Clean Drinking Water Act
and increased support of small systems and of private well owners will ensure safer
drinking water supplies. It must be remembered that although conditions will be wetter
in general, sharp droughts should be expected and must be part of any water management
plan.
3.3.5 Priorities for Research and Information
Research must focus on how wetter conditions and severe storms affect water resource
management in the Mid-Atlantic Region. Impacts and response strategies for water
delivery and water quality must be studied for all primary through tertiary sectors.
Because winter 1993-94 was a record snow year throughout most of the region, and
because 1996 was by far the wettest year on record in much of the region, there are good
opportunities to use these years as analogs for future wet conditions in the Mid-Atlantic.
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Case Study
Water Managers Report Systems Are Vulnerable to Weather Variability
(O'Connor)
In 1998, 504 managers of water systems in Pennsylvania responded to a mail survey.
The table shows that most managers reported that their systems suffer difficulties from
weather events in a typical year. Power outages from storms, affecting the ability to
pump water, are the most frequent weather-related problems.
The Question: "How many times in a typical year has your current system experienced
some form of difficulty due to the types of events listed below?"
The Answers: (Percent who responded...)
Drought conditions lowered the supply of water in the
system
Drought conditions forced us to seek out another
source
Drought conditions led to significant increased
demand on our system
Flash floods have overloaded our recharge area's
ability to filter surface water naturally
Flash floods have increased the turbidity in our
surface water system
Storm water runoff has threatened our recharge
areas
Extremely high air temperatures have overloaded
electrical circuits and knocked out pumping stations
Extremely high air temperatures have increased
demand and thus strained our water supply
Extremely low air temperatures have frozen water
pipes that expanded and broke water lines
Electrical storms have led to power outages that
affected our ability to pump water
Heavy, wet snows have led to power outages that
affected our ability to pump water
Heavy winds have led to power outages that affected
our ability to pump water
"never"
60
88
58
94
75
89
90
72
67
32
55
56
"1 or 2 per
year"
35
9
34
6
14
9
9
23
27
58
42
42
"more often"
5
2
9
1
12
2
1
5
5
11
2
3
Managers who are having problems now expect that they will continue to suffer
disruptions in their daily operations in the next 5 years.
Perhaps surprisingly, larger systems report even more weather-related problems than do
smaller systems. Larger systems are often able to draw on more than one source of
water, yet this very complexity may increase their vulnerability to extreme weather
events. In any event, these data call into question assumptions that the current trend of
water system consolidation is reducing the vulnerability of water systems to weather
variability.
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Water system managers are ambivalent about climate change. When provided options,
only 9 percent checked that they are not concerned about climate change because it is
unlikely to happen. A much larger percentage (21 percent) checked that "global
warming is real" and that they are concerned. Still, the highest percentage (50 percent)
checked that they simply do not know what to believe about climate change. Another 18
percent checked that climate change may happen, but it is so long-term that adaptations
are beyond their planning horizon. Seventy-eight percent of managers indicate their
planing horizon is 5 or fewer years.
In summary, most managers of water systems in Pennsylvania today face disruptions
from weather events and expect these problems to continue. They report that they are
concentrating their energies on addressing current concerns amid a changing regulatory
climate. While only a small number reject climate change as unlikely, most are unsure
about climate change and, in any event, few plan beyond 5 years. This ambivalence
may well change in the direction of concern for adaptation if managers of water systems
learn that climate change impacts include more frequent and intense weather events.
Case Study
Recreational Fishing and Climate Change in MAR
(Heberling and Thornton)
The Mid-Atlantic Region (MAR) is home to many recreational freshwater fish species
including brook trout, rainbow trout, brown trout, largemouth bass, smallmouth bass,
catfish, carp, and panfish. Bass and trout species are the most popular for recreational
fishing.
Different types of fish thrive in different temperatures. Bass can be found throughout
warm waters in the MAR, but trout are cold-water species. The current southern
distribution of trout is limited to the Appalachian Mountains of North Carolina and Virginia
where higher elevations have cooler summer water. Brook trout typically are found in
the highest elevations, with rainbow and brown trout in lower elevations. Brown and
rainbow trout compete with brook trout for food and habitat; they also prey on brook
trout.
Water temperatures are affected by both groundwater temperature and local average
annual air temperature. If the climate warms, trout habitat will shrink in low elevations
and low latitudes, and bass habitat will increase. Increasing stream temperatures could
increase the mortality rate for brook trout, the least tolerant to temperature fluctuations.
It could also increase the competitive advantage of both brown and rainbow trout over
brook trout. Brook trout might be lost from many MAR streams.
Fish populations also follow changes in stream flow (one measure of fish habitat space).
Even though the Mid-Atlantic region may have more summer precipitation, warmer
temperatures may mean less snowpack and thus lower summer stream flows and lake
levels; in turn, this would reduce trout habitat. However, not all fish species will be hurt
by decreases in stream flow. In the Susquehanna River, low flows actually benefit
smallmouth bass populations while above-normal stream flows have produced the
smallest populations of smallmouth bass.
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Riparian and instream habitat restoration in upper reaches of some watersheds could
help maintain brook trout habitat, but two factors could offset the effectiveness of this
management option. First, increased temperatures will likely continue to favor brown
trout, which are likely to migrate into these improved habitats and continue to compete
with the brook trout population. Second, recent evidence indicates that past land use,
particularly for agriculture and poorly managed forestry, continues to have a present day
influence on stream invertebrates and fish. Instream sedimentation and loss of gravel
spawning areas are difficult to restore, even if the terrestrial watershed is restored to
forested land cover.
Thus the prospects for recreational fishing are complex in a climate change scenario.
Gains in warm-water habitat could moderate losses in cold-water habitat in terms of total
fish populations, but there still will be economic damages. Some economic damage
could be offset by anglers switching to other fish (i.e., cold-water anglers become warm-
water anglers). However, some cold-water habitat will not be suitable for warm-water
fish, so some fishing opportunities will be lost. In a related study, anglers were asked
their willingness to pay to avoid decreases in cold-water fishing opportunities
accompanied by increases in warm-water fishing opportunities (Heberling et al). On
average they were willing to pay around $4.00 per angler per year to avoid the changes.
Case Study Box PLACEHOLDER
Skiing (Dane)
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3.4 Coastal zones (Najjar)
3.4.1 Current status and stresses of the Mid-Atlantic Coastal zone
Three important characteristics are pertinent to understanding the Mid-Atlantic Coastal
Zone (MACZ) response to global climate change. First, Mid-Atlantic coastal waters are
highly productive, largely due to the region's numerous estuaries, including Chesapeake
Bay, the largest and most productive estuary in the United States. Second, the Mid-
Atlantic region's coastal counties are densely populated, containing 38% of the Mid-
Atlantic population in only 19% of its area. Third, the MACZ already is stressed by sea
level rise; this process is likely to accelerate as a result of climate change. Even without
climate change, these three characteristics will be important to the future of the MACZ.
3.4.1.1 Land Forms
Sea level has been rising throughout the past 20,000 years, molding the current Mid-
Atlantic coastline, which has three primary forms: drowned major river valleys, barrier
islands and coastal headlands. Drowned major river valleys, such as Chesapeake and
Delaware Bays, accumulate sediment that helps mitigate the impact of sea level rise.
Barrier islands are sand accumulations that separate a body of water, such as Pamlico
Sound, from the open ocean. Barrier islands have sand beaches, dunes, washover
features, and inlets. Estuaries near barrier islands, such as Barnegat Bay, tend to be
elongated in a direction parallel to shore. Coastal headlands, such as much of Monmouth
county and the northern portion of Ocean County in New Jersey, much of the Delaware
ocean shoreline, and Virginia Beach, have cliffs of various heights with a narrow fringing
beach and sometimes minor dune forms draped over the bedrock.
Slow sea level rise over the past 2500 years allowed barrier islands to form from sands
created by erosion during the previous period of rapid sea level rise. However, the
present rates for most of the MACZ are more than twice the rate of sea-level rise in these
same locations in the several millennia prior to 2500 years before now, on the order of
0.12 inch (3-4 mm) per year. This present rate is much faster than the delivery of
sediment to the barrier islands and estuaries, causing a loss of barrier island mass, with a
concomitant loss of wetland area. The larger estuaries have a similar scenario but for
different reasons. Clearing land for agriculture peaked in the 19th century, and caused
extensive erosion of upland soils. This material was transported to estuaries and near-
shore areas, contributing to the maintenance and development of marshes while the rate
of sea level rise was increasing. Since 1930, the sediment yields of major mid-Atlantic
rivers have decreased due to land abandonment, dam construction and efforts to curb soil
erosion. As a result, coastal areas along drowned major river valleys are, like barrier
islands, becoming inundated as a result of sea level rise. For example, records of the
Cape Henlopen lighthouse in Lewes, Delaware, show that over its 163 year life span, the
land it sat on eroded an average of 9 feet per year, finally causing the lighthouse to fall
into the sea (Kraft, 1992).
SAY SOMETHING ABOUT HEADLANDS? NOTHING TO WORRY ABOUT?
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3.4.1.2 Water quality
Overall, Mid-Atlantic estuaries are high in chlorophyll, nutrients and turbidity and low in
SAV. Hypoxic or anoxic conditions exist in at least one salinity zone in half of the 18
estuaries evaluated in the Estuarine Eutrophication Survey of the National Oceanographic
and Atmospheric Administration (NOAA, 1997a; NOAA, 1997b). Significant nuisance
algae are reported for half of the estuaries, but toxic algal blooms have had resource
impacts in only four bays, three of which are in North Carolina. Some water quality
parameters show definite trends over the past 25 years. Chlorophyll concentrations are
increasing, probably as a result of overenrichment, in Chesapeake Bay and in several of
its tributaries. Anoxia is increasing in the Bay and its Choptank River tributary.
3.4.1.3 Coastal ecosystems
Wetlands (MAY NEED TO SHORTEN)
Wetlands have many biological, physical, hydrological and chemical functions as well as
recreational, economical and aesthetic values. For example, wetlands provide coastal
protection by buffering storms. About half of normal wave energy is dissipated within
the first 10 feet of encountering marsh vegetation such as cordgrass, and completely
absorbed within 30 feet (Kesselheim, 1995). Wetlands also help to slow down erosion
and buffer against flooding. The roots of wetland plants hold soils in place against tides,
waves and wind. In turn, the soils act as giant sponges to soak up additional water from
heavy rains and increased river flow. Runoff is slowed by wetlands so that extra
nutrients, sediments, and pollutants are trapped before they enter coastal waters.
Because of their high primary productivity, wetlands in the Mid-Atlantic are important
grounds for food, shelter, spawning, nesting and predation. Fish and invertebrates, such
as weakfish, black sea bass, striped bass, herring, spot, summer flounder, blue crab,
eastern oyster, and horseshoe crab, need the wetlands of the coast to survive and
reproduce.
The dominant wetland ecosystems in the coastal zone (floodplain forest, tidal freshwater
marsh, and salt marsh) sort out on the landscape according to gradients in flooding
frequency, tidal amplitude and salinity. Floodplain forests occur adjacent to coastal plain
rivers in the Mid-Atlantic region. The forests are dominated by deciduous trees that
tolerate short to moderate periods of flooding. Southern Virginia and North Carolina
have more extensively flooded forests dominated by bald cypress and water tupelo.
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8 •*-
Coastal Ecosystem Divisions
Upland or
Wetland Forest
Shorebirds
High Salt Marsh
Low Salt Marsh
& Mudflats
Waterfowl
CO
£
o
0.
Depth of inundation
Benthic Algae
&SAVs
0 - 4 inches 4 inches 20 inches 3 to 6 feet
(0.1m) (0.1m) (0.5m) (1to2m)
Frequency of sea inundation (days)
Rare
Monthly
Twice Daily
Salinity
Continuous
< 0.5 ppt
35ppt
Adapted from
Bhnson, M.M., R.R. Christian, and L.K. Blum. 1995. Multiple states in the sea-level induced transition from terrestrial forest to estuary.
Estuaries 18:648-659.
The upper reaches of river-estuarine systems in the Mid-Atlantic region are both tidal and
freshwater. Such tidal-freshwater systems are dominated by marshes but may support
swamp forests too. They are habitat for diverse plant communities - including many that
are rare or endangered - and they are sources of the Chesapeake Bay's shad fisheries.
Salt marsh ecosystems (salinity >0.5 ppt) support little biodiversity compared with tidal
freshwater marshes, but there are several distinct community types that are characteristic
of particular salinity ranges. Because the salinity gradient in an estuary determines where
fresh- and saltwater marshes occur, these ecological communities are sensitive to changes
in both sea level and freshwater flow in rivers. Fresh- and saltwater marshes depend on
the vertical and horizontal accretion (build up) of the soil surface in order to maintain
themselves within the tidal range as sea level rises. The elevation of a sediment surface
is a dynamic balance between mineral and organic matter deposition, and subsequent loss
through subsidence (land level sinking) or erosion. For wetlands to persist, surface
accretion must balance both subsidence and sea level rise over a period of decades.
Furthermore, marshes must be able to migrate inland to balance losses to shoreline
erosion.
Sediment deposition includes organic and inorganic material settling from flood waters,
surface litter, and organic material injected through root growth. Organic matter can
account for 50 to 90% of salt marsh sediment volume (Bricker-Urso et al. 1989).
However, marshes with low rates of mineral deposition may be particularly sensitive to
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sea level rise because organic matter is more compressible than mineral matter and it can
be lost through decomposition. Such sites (e.g. microtidal marshes) typically lack
significant sources of riverine sediment.
Shorebirds (Richards)
Migrating shorebirds depend on the ecosystems of Mid-Atlantic for their survival. The
Delaware Bay, for example, is home to the second largest concentration of migratory
shorebirds in the Western Hemisphere (WHAT IS THE LARGEST?), with numbers
approaching 1.5 million birds stopping at the Bay to feed each spring (EPA, 1997). The
predominant species of shorebirds are the red knot, dunlin, sanderling, semipalmated
sandpiper, and ruddy turnstone. With up to 70 percent of the entire North American
population of the red knot in the Delaware Bay at one time (Sutton, 1996), these birds are
extremely vulnerable to any environmental changes to the ecosystem.
SECTION HERE ON OTHER PARTS OF COASTAL ECOSYSTEMS (by VIC)
3.4.1.4 Coastal development and use
The coastal areas of the Mid-Atlantic Region have important aesthetic and economic
value. The shore is a focal tourist destination, inviting investment in facilities to serve
their needs and desires. For many coastal areas, visitors and temporary residents exceed
the permanent resident population by an order of magnitude or more. The annual flux of
visitor populations to the coast is concentrated during the peak summer holiday but
extends to late Spring and early Fall, as well as weekends, holidays, and even winter
when one would expect few visitors. Even without climate change and sea level rise,
coastal communities and the natural resources that prove so attractive have been
vulnerable to the inexorable forces of nature-storms, coastal erosion, beach dynamics -
all anathema to the permanency of investment in such areas.
The intensive development of the MACZ is demonstrated by NOAA's data on building
permits for 1970-1989. Some X permits were issued for residential developments and Y
for non-residential purposes. While the most development occurred in the metropolitan
areas of Chesapeake Bay (Baltimore-Washington), the New Jersey shore, and Virginia
Beach, substantial growth also occurred on the Delmarva Peninsula and Outer Banks.
The Mid-Atlantic paralleled the nation as a whole in having a majority of the region's
single family construction and a significant proportion of new multiple family dwellings
occurring in coastal counties.
Location on the shore (versus near the shore) has an important contribution to coastal
property values. For Delaware, Parsons and Powell (1998) estimate that $90,000 of a
$200,000 home along the coast could be attributed to ocean frontage; bay frontage is
worth $15,200 and canal frontage $46,200. Thus proximity (and vulnerability) have
highest market value.
CAN WE FIND SIMILAR DATA FOR ESTUARY LOCATIONS?
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Beach communities in Delaware have housing units valued in aggregate at $3.5 billion;
5.1 million annual person-visits to the beaches generate $380 million plus $573 million in
trip-related expenditures (Faucett 1998). Beach nourishment to counteract Delaware's
annual 2-4 foot erosion rate costs about $1.8 million per year. In Maryland, some 277
miles of shoreland armoring (plus 52.6 miles of replacements) were permitted in the
period from 1978-1994 (Titus 1998: 1399). CAN JIM TITUS SEPARATE THIS INTO
BAY VERSUS OCEAN? Many homes and businesses are clearly within the limits of
designated floodplains (Figure here: FEMA MAPS). The high values of structures built
in vulnerable coastal locations are a concern to insurers and to the Federal Emergency
Management Agency (FEMA), which subsidizes insurance in these locations.
Public ownership of shoreland differs among the Mid-Atlantic states (Titus, 1998). New
Jersey provides for public ownership along the wet beach (i.e., below mean high tide),
with access along the dry beach (although communities effectively block such access by
parking restrictions for non-residents or those who are not local paying guests).
Maryland's public ownership also extends to the wet beach. In Pennsylvania, Delaware
and Virginia, the public owns only to the low water mark, but with tideland access for
navigation, hunting and fishing. These distinctions affect whether the policies in place
can maintain existing shoreline contours and uphold public access to the shore. (TITUS
EXPAND?)
Land use and tourist dynamics differ between the estuary shores and the oceanfront of
Mid-Atlantic states. Even at the oceanfront, important land use and property gradients
apply between oceanfront, inland, canal, and bay locations. The US Geological Survey
identifies 14 land use and land cover types in the coastal zone. Of these uses, eight have
moderate to high vulnerability to human-induced and natural processes of change. Many
of these land uses are subject to natural physical processes that will be exacerbated by sea
level rise.
The shores of the Mid-Atlantic region may not be among the nation's most spectacular in
terms of scenic beauty, but their proximity to large urban populations gives them special
significance. Many of these areas are already under stress from population pressure in
sensitive ecological zones. The existing dynamics of the coastal environment already
threaten some coastal areas, requiring substantial public investment to maintain a quasi-
status quo on the coast.
3.4.21 Climate sensitivity of the Mid-Atlantic Coastal zone
3.4.2.1 Water quality
Coastal water quality is generally correlated with water flow, which in turn depends on
precipitation and temperature. Most Mid-Atlantic estuaries have their lowest salinity
concentrations at the end of the spring, when high flows have diluted coastal waters.
Interannual variations in flow also affect salinity. Gibson and Najjar (1999), for example,
show that a 10% increase in the annual flow of the Susquehanna River results in an
annual average salinity decrease of 8% at the mouth of the Susquehanna River; the
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decrease is still 2% at the mouth of Chesapeake Bay. Other water quality variables in
Chesapeake Bay, such as nutrients, oxygen and water clarity, all have statistically
significant relationships with flow. Flow is positively correlated with chlorophyll and
nitrogen concentrations and negatively correlated with phosphorus concentrations and
water clarity (as indicated by Secchi depth measurements). These correlations result
from the input of nutrient rich water from rivers, which supports plankton blooms.
Seliger and Boggs (1988) demonstrated a highly significant correlation between
summertime anoxic volume in Chesapeake Bay and the spring flow of the Susquehanna
River, arguing that the density stratification induced by lower flow inhibits vertical
mixing and the aeration of bottom waters. The types of relationships with flow found in
Chesapeake Bay also show up in the Patuxent River (Jody has ref) and Delaware Bay
(Sharp et al, 1986).
3.4.3.2 Ecosystems
Temperature and salinity have important effects on ecosystems in the MACZ. (NEEDS
TO BE MORE GENERAL. VIC WILL FILL THIS OUT. HERE ARE SOME
SPECIFICS.) During the mid-1980s, low riverine flows resulting from low precipitation
over the watersheds of Mid-Atlantic states caused estuarine salinities to be higher than
normal. Oyster diseases responded positively to these saltier waters and decimated the
oyster population in much of the region. On the other hand, oysters cannot tolerate very
low salinity. Massive oyster mortality occurred in 1972 when Hurricane Agnes caused
extremely high flows into Chesapeake Bay and a corresponding drop in salinity. Rivers
also affect the nutrient content, sediment input and circulation of estuaries. All of these
play pivotal roles in production of phytoplankton, which lie at the base of food chain in
the Mid-Atlantic estuaries.
Temperature variations also influence the MACZ. For example, severe winters in 1977
and 1981 resulted in high mortality of blue crabs in the Delaware estuary, leading to very
low catches of this commercially important shellfish. Extremely high temperatures, on
the other hand, can cause oxygen concentrations to drop perilously low. Warm events in
the late 1980s are thought to be partly responsible for the large menhaden kills in the
upper Chesapeake Bay. Recent outbreaks of the fish-killing dinoflagellate, Pfiesteria
piscicida, have been partly attributed to nutrient overenrichment and abnormally warm
and salty environmental conditions.
3.4.3 Predicted response to climate change
3.4.3.1 Sea level rise
Direct Impacts
The obvious modification of the shoreline with sea-level rise is an inland displacement
that is proportional to the amount of rise and the slope across which the water is rising.
The displacement is not a linear shift of the shoreline, but depends on the mass of
sediment required to establish a new equilibrium beach profile and the source of that
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sediment (i.e., how much sand will be needed and where will it come from to build the
profile). Thus, shorelines with meager sand resources will have greater displacements for
any unit of sea-level rise than those with fully developed beaches and dunes. For barrier
islands, the situation is compounded because the shoreline is affected on both sides of the
island.
Among coastal ecosystems, salt marshes are most vulnerable to sea level rise because of
natural, historical and human factors that affect their ability to respond. A key historical
factor is reduced sediment yields in major mid-Atlantic rivers, implying less amelioration
of natural subsidence. A key human factor is coastal development that can block inland
migration of marshes. Wetlands that lack significant inputs of riverine sediments include
micro tidal marshes of the Chesapeake Bay and wetlands of the Albemarle-Pamlico
Peninsula of North Carolina. Such sites are relatively dependent on organic matter
storage for accretion of sediment and many have converted to open water in recent
decades (Kearney and Stevenson 1991). The Albemarle-Pamlico Peninsula is
particularly sensitive due to large areas with less than 6 feet of elevation, very gentle
slopes, and the absence of tides (which transport sediments). Thus, coastal wetlands in
the southern mid-Atlantic region tend to be unusually dependent on peat formation for
vertical accretion. Changes in the net primary production or decomposition will
influence their vulnerability to sea level rise.
Changes in storm surge due to sea level rise
Storm surge levels vary as a function of the storm's strength. However, the change in
relative sea level through time also affects the level to which any storm can raise the
water elevation and penetrate inland. Sea level is rising. Therefore, the base upon which
storms have occurred is changing. Recent storms are now capable of reaching historical
flood levels with lower surges. In addition, a rise in sea level allows stronger, less
frequent events to reach coastal areas that were once safe from storm activity, exposing
more areas to the storm erosion and flooding effects.
[SOME OF THESE HAVE BEEN CONVERTED TO "ENGLISH" UNITS; OTHERS
ARE METRIC—PLEASE MAKE SURE THE NUMBERS ARE CORRECT, AND
THAT INCLUDE THE MORE FAMILIAR ENGLISH UNITS.]. Using the past
century's sea level rise rate of 0.0126 ft./yr. (3.84 mm/yr.), the March 1962 storm had
water levels equal to that of Hurricane Gloria in 1985, 7.2 ft. above NGVD (DEFINE), a
1 in 35 year water level. With EPA's projected "best estimate" rate of sea level rise of
0.018 ft./yr. to the year 2050 (Titus, 1995), the equivalent storm would reach a water
level 8.62 ft. (2.63 m) above NGVD, a 1 in 60 year water level.
3.4.3.2 Water quality
[To be written]
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3.4.3.3 Ecosystems
Elevated carbon dioxide (CCy could alter carbon storage in wetlands. A Maryland salt
marsh exposed to elevated (ambient+340 ppm) levels of CC>2 since 1987 shows an
increase in net ecosystem production (net ecosystem production = net primary production
- ecosystem respiration). Shoot biomass was largely unaffected by elevated CO2, but root
production increased by an average of 44% (Drake et al. 1996). Assuming that all the
"extra" carbon allocated below ground is stored, the surface elevation of the high COa
plots should be increasing faster than the ambient CC>2 plots by about 0.4 inch (1 cm) per
year. This value can be considered an upper limit for the influence of elevated CC>2 on
surface elevation in mid- Atlantic high marshes, because it is clear that such a large effect
has not been maintained over the 1 1 years of this study. Research underway will
determine the extent to which elevated CC>2 has stimulated accretion in this experimental
system (Megonigal, per. comm.).
The stimulation of carbon cycling by elevated CC>2 has caused other ecosystem changes
including an increase of 68% in emissions of methane (CH4, an important greenhouse
gas), an increase in nitrogen fixation and invertebrate activity, and improved plant water-
use efficiency. No direct effects on decomposition rates have been observed. The effects
of elevated CC>2 on this brackish marsh was different for plants with C3 or C4
photosynthesis. C3 plants use sunlight to make sugar starting with three carbon atoms,
and are well suited to climates that are not too hot or too dry. Plants that originated in
drier, hotter climates are called C4 plants because their first product from photosynthesis
has 4 carbon atoms. C4 plants (such as corn, sugar cane, and lawn grasses) grow rapidly
in conditions of high sunlight and use C02 efficiently. Increasing atmospheric CC>2 is
likely to benefit C3 plants more than the already efficient C4 plants. A community
dominated by the C3 sedge Scirpus olneyi responded strongly, while a community
dominated by the C4 grass Spartina patens showed no significant changes after 7 years of
elevated CO2 treatment (Drake et al., 1996). In a community composed of both C3 and
C4 plants, the density of C3 species increased significantly at the expense of C4 species.
Thus, elevated CC>2 could increase the abundance of C3 communities in coastal marshes;
the consequences on ecosystem function are uncertain.
Regional warming will likely change the amount of photosynthesis (and therefore carbon
storage) in marshes because increasing temperature speed up photosynthesis (until it gets
too hot at about 84° F or 29° C). One way to examine potential effects of temperature
outside a laboratory is to compare rates at different latitudes. Southern marshes are more
productive than more northern ones, but part of the difference is explained by a longer
growing season in the South (Turner, 1976). Although higher temperatures usually mean
faster decomposition rates, Callaway et al. (1996) found more soil organic matter in
Southern marshes, suggesting that accretion rates are likely to be higher at higher
temperatures.
There have been relatively few estimates of potential vertical accretion rates in coastal
wetlands. Bricker-Urso et al. (1989) suggested an upper limit of 0.63 inch (16 mm) per
year, a rate that easily exceeds the highest projections for the mid- Atlantic region of
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about 0.28 inch (7 mm) per year. Callaway et al. (1996) calibrated a sediment accretion
model for a coastal Louisiana marsh and concluded that rates of sea level rise more than
0.12 inch (3 mm) per year would cause a state change toward more flood-adapted plant
communities (Brinson et al. 1995) and that rates more than 0.2 inch (5 mm) per year
would cause complete wetland loss. More research on vertical accretion rates is needed.
Tidal freshwater wetlands may be more stable than saltmarshes in the face of sea level
rise because they receive riverine sediments. However, lateral migration of these
wetlands will be limited by steep valley slopes in the upper reaches of such river systems,
and upriver migration will be limited by increasingly narrow channels. Increasing
freshwater flow might increase the area of these wetlands.
SECTION HERE BY VIC ON NON-WETLAND ECOSYSTEMS
[BOXES BY ANDERSON AND SORENSON COULD GO HERE-LATER,
GALBRAITH MAY PROVIDE INFO TO LINK SHOREBIRDS' HABITAT TO
WATERFOWL]
3.4.3.4 Coastal development and use
Climate change could have both positive and negative impacts on development and use in
the MACZ. On the positive side, warming could extend the coastal recreation season,
making the northerly areas' seasons as long as those now occurring in North Carolina and
Virginia. On the negative side, there appear to be far greater risks. Among these are the
ecological and management challenges of increased recreational demand (for water,
waste management, traffic, public safety); the threat of sea-level rise; vulnerability to
coastal storms and storm surges; AND... Dolan et al. (1980a) illustrate the compounding
effects of physical processes at the coast, including storm waves, daily tide, spring or
neap tide, storm surges, and sea level rise effects on resultant water levels. The timing of
such events-and the structural and non-structural policies in anticipation of them-may be
crucial for the well-being of coastal zones. Less direct impacts might also be important.
Coastal wetlands are important sites in both estuarine and oceanic food chains, affecting
both sport and commercial fishing locally and more widely. [INTERNS BORNSTEIN
AND SONG ARE WORKING ON THIS...]
Although the MACZ is not particularly vulnerable to hurricanes, with the exception of
the Outer Banks, September is the most common period of direct hits (Pielke 1998).
Thus the extension of the peak season into September could mean greater populations
vulnerable to storms at that time, with concomitant difficulty in evacuation and disaster
management.
3.4.4 Management and adaptation options
Sea level rise poses an important challenge to desirable beach environments and
beachfront developments. Parsons and Powell (1998) suggest that based on values of a
Delaware coastal location, it will remain sensible for investments to be made in beach
nourishment, given the costs of beach retreat over the next fifty years in present value of
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$200 million. [NOT CLEAR WHETHER THIS $200M IS JUST FOR THE LOCATION
OR FOR MAR, AND IS THIS BASED ON CURRENT SLR WITHOUT CLIMATE
CHANGE INCREMENTAL IMPACT?] They argue that beach nourishment remains a
viable strategy for the next half-century. Data from Faucett (1998) appear to confirm this
conclusion: beach replenishment is far less costly than the present value of permitting
beach retreat. Nevertheless, Delaware has taken the stance of allowing "strategic retreat"
(NEED TO CONFIRM) for state owned coastal lands.
There are important legal dimensions to the process of beach retreat in coastal areas
(Titus 1998). (NEED JIM TITUS TO SUMMARIZE HIS 1998 PIECE)
In general, we may reasonably anticipate that in the ocean coastal areas, society will
continue to build structures like seawalls to maintain the status quo, regardless of how ill-
founded this strategy may be in the long run. In the estuaries, only socially-significant
locations are likely to be protected from submergence, while other lands will be yielded
to wetlands and water. All of this, of course, will exact a cost from society that will
increase through time.
3.4.5. Research and information priorities [BEING WRITTEN]
Case study
Riparian Forest Loss
(Anderson)
Figure n\ Future coastline map of Chesapeake Bay in 2100 overlaid on land use map
from 1993.
The predicted average sea level rise (SLR) of 70 cm by the year 2100 is likely to have
major impacts on coastal ecosystems, especially in terms of habitat loss. There is a
potential for species migration, but it is limited by the concentration of human
populations and urban/suburban development in coastal counties. In addition, the speed
with which habitat is lost has a major impact on the ability of slow moving species (such
as most plants) to migrate to new, appropriate habitats. Although this study is based on
the loss of forest to coastal flooding, it is possible that forest areas will have been lost to
human development long before the sea rises over them.
Rising sea levels could flood marshes and push back coastal vegetation without
eliminating any vegetation types. That is, unless soil and hydrological conditions change
or unless a barrier to this migration exists. For example, steep slopes, sea walls and
bulkheads, or upland construction could squeeze coastal ecological communities.
Coastal forests are especially vulnerable to the squeeze because they are often found
nearest to human development projects. People often recognize the value of these
wetland forests as habitat for wildlife, as filtration systems for excess nutrients from
agricultural runoff and atmospheric deposition of acid rain, and as sponges for soaking
up heavy metals, but this environmental value may not overcome human demand for
dwelling and recreational spaces.
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To understand the potential loss of coastal forests, assuming the best case (no loss of
forest and no increase in development since 1993), we analyzed the land use map along
with the map of coastal flooding to determine the loss of forest land in coastal counties
(those counties defined as coastal by NOAA): In 1993, X acres within the Chesapeake
region were covered by forests. The predicted forest land cover in 2100 (based only on
SLR with no increase in other land use types) is Y acres. Of course, species that thrive
in coastal wetland forests require special growing conditions found in the wetter soils to
which they have adapted. Even if there is room for forests to migrate inland, they might
not survive if inland is too far upland where hydrological conditions favor different plant
species. Migration is also more likely if the flooded forest is adjacent to other forests or
even agricultural land rather than abutting developed areas.
Case Sftody
Impacts of Global Warming on Waterfowl Wintering in the Chesapeake Bay
(Sorenson)
The Chesapeake Bay is a famous waterfowl wintering site. Nearly 1 million ducks,
geese and swans use this estuary to feed and rest during the winter and thousands
more use it as a migration stopover point. The commercial hunting that once took place
here and nearly decimated the populations has been replaced by managed hunting
(numbers of harvested ducks in recent years, economic values of hunting). Table 3.4.1
shows recent average population sizes for species wintering in the Bay. Wintering
population sizes of most duck species have declined since the 1950s while population
sizes of Canada Geese and Snow Geese have increased (Perry and Deller 1995).
Most of these changes are attributed to changes in waterfowl food resources in and
around the Bay, particularly the widespread decline of submerged aquatic vegetation
(SAV), a prime waterfowl food (Perry and Deller 1996). Loss of SAV is attributed to
excessive nutrients and sedimentation. The resulting high turbidity shades SAV and
limits its growth (Hurley 1991). Some species have been able to adapt by changing their
diet. For example, swans and geese now feed largely in upland agricultural areas on
waste corn and winter cover crops (Munro 1980, Perry 1987). Canvasbacks switched
from a diet of wild celery and sago pondweed to Baltic clams, an invertebrate that has
become more plentiful (Perry and Uhler 1988, Haramis 1991 a). Species that were
apparently unable to adapt to the loss of SAV have shown drastic declines in numbers;
the Northern Pintail, Redhead, and American Wigeon have largely abandoned the Bay
as a major wintering site (Haramis 1991b, Perry and Deller 1995).
Global warming is likely to have a major impact on waterfowl populations in the coming
decades, with changes projected to occur in both breeding and wintering habitats.
Warmer and drier conditions are projected for the Prairie Pothole Region (PPR) of the
north-central US and south-central Canada, an area known as the continent's "duck
factory" (Sorenson et al. 1998; Sorenson et al. in prep). These changes could reduce
the number of pothole wetlands and correspondingly, the number of ducks breeding in
the region and their reproductive success. Diminished population size and productivity
on the breeding grounds could decrease waterfowl abundance in the Bay, because
many of the ducks that winter in the Bay breed in the Prairie Pothole Region. These
include the Mallard, Northern Pintail, American Wigeon, Canvasback, Redhead, Lesser
Scaup, Common goldeneye, Ruddy Duck, and Bufflehead. Breeding population sizes of
these species fluctuate from year to year depending on conditions on the breeding
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grounds. Historical fluctuations in numbers of-birds using the Bay reflect, in part, these
continental trends (Perry and Deller 1995).
Approximately Xx% of Canvasbacks wintering in Chesapeake Bay come from the PPR.
Band recovery data show that the Chesapeake Bay population represents xx% of the
Atlantic Flyway wintering population and xx% of the total number of birds counted in the
mid-winter surveys. If ponds on the breeding grounds dry up causing declines in
breeding population sizes and numbers of young produced, then we can expect similar
declines in the number of ducks using the Chesapeake Bay (give range of numbers for
different GCM scenarios).
Discuss caveats accompanying the analysis:
A. Don't know how birds will move around on breeding grounds and how probable
changes in habitat use will influence productivity and sizes of wintering populations.
B. Need to know how SLR from global warming as well as future management of the
Bay will impact Chesapeake Bay as wintering habitat for waterfowl (how will distribution
and abundance of SAV be affected). This will be an equally or perhaps more important
determinant of future waterfowl numbers in the Bay.
Table 3.4.1. Fifteen year average (1984-1998) population sizes
for 9 species of waterfowl wintering in Chesapeake Bay
Species
American Black Duck
Mallard
Northern Pintail
American Wigeon
Canvasback
Redhead Scaup
Goldeneye
Bufflehead
Ruddy Duck
Canada Goose
Snow Goose
Brant
1984-1 998 averages (SE)
43,411 (1462)
58,059(2391)
2335 (277)
57,271 (2,488)
50,869 (6,808)
392,896 (29,696)
85,018(9,089)
21,102(2,564)
Case Study Box PLACEHOLDER
I Marine Fishing I
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3.5 Ecosystems (Rogers and McCarty)
3.5.1 What is the status of resources and what are the current stresses?
Human activities in the Mid-Atlantic are changing the structure and functioning of
ecosystems, "interacting systems of biological communities and their non-living
surroundings" (US EPA 1999). The impacts upon ecosystems of changes in long-term
climate patterns and climatic variability need to be assessed in the context of other
stresses, such as habitat loss, pollution, and non-indigenous invasive species. Impacts
may take the form of changes in ecosystem composition, including the species that
inhabit them and the relative abundance, distribution, and density of those species.
Ecosystems function in ways that confer a variety of benefits that are valued by human
populations. For example, forested ecosystems provide valuable recreation opportunities
and habitat for wildlife; wetlands can help clean water pollution and filter heavy metals.
Benefits from ecosystem functions range from being very specific, such as crop
pollination, to being very general, such as waste decomposition. Changes in ecosystem
structure may lead to changes in their valuable functions (Chapin et al. 1997).
Criteria for evaluating the status and stresses of Mid-Atlantic ecosystems depend partly
on what aspects of ecosystems are important to people in the region. The Mid-Atlantic
region includes a variety of ecosystems ranging from deciduous forests and mountain
streams, to agricultural, suburban and urban dominated areas in the lowlands, to wetlands
and estuaries near the Atlantic coast. The Chesapeake Bay's most valued ecological
attributes are different from those for Shenandoah National Park, which in turn differ
from those for a farmer's field or the center of a major city or a suburban neighborhood.
We hypothesize that particular regional populations of species, ecosystem functions, and
places are valued by stakeholders in the Mid-Atlantic Region (as explained in Appendix
C). Our hypotheses can be refined as we learn more about stakeholder perspectives and
values, and as the scientific understanding of how people and their activities depend upon
ecosystems improves.
The Mid-Atlantic region, with its mountains, valleys and coastal plains, is among the
most diverse physical and ecological regions in the United States (US EPA 1997,
Landscape Atlas). The region is especially notable for the diversity and endangerment of
its freshwater fauna. Nationwide, the four groups of species with the greatest proportion
of species at risk (40-67%, compared to birds, mammals and reptiles, with imperilment
rates of 15-20%) are freshwater mussels, crayfish, amphibians, and freshwater fish, all of
which depend upon freshwater habitats (Stein and Flack 1997). The US ranks first in the
world in the number of described species of freshwater mussels, crayfish, freshwater
snails, and several aquatic insects: stoneflies, mayflies and caddisflies; and ranks seventh
in freshwater fish species (Master et al. YEAR?). Of the Mid-Atlantic states, Virginia
has the greatest number of historically occurring freshwater mussels, the greatest
percentage of imperiled mussels and fish, and the greatest number of at-risk fish species
(Table 3.5.1). Virginia has also lost the greatest number of species (terrestrial and
aquatic) to extinction. Of the 2,100 small watersheds in the US, 87 are hot spots with 10
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or more at-risk freshwater fish and mussels species. Of these 87, 6 are located in NC, 4
in VA, and 2 in WV) (Master et al. YEAR?).
Table 3.5.1.
State
NC
VA
WV
MD
DE
PA
NJ
NY
Total # Species:
Presumed + Possibly
Extinct
(Stein & Flack 1997)
5 + 9
8 + 17
2 + 3
6 + 2
1 + 1
2 + 5
3 + 2
3 + 5
# At-Risk
Freshwater Fishes
(% Imperiled)
(Master et al. Year?)
39 (18%)
41 (20%)
16(11%)
3 (3%)
5 (5%)
13 (8%)
3 (4%)
13 (8%)
# Freshwater Mussels
Historically Known to Occur
(% Imperiled)
(Williams and Neves 1995)
50 (60%)
80(71%)
50 (46%)
19(68%)
12 (58%)
63 (49%)
12 (58%)
30 (47%)
For eastern fishes and mussels, altered sediment loads from agriculture, and non-native
invasive species are the dominant threats (Richter et al. 1997). Eastern fishes are also
threatened by municipal sources of nutrients and sediments, while eastern mussels face
greater threats from altered nutrient input associated with both agriculture and
hydroelectric dams.
The history of sediment and nutrient pollution through the present is discussed in section
3.1 above. Sediment pollution from agricultural and urban lands reduces water clarity,
smothers bottom organisms, and clogs waterways, affecting freshwater and estuarine
ecosystems. Nitrogen, phosphorus and sediments from agricultural and urban areas are
the greatest threats to the Chesapeake Bay (US EPA 1995). Excess quantities of these
nutrients feed algal blooms, which contribute to low oxygen conditions in bottom waters
and to losses of submerged aquatic vegetation. This, in turn, has reduced the ability of
the Bay and associated wetlands to support fish, crabs and waterfowl (See section 3.4).
River and stream ecosystems are adapted to naturally varying levels of stream-flow, but
are stressed by human alterations of flow regimes (Karr et al. 1986; see also Part 2: A
View of the Mid-Atlantic Region in 2030). Urban development alters flow regimes
because while rain falling on a vegetated surface can be absorbed into the ground and
slowly released to streams, rain falling on paved surfaces in urban areas cannot be
absorbed. This can lead to sharply increased peak flows during storms, and to very low
flows during dry periods. Peak flows can be physically destructive, while low flows can
significantly reduce available habitat for fish and aquatic insects. Withdrawals of water
for human uses can further deplete baseflows.
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Freshwater and other ecosystems of the Mid- Atlantic are stressed by non-native invasive
species. Nine of the twelve non-native invasive species identified by the Nature
Conservancy (1996) as posing great threats to US ecosystems occur in the Mid- Atlantic
region: zebra mussels, flathead catfish, and hydrilla in freshwaters; purple loosestrife in
wetlands; balsam wooly adelgid, leafy spurge and tamarisk in terrestrial; and Green Crab
and Chinese Tallow pose threats to small parts of the region. Up to 10,000 zebra mussels
can adhere to a single native mussel, interfering with the native mussels' feeding, growth,
movement, respiration and reproduction. Non-native invasive species are believed to
have contributed to the decline of 42% of US threatened and endangered species, and to
the extinction of 27 of 40 North American freshwater fishes (Nature Conservancy 1996).
Non-native invasive agricultural weeds, pests and pathogens threaten agriculture, and
European starlings, ragweed, and gypsy moths cause various kinds of damage in both
natural and more human-dominated landscapes. While some introduced species (e.g.,
wheat, rice, corn, cattle, poultry) are valued parts of agricultural ecosystems, 79 non-
native invaders were estimated to cause approximately $97 billion in damages from 1906
to 1991 (OTA 1993). $764 million of damage were attributed to European gypsy moths
by the US Department of Agriculture, and zebra mussels could cause up to $5 billion in
damages by 2002 (Nature Conservancy 1996). In addition, invasive species may impair
valued ecosystem functions (Vitousek and Walker 1989, Vitousek et al. 1996).
Freshwater ecosystems have been degraded by other stresses, including toxic chemical
pollution, acid deposition and physical alterations, such as dams, road crossings and
channelization. Each of these stresses is related to human activities. Lifestyles in the
Mid- Atlantic are associated with the generation of sewage and trash, and with demands
for transportation, electricity, manufacturing, food, water, recreation and a variety of
services. Although primarily a by-product of manufacturing and agricultural pesticide
applications, toxic chemical pollution also is associated with services such as dry-
cleaning and automotive maintenance. The Elizabeth River, the Patapsco and Back
Rivers of the Baltimore Harbor area, and the Anacostia River have sediment
contamination concentrations in excess of hazardous levels (PELs or Probable Effect
Levels) (US EPA 1995). Nitrogen and sulfur emissions from cars and power plants,
along with mine drainage, contribute to the acidification of lakes and streams. Rivers and
streams are physically altered for human convenience. Natural, meandering forest
streams are often replaced in urban and suburban neighborhoods by straightened channels
with frequent road crossings, and are sometimes routed into underground drainage pipes.
These alterations tend to make the flow faster and "flashier" as well as degrading or
eliminating shoreline habitat.
presence in the Mid- Atlantic region has altered all of the region's ecosystems
to varying degrees. Forested, agricultural and coastal ecosystems are discussed in other
sections (3.1-3.4), as are human health issues that arise in human-populated ecosystems
(3.6). Some natural forests, wetlands and other systems have essentially been lost when
they have been converted into urban, suburban and agricultural areas to meet the growing
population's growing demands for homes, offices, shops, transportation and recreation.
Cities in the Mid- Atlantic generally support large human populations and provide more
amenities for people than less human-dominated systems, and agricultural lands produce
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large quantities of food. Unfortunately, these landscape changes also result in losses of
habitat for many non-human species and losses of the functions performed by these
ecosystems. The fragmentation of forests into isolated patches exacerbates the problem
of habitat loss for Mid-Atlantic species that require large, connected areas to thrive.
Ecosystems are interconnected. Human activities within urban and agricultural
ecosystems are responsible for many stresses on freshwater and estuarine ecosystems,
including disrupted hydrologic cycles, sedimentation and eutrophication. Most, or
perhaps all, of the region's ecosystems have somewhat diminished capacities to support
their natural composition and functions. Appendix F lists all species on the federal list of
endangered species that occur within the MAR. It also summarizes stresses on declining
species within the Chesapeake Bay Region.
PLACEHOLDER: Could add overharvesting, native pests and pathogens to list of
stresses. Could add that climate currently stresses Mid-Atlantic ecosystems through
temperature extremes, droughts, floods, and other variations in the timing and geographic
distribution of precipitation. Could add discussion of current status and stresses for
national parks and other federal, state, local and private (including nonprofit) nature
reserves. Current stresses are a problem for these areas, e.g., non-native invasive species,
runoff from adjacent urban and agricultural lands, acid deposition. Climate stress will
interact with current stresses in ways that may be very detrimental to lands protected for
nature.
3.5.2 How might climate change and changes in climate variability exacerbate or
ameliorate current conditions?
Predictions for the Mid-Atlantic for the next 100 years suggest that temperature and
precipitation patterns will change - probably in the direction of a warmer, wetter climate
- and that sea level will rise (Part 2.4). These changes can be expected to affect Mid-
Atlantic ecosystems. Studies not specific to the Mid-Atlantic have linked changes in the
distribution and ecology of species to the climate warming of the last century (Barry et al.
1995, Beebee 1995, Parmesan 1996, Crick et al. 1997). During rapid climatic changes
occurring in the last 100,000 years, many species' ranges shifted in conjunction with
global temperature changes (Pitelka et al. 1997). For instance, spruce trees shifted their
ranges from Southeastern to Northeastern North America (but at a time when there were
few m:an-made barriers such as cities and farms). Researchers have linked local climate
conditions to survival and reproduction in many plants and animals (Huntley et al. 1989,
Visser et al. 1998) and have established that climate directly or indirectly determines the
geographic ranges of many species (Root 1988, Huntley et al. 1995; Shao and Halpin
1995).
Changes in carbon dioxide concentration, temperature, precipitation, and sea level in the
Mid-Atlantic will affect individual species differently - benefiting some species, while
harming others. For example, see section 3.4 discussion of how carbon dioxide has
differential impacts on plant species within a marsh. While some species may be
physiologically well suited to new conditions, others will need to adapt. The speed and
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success of adaptation will vary across species. If temperature extremes, droughts, and
floods increase in frequency or intensity, these conditions could be particularly stressful
for many species. Species that can evolve rapidly are more likely to cope effectively with
environmental changes (Cronin and Schneider 1990, Geber and Dawson 1993).
Significant adaptive evolution is more likely in species with short generation times, such
as microbes, insects and annual plants than in long lived species, such as trees. Rapid
evolution is also favored in species that possess high levels of genetic variability for traits
related to climate tolerance (Geber and Dawson 1993). Some species will shift their
geographic range by invading more hospitable climates. Other species will fail because
they can not move fast enough to keep pace with change, because landscape features
(such as cities) block their movement, or because new suitable habitats are simply not
available (Pitelka et al. 1997). A species may fail to colonize a prospective habitat with a
newly favorable climate if it can not adapt to that habitat's soils, terrain, level of human
development, or compete with other species already in residence. Since species will be
affected differently by climatic changes, relationships among species will be altered.
[PLACEHOLDER: discussion of effects on parks and nature reserves.]
Invasive species share a set of traits that predispose them to successfully invade pre-
existing communities. Successful invaders such as agricultural weeds and pests,
European starlings, ragweed, gypsy moths and purple loosestrife tend to have these traits,
including a high rate of population growth, which contributes to rapid colonization;
ability to move long distances, which contributes to colonizing distant habitats; tolerance
of close association with humans, which is important in increasingly human-dominated
landscapes; and tolerance of a broad range of physical conditions (Ruesink et al. 1995,
Rejmanek and Richardson 1996). The same traits that allow these species to invade
communities might enable them to adapt to climate changes and other types of human
disturbances. Thus, climate change could accelerate the loss of species already imperiled
by pre-existing stresses yet fail to exert the same negative pressures upon invasive, weedy
species. Climate change could work in concert with other stresses to further reduce
populations of rare and endemic species while increasing populations of already
abundant, widespread species.
Climate change will interact with pre-existing stresses. Ecosystems of the Mid-Atlantic
are already experiencing severe stress from habitat loss, degradation and fragmentation.
Losses of coastal habitats are predicted consequences of sea level rise. For example, see
case study boxes on forests subject to sea-level rise, waterfowl in the Chesapeake Bay,
[later shore birds in the Delaware Bay], and the discussion of the effects of COi, sea level
rise, and sediment accretion on marsh grasses of Chesapeake Bay wetlands, all in Section
3.4.
Intensive human development of coastal areas exerts stresses on coastal ecosystems and
can prevent coastal wetlands from migrating inland as sea level rises. Adaptation
measures to protect developed areas from sea level rise, such as increased use of sea-
walls, can directly harm ecosystems. In upland habitats, such as forests, populations of
plants and animals isolated by habitat fragmentation will be stressed by climate changes
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and their small size and isolation can make it more difficult for those populations to
survive (Peters and Darling 1985).
Increased precipitation in the Mid-Atlantic might ameliorate the impacts of urban
reductions of baseflows, but more intense storms could exacerbate existing problems of
flooding, erosion and other stresses. The combined effects of increased carbon dioxide,
precipitation and temperature need to be considered to understand the overall impact on
ecosystems. Dissolved oxygen concentrations (vital, for example, to fish) depend upon
temperature and streamflow. (See recreational fishing case box in Section 3.3.)
Temperature increases would tend to lower dissolved oxygen concentrations, as would
decreases in turbulent streamflow. A warmer climate would, therefore, increase the
problem of low dissolved oxygen concentrations. A wetter climate might moderate this
type of stress if resulting flows increase streamflow turbulence during critical periods
when dissolved oxygen concentrations were low.
The movement of soils, nutrients and pesticides from terrestrial systems to aquatic ones is
a complex process influenced by a variety of human and natural factors. In the absence
of adaptive human intervention, increases in overall precipitation and/or storm intensity
might increase runoff from agricultural and urban areas, exacerbating stresses on aquatic
systems. The same processes might increase acidic drainage from mines. The effects of
climate change on the acidification of lakes and streams remain speculative due to
uncertainties regarding the effects of climatic changes on acid deposition and hydrology.
The timing of rainfall is an important factor in determining the impacts of climate
change.
3.5.3 What are the potential strategies for coping with risk and taking advantage of new
opportunities?
The key to effective ecosystem management is a strategy that addresses existing stresses
as well as additional stresses from climate change. The dynamic and interactive nature of
ecosystems is revealed in the network of pathways by which multiple current stresses
interact with climate stresses and natural processes to produce ecosystem changes.
Action is needed to protect highly valued regional populations of species, ecosystem
functions, and special places. Management strategies that reduce the impacts of other
stresses (such as preserving forests and wetlands, minimizing urban and agricultural soil
erosion and nutrient runoff, conserving energy and water, protecting stream habitat, and
reducing waste and the release of toxic chemicals) already have been identified. The flip
side of multiple pathways for doing harm is multiple pathways for reducing harm. For
example, some agricultural practices reduce the use of toxic chemicals, reduce soil
erosion and nutrient runoff, and conserve energy and water. In doing so, these practices
protect soil fertility and reduce harmful impacts on terrestrial and aquatic plants and
animals. Some strategies that promote energy conservation (such as insulation in
buildings or energy-efficient transportation) reduce emissions of nitrogen and sulfur and
greenhouse gases, thereby addressing acid rain, atmospheric nitrogen deposition, and
climate change simultaneously. Many opportunities are available that would protect, and
sometimes restore, attributes of ecosystems that are important to stakeholders in the Mid-
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Atlantic. (Appendix F suggests key locations for which protecting small watersheds
would reduce substantial threats to imperiled freshwater fish and mussels.) Research and
public education can contribute to developing a public understanding of the value of
ecological resources, the most serious threats to those resources, and the possibilities for
protection and restoration. Improved protection of ecological resources will then depend
upon people evaluating the economic, social, and ecological consequences of various
alternatives and then taking action.
3.5.4 What are the policy-relevant research gaps?
Policy-relevant research could improve the foundation for decision-making in the Mid-
Atlantic region. We have a very incomplete understanding of how we depend upon
ecosystems, how ecosystems sustain themselves, and how human activities alter
ecosystem processes. Because innumerable aspects of ecosystems contribute to human
health and well-being in ways that are still poorly understood, it is difficult to set
priorities for protecting or restoring ecosystem components and processes (e.g., Daily et
al. 1997). There is a critical need for research into ecosystem processes.
Valuation of ecosystem components and processes poses another challenge: linking a
scientific understanding of ecosystems to a characterization of stakeholder values. This
requires identifying how ecological attributes change in response to human activities
(including ecological attributes that change as a result of a cascading set of effects
originally caused by human activity), describing ecosystem changes in terms that relate to
human values, and providing stakeholders with adequate information upon which to base
decisions. Preliminary plans for investigating these topics are described in Appendix C.
The study of how particular attributes of ecosystems change in response to human
activities is an interdisciplinary endeavor. Economists and other social scientists
contribute information about human behavior, while ecologists, climatologists,
hydrologists, biologists, and chemists provide insights into natural processes and how
they can be altered by human actions. To improve our understanding of how ecosystems
might respond to climate change, collaboration among ecologists, climatologists and
hydrologists is needed to identify and characterize those aspects of climate of the greatest
relevance to ecosystems. Key issues for freshwater ecosystems in the Mid-Atlantic
include potential changes in the amount, geographic distribution and timing of rainfall; in
interactions of precipitation patterns with current and future landscapes on already
varying streamflow levels and water quality; and the consequences of these changes on
the abundance and distribution offish and other aquatic organisms. Similarly, a key issue
for the Chesapeake Bay is how changes in rainfall and water quality might affect nutrient
inputs to the Bay. Future assessments of the impacts of climate change on forests could
benefit from improved understanding of the sensitivity of terrestrial ecosystems to
changes in carbon and nutrient cycles caused by increased carbon dioxide concentrations
as well as to changes in temperature and rainfall. These assessments also need better
characterization of how temperature and rainfall might change. Since climate change and
other stresses can be expected to have negative effects on some species and ecosystems,
it would be useful to improve our ability to predict which species or ecosystems are least
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likely to be replaced by functionally equivalent components. Focusing some research
effort upon the study of how non-native species invade new ecosystems might improve
this predictive ability.
Ecosystems are, by definition, interactive systems. The emphasis here on assessing
current status and stresses is a useful baseline, but baseline research should be continued
to improve our understanding of ecosystem structure and functioning. This will serve as
a firmer foundation for research needed on the interactive effects of multiple current and
climatic stresses, and the effectiveness of alternative management strategies. Because
natural ecosystems play a role in the global carbon cycle, research into feedbacks
between ecosystems and climate change would be helpful for evaluating management
strategies.
Case Study
Climate Change and Bird Distributions in the Mid-Atlantic Region
(Price)
There are both economic and ecological reasons to care about the birds of the Mid-
Atlantic region. Birdwatching is big business: non-consumptive bird 'use' (watching and
feeding birds) generates $884.7 million in annual retail sales in the states within the Mid-
Atlantic region (Southwick Associates 1991). This, in turn, supported more than 23,000
jobs in those states (Bird Conservation 1997). While people care about birds, it is
difficult to estimate how changes in bird distributions might affect the economics of non-
consumptive bird use. Shifts in regional spending on this activity are likely as some
birdwatching sites become less favorable and different sites become more favorable.
Although many bird watchers might simply adjust to the reduction in species richness in
their areas, they will experience the loss of well-being that accompanies a reduction in
their preferred activities.
Also of concern are the potential indirect economic costs of changes in bird distributions
and how these changes will affect ecosystems. Birds provide important ecological
services including seed dispersal, plant pollination and pest control.
For example:
• Blue Jays are a major disperser of oak seeds.
• Birds have been known to eat up to 98% of the overwintering codling moth larvae in
orchards.
• Several species of wood warblers are largely responsible for holding down numbers
of spruce budworm larvae, eating up to 98% of the non-outbreak larvae.
• While birds are not the principal vertebrate predator of gypsy moths, they do play a
role in holding down numbers of this pest.
The results presented in Tables 3.5.2 and 3.5.3 came from logistic regression models
developed to associate bird distributions with current climatic conditions (1985-1989).
These models were then coupled either to a sensitivity analysis where temperatures
were increased by 1.8° F (1° C) or to equilibrium output from the Canadian Climate
Center (CCC) general circulation model (in press; 1995).
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Models have been developed for almost all perching (passerine) bird species except the
thrushes and some miscellaneous species. The results in Table 3.5.2 show how climate
change might change the number of species found in the Mid-Atlantic region and for
each of its states. The gross change represents the overall loss of species currently
found in the area. The net change represents the loss of species currently found in an
area offset by species moving into the area from outside of the region. For example,
even a 1° C increase in temperature could lead to a loss of 11% of the passerine
species currently found in the states of the Mid-Atlantic region. These losses would be
somewhat offset by birds colonizing from outside the region so the net change would be
5% fewer species than currently found there. Table 3.5.3 provides data on a subset of
the species from Table 3.5.2, the wood warblers. These colorful species are popular
among bird watchers, are important predators of insects and may be especially sensitive
to climate change. Even a 1 ° C increase in temperature could lead to a gross loss of
29% of the warblers in the region. This could be important because it is unknown
whether the species colonizing the region would perform the same ecological services of
the species currently found there. Even if they did, the net change would still be a 19%
reduction in the number of warbler species currently found in the region.
Table 3.5.2. Changes in number of perching bird species under a 1° C temperature
change and under the equilibrium conditions from the Canadian Climate Center GCM.
Region
Delaware
Maryland
New Jersey
New York
North Carolina
Pennsylvania
Virginia
West Virginia
With 1 ° C temperature increase
Gross Change (%)
-11
-9
-8
-8
-14
-8
-13
-10
-12
Net Change (%)
-5
-6
-3
-2
-5
-4
-6
-6
-7
Under conditions of CCC -GCM
Gross Change (%)
-36
-29
-33
-24
-41
-26
-40
-34
-38
Net Change (%)
-18
-21
-18
-11
-18
-16
-17
-21
-20
How quickly these changes might occur is unknown. It is possible they could occur
relatively quickly. For example, the average latitude of occurrence of 43% of the
warblers has shifted north in the last 20 years, by an average of more than 70 km (Price,
unpublished data). Only three species (6%) were found significantly farther south and
these represented overall expansions of the species' ranges. In most of the remaining
species, the range showed a northward trend but it was not enough to be statistically
significant.
In summary, climate change will cause changes in the distributions of birds. These
changes could occur (and probably are occurring) relatively quickly. While these
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changes will have ecological effects and possible economic effects, the magnitude of
these effects is unknown.
Table 3.5.3. Changes in number of warbler species under a 1 ° C temperature change
and under the equilibrium conditions from the Canadian Climate Center GCM.
Region
Delaware
Maryland
New Jersey
New York
North Carolina
Pennsylvania
Virginia
West Virginia
With 1 ° C temperature increase
Gross Change (%)
-29
-22
-29
-19
-31
-19
-31
-29
-29
Net Change (%)
-19
-19
-28
-7
-15
-14
-17
-28
-24
Under conditions of CCC -GCM
Gross Change (%)
-63
-52
-58
-52
-72
-48
-68
-59
-57
Net Change (%)
-45
-46
-47
-31
-43
-43
-47
-51
-44
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Section 3.6 MAR Draft Preliminary Human Health Sector Assessment (Shortle,
Benson, Kocagil, Wang)
Interest in the impacts of climate change on human health was strongly expressed at the
US GCRP Workshop on Climate Change Impacts in the Mid Atlantic Region (MAR)
(Fisher et al., 1999). Concerns included increased illness and mortality related to more
frequent and/or severe extreme heat events, new or re-emergent diseases because of
changes in the dynamics of transmission, distribution and resistance of disease agents,
and increased contamination of public and private water supplies due to increased
flooding. Our goal is to examine the potential effects of climate change and variability
on the health of the people in the MAR population over the next twenty to one hundred
years, recognizing possible positive, as well as negative effects.
3.6.1 Current Health Status and Stresses
Climate is one of many factors that influence human health. Other factors are lifestyle
choices (e.g., cigarette and alcohol consumption, diet, fitness), access to medical care
(availability, quality and price of care and health insurance status), medical technology,
genetic endowment (predisposition to certain diseases), and characteristics of the built
and natural environment. The current health status of the region's population reflects the
combined effects of climate and nonclimatic factors, and provides a backdrop for
evaluating the potential impacts of climate change in the region. We present selected
health assessment measures for four physiographic subregions of the MAR: Coastal,
Piedmont, Plateau and Ridge and Valley (R&V). County level data was collected and
aggregated by state and physiographic region.
Mortality
Table 3.6.1 presents crude mortality rates for the top 10 causes of death in the US and in
the region. It also includes crude mortality rates from homicides, and motor accidents,
and deaths directly attributable to cold, heat, lightning, storms, and flooding. We use
crude mortality and morbidity rates for 1995, the most recent year for which
comprehensive data is available to examine causes of death. Age-adjusted mortality rates
are the preferred measure but crude mortality rates are adequate for our purposes and
more easily obtained.
The three leading causes of death in the MAR, are heart disease, cancer and stroke. This
ranking is the same as for the US but the MAR mortality rate is somewhat higher than the
national rate for all three causes of death. In addition, the mortality rates for death by all
causes, lung disease, pneumonia/influenza, and diabetes exceed the US national death
rates. However, the MAR has lower mortality rates for AIDS, cirrhosis/liver disease,
homicide, suicide, accidents, and motor vehicle accidents than the US.
Within the MAR, the Plateau and R&V regions have death rates that are much higher
than the national mortality rate for heart disease, cancer, stroke, pneumonia, diabetes, and
death by all causes while the Piedmont and Coastal regions have death rates similar to the
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US rate. A comparatively large percentage of the population in the later years of life may
explain these higher mortality rates in the Plateau and R&V regions. More than 15% of
the population in these two regions is aged 65 and older as compared to the national
average of 12.8%. For AIDS, liver disease, and homicide, the Coastal region has a higher
mortality rate than the US, while the other regions have lower mortality rates.
Climate related conditions can be an aggravating or contributing factor in many of the
leading causes of death (WHO 1990; IPCC 1996). We are currently unable to quantify
the magnitude of the climate effect, but the literature indicates that lifestyle choices,
genetic endowment and age are the most important contributing factors.
Table 3.6.1. 1995 Death Rates per 100,000 Population
by Physiographic Region for Selected Causes of Death
Cause of Death
All Causes
Top 10 Causes:
Heart Disease
Cancer
Stroke
Lung Disease
Accidents
Motor Accidents *
Pneumonia/Influenza
Diabetes
AIDS/HIV
Suicide
Cirrhosis/Liver
Selected Other Causes:
Homicide
Cold Related
Heat Related
Storm/Flood/Lightning
US
880.00
280.70
204.90
60.10
39.20
35.50
16.50
31.60
22.60
16.40
11.90
9.60
8.70
0.21
0.27
0.06
Coastal
913.11
276.91
220.35
57.64
33.51
33.94
12.34
28.59
24.44
25.04
9.28
9.95
12.34
0.17
0.32
0.02
Piedmont
871.90
265.57
209.00
61.73
35.23
30.32
12.98
29.80
23.00
14.61
10.80
7.18
6.49
0.18
0.12
0.01
Ridge and
Valley
1049.09
366.06
239.53
68.25
45.25
34.83
13.24
37.61
26.33
4.19
11.08
5.60
2.24
0.43
0.03
0.03
Plateau
1058.03
367.29
254.48
66.60
50.35
32.85
13.54
37.66
26.89
5.49
10.01
7.56
2.44
0.29
0.09
0.00
MAR
951.53
305.42
227.66
62.14
39.44
32.71
12.92
32.13
24.84
15.02
10.08
8.12
7.15
0.23
0.18
0.01
Crude death rates for 1995. Source: Office of Analysis and Epidemiology, National Center for Health
Statistics, Centers for Disease Control and Prevention.. Data collected July 1998 from cdc.wonder.gov.
Missing values have been converted to zeros.
* Motor accidents are a subset of all accidents and are included in the accident category.
Currently, very little mortality in the MAR is directly attributable to cold, heat, storms,
flooding or lightning. The death rate from mortality directly attributable to cold in MAR
was similar to that of the US in 1995. The highest death rate from cold in MAR in 1995
occurred in the R&V region. Death rates from heat in the MAR were lower than the US
rate but the death rate. There is, however, significant variation within the region, with
much lower rates in the Piedmont, R&V and Plateau regions than in the Coastal region.
Death rates from storms, floods, and lightning in the MAR were much lower than in the
US as a whole.
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Because heat, cold, storm, flood, and lightning related mortality is likely to show more
year to year variability than the leading causes of death, we examined MAR and US
crude mortality rates for these causes of death for the years 1990-1996. The average
annual mortality rate in the MAR was 0.20, 0.07, and 0.02 per 100,000 population for the
cold, heat and combined storm/flood/lightning category, respectively. Death rates from
these causes in the MAR are less than the corresponding US mortality rates of 0.24, 0.12,
and 0.06. Although these data exhibit a degree of year to year variability, especially at
the sub-regional level, the mortality rates are consistently minuscule by comparison to the
major causes of death.
Morbidity
Our analysis of morbidity focuses on selected diseases that have direct or indirect
linkages with climatic conditions and for which regional data was available. Three of the
diseases, Cryptosporidiosis, Giardiasis and Cholera, are water/food borne diseases. Four
of the diseases, Malaria, Lyme Disease, Hanatavirus and Dengue Fever, are vector-borne
diseases. Importantly, we do not have data on heat related morbidity or weather-related
accident morbidity.
Table 3.6.2. MAR Morbidity Baseline Assessment: 1995 CDC & State Data
# of reported cases
# of cases per 100,000 population
Region
Coastal
Piedmont
Ridge &
Valley
Plateau
North
South
Giardiasis
568
4.48
719
7.12
231
6.64
1140
72.75
2658
9.98
0(")
0
Crypto-
sporidiosis
5
.03
1
.009
0
0
66
.73
72
.27
0
0
Cholera
1
.007
0
0
0
0
0
0
1
.003
0
0
Lyme
Disease
1231
9.72
2098
20.78
44
1.26
471
5.27
3782
14.20
62
.72
Malaria
91
.71
47
.46
1
.02
20
.22
128
.48
31
.36
Dengue
Fever
0
0
1
.009
0
0
0
0
0
0
1
.003
Hantavirus
0
0
0
0
0
0
0
0
0
0
0
0
The limitations of the morbidity baseline are:
1. Giardiasis cases are missing for North Carolina and Virginia in 1995. Therefore, the number of
cases could be under reported.
2. West Virginia AIDS cases were reported at the state-level. We calculated a population-weighted
average to determine how the cases would be distributed. Thus, the figures may not reflect actual
cases in the different regions (plateau - ridge & valley).
3. Cryptosporidiosis was not a reportable disease for 1995.
4. We can not distinguish between imported and autochthonous (local) cases.
5. There were no cases of Hantavirus in the MAR in!995, however, there have been 6 cases in the
I region since 1994.
There were 6,735 reported cases of communicable diseases of interest in the MAR, of
which 59.5% were vector borne and 40.5% were water-food borne diseases (Table 3.6.2).
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The R&V subregion had the fewest (276 cases) of these communicable disease while the
Piedmont had nearly half of the total (2865 cases).
Giardiasis was the most prevalent water-borne disease and found throughout the MAR.
Most of these cases (1140) occurred in the Plateau region. Lyme Disease was the most
prevalent vector-borne disease in the MAR with 3,844 cases. Nearly 60% of the Lyme
Disease cases were clustered in fifteen counties along or near the New Jersey-
Pennsylvania border. In 1995, there was one case of imported dengue fever, one case of
imported cholera and no cases of hantavirus.
Key Trends Affecting Health Status in the MAR
While some of the factors that influence health status are likely to remain fairly constant
in the next several decades, such as the genetic endowment of the general population,
others are likely to change with potentially large impacts on health in the region. For
example, managed care will continue to have a strong influence on the payment and
provision of health care services.
Factors that we expect to greatly influence future scenarios include changes in the total
population of the region, age distribution of the population, per capita income,
employment status, health insurance, medical technology, public health systems.
Changes will have various positive and/or negative impacts on health status and the
vulnerability of the MAR population. While the dynamic nature of the health sector
makes future changes difficult to predict, some future trends that may occur are (the
impact on health status indicated with a + (positive) or - (negative)):
• An increase in per capita income in the region (+)
• Population growth in the Coastal and Piedmont regions, maintenance in Plateau and
R&V regions (+ & -)
• An increase in the total and proportion of the population aged 65+(+)
• Continued growth in cost of medical care (-)
• Continued growth of managed care systems (+ & -)
• Continued movement of care from the hospital to the home (+)
• Technological improvements (genetic engineering, biotechnology, medical devices,
provision of services, etc.) will continue to improve disease prevention and treatment (+)
• Transfer of responsibility for health care costs to individuals (e.g. higher insurance
rates for smokers) (+)
• Improvements in public insurance programs (e.g., Medicare and Medicaid) will
influence the health of recipients, such as the elderly and the indigent (+)
3.6.2 Effects of Climate Variability
Climate variability clearly affects human health in the MAR. Temperature extremes and
extreme weather events such as storms or floods currently do cause some death and
injury in the region. However, the risk of death from extreme heat or other events is
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quite low when compared to death by other causes. Important factors protecting the
MAR population from climate variability include:
• Most people in the MAR live and work in structures that protect them from the
elements, and many in structures with sophisticated climate control systems. These
structures can be viewed as adaptations to the existing climate.
• Most people in the MAR have access to water and sanitation systems that provide
potable water and treat wastes. The region also has significant regulation to protect
the safety of drinking water and foods. These systems reduce risks from water borne
and other diseases.
• Most people in the MAR have access to modern medical services that can provide
them with vaccines and treatments against most communicable disease that may
migrate to the region.
• The region has modern food and energy distribution systems that disconnect food and
energy supplies from local production and climate for most people.
However, some factors leave the population vulnerable to climate variability. These
include:
• MAR has a relatively older population. The elderly tend to be more sensitive to
infectious diseases and thermal extremes.
• There areas within the MAR of significant poverty (e.g. Appalachia).
• MAR has numerous small water supply systems that do not provide customers the
safegaurds of large systems.
• MAR has significant coastal populations and narrow, flood prone valleys.
• The primary disease vector of concern is the deer tick, which transmits Lyme disease.
The disease vectors that transmit malaria, dengue fever, and hantavirus are present in
the MAR even though the diseases are not currently present.
Human health sensitivity to climate variability in the MAR has diminished historically
with changes in the economy, characteristics of the population, medical technology, and a
range of other factors. In the absence of climate change, we would expect climate-related
risks to continue to diminish.
3.6.3 Impacts of Climate change
Following the IPCC format, the impacts of climate change on human health can be
subdivided into direct effects and indirect effects (WHO 1990; IPCC 1996). Direct
effects would occur predominantly through changes in the frequency and severity of
weather events (e.g., temperature, wind, precipitation) that have direct impacts on the
human physiology or psychology. Examples include changes in the incidence of
illnesses or deaths from exposure to thermal extremes or extreme weather events.
Indirect impacts would occur predominantly through the effect of climate on other
biological or geophysical systems that influence human health. For example, climate
change could influence the range and activity of disease vectors and infective parasites,
the ecology of waterborne and food borne infectious agents, the levels and biological
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impacts of air pollutants, and the productivity of food systems. Factors affecting regional
vulnerability to climate change include 1) the nature and extent of the change in regional
climatic variables that directly or indirectly affect human health, 2) the degree to which
humans or our biophysical support systems are sensitive to these changes, and 3) the
ability of humans and our biophysical support systems to adapt to new climates.
We do not attempt a comprehensive assessment of the range of risks in this report.
Instead we focus on heat-related illness, cryptospridiosis, malaria, and cholera. These
cases are of special interest in the region and involve major direct and indirect health
impacts. A more complete assessment is found in Kocagil et al., 1999.
Heat-Related
In general, temperature has a u-shaped relationship with mortality: very cold and very
warm temperatures are related with higher mortality. If climate change causes higher
temperatures in the MAR, as predicted by the Hadley Center climate model, there may be
reductions in cold related mortality and morbidity and increases in heat related mortality
and morbidity. The Hadley Center climate model predicts an increase in annual mean
temperature in the MARA region of 4.5° F for the period from 1990 to 2099 with
minimum and maximum temperatures increasing 4° F and 5° F respectively. By 2030,
minimum and maximum temperatures are expected to increase by 2° F with much of the
change occurring in the summer. Recognizing that a significant proportion of the MAR
population lives in urban areas and that this population is more susceptible to warmer
temperatures because urban areas act as heat traps, the predicted change in temperature is
likely to increase heat related mortality and morbidity in the Mid-Atlantic region.
Kalkstein and Swift (1998) and Kalkstein and Greene (1997) examine the relationship
between weather and mortality in several Mid-Atlantic cities: Baltimore, Greensboro,
Philadelphia, Pittsburgh, Washington, DC. They identify air masses currently associated
with high mortality during summer and winter months. Using three GCMs (the
Geophysical Fluid Dynamics Laboratory (GFDL), the United Kingdom Meteorological
Office, and the Max Planck Institute for Meteorology models) they predict how the
frequency of these high-risk air masses might change in 2020 and 2050 with climate
change. They apply the current mortality-climate relationships assuming full
acclimatization to the future scenarios to estimate excess mortality due to the high-risk air
masses. For instance, summer mortality for the 5 cities combined either decreases by
21% or increases by 26% or 126% for the 2020 scenarios and increases by 71% to 181%
for the 2050 scenarios, depending on the GCM used.
Examination of the individual cities reveals that Washington, DC is the least vulnerable
to summer high risk air masses. Kalkstein and Swift (1998) and Kalkstein and Greene
(1997) estimate that for Philadelphia, excess deaths in 2050 could increase by 91 to 270%
over current levels depending on the GCM used. Summer mortality would increase 48 to
95% for Baltimore, 56 to 144% for Pittsburgh, and 32 to 105% for Greensboro. Winter
mortality is not found to be as strongly associated with air mass as summer mortality.
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The decreases in winter mortality are not large enough to offset the increases in summer
mortality.
It is important to note that the results vary widely depending on the GCM used. In
addition, adjustments for future demographic changes are not included in the analysis.
Changes in the age distribution of the population may be especially important for heat
related mortality.
Cryptosporidiosis: In the past twenty years, Cryptosporidium parvum has gained
attention as a potentially deadly protozoan infecting water supplies. Since the first
reported case, in 1976, Cryptosporidium has been confirmed as the cause of several
waterborne and foodborne illness outbreaks. The most devastating outbreak to date is the
1993 Milwaukee, Wisconsin outbreak causing several deaths and more than 400,000
illnesses. Waterborne outbreaks have been confirmed in all regions of the United States
from both ground and surface water sources.
Some researchers believe the health risks from Cryptosporidium could increase if
environmental conditions change. Warmer and wetter conditions in the United States
may increase oocysts viability or increase the transportation of oocysts. A wetter climate
projection may be more consequential because Atherholt et al. (1998: 78) found most of
the Cryptosporidiosis outbreaks "occur during or following rainfall." These outbreaks
may occur because of greater water run off through livestock pastures and/or a severe
influx of storm water leading to a failure of waste water treatment plants. If the MAR
climate change scenarios hold true, people could have more chances to come in contact
with Cryptosporidium. More exposure opportunities would likely increase the number of
Cryptosporidiosis cases within the region.
Changes in "crypto" risks for a water system are extremely difficult to predict. They will
depend on the way that climate (e.g., temperature and precipitation) changes in the source
watershed, characteristics of the watershed (e.g., location of cattle or other crypto pools
in hydrologically active areas), water supply system characteristics (e.g., ground and/or
surface supplies, location of surface intakes or well fields, types and management of
source water treatment). These characteristics are highly system specific.
The impacts of climate change on crypto risks for the MAR region are highly speculative
at this time. Because humans and animals are the source of Cryptosporidium, we expect
risks to continue to be present in the MAR. Dairy, swine, and poultry are important
agricultural activities in the region and often occur in highly populated watersheds. We
do not expect significant growth in these activities, and significant decline is plausible.
In either case, we anticipate increased regulation of agriculture to reduce contamination
of surface and ground water supplies. A critical factor affecting the incidence of the
disease is water treatment. Effective water treatment limits the risk of massive outbreaks.
Under current and proposed EPA regulations, water systems classified as large or very
large should, theoretically, have filtration systems that greatly reduce the threat of
Cryptosporidiosis.
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Although additional research is needed to better understand crypto risks and how they
may change, our preliminary assessment is that climate change will not lead to
Cryptosporidiosis becoming a primary health risk to the citizens of the MAR. We are
working present case studies of the potential risk from Cryptosporidium within the
Delaware River Basin and Lancaster County, PA. These case studies illustrate linkages
between climate change and crypto risks, the economic costs of contamination events,
and adaptation strategies and their costs.
Malaria
Malaria is the most consequential insect transmitted disease in the world. Latest WHO
estimates between 300-500 million cases of clinical malaria per year, with 1.4-2.6 million
deaths, mainly among African children. Malaria remains the only insect-borne, parasitic
disease comparable in impact to the world's major killer transmissible diseases.
Previously extremely widespread, the disease is now mainly confined to the poorer
tropical areas of Africa, Asia and Latin America. Inadequate health structures and poor
socioeconomic conditions aggravate the problems of controlling malaria in these
countries. During the late 1940s, a combination of improved socioeconomic conditions,
water management, vector-control efforts, and case management successfully interrupted
malaria transmission in the United States. A malaria case surveillance program is
operational to detect locally acquired cases. Most of the Malaria cases documented
within the MAR are imported cases. An imported case is when a person contracts the
disease while traveling in a malaria endemic area.
Since the 1940s, there have been fifty-seven outbreaks of probable mosquito-transmitted
malaria in the United States. Some outbreaks, especially those in the northern parts of
the US (e.g., Michigan, New York and New Jersey), are associated with above-average
periods of temperature and precipitation. Climate change projections indicate a warmer
and wetter climate, thereby improving the conditions for parasite reproduction.
Examination of the 1995 Malaria case location reveals two important points. First, 50
percent (79 cases) of the malaria cases occur in the Baltimore-Washington, D.C.
metropolitan area. Second, six counties in New Jersey had 16.3 % of the total cases in
the MAR. These two facts could be significant with the development of autochthonous
or localized cases in the MAR.
Even if climate change does lead to improved conditions for malaria transmission, there
are measures that could prevent malaria from becoming a serious health risk. Australia
provides a useful analogue. Australia has had localized cases of malaria, and still has
mosquitoes capable of transmitting malaria, yet has not been considered a malaria risk
area since 1981. The success of Australia's antimalarial program has been based to a
large degree on the rapid diagnosis and treatment of humans with malaria. Similar public
health measures could be implemented in the MAR.
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Cholera
Cholera is currently thought to be a disease of the tropics. However, this was not always
the case. John Snow conducted one of the first epidemiological investigations with his
examination of an 1854 cholera outbreak in London, England. Although endemic cholera
is generally confined to the tropics, a warmer and wetter climate could improve the
conditions for cholera to thrive in the MAR waters.
No major outbreaks of this cholera have occurred in the United States since 1911. The
majority of cases that occurred between 1973 and 1996 in the US are imported or when
people contract cholera while traveling in a cholera endemic area. Many of the cholera
outbreaks that have occurred in the Western Hemisphere are associated with the
consumption of raw, improperly cooked or recontaminated shellfish. However, sporadic
cases occurred between 1973 and 1991, suggesting the possible reintroduction of the
organism into the US marine and estuarine environment. Analysis of the localized
outbreaks from 1978-1999 shows that the Louisiana delta region has had 5 localized
outbreaks and Florida 3 outbreaks of cholera cases.
In 1991, outbreaks of cholera in Peru quickly grew to epidemic proportions and spread to
other South American and Central American countries, including Mexico. They have
reported more than 340,000 cases and 3,600 deaths in the Western Hemisphere since
January 1991. Environmental studies have shown that strains of this organism may be
found in the temperate estuarine and marine coastal areas surrounding the United States.
Thus, the potential for a serious outbreak does exist, although proper water treatment and
shellfish preparation greatly reduces the risk.
3.6.4 Management Options and Strategies
Policy makers, governmental agencies, private organizations and even individuals can
take various actions to respond to climate related problems. There is the potential for
humans to create adaptive capabilities to mitigate health consequences from climatic
changes in the MAR. For example, the recent introduction of a Lyme Disease vaccine
will diminish the increased risk that may occur from a climate change conducive to
spirochete production. Patz (1996) categorizes adaptive measures into
administrative/legislative, engineering and personal behavior. Table 3.6.3, adapted from
Patz's Table 1 (1996: 456), shows potential responses to climate related problems.
Comparison of costs with benefits (e.g., improved health) should dictate which
response(s) should be chosen. The most cost effective method may vary by disease and
by the anticipated disease incidence under climate change. For example, in instances
where the number of cases is likely to be small, it may be cost effective to treat people
who become ill rather than develop a new vaccine. Kocagil et al., (1999) present a
framework for evaluating and categorizing adaptive responses by based on effectiveness,
complexity and cost.
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Table 3.6.3
Responses to climate-related problems
Adaptive Measure
Heat-Related Illness
Cryptosporidiosis
Malaria
Administrative/
Legislative
Engineering
Personal behavior
Implement weather
watch/warning systems
Plant trees in urban areas
Implement education
campaigns
Transport high risk
individuals (i.e. elderly)
to air conditioned
locations during peak
periods
Insulate buildings
Install high-albedo
materials for roads
Maintain hydration
Schedule work breaks
peak daytime
temperatures
Regulate water/sewer
treatment systems
Provide funding for
development of treatment
Pharmaceuticals
Maintain a disease
surveillance system
Mandate agricultural
waste disposal
Issue Boil Water
Advisories
Install Cryptosporidium
specific
filtration/treatment
systems
Drink bottled water
Use proper and frequent
handwashing techniques
Monitor breeding sites
Maintain disease
surveillance system
Implement education
campaigns to eliminate
breeding sites
Fund research for
development of a vaccine
Implement provider
awareness program on
non-endemic diseases
Install window screens
Release sterile males
Apply spot pesticides
Use topical insect
repellents
Use pyrethroid-
impregnated bed nets
3.6.5 Conclusions
Currently, mortality directly attributed to heat, cold, storms, flooding and lightning is
very small in the MAR. Even if climate change greatly increased the risk of such
mortality over current levels, the risks would remain small. The leading causes of death
in the region are heart disease, cancer, and stroke. Climate can be an aggravating or
contributing factor in these diseases, and climate change can be expected to have some
effect on mortality risks. However, it reasonable to believe that lifestyle choices, such as
smoking, diet, and fitness, and genetic endowment will remain much more important to
the health status of the region.
Research suggests that projected climate changes for the region will increase heat-related
mortality, although not by a large factor. We know less about the impact of climate on
heat-related morbidity. We expect climate change will influence heat-related morbidity
but are unable to quantify the change at this time.
We expect climate change to affect health risks through affects on water borne and vector
borne diseases. Scenarios can be constructed in which risks are increased, however, this
is a highly speculative area because of the uncertainty about local climates, the impacts of
those changes on biological and geophysical systems influencing infectious disease
pathogens and organisms that spread infectious diseases. We are guardedly optimistic
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that the current and future MAR health infrastructure has significant capability and
adaptability to cope with increased health risks from a climate change. Even in this case,
however, there are groups who would be disproportionately affected. These would be
mainly the elderly and those with limited access to health care.
There are events (scenario breakers) that would alter our preliminary conclusions.
Examples include:
• Unanticipated health impacts from climate-induced changes in ecosystems.
• A large influx of environmental refugees from outside the MAR. A sudden increase
of refugees, for example from Africa, could over tax the health care delivery system,
especially if these refugees bring native diseases to the MAR.
• Economic or other events that cause collapse of the region's public health
infrastructure.
• Collapse of Medicare that left elderly populations extremely vulnerable to many types
of health risks.
3.6.6 Research Priorities
In order to identify and prioritize the risks faced by the population, research on
comparative risk assessment is needed. A preliminary illustration based on Viscusi
(1993) and the current research shows that the relative risk of climate related mortality
and morbidity is quite low in the MAR in comparison to risk of fatality due to cigarette
smoking, fire, or car or plane accident (Appendix B). However, more research is needed
to understand and quantify climate change impacts on health. Research issues include:
• epidemiological research to better understand the linkage between human health and
climate (e.g., how changes in temperature and humidity may affect the human
immune system).
• how climate change will affect ecosystems and how ecosystem changes will in turn
affect disease vectors.
• research on the costs and benefits of various adaptation options to determine the
optimal policy choice.
• how shifts in health policy may affect vulnerability to climate change.
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Part 4: Where Do We Go from Here?
4.1 Summary of Key Findings (Fisher)
This report's earlier sections demonstrate many uncertainties in projecting, for 30 years
from now and for 100 years from now, a) how the socioeconomic/ecological structure of
the MAR will change, b) how the region's climate will change, and c) how changes in the
region's climate will affect the socioeconomic/ecological picture, particularly because
different people, organizations, species and ecosystems have differing capabilities to
adapt to the additional opportunities or stresses accompanying climate change. Despite
these uncertainties, we are able to derive significant results that the region's citizens and
decision makers can use to improve near-term decisions that affect the future of the
MAR.
4.1.1 Impacts/consequences from increased climate variability and change
Because of its advanced state of economic and social development, diverse topography,
and land cover, the Mid-Atlantic region has the good fortune of being reasonably resilient
to changes ranging from population growth and redistribution within the region to rapid
evolution in the types of goods and services people want to dramatic changes in the
technology for producing commodities. This resilience also will help the MAR adapt to
increased climate variability and climate change. On the other hand, lingering effects
from earlier degradation are compounded by continuing pressures on many of the
region's ecological resources. Increased recognition of these pressures has come at a
time of growing societal demand for ecological resource protection, both for its own sake
and because rising incomes increase the demand for recreation, including outdoor
recreation in natural areas. These features—substantial overall resilience in concert with
pressure on ecosystems—summarize the region's basis for taking advantage of new
opportunities created by climate change and for coping with negative impacts from
climate change.
MARA's preliminary results suggest the following impacts from increased climate
variability and change:
Agriculture
Agriculture has declined in importance within the MAR (reflecting national trends) as
well as adapting rapidly to changes in production and processing technology and to
changing demands for different agricultural products. Because farmers are adaptable,
climate change is likely to increase production of soybeans and tree fruits, and possibly
corn. Climate change could have negative impacts on the region's tobacco, primarily
because of increased competition from outside the MAR. The region's other two major
agricultural categories, dairy and poultry, are not expected to be affected by climate
change. The main environmental side effects from agricultural production are nutrient
and pesticide runoff, and erosion. If raising livestock continues to be as important in the
MAR, nutrient leaching and runoff could increase. Risk from some waterborne diseases,
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such as Cryptosporidiosis, could also be affected. The other impacts on agriculture from
climate change are not expected to affect water quality, unless there is a substantial
change in extreme weather events. The impacts of changes in agricultural production on
rural amenities could be significant but there is little research for identifying threats or
opportunities.
Forests
Whether forests are managed for watershed protection, harvesting of sawtimber, or
maintenance of forest aesthetics, their managers report increased operating costs when
extreme weather occurs. These costs would be higher if extreme weather becomes more
frequent or intense. Climate change is likely to reduce the dominance of maple-beech-
birch forests in the MAR, with an increase in oak-hickory forests, and, to a lesser extent,
southern pine and mixed oak-pine forests. This could decrease the competitiveness of the
many small hardwood processors (e.g., for furniture and cabinetry).
Fresh water quantity and quality
Rapid development in some parts of the MAR that rely more heavily on ground water
wells, especially for residential use, has created stresses because of potential surface
water infiltration, or in the coastal zone, salt water intrusion. The potential for a wetter
regional climate, punctuated by droughts, suggests higher water supply management
costs, to accommodate source protection (for water quality of both surface water and
ground water) and more storage capacity. Smaller water systems and individual well
owners have the disadvantage of not being able to spread these costs over large numbers
of users. There is, however, significant potential for improved efficiency of water use
through innovations in demand management. The effects of changed climate on the
MAR hydrology also are likely to stress ecosystems, because "flashier" runoff will carry
more contaminants and sediment to streams that simultaneously are somewhat warmer
because of higher average air temperatures. Changes in the amount, timing, and quality
of water might affect ecosystems from the headwaters and throughout the drainage basins
until they reach the estuaries and bays of the MAR. Land use decisions can have a large
impact on the quantity and quality of runoff. Because these decisions have long-lasting
effects, future vulnerability related to water resources will be influenced by decisions
now and in the near future.
Coastal zones
The MAR coastal zone's dense population puts people in harm's way from storm surges
that will be exacerbated by the combined effects of sea level rise from climate change
and subsidence. The costs of protecting valued infrastructure or natural areas could be
quite high. The benefit of longer coastal recreation seasons could be more than offset, if
severe storms become more frequent or intense. The very diverse and productive
ecosystems in salt marshes are vulnerable to sea level rise, because sediments and organic
matter are not deposited fast enough to allow them to keep up with sea level rise and
barriers often prevent inland migration.
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Ecosystems
Many ecosystem components are quite resilient, while others are very fragile. In many
locations, the MAR ecosystems already are stressed. Changes in carbon dioxide
concentration, temperature, precipitation and sea level in the MAR will affect individual
species differently. Species that benefit could crowd out others not directly affected by
changes in climate variables, as well as those that suffer directly. Although some desired
species might become more abundant, the overall result is likely to be a reduction in
biodiversity, with uncertain implications for the ecosystem functions that are crucial for
ordinary ecosystem evolution as well as functions that people value, such as pollinating
crops, moderating and purifying water flows, and providing diverse wildlife to observe.
Human health
Morbidity and mortality directly related to heat, cold, storms, flooding and lightning are
likely to continue to be very small in the MAR, although climate change could increase
these somewhat. Climate change could aggravate or contribute to the region's leading
causes of death (heart disease, cancer, strokes), but lifestyle choices (smoking, diet,
fitness) and genetic endowment are likely to continue dominating the health status of the
region. Although more speculative, climate change could increase the region's risk from
water borne and vector borne diseases. The region's current and future health
infrastructure are expected to be able to cope with these risks, but the elderly and those
with limited access to health care still could be disproportionately affected.
4.1.2 Win-win strategies identified for early action
Education and public information strategies so that people in the MAR will know
what they can do to capitalize on benefits and ameliorate damages from climate
change.
- In agriculture, continued adaptation, especially for biotechnology and precision
agriculture. This requires disseminating information in a form farmers can use.
Extend provisions of the Clean Water Act beyond small community water systems to
help individual well owners reduce their vulnerability to potential variability in the
amount and quality of water.
- Water demand management policies to increase the efficiency of water use.
Land use planning procedures and ordinances that reduce risks from flooding and
runoff pollution, and protect well fields.
- Watershed-based water quality protection programs, especially for nonpoint sources.
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For ecosystems, strategies that address current stresses also will reduce potential
stresses from climate change. These include preserving forests and wetlands,
minimizing urban and agricultural soil erosion and nutrient runoff, protecting stream
habitat, and reducing the release of toxic chemicals.
Beach nourishment is cost-effective for protecting some coastal developments;
allowing coastal retreat is more cost-effective in other areas (although this would be
accompanied by net losses in coastal ecosystems if barriers prevent their migration).
- Silvicultural practices to encourage species that thrive in pine and oak-hickory forest
types, including cutting to minimize wind and ice damage, and monitoring for
potential increases in any pests and diseases expected to be more prevalent under
climate change conditions.
- (- Monitoring for disease vectors identified as moderate-to-high risk in the MAR.)
4.2 Next Steps
4.2.1 Maintaining and enhancing the mechanism for public involvement (O'Connor)
The guiding philosophy of the MARA team has been to work with stakeholders to design
and implement the assessment. The process has been dynamic and interactive, with this
report a milestone, not an endpoint. With this milestone completed, the challenge is to
engage stakeholders in responding to its findings and to move ahead to elicit new
information and deliver it to stakeholders. We will continue to work with our Advisory
Committee as well as use other mechanisms to enhance the two-way flow of information.
Identifying win-win strategies for specific stakeholder groups will have little impact on
the Mid-Atlantic region unless those groups get this information in a manner they find
credible and useful. We have three mutually supportive types of activities planned:
Advisory Committee involvement, public information materials, and focused efforts at
identifying stakeholder informational needs and preferences.
Advisory Committee
Advisory Committee members will continue to work on a one-on-one basis with MARA
team members to address specific concerns and interests. In early May 1999 the
Advisory Committee will meet at Perm State to review the initial assessment, discuss next
steps in assessment, and work on planning a dissemination strategy. During the next year
the Advisory Committee will continue to review documents and work on dissemination.
Public Information Materials
The MARA team does not have the resources to mount a full public information
campaign. Nevertheless, the team is committed to develop materials and distribute them
to interested groups and the media. Where we have identified win-win strategies
applicable to particular stakeholder groups, we will work with organizations associated
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with those groups to ensure that they get this information in a form that is appropriate,
timely, and useful. For the general public, we are working with public information
specialists at Perm State and on our Advisory Committee (e.g., Bud Ward of the National
Safety Council) to devise a strategy that will leverage our resources by providing
materials to groups and the media. Materials may include real-time computer simulations
to demonstrate potential impacts.
Focus Groups and Scientific Surveys
The initial assessment shows that certain groups in the region face significant stress from
growth and other factors having nothing to do with climate change, but that climate
change adds an additional element of uncertainty and concern. There may be win-win
strategies for these groups that would reduce risks and help to preserved a valued way of
living. We intend to carry out a number of focus groups with members of these groups
(e.g., barrier island residents, commercial anglers) to learn what information they would
like in what form, and what research they think needs to be done. We will work with the
Advisory Committee in designing this work and recruiting some participants. The results
of these focus groups will both help inform additional assessment activities and feed into
the design and implementation of scientific surveys of different stakeholder groups as
well as the general population of residents of the Mid-Atlantic region. We expect to learn
how better to communicate with stakeholders about climate change in the context of
planning for themselves and their communities. We expect this work to produce
important substantive findings as well as methodological advances that can be used for
ongoing assessments here and outside this region.
One research priority is to identify stakeholder preferences for ecological assessment and
protection. The ecology of the region faces many stresses, with some ecologically valued
places and objects more vulnerable than others to climate change impacts. The initial
assessment produced much information on how and where the ecological is threatened,
but little information on exactly what stakeholders value about the ecology to a greater or
lesser degree. The research task is to use what we have already learned to ascertain how
residents of the Mid-Atlantic Region understand and value the ecology. A vast array of
ecological changes is likely, but we do not know how to focus resources on particular
places or species to reflect societal values.
Still to be addressed are issues of a) How to better communicate uncertainty. Insurance
companies do this..., and b) How to better relate potential climate change impacts on
people and the environment to their everyday lives.
4.2.2 Identified research priorities and information needs (Fisher)
- Improved ability to project frequency, timing and intensity of extreme weather events
at a regional level
- impacts on agriculture, forests, ecosystems, and coastal zones
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- impacts on environmental side effects from agriculture, silviculture,
development patterns
How climate change would affect weeds, and insects and diseases in crops, livestock,
and forests.
How climate change would affect environmental side effects from agriculture
How a warmer, wetter climate will affect the amount, timing, and quality of water
available for human and ecosystem use.
Research into ecosystem processes, how people depend on them, and how human
activities alter them.
Specific research on how changes in climate variables affect different types of
ecosystems (with respect to ecosystem function, especially fragile components that
might be replaced by invasive species or affected by indirect impacts because of
changes in nutrient runoff, how ecosystem changes affect disease vectors).
Valuation of ecosystem components and processes.
Research on the linkage between human health and climate, such as how temperature
and humidity affect the immune system
Identification of methods for evaluating how proposed shifts in policy (e.g., health
policy, land use policy, agricultural policy) would affect vulnerability to climate
change.
Research on the benefits and costs of alternative adaptation options, so that efficiency
can be considered in management and policy decisions.
4.2.3 Types of issues still to be addressed [add issues that can and should be addressed in
the next phase of the first MARA, or in future assessments]
4.3 Summary
Positive impacts:
- Overall benefits to agriculture in the region, especially soybean and tree fruit
production, and possibly corn.
Extended outdoor recreation seasons, particularly for those visiting coastlines.
Decreased cold-related mortality
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- Increased primary forest productivity
- Increased water supply
Negative impacts:
- Reduction in biodiversity and ability of ecosystems to function effectively for societal
values such as water storage and purification, fish and wildlife for recreation,
Shorter winter outdoor recreation seasons, particularly for the snow skiing resorts in
the MAR.
Increased heat-related mortality risks in some cities.
- Increased probability of floods.
Property and ecosystem damages from sea level rise.
110
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Caspar, J., and E. L. Glaeser (1998), "Information Technology and the Future of Cities,"
Journal of Urban Economics, 43:136-156.
Goldin, I., O. Knudsen, and D. van der Mensbrugghe (1993), Trade Liberalisation:
Global Economic Implications, Paris: OECD.
Hardie, I. W., and P. J. Parks (1997), "Land Use in a Region with Heterogeneous Land
Quality: An Application of an Area Base Model," American Journal of Agricultural
Economics, 79:299-310.
Hertel, T. W., ed. (1996), Global Trade Analysis: Modeling and Applications,
Cambridge, UK: Cambridge University Press.
Huttner, S. L. (1996), Biotechnology and Food, New York: ACSH.
Islam, N., ed. (1995), Population and Food in the Early Twenty-First Century: Meeting
Future Food Demand of an Increasing Population, Washington, DC: International Food
Policy Research Institute. !
Johnson, D. G. (1998), "Food Security and World Trade Prospects," American Journal of
Agricultural Economics, 80:941-947.
Johnson, D. G. (1991), World Agriculture in Disarray, second edition, New York: St.
Martin's Press.
Johnson, S. R., and M. T. Holt (1997), "The Value of Weather Information," Economic
Value of Weather and Climate Forecasts, ed. R. W. Katz and A. H. Murphy, Cambridge,
UK: Cambridge University Press, pp. 75-107.
Mjelde, J. W., H. S. J. Hill, and J. F. Griffiths (1998), "A Review of Current Evidence on
Climate Forecasts and their Economic Effects in Agriculture," American Journal of
Agricultural Economics, 80:1089-1095.
National Research Council (1997), Precision Agriculture in the 21st Century,
Washington, DC: National Academy Press.
Plucknett, D. L., and D. L. Winkelmann (1996), "Technology for Sustainable
Agriculture," Key Technologies for the 21st Century, New York: Freeman, pp. 133-138.
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Rifkin, J. (1998), The Biotech Century: Harnessing the Gene and Remaking the World,
New York: Jeremy P. Tarcher/Putnam.
Thompson, G. D. (1998), "Consumer Demand for Organic Foods: What We Know and
What We Need to Know," American Journal of Agricultural Economics, 80:1113-1118.
Tribbia, J. J. (1997), "Weather Prediction," Economic Value of Weather and Climate
Forecasts, ed. R. W. Katz and A. H. Murphy, Cambridge, UK: Cambridge University
Press, pp. 1-18.
Tweeten, L. (1998), "Dodging a Malthusian Bullet in the 21st Century," Agribusiness,
14:15-32.
U.S. Department of Agriculture, Interagency Agricultural Projections Committee (1999),
USDA Agricultural Baseline Projections to 2008, Staff Report WAOB-99-1,
http://www.econ.ag.gov/epubs/pdf/baseline/index.htm.
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Appendix A:
People and Partners
MARA Advisory Committee Members
Balbus, John, Dept. of Env. & Occupational Health, George Washington U (DC)
Ball, Christopher, Ozone Action (DC)
Banfield, Timothy, Allegheny Power (PA)
Bechis, Maria, Sierra Club (PA)
Birdsey, Richard, USDA Forest Service (Radnor, PA)
Bissell, Perry, Consul, Inc. (coal) (PA)
Bloomfield, Janine, Environmental Defense Fund (NY)
Brooks, Irene, PA DEP, Office for River Basin Cooperation
Buchanan, Claire, ICPRB Potomac River Basin, MD
Butt, Arthur, VA DEQ
Calaban, Michael, NY DEC
Carson, Charles, U.S. Steel (PA)
Carter, Lynne, National Assessment Coordination Office (DC)
Colket, Peter, American Reinsurance Co. (NJ/MD)
Connor, Betty, PA League of Women Voters (PA)
Cronin, Tom, USGS (DC or VA)
DeMoss, Thomas, Chesapeake Bay Program (MD) (Eric Walbeck, alternate)
Donaldson, Guy, Pennsylvania Farm Bureau
Esposito, Gerald, Tidewater Utilities (DE)
Falconer, John, American Forests (DC)
Featherstone, Jeffrey, DRBC (NJ)
Flemming, Agnes, Norfolk Department of Public Health (VA)
Freudberg, Stuart, Washington Area COG
Fromuth, Richard, DRBC (NJ)
Garvin, Don, Trout Unlimited (WV)
Gilbert, Phyllis, Sierra Club (PA)
Glotfelty, Caren, ClearWater Conservancy (PA)
Hoffman, Joe, Interstate Commission on the Potomac River Basin
Jarrett, Jan, PA Campaign for Clean Affordable Energy
Johnson, Zoe, MD DNR Coastal Zone Management Division
Kaiser, Marshall, Safe Harbor Water Power Corp./Alliance for the Chesapeake Bay (PA)
Kauffman, John, Alliance for the Chesapeake Bay (former chairman, PP&L) (PA)
Kunreuther, Howard, University of Pennsylvania
Kusler, Jon, Association of State Wetland Managers (NY)
Lassiter, Ray, Ecosystem Research Division, NERL, USEPA (GA)
Leathers, Daniel, DE state climatologist
Linky, Ed, U.S. EPA Region 2 (NY and NJ)
MacSparran, John, SRBC (Gil Hirschel, alternate) (PA)
Markham, Adam, World Wildlife Fund (DC)
Mortsch, Linda, Environment Canada (Waterloo, Ontario)
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Nagourney, Stuart, NJ DEP
Nichols, Georgej Washington Area COG
Nunez, Albert, ICLEI, Tacoma Park, MD
Pearsall, Sam, The Nature Conservancy (NC)
Pena, Michelle, Climate Institute (DC)
Penn, Robert C., Vanguard Management Group, Baltimore (MD)
Pitelka, Lou, U of MD Center for Environmental Science
Plaut, Jon, NAFTA Environmental Commission (NJ)
Raman, Sethu, NC Climatologist
Ratzell, Lynn, PP&L environmental manager (PA)
Ross, Sharon, Allegheny Power, MD
Rotz, J.B., PA Farm Bureau
Rudd, Ralph, ClearWater Conservancy (PA)
Scheraga, Joel, U.S. EPA (DC)
Schmidt, Michael, CIGNA (PA)
Schultz, Gwynne, MD DNR Coastal Zone Management Division
Schwarzwaelder, David, Columbia Gas (PA)
Small, David, Department of Natural Resources and Environmental Control (DE)
Smith, Betsy, US EPA National Environmental Research Lab (NC)
Stevens, Jack, Professor of Management, PSU
Swanson, Ann, Chesapeake Bay Commission (MD)
Way, Brooks, Way Fruit Farm (PA)
Wertz, Fred, PA Department of Agriculture
Collaborators
Epp, Donald, Dept. of Ag. Econ. & Rural Soc., Penn State University
Evans, Barry, Environmental Resources Research Institute, Penn State University
Galbraith, Hector, Stratus Consulting (CO)
Handcock, Mark, Department of Statistics, Penn State University
Iverson, Louis, Forest Service (OH)
Kalkstein, Laurence, University of Delaware
Kennedy, Vic, Horn Point Lab, University of Maryland
Lynch, James A. School of Forest Resources, Penn State University
Matthews, Stephen, Population Research Institute, Penn State University
Megonigal, Pat, George Mason University (VA)
Orr, Wil, and Hoyt Johnson, Prescott College (AZ)
Patz, Jonathan, Johns Hopkins University School of Public Health (MD)
Petersen, Gary, Penn State University
Pielke, Roger, Jr., National Center for Atmospheric Research (CO)
Price, Jeff, American Bird Conservancy (CO)
Psuty, Norbert, Department of Marine and Coastal Sciences, Rutgers University (NJ)
Richards, Bruce, Center for Inland Bays (DE)
Rogers, Catroina, EPA/ORD (DC)
Sorenson, Lisa, Boston University (MA)
Thornton, Kent, FTN Associates (AR)
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Walker, Hal, EPA/ORD (RI)
Ward, Bud, Environmental Health Center, National Safety Council (DC)
Wilbanks, Thomas, Oak Ridge National Lab/NCEDR
Corresponding Members
Bemabo, Chris, RAND Corporation (formerly Science and Policy Associates, Inc. (DC)
Blankenship, Karl, Bay Journal (PA)
Burns, Doug, USGS (Troy, NY)
Denworth, Joanne, 10,000 Friends of PA
Falke, Tom, coal industry (PA)
Friedman, David, American Forest and Paper Association
Goldberg, Annette, PA Economy'League (until 2/99)
Gornitz, Vivien, NASA Goddard Institute for Space Studies, Columbia University (NY)
Herkness, Diane, CIGNA (PA)
Hunter, Scott, Philadelphia Energy Coordinating Agency
McDonnell, Arch, Perm State University, Environmental Resources Research Institute
McKinnon, Hugh, U.S. EPA National Risk Management Research Lab (OH).
Mongan, Edward, DuPont (DE)
Parks, Nancy, Sierra Club, PA
Reichert, Joshua, Pew Charitable Trusts (PA)
Shinn, Robert, NJ DEP Commissioner
Simns, Larry, MD Watermen's Association
Smith, Joel, Stratus Consulting (CO)
Tropea, Larry, AMP (PA)
Tulou, Christophe, Secretary, Dept. of Natural Resources and Env. Control (DE)
Winebrake, James, James Madison University (VA)
Post-Docs/Research Associates
Parti Anderson
Keith Benson
JeffCarmichael
Bahar Celikkol
Mary Easterling
Patricia Kocagil
Students
Richard Caplan (summer 1999)
Bahar Celikkol (summer 1999)
Marta Galopin (summer 1999)
Eric Houston (through summer 1999)
Loan Lee
Eric Steele
Katie Filbert
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Tao Zhu (through summer 1999)
Byeong-ok Song
Steve Norman
Debo Oladosu
Brett Bornstein
Brian Schorr
Sponsors and Institutional Support
MARA has received financial support from the US Environmental Protection Agency.
Institutional support is being provided by the following Penn State units: Earth System
Science Center and Center for Integrated Regional Assessment within the College of
Earth and Mineral Sciences, Department of Agricultural Economics & Rural Sociology
and the College of Agricultural Sciences, Environmental Resources Research Institute,
and Office of Research. Institutional support also is being provided by the home
organizations of our collaborators, especially US EPA and US Forest Service. Additional
institutional support comes from National Center for Atmospheric Research, University
of Delaware, University of Maryland, Rutgers University, Johns Hopkins University,
Boston University, George Mason University, Prescott College, National Safety Council,
Oak Ridge National Laboratory, FTN Associates, Stratus Consulting, and American Bird
Conservancy.
As part of the national assessment process, the regional and sectoral teams also receive
diverse types of information, data, input and feedback from NAST, NACO and NAWG.
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Appendix B:
Introduction
The MARA team has used a variety of research methods to prepare the results for this
report and in their ongoing work. These methods include:
• Empirical downscaling to improve the regional resolution of changes in temperature
and precipitation available from the models and projections provided by NAST,
NAWG, and NACO,
• Statistical estimation of models for examining relationships between climate and
water resources, forest resources, ecosystems and health risks.
• Geographic Information Systems for processing and displaying baseline and other
data.
• Social Accounting Matrices, Input-Output Models, General Equilibrium Models for
characterizing the regional economy and analyzing economy-wide impacts of climate
change.
• Comparative Risk Assessment and Event Trees for analyzing impacts of climate
change on disease risks.
• Collection and analysis of primary data from targeted groups such as community
water system managers and forest managers.
Below we provide additional detail on selected methods and topics.
Agricultural Baseline
This section discusses baseline futures for agriculture in the Mid-Atlantic region - in
other words, how agriculture might change between now and 2030 independent of any
impacts due to climate change. As discussed in Part 3, the uncertainties surrounding the
year 2100 are so overwhelming that it is very difficult to think about baseline agricultural
scenarios for that year.
Key Socioeconomic Variables
In thinking about key socioeconomic variables for Mid-Atlantic agriculture that
determine future baseline scenarios, the focus should be on variables that are highly
relevant to the future of agriculture in the region. Key socioeconomic variables for Mid-
Atlantic agriculture can be grouped into four broad categories:
1. Markets for agricultural commodities in the Mid-Atlantic region. This can in turn
be grouped into two subcategories:
a. Real (inflation-adjusted) prices oftradable agricultural commodities. These are
commodities that the Mid-Atlantic region either imports or exports to other
regions of the U.S. or other countries.
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b. Demands within the Mid-Atlantic region for nontraded agricultural commodities.
These are commodities that are produced and then entirely consumed within the
Mid-Atlantic region.
Most agricultural commodities produced in the Mid-Atlantic, and certainly the ones
accounting for the vast majority of farm income, are tradable between the Mid-
Atlantic and other regions and countries. Major tradable commodities currently
produced in the region include dairy products, beef, pork, poultry, eggs, corn, wheat,
other grains, soybeans, tobacco, mushrooms, other greenhouse products, apples, and
peaches. Given probable future developments in transportation and communications,
we anticipate that the degree of tradability of agricultural commodities in the Mid-
Atlantic will increase even further.
The Mid-Atlantic region is an important producer of many tradable agricultural
commodities. Nevertheless, even for commodities such as milk, the Mid-Atlantic
region currently accounts for a small percentage of total US production and an even
smaller percentage of global production and trade. For this reason, supplies and
demands for these commodities in the Mid-Atlantic are unlikely to have significant
effects on prices of these commodities. The Mid-Atlantic region, in other words, is a
price taker for these commodities.
r>
The most important commodity for which little or no interregional or international
trade occurs at present is hay. Other commodities in this same category include some
seasonal fresh fruits and vegetables, for which it is uneconomic at the present time to
ship them interregionally or internationally. Demands for these commodities are
likely to depend on population growth in the region, per capita income growth in the
region, and trends in prices of substitute commodities.
2. Markets for agricultural inputs in the Mid-Atlantic region. This can be broken
down into two subcategories:
a. Real prices of capital, labor, and purchased materials. Labor includes both farm
operator labor and hired labor, while purchased materials include seeds, processed
livestock feed, fertilizers, pesticides, and energy.
b. Competing demands within the Mid-Atlantic region for nontraded inputs,
principally land.
Capital and labor, at least in the long run, are highly mobile between Mid-Atlantic
agriculture and other sectors of the Mid-Atlantic economy. They also tend to be quite
mobile between regions of the country, and in the case of capital also quite mobile
internationally. In the short run, capital and labor do not tend to move quickly
between sectors or regions of an economy. However, over the long time horizons
contemplated for climate change, these short-run rigidities disappear. At the same
time, Mid-Atlantic agriculture is a small part of the Mid-Atlantic labor force and
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economy, and a trivial part of the entire U.S. labor force and economy. For these
reasons, returns to capital and labor in agriculture are determined by economic forces
in the U.S. and global economies as a whole (Johnson, 1991). The Mid-Atlantic
region, in other words, is a price-taker for capital and labor.
Purchased materials also tend to be highly mobile between Mid-Atlantic agriculture
and agriculture in other regions and countries. Furthermore, in the long run, the
capital, labor, and natural resources used to produce purchased agricultural materials
can be withdrawn at relatively low cost for use in other sectors of the economy. For
these reasons, Mid-Atlantic agriculture is also a price-taker for purchased materials.
Mid-Atlantic agriculture is definitely not a price taker for land, since it accounts for
about one-fourth of total land use in the region. Forests and urban development are
major competing land uses. Historically, much abandoned agricultural land in the
region has gone back into forests, while in more recent years significant areas of
agricultural land have been converted into urban uses.
Another input that would fall into the same category as land in many other regions of
the US is water, since agriculture is a major user of water for irrigation in many parts
of the country. As discussed in Part 2, however, irrigation is of negligible importance
in the Mid-Atlantic at the present time.
3. Technologies available to agricultural producers within the Mid-Atlantic region.
This can be divided into three subcategories:
a. New technologies for producing existing agricultural commodities.
b. Technologies that lead to the production of new agricultural commodities. These
could be either commodities that do not yet exist or commodities that exist but are
not currently being produced in the Mid-Atlantic.
c. Technologies that lead to the use of new agricultural inputs. These could be
inputs that do not yet exist or inputs that exist but are not currently being used in
the Mid-Atlantic.
For the Mid-Atlantic region, available technologies are largely exogenous because
most technologies are developed for national or global markets, and the Mid-Atlantic
is too small economically to significantly affect national and global rates of technical
change. Of course, the fact that a technology is available does not mean that it must
or will be used. Individual farmers, companies, households, and organizations within
the Mid-Atlantic will make choices about what technologies to adopt among the
available set of options.
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4. Public policies and institutions. This is potentially a very broad category, but some
key components would include:
a. Agricultural price and income support policies.
b. Environmental policies facing agricultural producers.
c. Agricultural land preservation policies, land retirement and set-aside policies, '
and land use regulations and institutions.
Among all the policies and institutions that could be listed here, these three stand out
as particularly important because of (1) their large, or potentially large, impacts on
farmers and farm management decisions, and (2) the high likelihood that these
policies will change significantly over the next 25-50 years.
Agricultural price and income support policies in the U.S. are almost exclusively the
province of the federal government. For the Mid-Atlantic region, public policies and
institutions at the federal level are largely exogenous because the Mid-Atlantic is too
small (in economic and political terms) to influence the structure of overall policies or
institutions at this level.
«*
Environmental policies toward agriculture have traditionally been the province of the
states, rather than the federal government, with the notable exception of pesticide
regulation. All three levels of government (federal, state, and local) have traditionally
had some authority over land use. Local governments within the Mid-Atlantic region
can influence land use through zoning, tax, and other policies. State governments in
the region have policies designed to foster the preservation of agricultural land. At
the federal level, land retirement and set-aside policies have long been a part of
agricultural price and income support programs.
Agricultural Commodity Markets
A number of near-term futuring exercises have been conducted in recent years for world
agricultural commodity markets, with time horizons ranging from 2008 to 2030. These
studies include economic simulation models constructed by the International Food Policy
Research Institute (Islam, 1995) and the U.S. Department of Agriculture (1999), as well
as more qualitative analyses by Crosson and Anderson (1992).
Baseline projections by the International Food Policy Research Institute (Islam, 1995)
and the U.S. Department of Agriculture (1999) suggest that real prices for major
agricultural commodities such as wheat, corn, other grains, soybeans, dairy products,
beef, pork, chicken, and eggs are all likely to decline in the coming decade, perhaps
significantly. These projections imply that productivity growth in world agriculture is
likely to outstrip growth in food demand caused by population growth and growth in per
capita income. As Johnson (1998) notes, estimates of future population growth have
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dropped since these baseline projections were constructed, suggesting even larger
declines in agricultural commodity prices. These projections are consistent with trends in
global agricultural prices since the end of World War II. In real terms, prices of
agricultural commodities as a whole stand today at about one-fourth of their 1950 levels.
The more qualitative analyses by Crosson and Anderson (1992) for the year 2030 are
consistent with these projections.
Others, such as Tweeten (1998) and Brown (1996), are more pessimistic about the
potential for productivity growth in world agriculture to outstrip the growth in global
food demand. They suggest that real prices of agricultural commodities could rise
somewhat over the next few decades. However, as Johnson (1998) emphasizes, such
projections have consistently been proven wrong in the past.
These price projections are particularly important for the Mid-Atlantic region because the
region is a marginal producer of a number of agricultural commodities. Unlike many
parts of the Corn Belt, Great Plains, or numerous other countries, agricultural land in the
Mid-Atlantic has a number of competing uses. Many other regions and countries can
produce grains, dairy products, poultry, and other commodities at lower prices than the
Mid-Atlantic and still remain profitable because there are few economically viable
alternative uses for their agricultural land.
Demand could potentially grow in the Mid-Atlantic region over the coming decades for
fresh fruits and vegetables, in particular organically grown fruits and vegetables. A
growing demand for organic produce is already evident in Western Europe and, to a
lesser extent, the United States (Thompson, 1998). Given costs of transporting fruits and
vegetables, much of the demand in the Mid-Atlantic region may be satisfied by
production within the region. However, the nature of fruit and vegetable production is
that significant amounts can be grown on small parcels of land. Thus, we do not
anticipate that fruit and vegetable production will become a dominant part of Mid-
Atlantic agriculture.
Agricultural Input Markets
There probably will not be significant long-term changes in real interest rates facing the
Mid-Atlantic region, while a continuation of the long-term upward trend in real wages
and salaries is probable. The U.S. economy appears to be close to an economic steady
state in which long-term real interest rates are constant and the long-term growth rate in
real wages and salaries is also constant (Barro and Sala-i-Martin, 1995). This means that
Mid-Atlantic farmers, and prospective farmers, will face increasingly better earnings
prospects outside of agriculture, and thus returns to labor in agriculture will have to rise
to keep pace. Historically this has meant larger farm sizes and fewer farmers, a trend that
is likely to continue.
Future increases in population in the Mid-Atlantic region may lead to additional
conversion of farmland to residential and commercial uses. Future increases in per capita
income could manifest themselves in larger homes and lot sizes, and thus more
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residential land use, a tendency evident over the last 30 to 40 years. Studies of land use
confirm that population and per capita income are important determinants of the
conversion of farmland and forestland to urban uses (Hardie and Parks, 1997; Bradshaw
and Muller, 1998).
Probable futures for the spatial pattern of development within the Mid-Atlantic region are
more difficult to assess than an overall tendency toward urbanization. There may be a
"fill in" of areas between existing major urban centers, such as the area between
Baltimore and Washington, DC (Bockstael and Bell, 1998; Bockstael and Irwin, 1997).
Some have speculated that future information technologies could render cities obsolete,
leading to spatial population dispersion. However, this presumes that information
technologies are a substitute for the regular face-to-face interactions that a city makes
possible, when they in fact could be complements (Caspar and Glaeser, 1998).
Friendships and business relationships that begin with an e-mail message may continue
over lunch or dinner.
Agricultural Technologies
Technology is a "wild card," both because of its unpredictability and because of its
potential to trump other changes in Mid-Atlantic agriculture, including climate change.
Based on what we know now, three technologies would appear to have significant
implications for Mid-Atlantic agriculture: biotechnology, precision agriculture, and
improved weather forecasting.
The basic science of biotechnology is progressing very rapidly, and already tens of
millions of crop acres in the U.S. have been planted with genetically modified organisms
(GMOs). Corn has had the most GMO releases to date, and should have substantially
more during the next decade. Plant biotechnology has the potential to yield crops with
significantly greater resistance to a whole host of pests, greater resilience during periods
of temperature and precipitation extremes, and even cereal varieties that fix atmospheric
nitrogen in the same manner as legumes (Plucknett and Winkelmann, 1996; Huttner,
1996). Work is also underway to engineer pest vectors into beneficial insects as part of
integrated pest management (IPM) strategies. Animal biotechnology has the potential to
yield livestock that process feed more efficiently, leading to reduced feeding
requirements and fewer nutrients in animal wastes. Feed may also be genetically
modified so as to reduce nutrients in livestock wastes. Genetically engineered vaccines
and drugs could significantly reduce livestock mortality and increase yields. Plant
biotechnology is already being used to develop higher quality crops that have attributes
desired by consumers, and the same may occur with animal biotechnology.
We recognize that many people, such as Rifkin (1998), are very skeptical about the
effectiveness and environmental consequences of agricultural biotechnology. For
example, GMOs with tolerance to herbicides are also being developed and released, and
concerns have been raised that these may promote herbicide usage. The challenge here
will be to encourage herbicide-tolerant GMOs only insofar as they lead to use of more
effective and more environmentally benign herbicides.
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Precision agriculture has the potential to significantly increase agricultural productivity
by giving farmers much greater control over microclimates and within-field variations in
soil conditions, nutrients, and pest populations (National Research Council, 1997). This
may be accompanied by significant improvements in computer-based expert systems to
aid farmers with production decision-making (Plucknett and Winkelmann, 1996). The
environment could also benefit insofar as precision agriculture permits fertilizers and
pesticides to be applied more precisely where they are needed at the times of the year
when they are needed.
Future improvements in computer technology and in modeling smaller scale climatic
processes such as thunderstorms can be expected to lead to improved weather forecasts
(Tribbia, 1997). Improved forecasts may lead farmers to make better choices about what
crops to plant, when to plant and harvest, when to protect temperature-sensitive crops
such as tree fruits, when to fertilize, and other farm management decisions (Johnson and
Holt, 1997; Mjelde et al., 1998). This can be expected to increase agricultural
production.
Public Policies and Institutions
With respect to agricultural price and income support policies, there appears to be slow
trend toward global agricultural trade liberalization. Accompanying this may be a
dismantling of traditional agricultural price and income support policies and a move
toward direct payments to farmers that are not tied directly to their current production.
Agricultural trade liberalization should have modest effects on prices of most agricultural
commodities (Goldin et al., 1993; Hertel, 1996). An exception may be dairy products.
Dairy markets in most countries, including the U.S., are highly protected from imports at
present. Trade liberalization could significantly reduce prices of dairy products, which is
significant given the current importance of dairy production in the Mid-Atlantic region.
Dairy production could shift to other regions and countries that have a comparative
advantage in dairy.
The future of environmental policies toward agriculture in the Mid-Atlantic is uncertain.
To the extent that growth occurs in large-scale, highly intensive livestock operations in
the Mid-Atlantic, and to the extent that population growth within the region places more
people in close proximity to these operations, state and federal environmental regulations
may increase. Prospects for changes in regulation of less intensive livestock operations
or of grain farms are more difficult to assess.
All eight states in the Mid-Atlantic region have programs such as agricultural protection
zoning, differential property assessment, and conservation easements that are designed to
protect farmland from development (American Farmland Trust, 1997). However, these
programs may have limited impact because the lands enrolled are generally ones that
would not have been developed anyway (Bockstael and Bell, 1998).
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Two Future Baseline Scenarios for Mid-Atlantic Agriculture
As discussed in Part 2 of this report, the purpose in constructing future baseline scenarios
cannot and should not be to assemble an exhaustive list of all possible futures. Nor
should the purpose be confined to single point forecasts of future baseline socioeconomic
conditions. Instead, a relatively small number of scenarios should be chosen. When
taken as a whole, the scenarios should, with some reasonable degree of confidence, yield
upper and lower bounds on potential climate change impacts. With this in mind, we
consider two future baseline scenarios for Mid-Atlantic agriculture:
1. Smaller, More Environmentally Friendly Agriculture (SEF). Under this scenario,
real prices of most agricultural commodities continue their historical downward trend,
reducing the profitability of agriculture in the Mid-Atlantic region. Agricultural
production shifts to a large extent to other regions and countries. The downward
trend in production within the region is accelerated by a growing demand for land in
urban uses. The decline in dairy production is also accelerated by trade liberalization,
which leads to an even greater shift in production to other countries.
The decline in agricultural production is accompanied by a significant decrease in
both the amount of land in agriculture and the agricultural labor force. Agricultural
production as a percentage of the region's total economic output also declines.
However, the farmers that remain enjoy significantly higher living standards than do
farmers within the region today. Overall economic growth raises wages and salaries
in competing economic sectors, meaning that returns to labor in agriculture must rise
to keep pace. The farms that remain are significantly larger (in acreage and in the
number of livestock) than are farms within the region today.
i
Development and adoption of precision agriculture permits Mid-Atlantic farmers to
better adjust their production practices to microclimates and to variations in other
growing conditions within and between farm fields. Because of biotechnology, the
crops that are grown in the region are more tolerant of temperature and precipitation
extremes, and have substantially improved resistance to pests and diseases. Livestock
within the region consume feeds designed to reduce content of animal wastes, and
livestock are also genetically engineered to produce wastes with lower nutrient
contents. Pesticide use decreases significantly. There are stricter environmental
regulations over intensive livestock operations.
2. Continuation of the Status Quo (SQ). We consider this scenario not because we
think it is likely that agriculture in the Mid-Atlantic will continue as it is now.
Indeed, this is an exceedingly unlikely scenario. However, this scenario in
conjunction with the SEF scenario helps to establish bounds on climate change
impacts.
i
The upper and lower bound established by each scenario are listed in Table B.I. The SQ
scenario establishes an upper bound on negative climate change impacts on production
simply because agriculture is much larger in the SQ scenario than in the SEF scenario.
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The SEF scenario establishes an upper bound on positive climate change impacts on
production because, even though agriculture is smaller than in the SQ scenario, it is much
better equipped to take advantage of positive climate developments. The SQ scenario
establishes upper bounds on positive and negative environmental side effects because
agriculture in this scenario is larger than in the SEF scenario. In addition, biotechnology
and precision agriculture in the SEF scenario that are unavailable in the SQ scenario help
minimize negative environmental side effects.
Table B.I. Upper and Lower Bounds Established by
the Two Agricultural Baseline Scenarios
Negative Positive
Negative Impacts Positive Impacts Environmental Environmental
on Production on Production Side Effects Side Effects
Upper
Bound SQ SEF
Lower
Bound SEF SQ SEF SEF
SQ = Status Quo Scenario
SEF = Smaller, More Environmentally Friendly Scenario
Methods for Assessing the Potential Impacts of Climate Change on Coastal forests
of the Chesapeake Bay Region
The study site for this GIS analysis was the area defined by coastal counties of the
Chesapeake Bay. We determined land uses from 1993 MRLCs for the Chesapeake and
based coastal flooding projections on an estimated sea level rise (SLR) of 73 cm. This is
the mean sir estimated from the EPA report, The Probability of Sea Level Rise, in which
vertical accretion of 2mm/year by marshes was [or was not] included.
Elevation data were gathered from 7.5 minute (1:24,000) digital elevation models
(OEMs) from the U. S. Geological Survey (USGS). DEMs are digital elevation data sets
in raster format. The DEMs used in this analysis were manipulated with Arc Info
software. The 7.5 minute DEMs use spacings of 30 x 30 m on a UTM (Universal
Transverse Mercator) projection. Most of the analysis contains 30 m DEMs [, but a few
were 90 m-if any of the 90 m DEMs are within Eric's boundaries]. The reliability of our
projection and analysis of potential impacts relies on the quality of the maps that were
available from USGS. We did no ground truthing nor systematic corrections of these
maps. Our efforts to predict the effects of sea level rise on coastal forests also depended
on 1993 MRLC land use data for the Chesapeake Bay. These data were also collected for
30 x 30 m grids.
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Using Arc View software, our team calculated the amount of forest likely to be lost to
coastal flooding by 2100 with a predicted SLR = 73 cm. If the future coastline indicates
an area described as "forest" on the MRLC land use map will be under water in 2100,
then that forest is considered "lost" as a land use. If a 30 x 30 m area on the MRLC map
has mixed use, it is assigned a forest category if over 50% of the area is forested.
Comparative Health Risks
As part of the MARA health sector assessment, we are attempting to compare climate
related health risk to other sources of health risk. A preliminary analysis is presented
below.
Appendix B.2. Comparative Risks
Source of Risk Annual Risk
Risk of fatality in the US:
Cigarette smoking (per smoker)a 1/150
Motor Vehicle Accidenta 1 / 5,000
Asteroid impact (doomsday rock)a 1 / 6,000
Firea 1 / 50,000
Aviation Accidenta 1 / 250,000
Approximate mortality risk within MAR derived from 1995 mortality rates:
Cold 1 / 450,000
Heat 1/600,000
Storm/Flood/Lightning 1 /10,000,000
Approximate risk of contracting disease in MAR derived from 1995 morbidity rates:
Lyme Disease 1 / 9,500
Giardia 1 /13,500
Viscusi (1993).
Coastal Assessment
The current status of Mid-Atlantic estuaries has been evaluated by the Estuarine
Eutrophication Survey of the National Oceanographic and Atmospheric Administration
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(NOAA, 1997a; NOAA, 1997b). The descriptor ratings (typically low, medium or high)
were based on discussions with eutrophication experts and reflect comparison with
nationwide averages. The survey includes 18 estuaries in the Mid-Atlantic Region.
Based on their salinity in parts per thousand (ppt), estuaries are characterized as follows:
tidal fresh (0-0.5 ppt), mixing (0.5 to 25 ppt) and seawater (>25 ppt). Other water quality
parameters include: turbidity, total dissolved nitrogen and phosphorus concentration, and
dissolved oxygen concentration. Chlorophyll concentration (an indicator of
phytoplankton biomass), the presence of nuisance and toxic algal blooms, and the amount
of submerged aquatic vegetation (SAV) are included because of their effects on the upper
trophic levels of estuarine ecosystems.
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Appendix C:
Communication, Outreach, and Education
The MARA team involved stakeholders even before beginning the actual assessment
work in 1998. In September 1997 the group that was to become the MARA team
convened a meeting of stakeholders for the Chesapeake Bay and Delaware River Basin.
Held at Perm State, the participants included both researchers and representatives of
groups likely to be particularly affected by climate change. Participants identified those
issues they judged deserving of special attention in an assessment of potential climate
change impacts for the region. It was at this meeting that the potential importance of
health effects emerged as a significant issue.
One group of stakeholders is the climate change research community. As soon as we
received funding for the assessment, we convened a June 1998 meeting of researchers,
held at Perm State. Working for universities and government agencies, this group
provided a state-of-the-art explanation of available knowledge and resources. The
researchers also participated in formal and informal interchanges regarding the structure
and process of the assessment.
These researchers are one component of the MARA Advisory Committee. Researchers
however, are certainly not the only ones with a stake in how climate change might affect
the Mid-Atlantic region. In one sense, everyone in the region is a stakeholder in the
MARA project because all of the regions' citizens could be affected by climate change.
In seeking to identify stakeholders to participate in the assessment process, MARA is
paying special attention to groups likely to be particularly affected by climate change and
to groups that have expressed an interest in the issue. The non-researcher component of
the MARA Advisory Committee represents a myriad of experiences, including members
from mining companies, non-governmental voluntary organizations, and government.
The process for selecting Advisory Committee members was informal and broad. We
identified individuals and groups that had expressed interest in climate change. We also
made a strong effort to bring in a diversity of backgrounds and positions. For reasons of
manageability of size, we decided not to invite elected officials to join the Advisory
Committee, but everyone who sought to participate has been welcomed to the Advisory
Committee.
A number of individuals want to help with the assessment, but are unable to participate in
the October 1998 or May 1999 meetings. These individuals provide feedback to
assessment designs and documents by e-mail, phone, and mail. They are Corresponding
members of the Advisory Committee. The regular and corresponding members are listed
in Appendix A.
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The Advisory Committee is improving the assessment in four ways:
• Early in the project, members explained what kinds of information they need to help
them make decisions in the context of regional climate change.
• During implementation of the project, members are reviewing chapter outlines and
potential scenarios to be used in writing the report.
• At completion of the draft assessment report, the Committee will review the
document and suggest ways to improve it.
• Members are advising the MARA team regarding ways to disseminate the results in
the region.
The stakeholders already have helped us refine the research questions. For example,
participants at the October 19-20,1998, Advisory Committee meeting made sure that the
assessment would be responsive to climate-related issues most important to the people
who live and work in the region, such as the need for reliable seasonal climate projections
by water system managers and farm operators. They also expressed concerns about the
implications of climate change for insurance coverage and the insurance industry.
Stakeholders are scheduled to meet again on May 2-3, 1999, to review their draft
preliminary assessment and offer advice about developing materials and disseminating
the assessment results to a wide audience.
In addition to coming together for working meetings and reviewing draft documents,
many stakeholders have maintained informal communications with team members
working on particular parts of the report. In our view, successful stakeholder
involvement must be ongoing, two-way, and substantive. One part of the two-way
communication is making sure stakeholders understand how their participation makes a
difference in the assessment process. Ongoing contact between researchers and
stakeholders facilitates this understanding.
One of the next steps is to develop and implement a process of selecting priorities for
ecological assessment that involves stakeholders and researchers in the MAR. The initial
MARA shows that climate change has potentially significant ecological impacts. The task
now is to devise procedures and methods for ascertaining how stakeholders think about
ecological impacts from climate change and other stressors. What do they think should
be the priorities for further assessment? Their answers to what they want us to assess
would reflect their mental maps and values. A key to understanding stakeholder
perceptions may be how they see tradeoffs and options.
The process will involve two components: (1) researchers will identify ecological
resources that may be at risk, (2) researchers will communicate with stakeholders to
determine which ecological resources are most highly valued and which risks are of the
most concern. The process will be iterative, with researchers refining the scope of their
work in accordance with stakeholder values and with stakeholders refining their
statements about their concerns as they learn about how things that they value can be
related to ecological risks that can be assessed
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Improving methods for obtaining public and stakeholder views is an important task in the
assessment process. There is a common assumption that most people care mostly about
big animals (charismatic mega-fauna). Our Advisory Committee and some of our earlier
work (Lazo et al. in press) has convinced us that this is not true, that how many people
think about ecosystems is more complex and quite subtle. A method for the research
might involve some sort of "snowball" approach that would look at the literature, speak
with some key informants (e.g., regional planners, elected officials, EPA experts),
conduct a series of focus groups with different groups (e.g., people who fish
commercially, farmers, recreational anglers, developers, foresters), and then use what we
have learned to design and implement a general survey in the MAR. The findings would
pertain to this region, but the methodological advances would be useful to all
assessments.
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Appendix D:
[To be provided by NACO, NAST, and NAWG.]
142
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Appendix E:
Glossary
accretion: An increase in land area because of sediments deposited by flowing water,
especially along shores. If accretion keeps pace with sea level, then relative sea level rise
has little impact on coastal wetlands. If sea level rises faster than organic matter and
mineral deposits can accumulate (or if sediments are trapped behind dams) coastal land
can be inundated, especially during spring tides when tides are highest, for example at
full moon.
algal blooms: A population explosion of aquatic plants, often as the result of nutrient-
rich runoff
anoxic: Without oxygen
aquatic: Living or growing in fresh water (in contrast with marine organisms found in
salt water)
benthic: Bottom dwelling aquatic or marine organisms
biomass: The mass of living matter in an area (for example, grams of leaves and stems
per cubic meter)
dinoflagellate: A group of marine protozoans (single-celled organisms) with two
flagella (whip-like filaments used for propulsion)
downscaling: Reducing the scale of the model from global to regional scale
ecosystem: A unit of ecological analysis in which the physical and biological entities are
considered in relation to each other, including energy flows and chemical feedbacks
within a defined geographical area.
estuary: An estuary is in essence an interface: it is an area where a river meets the sea,
where aquatic and marine life meet terrestrial life in marshes and wetlands, and where
fresh water can still be influenced by tides. Estuaries can be defined by a salinity
gradient that ranges from ocean salinity of 35.0 ppt (parts per thousand) to fresh water
with salinity of less than 0.5 ppt.
eutrophication: An oversupply of the essential elements necessary for growth of tiny
(microscopic) floating organisms can cause them to experience a population explosion
that can quickly cover the surface of the water and block sunlight from larger plants
growing underwater and deplete dissolved oxygen.
fauna: Animal life, especially the animals found in a particular region
flora: Plant life or vegetation of a region
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geomorphology: The study of land configuration and evolution, primarily by geologists
greenhouse gases: Several gases that allow the earth's atmosphere to trap solar radiation
by absorbing heat radiated back from the surface of the earth. These gases include
carbon dioxide, methane, water vapor, and nitrous oxide.
human capital: refers to the knowledge, information, skills, and abilities possessed by
people. Physical capital refers to machines, transportation and communications
infrastructure, water resource management structures, buildings, and other tangible
investment goods. Physical capital is usually just known as "capital," but referring to it
as physical capital helps distinguish it from other forms of capital. Natural capital
encompasses all renewable and nonrenewable natural resources, and all market and
nonmarket natural resources. It includes not only conventional commodity resources,
such as fossil fuels, metals, fisheries, and forests, but other elements of nature that
directly or indirectly affect human welfare (e.g., genetic material, the ozone layer, and
hydrologic and carbon cycles).
hypoxia: A condition of having low levels of oxygen, often too low to support animal
life
invertebrate: An animal without a backbone
mesoscale models: Models that focus on a regional, rather than a global or local, level
nutrient: An element that is necessary for growth and replacement of tissues, such as
nitrogen, phosphorus, and potassium
passerine: Birds of the order Passeriformes, including perching birds and warblers such
as sparrows, finches, and jays
physiographic region: Area with similar land form
phytoplankton: Microscopic plants that float in aquatic or marine environments (fresh
or salty water)
primary productivity: The products of photosynthesis, the primary conversion of the
sun's energy into chemical energy that can be stored as sugars or starches in plants. Net
primary productivity is the amount of energy available after the plant has met its own
energy needs.
sediment: Fine grains of solid material suspended in water or settled out of water to be
deposited on land
surficial: Taking place on or relating to the surface of the earth
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topography: The physical features, such as elevation, of an area or the representation of
its features on a map
transpiration: Evaporation from plant foliage
trophic level: Trophic levels refer to particular positions in a food web. Eutrophic
means well nourished (or over fed); oligotrophic means underfed or with low nutrient
levels.
turbidity: In water bodies, the condition of having suspended particles that reduce the
ability of light to penetrate beneath the surface. Some rivers and streams are naturally
more turbid than others; soil erosion and runoff into streams can increase turbidity.
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List of Acronyms and Abbreviations
AgSci: College of Agricultural Sciences (PSU)
CBP: Chesapeake Bay Program (EPA)
CCC: Canadian Climate Center (global climate model)
CDIAC: Carbon Dioxide Information Analysis Center
CENR: Committee on Environmental and Natural Resources (NSTC)
CIESIN: Consortium for International Earth Science Information Network
CIRA: Center for Integrated Regional Assessment (PSU)
COi: carbon dioxide (a greenhouse gas)
CWC: Cooperative Wetlands Center (PSU)
DHHS: Department of Health and Human Services
DOD: Department of Defense
DOE: Department of Energy
DOI: Department of Interior
EHC: Environmental Health Center (National Safety Council)
EMS: College of Earth and Mineral Sciences (PSU)
EPA: U.S. Environmental Protection Agency
EPIC: Environmental Planning Information Center
ERRI: Environmental Resources Research Institute (PSU)
ESSC: Earth Systems Science Center (PSU)
FEMA: Federal Emergency Management Agency
FIPS: Federal Information Processing System (a code that identifies counties)
FS: Forest Service (USDA)
GCLP: Global Change/Local Places
GCM: General circulation model; also global climate model
I
GCOS: Global Climate Observing System
GEIA: Global Emissions Inventory Activity
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GENESIS: a global climate model
GFDL: Geophysical Fluid Dynamics Laboratory (NOAA, Princeton)
GHCN: Global Historical Climatology Network (at CDIAC)
GHG: Greenhouse Gas(es)
GIS: Geographic Information System
GISS: Goddard Institute for Space Studies (NASA)
HAD: Hadley Centre global climate model
HDGC: Human Dimensions of Global Change
IPCC: Intergovernmental Panel on Climate Change
JSTC: Joint Scientific and Technical Committee (of GCOS)
MACZ: Mid-Atlantic coastal zone
MAHA: Mid-Atlantic (Mid-Appalachians) Highlands Assessment
MAIA: Mid-Atlantic Integrated Assessment (EPA/ORD)
MAR: Mid-Atlantic region
MARA: Mid-Atlantic Regional Assessment
MLRA: Major Land Resource Area (with uniform soil, climate, water resources and
land use)
MPE: Mission to Planet Earth (NASA)
MSA: Metropolitan Statistical Area
NACO: National Assessment Coordination Office
NAS: National Academy of Sciences
NAST: National Assessment Synthesis Team
NASA: National Aeronautics and Space Administration
NAWG: National Assessment Working Group
NCAR: National Center for Atmospheric Research
NCDC: National Climatic Data Center
NCEDR: The National Center for Environmental Decision-Making Research
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NGVD: National Geodetic Vertical Datum
NIEHS: National Institute for Environmental Health Services (DHHS)
NIGEC: National Institute for Global Environmental Change
NOAA: National Oceanic and Atmospheric Administration
NOX: oxides of nitrogen
NPA: NPA Data Services, Inc.
NSF: National Science Foundation
NSTC: National Science and Technology Council
OMB: Office of Management and Budget
OPPE: Office of Policy, Planning and Evaluation (EPA)
ORD: Office of Research and Development (EPA)
ORNL: Oak Ridge National Laboratory (TN)
OSTP: Office of Science and Technology Policy
Penn State (PSU): Pennsylvania State University
PPR: Prairie Pothole Region
ppt: parts per thousand
RTA: Regression Tree Analysis
RTP: Research Triangle Park, NC
SAV: submerged aquatic vegetation
SEF: smaller environmentally friendly
SGCR: Subcommittee on Global Change Research (in NSTC's CENR)
SOX: oxides of sulfur
SQ: status quo
UCAR: University Corporation for Atmospheric Research
UKMO: United Kingdom Meteorological Office (global climate model)
UNEP: United Nations Environmental Program
USGCRP: U.S. Global Change Research Program
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USFS: U.S. Forest Service (USDA)
USGS: U.S. Geological Survey
USDA: U.S. Department of Agriculture
WMO: World Meteorological Organization
149
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The Counties of the Mid-Atlantic Region
with FIPS Codes and Physiographic Regions
10001
10003
10005
11001
24001
24003
24005
24009
24011
24013
24015
24017
24019
24021
24023
24025
24027
24029
24031
24033
24035
24037
24039
24041
24043
24045
24047
24510
34001
34005
34007
34009
34011
34015
34019
34021
34025
34027
34029
34033
34037
34041
36003
DE
DE
DE
DC
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NY
£#! count/ ; :„
Kent County
New Castle County
Sussex County
District of Columbia
Allegany County
Anne Arundel County
Baltimore County
Calvert County
• Caroline County
Carroll County
Cecil County
Charles County
Dorchester County
Frederick County
Garrett County
Harford County
Howard County
Kent County
Montgomery County
Prince George's County
Queen Anne's County
St. Mary's County
Somerset County
Talbot County
Washington County
Wicomico County
Worcester County
Baltimore City
Atlantic County
Burlington County
Camden County
Cape May County
Cumberland County
Gloucester County
Hunterdon County
Mercer County
Monmouth County
Morris County
Ocean County
Salem County
Sussex County
Warren County
Allegany County
.RtgiQE^i^i
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Ridge and Valley
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
Piedmont
Piedmont
Coastal Plain
Coastal Plain
Piedmont
Plateau
Piedmont
Piedmont
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Ridge and Valley
Coastal Plain
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
Plateau
Piedmont
Plateau
150
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36007
36011
36015
36017
36023
36025
36039
36043
36051
36053
36065
36067
36069
36071
36077
36095
36097
36101
36105
36107
36109
36111
36123
37001
37005
37013
37015
37029
37033
37041
37053
37055
37065
37067
37069
37073
37077
37081
37083
37091
37095
37117
37127
37131
37135
37137
37139
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
Broome County
Cayuga County
Chemung County
Chenango County
Cortland County
Delaware County
Greene County
Herkimer County
Livingston County
Madison County
Oneida County
Onondaga County
Ontario County
Orange County
Otsego County
Schoharie County
Schuyler County
Steuben County
Sullivan County
Tioga County
Tompkins County
Ulster County
Yates County
Alamance County
Allegheny County
Beaufort County
Bertie County
Camden County
Caswell County
Chowan County
Currituck County
Dare County
Edgecombe County
Forsyth County
Franklin County
Gates County
Granville County
Guilford County
Halifax County
Hertford County
Hyde County
Martin County
Nash County
Northampton County
Orange County
Pamlico County
Pasquotank County
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Piedmont
Ridge and Valley
Coastal Plain
Coastal Plain
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Piedmont
Piedmont
Coastal Plain
Piedmont
Piedmont
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
151
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37143
37145
37147
37157
37169
37171
37177
37181
37185
37187
37195
42001
42003
42005
42007
42009
42011
42013
42015
42017
42019
42021
42023
42025
42027
42029
42031
42033
42035
42037
42039
42041
42043
42045
42047
42049
42051
42053
42055
42057
42059
42061
42063
42065
42067
42069
42071
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
Perquimans County
Person County
Pitt County
Rockingham County
Stokes County
Surry County
Tyrrell County
Vance County
Warren County
Washington County
Wilson County
Adams County
. Allegheny County
Armstrong County
Beaver County
Bedford County
Berks County
Blair County
Bradford County
Bucks County
Butler County
Cambria County
Cameron County
Carbon County
Centre County
Chester County
Clarion County
Clearfield County
Clinton County
Columbia County
Crawford County
Cumberland County
Dauphin County
Delaware County
Elk County
Erie County
Fayette County
Forest County
Franklin County
Fulton County
Greene County
Huntingdon County
Indiana County
Jefferson County
Juniata County
Lackawanna County
Lancaster County
Coastal Plain
Piedmont
Coastal Plain
Piedmont
Piedmont
Piedmont
Coastal Plain
Piedmont
Piedmont
Coastal Plain
Coastal Plain
Piedmont
Plateau
Plateau
Plateau
Ridge and Valley
Piedmont
Ridge and Valley
Plateau
Piedmont
Plateau
Plateau
Plateau
Ridge and Valley
Ridge and Valley
Piedmont
Plateau
Plateau
Plateau
Ridge and Valley
Plateau
Ridge and Valley
Ridge and Valley
Coastal Plain
Plateau
Plateau
Plateau
Plateau
Ridge and Valley
Ridge and Valley
Plateau
Ridge and Valley
Plateau
Plateau
Ridge and Valley
Plateau
Piedmont
152
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42073
42075
42077
42079
42081
42083
42085
42087
42089
42091
42093
42095
42097
42099
42101
42103
42105
42107
42109
42111
42113
42115
42117
42119
42121
42123
42125
42127
42129
42131
42133
51001
51003
51005
51007
51009
51011
51013
51015
51017
51019
51021
51023
51025
51027
51029
51031
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
Lawrence County
Lebanon County
Lehigh County
Luzerne County
Lycoming County
Me Kean County
Mercer County
Mifflin County
Monroe County
Montgomery County
Montour County
Northampton County
Northumberland County
Perry County
Philadelphia County
Pike County
Potter County
Schuylkill County
Snyder County
Somerset County
Sullivan County
Susquehanna County
Tioga County
Union County
Venango County
Warren County
Washington County
Wayne County
Westmoreland County
Wyoming County
York County
Accomack County
Albemarle County
Allegheny County
Amelia County
Amherst County
Appomattox County
Arlington County
Augusta County
Bath County
Bedford County
Bland County
Botetourt County
Brunswick County
Buchanan County
Buckingham County
Campbell County
Plateau
Piedmont
Piedmont
Ridge and Valley
Plateau
Plateau
Plateau
Ridge and Valley
Plateau
Piedmont
Ridge and Valley
Piedmont
Ridge and Valley
Ridge and Valley
Coastal Plain
Plateau
Plateau
Ridge and Valley
Ridge and Valley
Plateau
Plateau
Plateau
Plateau
Ridge and Valley
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Piedmont
Coastal Plain
Piedmont
Ridge and Valley
Piedmont
Piedmont
Piedmont
Coastal Plain
Ridge and Valley
Ridge and Valley
Piedmont
Ridge and Valley
Ridge and Valley
Piedmont
Ridge and Valley
Piedmont
Piedmont
153
-------
draft
51033
51035
51036
51037
51041
51043
51045
51047
51049
51051
51053
51057
51059
51061
51063
51065
51067
51069
51071
51073
51075
51077
51079
51081
51083
51085
51087
51089
51091
51093
51095
51097
51099
51101
51103
51105
51107
51109
51111
51113
51115
51117
51119
51121
51125
51127
51131
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
Caroline County
Carroll County
Charles City County
Charlotte County
Chesterfield County
Clarke County
Craig County
Culpeper County
Cumberland County
Dickenson County
Dinwiddie County
Essex County
. Fairfax County
Fauquier County
Floyd County
Fluvanna County
Franklin County
Frederick County
Giles County
Gloucester County
Goochland County
Grayson County
Greene County
Greensville County
Halifax County
Hanover County
Henrico County
Henry County
Highland County
Isle of Wight County
James City County
King and Queen County
King George County
King William County
Lancaster County
Lee County
Loudoun County
Louisa County
Lunenburg County
Madison County
Mathews County
Mecklenburg County
Middlesex County
Montgomery County
Nelson County
New Kent County
Northampton County
Coastal Plain
Ridge and Valley
Coastal Plain
Piedmont
Coastal Plain
Ridge and Valley
Ridge and Valley
Piedmont
Piedmont
Ridge and Valley
Coastal Plain
Coastal Plain
Coastal Plain
Piedmont
Ridge and Valley
Piedmont
Piedmont
Ridge and Valley
Ridge and Valley
Coastal Plain
Piedmont
Ridge and Valley
Piedmont
Coastal Plain
Piedmont
Coastal Plain
Coastal Plain
Piedmont
Ridge and Valley
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Ridge and Valley
Piedmont
Piedmont
Piedmont
Piedmont
Coastal Plain
Piedmont
Coastal Plain
Ridge and Valley
Piedmont
Coastal Plain
Coastal Plain
154
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draft
51133
51135
51137
51139
51141
51143
51145
51147
51149
51153
51155
51157
51159
51161
51163
51165
51167
51169
51171
51173
51175
51177
51179
51181
51183
51185
51187
51191
51193
51195
51197
51199
51510
51515
51520
51530
51540
51550
51560
51570
51580
51590
51595
51600
51610
51620
51630
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
Northumberland County
Nottoway County
Orange County
Page County
Patrick County
Pittsylvania County
Powhatan County
Prince Edward County
Prince George County
Prince William County
Pulaski County
Rappahannock County
. Richmond County
Roanoke County
Rockbridge County
Rockingham County
Russell County
Scott County
Shenandoah County
Smyth County
Southampton County
Spotsylvania County
Stafford County
Surry County
Sussex County
Tazewell County
Warren County
Washington County
Westmoreland County
Wise County
Wythe County
York County
Alexandria City
Bedford City
Bristol City
Buena Vista City
Charlottesville City
Chesapeake City
Clifton Forge City
Colonial Heights City
Covington City
Danville City
Emporia City
Fairfax City
Falls Church City
Franklin City
Fredericksburg City
Coastal Plain
Piedmont
Piedmont
Ridge and Valley
Piedmont
Piedmont
Piedmont
Piedmont
Coastal Plain
Piedmont
Ridge and Valley
Piedmont
Coastal Plain
Ridge and Valley
Ridge and Valley
Ridge and Valley
Ridge and Valley
Ridge and Valley
Ridge and Valley
Ridge and Valley
Coastal Plain
Piedmont
Piedmont
Coastal Plain
Coastal Plain
Ridge and Valley
Ridge and Valley
Ridge and Valley
Coastal Plain
Ridge and Valley
Ridge and Valley
Coastal Plain
Coastal Plain
Piedmont
Ridge and Valley
Ridge and Valley
Piedmont
Coastal Plain
Ridge and Valley
Coastal Plain
Ridge and Valley
Piedmont
Coastal Plain
Coastal Plain
Coastal Plain
Coastal Plain
Piedmont
155
-------
draft
51640
51650
51660
51670
51678
51680
51683
51685
51690
51700
51710
51720
51730
51735
51740
51750
51760
51770
51775
51790
51800
51810
51820
51830
51840
54001
54003
54005
54007
54009
54011
54013
54015
54017
54019
54021
54023
54025
54027
54029
54031
54033
54035
54037
54039
54041
54043
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
Galax City
Hampton City
Harrisonburg City
Hopewell City
Lexington City
Lynchburg City
Manassas City
Manassas Park City
Martinsville City
Newport News City
Norfolk City
Norton City
. Petersburg City
Poquoson City
Portsmouth City
Radford City
Richmond City
Roanoke City
Salem City
Staunton City
Suffolk City
Virginia Beach City
Waynesboro City
Williamsburg City
Winchester City
Barbour County
Berkeley County
Boone County
Braxton County
Brooke County
Cabell County
Calhoun County
Clay County
Doddridge County
Fayette County
Gilmer County
Grant County
Greenbrier County
Hampshire County
Hancock County
Hardy County
Harrison County
Jackson County
Jefferson County
Kanawha County
Lewis County
Lincoln County
Ridge and Valley
Coastal Plain
Ridge and Valley
Coastal Plain
Ridge and Valley
Piedmont
Piedmont
Piedmont
Piedmont
Coastal Plain
Coastal Plain
Ridge and Valley
Coastal Plain
Coastal Plain
Coastal Plain
Ridge and Valley
Coastal Plain
Ridge and Valley
Ridge and Valley
Ridge and Valley
Coastal Plain
Coastal Plain
Ridge and Valley
Coastal Plain
Ridge and Valley
Plateau
Ridge and Valley
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Ridge and Valley
Plateau
Ridge and Valley
Plateau
Ridge and Valley
Plateau
Plateau
Ridge and Valley
Plateau
Plateau
Plateau
156
-------
draft
54045
54047
54049
54051
54053
54055
54057
54059
54061
54063
54065
54067
54069
54071
54073
54075
54077
54079
54081
54083
54085
54087
54089
54091
54093
54095
54097
54099
54101
54103
54105
54107
54109
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
Logan County
McDowell County
Marion County
Marshall County
Mason County
Mercer County
Mineral County
Mingo County
Monongalia County
Monroe County
Morgan County
Nicholas County
Ohio County
Pendleton County
Pleasants County
Pocahontas County
Preston County
Putnam County
Raleigh County
Randolph County
Ritchie County
Roane County
Summers County
Taylor County
Tucker County
Tyler County
Upshur County
Wayne County
Webster County
Wetzel County
Wirt County
Wood County
Wyoming County
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Ridge and Valley
Plateau
Plateau
Plateau
Ridge and Valley
Plateau
Plateau
Ridge and Valley
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
Plateau
157
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draft
Appendix F:
F.I. Ecosystem Stresses
Complete Inventory of Federally Listed Endangered Species within the Mid-Atlantic
Region
US Fish and Wildlife Service Division of Endangered Species
(http://www.fws.gov/r9endspp/endspp.html)
T = threatened or E = endangered
Delaware
Animals-5 species
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatuiri)
T - Plover, piping (Charadrius melodus)
E - Squirrel, Delmarva Peninsula fox (Sciurus niger cinereus)
T - Turtle, bog (=Muhlenberg) (Clemmys muhlenbergii)
Plants-4 species
T - Swamp pink (Helonias bullatd)
T - Small whorled pogonia (Isotria medeoloides)
E - Canby's dropwort (Oxypolis canbyi)
T - Knieskern's beaked-rush (Rhynchospora knieskernii)
Maryland
Animals-10 species
E - Bat, Indiana (Myotis sodalis)
T - Beetle, northeastern beach tiger (Cicindela dorsalis dorsalis)
T - Beetle, Puritan tiger (Cicindela puritand)
E - Darter, Maryland (Etheostoma sellare)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatuni)
T - Plover, piping (Charadrius melodus)
E - Squirrel, Delmarva Peninsula fox (Sciurus niger cinereus)
T - Turtle, bog (=Muhlenberg) (Clemmys muhlenbergii)
E - Wedgemussel, dwarf (Alasmidonta heterodon)
Plants-6 species
T - Sensitive joint-vetch (Aeschynomene virginicd)
E - Sandplain gerardia (Agalinis acuta)
158
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draft
T - Swamp pink (Helonias bullatd)
E - Canby's dropwort (Oxypolis canbyi)
E - Harperella (Ptilimnium nodosum (=fluviatile))
E - Northeastern (=Barbed bristle) bulrush (Scirpus ancistrochaetus)
New Jersey
Animals-7 species
E - Bat, Indiana (Myotis sodalis)
T - Beetle, northeastern beach tiger (Cicindela dorsalis dorsalis)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatum)
T - Plover, piping (Charadrius melodus)
E - Tern, roseate (Sterna dougallii dougallii)
T - Turtle, bog (=Muhlenberg) (Clemmys muhlenbergii)
E - Wedgemussel, dwarf (Alasmidonta heterodori)
Plants-5 species
T - Sensitive joint-vetch (Aeschynomene virginica)
T - Swamp pink (Helonias bullatd)
T - Small whorled pogonia (Isotria medeoloides)
T - Knieskern's beaked-rush (Rhynchospora knieskernii)
E - American chaffseed (Schwalbea americand)
New York
Animals-9 species
E - Bat, Indiana (Myotis sodalis)
E - Butterfly, Karner blue (Lycaeides melissa samuelis)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatum)
E - Plover, piping (Charadrius melodus)
T - Snail, Chittenango ovate amber (Succinea chittenangoensis)
E - Tern, roseate (Sterna dougallii dougallii)
T - Turtle, bog (=Muhlenberg) (Clemmys muhlenbergii)
E - Wedgemussel, dwarf (Alasmidonta heterodon)
Plants-6 species
T - Northern wild monkshood (Aconitum noveboracense)
E - Sandplain gerardia (Agalinis acutd)
T - Seabeach amaranth (Amaranthus pumilus)
T - American hart's-tongue fem (Asplenium scolopendrium var. americanum)
T - Leedy's roseroot (Sedum integrifolium ssp. leedyi)
T - Houghton's goldenrod (Solidago houghtonii)
159
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draft
North Carolina
Animals-23
E - Bat, Indiana (Myotis sodalis)
E - Bat, Virginia big-eared (Corynorhinus (=Plecotus) townsendii virginianus)
E - Butterfly, Saint Francis' satyr (Neonympha mitchellii francisci)
T - Chub, spotfm (=turquoise shiner) (Cyprinella (=Hybopsis) monacha)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Elktoe, Appalachian (Alasmidonta raveneliand)
E - Falcon, American peregrine (Falco peregrinus anatum)
E - Heelsplitter, Carolina (Lasmigona decorata)
E - Manatee, West Indian (Trichechus manatus)
E - Pearlymussel, littlewing (Pegias fabuld)
T - Plover, piping (Charadrius melodus)
E - Shiner, Cape Fear (Notropis mekistocholas)
T - Shrew, Dismal Swamp southeastern (Sorex longirostris fisheri)
T - Silverside, Waccamaw (Menidia extensd)
T - Snail, noonday (Mesodon clarki nantahald)
E - Spider, spruce-fir moss (Microhexura montivagd)
E - Spinymussel, Tar River (Elliptic steinstansand)
E - Squirrel, Carolina northern flying (Glaucomys sabrinus coloratus)
T - Tern, roseate (Sterna dougallii dougallii)
T - Turtle, loggerhead sea (Caretta carettd)
E - Wedgemussel, dwarf (Alasmidonta heterodori)
E - Wolf, red (Canis rufus)
E - Woodpecker, red-cockaded (Picoides borealis)
Plants-26 species
T - Sensitive joint-vetch (Aeschynomene virginica)
T - Seabeach amaranth (Amaranthus pumilus)
E - Small-anthered bittercress (Cardamine micrantherd)
E - Smooth coneflower (Echinacea laevigatd)
E - Spreading avens (Geum radiatum)
E - Rock gnome lichen (Gymnoderma lineare)
E - Roan Mountain bluet (Hedyotis purpurea var. montana)
E - Schweinitz's sunflower (Helianthus schweinitzif)
T - Swamp pink (Helonias bullata)
T - Dwarf-flowered heartleaf (Hexastylis naniflora)
T - Mountain golden heather (Hudsonia montana)
T - Small whorled pogonia (Isotria medeoloides)
T - Heller's blazingstar (Liatris helleri)
E - Pondberry (Lindera melissifolid)
E - Rough-leaved loosestrife (Lysimachia asperulaefolid)
E - Canby's dropwort (Oxypolis canbyi)
E - Harperella (Ptilimnium nodoswn (-fluviatilej)
E - Michaux's sumac (Rhus michauxii)
160
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draft
E - Bunched arrowhead (Sagittaria fasciculatd)
E - Green pitcher-plant (Sarracenia oreophild)
E - Mountain sweet pitcher-plant (Sarracenia rubra ssp. jonesii)
E - American chaffseed (Schwalbea americand)
E - White irisette (Sisyrinchium dichotomum)
T - Blue Ridge goldenrod (Solidago spithamaed)
1 - Virginia spiraea (Spiraea virginiana)
E - Cooley's meadowrue (Thalictrum cooleyf)
Pennsylvania
Animals-13 species
E - Bat, Indiana (Myotis sodalis)
E - Clubshell (Pleurobema clava)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatuni)
E - Mucket, pink (pearlymussel) (Lampsilis abrupta)
E - Pearlymussel, cracking (Hemistena latd)
E - Pigtoe, rough (Pleurobema plenum)
E - Pimpleback, orangefoot (pearlymussel) (Plethobasus cooperianus)
E - Pink, ring (mussel) (Obovaria retusa)
E- Plover, piping (Charadrius melodus)
E - Riffleshell, northern (Epioblasma torulosa rangiana)
T - Turtle, bog (=Muhlenberg) (Clemmys muhlenbergii)
E - Wedgemussel, dwarf (Alasmidonta heterodori)
Plants-3 species
T - Small whorled pogonia (Isotria medeoloides)
E - Northeastern (=Barbed bristle) bulrush (Scirpus ancistrochaetus)
T - Virginia spiraea (Spiraea virginiana)
Virginia
Animals—40 species
E - Bat, gray (Myotis grisescens)
E - Bat, Indiana (Myotis sodalis)
E - Bat, Virginia big-eared (Corynorhinus (=Plecotus) townsendii virginianus)
E - Bean (mussel), purple (Villosaperpurpured)
T - Beetle, northeastern beach tiger (Cicindela dorsalis dorsalis)
E - Blossom, green (pear\ymusse[)(Epioblasma torulosa gubernaculum)
T - Chub, slender (Erimystax (=Hybopsis) cahni)
T - Chub, spotfin (=turquoise shiner) (Cyprinella (=Hybopsis) monachd)
E - Combshell, Cumberlandian (Epioblasma brevidens)
E - Darter, duskytail (Etheostoma percnurum)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatum)
161
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draft
E - Fanshell (Cyprogenia stegarid)
E - Isopod, Lee County cave (Lirceus usdagaluri)
T - Isopod, Madison Cave (Antrolana lira)
E - Logperch, Roanoke (Percina rex)
T - Madtom, yellowfin (Noturus flavipinnis)
E - Monkeyface, Appalachian (pearlym\\ssQ\)(Quadrula sparsd)
E - Monkeyface, Cumberland (pear\ymusse\)(Quadrula intermedia)
E - Mucket, pink (pearlymussel) (Lampsilis abruptd)
E - Mussel, oyster (Epioblasma capsaeformis)
E - Pearlymussel, birdwing (Conradilla caelatd)
E - Pearlymussel, cracking (Hemistena lata)
E - Pearlymussel, dromedary (Dromus dramas)
E - Pearlymussel, littlewing (Pegias fabuld)
E - Pigtoe, finerayed (Fusconaia cuneolus)
E - Pigtoe, rough (Pleurobema plenum)
E - Pigtoe, shiny (Fusconaia cor (=edgariana))
T - Plover, piping (Charadrius melodus)
E - Rabbitsfoot, rough (Quadrula cylindrica strigillatd)
E - Riffleshell, tan (Epioblasma florentina walkeri)
E - Salamander, Shenandoah (Plethodon shenandoah)
T - Shrew, Dismal Swamp southeastern (Sorex longirostris fisheri)
E - Snail, Virginia fringed mountain (Polygyriscus virginianus)
E - Spinymussel, James (=Virginia) (Pleurobema collind)
E - Squirrel, Delmarva Peninsula fox (Sciurus niger cinereus)
E - Squirrel, Virginia northern flying (Glaucomys sabrinus fuscus)
E - Tern, roseate (Sterna dougallii dougallii)
E - Wedgemussel, dwarf (Alasmidonta heterodon)
E - Woodpecker, red-cockaded (Picoides borealis)
Plants-13 species
T - Sensitive joint-vetch (Aeschynomene virginicd)
E - Shale barren rock-cress (Arabis serotind)
T - Virginia round-leaf birch (Betula uber)
E - Small-anthered bittercress (Cardamine micrantherd)
E - Smooth coneflower (Echinacea laevigatd)
T - Virginia sneezeweed (Helenium virginicuni)
T - Swamp pink (Helonias bullatd)
E - Peter's Mountain mallow (Iliamna corei)
T - Small whorled pogonia (Isotria medeoloides)
T - Eastern prairie fringed orchid (Platanthera leucophaea)
E - Michaux's sumac (Rhus michauxii)
E - Northeastern (=Barbed bristle) bulrush (Scirpus ancistrochaetus)
T - Virginia spiraea (Spiraea virginiand)
162
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West Virginia
Animals-14 species
E - Bat, gray (Myotis grisescens)
E - Bat, Indiana (Myotis sodalis)
E - Bat, Virginia big-eared (Corynorhinus (=Plecotus) townsendii virginianus)
E - Blossom, tubercled (pearlymussel) (Epioblasma torulosa torulosd)
E - Clubshell (Pleurobema clava)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatum)
E - Fanshell (Cyprogenia stegarid)
E - Mucket, pink (pearlymussel) (Lampsilis abruptd)
E - Riffleshell, northern (Epioblasma torulosa rangiand)
T - Salamander, Cheat Mountain (Plethodon nettingi)
T - Snail, flat-spired three-toothed (Triodopsis platysayoides)
E - Spinymussel, James (=Virginia) (Pleurobema collina)
E - Squirrel, Virginia northern flying (Glaucomys sabrinus fuscus)
Plants-6 species
E - Shale barren rock-cress (Arabis serotind)
T - Small whorled pogonia (Isotria medeoloides)
E - Harperella (Ptilimnium nodosum (=fluviatile))
E - Northeastern (=Barbed bristle) bulrush (Scirpus ancistrochaetus)
T - Virginia spiraea (Spiraea virginiand)
E - Running buffalo clover (Trifolium stoloniferum)
The District of Columbia
Animals-3 species
E - Amphipod, Hay's Spring (Stygobromus hayi)
T - Eagle, bald (Haliaeetus leucocephalus)
E - Falcon, American peregrine (Falco peregrinus anatum)
Plants-0 species
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F.2.
Summary of identified stresses for currently or previously declining species within the Chesapeake Bay Region
Consult 'Description of stresses.. .Chesapeake Bay Region1 for interpretation and discussion of values for parameters.
Rank: 1 Major Stress 2 Moderate Stress 3 Minor Stress * Recovering from Stress
Blank boxes indicate insufficient data available or not applicable to the species.
ORGANISM
SAV: Eelgrass (Ruppia
maritima) & Widgeon grass
(Zostera marina)
Striped Bass
(Morone saxatilis)
American Shad
(Alosa sapidissima)
Blue Crab
(Callinectes sapidus)
American Oyster
(Crassostrea virginica)
Swamp Pink
(Helonias bullata)
Black Duck
(Anas rubripes)
Mallard
(Anas platrhynchos)
Piping Plover
(Charadrius melodus)
Bald Eagle
(Haliaeetus leucocephalus)
HABITAT STRESS
Agriculture
2
2
3
2
1
1
1
1*
Industry &
Commerce
2
3
1
1
1
1*
1
Tourism
3
3
2
2*
1
3
Water
Pollution
1
1
1
1
1
2
2
2
3
SPECIES STRESS
Overharvestina
3
1*
1*
3*
2
3
3
3*
1*
Disease
3*
3
2
Introduced
species/
predation
3
3
3
3
3
• Weather
Disturbances
3*
3
3
3
164
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F.3. Description of stresses on target species in the Chesapeake Bay Region
Submerged Aquatic Vegetation:
Eelgrass (Ruppia maritima)
Widgeon grass (Zostera marina)
Habitat Stress
Agriculture and water pollution: fertilizer runoff and municipal wastewater discharge
increase turbidity, reducing available light for underwater plants to grow and reproduce
(EPA 1998)
Industry and commerce: commercial clam dredging uproots SAV and increases turbidity
and its effects (Funderburk 1991)
Tourism: as boat traffic increases, plants are more often uprooted by boat propellers;
development of marinas and construction of waterfront properties causes loss of shallow
water habitat, reducing potential habitat for underwater plants to recolonize (Funderburk
1991)
Species Stress
Overharvesting: foraging by localise waterfowl reduces underwater plant distribution and
abundance (http://www.fws.gov/r5cbfo/savpage.htm)
Disease: in the 1930s, infestation of slime mold (Labyrinthald) almost eliminated
eelgrass (http://www.fws.gov/r5cbfo/savpage.htm)
Weather disturbances: in 1972, Hurricane Agnes caused reductions in salinity of the bay,
impeding the recolonization of underwater plants; temperature fluctuations such as warm
winters inhibit plant growth and reproduction (http://www.fws.gov/r5cbfo/savpage.htm)
Current status: total acreage of SAV increased in 1996 and 1997 after a decline in 1994 to
1995 (http://www.chesapeakebay.net/bayprogram/indicatr/measure/indover.htm}
Striped Bass (Morone saxatilis)
Habitat Stress
Agriculture and water pollution: fluctuation of water temperature disturbs spawning
grounds for hatchery; toxic heavy metals that include arsenic, copper, cadmium and
aluminum, and a commonly used pesticide, malathion, reduce the number of larval
stripped bass (http://www.fws.gov/r5cbfo/striper.htm)
i
Industry and commerce: chlorination of effluent from sewage plants and electric power
stations reduces zooplankton leading to starvation of young hatchlings
(http://www.fws.gov/r5cbfo/striper.htm)
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Species Stress
Overharvesting: between the late 1970s and the early 1980s, increased fishing pressures
have reduced the number spawning female striped bass
(http://www.fws.gov/r5cbfo/striper.htm)
Weather: acid rain reacts with soil aluminium, which then runs off into the bay water and
reduce hatchlings survival (http://www.fws.gov/r5cbfo/striper.htm)
Current status: since 1995 an increase in interim stocking programs and implementing
harvest limits has restored stocks
(http://www.chesapeakebay.net/bayprogram/indicatr/measure/indover.htm}
American Shad (Alosa sapidissimd)
Habitat Stress
Water pollution: nutrient inputs from stormwater runoff and atmospheric deposition
stimulate phytoplankton growth which leads to increased water temperatures, low
dissolved oxygen, and eutrophication hindering upstream migration to spawning grounds
(http://www.fws.gov/r5cbfo/SHAD.HTM)
Industry and commerce: since the early to mid-1900s, construction of feeder dams and
hydroelectric dams have caused blockage of spawning grounds and an increase in turbine
mortality (http://www.fws.gov/r5cbfo/SHAD.HTM)
Agriculture: improper farming practices, timber harvest and stream channelization
accelerates erosion of surface soils and further degrades rivers and streams
(http://www.fws.gov/r5cbfo/SHAD.HTM)
Species Stress
Overharvesting: between the late 1800s and the 1940s, commercial and recreational
fishing decreased stocks to extremely low levels (Funderburk 1991)
Current status: in the 1980s, prohibiting stock harvesting has been effective but stocks
have yet to be restored
(http://www.chesapeakebay.net/bayprogram/indicatr/measure/indover.htm)
Blue Crab (Callinectes sapidus)
Habitat Stress
Water pollution: exposure to high levels of heavy metals , PCBs, PAHs, and pesticides in
the sediment, to runoff from urban, suburban, and agricultural areas, and to contaminated
food sources has cause a major decline in blue crab populations (Funderburk 1991)
Tourism: pollution from marinas and paint, mechanical disturbances, and direct contact
disturbs near-shore habitats, thus decreasing populations somewhat (Funderburk 1991)
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Species Stress
Overharvesting: increased fishing pressure is a potential stress (EPA 1998)
Introduced species/predation: species such as the American eel, croaker, trout, and bass
have been linked to an increase in mortality of juvenile and adult crabs (Funderburk
1991)
Weather disturbances: periodic severe wind generates currents that disturb spawning
(EPA1998)
Disease: marine fungus (Lagenidium callinectes) and nemertean worm (Carcinonemertes
carcinophilia) prevalent in some years and in some localities reduce the number of eggs
hatched (Funderburk 1991)
Current status: spawning stock has increased to 1970s level
(http://www.chesapeakebay.net/bayprogram/indicatr/measure/indover.htm)
American Oyster (Crassostrea virginica)
Habitat Stress
Water pollution and agriculture: major contaminates include heavy metals, pesticides,
PCBs, PAHs, chlorine-produced oxidants, and petroleum hydrocarbons from agricultural
runoff and wastewater effluent discharge causes mortality of oysters (Funderburk 1991)
Species Stress
Overharvesting: since the late 1800s, increases in annual harvest has severely depressed
the oyster population, degrading oyster grounds, and a reduction of these "filter feeders"
increases water turbidity and contributes to the decline in SAV (EPA 1998)
Disease: over the past 40 years, pathogens (Haplosporidium nelsoni and Perkinsus
marinus) are known to cause mortality and inhibited growth and gemetogenesis in oyster
populations (Funderburk 1991)
Introduced species/predation: predation by ctenophores and benthic carnivores such as
the sea anemones cause great loss of gametes, fertilized eggs, and larvae (Funderburk
1991)
Current status: reproduction has declined significantly and the survival of stock to
harvestable size is severely hampered by disease
(http://www.chesapeakebay.net/bayprogram/indicatr/measure/indover.htm)
Swamp Pink (Helonias bullata)
Habitat Stress
Agriculture and industry & commerce: urban and agricultural development and off-site
disturbances have increased siltation from uncontrolled soil erosion, discharge from
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sewage treatment plants, and deposition of nutrients and chemicals into the water, thus,
causing an increase in natural succession of competing species (e.g. common reed and
mountain laurel) (http://www.fws.gOV/r9endspp/i/q/saq54.html)
Water pollution: fertilizer runoff and wastewater discharge change soil conditions
necessary for plants to grow and reproduce
(http://www.fws.gOv/r9endspp/i/q/saq54.html)
Species Stress
Overharvesting: attractiveness of the plant makes it vulnerable to illegal collectors
(http://www.fws.gOv/r9endspp/i/q/saq54.html)
Black Duck (Anas rubripes)
Mallard (Anasplatyrhynchos)
Habitat Stress
Agriculture and water pollution: toxic substances and excess nutrient loading are
principal factors in the decline of food resources (e.g. submerged aquatic vegetation);
decline in duck populations correlates with the decline in S AV unless substitute food
sources are found (Funderburk 1991)
Tourism and industry & commerce: with the increase in development since the early
1970s, an increase in dredging, shoreline erosion, marina developments, and marshland
filing and draining disturbances have affected duck habitat selection and use (Funderburk
1991)
Species Stress
Overharvesting: in spite of annual harvest regulations, seasonal hunting has affected
waterfowl populations (EPA 1992)
Introduced species/predation: increase in development introduces predatory species such
as raccoons, foxes, and crows all of which destroy waterfowl nests (Brooke 1982)
Current status: black duck populations have not been restored; mallard populations have
been restored and exceeded expected levels
(http://www.chesapeakebay.net/bayprogram/indicatr/measure/indover.htm)
Piping Plover (Charadrius melodus)
Habitat Stress
Tourism and industry & commerce: growing human population increases recreational,
housing, and seawall development accompanied by irreversible loss of breeding habitats
(http://bluegoose.arw.i9.fws.gov/NWRSFiles/WildlifeMgmt/SpeciesAccounts/Birds/AtlP
ipingPlover/AtlPipingPloverIndex.html)
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Water pollution: oil spills pose a serious threat to plovers
(http:/^luegoose.arw.r9.fvvs.gov/NWRSFiles/WildlifeMgmt/SpeciesAccounts/Birds/AtlP
ipingPlover/AtlPipingPloverIndex.html
Species Stress
Introduced species/predation: urbanisation and recreational pressures encourage predators
(e.g. skunks, raccoons & gulls) which prey on plover chicks and eggs and often resulting
in abandonment of nest sites
(http://bluegoose.arw.r9.fws.gov/NWRSFiles/WildlifeMgmt/SpeciesAccounts/Birds/AtlP
ipingPlover/AtlPipingPloverIndex.html
Weather: extended cold weather, storms, and hurricanes result in direct mortality and
habitat loss; if population is low enough or remains sparse, population recovery may be
impaired
(http://bluegoose.arw.r9.fws.gov/NWRSFiles/WildlifeMgmt/SpeciesAccounts/Birds/AtlP
ipingPlover/AtlPipingPloverIndex.html
Bald Eagle (Haliaeetus leucocephalus)
Habitat Stress
Tourism: transformation of shoreline forest to housing development and marinas reduces
trees used for nesting and perching and an increase in contact between humans and eagles
reduces and potentially eliminates eagles' use of those areas (Funderburk 1991)
Agriculture: post WWII (late 1960s), use of DDT to control mosquitoes contaminated
food resources, DDE (breakdown product of DDT) caused birds to lay thin-shelled eggs,
thus, causing reproductive failure; in 1972, the use of DDT was banned in the United
States (http://www.fws.gov/r5cbfo/baldeagl.htm)
Species Stress
Overharvesting: pre-1940, hunting reduced eagle population; in 1940, the Bald Eagle
Protection Act was passed which made it illegal to kill, harm, harass, or possess bald
eagles (http://www.fws.gov/r5cbfo/baldeagl.htm)
Current status: August 11,1995, the bald eagle was reclassified from endangered to
threatened; during the past 25 years of recovery, bald eagles have responded to the 1972
DDT ban and the protection sustained by the Bald Eagle Protection Act of 1940; the
Chesapeake Bay area now has one of the highest concentrations of bald eagles in the
United States (http://www.fws.gov/r5cbfo/baldeagl.htm)
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F.4. Watersheds Key to Conserving At-Risk Fish and Mussels
Remarkably, the Nature Conservancy has determined that at least 2 populations of all
imperiled freshwater fish and mussels could be conserved by protecting only 15% of the
2,100 small watersheds in the continental United States (Master et al. YEAR?). Many of
these key watersheds are in the Mid-Atlantic; 19 are in NC and 14 are in VA. Thus, a
strategy to protect freshwater fish and mussels from current and climatic stresses could
prioritize protection of the key watersheds. If further research suggests that these key
watersheds are threatened by climate change, strategies could focus upon preventing
negative climatic impacts in these watersheds or upon locating other more climate-
resilient watersheds to protect imperiled species.
State
NC
VA
wv
MD
DE
PA
NJ
NY
Critical Watersheds to Conserve At-Risk Fish and Mussel Species
(Master et al. YEAR?) (Some watersheds include parts of more than 1
state.)
19: Upper Tar, Upper Neuse,Waccamaw, Upper Little Tennessee,
Fishing,
Lower Yadkin, Deep, Rocky, Lower Cape Fear, Black, Lumber,
Little Pee Dee, Albemarle, Lynches, Lower Catawba, Nottoway, Upper
Dan, Meherrin, Upper New
14: Cacapon-Town, Upper Clinch, Powell, South Fork Holston, North
Fork Holston, Upper Roanoke, Nottoway, Upper James, Upper Dan,
Meherrin, Upper New, Middle James-Buffalo, Pamunkey, Middle New
7: Cacapon-Town, Upper James, Upper Kanawha, Middle New,
Greenbrier, Cheat, Tygart Valley
2: Cacapon-Town, Cheat
6: Cacapon-Town, Lower Delaware, Middle Delaware-Mongaup-
Brodhead, French, Middle Allegheny-Tionesta, Cheat
2: Lower Delaware, Middle Delaware-Mongaup-Brodhead
2: Middle Delaware-Mongaup-Brodhead, French
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