Compiled by

Penn State’s core MARA Team:

Ann Fisher, David Abler, Eric Barron, Richard Bord,

Robert Crane, David DeWalle, C. Gregory Knight,

Ray Najjar, Egide Nizeyimana, Robert O’Connor,

Adam Rose, James Shortle, Brent Yarnal

and the research associates, assistants,

and external collaborators listed in Appendix A

Sponsored by

The U.S. Environmental Protection Agency,

Office of Research and Development,

Global Change Research Program

(Cooperative Agreement No. CR 826554-01)

with Janet Gamble as Project Officer

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

PREPARING FOR A CHANGING CLIMATE

The Potential Consequences

of Climate Variability and Change

Mid-Atlantic

Overview

A Report of the

Mid-Atlantic Regional

Assessment Team

March 2000

Printed on Recycled Paper
About this publication

This report summarizes the methods, findings and recommendations from the first 18 months of the Mid-Atlantic Regional

Assessment (MARA) of potential impacts from increased climate variability and change. The MARA examines both beneficial

and damaging impacts, accounting for how people and ecosystems are likely to respond to these changes.

This overview is intended for use by federal, state and local elected officials and by people in their role as citizens, employees,

and members of the community. It also gives regional texture intended to complement the national overview report being

prepared by the National Assessment Synthesis Team (NAST).

Our assessment includes two key features: 1) an interdisciplinary approach using the best science available, and 2) substantial

stakeholder participation. Aided by financial support from the U.S. Environmental Protection Agency (EPA), Penn State’s

MARA team (i.e., the core faculty, research associates and assistants, and external collaborators, listed in Appendix A) is

commited to an integrated assessment approach. Few studies have taken an integrated approach at the scale of a region such as

the Mid-Atlantic, so our initial plan was to demonstrate the MARA approach on two or three sectors likely to be affected by

climate change. Meetings with our Advisory Committee confirmed available research that suggests a broad range of potential

impacts, with none overwhelming the others for this region. This convergence of scientific implications and stakeholder

interests led to assessing impacts for each of the following: agriculture, forests, water resources, coastal zones, ecosystems and

human health. The assessment focuses on the year 2030 because the discussion of impacts for 2100 (the other date emphasized

in the National Assessment) is necessarily more speculative.

Enthusiastic teamwork has accomplished an astounding amount on a very compressed schedule. My thanks to each MARA

team member for his or her work. Appendix A lists the full team as well as those on our large Advisory Committee. Synergism

between researchers and the Advisory Committee has resulted in many modifications and improvements as a result of their

input and feedback. On behalf of the MARA team, thanks to the Advisory Committee members for their insights and

thoughtful responses. Special thanks to EPA Project Officer Janet Gamble. Thanks also to Ron Smart of Elemental Media for

the report’s layout and to Grabhorn Studio for the cover design.

Three features can help in getting the most from this report: 1) The glossary in Appendix D, 2) the list of acronyms in

Appendix E, and 3) a broad range of information available on the MARA web site http://www.essc.psu.edu/mara/. More

technical information about the MARA will appear in a special issue of Climate Research, to be printed in Spring 2000. Even

more detail will be in a longer Foundations report, expected to be on the MARA web site by July 2000.

A draft of this overview was circulated widely for peer review. This revision responds to more than 40 sets of comments from a

broad range of experts and stakeholders. The MARA team appreciates their constructive input. Full documentation of our

responses to the comments appears on the MARA web site: http://www.essc.psu.edu/mara/.

We welcome feedback so that we can maximize the usefulness of our continuing assessment efforts (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

February 14, 2000
Table of Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Setting the Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

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

The Mid-Atlantic Region: Present Status and Potential Futures . . . . . . . 5

2. The Mid-Atlantic region’s geography and economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

3. Envisioning the Mid-Atlantic region’s socioeconomic, environmental and climate future . . . . . . . . . . . . . .9

Impacts, Challenges and Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4. Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

5. Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

6. Fresh water quantity and quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

7. Coastal zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

8. Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31

9. Human health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

Planning for the 21st Century . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

10. What can we do now? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

11. What do we still need to know? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Appendices

Appendix A. Partners and Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

Appendix B. National Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

Appendix C. Stakeholder Participation in the MARA Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63

Appendix D. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65

Appendix E. Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
i

MARA OVERVIEW 2000 • Executive Summary

Executive Summary

Purpose

The U.S. Global Change Research Program (USGCRP)

and federal agencies are sponsoring 16 regional assessments

across the nation—including the Mid-Atlantic Regional

Assessment (MARA). The goal is to identify:

- how people and their surroundings are affected now

by climate variability and how they will be affected

by climate change,

- how individuals and communities can take advantage of opportunities and reduce vulnerabilities resulting from climate variability and change, and

- what additional information and research are needed

to improve decision-making related to impacts from

climate variability and change.

The assessments are challenging because of uncertainties

in projecting both climate change and how our society will

evolve – with or without climate change. These assessments

rely on multi-disciplinary integrated approaches and broad

stakeholder participation to ensure that the most important questions are examined and that the findings are useful.

The Mid-Atlantic Region (MAR)

The Mid-Atlantic Region, or MAR, includes all or parts of

eight states (NY, NJ, PA, DE, MD, WV, VA, and NC) and

the District of Columbia. More than half of the region’s

35 million people live in Philadelphia, Pittsburgh,

Baltimore, Washington, Richmond, and Norfolk. (New

York City is the topic of another assessment.)

The region’s annual economic output amounts to about

13 percent of the nation’s. Manufacturing and Services

are the largest economic sectors, accounting for 26 percent

and 20 percent of MAR output respectively. Agricultural

production covers 25 percent of the region’s land and Forests cover nearly 65 percent, but each represents only about

1 percent of the region’s gross output.

The region’s relatively large population (15 percent of the

nation’s population in 5 percent of the contiguous land

area) and development have stressed many of its ecological

resources, including the nation’s two largest estuaries, the

Chesapeake Bay and Albemarle-Pamlico Sounds, as well

as the Delaware River basin.

Over the last century, the average annual temperature in

the MAR has been about 52o

F, and average annual precipitation has been about 41 inches. Although there have

been upward trends in the last century’s precipitation (of

about 10 percent) and temperature (of about 1°F), the yearby-year and decade-by-decade variations in extreme events

such as droughts and floods are far more noticeable than

these small changes in average conditions.

Projecting future socioeconomic status

and climate

In 1900, it was difficult to predict the 20th century’s

dramatic socioeconomic changes, ranging from the decline

in farm employment that accompanied huge increases in

agricultural productivity to the dependence on computers.

The picture for the year 2100 is just as unclear. Even so,

socioeconomic projections are a starting point for

developing plausible scenarios that establish ranges for how
ii

sectors might be affected by climate change. The

USGCRP provided high, medium and low

projections of population, income and

employment growth for the region, assuming no

climate change. These scenarios serve as a

baseline for identifying incremental

socioeconomic impacts from changes in climate.

The USGCRP also provided scenarios from two

state-of-the-art global climate models (the

Hadley model from the Hadley Centre for

Climate Prediction and Research in Great

Britain, and the CCC model developed by the

Canadian Centre for Climate Modeling and

Analysis), to enable comparisons across regions.

As is the case for the socioeconomic changes,

these provide plausible ranges rather than

predicting what actually will happen. Both

models account for increasing emissions of

atmospheric greenhouse gases (which tend to

increase global surface temperatures) as well as

sulfate aerosols (which tend to reduce surface

temperatures).

Despite these similarities, the models produce

quite different results. For the MAR, Figure E1

shows their projected differences in maximum

temperature and precipitation. Both show

warming by the year 2030; the CCC model

shows much more warming by 2100. However,

the Hadley model shows substantial increases in

precipitation while the CCC model does not.

Two earlier studies used high resolution empirical

and numerical models to extract regional

information from a third global climate model

(GENESIS). These studies projected an increase

in MAR precipitation, similar to the Hadley

results. Because of the uncertainties in projecting

future climate, especially at a regional scale, we

use the model results in Figure E1 to represent

plausible ranges for changes in MAR temperature

and precipitation, rather than predictions of

what actually will happen at any particular place

or time.

Figure E1. Hadley and CCC model differences from

the observed 1960-1989 base period, for the MAR.
iii

MARA OVERVIEW 2000 • Executive Summary

How people and their

surroundings will be affected

by climate change

The assessment is based on the convergence of climate

model projections that the MAR will be somewhat warmer

and perhaps wetter, with potentially more variability in climate. Table E1 summarizes impacts from climate change

that might occur in the MAR by the year 2100. Because

of the large uncertainties about projecting changes in the

region’s climate, economy, and ecosystems, we put the impacts in categories according to their likelihood. The size

of an arrow indicates an impact’s expected magnitude in

relative terms.

Most certain, climate change in the MAR is likely to

increase slightly its agricultural production of soybeans,

and possibly corn and tree fruits, while making tobacco

less competitive. Enhanced sea-level rise almost certainly

will occur, with the potential for substantial damage to the

coastal zone’s structures, wetlands and estuaries, and to

water supplies because of salt water intrusion. Increased

temperatures will make urban heat stress more likely;

although the change is relatively large, heat stress will remain

a small factor in the region’s health status.

In the moderately certain category, forests might grow a

little faster, but extreme events could disrupt the pattern

of revenues from MAR forestry operations. People also

might experience even fewer health impacts from cold stress.

More uncertain is the potential for very large negative impacts on MAR biodiversity, especially because many plants

cannot migrate quickly and because plants and animals

may not be able to negotiate barriers such as mountains,

cities, or sea walls. These negative effects might be offset
iv

slightly by faster growth in warmer, wetter conditions. Also

uncertain are impacts on water quantity and quality. Additional precipitation will increase stream flows, making

more water available for public water supplies in most years.

If the additional precipitation tends to be from thunderstorms or rapid snow melt, runoff could make stream flows

more variable. Greater year-to-year variation in precipitation combined with higher temperatures means that streams

could still go dry in some years. Thus although there may

be a slight increase in available water, water quantity may

be slightly more variable because of droughts and floods.

Fresh water quality may suffer moderately from sediments

and contaminants flushed into streams or well casings by

thunderstorm runoff and rapid snow melt. There also could

be large impacts on ecological functioning as in a less diverse mix of forest species. There would be less habitat to

support cold water fisheries but more for warm water fisheries. Another example is the potential for a mismatch

between when birds migrate and when key food sources

are available for them. Biodiversity and ecological effects

could be among the region’s largest impacts—but are very

uncertain. Other uncertain impacts are expected to be

much smaller, such as the potential for climate change to

induce more disease carried by insects and animals or from

contaminated water. Another example is the potential for

nutrient leaching and runoff from livestock production to

increase the environmental damages from agriculture.

Economic analysis suggests that the MAR economy will be

resilient to projected climate change. The region’s

diversified, technologically advanced economy is highly

integrated with the rest of the United States and the world

and has relatively little dependence on climate-sensitive

economic sectors. Although there are exceptions (e.g.,

settlement along coasts and in flood plains), economic,

technological, institutional and behavioral changes

generally have reduced the region’s vulnerability to the

prevailing climate. Examples include the huge decline in

the relative importance of climate-sensitive economic

activities (agriculture and forestry), the regional

diversification of food and energy supplies, the climatecontrolled structures in which we live, and advances in

disease prevention and treatment. We expect similar

adaptations to occur in response to climate change.

The MARA suggests that climate change poses diverse and

potentially large risks to the region’s ecosystems. Lingering

effects from earlier degradation are compounded by

continuing pressures on many of the region’s ecological

resources at a time of growing societal demand for ecological

resource protection, both for its own sake and for

recreational uses.

Table E1 shows that the MAR can expect both positive

and negative impacts from climate change. Despite efforts

to identify as many positive impacts as possible, results show

that benefits are fewer and smaller than potential damages.

The impacts will make some of the region’s citizens and

organizations better off while making others worse off, so

that the distribution of impacts is also an issue. These

features—substantial overall economic resilience in concert

with pressure on ecosystems and concern about inequitable

impacts for vulnerable groups—are the region’s starting

point for taking advantage of new opportunities and coping

with negative impacts from climate change. Rather than a

straightforward summation of simple measures, overall

social well-being is a complex agglomeration of economic,

ecological and distributional considerations. Thus the

MARA findings provide insights for making better decisions

under uncertainty, with the goal of optimizing social wellbeing.
1

MARA OVERVIEW 2000 • Setting the Stage

Setting the Stage

Chapter 1. Introduction

The summer 1999 widespread drought in the Mid-Atlantic

region followed by the largest peace-time evacuation in U.S.

history to protect people from Hurricane Floyd are

reminders of how much weather and climate influence

people and their well-being. Growth in population and

the corresponding increase in impervious surfaces lead to

more severe impacts from the same shortfall in

precipitation, or from the same intensity of hurricane. It

is challenging to understand interactions among influences

such as population growth, changes in land use, and climate

variability, to determine the seriousness of consequences

from these interactions, and to evaluate options for

moderating undesirable consequences. Fortunately,

emerging integrated assessment techniques can help with

such challenges.

Integrated assessment uses diverse perspectives and many

types of expertise to focus attention on the most important

aspects of a societal issue (Carter et al. 1994). Integrated

assessment can identify which influences will make the

situation better or worse, and accounts for the interaction

among influences. It can be a starting point for evaluating

individual, community and societal actions to improve the

situation. It also highlights what is known and what is not

known about the issue and alternative actions. The

assessment process described here examines the regional

implications of climate change. The results are intended

as input for making smarter decisions in the Mid-Atlantic

region, as related to climate change. However, the

description also can be the basis for similar integrated

assessments of other issues, ranging from population growth

to land use change to education to health care.

The Mid-Atlantic Regional Assessment

(MARA) as part of a national assessment

process

The impacts of climate variability and 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 U.S. Global Change Research

Program (USGCRP) has been collaborating with federal

agencies (represented in the National Assessment Working

Group, NAWG) to sponsor 16 regional assessments that

span the nation and its territories. The USGCRP also is

sponsoring nationwide assessments of five cross-cutting

“sectors”: coastal areas and marine resources, fresh water,

agriculture, forests, and human health.

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 variability and change. (Note that these

assessments are not examining the need for, or ways to

accomplish, reductions in greenhouse gas emissions; other

activities are exploring these issues.) The assessments are

challenging because of the uncertainties in projecting both

climate change and how our society will evolve – with or

without climate change. These assessments rely on multidisciplinary integrated approaches and substantial

stakeholder participation.
2

An interdisciplinary National Assessment Synthesis Team

(NAST), whose members represent academia, government,

and business, is summarizing the potential national impacts

from increased climate variability and change. The NAST

will submit a First National Assessment report to Congress

in the year 2000. Appendix B has more information about

the national assessment process.

Goals of this report

This report summarizes the Mid-Atlantic Regional

Assessment (MARA) methods, findings, and

recommendations. Four questions guide MARA:

1. What are the region’s current environmental stresses

and issues?

2. How could climate change and variability affect these

stresses, or create new ones?

3. What actions could 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?

The MARA goals are to provide information about how

the region’s climate might change, what the changes might

mean for the region’s society and ecosystems, and actions

that could reduce vulnerabilities or exploit opportunities

created by climate variability and change. The results also

provide region-specific context for the first national

assessment. Another goal is to demonstrate that substantive

stakeholder involvement can help focus the assessment so

that it is more useful to the people who live in the MAR.

This report summarizes progress toward these goals.

Assessment approach

The MARA approach to these questions is based on an

integrated assessment framework developed by Penn State’s

Center for Integrated Regional Assessment (CIRA) and

shown in Figure 1 (Knight et al. 1999). Assessment can

begin at any point in the diagram; the logic follows a continuous loop from 1) causes to 2) climate changes to 3) the

biophysical and socio-economic consequences of these

changes to 4) human responses to the consequences. Dialogue with the policy community and other stakeholders

helps identify the most important components and focus

the framework’s iterative, increasingly complex quantitative and qualitative analyses. The framework diagram also

accounts for hierarchical relationships among different

scales. MARA is based on the assumption that the causes

of climate change are mainly outside the region, but that

climate change could engender important interactions

among the ecological and physical responses within the

region. However, because human responses include actions that in turn generate climate change at a national or

global scale, the national synthesis needs to account for

the aggregate effects of similar actions across regions.

The first step for the MARA has been to describe the region’s

land forms, natural resources, demographics, economy and

climate as they are at present, with some historical context

(Chapter 2). This description provides a starting point for

understanding the impacts of climate change. Next, because the region’s society and economy will evolve regardless

of whether climate change occurs, the second step is to

envision that evolution, with special attention to components that are sensitive to climate (Chapter 3). The third

step is to assess how the region’s climate might change over

the next century (also Chapter 3). The fourth step builds

on the first three, to assess the incremental impacts from

climate change on the MAR (Chapters 4-9). This step

accounts for the responses by people and their institutions,

as well as ecosystems, to take advantage of new opportunities or to reduce damages presented by climate change. The

fourth step also identifies anticipative actions that can be

taken now or in the near future that would reduce vulnerabilities or enhance opportunities for the future, plus

information and research still needed to improve decisions
3

MARA OVERVIEW 2000 • Setting the Stage

related to the regional impacts of climate variability and

change (Chapters 10-11).

The enormity of the assessment task made it necessary to

choose both a set of impact categories and the depth with

which each would be examined. The MARA team used

several criteria, including: 1) the importance of the impact

category to the region’s economic, social and environmental well-being; 2) expected sensitivity of the impact category

to climate variability and change; and 3) the feasibility of

performing a credible assessment of each impact category,

given the available time and resources. In applying these

criteria, we were informed by existing knowledge about

the region’s environmental stresses, and expectations about

which ones might be affected by climate variability and

Figure 1. Integrated Regional Assessment Framework

change. In addition to the team members’ prior research

and literature reviews, our input was supplemented by participation at the Summer 1997 Aspen Global Change

Institute workshop, “Preparing for a US National Assessment,” the November 1997 “U.S. Climate Forum,” and

the Summer 1998 and Spring 1999 workshops, “U.S.

National Assessment: The Potential Consequences of Climate Variability and Change.” These meetings led to

interactions among the teams conducting the regional assessments, the sectoral assessments, and the National

Assessment.

Crucial input also came from extensive interaction with

stakeholders - those who might be affected by climate

change in the MAR or who might make decisions based
4

on output from the assessment. Much of this interaction

has been with the MARA Advisory Committee, which has

more than 90 stakeholders and experts. The Advisory

Committee has met 4 times for interactive dialog on the

MARA plans and progress. Its members also have provided input and feedback on draft outlines, scenarios, an

April 1999 draft of preliminary assessment findings, and

an earlier draft of this Overview. Their diverse perspectives

emphasized the need to cover more impact categories than

originally planned, often using information for which they

had special access or knowledge. Evidence of the MARA

team’s responsiveness to stakeholder participation is shown

by the fact that more than 40 of them took the time to

prepare comments on the overview draft. Its revision is

more accurate and more useful as a result of their thoughtful feedback. The MARA team particularly looks forward

to working with stakeholders to develop messages and dissemination strategies that will make it easier for people to

understand how climate variability and change in the MAR

might affect their family, their employment, and their community—and what they might do to adapt to negative

impacts or enhance positive impacts. Appendix C includes

additional information about stakeholder participation in

the MARA process.

This report summarizes the methods, findings and recommendations from the first 18 months of MARA activities.

The MARA team now is filling gaps in the assessment.

Some of these gaps are for components that simply could

not be completed during the initial activities; other are for

issues identified during the assessment process as potentially more important than anticipated. These include more

attention to a) interactions among sectors, b) feedback effects that could strengthen or weaken impacts, and c)

consequences for special populations and special places.
v

MARA OVERVIEW 2000 • Executive Summary

What can we do now?

MARA findings suggest win-win actions that have

substantial benefits even if climate stays the same, plus a

bonus of making the region more resilient to climate

variability and change. The most important actions are

to:

- Use a watershed perspective to reduce flood and

drought damages and protect water quality (in streams

and rivers, lakes and reservoirs, and ground water).

- Remove incentives for practices (e.g., that promote

building in areas vulnerable to erosion and flooding)

that place people, investments, and (especially coastal)

ecosystems at greater risk to climate variability.

- Set up communication and learning tools and

programs that help the region’s people identify how

they can capitalize on benefits and reduce damages

from climate change.

The first two strategies would reduce risks from several

causes—including climate change. They imply actions

(such as preserving forests and wetlands, minimizing urban

and agricultural runoff, protecting stream habitat and

reducing the release of toxic chemicals) that also reduce

ecosystem stresses; although less certain than the large

threats to the coastal zone, threats to ecosystems also could

be quite large.

What do we still need to know?

People in the MAR can make better decisions if they know

more about potential impacts from climate variability and

change and the effectiveness of alternative actions. Knowing

more also would reduce uncertainty about the direction

and size of impacts in Table E1, making it less likely that

the region would face major surprises. The most important

information and research needs are to:

- Improve projections for frequency, timing and

intensity of average and extreme weather (especially

precipitation), at a regional level.

- Improve projections of how average and extreme

weather affect agriculture, forests, fresh water quantity

and quality, coastal zones, ecosystems, and human

health, and how adaptation would moderate negative

impacts and enhance opportunities.

- Improve models to evaluate the benefits and costs of

alternative adaptation options, so that economic

efficiency can be considered in management and

policy decisions.

- Improve methods for evaluating how proposed shifts

in policy (e.g., health policy, land use policy,

agricultural policy) might affect vulnerability to

climate variability and change.

More specific recommendations about what can be done

in the near-term and the needs for information and research

are listed in Chapters 10 and 11.
5

MARA OVERVIEW 2000 • Present Status & Potential Futures

Figure 3. The MAR, with major

watersheds and highlands

Figure 2. The MAR, with state boundaries

Chapter 2. The Mid-Atlantic

Region’s geography and

economy

Defining the MAR

Figure 2 shows that 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. Several

factors influenced the choice of regional boundaries. A

primary consideration was the region’s major watersheds

(Chesapeake Bay, Delaware River basin, Albemarle-Pamlico

Sounds, as shown in Figure 3), particularly because of interstate compacts that enable management across political

boundaries. For example, small portions of New York are

included as parts of the watersheds for the Chesapeake and

Delaware Bays.

A second factor was the need for data on land use, land

cover, and ecological characteristics. The U.S. Environmental Protection Agency’s (EPA’s) Mid-Atlantic Integrated

Assessment (MAIA) and Mid-Atlantic Highlands Assessment (MAHA) provide baseline data on the environmental status of PA, WV, MD, DE, VA (and the District of

Columbia) (Jones, et al. 1997). Given the economic linkages between the Chesapeake Bay watershed and the areas

to its west, and the fact that many environmental and land

management decisions occur at a state level, we expanded

the MAR to cover all of the MAIA territory.

Third, NJ’s western counties are in the MAR as part of the

Delaware River basin. We added the southeastern NJ counties to ensure their inclusion among the regional assessments. Remaining portions of NJ, NY and NC are being

covered by other regional assessments.

The Mid-Atlantic Region:

Present Status and Potential Futures
6

Figure 5 shows the regional distribution of major land cover

categories. Going inland, the landscape becomes less agricultural (25 percent of total MAR land cover) and more

forested (nearly 65 percent of land cover).

MAR land forms and land cover

The MAR’s 358 counties are in four geographic regions

(Cuff, et al. 1989), as shown in Figure 4. The relatively

flat Coastal Plain extends inland, traversing all of DE and

parts of NJ, MD, VA and NC. The MAR’s foothills of the

Appalachian Mountains are in the Piedmont Plateau, covering north-central NJ, southeastern PA and central portions of MD, VA, and NC. From northwestern NJ to the

southwest through PA, MD, and VA, the parallel eroded

mountains are called the Ridge and Valley zone. The Appalachian Plateau extends from the NY portion of the

MAR, through northern and western PA, the western edge

of MD, and most of WV. This subregion varies from rolling hills to relatively flat areas, with meandering waterways throughout.

Figure 5. Mid-Atlantic land cover

Figure 4. Geographic subregions of the MAR
7

MARA OVERVIEW 2000 • Present Status & Potential Futures

The MAR population and economy

Less than 4 percent of the MAR’s land is urban (Figure 5),

but more than half of the MAR’s 35.2 million people live

in its six largest urban areas: Philadelphia, Pittsburgh, Baltimore, Washington DC, Richmond, and Norfolk (U.S.

Bureau of the Census 1998). The region’s population

growth of nearly 20 percent between 1967 and 1995 (the

most recent year for which information is available) is more

modest than the nation’s growth of 33 percent. Regional

growth has been fastest in the retirement-age category (with

a decrease in the proportion under 20); 14 percent of the

region’s population is 65 or older (compared with 13 percent for the nation), with higher shares in the Ridge and

Valley (15 percent) and Plateau (16 percent) subregions.

About two-thirds of the working-age population, jobs and

incomes are in the Coastal Plain and Piedmont subregions

(NPA 1998a). For the entire region, jobs increased by 55

percent over the 1967-1995 period, and real per capita income increased by 82 percent.

The region’s annual output ($1.67 trillion in 1995) amounts

to about 13 percent of the nation’s (IMPLAN 1997).

Manufacturing and Services are the largest economic sectors, accounting for 26 percent and 20 percent of MAR

output respectively. Sectors directly sensitive to climate—

Agriculture and Forestry—each represent about 1 percent

of the region’s gross output.

Input-output analysis shows strong linkages among economic sectors in the MAR, with all industries except Agriculture and Mining (at the broad 1-digit Standard Industrial Classification or SIC level) purchasing between 50

and 80 percent of their intermediate inputs within the region. This means that activity in one part of the region’s

economy can have ripple effects in other sectors. The region also is highly integrated with economies in the rest of

the nation and the rest of the world. For example, imports

are 32 percent and exports are 28 percent of the region’s

total gross output. Agriculture, Mining, and Manufacturing account for the largest shares of this trade. The region’s

economy also is affected by migration, tourism, and communications. These linkages to other regions provide a

buffer against impacts within the MAR. They also transmit impacts from other regions to the MAR.

Human stresses on the environment

The region’s relatively large population (15 percent of the

nation’s population in 5 percent of the contiguous land

area) and economic development have stressed many of its

ecological resources, particularly the nation’s two largest

estuaries: the Chesapeake Bay and Albemarle-Pamlico

Sounds. These estuaries, along with the Delaware River

basin, are stressed by nutrient runoff from agricultural and

urban areas. The region’s forests, wetlands, and fresh water streams are affected by habitat loss and degradation,

pollution and nonnative invasive species. The primary

habitat threats for these ecosystems are forest fragmentation and loss, wetland drainage, stream channelization and

dams. Existing and future ecosystem stresses are described

more fully in Chapter 8.

MAR climate

Over the last century, the average temperature in the MAR

has been about 52o

F and average annual precipitation has

been about 41 inches.

Box 1 Weather and Climate

Distinguishing clearly between weather and

climate can improve understanding of this section.

Weather is the hour-to-hour and day-to-day state

of the atmosphere, such as being rainy or sunny,

warm or cold, windy or calm. Climate can be

thought of as average weather, and encompasses

a locale’s typical weather patterns as well as the

frequency and intensity of storms, cold outbreaks,

and heat waves.

Some reports define climate variation as natural

variation in climate, and climate change as those

variations and trends in climate attributable to

human activity. For this report, whether the cause

of an impact is natural or anthropogenic is less

important than whether it has to do with long-term

trends or shorter patterns of variation. Thus we

use more intuitive definitions: climate variability

refers to day-to-day, year-to-year, and decade-todecade patterns of weather and climate. Climate

change refers to longer term trends in average

weather and climate, usually measured by

temperature and precipitation.
8

Identifying whether climate is changing is difficult because of the complexity of the climate system and the

large variations in year-to-year and decade-to-decade

weather, the climate system’s tendency to change slowly

because of the basic physics that determine a locale’s

climate, and because the record of climate measurements is relatively short—only about 100 years. Over

this period, there has been an upward trend in precipitation amounting to about a 10 percent increase.

There also has been an upward trend of nearly 1 degree Fahrenheit in temperature. Still, the year-by-year

and decade-by-decade variations in extreme events such

as droughts and floods are far more noticeable than

these gradual trends (Polsky et al. 2000).

From 1901 to 1998, the average number of very hot

days per year—at least 90°F—decreased slightly from

roughly 20 to 18 in the MAR. Over the same period,

the average number of very cold days when

temperatures dipped below 0°F increased from 1 to

nearly 4 per year. Thus although average temperatures

are rising, the MAR is experiencing fewer really hot

days yet more really cold days. Variation in the region’s

climate also shows up in seasonal patterns. For

example, the three coldest winters on record were

1976-77, 1977-78 and 1978-79, while some of the

warmest winters occurred in 1982-83, 1994-95 and

1997-98.

If we express extreme precipitation events as occurring when precipitation exceeds two inches in 24 hours,

then the MAR has slightly fewer of these events now

than it did 100 years ago. Precipitation is increasing,

but it does not seem to be because of more frequent

severe rainfalls and snowfalls.

Box 2 Climate History in the

Mid-Atlantic Region

Even the short record of measurements can help in

understanding climate. For example, observations

of winds up to 10 miles above the Earth’s surface

have been available only since World War II. These

show that changes in atmospheric circulation can

explain variations in MAR climate. A strong westto-east atmospheric flow prevailed over North

America through the late 1940s and early 1950s

(Yarnal and Leathers 1988), producing average to

slightly above-average temperatures and variable

precipitation over the MAR. Then the circulation

shifted to a weaker north-to-south flow that became

entrenched by the 1960s. A deep trough of continental polar air and a storm track southeast of its longterm position prevailed during this decade, so that

precipitation often fell off the Atlantic coast. This

regime led to a relatively cool, dry MAR climate. The

MAR experienced higher temperatures and more precipitation when the trough migrated westward during the 1970s. Since the late 1970s, large variations

in the shape and positioning of the month-to-month

and year-to-year jet stream flow over North America

have produced a highly variable surface climate.

Insights about earlier climate can be gleaned from

cores taken from sediments of the Chesapeake Bay

(Cronin et al. 2000), from tree rings (Cook and

Jacoby 1977, 1983), and from diaries, newspapers

and periodicals (Baron 1995). Such paleoclimate

reconstructions suggest as much climate variability

in the MAR during the 16th-19th centuries as observed

during the 20th century. Especially noticeable are

“megadroughts” in the 16th and 17th centuries that

were more severe than 20th century droughts, as well

as very wet periods that occurred once or twice every century and lasted nearly 20 years. The effects

of El Niño-Southern Oscillation events and the North

Atlantic Oscillation also are observed in the record

(Cronin 1997).
9

MARA OVERVIEW 2000 • Present Status & Potential Futures

Chapter 3. Envisioning the

MAR’s socioeconomic,

environmental and climate

future

By definition, regional climate change impact analysis involves comparing what the region would be like “with”

and “without” climate change. Climate impact research

typically generates climate scenarios using a range of assumptions about changes in emissions of greenhouse gases

and other forces that drive climate change. Then societal

and other impacts are analyzed, based on the climate scenarios. Because climate change is a long-term phenomenon, this exercise requires consideration of potential economic, demographic, technological, institutional and ecological conditions many years into the future. The MAR,

like other regions of the United States and the World, has

changed dramatically over the last century, and there is no

reason to expect slowing in its pace of rapid economic

change. The region undoubtedly will be substantially different in the future than today, in terms of its sensitivity to

climate change and potential for response and adaptation.

Therefore it is important to envision the impacts of climate change in an evolving society that will differ in many

ways from the current society. Our limited ability to forecast reliably beyond just a few years implies large uncertainties about what the future will be like—with or without climate change.

Rather than assuming that a particular future will exist, it

is more useful to explore scenarios that could exist, examining the ramifications of those “what if” scenarios. Imagining where we might be headed reduces the complexity

and unpredictability, allowing decisions that can accommodate both positive and negative impacts (Schwartz

1991). Developing useful scenarios involves both art and

science. The MARA team relied on broad input from experts and stakeholders in identifying crucial scenario components and the ranges that guided the assessments described in Chapters 4-9. The scenario development process and results are summarized in this chapter.

Socioeconomic and environmental future

In 1900, it was difficult to predict dramatic 20th century

changes such as the decline in farm employment that

accompanied huge increases in agricultural productivity,

the widespread use of computers, or the shift from horses

to cars that reduced manure disposal problems in large cities

but increased air pollution. The socioeconomic and

environmental picture for the year 2100 is just as unclear.

For example, population cannot be predicted accurately at

a regional level because of the difficulty in predicting

regional migration patterns. Similarly, predicting

employment and income is complicated by the ease with

which technology and other resources move across regional

boundaries. Point estimates almost certainly would be

incorrect. However, trends and expectations about future

labor force participation rates, birth rates, immigration,

capital investment and improvements in productivity can

be used to calculate ranges for potential population,

employment and income. Trends suggest a continued

increase in population over the next 30 years in the MAR,

with a continued shift to the Coastal and Piedmont

subregions. This population growth will create additional

pressures for converting agricultural land to urban and

suburban uses, especially in the corridor from Norfolk to

New York City. In turn, land development is likely to create

additional stresses on the region’s ecosystems, particularly

the Chesapeake and Delaware Bays. General expectations

such as these help determine the input for more formal

projections provided by USGCRP and shown in Figure 6

(NPA 1998a). These calculations serve as a baseline for

the region’s future socioeconomic conditions in the absence

of climate change. Note that the uncertainties increase

dramatically as the horizon moves farther into the future.

These baseline ranges establish upper and lower bounds

for socioeconomic conditions on which climate change is

overlaid. Within specific sectors, these aggregate projections

sometimes are less relevant than more detailed projections

of land uses, demand for agricultural products, or

technologies. The chapter-by-chapter assessments reflect

available detailed projections where appropriate.
10

The most serious risk of adverse impacts emerges from

scenarios that combine the greatest increases in

socioeconomic or ecosystem sensitivities with

increased climate stresses and little socioeconomic

and/or ecological adaptation. For a given climate

change, Table 1 shows that large socioeconomic or

ecosystem sensitivities to climate variability and

change will have small impacts if there are large

adaptive capabilities in the socioeconomic structure

or ecosystem, but large impacts if adaptive capabilities

are small. Similarly, reduced risks or the lower bounds

for adverse climate impacts emerge in scenarios that

combine reduced baseline socioeconomic or

ecosystem vulnerability with substantial ability to

adapt. Such tables can be a starting point for

examining a range of climate, socioeconomic and

ecosystem conditions. Combinations of climate and

socioeconomic scenarios with offsetting effects may

yield greater or smaller risks. The ranges between

the upper and lower bounds could be viewed as

confidence intervals.

The set of scenarios is kept tractable by first

identifying sectors likely to be sensitive to climate

change and then by identifying and selecting risks

within those sectors. The resulting scenarios serve as

a starting point for identifying incremental

socioeconomic impacts from changes in climate. To

illustrate, the primary climate-sensitive sectors

identified for the MAR are: agriculture, forests, fresh

water, coastal zones, and human health—with

ecosystems as a cross-cutting issue. Within the

agriculture sector, for example, we identified four key

potential risks: food availability and cost, agricultural

income and employment, rural landscape, and

environmental impacts of agricultural production.

Because food availability and cost are almost entirely

determined by factors external to the region, we chose

to focus on the latter three. Similar decisions were

made for the remaining sectors.

Figure 6. NPA’s Population, Employment and

Income Projections for the MAR , to 2050
11

MARA OVERVIEW 2000 • Present Status & Potential Futures

Future climate

The U.S. Global Change Research Program (USGCRP)

provided data from two state-of-the-art global climate models

to promote a common ground for developing climate change

scenarios across the country. Two models were provided

because the organizers of the National Assessment preferred

to have data from more than one modeling group to reflect

the uncertainties in projecting future climate. They also

stipulated that the models had to:

• cover both the last century and the next century,

• use a consistent set of assumptions about the rate of

increase in greenhouse gas concentrations and sulfate

aerosols, and

• be available prior to the start of the formal

assessment process.

Given these guidelines, two models were available—the

Canadian Climate Centre (CCC) model, and a model from

the Hadley Centre for Climate Prediction and Research in

Great Britain.

Both models have done a reasonable job of reproducing

U.S. climate trends. For example, Table 2 shows the

similarity between observations and model results for a

recent decade. We chose 1984-1993 to represent the

current situation because of the wealth of socioeconomic,

ecological and climate data available for this period.

However, the climate simulations diverge as they run into

the future. Both models show increasing temperatures,

but with a more rapid increase in the case of the CCC

model. Figure 7 shows the U.S. temperature change from

Table 2. Observed and simulated temperature (°F) and precipitation (inches) for 1984-93.

 Temperatures are daily maximum temperatures and minimum temperatures.

Maximum Minimum Precipitation Temperature Temperature

mean standard mean standard mean standard

deviation deviation deviation

Observed 63.7 16.5 41.4 15.2 43.1 4.4

Hadley 63.5 17.5 41.0 15.5 42.8 3.0

CCC 63.6 16.7 41.6 15.1 45.0 2.8

Table 1. Potential Size of Climate Impacts.

Socioeconomic/Ecosystem Adaptation

Large Small

Socioeconomic/Ecosystem

 Sensitivities to Climate

Large Small impacts Largest impacts

Small Smallest impacts Small impacts
12

both models, together with the results from

several other models. The simulations tend

to fall into two groups, one showing a more

rapid temperature increase than the other. The

CCC model falls into the former group and

the Hadley model into the latter. At this point,

there are no grounds for suggesting that any

one model or simulation is more accurate or

realistic than another, and both models

produce climate changes that are possible

given the projected changes in atmospheric

composition. As is the case for the

socioeconomic changes, the simulations

produced by these models represent plausible

ranges for possible climate change, rather than

predictions of what actually will happen for

any particular time or place.

Both models were run for the 1992 businessas-usual (i.e., unconstrained emissions)

scenario of the Intergovernmental Panel on

Climate Change. This scenario provides for

a relatively rapid rise in the emissions of carbon

dioxide and other greenhouse gases (which

tend to increase global surface temperatures)

and of sulfate aerosols from coal combustion

(which tend to decrease surface temperatures).

On balance, the warming effect is significantly

larger, hence the upward trend in temperature

change depicted in Figure 7.

While virtually all models show a consistent

temperature response (an increasing trend

with the rate of change varying among

models), there is much less consistency in

projected precipitation changes. This is also

true of the Hadley and CCC models for the

Mid-Atlantic region. Figure 8 shows the

projected changes in maximum temperature

(upper panel) and in precipitation (lower

panel) for the MAR. The heavier lines show

9-year running averages, while the finer lines

Figure 7. Simulations for the United States of average changes

in temperature from leading climate models based on historic

and project changes of CO2

 and sulfate emissions. The Red

and Black lines indicate the models chosen for use by the

National Assessment.
13

MARA OVERVIEW 2000 • Present Status & Potential Futures

capture the variability likely to occur annually.

Both models show warming throughout the

next century. The Hadley model also shows a

substantial increase in precipitation by 2100,

while the CCC model does not. Two earlier

studies of the region used high resolution

empirical and numerical models to extract

regional information from a third global

climate model (GENESIS). These

methodologies, referred to as “climate

downscaling techniques,” provide finer

resolutions more appropriate for regional

assessments. Both studies projected an

increase in MAR precipitation similar to the

Hadley results (Crane and Hewitson, 1998;

Jenkins and Barron, 1997).

The CCC and Hadley simulations, when

considered in the light of the additional

downscaling results, suggest that the most

likely MAR climate change scenario will show

increasing temperatures and increased

precipitation across the region. Temperature

increases are likely to be on the order of 2°F

by 2030 and may increase an additional 3°F

to 8°F by the end of the 21st century. There

is a high likelihood that average annual

precipitation will increase, but the magnitude

and seasonal distribution of the increased

precipitation is uncertain. The MAR has

experienced natural weather disasters and

weather extremes at different times of the year.

The current spatial resolution of global climate

models is not fine enough to show

thunderstorms or hurricanes, but both the

CCC and Hadley models indicate slight

increases in the frequency and intensity of

winter storms with little change in storm track

over the MAR. Because storm impacts can

be substantial, Chapters 4-9 acknowledge the

uncertainty and address the implications of

more storminess.

Figure 8. Hadley and CCC model differences from

the observed 1960-1989 base period, for the MAR
14

It is also important to note that climate is highly variable

from year-to-year and that some of this variability is due to

features of the climate system (such as El Niño events) that

are not well simulated by current global climate models.

This variability will continue and may even increase in the

future. On time scales of years-to-decades, the climate in

any given period in the next century could be considerably

warmer/colder or wetter/drier than indicated in Figure 8.

Consequently, Figure 8 should be used only to infer the

overall trend in regional climate change.

Table 3 summarizes the climate change scenarios for the

MAR, together with projections of other important

environmental parameters such as sea level and runoff. An

indication also is given of our current confidence in these

projections. The most likely change is the rise in

atmospheric carbon dioxide (CO2

). (The great uncertainty

in projections of storminess keeps this category from

appearing in Table 3.) These environmental changes are

linked with the socioeconomic scenarios to project potential

impacts, challenges and opportunities in Chapters 4-9. The

assessments in Chapters 4-9 use the scenario information

in a variety of ways. Where a quantitative analysis is possible

(in the case of stream flow, for example) the Hadley and

CCC model projections are used to present a range of

possible outcomes. Where only more qualitative

assessments are possible, the judgement is based on the

generalized trend of increasing temperatures and rainfall.

In some cases the assessments use information from prior

analyses utilizing different climate models, and details of

these models are presented when relevant.

Table 3. Important projections for the years 2030 and 2095 with respect to 1990.

Parameter 2030 2095 Confidence in

projection

CO2 (%) a +20 to +30 +50 to +120 Very high

Sea level (inches) b +4 to +12 +15 to +40 High

Temperature (

 oF) c +1.8 to +2.7 +4.9 to +9.5 High

Precipitation (%) c -1 to +8 +6 to +24 Medium

Runoff (%) d -2 to +6 -4 to +27 Low

a. Range reflects IS92d and IS92f CO2 emission scenarios (Watson et al., 1996).

b. Low and high projections of Warrick et al. (1996) for IS92a scenario, plus a local component of 0.008 inches per year.

c. Range given by Hadley and CCC models for the Northeast U.S. (Felzer et al., 1999).

d. For the Susquehanna River Basin, using a water balance model forced with the CCC and Hadley output (Neff et al., 2000).
15

MARA OVERVIEW 2000 • Present Status & Potential Futures

Some of the assessment results rely on subregional data

extracted from the models. For illustration, Figure 9 uses

the Hadley model to show the pattern of temperature

change for winter and summer average temperatures. The

maps depict the simulated temperature change between

the average temperatures for 2025-2034 compared with

1984-1993. These data are extrapolated to the higher

spatial resolution of the VEMAP grid used to display

present-day climate in the National Assessment data sets

(Felzer et al. 1999). They do not represent downscaled

regional analyses of climate change. Similar maps can be

produced for other variables and for the change by 2100

(which, along with 2030, is a benchmark in the National

Assessment process). The spatial distribution of the

Figure 9. January (left) and July (right) differences between future (2025-2034)

and contemporary (1984-1993) average temperatures for the Hadley model

simulated temperature change in Figure 9 apparently

reflects some combination of latitudinal effects and the

physiographic divisions in the region. The summer-time

increases are greater over the Coastal Plain and Piedmont

than over the Ridge and Valley and Plateau regions. While

regional patterns such as these are quite likely to occur, we

can not place too much confidence in the specific grid-cell

values shown in Figure 9. Just as is the case for the

socioeconomic projections, uncertainties increase as the size

of the region becomes smaller. Both the socioeconomic

and the climate projections tend to be more reliable for

general trends across broad areas than for specific changes

in small areas.
17

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

Chapter 3 describes many uncertainties that arise in projecting

a) how the population, economy and environment of the MAR

will evolve, b) how the region’s climate will change, and c) how

changes in the region’s climate will affect its population, economy

and environment over the next 30 and 100 years. The uncertainties are compounded by the fact that different people, manmade systems, species and ecosystems have differing capacities

for adaptation to climate change. Despite these uncertainties,

this assessment has produced insights about how the region’s

citizens and decision makers can improve near-term decisions

to enhance the long-term future of the MAR. The impacts of

climate variability and change summarized in Table 4 are based

on climate model projections that the MAR will be generally

somewhat warmer and somewhat wetter, with potentially more

variability in the region’s climate in the next 30 to 100 years;

Impacts,

Challenges and Opportunities

this implicitly presumes that the causes of global climate change

will not be abated. Relatively little can be said about the timing

of these impacts, except that they would occur by 2100. Although the summary in Table 4 is useful for indicating the

relative direction, magnitude, and certainty of potential impacts, management decisions will need more information about

the data behind these indicators. Thus sector-by-sector impacts are described more fully in Chapters 4 through 9. These

chapters assess agriculture, forests, fresh water, coastal zones,

ecosystems and human health. Each chapter covers current

status and stresses for that topic, and how climate variability

and change would affect the current status and stresses. Actions that might take advantage of opportunities or reduce vulnerabilities, and the most important information and research

gaps are summarized in Chapters 10 and 11.
18

Chapter 4. Agriculture

Agriculture’s dependence on climate makes this sector an

obvious candidate for examining potential impacts from

climate change. Reflecting national trends, agriculture

within the MAR has declined in importance while simultaneously adapting rapidly to changes in production and

processing technology and to changing demands for different agricultural products.

Compared to other parts of the nation, the MAR has smaller

farms and produces a wider range of crops and livestock

(U.S. Department of Agriculture 1999). Historically adequate rainfall has led to irrigation on less than 3 percent

of MAR crop acreage, compared to a national average of

about 13 percent. Crops account for about one-fourth of

the MAR’s agricultural land, with the remainder in hay

and pasture. About 65 percent of MAR agricultural sales

are from livestock and livestock products. However, in the

context of the total value of production, which includes

both sales and products consumed on the farm and thus

not sold, crops are much more important (about threefourths of total value).

About one-fourth of the MAR’s land area is agricultural

(Abler et al. 1999), so its presence defines many rural landscapes. Rural and urban populations within and outside

the region enjoy agricultural vistas and consider them to

be a valuable amenity. Agricultural land is an important

habitat for a variety of wildlife species. Agriculture is also

a source of negative environmental impacts in the MAR,

mainly nutrient and pesticide runoff, and erosion (Kellogg

et al. 1997, 1999).

Using the socioeconomic approach outlined in Chapter 3,

two baseline scenarios were developed. The first one, called

the Status Quo (SQ) scenario, assumes agriculture continues much like it is today in the MAR. However, it is realistic to expect that agriculture will become less important

in the MAR because of a) environmental regulations, b)

competition from other agricultural areas across the nation and in the rest of the world, and c) higher values for

MAR land in uses other than agriculture. These expectations and the expectation that people will learn and adapt

are represented in the second scenario, called the More Environmentally Friendly and Smaller (EFS) scenario. Table

5 shows the assumptions for the scenarios.

Table 6 shows how the two scenarios provide upper and

lower bounds on positive and negative agriculture-related

impacts from climate change. For example, the EFS assumptions provide the upper bound on increased agricultural production; even though agricultural production is

smaller than under the SQ scenario, it is much better

Table 5. Baseline Agricultural Scenarios for the Year 2030 in the Mid-Atlantic Region

Scenario Scenario Assumptions

Status Quo (SQ) • Agriculture as it exists today

“Environmentally Friendly” • Major decline in field crops

and Smaller Agriculture (EFS) • Smaller but significant decline in livestock

• Substantially fewer farms

• Higher productivity due to biotechnology and precision agriculture

• Much more production per farm

• Much less sensitivity to climate variability due to biotechnology, precision

agriculture, and improved climate forecasts

• Conversion of land to urban uses, but slowed by farmland protection programs

• Some reforestation of economically marginal agricultural lands

• Much less commercial fertilizer and pesticide use due to biotechnology

• Less runoff and leaching of nutrients and pesticides due to precision agriculture

• Stricter environmental regulations, especially for intensive livestock operations
19

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

equipped to take advantage of positive climate developments. Correspondingly, the smaller production under EFS

assumptions yields lower bounds on environmental impacts (accompanying climate-induced changes in agriculture).

Climate change impacts on each of the MAR’s major agricultural products can be analyzed by applying these baseline

scenarios. Potential direct impacts from climate change

include increased photosynthesis (because higher carbon

dioxide concentrations act as a fertilizer), reduced transpiration, warmer temperatures, increased precipitation,

changes in extreme weather, and changes in weeds, insects

and diseases. (Although potentially important, there is

more uncertainty about the extent of changes and their

potential impacts for extreme weather, weeds, insects and

diseases.) The assessment also considers indirect effects

due to changes in farm commodity prices (because of climate change impacts in other regions or countries) and

economy-wide effects (because of climate change impacts

on prices Mid-Atlantic farmers must pay for inputs, for

example).

Table 7, which is part of Table 4, summarizes the assessment results. Because farmers are adaptable, climate change

is likely to increase production of soybeans, and possibly

corn and tree fruits. Tobacco could become less profitable, mainly because climate change will improve its growing conditions more in areas other than the MAR and thus

increase competition from outside producers. The region’s

other two major agricultural categories, dairy and poultry,

are not expected to be affected by climate change.

If livestock production continues to be as important in the

MAR as it is now, nutrient leaching and runoff could increase—which could raise the risks of waterborne diseases

such as Cryptosporidiosis and of ecological damages to fresh

water and estuarine resources from eutrophication. 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 has been little research to identify these

threats or opportunities.

Table 6. Upper and Lower Bounds Established by the Two Agricultural Baseline Scenarios

Negative Positive Negative Impacts Positive Impacts Environmental Environmental on Production on Production Effects Effects

Upper Bound SQ EFS SQ SQ

Lower Bound EFS SQ EFS EFS

SQ = Status Quo Scenario

EFS = More Environmentally Friendly and Smaller Scenario
20

Chapter 5. Forests

Forests cover about 65% of the MAR’s total land area (Figure 5) and support a rich mix of species, from the pine and

coastal wetlands regions in the south to the northern upland hardwoods (Jones et al. 1997). The top part of Figure 10 shows the current distribution of major forest types:

oak-hickory (46% of forested area), maple-beech-birch

(37% of area), and pine and mixed pine-hardwood forests

(8% of area) (based on data in Hansen et al. 1992). Dominant hardwood species are red oaks, white oak, yellow-poplar, red maple, sugar maple, black cherry, beech and

sweetgum. Softwood forests are dominated by hemlock

and by loblolly, shortleaf, and white pines.

Although cut extensively for wood products in the early

1900s, the region’s second-growth forests are rapidly approaching maturity. Forest area in the MAR has been

relatively stable over the past 30 years, but total standing

biomass has increased as forests mature (Powell et al. 1996).

disease and insects (especially the gypsy moth), deer browsing, and occasional wildfires. Urban/suburban

development also contributes to fragmentation of forest

tracts, reducing the ability of plants and animals to survive

and migrate. Most of the forests (88% of acreage) in the

region are privately owned, so management decisions rest

largely with non-industrial private landowners.

Insights about potential impacts of climate change can be

gleaned from current effects of climate in forests. Because

most of the available information was anecdotal rather than

representative of MAR forests, we designed, pretested, and

revised a questionnaire to determine how weather affects

forest operations. The questionnaire was sent to 592 federal and state agencies, consulting foresters, loggers,

industrial foresters, and urban and municipal foresters

within the MAR; 57 percent responded. Whether forests

are managed for watershed protection, harvesting of sawtimber or pulpwood, or maintenance of forest aesthetics

and habitat, their managers report increased operating costs

when extreme weather occurs.

Forest products in this region are primarily sawlogs, pulpwood, fuelwood, and

veneer logs, and other products such as

maple syrup, nuts and edible plants. The

combined total gross output (sales revenue) of 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) in 1995 was $41.8

billion, or 2.5% of the $1,671.1 billion

total gross output in the MAR.

MAR forests also provide important nonmarket benefits, including recreational

opportunities, watershed and riparian

buffers, wildlife habitat, enhanced

biodiversity, and aesthetic appeal. Stresses

from anthropogenic and natural factors

include loss of forest land to urban/suburban development, atmospheric

pollution (deposition of acidic compounds and high ground levels of ozone),

Figure 10. Dominant MAR forest types for current climate,

and potential distributions for CCC and Hadley

equilibrium climate scenarios, for a CO2

 doubling.
22

et al. 1999a). For example, high nutrient loadings from

agriculture and other non-point source runoff reduce oxygen levels, which in turn reduce the habitat available for

animals living in water. High nutrient loadings also increase blooms of algae, which shade deeper water and thus

decrease the amount of submerged aquatic vegetation

(SAV).

Impacts from climate change on stream flow were estimated

using a water balance model (Najjar 1999) for average

monthly air temperature and precipitation over the

Susquehanna River Basin (SRB), comparing the 1900-1987

record with output from the CCC and Hadley models for

2025-2034 and 2090-2099. The model projects modest

changes in annual streamflow for 2025-2034: +7 percent

for the Hadley scenario and -2 percent for the CCC scenario (Figure 11). By 2090-2099, the model projects substantially larger annual changes of +24 percent for the

Hadley scenario and -4 percent for the CCC scenario.

Recall that the CCC model projects much larger temperature increases and much smaller precipitation increases than

the Hadley model. Consequently, the offsetting effects of

increased temperature and precipitation cause the water

balance model to show different directions in streamflow

changes for each scenario. A further complication is that

elevated CO2

 may allow plants to use water more efficiently,

thereby reducing water need and increasing streamflow

(Wigley and Jones 1985). These factors highlight the difficulty in predicting future streamflow. Similar difficulties

arise in pinpointing the impact of climate change on future groundwater levels.

Despite the uncertainties in projecting future water availability, insights into potential adaptation strategies can be

gleaned from how water managers currently respond to

climate variability. Results from a survey of Pennsylvania

water managers appear in Box 3.

The potential for a wetter regional climate, punctuated by

droughts, suggests higher water supply management costs

to protect the quality of both surface and ground water

sources and to provide more storage capacity. Increased

storage capacity could buffer the region against additional

flooding if extra precipitation tends to arrive in intense

Chapter 6. Fresh water

quantity and quality

The MAR’s annual precipitation average of 41 inches provides water for many purposes. These include power

generation, public and private water supplies, industry, agriculture, waste treatment, aquatic and riparian habitat, and

recreation on the region’s streams, lakes and reservoirs. In

the MAR, the dominance of forests and agricultural lands

means that evapotranspiration accounts for approximately

two-thirds of all precipitation. The rest moves as groundwater or surface water to streams, lakes and reservoirs; both

flows are valuable for human and ecosystem use.

Approximately 95 percent of all MAR withdrawals are from

surface water (Neff et al. 2000). Thermoelectric power

generation withdraws more water than any other use (60

percent of all withdrawals), although much of this water is

returned to the watershed rather than consumed. The second and third largest users of water are public water supplies

(20 percent) and industry (14 percent). As mentioned in

Chapter 4, relatively little MAR agriculture is irrigated,

accounting for less than one percent of the region’s total

fresh water use.

Groundwater use is much smaller than surface withdrawals, but important. Within-region use of groundwater varies

from 3 percent of total withdrawals in West Virginia to 15

percent in Delaware and 17 percent in Maryland. Nearly

one-fourth of the region’s residential water supply comes

from privately owned wells. Nationally, only the southeastern United States has a larger share of self-supplied

residential water.

Although the supply of fresh water in the MAR generally

is adequate to meet withdrawal demands, exceptions include drought and occasional disruption or contamination

by floods or pathogen outbreaks (Solley et al. 1998, Yarnal

et al. 1997). Rapid development in parts of the MAR that

rely on ground water wells, especially for residential use,

has created stresses because of surface water infiltration, or

salt water intrusion in coastal areas. Human activities have

affected the region’s water resources in many ways (Walker
26

Chapter 7. Coastal zones

The Mid-Atlantic’s coastal areas, especially the Chesapeake

Bay, Delaware Bay, and Albemarle/Pamlico Sounds (Figure

3), have important aesthetic and economic values. In

Delaware, for example, Parsons and Powell (1998)

estimated that $90,000 of the value 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. The

MAR coastal counties (see Figure 4) are densely populated,

with 38 percent of the region’s population in only 19

percent of its area. Five of the MAR’s six largest cities

(Philadelphia, Baltimore, Washington, Norfolk,

Richmond) are in its coastal zone. The shore is a tourist

destination; many coastal communities have more visitors

than permanent populations.

Coastal communities and the natural resources that

prove so attractive have been vulnerable to the inexorable forces of nature: storms, coastal erosion

and beach dynamics. Despite this vulnerability,

coastal development has been subsidized through

federal activities such as shoreline protection and

beach replenishment, federal disaster assistance,

and the National Flood Insurance Program (NFIP).

In the 1978-1998 period, coastal counties of four

MAR states (NJ, DE, MD, VA) had 177,758 NFIP

policies in effect with $21 billion in coverage

(Kunreuther et al. 1999). During that time, $81

million in premium revenues were collected; $327

million were paid in 46,670 claims, $138 million

of which were repetitive.

Because higher air temperatures from increases in

CO2

 (see Table 3) are expected to warm and thus

expand the oceans, climate-induced sea-level rise

is very likely—and will add to the effects of local

subsidence in the MAR. Sea-level rise will flood

coastal regions. Figure 12 shows a rough estimate

of how a 24-inch rise in sea level (which is in the

range projected for the year 2095, as shown in Table

3) would affect Delaware’s shoreline (Najjar et al.

2000). The areas shown in blue are less than two

feet above current sea level. The actual response Figure 12. Potential Flooding in Delaware from 2 foot sea-level rise.

will depend on shoreline dynamics and wetland accretion,

as discussed below. About 22,000 acres would be inundated. The largest impact would be on emergent wetlands,

which account for less than 2 percent of Delaware’s area.

About 21 percent of this category would be inundated.

The next largest impacts on land use categories are much

smaller, but could be far more important economically.

For example, flooding would cover less than one percent

of land currently in high intensity developments, but would

be exacerbated by future development in coastal areas.

Coastal water temperatures track air temperatures, especially in shallow waters that have smaller volumes to be

heated or cooled and less influence from ocean circulation

and mixing. Warmer inland air may encourage more visits

and permanent residences at the shore, where warmer air

and water will extend the recreation season. Table 3 shows
31

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

Chapter 8. Ecosystems

The prominence of ecosystem discussions in the chapters above

reflects their importance as well as their cross-cutting nature.

Ecosystem services are “conditions and processes through

which natural ecosystems, and the species that make them

up, sustain and fulfill human life” (Daily 1997, p. 3). These

include actual life support functions, maintenance of

biodiversity, and production of goods (including aesthetics).

The land cover diversity shown in Figure 5 signals diversity

in the MAR’s ecosystems, which range from natural to

managed. The MAR’s forested, wetland, fresh water and

coastal ecosystems are on the natural side of the continuum;

its city, suburban and farm ecosystems are on the managed

side. This chapter highlights selected MAR ecosystems and

their sensitivity to climate change.

The Chapter 5 discussion emphasizes managing forests for

their products. The region’s forests also include rare

vegetation types, such as shrublands that provide crucial

habitat for wildlife and for endangered plant species.

Similarly, the coastal plain’s northern pine-oak forest

(known as pine barrens) form a unique ecosystem home to

rare plants and animals. The region’s public natural areas,

such as national and state parks and forests, provide

recreational opportunities and protected habitat for wildlife

species and communities. Current stresses on MAR forest

ecosystems include fragmentation, especially near urbanized

areas and on the Delmarva Peninsula (Jones et al. 1997);

pollutants such as acid rain (Likens et al. 1996); and

invasion of non-native organisms and insect pests

(Schlarbaum et al. 1999)

Chapter 7 describes coastal wetlands and Chapter 6

describes freshwater fish habitat. But neither chapter

considers fresh water wetlands and their important roles in

recycling nutrients, in providing crucial wildlife habitat,

and in removing pollutants from water (Hammer 1997,

National Research Council 1995). Some of these fresh

water wetlands are forested; others are marshes dominated

by plants such as cattails. Several unique and rare wetland

ecosystems are threatened in the MAR. Losses of lowland

evergreen shrub bogs and upland sphagnum bogs have

exceeded 85 percent in some MAR states, and more than

98 percent of the original stands of Atlantic white-cedar

swamp forest—such as those found in the Great Dismal

Swamp of VA and northern NC—have been destroyed

(Noss et al. 1995). Drainage is the major threat to fresh water

wetlands, but they also are affected by pollution (e.g., chemical

wastes) and non-native invasive species (e.g., purple loosestrife).

The MAR has substantial diversity in its freshwater fauna

which helps explain the importance of freshwater

ecosystems for recreation (as illustrated in Box 4). For

example, the number of native freshwater fishes ranges from

70 to 201 across the MAR states (Warren and Burr 1994).

The number of mussel species ranges from 12 to 80 across

the region’s states (Williams and Neves 1995). Like

terrestrial and wetland ecosystems, freshwater ecosystems

are stressed by habitat alteration, pollution, and non-native

invasive species. For example, the percentages of mussel

species at risk of extinction range from 46 percent to 71

percent in the MAR states.

Many ecosystem components are quite resilient, while others are very fragile. The survival, reproduction and the

geographic ranges of many plants and animals is linked to

climatic conditions (Root 1988, Shao and Halpin 1995,

Visser et al. 1998, Pitelka et al. 1997). Changes in CO2

concentration, temperature, precipitation and sea level in

the MAR will interact with other current and future stresses

on ecosystems and affect individual species differently.

While warmer temperatures, CO2

 fertilization, and more

precipitation will facilitate the growth of some (particularly plant) species, their expansion could come at the

expense of other species (e.g., Drake et al. 1996). The

complexity and interconnectedness of ecosystems makes it

difficult to predict impacts on particular species and ecosystem functioning.

Climate change might alter natural disturbance patterns,

such as the frequency of severe storms or fire. Rapid evolution might help some species with short generation times,

such as insects and annual plants, to adapt to environmental changes (Cronin and Schneider 1990). Adaptation will

be slower in long-lived species such as trees. Some MidAtlantic species might shift their geographic range by

invading more hospitable climates, such as the shifts in

forest types projected in Chapter 5. Species will shift their

geographic ranges at different rates; some will fail because

they cannot disperse fast enough to keep pace with change,

especially if landscape features such as cities block their

movement, or if suitable new habitats simply are not available (Pitelka et al. 1997). The Box 7 case study illustrates

potential species shifts for birds in the MAR.
34

Chapter 9. Human health

Chapter 8 stresses the cross-cutting importance of ecosystems. Another cross-cutting topic is the relationship between climate change and human health in the MAR.

Changes in the frequency and severity of weather events

could have direct effects on human physiology or psychology (WHO 1996). Climate change impacts on other biological or geophysical systems that influence human health

also could lead to indirect health effects. For example, climate change could influence the range and activity of insects or animals that transmit disease (affecting the incidence of vector-borne disease), the life cycles of pathogens,

the levels and biological effects of air pollutants, and the

productivity of food systems.

We focused this assessment on how projected climate

change would affect health through extreme events, heat

related mortality and selected vector/water/food borne diseases. We also initiated assessment of health impacts related to air quality and mental health. As is the case for

Chapters 4 through 8, our choices were guided by the input of MARA stakeholder groups, the climate model projections of climate change for the region, and results of

prior research.

Current Health Status and Stresses

The effects of climate change will occur against the backdrop of broader developments in health challenges and the

health system. Health is a complex function of many factors including behavioral choices (e.g., cigarette and alcohol consumption, diet, fitness); access to medical care (availability, quality and price of care as well as health insurance

status); medical technology; genetic endowment (predisposition to certain diseases); and characteristics of the natural environment and buildings in which people live, work

and play (Banta and Jonas 1996).

The health systems of the United States and other developed nations have experienced an incredible evolution over

the past 150 years, greatly improving the health status of

their citizens and the capacity to manage public health risks

(McKeown 1976). For example, the overall mortality rate

in 1900 was 1720 deaths per 100,000 people and life expectancy was 47.3 years. By 1990, that rate had dropped

to 860 and life expectancy had increased to 75.4 years

(Banta and Jonas 1996). These advances and other technological, institutional and economic advances have reduced vulnerability to climate-related health risks (Abler

et al. 2000). For instance, the relationship between mortality and extreme temperatures declined through the 20th

century (Larsen 1990). A century ago the predominant

health problems were epidemics of acute infections (Banta

and Jonas 1996). Today’s major health risks are chronic

diseases such as heart disease and cancer, which are strongly

related to personal behaviors. The shift in the sources of

health risks explains the changing emphasis in U.S. health

policy to issues such as access to health care (organizing

the health care system to reach vulnerable populations) and

promoting healthy behaviors (U.S. DHHS 1992).

The current health status of the MAR population is similar to that for the U.S. The four leading causes of death in

the region and the nation, which account for approximately

two thirds of all deaths, are heart disease, cancer, stroke

and lung disease (Kocagil et al. 1999). Climate can be an

aggravating or contributing factor, but genetic endowment

and behavioral choices (e.g., smoking, diet, fitness) are

major determinants of these causes of death (CDC 1999a,

CDC 1998).

Presently, acute health risks that are largely a function of

climate are not major determinants of the health status of

the MAR population. The Mid-Atlantic region has very

little mortality directly attributable to cold, heat, storms,

flooding or lightning. However, the region exhibits more

temperature-related mortality, particularly heat-related,

than other regions of the U.S. (Kalkstein and Greene,

1997). Examples include deaths from heart disease during

heat waves. While this is a matter of concern, heat- and

cold-related mortality rates are small compared to the leading causes of death (Chestnut et al. 1995).
39

MARA OVERVIEW 2000 • Planning for the 21st Century

The Mid-Atlantic Region’s economy is robust because it is

highly diversified, technologically advanced, and highly integrated with the rest of the United States and the world.

Our analysis shows that the MAR economy has relatively

little dependence on its climate-sensitive sectors. Even

within climate-sensitive sectors such as Agriculture and Forestry, impacts are moderated over time because buyers and

sellers within MAR as well as in the rest of the nation and

other countries can adapt to changes in availability of Agriculture and Forestry products.

These features make the MAR economy resilient to current climate variability. Along with anticipated

technological, institutional and behavioral adaptations,

these features also make the MAR economy reasonably resilient to projected future climate change and its impacts

on the region’s natural resources.

On the other hand, the MARA suggests that climate change

poses diverse and potentially large risks to the region’s ecosystems, which already show signs of stress for diverse

reasons. The 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 for recreational uses.

Recall that Table 4 shows that the MAR can expect both

positive and negative impacts from climate change. Despite efforts to identify as many positive impacts as possible,

results show that benefits tend to be fewer and smaller than

damages. Underestimating potential damages would be

more worrisome than underestimating potential benefits.

For benefits that are traded in markets, people and organizations will more-or-less automatically take advantage of

opportunities created by climate change, and thus experience the consequent improvement in well-being.

Assessment that identifies potential market benefits can spur

the market to work more quickly. By identifying potential

Planning for the 21st Century

nonmarket benefits, assessment can provide information

for pro-active decisions that enable society to reap those

benefits. Even more important, assessment can identify

potential damages over which individuals have little control, or that might be managed more effectively at a

community or regional level than by individual citizens or

firms. Identifying such risks can be a first step in evaluating options for reducing or adapting to them.

The impacts summarized in Table 4 will make some of the

region’s citizens and organizations better off while others

will be worse off. This uneveness in benefits and damages

or costs is referred to as distributional impacts. Distributional impacts merit special attention because small average

effects can mask impacts that are substantial for especially

vulnerable individuals, groups, communities, industries,

or subregions. These features—substantial overall resilience in concert with pressure on ecosystems and concern

about distributional impacts—summarize the region’s basis for taking advantage of new opportunities created by

climate change and for coping with negative impacts from

climate change.

Table 4 also shows differing levels of certainty about potential impacts. The more we know about the particular

climate change and its impacts, and the more such impacts are managed through existing institutions, the more

confident we can be that effective adaptation strategies can

be implemented now (Downing 1999). As uncertainty

increases, it becomes more difficult to determine whether

specific adaptive actions are warranted. Box 10 suggests

how many of the uncertainties described in earlier chapters combine to signal substantial potential climate threats

to the region’s urban areas. This complex topic needs additional research and assessment. Fortunately, however,

the MAR can reduce much of its vulnerability to climate

change by taking actions already justified for other reasons, as summarized in Chapters 10 and 11.
42

Chapter 10. What can we do

now?

MARA findings suggest win-win actions that have substantial benefits even if climate stays the same, plus a bonus

of making the region more resilient to climate change.

Many of these win-win strategies will be cost-effective even

in the absence of climate change but have not been high

on society’s agenda. It is desirable to consider such actions

sooner rather than later because they make sense even in

the face of substantial uncertainties in projecting global

climate change and its impacts on the MAR:

- Improve watershed management to reduce flood and

drought damages and protect water quality (in streams

and rivers, lakes and reservoirs, and ground water). For

example, many communities do not have a watershed

management plan even when the state requires one;

implementation of existing plans tends to be uneven,

too.

- Remove incentives for practices (e.g., that promote

building or subsidize agriculture in areas vulnerable to

erosion and flooding) that place people, investments,

and (especially coastal) ecosystems at greater risk to climate variability.

- Establish communication and learning tools and programs that help the region’s people identify how they

can capitalize on benefits and reduce damages from climate change.

The first two strategies would reduce risks from several

causes—including climate change. They imply actions

(such as preserving forests and wetlands, minimizing urban and agricultural runoff, protecting stream habitat and

reducing the release of toxic chemicals) that also reduce

ecosystem stresses; although less certain than the large

threats to the coastal zone, Table 4 indicates that threats to

ecosystems could be quite large.

We already know about actions that can implement many

of these strategies. One example is the weather warning

system which has been effective for reducing heat stress in

Philadelphia. Such demonstrations of effectiveness can be

implemented elsewhere in the MAR.

More specific recommendations are to:

- Identify where coastal protection options such as beach

nourishment, dikes or seawalls are cost-effective and

where allowing coastal retreat is more cost-effective;

prepare strategies for preventing or dealing with losses

(in wetlands, infrastructure) from sea-level rise.

- Improve water pricing to increase the efficiency of water

use.

- Give higher priority to implementing government programs that indirectly reduce vulnerability to climate

variability and change. Examples include the Safe

Drinking Water Act (SDWA) regulations, coastal zone

management plans, building codes, and land use planning.

- Foster forestry practices to encourage pine and oakhickory forests, including cutting to minimize wind and

ice damage, and monitoring for potential increases in

fire, insect pests and diseases that might be more prevalent under climate change conditions.

- Foster continued adaptation in agriculture, especially

for precision agriculture and biotechnology (if concerns

about unintended effects of biotechnology can be addressed).

- Monitor for the higher-risk climate-related disease vectors identified in the MAR.

The MARA team judges the first three of these specific

recommendations to be particularly important.
43

MARA OVERVIEW 2000 • Planning for the 21st Century

Chapter 11. What do we still

need to know?

People in the MAR can make better decisions related to

climate change if they know more about potential impacts

from climate variability and change and the effectiveness

of alternative actions. People tend to think of climate

change as happening slowly. Of particular concern, however, is the possibility that there could be major surprises

because of the many uncertainties about how the region’s

climate will change and how these changes will affect the

region’s citizens and ecosystems. Surprises can be defined

as rapid, non-linear responses (Watson et al. 1996). An

example surprise might be several extreme weather events

in a short time span, strong enough to reverse the direction of arrows in Table 4. Efforts to reduce vulnerability

are important when such surprises could create serious damages; Downing (1999) reinforces the need to monitor

impacts so that people—as individual stakeholders and as

members of organizations—will know when they need to

accelerate adaptation. Uncertainties about trends, variability, and surprises suggest that the most important

information and research needs are to:

- Improve projections for frequency, timing and intensity of average and extreme weather (especially

precipitation), at a regional level.

- Improve understanding of how average and extreme

weather affect agriculture, forests, fresh water quantity and quality, coastal zones, ecosystems, and human

health—including differences in the sensitivities and

vulnerabilities of these systems, and whether they have

climate-sensitive thresholds—and how adaptation

would moderate negative impacts and enhance positive impacts.

- Improve models to evaluate the benefits and costs of

alternative adaptation options, so that economic efficiency can be considered in management and policy

decisions. For example, such decisions range from insurance coverage versus structural “hardening” against

extreme events to land use restrictions versus subsidized changes in land use.

- Improve methods for evaluating how proposed shifts

in policy (e.g., health policy, land use policy, agricultural policy) might affect vulnerability to climate

variability and change.

More specific information needs include understanding:

- How changes in climate variables affect different ecosystems (with respect to how they function; what fragile

components might be affected by invasive species or by

changes in nutrient runoff; and how ecosystem changes

affect disease vectors).

- How to assign values to climate-related changes in ecosystem components and processes.

- How biophysical impacts from climate change, especially related to ecosystem processes, affect people in

different locations (e.g., rural versus urban, coastal versus inland) and with different characteristics (e.g., age,

income, education) through impacts on their health,

institutions, and other determinants of the quality of

life.

- How best to provide information about climate variability and change (i.e., what types of information, what

communication modes, what types of interaction strategies) so that diverse stakeholders can make more

informed choices about actions that affect future opportunities and vulnerabilities.

- How temperature and precipitation interact, and implications for evapotranspiration, as well as direct CO2

impacts on evapotranspiration.

- How climate change would affect environmental impacts from development patterns, agriculture and

silviculture, including water quality, landscape amenities, and carbon sequestration.

- How temperature and humidity affect the human immune system; how they interact with air pollution to

affect conditions such as asthma.
45

MARA OVERVIEW 2000 • References

References

Abler, D.G., Shortle, J.S., Rose, A., Oladosu, G. 2000. “Characterizing regional economic impacts and responses to

climate change.” Global and Planetary Change, in press.

Abler, D.G., Shortle, J.S., Nizeyimana, E., and Corradini, K. 1999. “Agriculture.” In Mid-Atlantic regional assessment

(MARA) draft preliminary report on impacts from climate change. The Pennsylvania State University: University Park,

PA. pp. 40-53.

Atherholt, T.B., LeChevallier, M.W., Norton, W.D., and Rosen, J.S. 1998. Journal of the American Water Works Association 90(9):66-80.

Banta, H.D., and Jonas, S. 1996. “Health and health care.” In Jonas’s Health Care Delivery in the United States, fifth

edition. Kovner, A.R. (ed). Springer: New York. pp. 11-33.

Baron, W.R. 1995. “Historical climate records from the northeastern United States, 1640 to 1900.” In Climate Since

A.D. 1500. Bradley, R.S., and Jones, P.D. (eds). Routledge, London. pp. 74-91.

Bazzaz, F.A. 1990. “The response of natural ecosystems to the rising global CO2

 levels.” Annual Review of Ecological

Systems 21:167-196.

Bird Conservation. 1997. “What’s a bird worth?” Bird Conservation, Spring Migration 1997. pp. 6-8.

Bricker-Urso, S., Nixon, S.W., Cochran, J.K., Hirschberg, D.J., and Hunt, C. 1989. “Accretion rates and sediment

accumulation in Rhode Island salt marshes.” Estuaries 12:300-317.

Brinson, 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.

Callaway, J.C., Nyman, J.A., and DeLaune, R.D. 1996. “Sediment accretion in coastal wetlands: A review and a simulation model of processes.” Current Topics in Wetland Biogeochemistry 2:2-23.

Carter, T.R., Parry, M.L., Harasawa, H., and Nishioka, S. 1994. IPCC Technical Guidelines for Assessing Climate

Change Impacts and Adaptations. CGER-I015-’94. University College: London.

CDC. 1999a. “Achievements in public health, 1900-1999: Decline in deaths from heart disease and stroke – United

States, 1900-1999.” MMWR 48(30):649-656.

CDC. 1999b. “Recommendations for the use of Lyme disease vaccine.” MMWR 48(RR-7):1-26.
55

MARA OVERVIEW 2000 • Appendices

Graduate Research Assistants

Jason Allard**

Tony Buda

Yiqing Cao**

Richard Caplan**

Bahar Celikkol**

Heejun Chang

Onelack Choi

Ken Corradini

Nate Currit

Kerri Dane**

Jan Dutton

Marta Galopin**

Jody Gibson

Gauri Guha

Matt Heberling

Hyun Jin**

Rob Neff

Steve Norman**

Debo Oladosu

Adetokunbo Oluwole**

Colin Polsky

Byeong-ok Song**

Bob Swanson

Robin (Lubing) Wang

Grant (Gwo Liang) Yang

Tao Zhu**

Core Penn State Faculty

Ann Fisher

David Abler

Eric Barron

Richard Bord

Robert Crane

David De Walle

C. Gregory Knight

Ray Najjar

Egide Nizeyimana

Robert O’Connor

Adam Rose

James Shortle

Brent Yarnal

Post-doctoral Scholars

and Research Staff

Patti Anderson**

Keith Benson**

Jeff Carmichael

Mary Easterling

Pat Kocagil

Shuang-Ye Wu

Appendices

Appendix A

Partners and Participants

An interdisciplinary Pennsylvania State University (Penn State) team is leading the first Mid-Atlantic Regional Assessment

(MARA) of Climate Change Impacts. The core team includes 13 faculty members, 6 post-doctoral or associate researchers,

26 graduate assistants, and 11 undergraduates or interns. The core team’s expertise is being expanded by substantive

collaboration with another 14 researchers at Penn State and other universities plus 3 at private organizations and 5 in

government. This entire group interacts frequently with the Advisory Committee, which provides input about what

questions are most important to a broad range of stakeholders in the region. The Advisory Committee also provides

feedback on draft assessment plans, approaches, and results. Interested stakeholders who cannot participate as fully are

Corresponding Advisors. Each of these groups is listed below. Interactions are described in Appendix C.

In addition, sections at the end of this appendix list MARA sponsors as well as primary authors for report chapters.
68

Mid-Atlantic Contribution to

the National Assessment on the Potential Consequences

of Climate Change for the United States

The overall goal of the National Assessment is to evaluate what is known about the potential consequences of

climate variability and change in the context of other pressures on the public, the environment, and the

Nation’s resources. The National Assessment process has been broadly inclusive, drawing on inputs from

academia, government, the public and private sectors, and interested citizens. Starting with broad public concerns about the environment, the Assessment is exploring the degree to which existing and future variations

and changes in climate might affect issues that people care about.

The National Assessment has three major components:

1. Regional analyses: Workshops and assessments are characterizing the potential consequences of climate variability and change in regions spanning the US. The reports from these activities address the interests of those in

the particular regions by focusing on the regional patterns and texture of changes where people live. Most

workshop reports are already available (see http://www.nacc.usgcrp.gov) and regional assessment reports of

which this is the first will become available over the next several months.

2. Sectoral analyses: Workshops and assessments are characterizing the potential consequences of climate variability and change for major sectors that cut across environmental, economic, and societal interests. The sectors

being focused on in this first phase of the ongoing National Assessment include Agriculture, Forests, Human

Health, Water, and Coastal Areas and Marine Resources. Publications and assessment reports started to become

available in late 1999.

3. National overview: The National Assessment Synthesis Team is summarizing and integrating the findings of

the regional and sectoral studies with the broader literature, and then drawing conclusions about the importance of climate change and variability for the United States. Their report is to be available in 2000.

Each of the regional, sectoral, and synthesis activities is being led by a team comprised of experts from both

the public and private sectors, from universities and government, and from the spectrum of stakeholder communities.

Mid-Atlantic Regional Assessment

The Pennsylvania State University

107 Armsby Building

University Park, PA 16802-5600
56

Undergraduates/Interns

Matt Balazik

Brette Bornstein

Andrea Denny**

Katie Filbert**

Eric Houston**

Loan Le**

Brian Schorr**

Andrea Soltysik

Eric Steele

Melanie Swartz

Marisa Trenkle

** indicates finished working

 on MARA by 7/31/99

Collaborators

Dennis Calvin

Entomology Department

Penn State University

William Easterling

Earth Systems Science Center

Penn State University

Donald Epp

Dept. of Agricultural Economics & Rural Sociology

Penn State University

Paola Ferreri

School of Forest Resources

Penn State University

Louis Iverson

Forest Service

Hoyt Johnson

Prescott College

Laurence Kalkstein

University of Delaware

Victor Kennedy

Horn Point Lab

University of Maryland

Dan Knievel

Agronomy Department

Penn State University

John McCarty

AAAS Fellow,

Environmental Protection Agency

J. Patrick Megonigal

George Mason University

Wilson Orr

Prescott College

Jonathan Patz

Johns Hopkins University School of Public Health

Roger Pielke, Jr.

National Center for Atmospheric Research

Jeff Price

American Bird Conservancy

Norbert Psuty

Department of Marine and Coastal Sciences

Rutgers University

Bruce Richards

Center for Inland Bays

Catriona Rogers

Office of Research and Development

Environmental Protection Agency

Lisa Sorenson

Boston University

Kent Thornton

FTN Associates

Henry Walker

Office of Research and Development

Environmental Protection Agency

Bud Ward

Environmental Health Center

National Safety Council
57

MARA OVERVIEW 2000 • Appendices

MARA Advisory Committee Members

William Adams

PA Farm Bureau

John Balbus

Department of Environmental & Occupational Health

George Washington University

Christopher Ball

Ozone Action

Timothy Banfield

Allegheny Power

Maria Bechis

Sierra Club

Richard Birdsey

USDA Forest Service

Perry Bissell

Consul, Inc.

Janine Bloomfield

Environmental Defense Fund

Barbara Blonder

NC Division of Coastal Management

Irene Brooks

Office for River Basin Cooperation

PA Department of Environmental Protection

Donald Brown

Senior Counsel for Sustainable Development

PA Department of Environmental Protection

Claire Buchanan

Interstate Commision on the Potomac River Basin

Arthur Butt

VA Department of Environmental Quality

Michael Calaban

NY Department of Environmental Control

Charles Carson

U.S. Steel

Lynne Carter

National Assessment Coordination Office

Peter Colket

American Reinsurance Co.

Betty Connor

PA League of Women Voters

Thomas Cronin

U.S. Geological Survey

Thomas DeMoss

EPA Mid-Atlantic Integrated Assessment Team

Guy Donaldson

PA Farm Bureau

Gerald Esposito

Tidewater Utilities

Barry Evans

Environmental Resources Research Institute

Penn State University

John Falconer

American Forests (through 4/99)

Jeffrey Featherstone

Delaware River Basin Commission

Agnes Flemming

Norfolk Department of Public Health

Stuart Freudberg

Washington Area Council of Governments

Richard Fromuth

Delaware River Basin Commission

Hector Galbraith

Stratus Consulting

Donald Garvin

Trout Unlimited

Phyllis Gilbert

Sierra Club
58

Caren Glotfelty

ClearWater Conservancy

Mark Handcock

Department of Statistics

Penn State University

Gil Hirschel

Susquehanna River Basin Commission

Joseph Hoffman

Interstate Commission on the Potomac River Basin

Richard Janoso

PP&L

Jan Jarrett

PA Campaign for Clean Affordable Energy

Zoe Johnson

Coastal Zone Management Division

MD Department of Natural Resources

Marshall Kaiser

Safe Harbor Water Power Corp.

Alliance for the Chesapeake Bay

John Kauffman

Alliance for the Chesapeake Bay

Howard Kunreuther

University of Pennsylvania (through 2/99)

Jon Kusler

Association of State Wetland Managers

Ray Lassiter

National Environmental Research Lab

Environmental Protection Agency

Daniel Leathers

DE Climatologist

Robert Leipold

American Forests

Ed Linky

Region 2

Environmental Protection Agency

James Lynch

School of Forest Resources

Penn State University

John MacSparran

Susquehanna River Basin Commission

Adam Markham

World Wildlife Fund

Stephen Matthews

Population Research Institute

Penn State University

Linda Mortsch

Environment Canada

Stuart Nagourney

NJ Department of Environmental Protection

George Nichols

Washington Area Council of Governments

Albert Nunez

ICLEI

Sam Pearsall

The Nature Conservancy (through 4/99)

Michele Pena

Climate Institute

Robert Penn

Vanguard Management Group

Gary Petersen

Penn State University

Lou Pitelka

Center for Environmental Science

University of Maryland

Jon Plaut

NAFTA Environmental Commission

Sethu Raman

NC Climatologist

Lynn Ratzell

PP&L environmental manager
59

MARA OVERVIEW 2000 • Appendices

Sharon Ross

Allegheny Power

Joel Rotz

PA Farm Bureau

Ralph Rudd

ClearWater Conservancy

Joel Scheraga

Environmental Protection Agency

Michael Schmidt

CIGNA

Gwynne Schultz

Coastal Zone Management Division

MD Department of Natural Resources

David Schwarzwaelder

Columbia Gas

Dick Shafer

College of Health & Human Development

Penn State University

David Small

DE Dept. of Natural Resources and

Environmental Control

Betsy Smith

National Environmental Research Lab

Environmental Protection Agency

Jack Stevens

Professor of Management,

Penn State University

Ann Swanson

Chesapeake Bay Commission (MD)

Eric Walbeck

EPA Mid-Atlantic Integrated Assessment Team

Brooks Way

Way Fruit Farm

Fred Wertz

PA Department of Agriculture

Thomas Wilbanks

Oak Ridge National Lab/NCEDR

Corresponding Advisors

Chris Bernabo

RAND Corporation

Karl Blankenship

Bay Journal

Doug Burns

U.S. Geological Survey

Joanne Denworth

10,000 Friends of PA

Tom Falke

Coal industry representative

David Friedman

American Forest and Paper Association

Annette Goldberg

PA Economy League (through 2/99)

Vivien Gornitz

NASA Goddard Institute for Space Studies

Columbia University

Diane Herkness

CIGNA

Scott Hunter

Philadelphia Energy Coordinating Agency

Arch McDonnell

Environmental Resources Research Institute

Penn State University

Hugh McKinnon

National Risk Management Research Lab

Environmental Protection Agency

Edward Mongan

DuPont

Nancy Parks

Sierra Club
60

Joshua Reichert

Pew Charitable Trusts

Robert Shinn

NJ Department of Environmental Protection

Larry Simns

MD Watermen’s Association

Michael Slimak

Office of Research and Development

Environmental Protection Agency

Joel Smith

Stratus Consulting

Lawrence Tropea

AMP Corporation

Christophe Tulou

DE Dept. of Natural Resources and

Environmental Control

Melanie Wertz

The Chesapeake Bay Foundation

James Winebrake

James Madison University

Sponsors and Institutional Support

MARA has received financial support from the U.S. 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 U.S.

EPA and U.S. 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 the National Assessment Synthesis Team (NAST), the National Assessment Coordinating

Office (NACO) and the National Assessment Working

Group (NAWG).

Primary Authors

Preparing this report truly has been a team effort.

Nevertheless, the enormity of the assessment task dictated

that we share writing responsibilities on the basis of

expertise. Thus primary authors are indicated below, by

report section.

Chapter 1: Fisher, Shortle, Knight

Chapter 2: Polsky, Rose, Yarnal

Box 1: Fisher

Box 2: Yarnal

Chapter 3: Polsky, Crane, Allard, Abler, Fisher,

Shortle, Najjar

Chapter 4: Abler, Shortle

Chapter 5: DeWalle, Easterling, Rose, Buda,

Iverson, Prasad

Chapter 6: Yarnal, Neff

Box 3: O’Connor

Box 4: Thornton, Heberling

Chapter 7: Najjar, Anderson, Knight, Walker,

Megonigal, Psuty, Kennedy, Swanson,

Gibson, Steele

Box 5: Fisher, Choi

Box 6: Sorenson

Chapter 8: Rogers, McCarty

Box 7: Price

Chapter 9: Benson, Kocagil, Shortle

Box 8: Fisher, Shortle, Kocagil

Box 9: Kocagil

Box 10: Knight

Chapter 10: Entire MARA Team

Chapter 11: Entire MARA Team
61

MARA OVERVIEW 2000 • Appendices

The influence of climate permeates life and lifestyles in the

US. Year-to-year variations are reflected in such things as

the number and intensity of storms, the amount of water

flowing in our rivers, the extent and duration of snow cover,

and the intensity of waves that strike our coastal regions.

Science now suggests that human activities are causing the

climate to change. Although the details are still hazy about

how much the changes will be in each region of the country, changes are starting to become evident. Temperatures

have increased in many areas, snow cover is not lasting as

long in the spring, and total precipitation is increasing with

more rainfall occurring in intense downpours. These

changes appear to be affecting plants and wildlife. There

is evidence of a longer growing season in northern areas

and changing ranges for butterflies and other species. The

international assessments of the Intergovernmental Panel

on Climate Change (http://www.ipcc.ch) project that these

changes will increase over the next 100 years.

The Global Change Research Act of 1990 [Public Law 101-

606] gave voice to early scientific findings that human activities were starting to change the global climate: “(1) Industrial, agricultural, and other human activities, coupled

with an expanding world population, are contributing to

processes of global change that may significantly alter the

Earth habitat within a few generations; (2) Such humaninduced changes, in conjunction with natural fluctuations,

may lead to significant global warming and thus alter world

climate patterns and increase global sea levels. Over the

next century, these consequences could adversely affect

world agricultural and marine production, coastal habitability, biological diversity, human health, and global economic and social well-being.”

To address these issues, Congress established the U.S. Global Change Research Program (USGCRP) and instructed

the Federal research agencies to cooperate in developing

and coordinating a “comprehensive and integrated United

States research program which will assist the Nation and

the world to understand, assess, predict, and respond to

human-induced and natural process of global change.”

Further, the Congress mandated that the USGCRP

“shall prepare and submit to the President and the

Congress an assessment which

1) integrates, evaluates, and interprets the

findings of the Program and discusses the

scientific uncertainties associated with such

findings;

2) analyzes the effects of global change on

the natural environment, agriculture,

energy production and use, land and water

resources, transportation, human health

and welfare, human social systems, and

biological diversity; and

3) analyzes current trends in global change,

both human-induced and natural, and

projects major trends for the subsequent 25

to 100 years.”

The USGCRP’s National Assessment of the Potential Consequences of Climate Variability and Change, which is focused on answering the question about why we should care

about and how we might effectively prepare for climate

variability and change, is being conducted under the provisions of this Act.

The overall goal of the National Assessment is to analyze

and evaluate what is known about the potential consequences of climate variability and change for the Nation

in the context of other pressures on the public, the environment, and the Nation’s resources. The National Assessment process has been broadly inclusive, drawing on

inputs from academia, government, the public and private

sectors, and interested citizens. Starting with public concerns about the environment, the Assessment is exploring

the degree to which existing and future variations and

Appendix B

National Assessment of the Consequences of Climate Variability

and Change for the United States

Prepared by Michael MacCracken, National Assessment Coordination Office, 10/5/99
62

changes in climate might affect issues that people care about.

A short list of questions has guided the process as the Assessment has focused closely on regional concerns around

the US and national concerns for particular sectors:

• What are the current environmental stresses and

issues that form the backdrop for potential additional impacts of climate change?

• How might climate variability and change exacerbate or ameliorate existing problems? What new

problems and issues might arise?

• What are the priority research and information

needs that can better prepare the public and policy

makers for reaching informed decisions related to

climate variability and change? What research is

most important to complete over the short term?

Over the long term?

• What coping options exist that can build resilience

to current environmental stresses, and also possibly lessen the impacts of climate change?

The National Assessment has three major components:

1. Regional analyses: Regional workshops and assessments are characterizing the potential consequences of climate variability and change in regions

spanning the US. A total of 20 workshops were

held around the country, with the Native Peoples/

Native Homelands workshop being national in

scope rather than regional; to date, 16 of these

groups are preparing assessment reports. The reports from these activities address the interests of

those in the particular regions by focusing on the

regional patterns and texture of changes where

people live. Most workshop reports are already

available (see http://www.nacc.usgcrp.gov) and

assessment reports started to become available in

late 1999.

2. Sectoral analyses: Workshops and assessments are

being carried out to characterize the potential consequences of climate variability and change for major sectors that cut across environmental, economic

and societal interests. The sectoral studies analyze

how the consequences in each region affect the

Nation, making these reports national in scope and

of interest to everyone. The sectors being focused

on in this first phase of the ongoing National Assessment include Agriculture, Forests, Human

Health, Water, and Coastal Areas and Marine Resources. Assessment reports started to become

available in late 1999.

3. National overview: The National Assessment Synthesis Team has responsibility for summarizing and

integrating the findings of the regional and sectoral

studies and then drawing conclusions about the

importance of climate change and variability for

the United States. Their report is to be available

by Spring 2000.

Each of the regional, sectoral, and synthesis activities is

being led by a team comprised of experts from both the

public and private sectors, from universities and government, and from the spectrum of stakeholder communities. All their reports have gone through an extensive review process involving experts and other interested stakeholders. The assessment process is supported in a shared

manner by the set of USGCRP agencies, including the

departments of Agriculture, Commerce (National Oceanic

and Atmospheric Administration), Energy, Health and

Human Services, and Interior plus the Environmental Protection Agency, National Aeronautics and Space Administration, and the National Science Foundation. Through

this involvement, the USGCRP is hopeful that broad understanding of the issue and its importance for the Nation

will be gained and that the full range of perspectives about

how best to respond will be aired.

Extensive information about the assessment, participants

on the various assessment teams and groups, and links to

the activities of the various regions and sectors are available over the Web at http://www.nacc.usgcrp.gov or by

inquiry to the Global Change Research Information Office, PO Box 1000, 61 Route 9W, Palisades, New York

10964.
63

MARA OVERVIEW 2000 • Appendices

Appendix C

Stakeholder participation in the MARA process

Penn State’s early steps included a September 1997

Workshop focusing on the watersheds for the Chesapeake

and Delaware Bays (Fisher et al. 1999a). The workshop’s

purpose was to develop an initial formulation of “the

problem,” to identify what types of regional impacts a

diverse group of climate experts and regional stakeholders

thought might be related to climate variability and change.

The 92 participants, representing federal, state and local

government, industry, academia, and public interest groups,

reported learning 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.

A June 8-9, 1998 researchers’ meeting explored questions

raised during the September 1997 workshop, particularly

related to concerns stakeholders raised about potential

effects, 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.

These researchers are one component of the MARA

Advisory Committee. Researchers however, are hardly 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 varied experiences, including

representatives 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.

Members are listed in Appendix A.

The Advisory Committee met for 2 days each in October

1998 and May 1999. The October meeting focused on

identifying climate-related issues important to MAR

residents, while the May meeting reviewed the treatment

of these and other issues in the Draft Preliminary Report

(Fisher et al. 1999b).

A number of individuals have wanted to help with the

assessment, but were 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, often in response to postings on the

MARA web site: www.essc.psu.edu/mara. They are

Corresponding members of the Advisory Committee.

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 reviewed chapter outlines and

scenarios used in writing the report.
64

· At completion of draft assessment reports,

the Committee reviews documents and

suggests improvements.

· Members are advising the MARA team

regarding ways to disseminate the results

in the region.

The stakeholders helped 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

also are offering 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, twoway, 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.

For example, stakeholders have been invited to suggest

agenda items for meetings and to bring case study examples

to share with the MARA team as well as the full Advisory

Committee. As planning progressed for assessment

activities, draft outlines of plans and scenarios were sent

for their input. They also have been asked for feedback on

draft chapters and journal manuscripts. The Draft

Preliminary Report (Fisher et al. 1999b) and the earlier draft

of this Overview were circulated widely for review. Their

comments on the Draft Preliminary Report are being used

to prepare the Foundations Report, which has more detail

than could be included in this overview (and expected to

be available by July 2000). This Overview is much stronger

because of the more than 40 sets of comments provided by

stakeholders. Documentation of the MARA team’s

responses to those comments appears on the web site:

www.essc.psu.edu/mara/.

One of the goals of the National Assessment process has

been to involve stakeholders as actual assessors. This has

been difficult to accomplish as fully as hoped in the MARA,

partly because of the newness of such an approach (which

made it more difficult at the “front end” to set up an

effective involvement structure), and partly because of the

tight timeframes for the initial assessment. Even so, the

MARA team has been delighted by the shift of several

Advisory Committee members to the collaborator list.

Some of these collaborators are extensively involved in the

assessment, as indicated by the Appendix A list of primary

authors. The MARA team expects additional substantive

stakeholder collaborations during the continuing

assessment activities. An example emphasizing ecosystems

is described at the end of Chapter 8. Another example is

the work on implications of climate variability and change

for the insurance industry. Without stakeholder

collaboration that provides access to proprietary models

and data, the assessment of these implications would be

much less complete.
65

MARA OVERVIEW 2000 • Appendices

Appendix D

Glossary

algae: Plants having no true root, stem or leaf, including seaweeds and pond scum.

anoxia: Without oxygen.

aquatic: Living or growing in fresh water (in contrast

with marine organisms found in salt water).

atmospheric: In the air surrounding the Earth.

benthic: Bottom dwelling aquatic or marine organisms.

biodiversity: The range of organisms present in an ecosystem. Biodiversity can be measured by the numbers

and types of different species, or the genetic variations

within and between species.

CCC model: Canadian Climate Centre global climate

model.

Cryptosporidiosis: Illness with diarrhea as the main

symptom, caused by tiny cysts transmitted from animal

or human feces through contaminated water.

downscaling: Reducing the scale of the model from

global to regional level.

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, causing them to grow very quickly. This can

block sunlight from larger plants growing underwater

and deplete dissolved oxygen.

evapotranspiration: Loss of water from the soil both

by evaporation and by transpiration from trees and other

plants.

fauna: Animal life, especially the animals found in a

particular region.

flora: Plant life or vegetation of a region.

fragmentation: Occurs when habitat is split by changing

land use, leaving isolated pockets of the original habitat.

GENESIS model: Global climate model.

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.

Hadley model: Global climate model developed by

Hadley Centre for Climate Prediction and Research in

Great Britain.

hydrology: Properties, distribution and circulation of

water on the surface of the land, in the soil and underlying rocks, and in the atmosphere.

invasive species: Species that grow aggressively in an

area and stifle pre-existing species.

non-point source: Dispersed emitters of pollutants (such

as farms, automobiles, or city streets).
66

non-target species: Species not intended for particular

treatment (e.g., by pesticides aimed at other species) that

could be affected by the treatment.

nutrient loading: The amount of nutrients in a water

body.

passerine: Birds of the order Passeriformes, including

perching birds and warblers such as sparrows, finches,

and jays.

pathogen: Any microorganism or virus that can cause

disease.

phytoplankton: Microscopic plants that float in aquatic

or marine environments (fresh or salty water).

point source: A specific location (such as an effluent

pipe or a smokestack) that discharges pollutants into the

environment.

precision agriculture: Incorporates advanced remote

sensing, computer, and information technologies in order to achieve very precise control over agricultural input applications (chemicals, fertilizers, seeds, etc.) so that

farmers can compensate for small-scale variations within

a farm field in soil nutrients and crop pests.

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.

riparian: Along the bank of a river or stream, or sometimes of a lake or a tidewater.

rolling easement: The right of access to waterfront,

which “rolls” back as the beach is flooded or eroded so

as to maintain the same access distance from the water.

SIC level: Standard Industrial Classification of economic activities. A one-digit SIC is the most aggregated;

very detailed information is available at the disaggregated 4-digit SIC level.

silviculture, silvicultural: Development and care of

forests.

sphagnum bog: Wet, acid area where mosses grow;

their remains become compacted with other plant debris

and eventually form peat.

stakeholder: Potentially affected or interested person.

subsidence: Lowering of land elevation. Such sinking

can be caused by groundwater withdrawals or by longterm settling of the Earth’s crust.

sulfate aerosols: A suspension of fine particles in the

air, containing sulfates. These suspensions act like

clouds, making the Earth’s surface cooler.

transpiration: Evaporation from plant foliage.

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.

vector: An organism such as a mosquito or tick that

transmits disease from infected individuals or animals

to humans.

watershed: The drainage basin for a particular watercourse or body of water. Watershed scales range from

that for a small pond to much larger regions such as the

Chesapeake Bay drainage basin.
67

MARA OVERVIEW 2000 • Appendices

Appendix E

Acronyms and Abbreviations

CBP: Chesapeake Bay Program (EPA)

CCC: Canadian Climate Centre (global climate model)

CIRA: Center for Integrated Regional Assessment (PSU)

CO2

: carbon dioxide (a greenhouse gas)

EFS: Environmentally Friendly and Smaller

EPA: U.S. Environmental Protection Agency

GCM: General circulation model; also global climate

 model

GENESIS: a global climate model

GHG: Greenhouse Gas(es)

GIS: Geographic Information System

HADLEY: Hadley Centre global climate model

IPCC: Intergovernmental Panel on Climate Change

m: meter (39.37 inches)

MAHA: Mid-Atlantic (Mid-Appalachians) Highlands

 Assessment

MAIA: Mid-Atlantic Integrated Assessment (EPA)

MAR: Mid-Atlantic region

MARA: Mid-Atlantic Regional Assessment

NACO: National Assessment Coordination Office

NAST: National Assessment Synthesis Team

NAWG: National Assessment Working Group

NFIP: National Flood Insurance Program

NPA: NPA Data Services, Inc.

Penn State (PSU): Pennsylvania State University

PET: potential evapotranspiration

PPR: Prairie Pothole Region

ppt: parts per thousand

SAV: submerged aquatic vegetation

SIC: Standard Industrial Classification

SLR: sea-level rise

SQ: status quo

SRB: Susquehanna River Basin

SDWA: Safe Drinking Water Act

USDA: U.S. Department of Agriculture

USGCRP: U.S. Global Change Research Program

USGS: U.S. Geological Survey

VEMAP: Vegetation/Ecosystem Modeling and

 Analysis Project
46

CDC. 1998. Chronic Diseases and Their Risk Factors: The Nation’s Leading Causes of Death. Center for Disease Control:

Atlanta.

Chestnut, L.G., Breffle, W.S., Smith, J.B., and Kalkstein, L.S. 1998. “Analysis of differences in hot-weather-related

mortality across 44 U.S. metropolitan areas.” Environmental Sciences Policy 00:1-12.

Chestnut, L.G., Breffle, W.S., and Smith, J.B. 1995. “Estimates of willingness to pay for potential changes in risks of

mortality due to climate change.” Final Report funded under US EPA Cooperative Agreement No. CR-817693.

Collier, C.G., and Hardaker, P.J. 1995. “Weather, air quality and health.” Meteorological Applications 2:313-322.

Colwell, R., Epstein, P., Gubler, D., Hall, M., Reiter, P., Shukla, J., Sprigg, W., Takafuji, E., and Trtanj, J. 1998. “Global

climate change and infectious diseases.” Emerging Infectious Diseases 4(3):451-452.

Comrie, A.C.. 1994. “Tracking ozone: air-mass trajectories and pollutant source regions influencing ozone in Pennsylvania forests.” Annals of the Association of American Geographers 84:635-651.

Cook, E.R., and Jacoby, G.C. 1983. “Potomac River streamflow since 1730 as reconstructed from tree rings.” Journal of

Climate and Applied Meteorology 22:1659-1672.

Cook, E.R., and Jacoby, G.C. 1977. “Tree-ring drought relationships in the Hudson Valley, New York.” Science 198:399-

401.

Crane, R.G., and Hewitson, B.C. 1998. “Doubled CO2

 precipitation changes for the Susquehanna basin: down-scaling

from the GENESIS general circulation model.” International Journal of Climatology, 18:65-76.

Cronin, T., Willard, D., Karlsen, A., Ishman, S., Verardo, S., McGeehin, J., Kerhin, R., Holmes, C., Coleman, S., and

Zimmerman, A. 2000. “Climatic variability in the eastern United States over the past millenium from Chesapeake Bay

sediments.” Geology, in press.

Cronin, T. 1997. “Perspectives on current impacts and adaptation to climate variability in the Mid-Atlantic region.” Oral

presentation at the Workshop on Climate Change Impacts in the Mid-Atlantic Region. The Pennsylvania State University, University Park, PA, September 9-11.

Cronin, T., and Schneider, C.E. 1990. “Climatic influences on species: Evidence from the fossil record.” Trends in Ecology

and Evolution 5:275-279.

Cuff, D.J., Young, W.J., Muller, E.K., Zelinsky, W., and Abler, R.F. (eds). 1989. The Atlas of Pennsylvania. Temple

University Press: Philadelphia.

Daily, G.C. (ed). 1997. Nature’s Services: Societal Dependence on Natural Ecosystems. Island Press: Washington, DC.

Davies, J.D., and Mazurek, J. 1998. Pollution control in the United States. Evaluating the system. Resources for the

Future: Washington, DC. pp. 319.
47

MARA OVERVIEW 2000 • References

Downing, T.E. 1999. “Confidence and speculation in the assessment of climate change impacts.” Presented to the UNFCCC

Workshop, Bonn, Germany. September 21-24.

Drake, B.G., Peresta, G., Beugeling, E., Matamala, R. 1996. “Long-term elevated CO2

 exposure in a Chesapeake Bay

wetland: Ecosystem gas exchange, primary production, and tissue nitrogen.” In Koch, G.W., Mooney, H.A. (eds). Carbon Dioxide and Terrestrial Ecosystems. Academic Press: San Diego, CA. pp. 197-214.

Dunne, T., and Leopold, L.B. 1978. Water in Environmental Planning. W. H. Freeman: San Francisco.

Eamus, D. 1996. “Responses to field grown trees to CO2

 enrichment.” Commonwealth Forestry Review 75:39-47.

Erwin, R.M. 1999. Atlantic sea level rise, lagoonal marsh loss, and wildlife habitat implications. URL: http://

www.pwrc.usgs.gov/resshow/erwin1rs/erwin1rs.htm.

Faucett Associates. 1998. “The economic effects of a five year nourishment program for the ocean beaches of Delaware.”

Report for The Beach Replenishment Task Force, State of Delaware.

Felzer, B., Fisher, H., Rosenbloom, N., Kittel, T., and Schimel, D. 1999. “VEMAP Phase 2 future climatology for the

coterminus U.S. (CGCM1 and HADCM2).” Unpublished manuscript prepared for the U.S. National Climate Assessment.

Fine, A., Layton, M., Miller, J., Cimini, D., et al. 1999. “Update: west Nile virus encephalitis—New York, 1999.”

MMWR 48(41):944-946, 955.

Fisher, A., et al. 1999a. “Climate Change Impacts in the Mid-Atlantic - A Workshop Report.” Report to the US EPA,

Office of Research and Development, Global Change Research Program, (Coop. Agreement No. CR 826554-01) and

Office of Policy, (Coop. Agreement No. CR 824369-01): Washington, DC.

Fisher, A., et al. 1999b. Mid-Atlantic regional assessment (MARA) draft preliminary report on impacts from climate

change. The Pennsylvania State University: University Park, PA.

Gibson, J. 1999. “Modeling Chesapeake Bay salinity and phytoplankton dynamics in response to varying climate.”

Master’s thesis, The Pennsylvania State University: University Park, PA.

Glass, G.E. 1995. “Environmental risk factors for Lyme disease identified with Geographic Information Systems.” American

Journal of Public Health 85(7):944-948.

Grayson, M. 1998. “Report offers a glimpse into the future of health care.” Spectrum: The Journal of State Government

71(2):1-3.

Grey, G.W. 1996. The Urban Forest: Comprehensive Management. Wiley: New York.

Grunwald, M. 1999. “Fixing the beach, for $60 million a mile.” Washington Post, April 19, 1999. pp. A3.
48

Guinnup, D., and Collom, B. 1997. Final Report, Vol. I: Executive Summary OTAG Air Quality Analysis Workgroup. URL:

http://capita.wustl.edu/otag/reports/aqafinvol_I/animations/v1_exsumanimb.html.

Haines, A., and McMichael, A.J. 1997. “Climate change and health: Implications for research, monitoring and policy.”

British Medical Journal 315(7112):870-874.

Hammer, D.A. 1997. Creating freshwater wetlands, second edition. CRC Lewis Publishers: Boca Raton, FL.

Haramis, G.M. 1991a. “Canvasback.” In Funderbunk, S.L., Jordan, S.J., Mihursky, J.A., and Riley, D. (eds). Habitat

Requirements for Chesapeake Bay Living Resources, second edition. Chesapeake Research Consortium, Inc.: Solomons,

MD.

Haramis, G.M. 1991b. “Redhead.” In Funderbunk, S.L., Jordan, S.J., Mihursky, J.A., and Riley, D. (eds). Habitat

Requirements for Chesapeake Bay Living Resources, second edition. Chesapeake Research Consortium, Inc.: Solomons,

MD.

Heberling, M., Lazo, J.K., Wheeler, W.J., Fisher, A.N., and Epp, D.J. 1999. “Measuring the economic benefits of preserving fish habitat and population.” Department of Agricultural Economics and Rural Sociology Working Paper. The

Pennsylvania State University: University Park, PA.

Hewitson, B.C., and Crane, R.G. 1996. “Climate downscaling: techniques and application.” Climate Research 7:85-95.

Hurley, L.M. 1991. “Submerged Aquatic Vegetation.” In Funderbunk, S.L., Jordan, S.J., Mihursky, J.A., and Riley, D.

(eds). Habitat Requirements for Chesapeake Bay Living Resources, second edition. Chesapeake Research Consortium,

Inc.: Solomons, MD.

(IMPLAN) Impact Analysis for Planning System. 1997. Minnesota IMPLAN Group: Stilwater, MN.

Iverson, L. R. and Prasad, A. M. 1998. “Predicting abundance of 80 tree species following climate change in the Eastern

United States.” Ecological Monographs 68(4): 465-485.

Jenkins, G.S., and Barron, E.J. 1997. “Global climate model and coupled regional climate model simulations over the

eastern United States: GENESIS and RegCM2 simulations.” Global and Planetary Change 15:3-32.

Jones, C.G., Ostfeld, R.S., Richard, M.P., Schauber, E.M., and Wolff, J.O. 1998. “Chain reactions linking acorns to

gypsy moth outbreaks and Lyme disease risk.” Science 279(5353):1023-1026.

Jones, K.B., et al. 1997. An Ecological Assessment of the United States Mid-Atlantic Region: A Landscape Atlas. US

EPA, Office of Research and Development: Washington, DC. Document #EPA/600/R-97/130.

Kajander, J. 1996. “Future trends in the health care economy.” Journal of Health Care Finance 23(1):17-22.

Kalkstein, L.S., and Greene, J.S. 1997. “An evaluation of climate/mortality relationships in large U.S. cities and the

possible impacts of climate change.” Environmental Health Perspectives 105(1):84-93.
49

MARA OVERVIEW 2000 • References

Kalkstein, L.S., and Swift, J.J. 1998. “An evaluation of climate/mortality relationships in the Mid-Atlantic region and the

possible impacts of climate change.” University of Delaware. Prepared for Mid-Atlantic Regional Assessment. pp. 25.

Karl, T.R., Knight, R.W., Easterling, D.R., and Quayle, R.G. 1996. “Indices of climate change for the United States.”

Bulletin of the American Meteorological Society 77: 279-292.

Kearney, M.S., and Stevenson, J.C. 1991. “Island land loss and marsh vertical accretion rate evidence for historical sealevel changes in Chesapeake Bay.” Journal of Coastal Research 7:403-415.

Kellogg, R. L., Nehring, R., Grube, A., Plotkin, S., Goss, D. W., and Wallace, S. 1999. “Trends in the Potential for

Environmental Risk from Pesticide Loss from Farm Fields.” Conference Presentation at The State of North America’s

Private Land, Chicago, Illinois.

Kellogg, R. L., Wallace, S., Alt, K., and Goss, D.W. 1997. “Potential Priority Watersheds for Protection of Water Quality

from Nonpoint Sources Related to Agriculture.” Presentation at 52nd Annual Soil and Water Conservation Society Conference, Toronto, Ontario.

Knight, C.G., et al. 1999. “The CIRA Framework for integrated regional assessment.” Appendix A, pp. 45-48 in Progress

Report, Center for Integrated Regional Assessment. University Park, PA. March 1. URL: http://www.essc.psu.edu/cira/

progress99/1999 Report - Appendix A.pdf. (Accessed 1/27/2000.)

Kocagil, P., Benson, K., Shortle, J.S., and Wang, L. 1999. “Impacts of Climate Change on Human Health in the MidAtlantic Region.” Department of Agricultural Economics and Rural Sociology Working Paper. The Pennsylvania State

University: University Park, PA.

Kunreuther, H., et al. 1999. “The hidden costs of coastal hazards: Implications for risk assessment and mitigation.” The

H. John Heinz III Center for Science, Economics and the Environment: Washington, DC. Pre-publication report.

Larsen, U. 1990. “The effects of monthly temperature fluctuations on mortality in the United States from 1921 to

1985.” International Journal of Biometeorology. 34:136-145.

Likens, G.E., Driscoll, C.T., and Buso, D.C. 1996. “Long-term effects of acid rain: Response and recovery of a forest

ecosystem.” Science 272:244-246.

Loeb, P.D. 1985. “The efficacy and cost effectiveness of motor vehicle inspection using cross-sectional data.” Southern

Economic Journal 52(2):500-509.

Logue, J.N., Hansen, H., and Struening, E. 1979. “Emotional and physical distress following hurricane Agnes in Wyoming Valley of Pennsylvania.” Public Health Reports 94:495-502.

Longstreth, J. 1999. “Public health consequences of global climate change in the United States - some regions may suffer

disproportionately.” Environmental Health Perspectives 107(Supplement 1):169-179.

McCormick, J. 1991. Urban Air Pollution. United Nations Environment Programme: Nairobi.
50

McCosh, M.L. 1993. Abiotic factors affecting survival of young-of-the-year smallmouth bass. Thesis, The Pennsylvania

State University: University Park, PA. pp. 1-40.

McKeown, T. 1976. The Modern Rise of Population. Academic Press: New York.

McMichael, A.J., Ando, M., Carcavallo, R., Epstein, P.R., Haines, A., Jendritzky, G., Kalkstein, L.S., Odongo, R., Patz,

J.A., Piver, W., et al. 1996. “Human population health.” In Climate Change 1995: Impacts, Adaptations, and Mitigation

of Climate Change: Scientific-Technical analysis. Contributions of Working Group II to the Second Assessment report

of the Intergovernmental Panel on Climate Change. Watson, R.T., Zinyowera, M.C., and Moss, R.H. (eds). Cambridge

University Press, New York.

Meltzer, M.I., Dennis, D.T., and Orloski, K.A. 1999. “The cost effectiveness of vaccinating against Lyme disease.”

Emerging Infectious Diseases 5(3): 321-328.

Moeller, I., Spencer, T., and French, J.R. 1997. “Wind wave attenuation over saltmarsh surfaces: Preliminary results from

Norfolk, England.” J Coast Res 12:1009-1016.

Moorhead, K.K., and Brinson, M.M. 1995. “Response of wetlands to rising sea level in the lower coastal plain of North

Carolina.” Ecological Applications 5:261-271.

Najjar, R.G., Walker, H.A., Anderson, P.J., Barron, E.J., Bord, R., Gibson, J., Kennedy, V.S., Knight, C.G., Megonigal,

J.P., O’Connor, R.E., Polsky, C.J., Psuty, N.P., Richards, K.B., Sorenson, L.G., Steele, E., and Swanson, R.S. 1999. “The

potential impacts of climate change on the Mid-Atlantic coastal region.” Climate Research 14(2), in press.

National Research Council. 1995. Wetlands: Characteristics and boundaries. National Academy Press: Washington, DC.

Neff, R., Chang, H., Knight, C.G., Najjar, R.G., Yarnal, B., and Walker, H.A. 2000. “Impact of climate variation and

change on Mid-Atlantic region hydrology and water resources.” Climate Research, in press.

Noss, R.F., La Roe, E.T. III, and Scott, J.M. 1995. “Endangered ecosystems of the United States: A preliminary assessment of loss and degradation.” Biological Report 28. U.S. Department of the Interior, National Biological Services:

Washington, DC.

NPA Data Services, Inc. 1998a. Sources and Methods of Preparation of the 1998 Regional Economic Projections Series

Data Base. August. 12 pp.

NPA Data Services, Inc. 1998b. Regional Economic Projections Series. Washington, DC.

Oke, T.R. 1987. Boundary Layer Climates, second edition. Methuen, Inc.: New York. 435 pp.

Parsons, G.R., and Powell, M. 1998. “The cost of beach retreat: A hedonic price analysis of Delaware beaches.” Department of Economics Working Paper 98-14. University of Delaware.
51

MARA OVERVIEW 2000 • References

Patz, J.A. 1996. “Health adaptations to climate change: Need for farsighted integrated approaches.” In Smith, J.B., et al.

(eds). Adapting to Climate Change. Springer: New York.

Patz, J.A., and Yap, W.Y. 1998. “Regional Impact Assessment: Human Health-Mid Atlantic.”

Patz, J.A., Epstein, P.R., Burke, T.A., and Balbus, J.M. 1996. “Global climate change and emerging infectious diseases.”

JAMA 275(3):217-223.

Perry, M.C. 1998. “Wetland habitats for wildlife of the Chesapeake Bay.” In Majumdar, S.K., Miller, E.W., and Brenner,

F.J. (eds). Ecology of Wetlands and Associated Systems. The Pennsylvania Academy of Science.

Perry, M.C. 1987. “Waterfowl of Chesapeake Bay.” In Majumdar, S.K., Hall, L.W., and Austin, H.M. (eds). Contaminant Problems and Management of Living Chesapeake Bay Resources. The Pennsylvania Academy of Science.

Perry, M.C., and Deller, A.S. 1996. “Review of factors affecting the distribution and abundance of waterfowl in shallowwater habitats of Chesapeake Bay.” Estuaries 19:272-278.

Perry, M.C. and Deller, A.S. 1995. “Waterfowl population trends in the Chesapeake Bay area.” In Hill, P., and Nelson, S.

(eds). Toward a Sustainable Coastal Watershed: The Chesapeake Experiment. Proceedings of a Conference. Chesapeake

Research Consortium Publication No. 149. Chesapeake Research Consortium, Inc.: Edgewater, MD.

Perry, M.C., and Uhler, F.M. 1988. “Food habits and distribution of wintering canvasbacks, Aythya valisineria, on Chesapeake Bay.” Estuaries 11:57-67.

Pitelka, L.F., and the Plant Migration Workshop Group. 1997. “Plant migration and climate change.” American Scientist

85:464-473.

Polsky, C., Allard, J., Crane, R.G., and Yarnal, B. 2000. “The Mid-Atlantic Region and its climate: Past, present and

future.” Climate Research 14(2), in press.

Powell, D.S., Faulkner, J.L., Darr, D.R., Zhu, Z., MacCleery, D.W. 1996. “Forest Resources of the United States, 1992.”

USDA Forest Service, Rocky Mountain Forest and Range Experiment Station. General Tech. Report, RM-234 (revised).

133 pp.

Price, J.T. 1995. Potential impacts of global climate change on the summer distributions of some North American

grassland birds. Ph.D. dissertation, Wayne State University: Detroit, MI.

Price, J.T. In press. “Potential impacts of climate change on the summer distributions of some North American grassland

birds.” US Geological Survey, Northern Prairie Wildlife Research Center.

Root, T.L. 1988. “Environmental factors associated with avian distributional boundaries.” Journal of Biogeography 15:489-

505.
52

Schlarbaum, S.E., Hebard, F., Spaine, P.C., and Kamalay, J.C. 1999. “Three American tragedies: chestnut blight, butternut canker, and Dutch elm disease.” USDA Forest Service, Southeastern Research Station: Asheville, NC.

Schwartz, P. 1991. The Art of the Long View. Doubleday: New York.

Shao, G., and Halpin, P.N. 1995. “Climatic control of eastern North American coastal tree and shrub distributions.”

Journal of Biogeography 22:1083-1089.

Slanina, J., ten Brink, H.M., and Khlystov, A. 1999. “Fate of products of degradation processes: Consequences for

climatic change.” Chemosphere 38(6):1429-1444.

Solley, W.B., Pierce, R.R., and Perlman, H.A. 1998. Estimated Use of Water in the United States in 1995. US Geological

Survey Circular 1200. US Geological Survey: Washington, DC.

Sorenson, L.G., Goldberg, R., Root, T.L., and Anderson, M.G. 1998. “Potential effects of global warming on waterfowl

populations breeding in the northern Great Plains.” Climatic Change 40:343-369.

Sorenson, L.G., Goldberg, R., Anderson, M.G., and Root, T.L. 1999. “Potential consequences of global warming for

wetlands and ducks breeding in the Canadian Prairie Pothole Region.” In prep.

Sutton, C.C., O’Herron, J.C. II, Zappalorti, R.T. 1996. The Scientific Characterization of the Delaware Estuary. The

Delaware Estuary Program (DRBC Project No. 321; HA File No. 93.21).

Titus, J.G. 1998. “Rising seas, coastal erosion, and the takings clause: How to save wetlands and beaches without hurting

property owners.” Maryland Law Review 57:1279-1399.

US Bureau of the Census. 1998. Historical National Population Estimates. URL: http://www.census.gov/population/

estimates/nation/popclockest.txt.

USDA, Interagency Agricultural Projections Committee. 1999. USDA Agricultural Baseline Projections to 2008, Staff

Report WAOB-99-1, URL: http://www.econ.ag.gov/epubs/pdf/baseline/index.htm.

USDA, National Agricultural Statistics Service. 1999a. 1997 Census of Agriculture, URL: http://www.nass.usda.gov/

census/.

USDA, National Agricultural Statistics Service. 1999b. Published Estimates Database, URL: http://www.nass.usda.gov/

ipedb/.

US DHHS, Public Health Service. 1992. Healthy People 2000: National Health Promotion and Disease Prevention Objectives. US Department of Health and Human Services: Washington, DC.

US EPA. 1998. Condition of the Mid-Atlantic Estuaries. Office of Research and Development. Washington, DC. Document #EPA600-R-98-147.
53

MARA OVERVIEW 2000 • References

Visser, M.E., van Noordwijk, A.J., Tinbergen, J.M., and Lessells, C.M. 1998. “Warmer springs lead to mistimed reproduction in great tits (Parus major).” Proceedings of the Royal Society of London B 265:1867-1870.

Walker, H.A., Latimer, J.S., Dettmann, E.H., Najjar, R.G., Allard, J., Crane, R.G., Yarnal, B., and Berounsky, V. 1999b.

“Climate change and eutrophication responses in the Potomac Estuary and Chesapeake Bay.” Paper to be presented at the

Annual Meeting of the Estuarine Research Federation, New Orleans, September 1999.

Walker, H.A., Latimer, J.S., and Dettman, E.H. 1999a. “Assessing the effects of natural and anthropogenic stressors in

the Potomac Estuary: implications for long-term monitoring.” Environmental Monitoring and Assessment, in press.

Walker, H.A., Najjar, R.G., and Yarnal, B. 1999. “Historical changes in global scale circulation patterns, Mid-Atlantic

region climate, stream flow, and nutrient fluxes to Chesapeake Bay.” Climate Research, in prep.

Warren, M.L., and Burr, B.M. 1994. “Status of freshwater fishes of the United States: Overview of an imperiled fauna.”

Fisheries 19(1):6-18.

Warrick, R.A., Le Provost, C., Meier, M.F., Oerlemans, J., and Woodworth, P.L. 1996. “Changes in sea level.” In Houghton,

J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A., and Maskell, K. (eds). Climate Change 1995: The

Science of Climate Change. Cambridge University Press: Cambridge, UK. pp. 359-405.

Watson, R.T., Zinyowera, M.C., and Moss, R.H. (eds). 1996. Climate Change 1995: Impacts, Adaptations, and Mitigation of Climate Change: Scientific-Technical analysis. Contributions of Working Group II to the Second Assessment

report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York.

WHO. 1996. Climate Change and Human Health. McMichael, A.J., Haines, A., Slooff, R., and Kovats, S. (eds). World

Health Organization: Geneva, Switzerland.

Wigley, T.M.L., and Jones, P.D. 1985. “Influences of precipitation changes and direct CO2

 effects on streamflow.” Nature

314:149-152.

Williams, J.D., and Neves, R.J. 1995. “Freshwater mussels: A neglected and declining aquatic resource.” In La Roe, E.T.,

Farris, G.S., Puckett, C.E., Doran, P.D., and Mac, M.J. (eds). Our living resources: A report to the nation on the

distribution, abundance, and health of U.S. plants, animals, and ecosystems. US Department of the Interior, National

Biological Service: Washington, DC. pp. 177-179.

Yarnal, B., Neff, R., Allard, J., and Walker, H.A. 1999b. “Climate impacts on nutrient fluxes in stream flow in the MidAtlantic Region.” Paper presented at the Annual Meeting of the Association of American Geographers, Honolulu, HI,

March 1999.

Yarnal, B., Johnson, D.L., Frakes, B., Bowles, G.I., and Pascale, P. 1997. “The flood of ‘96 in the Susquehanna River

Basin.” Journal of the American Water Resources Association 33:1299-1312.

Yarnal, B. 1991. “The climatology of acid rain.” In Mujamdar, S.K., Miller, E.W., Cahir, J. (eds). Air pollution: Environmental issues and health effects. Pennsylvania Academy of Science: Harrisburg, PA. pp.155-169.
54

Yarnal, B., and Leathers, D.J. 1988. “Relationships between interdecadal and interannual climatic variations and their

effect on Pennsylvania climate.” Annals of the Association of American Geographers 78: 624-641.

Zlatoper, T.J. 1987. “Testing for functional form and autocorrelation in the analysis of motor vehicle deaths.” Quarterly

Review of Economics and Finance 27(4):6-17.
44

- How climate change might affect weeds, insects, and

diseases in crops, livestock, and forests, and how increases in such adverse impacts might be controlled.

- How a warmer, wetter climate will affect the amount,

timing, and quality of water available for human and

ecosystem use.

- How adaptations in turn will feed back into the production of greenhouse gases.

The information and research needs might seem daunting.

Although listed in order of the MARA team’s “first cut” at

priorities, refining priorities among them can be facilitated

by assessing public understanding of, and potential

reactions to, climate change and its multiple subtle yet

significant impacts. Working with stakeholders can clarify

misunderstandings about climate change and their priorities

for research on potential impacts from climate change. For

example, the MARA shows that ecosystems are especially

vulnerable to climate variability and change. The MARA

researchers and stakeholders are developing a process for

setting priorities for additional ecological assessment. This

process will include criteria such as those described in

Chapter 2, with an emphasis on how stakeholders’

perceptions of and values for potential ecological impacts

would affect their priorities about what to assess next.

Our experience shows that stakeholders can be an important contributing resource in the assessment process because

of their knowledge of local conditions and their access to

valuable information otherwise not available; some of this

is being used in the second-year MARA activities (e.g., see

page 33). As research and assessment results become available, continuing stakeholder collaboration is crucial for

designing and implementing effective strategies to disseminate the findings for citizens’ use.
40

Rather than a straightforward summation of simple measures, overall social well-being is a complex combination

of economic, ecological and distributional considerations.

The MARA findings provide insights for making better

decisions in an uncertain world, with the goal of optimizing social well-being. Chapter 10 summarizes the most

important actions for the relatively near term to take advantage of opportunities and enhance the region’s resiliency

to climate variability and change. Chapter 11 takes a longer

view, setting priorities for filling gaps in information and

understanding needed to improve the region’s future decisions related to climate variability and change.

The MAR’s 6 largest metropolitan areas (Baltimore, Norfolk, Philadelphia, Pittsburgh, Richmond, and Washington) account for more than

half of the region’s population. Expected growth

makes urban areas among special places where

impacts of climate change warrant particular

attention. In the context of the U.S. National

Assessment, the Metro East Coast assessment

examines how climate change might affect the

New York City area. Although not a primary focus in the initial MARA, chapters 4-9 indicate

the potential for substantial cumulative impacts

in urban areas. This box draws attention to several issues for additional analysis: urban climate, urban forests, air quality, and flood risk.

Box 10 Climate Change

and Mid-Atlantic Cities

Photographic and GIS views of Philadelphia
41

MARA OVERVIEW 2000 • Planning for the 21st Century

Air Quality

There are many links among cities, air quality, and

climate (McCormick 1991; Yarnal 1991). Both human activities and climate must be studied to understand future pollution trajectories. Future legislation

and urban development will both affect emissions of

ozone and acid rain precursors. Weather systems

develop and deliver ozone and acid rain to the MidAtlantic Region (Comrie 1994). Will future climatic

conditions increase or decrease the number of these

systems or affect their nature, thus influencing regional ozone and acid rain problems? This work is

yet to be done.

Flood Risk

Climate changes that influence the frequency and

magnitude of riverine or coastal floods may create

substantial threats specific to urban areas. The Federal Emergency Management Agency (FEMA) has

encouraged a change in society’s response to flood

risk. The old approach was largely structural: storing floodwater in dams or preventing it from entering threatened areas (using dikes, levees, or sea

walls). The new approach recognizes the importance

of limiting development in flood-prone areas through

zoning regulations and building codes. However,

application of these regulations is largely based on

the spatial extent of historical flood frequency, such

as a 100-year flood. Changing hydrological conditions upstream in a river basin (such as forest cutting, stream channelization, and increase of

impervious surfaces) can increase downstream flood

frequency and extent (Dunne and Leopold 1978), as

could changing climatic conditions. Flood plain

definitions that guide land use management for flood

hazard mitigation today are likely to be inadequate

in the future. A similar issue will affect coastal cities, where flood risk mapping is based on today’s

sea level.

Box 10, continued

Urban Climate

The climate of cities differs from that of surrounding

areas (Oke 1987). Cities are warmer, receive less

sunlight because they have higher concentrations of

pollutants, and alter wind speed and direction. Although cities (and nearby downwind areas) tend to

have higher precipitation than outlying areas, they

usually are less humid because of increased runoff

and lower evapotranspiration. How climate change

will affect urban climates and these urban-rural differences is not known. One reason is that each city

has a unique geography. Also, climate models and

climate downscaling techniques lack sufficiently

detailed spatial resolution.

If present differences between urban and rural climate continue under climate change, we might expect that cities would experience more health effects

from heat waves, a greater summer heat island effect when air conditioning is increased, even more

increases in downwind precipitation, lower costs for

snow removal with warmer winters, and greater

water use in hotter summers.

The Urban Forest

Urban trees increase human comfort levels and reduce cooling and heating needs by providing shade

and reducing air temperature and wind speed (Grey

1996). Trees are important for their aesthetic appeal, for urban wildlife habitat and for reducing runoff by enhancing water infiltration in soils. In

addition to outright removal to make way for development, trees in urban areas often are exposed to

stresses such as: damage to boles, roots and branches

during construction; increased levels of air pollution; higher urban air temperatures; less soil water;

and road salt in runoff water. Only the latter of these

would be improved by warmer conditions.
35

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

Extreme Events

The MAR experiences extreme heat and cold, floods, fogs,

hurricanes and other weather events that can cause or contribute to injuries and death. These events can have indirect consequences as well. For example, floods can contaminate drinking water supplies.

While climate change will affect the severity and frequency

of extreme events, little can be said at this time about resulting impacts on health in the MAR. This is partly because of uncertainty about how climate change will influence the frequency and severity of extreme events in the

region. Moreover, while the health consequences of hurricanes and floods are apparent, those of other types of

weather are not. For example, fewer motor vehicle accident fatalities occur in snowy or rainy weather (Loeb 1985,

Zlatoper 1987), perhaps because people drive less frequently

and/or more carefully in such weather.

Mortality risks from extreme events are currently very small

in the MAR (Kocagil et al. 1999). This suggests that modest changes in these risks would have little impact on the

region’s health status (not accounting for how morbidity

due to extreme events would affect health status). Furthermore, if weather events that pose threats to health become more frequent or severe, there are structural and

nonstructural measures that can be undertaken to reduce

vulnerability (see Box 8). These measures include building codes, land-use planning, and severe weather warning

systems. Health surveillance during and after extreme

events is important in order to choose the most appropriate responses and to evaluate the effectiveness of the responses.

Heat Related Mortality

Heat and cold currently are not major causes of death in

the MAR (Kocagil et al. 1999). Nevertheless, Kalkstein

and Swift (1998) estimate that compared with other U.S.

cities, MAR cities would experience larger increases in heatrelated mortality under climate change. Climate change is

projected to increase summer excess mortality in Philadelphia, Baltimore, Pittsburgh, and Greensboro (see Box 9).

Box 8 Health Risk versus Vulnerability

It is useful to distinguish between risk and vulnerability. For

instance, research might show that malaria-carrying mosquitos would find more suitable habitat in a region that

becomes warmer and wetter, giving the region a higher

risk from malaria. However, the region could adapt to

reduce its vulnerability through measures such as vector control, disease monitoring and medical treatment,

or even development of a vaccine. To be effective, adaptation must be technologically feasible, affordable, and

acceptable. There often are trade-offs among these characteristics. Examples include concerns about unintended

impacts on non-target species from spraying to control

mosquitos and concerns that control measures might not

be used by some vulnerable groups (such as susceptible

elderly people who feel they cannot afford insect repellent). Thus the availability of adaptation measures does

not ensure their adoption, but successful adaptation to

the (climate-induced) increase in risk means low vulnerability to that risk.

Infrequent but intense heat waves coupled with vulnerable

elderly and poor populations make Philadelphia residents

particularly susceptible to heat-related mortality. Winter

mortality is not as strongly associated with weather conditions as summer mortality, with winter excess mortality

projected to be lower in Philadelphia but higher in Pittsburgh and Washington, DC. Despite potentially large percentage increases in excess mortality, mortality rates due to

heat and cold extremes would remain small compared to

rates from the region’s leading causes of death (Chestnut et

al. 1995).

Vector/Water/Food Borne Diseases

Climate change can affect health risks from vector, water,

and food borne diseases. These indirect health effects involve a complex chain of causality from climate change

through biophysical systems to human disease risks, making them extremely difficult to quantify (Haines and

McMichael 1997). Yet, the emerging view of health impacts at the global level suggests that indirect impacts may

be substantially more important than direct impacts

(McMichael et al. 1996).
36

Box 9. Heat- and Cold-Related Mortality for five MAR cities*

Below are estimates of current excess mortality (i.e., the number of deaths attributable to climate) and projections of

the change in excess mortality, adapted from Kalkstein and Greene (1997) and Kalkstein and Swift (1998). Full

acclimatization is assumed, and the figures are adjusted for projected population changes. Three general circulation models (GCMs) recommended by the IPCC are used, from the Geophysical Fluid Dynamics Laboratory (GFDL),

the United Kingdom Meteorological Office (UKMO), and the Max Planck Institute for Meteorology. These models

were used because this research was done prior to the NAST recommendations. Nonetheless, these three GCMs

provide a wide range of projections (GFDL low end, UKMO high end) that most likely encompass the Hadley and

CCC climate projections. Cities that currently do not have a significant relationship between climate and mortality

have no projected excess mortality.

Excess Change in Excess Mortality Mortality

Present 2020 2050

Climate GFDL UKMO Max Planck GFDL UKMO Max Planck

Summer

Baltimore 93 -8 106 -3 127 171 134

Greensboro 29 28 38 28 55 59 49

Philadelphia 146 -8 361 122 282 682 416

Pittsburgh 46 -1 47 44 66 107 129

Washington, DC 0 0 0 0 0 0 0

Winter

Baltimore 0 0 0 0 0 0 0

Greensboro 0 0 0 0 0 0 0

Philadelphia 100 24 -79 13 -29 -82 63

Pittsburgh 23 6 15 7 22 27 16

Washington, DC 20 12 19 11 3 21 16

Total (Summer + Winter)

Baltimore 93 -8 106 -3 127 171 134

Greensboro 29 28 38 28 55 59 49

Philadelphia 246 16 283 135 254 600 478

Pittsburgh 69 4 62 51 88 134 145

Washington, DC 20 12 19 11 3 21 16

* Portions of the Greensboro metropolitan statistical area are not located in the MAR.
37

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

Much of the research on climate change and health has

focused on the migration of tropical infectious diseases (e.g.

malaria, carried by mosquitos) to more temperate zones

(Patz et al. 1996; Colwell et al. 1998). The recent outbreak of West Nile virus encephalitis in New York is an

example of a migrating disease. As of mid-October, 1999,

7 people had died and 49 had become ill from this mosquito-borne virus which also caused bird deaths in NY, NJ

and CT (Fine et al. 1999). This strain of encephalitis had

not been observed in the Western Hemisphere prior to this

outbreak. Malaria is another disease currently not common in the MAR (Kocagil et al. 1999). However, a warmer

and wetter climate could make conditions favorable for

the mosquito vector, thus increasing the risk of malaria.

Based on experiences in other developed countries (e.g.,

Australia), vulnerability to malaria can be reduced by vector controls, disease monitoring and medical treatment.

Key components to reducing vulnerability include educating physicians to recognize diseases not common to the

region and improving surveillance of mosquitos that carry

other diseases such as viral encephalitis.

Lyme disease is the most prevalent vector-borne disease in

the MAR. Lyme disease rates in the MAR are more than

twice the nation’s (Kocagil et al. 1999). One way this disease is linked to the climate is by the range and activity of

its host vector, the deer tick, which requires certain temperature and humidity conditions (Glass 1995). Links have

also been made between acorn production, gypsy moth

outbreaks and Lyme disease risk (Jones et al. 1998). The

impact of climate change on the risk of contracting Lyme

disease is a concern for the MAR (Patz and Yap, 1998).

Like malaria, adaptation measures can reduce vulnerability to the disease. Vector controls, prevention, vaccination

and early detection are some examples. Research suggests

that at this time, it is cost effective to vaccinate only those

individuals who spend a lot of time outdoors in areas of

high Lyme disease risk rather than everyone (Meltzer et al.

1999, CDC 1999b). In addition, vaccinated people inadvertently may put themselves at higher risk, especially if

they presume the vaccine will be more effective than clinical trials have shown. Its cost (currently about $250) may

leave lower income individuals at risk even if the region as

a whole has low future vulnerability to Lyme disease.

Giardiasis (caused by Giardia lamblia) and cryptosporidiosis

(caused by Cryptosporidium parvum) are two important

waterborne diseases in the MAR (Kocagil et al. 1999).

Cryptosporidiosis is of particular concern because of the

difficulty in removing Cryptosporidium from water supplies

and the potential deadliness of this disease. Although there

are many possible sources of Cryptosporidium, an important one is cattle. Increased precipitation over agricultural

lands could increase the number of Cryptosporidium parvum

in water sources (Atherholt et al. 1998). Vulnerability could

be reduced by public water treatment, households taking

actions to prevent exposure, and source water protection.

Current regulatory efforts such as the Enhanced Surface

Water Protection Rule are aimed at reducing the amount

of Cryptosporidium parvum in drinking water.

Vibrio Cholerae, the bacterium that causes cholera, is present

in the Chesapeake Bay (Colwell et al. 1998) although the

disease is not currently a health problem in the MAR. Preliminary research indicates that climate change could facilitate the growth of Vibrio Cholerae in the Chesapeake

Bay (Gibson 1999). Thus the risk of cholera in the MAR

could increase with climate change. However, areas where

cholera is currently a major health problem are countries

with less developed public health infrastructure than the

U.S. Proper food preparation, waste and water treatment

can effectively reduce vulnerability to the disease.

The view emerging from our assessment is that the MAR’s

technological and medical infrastructure should be able to

contain endemic and migrating vector/water borne diseases,

keeping them from becoming significant regional problems. Thus, while the risks from these diseases could increase, adaptive measures can be taken to reduce the region’s

vulnerability. However, there are economic costs associated with these measures. Accordingly, while health status

may be little affected, the costs of public health would increase. There also may be subgroups with less access to

public health measures. In addition, some adaptation

measures can have undesirable side effects (e.g., secondary

impacts of pesticides used to control disease vectors).
38

Air Quality

Without reductions in polluting emissions, heavily populated

urban areas could experience more severe air quality problems as a result of climate change effects on the composition,

concentration, and duration of chemical pollutants in the atmosphere (Slanina et al. 1999). One important air pollutant

is ground-level ozone, which can cause or exacerbate respiratory illness. Ozone precursors include volatile organic compounds (VOC) and nitrogen oxides (NOx). Wind speeds

and patterns influence regional transport of ozone and its precursors in the MAR and northeastern U.S. The largest point

source of NOx is in the Ohio River Valley while large urban

areas, such as the Washington-New York corridor, are sources

of both VOC and NOx (Guinnup and Collom, 1997). A

key issue for assessing air quality risks is future emissions.

Future emissions of air pollutants that are harmful to human

health will depend on a variety of factors (e.g., automobile

use, technology, regulations) that are not easily forecasted

(Davies and Mazurek, 1998). Efforts to reduce greenhouse

gas emissions would have the secondary benefits of reducing

pollutants harmful to human health.

Mental Health

Depression and psychological changes can be related to persistent cloudy skies, precipitation, or thunderstorms (Collier and

Hardaker 1995). Furthermore, experiencing extreme weather

such as hurricanes or floods can inflict psychological stress and

trauma (McMichael et al. 1996). This was found to be the case

in Pennsylvania from flooding after Hurricane Agnes (Logue

et al. 1979). Because such psychological impacts are not fully

understood in the current climate regime, it is very difficult to

evaluate them under future climate change scenarios.

Conclusions

Table 13 (drawn from Table 4) summarizes the assessed potential health impacts for the region. The MAR’s current and

future health infrastructure is expected to be able to respond

to the health risks associated with a warmer and wetter climate, although at increased cost from measures to protect the

safety of food and water, control disease vectors, and provide

health services. Historically the U.S. population has been

able to adapt and reduce vulnerability to climate related health

risks. With continued investment in public health infrastructure and systems and barring significant unforseen developments, major health policy challenges in the U.S. during coming decades will relate to cost, access, and disparity in care

(Kajander 1996, Grayson 1998).

It is evident that further research is needed, particularly to

increase our understanding of the indirect effects of climate

change on health. Integrated modeling efforts are needed to

quantify the complex biophysical and behavioral linkages

connecting climate to health. Of equal importance is research

on the costs, effectiveness and acceptability of adaptation options. Additional major research gaps are how climate change

affects air pollution and motor vehicle accidents. Motor vehicle fatalities currently are an important cause of death in

the MAR; the potential for additional motor vehicle fatalities

induced by climate change could be larger than other weatherrelated causes of death. Finally, research on the variation of

health risks within the region by location and population characteristics is needed to address the distribution of climateinduced health impacts, especially because different segments

of the population (e.g., elderly, poor, less educated) currently

tend to face greater health risks in general.
32

Box 7 Climate Change and Bird Distributions in the Mid-Atlantic Region

There are both economic and ecological reasons to care

about birds. Watching and feeding birds is big business,

generating about $885 million annually in retail sales

within the Mid-Atlantic region (MAR) (Bird Conservation 1997).

It is difficult to estimate how changes in bird distributions might affect the economics of watching and feeding birds. Spending would shift as some birdwatching

sites become less favorable and others become more favorable. Although many bird watchers might adjust to

diminished species richness, they will experience the loss

of well-being that accompanies a reduction in their preferred activities.

Also of concern are potential indirect 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 eat up to 98% of the overwintering codling

moth larvae in orchards.

· Wood warblers are largely responsible for holding down numbers of spruce budworm larvae,

eating up to 98% of the non-outbreak larvae.

· While the white-footed deer mouse is a more important predator of gypsy moths, birds also hold

down numbers of this pest.

The table shows results from statistical models that associate bird distributions first with current climatic conditions (1985-1989) and then with temperatures increased by 1.8° F (1° C) from the CCC model (Price,

in press; 1995). This temperature change is within the

ranges suggested in Figure 8. The gross change represents the overall loss in the number of perching (passerine) species currently found in the area. The net

change represents the loss of species currently found

there, offset by species moving in from outside the area.

Thus a 1.8° F increase in temperature could lead to a

loss of 7% of the passerine species currently found in

the MAR. These losses would be somewhat offset by

birds colonizing from outside the region so the net

change would be 3% fewer species than currently found

there. This 3% translates into fewer than 5 perching

species in the MAR.

The colorful wood warblers are a subset of the species

from the table, are popular among bird watchers, and

are important predators of insects. The same increase

in temperature could lead to a gross loss of 14% of

MAR warblers. 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 an 8% reduction in the number

of warbler species currently found in the MAR.

How quickly these changes might occur is unknown.

In the last 20 years, the average latitude for warblers

has shifted north by an average of more than 43 miles.

This suggests such changes could occur relatively

quickly.

In summary, climate change will affect bird distribution, perhaps quickly, and the magnitude of ecological

and economic effects is unknown.

Changes in number of perching bird species

With 1.8° F temperature increase

Gross Change (%) Net Change (%)

Region - 7 - 3

Delaware - 3 0

Maryland - 4 - 1

New Jersey - 5 - 1

New York - 10 - 4

North Carolina - 5 - 2

Pennsylvania - 9 - 3

Virginia - 4 - 1

West Virginia - 7 - 3
33

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

Because species will be affected differently by climate

changes, relationships among species will be altered and

this could affect ecosystem functioning. 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 a) the ecosystem functioning—i.e., how ecosystem components interact—that is crucial for ecosystem evolution as well as b)

functions that people value, such as pollinating crops, moderating and purifying water flows, and providing diverse

wildlife to observe.

Projecting potential ecological impacts of climate change

in the MAR is especially difficult because the MAR itself is

a complex interconnected system. Although specific impacts still cannot be predicted, ecosystem functioning and

biodiversity are likely to experience mostly negative effects

from climate change. These impacts on ecosystems in turn

could have as yet unpredicted impacts on the sectors described in Chapters 4-9. Monitoring will be important to

identify changes in habitat and biodiversity early enough

to evaluate whether actions to mitigate those changes are

warranted. In sum, the overall ecological impacts could be

quite large, even though very uncertain, as shown in Table

12, which is drawn from Table 4.

Because of the potentially significant ecological impacts from

climate change in the MAR, a next step is to develop and

implement a process of selecting priorities for additional ecological assessment. That process will involve stakeholders and

researchers in the MAR. 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 will reflect their mental maps of

how they perceive ecosystems, how ecosystem components

interact and function, and how ecosystems might change.

Their answers also will reflect their values related to these

concepts. 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.

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). The Advisory Committee and our earlier work have convinced us

that this view is too simple: 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. This in-depth work on ecological values and concerns is intended to complement the ongoing

activities with our Advisory Committee, filling a gap its

members helped identify.
27

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

Hurricane Floyd Effects on Franklin, VA

(Photo by Liz Roll/FEMA News Photo)

that the Hadley and CCC models project increases in precipitation by the year 2100. There is more uncertainty

about future storminess, but the benefit of longer coastal

recreation seasons could be more than offset by frequent

or intense storms.

Higher sea levels will raise storm surge levels, even if the

frequency and intensity of storms do not increase. Thus

the same strength storm will cause more damage, making

it seem like a stronger storm. Najjar et al. (2000) show

that what are now considered 100-year floods will occur

every 25 to 30 years. Recent experiences such as Hurricane Floyd emphasize that storm damages can be substantial both along the coast and inland. Strong storms can

disrupt transportation, settlements, waste treatment, emergency services, and ecosystems. (See Box 5.)

The costs of protecting valued infrastructure or natural areas

could be quite high. Parsons and Powell (1998) and Faucett

Associates (1998) find that beach replenishment is less

costly than the present value of losses from allowing beach

retreat. However, an emerging policy of beach replenishment in New Jersey is estimated to cost $60 million

per mile and $9 billion over

50 years (Grunwald 1999).

Policies for strategic retreat—

i.e., deliberately allowing selected low-lying areas to be

inundated by sea-level rise—

are likely to be controversial

and have legal complications,

but Titus (1998) argues for

rolling easements that would

maintain public access to

tidal lands as shorelines retreat. Delaware currently allows strategic retreat for state

owned coastal lands.

In addition to the aesthetic and economic values discussed

above, a changing climate would affect the MAR’s ecosystems. The complexity of coastal ecosystems is depicted in

Figure 13. The MAR’s dominant coastal wetlands are floodBox 5 Hurricane Floyd

In September 1999, approximately 2.6 million people

evacuated their FL, GA, SC and NC homes in advance

of Hurricane Floyd. Even so, there were 77 deaths

and more than $6 billion in damages in the Southeast

and Mid-Atlantic regions.

The environmental, agricultural and human disaster

were particularly severe in NC, where rivers were

fouled by human and livestock waste (spoiling water

supplies and the shrimp harvest), farm losses exceeded

$1 billion, millions of dollars in uninsured home and

car losses were incurred, and 51 fatalities were

attributed to Hurricane Floyd. Observing how NC

responds by shifting the types and locations of its

socioeconomic activities can be an indicator of how

other vulnerable subregions might respond to similar

storms in the future.
28

plain forest, tidal fresh water marsh, and salt marsh, which

sort out on the landscape according to gradients in flooding frequency, tidal amplitude and salinity. Wetlands buffer

the coast from storms; about half of normal wave energy is

dissipated within the first 10 feet of encountering marsh

vegetation such as cordgrass (Kesselheim 1995, Moeller et

al. 1997). Wetlands also slow erosion (because plant roots

hold soils in place) and moderate flooding (because the

soils soak up water from heavy rains and increased

streamflows). Wetlands slow runoff so that nutrients, sediments and pollutants are trapped before entering coastal

waters.

Figure 13. Coastal ecosystem divisions (adapted from Brinson et al. 1995).

MAR wetlands 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 coastal wetlands to survive and reproduce. The

Chesapeake and Delaware Bays are homes to two of the

largest concentrations of migratory shorebirds in the western hemisphere. Nearly 1.5 million birds, predominantly

red knot, dunlin, sanderling, semipalmated sandpiper and

ruddy turnstone, stop at the Delaware Bay to feed each

spring (Jones et al. 1997). With up to 70 percent of the

entire North American population of the red knot in the
29

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

Delaware Bay at one time, these birds are quite vulnerable

to any environmental changes (Sutton et al. 1996). See

Box 6 for a case study of potential impacts on waterfowl in

the Chesapeake Bay.

Human and ecological forces also interact with respect to sealevel rise. For instance, dams and development that reduce

the availability of sand and sediment would lead to greater

inland displacements. Land loss and erosion would be reduced if vertical marsh accretion could keep pace with sealevel rise. Salt marshes generally are vulnerable to sea-level

rise. For most of the MAR coast, sediments and organic matter

are not deposited fast enough to keep up with current sealevel rise and barriers often prevent inland migration (Kearney

and Stevenson 1991, Erwin 1999). Losses are very likely in

the extensive non-tidal wetlands of the Ablemarle/Pamlico

Peninsula, which receive little riverine sediment (Moorehead

and Brinson 1995). Some of this loss may be offset by an

increase in organic matter accumulation stimulated by the

higher levels of CO2

 (Drake et al. 1996) and warmer temperatures (Callaway et al. 1996, Bricker-Urso et al 1989).

Much of the response (for salinity, dissolved oxygen, submerged aquatic vegetation (SAV), phytoplankton, fish and

shellfish) of the Mid-Atlantic estuaries to climate change cannot be predicted reliably because of uncertainties in future

streamflow. What is clear is that relatively modest changes in

stream flow can have a dramatic impact on water quality. For

example, Table 10 shows how anoxia (lack of oxygen) would

increase in the Chesapeake Bay, given the stream flow changes

projected in Chapter 6. Fish and shellfish will migrate away

from increased anoxia, while decreased anoxia will be beneficial. This calculation does not include the direct impact of

warming, which is likely to exacerbate the already low summer oxygen levels in Mid-Atlantic estuaries because of increased oxygen demand and decreased oxygen solubility. Some

mobile estuarine species also will be displaced northward by

warming.

Sea-level rise is likely to have the largest climate-related impact on coastal wetlands. However, continuing current development practices will cause much larger impacts. This

suggests that strategies to minimize adverse ecological impacts

of human activities on coastal ecosystems in the MAR could

help mitigate some of the risks from future climate variability

and change. Table 11 (drawn from Table 4) summarizes the

impacts from climate variability and change on the MAR’s

coastal zones.

Table 10. Projected change in April-May flow of the Susquehanna River and

 Chesapeake Bay summertime anoxic volume (Najjar et al. 2000)

2030 2095

Change

Hadley CCC Hadley CCC

April-May flow (%) +12 -4 +4 -25

Anoxic volume (%) +31 -10 +10 -65
30

About a million ducks, geese and swans use the Chesapeake Bay estuary to feed and rest during the winter and

thousands more use it as a migration stopover point.

Average population sizes for most duck species have declined markedly since the 1950s while populations of

Canada Geese, Snow Geese and Brant have increased

(Perry and Deller 1995). See table below. Most of these

changes are attributed to changes in food resources in

and around the Bay, particularly the widespread decline

of submerged aquatic vegetation (SAV), a prime waterfowl food (Perry and Deller 1996). Excessive nutrients

and sedimentation result in blooms of algae and high

turbidity that shade SAV and limit its growth (Hurley

1991). A few species have adapted by changing their

diet. For example, swans and geese now feed largely in

upland agricultural areas on waste corn and winter cover

crops (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 1991a). Species apparently unable to adapt to the loss of SAV have shown drastic declines; the Northern Pintail, Redhead, and American

Wigeon have largely abandoned the Bay as a wintering

site (Haramis 1991b, Perry and Deller 1995).

Global climate change is likely to affect both waterfowl

breeding and wintering habitats. Warmer and drier conditions are projected for the Prairie Pothole Region of

the north-central U.S. and south-central Canada, an area

known as the continent’s “duck factory” (Sorenson et al.

1998; Sorenson et al. in prep). By reducing the pothole

wetlands, these changes could reduce the number of ducks

in the region and their reproductive success. Sorenson

et al. (in prep) project 20-40% declines in the number of

ducks breeding in Praire Canada for the 2030s. In turn,

this 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.

Projections of warmer water temperatures, possible

streamflow increases and larger human populations in

the MAR suggest that water quality in the Bay and thus

SAV will continue to decline. Sea-level rise might reduce the amount of shallow water habitat suitable for

wintering waterfowl. Thus climate change will add to

the habitat pressures already being experienced because

of human activity.

Many questions remain, such as how alternate breeding

grounds further north will be affected by climate change.

Even so, projections of declining breeding populations

producing fewer young due to drier conditions on the

prairies, coupled with likely impacts of climate change

on winter habitat quality bode poorly for future duck

populations in MAR waters.

Box 6 Impacts of Climate Change on Waterfowl in the Chesapeake Bay

Average waterfowl populations wintering in Chesapeake Bay

Species 1950-1959 1985-1999 % Change

Redhead * 76,000 2,000 -97%

American Wigeon * 77,000 5,000 -94%

Northern Pintail * 40,000 3,000 -93%

Common Goldeneye ** 22,000 6,000 -73%

American Black Duck ** 143,000 45,000 -69%

Canvasback ** 179,000 57,000 -68%

Ruddy Duck ** 66,000 33,000 -50%

Scaup ** 102,000 52,000 -49%

Mallard ** 71,000 60,000 -15%

Bufflehead ** 9,000 20,000 +55%

Canada Goose *** 178,000 386,000 +54%

Snow Goose *** 5,000 90,000 +94%

Brant *** 13,000 22,000 +41%

Major food: * SAV, ** invertebrates, *** agricultural fields
23

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

In 1998, 504 Pennsylvania water system managers responded to a mail survey. The table shows that many

systems suffer difficulties from weather events. Power outages from storms, affecting the ability to pump water, are

the most frequent weather-related problems. Managers

having problems now also expect disruptions in their daily

operations in the next 5 years.

Perhaps surprisingly, larger systems report more weatherrelated problems than do smaller systems. Larger systems

often draw on more than one source of water; this complexity may increase their vulnerability to extreme weather.

These data call into question assumptions that the current

consolidation trend is reducing the vulnerability of water

systems to weather variability.

Water system managers are ambivalent about climate

change. When provided options, only 9 percent checked

that they are not concerned because climate change is unlikely to happen. A much larger share (21 percent) checked

that “global warming is real” and that they are concerned.

Still, the highest share (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 is so long-term that adaptations are beyond their

planning horizon. Seventy-eight percent of managers indicate their planning horizon is 5 or fewer years.

In summary, most Pennsylvania water system managers

face disruptions from weather events and expect these problems to continue. 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;

in any event, few plan beyond 5 years. This ambivalence

may give way to concern for adaptation if water system

managers learn that climate change impacts include more

frequent and intense weather events.

 Box 3 Water Managers Report Systems Are Vulnerable to Weather Variability
24

storms. Although smaller water systems and individual

well owners cannot spread these costs over large numbers

of users, improved use of water markets could increase the

efficiency of water use for both large and small systems.

The effects of changed climate on the MAR hydrology are

likely to stress ecosystems. Because warmer water holds

less oxygen, dissolved oxygen levels—an indicator of the

general ecological health of water bodies—will decrease as

the higher average air temperatures cause the region’s

streams and lakes to warm. Runoff from increased thunderstorm activity and more rapid snow melt will carry more

contaminants and sediment to streams. Changes in the

amount, timing, and quality of water might affect ecosystems from the headwaters throughout the drainage basins

to the region’s estuaries and bays. For instance, under the

Hadley scenario, the increased spring runoff and stream

flow following fertilizer application will increase nutrient

loads in and from agricultural watersheds. Coastal areas

downstream of such areas will have greater risk of eutrophication. Under the CCC scenario of decreased spring and

summer stream flow, however, nutrient loads will decrease

at this time of year. Such changes will affect recreation

opportunities as well as the general quality of life. (As an

example, see Box 4 for a case study on recreational fishing.) Because land use decisions have long-lasting effects

on the quantity and quality of runoff, future water resource

vulnerabilities will be influenced by the interaction between

land use decisions and climate change impacts. The nature of these interactions and their outcomes require further research.

Table 9 (drawn from Table 4) summarizes the impacts on

the region’s fresh water quantity and quality.

Figure 11. Predicted streamflow changes
25

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

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 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 limit of trout is in 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. Higher

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 MAR 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 (McCosh 1993).

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 poorly

managed agriculture and forestry, continues to influence

stream invertebrates and fish. Related instream sedimentation and loss of gravel spawning areas are difficult to

restore, even if the watershed is restored to forested land

cover.

Protection and restoration require resources that otherwise could be put to alternative use. 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. 1998). On average they were willing to

pay around $4.00 per angler per year to avoid the changes.

Aggregating such estimates can help answer questions

about whether protection and restoration are worthwhile.

Overall recreational fishing prospects 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.

Box 4 Recreational Fishing and Climate Change in the MAR
21

MARA OVERVIEW 2000 • Impacts, Challenges & Opportunities

More than 20 percent of the respondents reported major

impacts from heavy rains, high winds or ice storms. About

15 percent reported major impacts from low rainfall or

heavy snowfall. Fewer than 5 percent reported major impacts from very high or low temperatures. Compared with

government operators, private foresters find high rainfall

to be a bigger problem, reflecting their interest in access to

harvest sawtimber and pulpwood. Not surprisingly, foresters in the upland hardwood subregion are more

concerned about heavy snowfall than those in the southern pine subregion. Analysis of the full set of responses

will serve as a baseline for predicting how forest managers

would respond to changes in extreme events—but we need

better ways of projecting how the magnitude and frequency

of extreme events might be affected by overall climate

change.

If projections of future patterns of extreme events are uncertain, there is greater certainty that higher temperatures

and changes in precipitation will directly affect tree growth

and survival. In addition, increased concentrations of atmospheric CO2

 may enhance growth and allow plants to

use water more efficiently (Bazzaz 1990, Eamus 1996).

And, changes in the distribution and abundance of pests,

frequency of fires, erosion and decomposition could cause

indirect impacts on forests (Watson et al. 1996). Recent

work forest sector analysis for the National Assessment has

suggested that temporary or long-term conversion of forest to grassland/savanna may occur in parts of the east/

southeast U.S.

We used a statistical procedure (Iverson and Prasad 1998)

to relate current environmental conditions to current abundance of 75 tree species; we then projected potential future

abundance based on future climatic conditions. The model

uses county-level data for 33 environmental variables. Climate data include monthly averages for precipitation, temperature, and potential evapotranspiration (PET) for

current conditions and for climate models. Unlike other

chapters in this report, the forest projections are based on

equilibrium climate scenarios for a doubling of CO2

.

However, the temperature and precipitation changes for

these equilibrium scenarios are similar to those for the 2090-

2099 period of the transient climate scenarios. Thus we

consider the two to be comparable. (Detailed transient

model data were not available in time for our forestry assessment, and use of the CO2

 doubling allows comparison

with prior studies.) Species are assumed to be able to colonize all suitable sites. Time lags in species’ migration are

not accounted for (so this assumes trees are able to migrate

in pace with climate), nor are competitive interactions

among species.

Climate change is likely to reduce the dominance of maplebeech-birch forests in the MAR, with an increase in

oak-hickory forests, and, to a lesser extent, southern pine

and mixed oak-pine forests. The lower part of Figure 10

shows how the dominant forest types would be distributed

under the CCC and Hadley models, for comparison with

the current distribution in the upper part. The shifts in

forest types and their locations could diminish the competitiveness of the many small hardwood processors (e.g.,

for furniture and cabinetry). Although overall primary

forest productivity might increase, the relatively rapid shift

in dominant forest types might foster invasive species and

decrease biodiversity in the region’s forests. Rapid shifts in

forest types also might affect hydrology so that forests provide less filtering and moderation of stream flow. Relatively

little is known about how the changes in forest types might

affect recreation opportunities in forests. Thus forest impacts are shown in the “moderately certain” and “uncertain”

categories in Table 8 (which is part of Table 4).