Pennsylvania Climate Impacts
    Assessment Update
    Submitted by:
    The Pennsylvania State University
    Submitted to:
    Commonwealth of Pennsylvania
    Department of Environmental Protection
    October 2013

    Pennsylvania Climate Impacts AssessmentU pdate
    Andrew Ross
    Graduate Student
    Charlotte Benson
    Undergraduate Student
    David Abler
    (Co-PI), Professor
    Denice Wardrop
    Associate Professor
    James Shortle
    (PI), Professor
    Marc McDill
    (Co-PI), Associate Professor
    Matthew Rydzik
    Graduate Student
    Raymond Najjar
    Richard Ready
    (Co-PI), Associate Professor
    Seth Blumsack
    Assistant Professor
    Thorsten Wagener
    Associate Professor
    Department of Agricultural Economics and Rural Sociology
    Energy and Mineral Engineering
    School of Forest Resources
    Civil and Environmental Engineering
    The Pennsylvania State University, University Park
    For correspondence about this report, contact James Shortle at -886145-7657, or
    This report is the product of all of the autho. Trhe
    s primary authors for the chapters are: Agriculture, David Abler;
    Energy, Seth Blumsack; Forests, Marc McDill; Climate Futures, Ray Najjar; Human Health and Outdoor Recreation,
    Rich Ready; Water, Thorsten Wagener; Aquatic Ecosystems, Denice Wardrop.

    Table of Contents
    Executive Summary ......................................................................................................... 6
    Introduction .................................................................................................................... 12
    Pennsylvania Climate Futures........................................................................................ 13
    Differences in GCM analysis between this update and the 2009 PCIA......................... 14
    Regional climate models, data sets, and analysis ........................................................... 15
    Results ............................................................................................................................ 17
    Model evaluation .................................................................................................... 17
    Model projections ................................................................................................... 21
    Historical temperature and precipitation change across Pennsylvania .......................... 26
    Conclusions .................................................................................................................... 29
    Agriculture ..................................................................................................................... 32
    The Near- and Long-Term Future for Pennsylvania Agriculture .................................. 32
    National and Global Agricultural Markets ............................................................. 32
    Agricultural Land Conversion ................................................................................ 35
    Pennsylvania Food Demand ................................................................................... 36
    Federal Agricultural Budgets .................................................................................. 38
    Recent Research on Climate Change and Agriculture ................................................... 39
    Climate Change and Crop Production .................................................................... 39
    Climate Change and Livestock Production ............................................................. 41
    Adaptation Strategies ..................................................................................................... 42
    Conclusions .................................................................................................................... 43
    Pennsylvania Climate Change and Water Resources .................................................... 46
    Historical Climate and Hydrology of PA ....................................................................... 46
    Climate Change Implications for the Water Cycle in PA .............................................. 48
    Precipitation – Rainfall and Snow .......................................................................... 48
    Evapotranspiration .................................................................................................. 49
    Streamflow/Runoff ................................................................................................. 50
    Soil Moisture ........................................................................................................... 51
    Groundwater ........................................................................................................... 52
    Stream Temperature ................................................................................................ 53
    Consequences for Pennsylvania Freshwater Services and Disservices ......................... 56
    Floods ...................................................................................................................... 56

    Droughts .................................................................................................................. 57
    .3.3 Water Quality ............................................................................................................. 58
    Salt Water Intrusion in the Delaware Estuary......................................................... 58
    Adaptation Strategies ..................................................................................................... 59
    Barriers and Opportunities ............................................................................................. 60
    Information Needs .......................................................................................................... 61
    Conclusions .................................................................................................................... 62
    Aquatic Ecosystems and Fisheries ................................................................................. 69
    Pennsylvania’s Aquatic Resources................................................................................. 69
    Definition and Description of Ecosystem Services ........................................................ 70
    Major Drivers of Aquatic Ecosystem Response to Climate Change ............................. 73
    Potential Climate Change Impacts to Pennsylvania Aquatic Ecosystems ..................... 74
    A Case Study for Climate Change Impacts to Hydrology: Comparison of the Little
    Juniata River and Young Woman’s Creek Watersheds ................................................. 77
    Stream Flow ............................................................................................................ 78
    Groundwater Levels ................................................................................................ 81
    Summary of Impacts ...................................................................................................... 83
    Adaptation Strategies ..................................................................................................... 85
    Informational needs for Aquatic Ecosystems................................................................. 85
    Energy Impacts of Pennsylvania’s Climate Futures ...................................................... 91
    Energy Supply in Pennsylvania...................................................................................... 91
    Energy consumption and pricing in Pennsylvania ......................................................... 93
    Greenhouse-gas impacts of energy production and consumption in Pennsylvania ....... 97
    Climate-related policy drivers affecting Pennsylvania’s energy sector ....................... 101
    Pennsylvania’s Alternative Energy Portfolio Standard ........................................ 102
    Energy conservation through Pennsylvania’s Act 129 ......................................... 104
    Uncertainties and Informational Needs in Assessing Climate-Change Impacts on
    Pennsylvania’s Energy Sector...................................................................................... 104
    Uncertainties Related to Natural Gas Impacts ...................................................... 104
    Uncertainties Related to the Transportation Sector .............................................. 105
    Uncertainties related to coupled energy and water systems ................................. 106
    Conclusions .................................................................................................................. 107
    Forests .......................................................................................................................... 111
    Climate Changes’ Effects on Pennsylvania Forests ..................................................... 114

    Tree Species Range shifts ..................................................................................... 114
    Tree Regeneration ................................................................................................. 115
    Tree Mortality ....................................................................................................... 115
    Phenological Mistiming ........................................................................................ 116
    Growth impacts ..................................................................................................... 116
    Atmospheric Impacts ............................................................................................ 117
    Insects, Pathogens and Invasive Species .............................................................. 117
    Fauna ..................................................................................................................... 118
    Mitigation ..................................................................................................................... 119
    Adaptation .................................................................................................................... 120
    Conclusions .................................................................................................................. 121
    Human Health Impacts of Climate Change in Pennsylvania ....................................... 129
    Temperature-related mortality...................................................................................... 129
    Air quality and health ................................................................................................... 130
    9.2 1
    Ground-level ozone ............................................................................................... 130
    Airborne particulates ............................................................................................. 131
    Pollen and mold .................................................................................................... 132
    Vulnerable populations ......................................................................................... 132
    Extreme weather events ............................................................................................... 132
    Vector-borne disease .................................................................................................... 133
    Water and air-borne disease ......................................................................................... 135
    Adaptation Strategies ................................................................................................... 136
    Information Needs ........................................................................................................ 136
    Conclusions .................................................................................................................. 137
    Outdoor Recreation and Tourism................................................................................. 141
    10.1 Winter Recreation ........................................................................................................ 141
    10.1.1 Evidence of winter climate change in Pennsylvania ............................................. 141
    10.1.2 Recent research on the impact of climate change on downhill skiing .................. 144
    10.2 Recreational Fishing ..................................................................................................... 145
    10.3 Water-Based Recreation ............................................................................................... 146
    10.4 Outdoor Sports and Exercise Activities ....................................................................... 147
    10.5 Adaptation Strategies ................................................................................................... 148
    10.6 Information Needs ........................................................................................................ 149

    10.7 Conclusions .................................................................................................................. 150
    Appendix ...................................................................................................................... 153
    11.1 Locations of Stream Temperature Measurements ........................................................ 153
    11.2 IPCC Emissions Scenarios ........................................................................................... 153

    Executive Summary
    The Pennsylvania Climate Change Act, Act 70 of 20, 0d8irected Pennsylvania’s Department of
    Environmental Protection (DEP) to initiate a study of the potential impacts of global climate change on
    Pennsylvania over the next century. This study was conducted for the DEP by a team of scientists at
    The Pennsylvania State University and presented to the edpartment in the 2009 reports:
    Climate Impacts Assessment
    Economic Impacts of Projected Climate Change in Pennsylvan
    report presents an update on those findings that were also mandated by the Pennsylvania Climate
    Change Act, Act 70 of 2008.
    The 2009
    Pennsylvania Climate Impacts Assessmen
    2009 PCIA) contained an assessment of the
    impacts of global climate change no Pennsylvania’s climate in the
    It presented assessments
    of the impacts of climate change in Pennsylvania on climate sensitive sectors (agriculture, ecosystems
    and fisheries, forests, energy, outdoor recreation and tourism, human hh, ealtwater and insurance) and
    the general economy. The 2012 update is based on a review and evaluation of pertinent scientific
    literature and data analyses conducted by hTe Pennsylvania State University team since the conculsion
    of the last report. The update indcleus new simulations conducted uinsg results from the North
    American Regional Climate Change Assessment Program (NARCCAP). It includes updates for all the
    sectors considered in the rpevious report except insurancea nd the general economy. The 2009 PCIA
    concludes that Pennsylvania’s insurance sector is wemllan- aged and not highly vulnerable to climate
    change. There was no new information indicating a need to revisit this coinocnl. usThe
    Impacts of Projected Climate Change in Pennsylvan
    dicates that while significant economic impacts
    could occur within certain climate sensitive sectors, Pennsylvania’s overall economy would be little
    affected by projected climate change. There was no new information suggesting a need to revisit this
    Pennsylvania’s Climate Futures
    The update on Pennsylvania’s climate futures includes new simulations that were conducteind g us
    results from the North American Regional Climate Change Assessment Program (NARCCAP). The
    2009 PCIA was based on Global Climate Models (GCMs) thhaad
    t a very coarse horizontal resolution
    (several hundred km). The NARCCAP resultus se higher resolution (50 km) Regional Climate Models for
    North America that are nested inside of GCMAst . 50 km resolution, Pennsylvania can be broken down
    into nearly 40 grid boxes, effectively providing results approaching the scale of an individual c. ounty
    Greater spatial resolution and potentially greater GCM based certainty allow for more accurate
    investigation of the impacts of climate changeS. imulations with the higher resolution models were
    conducted for the recent past (197-21000) and one future period (204-12070) for the A2 emissions
    scenario (a medium-high scenario) designed by the Intergovernmental Panoen l Climate Change.
    In addition to repeating and extending the analysis of the Pennsylv-awinidae averages of temperature
    and precipitation from the 2009 PCIA, this update takes advantage of the improved resolution of the
    NARCCAP models to evaluate theirab ility to simulate spatial variations in Pennsylvania’s clim. ate
    Furthermore, we present future projections of temperature, precipitatianond soil moisture change at
    50 km resolution. The update also presents a brief analysis of temperature change ovenr nPseylvania
    since 1900.

    The use of higher resolution models does not change the overall picture of simulated climate as
    presented in the 2009 PCIA. The regional climate models do not seem to reproduce the spatially
    averaged climate over Pennsylvania any btter
    e than the global climate modelsT. he regional climate
    models do, however, capture the broad spatial distribution of temperature across Pennsylvania, though
    this is not the case for precipitatio. Tnhe projections of future climate are not substantially different
    from the previous report (at least for the time period and scenario for which we
    uld cocompare the
    GCMs and RCMs).
    Our analysis of temperature change over the commonwealth duritnhg e past 110 years shows longter- m
    warming with a brief (but dramatic) m-20id
    century cooling. Global climate model simulations (with
    and without anthropogenic forcing) suggest that greenuhsoe gases are the main caue sof the lon-gterm
    The 2009 PCIA used the IPCC A2 and B1 emissions scenarioThs.e medium-high A2 scenario assumes
    high population growth, slow economic growth and locally based environmental policies with little
    global cooperation. The B1 scenario assumes a mid-century population growth which later declines,
    lower economic efficiency (but higehr than the A2 scenario) due to environmental and social concerns,
    and global integration leading to more environmentbalas- ed development. Figure 1.1 includes a
    summary of all four scenarios. An -dinepth look of all 4 storylines can be found in Appendi.2x 1. 1
    Figure 1.1.
    Source: Bates, B.C., Z.W. Kundzewicz, S. Wu and J.P. Palutikof, Eds., 2008: Climate Change
    and Water. TechnicalP aper of the Intergovernmental Panel on Climate Change, IPCC Secretariat,
    Geneva, 210 pp.

    The sectoral assessments in this udpate also refer to these scenario. sThe Climate Futures updates foucs
    on the value added of the additional spatial resolution provided by RCMs and utilizes the A2 sc. enario
    The findings of the analysis confirm the findings of the 2009 PfCoIA
    r the A2 scenraio. We would expect
    the same to be true for the B1 scenario. Figure 1.2 includes annu
    proCOjections for different
    emission scenarios (including A2 and B1 which were eusd in the 2009 PCIA).
    Figure 1.2.
    Annual CO2 emissions for the 2
    in gigatons of carbon (Gt C) for a range of possible
    world development path way. sSource: IPCC 2007a3.
    Analogous to our findings for Pennsylvania’s climate futures, our findings for climate sensitive sectors
    are largely consistent with the findings of the 2009
    Pennsylvania Climate Impacts Assessm
    differences found in the agricultural sectors concerne thnear-term economic environment between
    now and 2020 in which changes in climate will oc. cWure find that there is likely to be tight market
    situations for most agricultural products during the current decade in which extreme weather events are
    likely tolead to greater swings in global agricultural prices than would have been the case 10 or 20 years
    ago. We also find that conversion of agricultural land to uhsiong and other urban uses in southeastern
    Pennsylvania [where much of agriculture in the state cis
    oncentrated] will be lower between now and
    2020 than we anticipated in the 2009 PCIA. In addition, the difficult federal fiscal situation may restrict
    funding for crop insurance and agricultural researc. Shhould this occur, the private sector will neeo d t
    commit to a greater role in insuring against weather risks, and in developing new crop varieties and
    livestock breeds suited to a changed climate.

    Water Resources and Aquatic Ecosystems
    This update confirms the 2009 PCIA that risks related to hydrologic systems and aquatic ecosystems are
    significant and will pose challenges for water resource and ecosystem man. gTehrse update contains
    new information on vulnerabilities in these sectors in Pennsylvania based on recent sctasude ies.
    Adaptation strategies for water management under potential climate change have to be developed
    while considering scenarios for future regional population and economic development. Population
    growth, urbanization and other land cover change, anod lluption of water bodies could be equal or even
    more important stressors than climate change at - least in the near future. A holistic approach to
    developing adaptation strategies will be required, while the existing uncertainty in current projections of
    climate change impacts suggests that “no regret” strategies might be the best option for now. Strategies
    are classified as “no regret” if they lead to societal benefits regardless of the degree of climate change.
    Examples of such strategies include water ncoservation and better monitoring of hydrological and other
    environmental variables. Strategies to limit harm emphasize maintaining and improving the resiliency of
    aquatic systems through minimization of increased stream temperature, nutrient enrichment,
    hydrologic modification, habitat fragmentation and degradation, and species loss. Such actions would
    Protection of existing stream and wetland habitat, especially intact habitat for identified species
    of interest, such as eastern brook tro; ut
    Maintaining riparian forests for moderation of stream
    emtperature and treatment of runoff
    from adjoining lands;
    Implementation of Best Management Practices to reduce nutrient loading;
    Restoration of aquatic ecosystems such as streams and wetlands wherepover ssible; and
    Minimizing groundwater pumping (for irrigation, human consumption, etc.) that removes water
    from aquatic and wetland ecosystems.
    The likely impacts of climate change on energy production and utilization in Pennsylvania have not
    changed significantly from the 2009 PCIA. Warming in Pennsylvania is likely to increase the demand for
    electricity for cooling in the summertime, and can be expected to decrease demand for heating fuels (in
    Pennsylvania, the primary fuels uesd for heating are natural gas, fuel oil and electricity). The increase in
    cooling demand is likely to outweigh the decline in heating demand, implying that electricity
    consumption is likely to increase as a result of climate chan. Pgeerhaps more notably, peak-time
    electricity demand is likely to increas. eMeeting peak-time electricity demand without sacrificing
    reliability is challenging and costly, although recent policy initiatives to increase dem-sanide d
    participation in regional electricity markets may help to reduccoe sts and impacts on electric reliability.
    Climate impacts on Pennsylvania’s forest are likely to include species composition shifts, shifts in tree
    regeneration rates, greater tree stress, changes in the phenology of forest ecosystem spechiaesn, gecs in
    tree chemistry and growth rates, greater insect, disease and invasive species activity, and shifts in faunal
    populations. Many of these shifts have already begun to occur, and while many may be expected to lead
    to greater tree mortality, at leasfot r the present, increases in mortality that can be attributed to climate

    change have been mino. rThe effects of longer growing seasons and the
    CfeOrtilization on tree growth
    rates has not yet been observed in Pennsylvania’s forests, and may be ofy fsetht e bnegative effects of
    pollutants such as ozone and sulfate depositio. Tnhese effects will interact in very complex ways, making
    highly specific projections of future forest conditions difficult.
    As a significant reservoir of carbon, Pennsylvania’s esfots r can contribute to mitigating future climate
    change, but these effects are not likely to be large, as the growth rate of Pennsylvania’s forests is
    relatively slow and difficult to accelerat. eThe most promising forest management strategies for
    mitigating climate change in Pennsylvania are to reduce rates of conversion of forestland to
    uses and to plant trees in areas wrhe ethey are not currently found (e., ga.bandoned strip mines and
    some urban areas).
    A challenge for Pennsylvania’s foresmt anagers will be to actively adapt forests to climate chan. Ag ekey
    adaptation strategy will be to maintain or increase forest connectiv. Tithyis may be a significant
    challenge in areas where road and pipeline networks are being built and expanded to odp evneatl ural
    gas from the Marcellus Shale and other promising geological strataF. or some key species that are
    particularly vulnerable to climate change, assisted migration may be an option, but accomplishing this in
    practice for very many species will be difficult.
    Human Health
    Understanding of human health impacts of climate change has advanced from 2009 report rese. arch
    A consistent finding is that the impact of climate change on human health is uncertain, but likely to be
    small. Research has consistently shown that warming temperatures would result in increased
    heat-related deaths and decreased cold-related deaths. The net effect is uncertain, though recent
    research suggests that the increase in hearet-lated deaths will be larger than the decrease in
    cold-related deaths, so that total temperatu-rerelated deaths will increase. Adaptation strategies to
    reduce heat-related deaths include warning systems, provision of emergency shelters during heat waves
    and cold snaps, assistance to low incomeuh soeholds toas sure adequate heating and cooling in the
    home, and changes to building codes to reduce urban heat island ef. feRcetssearch on the impact of
    climate change on ozone and particulate concentrations in relation to significant poair
    llution related
    health risks is ambiguous. Warmer summer temperatures favor ozone and particulate creation.
    However, pollution concentrations depend on other factors as well, such as: cloud cover, precipitation,
    and air mixing. All of these are potentially affected by climate change. Regardless of whether climate
    change will increase or decrease pollution concentrations, other factors will have a larger effect on local
    air quality. Primary among these other factors is policies to reduce emissions of two volatile organic
    compounds: SO
    and NO
    Research on extreme weather events is not sufficient to project whether Pennsylvania will be subject to
    less or more severe storms or flooding. Pennsylvania is likely to experience fewer snowstorms and fewer
    freezing rain events. However,a s pointed out in the20 09 PCIA, traffic fatalities are not necessarily
    higher when roads are slippery. There is some evidence that Pennsylvania will experience a fewer
    quantity of rain events, but more intense rain events. Consequently, flood risk mcray
    easine. River
    monitoring is critical for effective warning and emergency respon. Cseareful hydrologic and land ue s
    planning can reduce flood risk and reduce the number of buildings at risk of flooding.
    As more research is conducted on the potential imps acotf climate change on infectuios disease, two
    things have become increasingly clear. First, our understanding of the biology and ecology of inufes ctio

    disease is insufficient to project with confidence what impact climate change might have on its
    distribution or prevalence. Second, factors other than climate change, such as habitat disturbance,
    human behavior, and health care access, will have a larger impact on disease incidence ancod moeus t
    than will climate change.
    The health impacts of climate change will fall disproportionately on vulnerable subpopulations. These
    include the very young, the elderly, those with low s-oeccoionomic status, those with chronic medical
    conditions, and those without access to health care. C-efosftective adaptation strategies should be
    targeted to those atr-isk groups.
    A consistent finding highlighted by several recent studies on the impacts of climate change on human
    health is that health impacts will vary within the population, with some identifiable groups more
    vulnerable to health impacts from climate change than others. For each climate change health impact
    discussed, this chapter will summarize: what is known about which subpopulations are more vulnerable
    and discuss how those vulnerabilities could be reduced.
    Outdoor Recreation and Tourism
    The main concul sions for outdoor recreation mirror those in the 2009 PC. IAThe outdoor recreation
    activity that will be most affected by climate change is winter recreation. Snowfall is expected to decline
    and winter temperatures are expected to rise. Both trends work against snow depth, which is the critical
    factor for snow-based recreation. There are few opportunities for adaptation for dispersed winter
    recreation such as cros-scountry skiing and snowmobiling. Downhill skiing canapad t for a limited time
    through increased and improved snowmaking. Moreover, ski resorts that depend upon summer revenue
    sources can remain financially viable for an extended period of time. As temperatures continue to rise
    through the latter half of the century, the only available adaptation approach for downhill skiers will be
    to travel to other regions located farther north or at higher elevations.

    The Pennsylvania Climate Change Act, Act 70 of 20, 0d8irected Pennsylvania’s Department of
    Environmental Protection (DEP) to initiate a study of the potential impacts of global climate change on
    Pennsylvania over the next centur. yThis study was conducted for DEP by a team of scientists at
    The Pennsylvania State University and presented to the department of in the 2009 reports
    Climate Impacts Assessment
    , and
    Economic Impacts of Projected Climate Change in Pennsylvan
    report presents an update of on those findings, also mandated by the Pennsylvania Climate Change Act,
    Act 70 of 2008.
    The 2009 PCIA contained an assessment of the impacts of global climate change on Pennsylvania’s
    climate in the 2
    Century. It presented assessments of the impacts of climate change in Pennsylvania
    on climate sensitive sectors (agriculture, ecosystems and fisheries, forests, energy, outdoor recreation
    and tourism, human health, water, insurance) and the general econ. oTmhiys update is based on a
    review and evaluation of pertinent scientific literature and data analyses conducted hbe y PTennsylvania
    State University team since the conculsion of the last repo. rtThe update includes new simulations were
    conducted using results from the North American Regional Climate Change Assessment Program
    (NARCCAP). It includes updates for all the sectors csoidnered in the previous report except insurance,
    and the general econom. yThe 2009 PCIA concludest hat the Pennsylvania’s insurance sector is
    well-managed and not highly vulnerable to climate chan. geNew information indicating a need to revisit
    this conclusion was not found. Our assessment of the impacts of climate change on Pennsylvania’s
    overall economy presented in the
    Economic Impacts of Projected Climate Change in Pennsylvania
    indicated that while significant economic impacts could occur within certcain
    limate sensitive sectors,
    Pennsylvania’s overall economy would be little affected by projected climate cha. nNgeew information
    suggesting a need to revisit this concliuosn was not found.
    The report begins with Pennsylvania’s climate futures, which uis nfdoational information for the entire
    report. It then presents the individual sector assessments.

    Pennsylvania Climate Futures
    This update presents new simulations of Pennsylvania’s future climate ing usresults from the North
    American Regional Climate Change Assessment Program (NARCCAP) (Mearnet sal ., 2009) . Further
    details of the NARCCAP can be found on the NARCCAP web site
    ). Results for Pennsylvania’s climate futures, presented
    in the 2009
    Pennsylvania Climate Impacts Assessment
    2009 PCIA) were based on Global Climate
    Models (GCMs), which have very coarse horizontal resolution (several hundred. Ikn mt)he NARCCAP,
    Regional Climate Models (RCMs) of higher resolution (50 km; m3il1 es) for North America are nested
    inside of GCMs. At this resolution, Pennsylvania can be broken down into nearly 40 grid boxes,
    effectively providing results approaching the scale of individual count. ieFigs ure 3.1 shows the domain of
    the NARCCAP modelsa nd the topography at a resolution of 50 km (31 mi. leCso)astlines and mountain
    ranges are resolved much better with RCM resolution than with GCM resolu. tiFoonr example, the
    Appalachian Mountains are barely noticeable in GCM topography (not shown) wherehaes y tshow up
    quite clearly at 50 km (3m1 iles) resolution. This improved resolution, though still not ideal, affords the
    possibility of investigating the impacts of climate change with greater spatial resolution and potentially
    greater certainty.
    Figure 3.1.
    The NARCCAP domain illutsrated by the topography (m) at a horizontal resolution of 50. km
    To illustrate the difference between a climate projection by a GCM nae loand one by an RCM nested
    inside a GCM, see Figure 3.2, which shows the projected change in precipitation during the winter by
    mid-century under the A2 emissions scenar. ioWhile the broad patterns are similar in the two
    projections, there are substantialre gional differences and there is clearly more detail in the RCM
    output. For example, in Pennsylvania the projected change simulated by the GCM is uniform across the
    commonwealth while the RCM projects a somewhat smaller change that increases from the thseaoust
    to the northwest.

    Figure 3.2.
    An example illustrating the difference between output from a global, coarse resolution
    climate model (left) and a hig-rehsolution regional climate model nested in a global climate model
    (right). Shown here is the projected winter precipitation change (percent) under the A2 emissions
    scenario by the middle of the
    simulated by the Coupled Global Climate Model Version 3
    (CGCM3, left) and the Regional Climate Model Version 3 (RCM3, rigehstte) dn in CGCM3.
    The main questions we seek to answer with the higher resolutions models are [1] do -rehsigholution
    regional models perform better than coars-ree solution global models ats imulating the
    Pennsylvania-average climate; and [2] is model conseuns s in future climate projections improved by
    using regional climate models?
    In addition to repeating and extending our analysis of the Pennsylv-awinidae averages of temperature
    and precipitation from the 2009 PCIA, we take advantage of the improved resolution of the NARCCAP
    models to evaluate their ability to simulate spatial variations in Pennsylvania’s clim. Faturethermore,
    we present future projections of temperature, precipitation, dan soil moisture change at a resolution of
    approximately 50 km (31 miles).
    Finally, building on related ongoing research at ThPee nnsylvania State University concerning climate
    trends within the Delaware River Watershed, we present a brief analysis of eteratmupre and
    precipitation change over Pennsylvania since the beginning of the
    Differences in GCM analysis between this update and the 20PC09I A
    We found ways to improve our analysis of GCM output since the 2009 ,P CaIAnd sot o understand why
    some of the GCM results shown here are slightly different, we clarify the three main changes that we
    made in the analysis.
    The first difference is in the total number of GCMes du, swhich aren ow 12 compared to the original
    14. Two of the models (CCSM3 and PCM) have been dropped buesce a they do not have complete daily
    precipitation files for future climate scenarios, which are needed for calculation of the extreme

    precipitation metrics. Note that these two models
    used in the 2009 PCIA for the analysis of other
    metrics, including those based on daily temperature (e.g., frost days).
    The second difference is in the treatment of realizatio. nA srealization is an individual simulation by a
    GCM with a specified forcing (e.g., greeunshe o gas scenario) and a specified initial state at the beginning
    of the simulations. The initial state includes, for example, the thrdeeim-ensional distribution of
    temperature in the ocean. Due to observational error, many different initial states are possible, anud s th
    to capture the impact of different initial states on the simulated climate, many different simulations are
    run with slightly different initial condition. sIn the 2009 PCIA, multiple realizations were ued
    s for the
    century (see Table 3.1) and ne o realization wasu sed (due to availability) for the
    scenarios. In the 2009 PCIA, metrics from the multiple realizations for the
    were averaged.
    Differences between the 2
    and 20
    century were computed by comparing the
    21century metric to
    the average of the 2
    century metrics. This creates some inconsistency becuase some of the difference
    computed in this way reflects a change in the initial state of th
    ce2n1tury simulation as opposed to
    the change in the greenhuose gas forcing, which the difference was intended to refle. Tco
    t address this
    inconsistency in this update, we only e usone 20
    century realization for each GCM, which corresponds
    to the single realization uesd for the 2
    The third difference is that we now eu sa common period for model evaluation, 19-719998 . In the
    2009 PCIA, we had used 1901-1997 for metrics based on monthly averages of temperature and
    precipitation and 1979-1997 for metrics based on daily averages of tepmerature and precipitation.
    Regional climate models, data sets, and analysis
    NARCCAP includes a wide range of possible GC-RMCM combinations so as to provide a measure of
    uncertainty in climate projections that is due to the climate models themsel. Ivn estotal, there are four
    GCMs and six RCMs participating in NARCCAP, though only 12 of the possible 24 combinations eare
    d us
    due to computational resource limitation. sWhen our analysis began in the fall of 2011, there were nine
    combinations available to U.S., which are listed in Table 3. .S2imulations with these models were
    conducted for the recent past (197-21000) and one future period (204-12070). These were conducted
    for the A2 emissions scenario, designed by the Intergovernmental Panel on Climate Ce han(IPgCC) and
    utilized in the 2009 PCIA( Nakiće& nSwoavrt,ić
    2000). The A2 scenario, which can be described as a
    medium-high scenario, assumes continued growth in global emissions of greeunshe o gases throughout
    the 21
    Originating Group(s)
    CMIP3 I.D.
    Bjerknes Centre for Climate Research
    National Center for Atmospheric Research
    Canadian Centre for Climate Modeling &
    Météo-France /Cent re National de
    Recherches Météorologiques
    CSIRO Atmospheric Research
    CSIRO Atmospheric Research
    Max Planck Institute for Meteorology

    Originating Group(s)
    CMIP3 I.D.
    Meteorological Institute of the University of
    Bonn, Meteorological Research Institute of
    KMA, and Model and Data group.
    Germany /
    U.S. Dept. of Commerce / NOAA / Geophysical
    Fluid Dynamics Laboratory
    U.S. Dept. of Commerce / NOAA / Geophysical
    Fluid Dynamics Laboratory
    Institute for Numerical Mathematics
    Center for Climate System Research (The
    University of Tokyo), National Institute for
    Environmental Studies, and Frontier Research
    Center for Global Change (JAMSTEC)
    Meteorological Research Institute
    National Center for Atmospheric Research
    Table 3.1.
    Global climate modelsu sed in the 2009 PCIA. This is similar to Table 5.1 in the 20P09
    except that it also shows the number of
    realizations used in the 2009 PCIA. Only one
    realization is used in the current reportA. n asterisk indicates a model not edu s in Section 3.3 of the
    current report.
    Model ID
    Modeling Group
    UC San Diego / Scripps
    Hadley Center
    Iowa State University
    UC Santa Cruz
    UC Santa Cruz
    Pacific Northwest National Lab
    Pacific Northwest National Lab
    Table 3.2.
    List of regional climate modelus sed in this update.
    Our analysis of the NARCCAP simulations follows the same approache du sfor the GCM analysis in the
    2009 PCIA. In short, we vealuated each model based on its ability to simulate mean annual cycles of
    Pennsylvania-wide averages of surface temperature and precipitation (mean, interannual variation, and
    intramonthly variation), and an overall ranking for each model was comp. uThtede time periods for
    analysis are 1979-1998 for the baseline and 204-62065 for the future time period.
    The two data sets for model evaluation of these six metrics are the same as theod se in usthe 2009 PCIA.
    The data setu sed for characterizing lon-gterm statistics in monthly means is from the University of
    Delaware (Matsuura & Willmot et al., 2007a,b). The version we are uinsg here is an update of the
    version used in the 2009PC IA. Here we are using Version 2.01 (released on June 22, 2009), whereas the
    2009 PCIA used a version downloaded from the Unirsveity of Delaware website in May 2008 (it is
    unclear what version this was. )The data set for computing metrics based on daily temperature and
    precipitation is from the North American Regional Reanalysis (NARR) (Mesinet
    al., 2006).

    In our examination of the spatial patterns of temperatuprere, cipitation, and soil moisture, wue se a
    horizontal grid spacing of 0.59° longitude and 0.575° latitude, chosen to create a 4 × 10 grid that is
    aligned with Pennsylvania’s southern, western and northern bord. eTrhse monthly observational data
    from the University of Delaware (0.5° resolution) were linearly interpolatto edt his grid. The RCMs, at 50
    km resolution, were u-pscaled to this grid by simply averaging any model grid points within each grid
    3.3.1 Model evaluation
    Figures 3.3, 3.4 and 3.5 show an evaluation of the NARCCAP models and the GCedM is n utshe 2009
    PCIA. Each figure shows 95p ercent confidence intervals (calculated uisng bootstrapping) of mul-tmi odel
    ensemble averages of the RCMs and GCMs; the observatioare
    ns shown as wellTh. ese figures show that
    there are some differences between the GCMs and RCMs in their simulation of the climate averaged
    across the commonwealth. We demonstrated in the 2009 PCIAt hat the GCMs have a slight cold and wet
    bias and here wes ee that this bias is slightly worse for the RCMs (Figure . 3W.3h)ereas the GCMs had
    mainly a winter cold bias, the RCM bias is more constant throughout the year, which means that the
    amplitude of the annual temperature cycle (summer mus inwinter) is actually improved for the RCM. s
    Except for the fall, the GCMs showed a wet bias and this is amplified for the RCMs, which show a wet
    bias in all months except Septemb. erInterannual and intramonthly variability in temperature is similar
    in the RCMs and GCMs, itwh modest biases that vary with season (Figures 3.4 and . 3In.t5e)rannual and
    intramonthly variability in precipitation, however, is clearly worse for the RCMs as it is too high
    compared to obsevr ations (Figures 3.4 and 3.5D)es. pite the differences and ligs ht degradation in skill of
    the RCMs, the simulations on the whole capture many features of Pennsylv-avaneiaraged climate, such
    as the clear annual cycles in temperatu-rebased metrics.
    Figure 3.3.
    Mean annual cycles of observed and simulated Pennsylvan-miaean temperature (left) and
    precipitation (right) for the period 197-199 98. Blue shading is the 95p ercent confidence interval of the
    average of all of the NARCCAP models (the nine RCMs) and readd insg
    h is the 95 percent confidence
    interval of the average of the CMIP3 models (the 12 GCMs), and the black line is the observations.

    Figure 3.4.
    Annual cycles of observed and simulated Pennsylvan-miaean interannual variability in
    temperature (left) and precipitation (right) for the period 1-9197998 . Blue shading is the 95p ercent
    confidence interval of the average of all of the NARCCAP models (the nine RCMs) and red shading is the
    95 percent confidence interval of the average of the CMIP3 models (1t2
    he GCMs), and the black line is
    the observations.
    Figure 3.5.
    Annual cycles of observed and simulated Pennsylvan-miaean intramonthly variability in
    temperature (left) and precipitation (right) for the period 1-9197998. Blue shading is the 95p ercent
    confidence interval of the average of all of the NARCCAP models (the nine RCMs) and red shading is the
    95 percent confidence interval of the average of the CMIP3 models (the 12 GCMs), and the black line is
    the observations.
    The superiority of the GCM simulations can be seen in the error index computuesd ing the mean annual
    cycles of the mean, interannual variability, and intramonthly variability of temperature and
    precipitation, following the exact same protocol wee ud sin the 2009 PCIA (Figure 3.6. )The error index
    ) for an average model is equal to onane d for a perfect model is equal to z. eThroe multi-model
    ensemble average for the GCMs is the best model representation of the commonwealth’s climate, with
    an error index of 0.29, and the RCM average is the second best, with an error index of 0.56.

    Figure 3.6.
    Error index for each model and the mu-mltiodel ensemble averages for the regional and
    global models.
    We also compared the RCMs and GCMs in their ability to simulate hydrological extremes (metrics that
    are described in detail in the 200P9 CIA). Results are shown in Figure 3.7. We see that botht s osef
    models do reasonably wellb; ut, again, the GCMs tend ptoer form slightly better, with the RCMs slightly
    on the extreme side.
    Figure 3.8 shows the spatial distribution of annumale-an temperature and precipitation for the
    multi-model RCM ensemble average and the observations, as well as the average mode. l Thbiase
    models, in spite of their cold and wet biases, are able to capture some aspects of the spatial patterns of
    temperature and precipitation patterns across PennsylvaniaS. urface air is relatively cool along the
    central portion of the northern border and
    elatr ively warm in the southeas. tThe temperature difference
    between these two regions is about °C 4 and is reproduced reasonably well by the RCM ensemble
    average. The distribution of annualm-ean precipitation across Pennsylvania indicates relatively wet
    regions along the northern and southern borders in the western portion of the state and in much of the
    east, with dry regions in the center and along the central portion of the western b. Tohrde emr odel
    ensemble average picks up these features, with the exticepon of the latter.

    Fig. 3.7.
    Evaluation of the RCM (NARRCAP) and GCM (CMIP3) simulation of hydrological extremes: the
    annual number of days with precipitation > 10 mm (upper left), the annual maxim-duam y 5precipitation
    total (upper right), the annual maximum number of consecutive dry days (lower left), and the fraction of
    the annual precipitation that comes from the topp e5 rcent of daily precipitation events (lower right).

    Figure 3.8.
    Observed (top panels) and ensembl-eaveraged RCM (middle panels) annu-aml ean
    temperature (left panels) and precipitation (right panels) for the 1-199798
    9 time periods. The bottom
    panels show the bias for temperature (simulated mus inobserved, left) and precipitation (simulated
    minus observed, expressed as a percent of the observed, right).
    3.3.2 Model projections
    Figures 3.9 and 3.10 show the season-malean temperature and precipitation changes by m-idcentury
    under the A2 scenario. The RCM results are consistent with many of our findings with the GCMs, as
    described in the20 09 PCIA. Specifically, we see that [1] all models warm, t[h2e] re is slightly greater
    median warming in summer than in winter, and m[3o] re than ¾ of the models get wetter in win. ter
    One difference we find is that more than ¾ of the RCMs get drier in summer, whereas the GCMs are
    nearly evenly split during this season between getting wetter and drier.

    Figure 3.9.
    Box-whisker plots of simulated seasonalm-ean temperature change across Pennsylvania by
    the 12 GCMs (CMIP3) and the nine RCMs (NARCCAP) by -cmenidtury under the A2 emissions scenar. io
    The red line is the median change, the blue horizontal lines represent th
    changes, and the black lines thextre eme.

    Figure 3.10.
    Box-whisker plots of simulated seasonalm-ean precipitation change across Pennsylvania by
    the 12 GCMs (CMIP3) and the nine RCMs (NARCCAP) by -cmenidtury under the A2 emissions scenario.
    Changes in hydrological extremes predicted by the RCMs tend to be similar to those predicted by the
    GCMs, as shown in Figure 3.. 1T1here is a slight tendency, however, for the RCMs to predict smaller

    Fig. 3.11.
    Box whisker plots of simulated changes of hydrological extremes averaged across Pennsylvania
    by the 12 GCMs (CMIP3) and the nine RCMs (NARCCAP) by -cmenidtury under the A2 emissions
    scenario: the annual number of days with precipitation > 10 mm p(eur p left), the annual maximum
    5-day precipitation total (upper right), the annual maximum number of consecutive dry days (lower left),
    and the fraction of the annual precipitation that comes from the topp er5
    cent of daily precipitation
    events (lower right).
    The spatial variability of projected change as well as the degree of conuss eanms ong models is very
    different for temperature and precipitation (Figure 3.1. 2T)emperature change is quite uniform, varying
    by no more than 10 percent for the multi-model ensemble average of the RCM. Tshere is also a strong
    consensus among models for warming throughout the commonwealt. Phrecipitation, on the other hand,
    has projected mult-imodel mean increases throughout the state ubt with substantial consenuss (at least
    eight of the nine models agreeing on the sign of the change) in only about half the commonwealth.

    Figure 3.12.
    Spatial distribution of temperature (top) and precipitation (bottom) change across
    Pennsylvania by mid-century under the A2 emissions scenario (mu-RltCi M average). Shading indicates
    where at least eight of the nine modelgs ree
    a on the sign of the change.
    Projected soil moisture change is shown in Figure 3.13 for the summThee r. multi-model mean for the
    RCMs shows a decline ranging from 0 top er6 cent throughout the commonwealth, and there is
    considerable consensus of drying among the mode. lsThe soil moisture declines presumably occur as a
    result of warming (which will increase potential evapotranspiration) and precipitation declines during
    the summer (Figures 3.9 and 3.10).

    Figure 3.13.
    Multi-model ensemble averages in the changes in summer average smooil isture from the
    regional climate models. Shading indicates where at least eight of the nine models agree on the sign of
    the change.
    Historical temperature and precipitation change across Pennsylvania
    We analyzed changes in temperature and preciptitioan since 1901 across Pennsylvania uins g data from
    24 stations that are part of the United States Historical Climate Network, Version 2 (Met , 2009;
    Menne et al., 2010). These data are similar to those that underlie the University eolaf wDare gridded
    atlas (Matsuura & Willmot, 2007a,b), which was used earlier (e.g., in Figures 3.3. and 3.4)u. sTHhCe N,
    however, is a hig-hquality data set specifically designed for lo-ntgerm trend analysis, and has undergone
    extensive quality control and adujstments to account for spuriuos trends due to, fr oexample, changes in
    station location and the time at which daily observations were made. Thesuse tmadjents can be
    considerable, as shown below.
    During the past 30 years, station trends vary between 0.1 to 0.5 °C (0.2
    to 0.9
    per decade with an
    average of 0.3 °C (0.6 °F) per decade (Figure 3.14). The temporal pattern of change is very similar across
    the state, as shown in the decad-avaleraged temperature anomalies (Figure 3.15, left pan. el)
    Temperature increased by about 0.7 C °(1.3
    from the beginning of the 1900s to the 195. 0sIt then
    dropped rapidly by about 0.5 °C (0.9
    over the next decade or s. oSince the 1960s, there has been a
    steady increase of about 1 °C (1.°F8 ). The temporal pattern of change in Pennsylvania is broadly similar
    to the global average temperature chang(e Trenberth et al., 2007). Overall, the temperature increase in
    Pennsylvania from the first decade of the
    to the first decade of the
    2c1entury is about
    1.3 °C (2.4

    Figure 3.14.
    Mean temperature trends at the
    USHCN stations in Pennsylvania between 1981 and 201. 0
    What caused the longt-erm warming in Pennsylvania? We can use the GCMs to help U.aS.
    nswer this
    question. The left panel in Figure 3.16 shows the observed and simulated temperature change from the
    average over the 1900-1919 period to the average over th19e 79-1998 period. The bar on the left is the
    average temperature change simulated by the 12 GCMseu d s in this study (0.7
    C, 1.3
    F) and agrees well
    with the mean of the UHSCN stations over Pennsylvania (0.6
    C, 1
    F). These models include the
    observed 20
    century increases in greenhouse gases as well as natural forcings, such as changes in solar
    output and volcanic aerosols. We searched for the output of GCM simulations that contained only the
    natural forcings in order to determine their impact. We foutwnd
    o such GCMs (CCSM3 and PCM), which,
    coincidentally, are the same GCMs that were dropped from the analysis in Section 3.3 due to a lack of
    daily output for future scenarios. Output was acquired from the Earth System Grid gateway at the
    National Center for Atmospheric Research (
    ). As Figure 3.16 shows, these two
    models simulate warming over the 2
    0century when all forcings are included (though less warming
    than the average of the 12 models) and less warming or cooling when only natural forcings are included.
    This result suggests that a substantial portion of the observed warming in Pennsylvania isu a lt reof s
    anthropogenic climate forcing (i.e., greenuhsoe gases).
    However, as indicated by the last set of bars in Figure 3.16, the actual amount of warming in
    Pennsylvania over the 2
    century is subject to substantial uncertainty resulting from uasdtmj ents
    made to the USHCN data. Without the audsjtments, very little increase in temperature is seen. This is
    consistent with the change estimated from the University of Delaware data, which also shows much less
    warming than the adujsted USHCN data. These adujstments are important and necessary for making our
    best estimate of temperature change over the commonwealth, but the uncertainty introduced by them
    is not well constrained and deserves further study. Note that the usatmdjents are very minor over the
    past several decades (not shown), which means there is little uncertainty in the trends over this time

    We conducted a similar analysis for precipitation change over PennsylvanFiaig. ure 3.15 (right panel)
    shows that Pennsylvania has become increasingly
    wetter, with substantial changes from one decade to
    the next. The 1960s were remarkably dry, with annual precipitation 10 cm (aboupt e1rc0 ent) less than
    normal, whereas the most recent decade was about p1e0 rcent wetter than norma. l Seager et al.(2 012)
    suggested that these anomalies were not a result of greenuhseo gas increases nor were they driven by
    changes in surface ocean temperature. Our analysis is consistent witht .t hTahe right panel in Figure 3.16
    shows large variations among modepl-redicted precipitation change over the 2
    including anthropogenic greenhouse gases can either result in a precipitation increase (PCM) or
    decrease (CCSM3).
    Figure 3.15.
    Decadal averages of temperature (left) and annual precipitation (right) anomalies at each of
    the 24 USHCN stations in Pennsylvania (gray lines) from 1901 to 20. 1T0he anomaly for each station was
    computed with respect to the
    1895-2010 mean. The black line is the average of all of the stations.

    Figure 3.16.
    A comparison of simulated and observed temperature (left) and annual precipitation (right)
    change in Pennsylvania from the early
    (1900-1919) to the late 2
    0 century (1979-1998).
    The left bar in each panel represents the mean ± 1 standard deviation of the 12 GCeMd s in
    usmost of
    this report, which include all forcings (anthropogenic and natu. ral)The next two sets of bars eprresent
    the GCMs CCSM3 (red) and PCM (blue) under all forcings (second set) and natural forcings only (third
    set). The last set of bars represents the observationUns: iversity of Delaware (red)U, SHCN (blue), and
    usHCN unadjusted (green, temperature only).
    The use of higher resolution models does not change the overall picture of simulated climate as
    presented in the 2009 PCIA. The regional climate models do not seem to reproduce the spatially
    averaged climate over Pennsylvania any better than the global climate mo. dTehlse regional climate
    models do, however, capture the broad spatial distribution of temperature and precipitation across
    Pennsylvania. The projections of future climate are not substantially different from our preus vreio port
    (at least for the time period and scenario for which we could compare the GCMs and RCMs). Finally, our
    analysis of temperature change over the commonwealth over the past 110 years shows -loterngm
    warming despite a brief (but dramatic) m-20id
    century cooling. Though the temperature data have
    been corrected for changes in station location and other factors, the uncertainty introduced by these
    changes is not well constraine. dNevertheless, global climate models simulate the observed temperature
    change and indicate that much of it is due to anthropogenic greeunshe ogases. Precipitation has
    increased as well, but this appears to be a result of natural climate variability.

    Matsuura, K., Willmot, C.J., 2007a. Terrestrial Air Temperature:-2
    0 Gridded Monthly Time Series.
    Matsuura, K., Willmot, C.J., 2007b. Terrestrial Precipitation: -12900006 Gridded Monthly Time Series.
    Mearns, L.O., Gutowski, W.J., Jones, R., Le-uYn.g,
    , ML.cGinnis, S., Nunes, A.M.B., Qian, Y., 2009. A
    regional climate change assessment program for North America. EOS, Transactionts he of American
    Geophysical Union 90, 311-312.
    Menne, M.J., Williams Jr, C.N., Vose, R.S., 2009. THhe istUoric.S. al Climatology Network monthly
    temperature data, version 2. Bulletin of the American Meteorological Society 90,- 1090793.
    Menne, M.J., WilliamJr.s , C.N., Vose, R.S., 2010. United States Historical Climatology NetUwSoHrk
    Version 2 Serial Monthly Dataset. Carbon Dioxide Information Analysis Center, Oak Ridge National
    Laboratory, Oak Ridge,
    Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P.C., Ebisuzaki, W., Jovic, D., Woollen, J.,
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    Chapter 9 of the 2009
    Pennsylvania Climate ImpactsA ssessment
    report reached the following
    conclusions about the impacts of climate change on Pennsylvania agriculture:
    1. Moderate climate change may raise Pennsylvania yields of hay, corn, and soybeans, but it may
    also raise yields elsewhere in the U.aS.
    nd around the world –in creasing global production and
    pushing down prices received by Pennsylvania farmers.
    2. Yields of coolt-emperature adapted fruits and vegetables such as potatoes and apples are likely
    to decline as a result of climate change, while yieoldf sfru its and vegetables better suited to a
    warmer climate such as sweet corn are likely to rise.
    3. In the dairy industry, heat stress and a decline in feed quality are likely to drive milk yields
    downward and increase production cost. sFor operations that rely on grazing and -ofarm
    production such as dairy and beef herds, changes in pasture yields and feed quality will impact
    production costs.
    4. For the state’s hog and poultry producers, while climate control costs are likely to increase with
    warmer summer months, this same effect in southern states may make Pennsylvania more
    attractive to these industries and could induce a northward shift in production operations.
    This chapter summarizes new knowledge of about these impacts that has been developed since the
    2009 PCIA.
    The Near- and Long-Term Future for Pennsylvania Agriculture
    Agriculture in Pennsylvania has changed dramatically since 1900 and will likely continue to change in
    profound ways between now and 210, 0regardless of whether climate change is large or smallTh. is
    section discusses some of the major forces in addition to climate changes that are likely to impact
    Pennsylvania’s agricultural sector in coming years and decades. This section also covers huor w o
    understanding of those forces has changed since 2009, and the implications of these forces for potential
    impacts of climate change on Pennsylvanian agriculture.
    4.1.1 National and Global Agricultural Markets
    Pennsylvania is part of local, regional, ionatnal and global markets for food and agricultural produ. cts
    In some cases, such as hay, certain seasonal fruits and vegetables, prices are determinloedc al by and
    regional markets. Changes in demand or supply within Pennsylvania will affect prices fomr farers,
    consumers and others in the supply cha. iInn other cases, such as dairy products and usmhrooms, prices
    are determined by national and global markets. However, Pennsylvania is a large enough producer of
    these products that changes in supply within thste ate will have a noticeable impact on markets. By
    contrast, in cases such as corn and soybeans, Pennsylvania has such as small share of the global market
    that what happens within the state has no significant impact on market prices.
    Prices on national and global agricultural markets have been quite volatile during the past five years,
    with prices in 2011 significantly above lon-gterm averages. Figure 4.1 illustrates monthly trends from
    January 1990 to October 2011 in the Food and Agriculture Organizatio(Fn AO) food price index
    The USDA, World Bank, and International Monetary Fund (IMF) also spublh mionthly food price indices, and their
    indices exhibit similar trends to the FAO index (Trostle et al., 2011).

    index is inflation-adjusted and scaled so that the 200-202 04 average is 100. The index measures
    international prices of a basket of food commodit. ieFsigure 4.2 illutsrates monthly trends for three of
    the commodity groups in that basket that are particularly important to Pennsylvania: meat, dairy, and
    Figure 4.1.
    FAO Food Price Inde. xPrice indices are inflation adujsted and scaled so that 200-22004 =
    100. Food and Agriculture Organization (2011)
    Figure 4.2.
    FAO Price Indices for Meat, Dairy, and Grains. Price indices are inflatioun stadedj and scaled
    so that 2002-2004 = 100. Food and Agriculture Organization (2011)
    Figure 4.1. FAO Food Price Index
    Figure 4.2. FAO Price Indices for Meat, Dairy, and Grains

    Agricultural commodity markets have a long history of booms anud stsb. Many Pennsylvania farmers in
    business today were also inub siness, or beginning their careers, in the m-1970id s . The agricultural
    commodity boom of that era was followed by a majour stb in the 1980s. In 2009 the boom of 2007-2008
    had just ended, prices of agricultural commodities wre edeclining, and it was difficult to project where
    prices would head nex. tSince then prices have risen again and the FAO’s food price index h-tiit mae ll
    highs in 2011. Contributing factors to recent high prices include (Troset
    tleal. , 2011):
    Macroeconomic factors:
    the global economic recovery since 2009 and the declining value of the
    U.S. dollar;
    Supply shocks:
    a series of adverse weather events around the world during 2-0201011 ;
    Consumer demand growth:
    growing demand for meat and dairy prodtus cin emerging market
    countries such as China and Ind; ia
    growing use of corn, sugarcane, and other crops in the global production of bio; fuels
    Agricultural export restrictions:
    taxes, quotas, and bans on exports of key commodities enacted
    by Argentina, Russia, Ukraine and several other agricultural exporting countries (some policies
    since removed).
    Based on past experience, markets will adustj and prices are likely to decline from their 2011 highs;
    however that could take several year. sUSDA agricultural baseline projections to 2020 show prices for
    grains, oilseeds, fruits and vegetables gradually declining during this decade before leveling off at prices
    significantly greater than average prices during the 19-9200 05 period (USDA, Economic Reesarch
    Service, 2011). Among livestock products, poultry and egg prices are projected to remain significantly
    above 1990-2005 averages while dairy and meat product prices are projected to decline close to their
    historical averages by 2020 U(SDA, Economic Research Service, 2011). If dairy and meat prices decline
    while grain and oilseed prices remain high, this would lead to -pcoriscte pressure on Pennsylvania meat
    and dairy producers and could lead to an increase in the number of farms exiting these tuwso triiesnd.
    Overall,US DA projections suggest a tight market situation for most agricultural products during this
    decade. During this period, extreme weather events are likely to lead to greater swings in global
    agricultural prices than would have been the case 10 or 20 years
    the effects of
    weather are policy responses to adverse weather events observed in several emerging market countries
    since 2008. These policies attempted to hold down domestic food prices in those countries but at the
    cost of restricting supplies to world markets and
    usphing up world price. sFor example, Russia’s ban on
    wheat exports during 2010-2011 in response to a severe drought in that country was one important
    factor behind the ru-nup in global wheat prices during hatt time.
    With the partial exception of dairy products, wheUre
    .S. prices are somewhat insulated from world
    prices by U.S. import tariffs and tariffrat- e quotas (TRQs), Pennsylvania producers of internationally
    traded commodities are exposed to developments in world market. sIf climate variability continues to
    increase this decade, Pennsylvania farmers are likely to face more price volatility than in the past (at
    least through 2020) due to weather shocks in vauris oregions of the world.
    The USDA projections are based on the assumption of normal weather worldwide during the projection period
    (2011 -2020), a standard assumption in projections of this type. Of course, the weather in any given year is never
    completely normal, and extreme weather events mayus
    cae agricultural prices to deviate from the values
    projected by USDA.

    Beyond 2020, the uncertainties involved in agricultural market projection— sincluding uncertainties
    about population growth, income growth, technological change, laannd d water availability, energy
    markets and biofuels, and agricultural policie— sbecome far greater. FAO published projections in 2006
    for global agricultural markets to 205. 0These projections indicate that global agricultural supplies will
    keep pace with growing demands and that average food consumption per person will increase
    significantly between now and 2050in emerging market countries– especially consumption of meat and
    dairy products. In developed countries such as thUe .S., where per capita food consumption levels are
    already high, increases in consumption between now and 2050 are projected to be mo. dIn esa t recent
    review of these projections in light of developments since 2006, Alexandratos (2011) concluded that
    they are still broadly valid for 2050.
    Beyond 2050, the uncertainties rise by another order of magnitude buesce a of the possibility of
    technological changes that lead to a dramatic transformation of the agricultural secDtourr. ing the
    century, tractors and other farm machinery virtually eliminated the e uosf draft animals and made
    it possible for a single farmer to cultivate tracts of land orders of magnitude larger than a century ago.
    Listed below are more changes that have occurred since the beginning of th
    Synthetic organic pesticides revolutionized the control of weeds and inse. cts
    Tremendous growth occurred in the use of manufactured fertilizers; hybrid seeds and more
    recently, genetically modified (GM) seeds were developed and widely adopted.
    Livestock production was transformed from a sm-alsclale basis to, in many cases, a very
    large-scale basis with productivity levles far higher than a century ago; farmers became highly
    specialized in the livestock products and crops they produce.
    Crops that were virtually unheard of 100 years ago, such as soybeans, grew to major importance
    It is likely that Pennsylvania agriculture in 2100 will bear only a faint resemblance to today, but we
    cannot say with any conidfence what it might look like.
    4.1.2 Agricultural Land Conversion
    One issue identified in the 2009 PCIA was future trends in farmland availability in light of agricultural
    land conversion to urban uses such as housing, retail, and office spac. Tehis speaks to the question of
    where agriculture will be located within Pennsylvania in the fut—ureclimate change can only impact
    agricultural production if agriculture conintues to exist. Agricultural land conversion is being driven in
    large part by growth in the number of suburban uhseoholds. using 2000 Census data, the U.S.
    Bureau (2005) projected that Pennsylvania’s total population would increase only abopuert 4c ent
    between 2000 and 2030, compared to about 2p9 ercent for the U.S. as a whole. Projections by the
    Pennsylvania State Data Center (2008) uinsg 2000 Census data suggest a somewhat higher population
    growth of about 7p ercent for the state between 2000 nad 2030.
    At the county level, Pennsylvania State Data Center (2008) projections indicate continued strong
    population growth in the southeastern Pennsylvania and population losses in most of western and
    northern Pennsylvania. Agriculture in Pennsylvania isc urrently concentrated in the southeast part of the
    state. Lancaster County, which accounted for nearly o-nfifte h (18 percent) of total agricultural product

    sales in Pennsylvania in 2007
    ,is projected to see its population increase by about p1e8 rcent between
    2000 and 2030. The population of neighboring Chester County, which accounted forp 1er0 cent of
    Pennsylvania’s agricultural product sales in 2007, is projected to rise by almospt e6rc0 ent. Other
    southeastern counties with high projected rates ofo ppulation growth during 2000-2030 include Adams
    (26 percent), Berks (32
    percent), Cumberland (32 percent), and York (2p7 ercent).
    The Chesapeake Bay Program has a land eu schange model that has been eud s to project farmland loss
    within the Chesapeake Bay region over the period of 2-0200625 (Irani, 2011). The principal driver of
    farmland loss in the model is projected coun-letyvel population growth. The farmland loss projections
    are shown in Figure 4.. 3The largest losses in farmland acreage in the Pennsaynlvia portion of the
    Chesapeake Bay region are projectedt o occur in Adams, Cumberland, FranklinLan, caster, and
    York counties.
    County-level population projections have not yet been updated to reflect the 2010 Cues nfigs ures or
    economic developments during the past few year. sWith the economic recovery from the 2007-2009
    recessions occurring more slowly than many anticipated two years ago, suburban population and
    household growth may be dampene. dFor example, the growth projected by Masnick (2010) in the
    number of households nationally between 2010 and 2020 is about -6to-7 percent lower than the
    projections they made in 2009. In some parts of the
    U.S. where there have been steep declines in
    residential land values and increases in farmland values, agricultural land that had been sold to
    developers but was never developed is being repurchased by farmers and put back into agriculture
    (Whelan, 2011). What these national trends will mean for new uhsoing starts in Pennsylvania is not
    entirely clear, considering that the excess supply of uhsinog is greater in many other states than in
    It seems likely that conversion of agricultural land to uhsiong and other urban uess in southeastern
    Pennsylvania between now and 2020 will be lower than what we anticipated in our 2009. PBCeIAyond
    2020, it is much harder to say as it depends on future population growth, economic growth, and the
    housing market.
    4.1.3 Pennsylvania Food Demand
    Two trends identified in the 2009 PCIA were growing demand for organic food products and growing
    demand for local food. sWe indicated that the result of the trend toward organic food combined with
    technological change through biotechnology could be a split in Pennsylvania agriculture into two
    production systems: one heavily invested in biotechnology, and one orga. Tnhice trend toward local
    food implies greater demand over time by Pennsylvania consumers for Pennsylvania food and
    agricultural products, particularly fresh fruits and vegetables.
    From the
    2007 Census of Agriculture
    (USDA, National Agricultural Statistics Service, 2009).

    Figure 4.3.
    Projected Farmland Loss in the Chesapeake Bay Watershed, 2-0200230. Source: Chesapeake
    Bay Land Change Model.
    The slow recovery from the 200-72009 recession led to slower growth in demand for organic products in
    2010 than the double-digit growth rates of previuos years, but total U.S. organic product sales still grew
    by about 8p ercent from 2009 to 2010 (Organic Trade Association, 2011). Statistics on growth in demand
    for localfo ods are scarcer and less reliable (Martinet
    ez al., 2010). The conclusion is that the growth in
    demand for organic food products will continue in the near term, even if the economy remains weak,
    and will accelerate once the economy recovers.
    Over the longer term, the typical assumption in economic projections is that the economy moves
    towards its long-run rate of growth, so that periods of be-loavwerage growth or negative growth
    (recessions) are followed by periods of abo-vaveerage growth. For example, the uDsA’s agricultural
    baseline projections to 2020 assume that inflatio-andjusted growth in gross domestic product (GDP)

    will rise from 2-.6per cent in 2009 to 2.8 percent in 2012 and then level off at 2p.6 ercent growth per
    year during 2013-2020 (usDA, Economic Research Service, 2011).
    4.1.4 Federal Agricultural Budgets
    The federal government is under considerable fiscal pressure at the present time due to high budget
    deficits and deb. t The long-term fiscal outlook for federal entitlement ropgrams, which account for the
    majority of federal spending, is pooTrh. is fiscal situation could result in an extended period of restricted
    federal funding for agricultural programs, including agricultural research, conservation programs, and
    crop insurance.
    As discussed in the 2009 PCIA, any future increase in the variability of temperature and precipitation in
    Pennsylvania is likely to increase the demand by farmers for risk management products, including
    insurance against losses due to drought, floodgin, hail, wind, frost, insects, and dise. asWee also noted
    that the ability of Pennsylvania agriculture to adapt to climate change hinges in part on the
    development and adoption of new crop varieties and livestock breeds suited to a warmer and more
    variable climate.
    With an extended period of restricted federal agricultural funding likely, this means that the private
    sector and/or the state government will need to play a greater role in helping Pennsylvania farmers
    adapt to climate chang. eThe market for pivr ate (not federally subsidized) crop insurance is currently
    negligible, but that market might grow if federal crop insurance subsidies were red. uAcs edGoodwin
    (2001) indicates, the question is whether governme-nsut bsidized crop insurance exists becuase private
    insurance markets have failed or whether the lack of private insurance is due to direct expenditure
    offsets by government involvemen. tOne common argument for why private crop insurance rmkets
    may fail is systemic risfok r example, a major crop failure due to drought texphat oses insurers to large
    losses that overwhelm their reserves. There is debate over whether the reinsurance iunsdtry would be
    willing to cover these systemic ris. ksSystemic risk also figures prominently in insurance againostht er
    extreme weather events such as hurricanes and floods (Hecht, 2008).
    Alternatively, the federal government could continue to play an active role in crop insurance but cut
    expenditures by trimming premium subsidies received by agricultural producers andr/eodr ucing
    subsidies to private insurance companies that deliver federal crop insuran. Scuebsidies to private
    insurance companies consist of administrative anod perating (A&O) subsidies and net underwriting gains
    (the portion of gains kept by insurance comanpies in years when premiums exceed claim. sF)igures in
    Babcock (2010) indicate that premium subsidies on federal crop insurance averaged nearlyp e6r0 cent of
    total premiums during 2005-2009, and that subsidies to private insurance companies (A&O subsidies
    plus net underwriting gains) averaged more than 4p0 ercent of total premiums during 2005-2009.
    With respect to agricultural research, Huffman and Evenson (2006) estimate that the private sector
    accounted for approximately 70 percent of total (public puls private) U.S. agricultural research
    expenditures in 2000. They also estimate that inflatio-nadjusted agricultural research expenditures
    during the 1980s and 1990s grew more rapidly in the private sector (p2e.r5 cent per year) than in the
    public sector (09 . percent per year). As such, the private sector has considerable scope for engaging in
    R&D to assist agricultural producers in adapting to climate chan. gAet the same time, it should be
    recognized that the public and private sectors have different focal areas in agricultural rese. Tarche h
    private sector is foucsed on the development of commercially successful products and services, whereas
    the public sector is foucsed more on basic research and issues such as natural resources and the

    environment where there may be no commercial payoff (Schimmelpfennig He& isey, 2009). In addition,
    an increase in research resources devoted to climate change adaptation might come partly at the
    expense of resources devoted to advancing agrictuulral technology in other ways.
    Recent Research on Climate Change and Agriculture
    This section recapitulates some key points frotm
    he 2009 PCIA and discusses recent scholarly research
    on climate change and agriculture relevant to PennsylvanNiao . recent research foucses specifically on
    Pennsylvania. As a result, other regions of the Uw.itS.
    h a similar climate and agriculture to Pennsylvania
    were used to draw conculsions.
    4.2.1 Climate Change and Crop Production
    Statistics from the
    2007 Cenus
    s of Agriculture
    indicate that the three most important feed crops in terms
    of acreage in Pennsylvania are hay, corn (for grain and for silage), and soybeans, and the most important
    in terms of sales are corn and soybean. Sstatistics from the
    2007 Cenus
    s of Agriculture
    also indicate that
    the largest food crop in terms of sales in Pennsylvania uis shmrooms; the two other food crops on the
    top-ten list in terms of sales are fruits and vegetab
    One issue identified in the 2009 PCIA is that elevated levels of C
    lead to an inrec ase in
    photosynthesis and thus increased yields of these three crops, a phenomenon often called the
    fertilization orenr ichment effect. Carbon dioxide is an indispensable component in the process of
    photosynthesis. This effect is commonly expected to
    be stronger for C
    crops than for C
    crops grown in Pennsylvania and worldwide are
    cCrops. C
    feed crops include soybeans and different
    types of hay, among them alfalfa, timothy, tall fescue, orchardgrass, and perennial ry. eCg
    crops include wheat, barley, fruits, vegetables, and potat. oCe
    include corn and sorghum.
    In a recent review of the literature on experimental approaches to investigating crop responses to
    elevated CO
    , Ainsworth and McGrath (2010) find that majo
    r gCrain crops show an increase in seed
    yield of approximately 13 percent at 550 ppm atmospheric CO
    , while C
    crops do not show a significant
    yield increase at elevated C
    They also found that additional crop growth comes at the expense
    of grain quality: crop growth at elevated
    the protein content of no-lneguminous grain crops
    by 10 to 14 percent and reduces the content of minerals such as iron and zinc by to1 30
    5 percent. These
    represent the effects of elevated atmospheric C
    and not the effects of changes in climate
    in response to elevated C
    There have been a number of studies in recent years that have examined the response of feed crop
    yields to changes in temperature and precipitation, and our 20P0C9 IA discussed some of that literature
    relevant to Pennsylvania. One study published since our earlier report was completed is Schlenker and
    Roberts (2009). Their study found that corn yields increase slightly with average temperature during the
    growing season up to an vaerage of about 29
    C (84
    , beyond which yields decline significant. lyThey
    found a similar pattern for soybeans, with a threshold of about 30
    C (86
    beyond which yields decline
    The top-ten list in order of sales is: 1. Dairy; 2. Poultry and eggs; 3. Cattle and calveuss;h 4.r ooMms; 5. Other
    nursery and greenhouse products (aside from mushrooms); 6. Hogs and pigs; 7. Corn; 8. Fruits, tree nuts and
    berries; 9. Vegetables, melons, potatoes and sweet potatoes; and 10. Soybeans. These ten product categories
    account for 93p ercent of total agricultural product sales in Pennsylvania.
    In the first step of photosynthesis,
    plC ants convert the carbon from carbon dioxide into a thr-cearebon
    molecule, while C
    plants convert it into a fou-crarbon molecule.

    with higher temperatures. Average growing season temperatures for corn and soybeans in Pennsylvania
    are on the order of 20
    C (68
    , depending on location, which is well below the thresholds identified by
    Schlenker and Roberts (2009. )The implications of this study are similar to the conciolunss reached in the
    2009 PCIA: moderate climate change on the order of -31
    C (1.8-5.4
    should increase Pennsylvania corn
    and soybean yields. Greater climate change (-56
    C; 9-11
    ) could harm yields in Pennsylvania insofar as
    it leads to a greater frequency of years in which average growinag sosn e temperatures exceed 2-930
    . The projections in hCapter 3 indicate warming of about 2-.2.1 6°C (3.8-4.7
    by middle of the
    With respect to mushrooms, our 2009 PCIA concluded that the effects of climate change are ambiguuso.
    Mushrooms in Pennsylvania are almost entirely cultivated inside of specialized growing
    usheos under
    carefully controlled temperature and humidit. yAs such, the effects of climate change onu smhroom
    production will primarily be manifested in changes in heating and cooling requirements for growing
    houses. With warmer outside temperatures, there will on average be less heating required during the
    winter months but additional cooling during the summer mon. thThse net effects on annual energy e us
    and annual production costs are unclear, and we cannot say with any confidence whether they will
    increase or decrease.
    Regarding fruits and vegetables, our 200P9 CIA concluded that yields of coo-tlemperature adapted crops
    such as potatoes and apples are likely to declinas e a result of climate change, while yields of fruits and
    vegetables better suited to a warmer clima, tesuch as sweet corn, are likely to ris. ePennsylvania farmers
    are likely to adapt to climate change by changing the types and varieties of fruits anged
    tablvees grown.
    Warmer temperatures will permit some of Pennsylvania’s food crop producers to grow a wider variety
    of crops by day length, particularly sweet co. Trnhis will allow sweet corn producers to deliver their
    product to market earlier in the year,i ncreasing their competitiveness with corn grown in southern
    states that have traditionally dominated the early summer market for sweet corn.
    One area of significant uncertainty disucssed in our 2009P CIA was the effects of higher atmospheric
    concentrations and climate change on plant pests and pathogens, and effects on natural enemies of
    crop pests such as birds and beneficial inse. cItt s does not appear that research published during the
    past two years has moved the science very far in the direcotif onre solving this uncertaint. yA review of
    the literature on crop diseases and climate change by Newtonal. et (2011) concluded that complex
    biological interactions among pests, pathogens, mutualists, and parasites can lead to outcomes that
    differ from those predicted from the responses of each individual organism to temperature,
    precipitation, or atmospheric C
    A review of the literature on climate change and invasive species
    (pathogens, insects and weeds) by Ziset
    kaal.(20 11) identified a number of ersearch gaps and
    concluded that the research to date is inadequate to characterize the impacts of climate change on
    invasive species beyond the micro scale (e.g. beyond the scale of a leaf).
    What the recent research on climate change and crop productioon es dnot answer is the question of
    how crop producers in Pennsylvania will fare relative to producers in other states and cou. ntries
    Moderate climate change on the order o-f 31
    C (1.8-5.4
    may raisePe nnsylvania feed crop yields, but
    it may also raise yields elsewhere in the Ua.nS.
    d around the world, increasing global production and
    pushing down prices received by Pennsylvania farme. rsGreater climate change could lower
    Pennsylvania yields of these crops, but it could also lower yields elsewhere, reducing global production
    and raising prices received by Pennsylvania farme. rsIn either case, the net effect on Pennsylvania farm
    revenues for these crops is likely to be ambiguuso. Our 2009 economic analysis of climate change

    impacts in Pennsylvania found that these changes in prices and yields essentially offset each other, with
    the result that there is very little change in revenues for Pennsylvania grain and oilseed producers.
    4.2.2 Climate Change and iLvestock Production
    Statistics from the
    2007 Cenus
    s of Agriculture
    indicate that four livestock products are among the top
    10 agricultural product categories in Pennsylvania in terms of sales: dairy, poultry and eggs, cattle and
    calves, and hogs and pigs. The 2009 PCIA focused on three potential impacts of climate change on
    Pennsylvania livestock production: [1]h eat stress among livestock kept outdoors during much of the
    year; [2] parasites, pathogens, and disease vectors; and [n3u] tritional stress due to changes in forage
    Like all warm-blooded animals, livestock require ambient temperatures that allow them to maintain a
    relatively constant body temperature (Boesch, 200. 8I)f their body temperature moves outside of their
    normal range, the livetos ck must expend excess energy to conserve or eliminate heaTht. is reduces
    energy that can be devoted to production of products such as milk, bodily growth and reprod. uction
    Heat stress can lead to reduced physical activity, reduced eating or grazing, er higmhortality and lower
    fertility (Nardone et al., 2010). Temperature thresholds vary according to the species and breed of
    livestock, as well as each individual animal’s genetics and health.
    In Pennsylvania dairy and cattle production, livestock are oftoeun tdoors much of the timeP. oultry and
    eggs in Pennsylvania are mostly produced in larg-sceale indoor facilities where the birds are kept in close
    quarters. Housing large numbers of birds with a high metabolism in these conditions makes them
    vulnerable to heat stress during the summer (Boesch, 200. 8Bird) s can be at least partially protected
    against heat stress through investments in insulation, ventilation, fans and air conditioning in growing
    facilities. The existence of larges-cale poultry production ins outhern states such as Alabama, Arkansas,
    Georgia and Mississippi suggests that these investments can be made an at acceptable cost (i.e., at least
    with current energy price)s. Higher energy prices might alter that calculatio. Hnogs and pigs in
    Pennsylvania are typically housed inside of growing facilities, with ventilation and faned
    s usto keep
    them cool during the summe. rThe existence of larg-escale hog production in southern states such as
    North Carolina and Oklahoma suggests that Pennsylvania hog pdrouction is likely to continue being
    economically viable in a warmer climate.
    Climate change is also likely to impact livestock production through parasites, pathogens and disease
    vectors (Boesch, 2008. )There is likely to be northward migration of livestock pests currently found in
    southern states and greater overwintering of pests already present in PennsylvanHiaig. h temperatures
    and moisture can also encourage the growth of mycoto-xpirnoducing fungi, which can cuase acute
    disease episodes among ivl estock if consumed in sufficient quantities (Nardone , 2010). The
    conclusion is that Pennsylvania livestock producers will face a different set of pest and disease
    management challenges than they face today.
    Research on the effects of changes in temperature and precipitation on forage quality has yielded
    conflicting results (Craine eal.t , 2010). Craine et al.(20 10) used a long-term, national database of cattle
    fecal chemical composition to analyze the impacts of temperature and precipitation roun de c protein
    (CP) and digestible organic matter (DOM) in forage c. roFposr forested regions with a climate similar to
    Pennsylvania, they find that higher annual temperatures are associated with lower levels of CP and
    DOM. They do not report impacts of chgaens in precipitation for these regions.

    The 2009 PCIA noted that a wetter climate may lead to higher levels of -dneotnergent fiber in alfalfa
    that could reduce the ability of dairy cows to convert feed into . mOnilk the other hand, longer growing
    seasons created by warmer temperatures would allow dairy and cattle producers to graze their livestock
    for more of the year, reducing expenses on purchased feed and the amount of feed crops that need to
    be grown on the farm.
    Like crop production, the recent resreach on climate change and livestock production does not answer
    the question of how Pennsylvania livestock producers will fare relative to producers in other states and
    countries. For products with national and global markets, such as meat and dairy pros,d ucchtanges in
    production elsewhere will impact prices facing Pennsylvania farme. rsIf declines in supply from other
    states and countries cuase prices to rise by a sufficiently large amount, Pennsylvania farmers could find
    it profitable to increase herd seizs and produce more meat and dairy products in spite of declines in
    livestock productivity. Our 2009 economic analysis of climate change impacts in Pennsylvania found this
    to be the case for all the livestock products considered in that analy— bseisef, dairy, poultry and eggs,
    and hogs and pigs.
    Adaptation Strategies
    As stated in the 2009 PCIA, the existence of a productive and dynamic agriculture in states to the south
    of Pennsylvania demonstrates that Pennsylvania agriculture can continue to prosper in a warmer
    climate, but changes will be require. Adny producers who fail to audsjt to climate change are likely to
    see their yields and profitability decline.
    Alfalfa may decline in importance; if so, farmers will plant other types of hay better tso uita edw armer
    climate. Farmers in South Carolina and Georgia grovw arious types of hay, such as orchardgrass,
    bermudagrass and tall fescu. eFarmers will need to plant corn and soybean varieties suitable to a
    warmer environment and better able to withstand a likely increase in the variability of temperature and
    precipitation. Acreage devoted to coo-ltemperature fruits and vegetables such as potatoes and apples is
    likely to decline, while acreage devoted to crops better suited to a warmer climate such as csowern et
    likely to rise. One factor that could limit the decline in acreage devoted to ctoeoml-perature fruits and
    vegetables is a demand for locally grown foods, if that demand increases significantly in coming
    decades. For bedding/garden plants and nursey r stock, climate change is likely to necessitate changes in
    the types of species that are grown and sold to consum. Ferosr grapes, Pennsylvania wineries may
    choose to replace some of their NativAe merican grape varieties with European varieties that do terb et
    in a warmer climate.
    Producers of dairy products, cattle and calves, and hogs and pigs can adapt to climate change by
    selecting breeds that are genetically adapted to a warmer climaHteo. wever, breeds that are more heat
    tolerant tend to be less productive (Boesch, 20. 08W)arming may lead producers who currently keep
    their livestock outdoors much of the time to move them indoors into clim-coatnetrolled facilities, which
    would increase energy use and costs of production relative to prese-dnat y production systems
    (Boesch, 2008). On the other hand, dairy and beef producers may benefit from a longer grazing season.
    An increase in the variability of temperature and precipitation is likely to increase the demand by
    farmers for risk management product. sTo the extent that federal funding for crop insurance is
    restricted due to the federal fiscal situation, Pennsylvania farmers may need to increasingly turn to
    private crop insurance rather than government insurance programs; or to federal insurance ate r high
    rates than at present if the federal government decides to reduce crop insurance subs. idies

    The ability of Pennsylvania agriculture to adapt to climate change hinges in part on the development
    and adoption of new crop varieties and livestock breeds esud itto a warmer and more variable clima. te
    Genetic diversity for response to temperature and water stress has already been identified in the
    primary gene pools of most major crop species, but there are significant challenges in introducing these
    genes into the crops grown by farmers (Trethowan , 2010). With public funding for agricultural
    research likely to be constrained in the coming years, the task of developing new varieties and breeds
    will fall mainly to the private ctseor. Land grant universitiesw ill need to play a major role in graduating
    scientists who can successfully carry out this research.
    This update is largely consistent with the20 09 PCIA in regards to climate change and Pennsylvania
    agriculture. The principal differences concern the nea-terr m economic environment between now and
    2020 in which changes in climate will occuWr. e find that there is likely to be a tight market situation for
    most agricultural products during the current decade in which extreme wear theveents are likely to lead
    to greater swings in global agricultural prices than would have been the case 10 or 20 years. Wage oalso
    find that conversion of agricultural land to uhsoing and other urban uess in southeastern Pennsylvania
    (where much of agriculture in the state is concentrated) will be lower between now and 2020 than we
    anticipated in our 2009 PCIA. In addition, the difficult federal fiscal situation may restrict funding for
    crop insurance and agricultural researc. hShould this occur, the pvrati e sector will need to play a greater
    role in insuring against weather risks, and in developing new crop varieties and livestock breeds suited
    to a changed climate.
    The existence of a productive and dynamic agriculture in states to the south of Penannsia ylv
    demonstrates that Pennsylvania agriculture can continue to prosper in a warmer climate, but changes
    will be required in order to attain productivity in a climate that is new to Pennsylvania. By identifying the
    key areas that need attention and taking steps today to overcome these obstacles, we can ensure that
    Pennsylvania agriculture remains vibrant for decades to com. e

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    Pennsylvania Climate Change and Water Resources
    This section is an update on Chapter 6 of t20he 09
    Pennsylvania Climate Impacts
    came to the following concluiosns.
    1. There is an expected increase of precipitation to fall in liquid rather than snow form. Increased
    evapotranspiration has been observed and is expected to continue. This has led to a lengthening
    of the growing season. Studies confirm a slight increase in streamflow ,an
    thderefore,ru noff. An
    increase in spring soil moisture is predictedh; owever, an overall trend towards decreased soil
    moisture has been observed. On the other hand, groundwater is expected to inec, reabust not
    at a rate that will compensate the withdrawal rate. Lastly, stream temperature is projected to
    increase, which will likely negatively impact aquatic ecosystems.
    2. Floods are difficult to predict under climate change scenarios; however, higher smnoew
    lt will
    likely increase winter and spring flooding. Also, Pennsylvania is likely to suffer -stehrom
    summer droughts. Decreased streamflowan d an increase in stream temperature could lead to a
    decrease in water quality. Lastly, the Delaware Estuary expcanec t to face higher salinity due to
    increasing sea-levels and streamflow changes.
    This update on the impacts of climate change on water resources integrates the results of recent
    studies, and includes some new and improved graphics. A notable inteogrn ais
    ti of the recent research
    that has evolved in the area of stream temperature in Pennsylvania. This subsequent literature confirms
    and builds upon conculsions drawn in the 2009 PCIA.
    Historical Climate and Hydrology ofeP nnsylvania
    Pennsylvania is a temperate region with a river drainage network that is defined by a large number of
    small perennial streams (Sankarasubramanian & Vogel, 2003; Freeman , 2007). Precipitation is
    distributed relatively uniformly throughout the year twh i little differences between monthly average
    amounts. The distribution of temperature, on the other han, dshows a strong seasonal trend with a
    summer peak. Potential evapotranspiration follows the same distribution. Streamflow distribution
    throughout the year is related to evapotranspiration amounts and the relative occurrence of
    precipitation as rain or snow. Spring snowmelt events, in particular rain on snow events, will generally
    produce the largest streamflow. Precipitation in Pennsylvania predominantly falls as rainfall with snow
    only accounting for 10 to 25 percent (depending on where in PA) of the total annual precipitation on
    average. Precipitation itself is also quite variable across the state, but averages slightly over 102 cm
    (40 in) per year. Te hpart of the precipitation that falls during the growing season is returned almost
    completely to the atmosphere as evapotranspirationab– out 53 cm (21 in) on average. Of the remaining
    precipitation, about 18 cm (7 in) produce runoff relatively quicklyile wthhe rest (33 cm; 13 in) recharges
    the groundwater. Most of this recharge occurs from rain and melting snow during early spring and late
    fall when the soil is not frozen and plants are not actively grow(Sinwg istock, 2007). Pennsylvania mainly
    has perennial streams, which typically receiv⅔
    e of their flow from groundwater. Groundwater aquifers
    in Pennsylvania can sometimes be found only a few feet below the surface, but most often are at depths
    greater than 30 m (100 feet). These aquifers provide a grefrat
    eshwater resource though the ue s of this
    resource (mainly for water supply) has remained relatively constant over the last few decades (Swistock,
    Pennsylvania’s climate, and therefore its hydrology, has already seen significant changes over the last
    century. These changes provide an indication of potential future changes (Table 5.1). The last 100 years

    have seen an increase in annual temperatures (by over .28 C; 0.5˚ F) and in annual precipitation in most
    of the state (UCS, 2008). Annual temperatures have risen over the NortheasUter.Sn
    . in general (+0.08
    0.01˚C/decade; +0.114
    0.018 ˚F/decade), especially since about 1970 where rates have been even
    higher (+0.25
    0.01˚C/decade; +0.45
    0.018˚F/decadeet ) al.(H, a200y7; hHoune
    tington et al., 2009).
    Warming was higher during the winter periods,u ts hleading to a decrease in snow cover and an earlier
    arrival of spring. Precipitation has also increased over the last century, with additional increases ranging
    between 5 and 20 percent throughout the state (UCS, 2008). This additional moisture falls in the winter
    months, while summers have actually seen a slight decrease in precipitation (UCS, 2008). Average
    annual precipitation increased from 97 to 112 cm (38 to 44 in) throughout the tw-firsent tycentury
    (UCS,2 008). Soil moisture related droughts have also increased due to increased summer temperatures
    and decreased rainfall. Simulations of the hydrology f tho e northeastern U.S.
    over a 50-year period
    (1950 to 2000) suggest a decline in available soil moisture during the period from June to At uogvuesr
    large areas (Sheffield & Wood, 2008). The primary ce
    ausof this decline is likely an increase in
    evapotranspiration during the summer period since rainfall amounts remained relatively stable over the
    same period. Historically, sho-rtterm droughts (lasting one to three months) occurred roughly once
    every three years over western
    Pennsylvania and once every two years over eastern Pennsylvan. ia
    Medium-term droughts (lasting three to six months) are far cleosms mon in Pennsylvania; they have
    occurred once every ten years in western parts of the state and rarely in most eastern areas-ter. Lm
    droughts (lasting more than simx onths) have occurred on aveagr e less than once in 30 years (UCS,
    2008). Streamflow has also increased, but to a lesser degree. Met
    illy al. (2005) calculated that runoff
    (streamflow) across the northeastern U.iS.
    s expected to have increased between 0 and pe5 rcent using
    GCM projections for the 20
    century. McCabe and Wolock (2002) found that this increase was mainly in
    the lower and intermediate flow quantiles for New England and the -MAtiland tic regions.
    Water Resource Change
    IPCC examples for North
    America (AR4)
    1-4 week earlier peak streamflow due to
    earlier warming-driven snowmelt
    ↑ U.S. West and U.S.
    New England regions
    ↑ Increase in growing
    season length
    Proportion of precipitation falling as snow
    ↓ U.S. West
    ↓ Decline
    Duration and extend of snow cover
    ↓ Most of North
    ↓ Decline
    Annual precipitation
    ↑ Most of North
    ↑ Up in winter months,
    constant in summer
    months. Overall
    Frequency of heavy precipitation events
    ↑ Most ofUS A
    ↑ Increase in heavy
    ↑ Most of the Eastern
    ↑ Overall increase, but
    lower in summer and
    Water temperatures of lakes (0.-1.1 5˚C;
    .18-2.7 ˚F)
    ↑ Most of North
    ↑ Increase
    Salinization of coastal surface waters
    ↑ Florida, Louisiana
    ↑ Delaware Estuary
    The Mid-Atlantic is not part of the New England region. Moreover, the Northeast comprises both theAt lManidti-c
    and the New England regions.

    Water Resource Change
    IPCC examples for North
    America (AR4)
    Periods of droughts
    ↑ Western U.S.
    ↑ Increase ofbo th soil
    moisture related
    droughts and
    decrease in summer
    / fall low flows
    Table 5.1.
    Observed changes to North American Water Resources during the
    2c0entury Pennsylvania
    trends are generally typical for the Northern U(.BS. ates et al., 2008, p.102).N ote: AR4 refersto the
    Annual IPCC Assessment.
    Climate Change Implications for the Water Cycle in PA
    The complexity of the hydrological cycle with nonlinearities, thresholds and feedbacks makes it hard to
    model potential future conditions ovf ariables such as soil moisture and streamflow with high reliability.
    This is particularly true considering the uncertainty already present in the projections of meteorological
    drivers (i.e. precipitation and temperature). One can probably have high ceonncfide in GCM projections
    of temperature, moderate confidence in temperature extremes, moderate confidence in precipitation
    and low confidence in precipitation extremes. This likely translates into moderate confidence in the
    directional change of hydrological variables and lower confidence in estimates of extreme conditions as
    discussed in more detail below.
    5.2.1 Precipitation – Rainfall and Snow
    For Pennsylvania, more than thre-qeuarters of all GCMs analyzed project an increase in precipitation
    regardless of scenario analyzed (See Chapter 3 in this report and Chapter 5 in the 2009) .P CMIAost of
    this precipitation increase is projected to occur in the winter months. The uncertainty of precipitation
    estimates for the summer is likely to be higher as reflected in reduced model couns sednusring this part
    of the year. All GCMs analyzed simulate increasing temperatures for Pennsylvania regardless of emission
    scenario analyzed (See Chapter 3 in this report and Chapter 5 in20 t09
    he PCIA). Warming projections
    reach about 4˚C (7.2˚F) for the A2 scenario by the end of the century, while the B1 scenario reaches
    about half of this value. For Pennsylvania, more than th-qreuarte er of all GCMs analyzed project an
    increase in precipitation regardless of scenario analyzed. oMst of this precipitation increase is projected
    to occur in the winter months. The uncertainty of precipitation estimates for the summer is likely to be
    higher as reflected in reduced model conseuns sduring this part of the year. All 21 GCMs analyzed in
    Chapter 3 of this report simulate increasing temperatures for Pennsylvania regardless of emission
    scenario analyzed. Warming projections reach about 4˚C (7.2˚F) for the A2 scenario by the end of the
    century, while the B1 scenario reaches about half of thaluis ev. This trend suggests that an increasing
    fraction of precipitation will fall in liquid form, rather than as snow, which is consistent with historical
    trends observed over the last 30 years in the region (Huntingtonal. et
    , 2004). Hydrological modeling
    studies – necessary due to a lack of historical snow observatio– nus sing historical precipitation and
    temperature data between 1970 and 1999 suggest statistically significant trends for decreasing snow
    water equivalent (about 3- mm/decade;-. 12 in) and fr odecreasing numbers of snow covered days
    (about -0.5 days/month/decade) (Hayhoe eal.t , 2007, Figure 5.1). Snow covered days are defined in
    Hayhoe et al.(2 007) as those days with a snow water equivalent larger than 5 mm (.2 in). Overall,
    Pennsylvania’s precipitation regime is projected to become more extreme, including longer dry periods
    and greater intensity of precipitation events. Increases in intense precipitation have already been
    observed for Pennsylvania (Madsen & Figdor 2007). The order of magndie tuof change and the direction

    of change are consistent with other climate change impact studies for the northeastern U.S.
    (e.g. Hayhoe et al., 2007; UCS, 2008). In general, the confidence in changes to winter climate is higher
    than the confidence in changes to summer climate.
    Figure 5.1.
    Results of HadCM3 and PCM GC-Moutput driven simulations with the VIC lan-sdurface
    model. Plots show snow-covered days per month from December to February averaged over -y3e0ar
    periods. ‘Change’ refers to the difference between the period 1-1996910 and future periods (Hayhoe
    et al., 2007).
    5.2.2 Evapotranspiration
    Evapotranspiration is the combined process of evaporation from the soil surface and open water bodies
    and transpiration from vegetation. The fractn ioof precipitation that evapotranspires is no longer
    available as freshwater supply for further eu. s GCM projections generally suggest an increase in
    potential evapotranspiration due to increased temperatures throughout the year for both scenarios.
    Increasing moisture availability, at least in the beginning of the summer, should also lead to increased
    actual evapotranspiration. Evapotranspiration is generally lower in the winter and is likely to decrease
    further with a decreasing snow pack and hence reducseud blimation (Hayhoe et al., 2007). The expected
    lengthening of the growing season (earlier spring and later fall) is likely to result in an additional increase
    in actual evapotranspiration. Such a lengthening of the growing season has already been obd seforvr e
    the study region and beyond (Christidis , 2007).

    5.2.3 Streamflow/Runoff
    Climate change impact studies performed so far generally suggest a slight increase in runoff across the
    northeastern U.S. (Millyet al., 2005) across scenarios (Figure 5.2). As a first order estimate of runoff, one
    can assume that, over a multyei-ar period, the average runoff is equal to the difference between
    precipitation and evapotranspiration (assuming no storage change), and
    us tto
    h the convergence of
    atmospheric moisture flux (Milly etal. , 2005). Sankarasubramanian and Vogel (2003) calculated a
    precipitation elasticity of streamflow across the northeasternU .S. between 1.5 and 2.5 for most areas
    based on an analysis of almost 1400 watersheds across the UE.lSas. ticity is an index describing the
    proportional change in streamflow to the proportional change in precipitation. This means that they
    suggest that for a 1p ercent change in precipitation, streamflow will change by 1.5 to p2e.r5 cent. The
    non-linearity in this relationship is a function of storage processes within the watershed. The overall
    increase in precipitation projected is uths likely to result in a slight increase in runoff. Hayhet oeal.
    (2007) drive a larg-escale hydrological model (VIC) with GCM outt pu(precipitation and temperature) for
    both a historical period (50 years) and for future projections over the northeastern (sUe.e S. also Figure
    5.1). Their results show slight changes in runoff over the historical period, but none of them statistically
    significant in strength. Future projections show wetter winters and generally warmer temperatures,
    leading to an overall increase in runoff in the order opf er5 cent (Figure 5.2). This runoff increase will
    likely be in the winter months thou, gahnd peak runoff is projected to shift to earlier in the year. While
    winter months will generally be warmer, frozen ground is still a facantod r thus plays a role in increased
    runoff. Frozen ground prevents infiltration of snowmelt or rainfall, leading to earlier and higher than
    expected observed spring runoff. Moreover, the snow on top might melt before the soil water does or
    top soil layers may be warmer than layers also contributing to ru. nFoofr f completeness, it should be
    noted that there is a possibility of increased icing if precipitation freezing on contact with frozen ground
    (which would decrease winte-rrunoff/flooding). (Niu & Yang etal. , 2006)
    Implications of climate change on annual runoff might also be smaller in watersheds that are (or will be)
    more urbanized (DeWallee t al., 2000). This is because urbanization controls the water balance more
    than the climate. Hencec, limate change has less fo an impact in urbanized watershed. sRecent results
    by Singh etal. (2011) on climate change impact projections across the Eastern Uf.Sur. ther suggests the
    streamflow response might be even stronger if it watershed behavior changears e consideredun der
    different climatic conditions. Overall, it should be considered that a high degree of uncertainty is still
    associated with any projections of this kind.

    Figure 5.2.
    Model-projected percentage change in annual runoff for future period, 2-0240160, relative
    to 1900-1970 baseline (using the A1B scenario projections). Any color indicates that >6pe6 rcent of
    models agree on the sign of change; diagonal hatching indicates >p9e0 rcent agreement. (Online
    supplement to Milly, P.C.D., K.A. Dunne, A.V. Vecchia, Global pattern of trends in streamflow and water
    availability in a changing climate,
    438, 347350- , 2005.)
    5.2.4 Soil Moisture
    The amount of water in the soil that is available for uptake by vegetation is generally referred to as soil
    moisture. Hayhoe et al. (2007) suggest a general increase in dry conditiotnhs ough (with respect to soil
    moisture) with large spatial variability under future conditions. This drying is maianuly
    sedc by increased
    evapotranspiration due to higher temperatures and decreased summer/early fall precipitnat. ioThe
    study also suggests that spring soil moisture is likely to be much higher (particularly for high emission
    scenarios) due to higher winter precipitation and earlier snowmelt (Hayhet oeal. , 2007). A trend
    towards decreasing soil moisture in North Americaas halso been shown in a recent study by Sheffield
    and Wood (2008).
    Using a parsimonious soil water balance model developed by and described in detail in Porpoet
    (2004), which represents a stochastic model of the soil moisture dynamics, we caen ssas
    thse probability
    distribution of soil moisture under different climatic conditions (Figure 5.3). For typical soil and
    vegetation characteristics we see that the soil moisture probability density functions (PDF) shift towards
    drier conditions (to the left) for both the A2 and B1 emission scenarios. A soil moisture value of 1 would
    mean that the soil is always wet, while a value of 0 indicates that the soil is always dry. The actual
    modeled values are less reliable than the relative change between time iopders and scenarios due to the
    simplicity of the model uesd. Differences between the A2 and B1 emissions scenarios are rather small,
    which is similar to results reported in Boesch (2008) for the state of Maryland. A summer decrease in
    soil moisture should hterefore be considered likely. In how far such drying will result in plant stress will

    depend, among other things, on the rooting depth of the vegetation. Figure 5.4 shows the variability of
    the PDF with variation in rooting depth. This result suggestst tvheagetation with shallow roots will feel
    more stressed.
    Figure 5.3.
    Soil moisture probability density functions for different soil and vegetation characteristics
    (RD: rooting depth) and for different climatic conditions (mainly average temperature, rainfall depth and
    frequency) during the growing season for control, fue tu1 r and future 2.
    Figure 5.4.
    Variability of soil moisture probability density functions with variability in rooting depth.
    5.2.5 Groundwater
    Precipitation that infiltrates into the ground and is not taken up by plants percolates deeper to deeper
    layers and eventually becomes groundwater. Pennsylvania groundwater characterization is difficult due
    to the complex geology of the state. Most groundwater, however, is stored in consolidated aquifers that
    consist of limestone, sandstone, granite or other rothcka t hold water in interconnected fractures and
    pore spaces. How much water is contained in an aquifer and how fast it moves depends on the aquifer’s
    specific characteristics. As mentioned earlier, most of the groundwater recharge occurs in the spring,
    while groundwater levels decline during the remaining year. More than a third of the population of
    Pennsylvania either uses groundwater from wells and springs as drinking water or for domeste ic in
    general. While higher winter precipitation and warmer temratpeures could lead to increased recharge,

    detailed studies of climate change impacts are not available yet. Any increases in precipitation are
    unlikely to replace groundwater substantially enough to compensate excessive withdrawals of some
    aquifers (Boesch, 2008), which means that future population (and us tdhemand) scenarios are as
    important for this resource as climate change projectio. nMs oreover, other environmental changes
    (e.g., increasing urbanization) also have significant impact on groundwater recharge that even exceed
    the impacts of climate change. Every .4 ha (1 acre) of land that is covered with an imus pseurrfacvio e
    generates 102 kiloliters (27,000 gallons) of surface runoff instead of groundwater recharge during a
    one-inch rainstorm (Swistock, 2007).
    5.2.6 Stream Temperature
    Stream temperature, an important measure of ecosystem health, is expected to be altered by future
    changes in climate and land ues, potentially leading to shifts in habitat distribution for aquatic organisms
    dependent onpart icular temperature regimes. The water temperature of streams has important direct
    and indirect implications for aquatic organisms. Generallya, che organism has a particular temperature
    range, which might change with life stage in which it can survIin
    vead. dition, temperature affects water
    properties (such as dissolved oxygen content and nutrient concentratio) ntshat are important for habitat
    quality. Stream temperature is strongly correlated with air temperature for many streams unless they
    receive considerable groundwater influx (Morrill et al., 2005). The relationship between air and stream
    temperature can thus be used to obtain a first order assessment of the likely implications of air
    temperature increase for streams. Figure 5.5 shows linear regsireons relationships between air and
    stream temperature derived for six streams of different size in Pennsylvania. Stream temperature can be
    estimated quite well for all but one of the streams (i.e., Big Spring Creek). The water temperature in this
    spring-fed stream is rather independent of air temperature. In most streams, an increas
    1.1 8˚F)
    in air temperature will lead to about 0.7 to
    to 1.
    6F) increase in water temperature. This result
    is typical for many streams in diverse geographical settings and should be usrt obeven if airs-tream
    temperature relationships can sometimes be slightly no-linn ear (Morrill etal. , 2005). The impact of
    assuming linearity should be considered minor considering other influences in the data.
    To assess the sensitivity of stream temperature to change across Pennsylvania where a temperature
    shift has the potential to occur, Kellehet
    er al.(20 11) examined the variability of and controls on the
    direct relationship between air and water temperature across the taste. They characterized the
    relationship between air and stream temperature via linear and nonlinear regression for 57 sites across
    Pennsylvania at daily and weekly timescales. Both models (linear and linnoenar) showed high
    performance,with the nonlinearre gression performing slightly better. To investigate the mechanisms
    controlling stream temperature sensitivity to environmnetal change, “thermal sensitivit,y” defined as
    the sensitivity of stream temperature of a given site to a change in air temperat, wuas
    re quantified as
    the slope of the regression line between air and stream temperature. Air temperature accounted for
    60-95 percent of the daily variation in stream temperature for sites at or above a Strahler stream order
    (SO) of 3, with thermal sensititivies ranging from low (0·02) to high (0·93). The sensitivity of stream
    temperature to air temperature was primarily controlled by streaom
    rder (SO) – an indicator of stream
    size – and baseflow contribution. Together, SO and baseflow index explainedp e4r3 cent of the variance
    in thermal sensitivity across the State (Figure 5.6), and per59c ent within the Susquehanna River Basin.
    In small streams, baseflow contribution was the major determinant of thermal sensitivity, with
    increasing baseflow contributions resulting in decreasing sensitivity values (Figure 5.7). In large streams,
    thermal sensitivity increased with stream size, as a function of accumulated heat throughout the stream
    network. Riparian buffer plantings that provide stream shading can serve ase ffean ctive adaptation
    strategy to mitigate variation in thermal change, particularly for smaller bodies of water.

    Figure 5.5.
    Pictures of analyzed rivers and linear regression plots of daily air temperaturesuv s ewars ter
    temperatures for all six locations. Most streams will see an increase in water temperature with
    increasing air temperature, though some sprin-fged streams might be relatively insensitive to these
    changes. The combined effect of higher water temperatures and lower summer streamflow is likely to
    cause problems for aquatic ecosystems and increased competition for freshwater resources.
    See Appendix 11.1 for further details on this analysis.

    Figure 5.6
    . Stream order (SO) vues rsthermal sensitivity, acorss 57 Pennsylvania streams. Color highlights
    baseflow contribution, in terms of BFI. Sites where thermal sensitivity is influenced by a unique site
    condition are noted on the figure. General controls on thermal sensitivity and their influence relative to
    stream size are conceptualized at the bottom of the figure (Kellehet er
    al., 2011).
    Figure 5.7
    . Relative influence of BFI on thermal sensitivity in small streams (first through third SO) and in
    large streams (fourth through seventh SO). (Kelleher al.e, t 2011)

    Consequences for Pennsylvania Freshwater Services and Disservices
    We can define the benefits received by nature (ecosystems) and humans from freshwater resources as
    services and the negative impacts as disservices (Wagener , 2008). This section is based on a
    mixture of quantitative results and qualitative interpretation of what is currently known or of data that
    is currently available. Consequently, mitigating factors should be considered before a definitive
    conclusion is made. Global climate models have particular limitations when it comes to capturing
    extremes (e.g, .in relation to meteorological drivers of floods and droughts) due to theiru s foonc longer
    term and large scale patterns. At the global scale, a region suche nas
    nsyPlvania has to be seen as a low
    stress region (i.e., based on a ratio of water withdrawals to available water). Potential population
    increase and urbanization might have impacts on the water cycle that can often exceed the direct
    impact of climate change, at least in the near future. At the same time, water conservation strategies
    can significantly reduce water ue s or maintain constant levels even under growing population
    (Boesch, 2008).
    5.3.1 Floods
    Flood events are generally the result of extreme precipitation events. They are as such relatively difficult
    to predict even under conditions of stationarity of the climatic and the environmental systems.
    Historically, the frequency of floods with certain magnitudes is typically predictedin g usstatistical
    hydrology. In this approac, hhistorical records are analyzed to identify a probability distribution that
    describes the historical occurrence of a flood of a given size. Assuming stationarity, these probability
    distributions can then be uesd to calculate dseign floods. However, the assumption of stationarity is not
    suitable for climate change impact assessme, nant d recent papers call for the development of new
    approaches to replace current techniques (e.g., Met
    illy al., 2008). Because past analyses of flood
    frequency were uncertain it is likely that estimates under conditions of climate change are even more
    unreliable – particularly since precipitation extremes are poorly captured in current GCMs due to the
    large spatial scales of the grid cells over whichh ey t average atmospheric conditions.
    Winter floods will likely be impacted by the changes in precipitation type (more rain rather than snow)
    and by the reduction in sno-wcover extent and snow water equivalent. There is a general trend towards
    higher winterpr ecipitation, which will translate into a tendency for streamflow to be higher in winter
    and spring. Rain on snow floods can be significant in tuhse quSehanna River Basin. During such events,
    large amounts of snow melt quickly during a precipitation et vepnotentially resulting in extreme flood
    events. The uSsquehanna River Basin witnessed an extreme of this flood type in January 1996. The
    projected reduction in snow pack might lead to a reduction of flood events of this type. Conversely,
    peak flooding is likely to increase in urban environments due to an increase in impues rarevioas and
    higher rainfall variability. A flashier runoff regime and increasing water temperature will likely have
    negative implications for aquatic ecosystems (Boesch, 2008).
    A more detailed investigation of potential future flooding in thue sqSuehanna River Basin is crucial since
    it represents one of the most flood prone areas in the whUo.lS.e , experiencing a major devastating
    flood on average every 14 years (SRBC, 2006). The etiffveenc ess of adaptation measures that reduce the
    fast runoff response, such as artificial infiltration areas or peurvs ipoavements, has to be investigated.

    5.3.2 Droughts
    Drought can be defined differently depending on its length and depending on the ohloygdicr al variables
    impacted (Figure 5.8). A meteorological drought is mainly based on lack of precipitation and is generally
    very short in duration (NWS, 2006). An agricultural drought relates mainly to a deficit in soil moisture
    with subsequent plant watr e stress and potential reduction in biomass production (NWS, 2006m) o– st
    climate change impact studies for the northeastern Uh.Sa. ve focused on this kind of drought. Droughts
    resulting from the longert-erm consequences of lack of precipitation and increased evapotranspiration
    are generally referred to as hydrological droughts. They manifest themselves in reduced streamflow,
    lower reservoir and lake levels as well as lower groundwater levels (NWS, 2006). Aus ssdeisd cabove,
    increased summer temperatures will likely shift soil moisture distributions to drier regimesu, s th
    increasing plant stress and potentially decreasing plant productivity. The strength of the impact will
    differ with plant rooting depth and uths moisture availability for individual plants. Pennsylvania will
    likely see an increased frequency of sho-trterm droughts while the overall annual runoff increases
    slightly. Lower summer and fall streamflow could provide problems through competinesg ,u s(e.g. power
    plant cooling versus environmental flows) due to a combination with increased streamflow
    temperatures (see 5.3.3. Water Quality of this Upd)at. eThe largest drought on record in Pennsylvania
    occurred during the period from 1962 to 1965 and will likely remain an extreme event despitree asinecs
    in future summer air temperatures (Hayhoet
    e al., 2007). This drought was the consequence of an
    extended period of low precipitation (Namias, 1966).
    At the same time thast ummer flows might be lower, winter precipitation will likely incre, aswheich
    could result in fuller freshwater reservoirs at the beginning of the summer anud
    s alloth w for an
    opportunity to reduce any impasses through audsjted water management. Water-supply drought is
    more heavily affected by periods of low precipitation extendinovg er multiple months, and is most
    strongly correlated with dry periods persisting through winter and spring when soil moisture, water
    tables, and reservoir levels would normally experience recharge (Boesch, 2008).
    Figure 5.8.
    Flow chart visualizing the differences between meteorological, agricultural and hydrological
    droughts (National Drought Mitigation Center,,
    Accessed February 2009).

    5.3.3 Water Quality
    Increased variability in streamflow is likely too ccur, which means that there will be a tendency for
    higher winter and spring flows but lower summer and fall flows. Figure 5.5 suggests that many
    freshwater streams are well mixed and that their temperature will respond quickly to increg asin
    atmospheric temperatures. Low flows and higher water temperatures are likely to decrease the habitat
    suitability for aquatic biota since it will lead to a decrease ins oldvised oxygen content. Increases to
    variability of flow, changes to the timing of peak spring flow and changing water temperature will likely
    negatively impact aquatic ecosystems. Lower summer stream flows could also lead to the loss of small
    wetlands, thus further impacting water quality throughout the river network negatively. Degraded
    streams and flashier runoff would further increase water quality impairments in the Chesapeake Bay
    due to ful shing of nutrients and sediments into the estuary (Boesch, 2008).
    5.3.4 Salt Water Intrusion in the Delaware Estuary
    When one thinks of Pennsylvania, the ocean
    oeds notus ually come to mind. However, Pennsyvl ania’s
    connection to the sea, the Delaware Estuary, is one of the state’s most valuable economic and ecological
    resources. The estuary is home to the largest freshwater port in the world, generating billi$1o9 n
    annually and receiving 70 percent of the oil shipped to the U.ESa. st Coast. The combined river and
    estuary system provides drinking water to 15 million people (including many Pennsylvanians), is a source
    of water for industrial processes, and recivees wastes from municipal and inudstrial wastewater
    treatment plants. The estuary also supports the largest horseshoe crab population in the world and is
    one of the world’s most important sites for shorebird migration.
    Climate change has the potential oaltf ering estuaries through changes in temperature, winds,
    streamflow, and sea level, which will affect numues roestuarine characteristics, such as circulation,
    water quality, and ecolog. ySalinity is an important defining characteristic of the Delawaret uearys ,
    regulating floral and faunal distributions and affecting humane uosf the estuar. yA major objective of
    the Delaware River Basin Commission (DRBC) is to regulate streamflow through the e ouf sreservoirs so
    as to keep water supplies safe for humann csoumption and industrial uses. The DRBC attempts to keep
    potable supplies at sodium concentrations less than 50 ppm, the New Jersey drinking water sta. ndard
    However, the American Heart Association (AHA) recommends sodium levels less than 20 . Thppe
    chloride recommended standard is 250 ppm for drinking water and is also the concentration at which
    water begins to taste salty (EPA, 201. 2D)RBC salinity controls are also in place to protect groundwater,
    which is fed in part from the estuar. It y has been dteermined that a chloride concentration at
    Philadelphia less than 180 ppm will keep well waters potable (Hull
    , 1986).
    There is strong evidence that past climatein-duced changes in bay salinity have had negative impacts on
    water supply systems. The drought of 1930 resulted in Delaware River chloride concentrations as high as
    500 ppm in Philadelphia, and xeceeding 1000 ppm in Chester, Pa(M. ason & Pietsch, 1940). In 1951,
    Chester changed its water source from the Delaware River to tuhse quSehanna RiverBa sin because of
    increases in salinity; it has been suggested that these were ced
    ausby sea-level rise (Parker, 1964) and
    low streamflow (Hull et al., 1986). In 1964, drought conditions resulted in chloride concentrations of
    250 ppm in Philadelphia, an eemrgency declaration by the DRBC, and economic damages (Hull
    Sodium and chloride levels of the Delaware River at Philadelphia have steadily increased from the early
    20th century to the present day (Philadelphia Water Department, 2007), yl ikfrelom changes in land ue s

    in the watershed, increased wastewater discharges, but also possibly from the increase in sea level of
    approximately 0.28 m (1 foot) (Zervas, 2001) that occurred during this . tCimurerent (2003-2005) mean
    levels of chloride and osdium are 23 ppm and 15 ppm respectively, and extrapolation of recent trends
    suggests that the mean sodium level will exceed the AHA criterion in 100 years.
    Sea level is very likely to keep increasing throughout the 21st Century, which will increases treof nd
    sodium and chloride in the Delaware River and Estu. arRyahmstorf (2007) estimated that global mean
    sea level will rise by 0.5 to 1.4 m (1.6 to 4.6 feet)by the end of this century, where the range expresses
    uncertainty in the particular C
    scenario and in the response of the climate to
    . CFOor the
    Delaware Estuary, these projections need to be increased to account for local impacts on the relative
    position of the land and the sea, including land subsidence due to geological proc. eGslsoebsal mean sea
    level rose at a rate of approximately 1.8 mm ypeaer r (0.07 in) during the second half of the
    per year (Hull et al., 1986). At the two locations in the Delaware Bay sampled during this time,
    Philadelphia, PA and Lewes, DE, sleeav-el rise was 2.74 ± 0.35 mm per year (±.1.01
    1 in) and 3.04 ±
    0.29 mm per year(. 12 ±.01 in), indicating a local component of sleeav-el rise of about 1 mm/year
    (.04in ). Thus sea-level projections for the Delaware estuary should be increased above the al
    average to 0.6 to 1.5 m ( 9 4.9 feet) by the end of this century.
    Three modeling studies have been conducted to assess the potential impact o-f levseael rise on the
    Delaware Estuary. Hull and Tortoriello (1979) uesd a 1-D model to investigate the mipact of a 0.13 m
    (5.12 in) rise in sea leveTlh. ey ran the model for conditions experienced during the 19-169465 drought
    and found that the se-alevel increase resulted in a-1 540 ppm (10-21 percent) increase in chloride
    concentration between river kilometers 129 and 185 (river miles 80 and 115). At Philadelphia’s
    Torresdale water intake (River Mile 110), the chloride increase was 4 . pTphme U.S. Army Corps of
    Engineers (1997) conducted simulations uinsg a 3-D model and found a 2-p0pm chloride increase at
    River Mile 98 (River km 158) for a seale- vel increase of 0.3 m (1 foo. tK)im and Johnson (2007) uesd an
    updated version of this model to evaluate the impact of a 0.16 m (6.3 in) increase in sea level, finding a
    chloride increase of 7 ppm (1p0 ercent) at the Ben Franklin Bridg. eThese studies collectively suggest
    that, in the vicinity of Philadelphia, chloride increases roughly 3 to 6 ppm for every 0.1 m (4 in) of sea
    level increase. Thus, sea level projections of 0.6 to 1.5 m (2 to 5 ft) to by thofe tenhis d century imply
    chloride increases of 18 to 90 pp—manywhere from a doubling to a quadrupling of current chloride
    In addition to se-alevel rise, salinity of the upper Delaware Estuary is likely to change as a result of
    climate-induced changes in streamflow. However, the projected change in annual streamflow is
    uncertain. Nonetheless, streamflow variability is expected to increase, so even though quantitative
    projections are not possible at this time, it seems likely that drouingdhut-ced saltwater intrusion events
    will increase throughout the 2
    Adaptation Strategies
    Any climate change policy must consider some degree of adaptation beucsae – even under the most
    optimistic emission scenario – we expect somede gree of climate change, the consequences of which
    can already be felt in many regions. Following the IPPC, adaptation can be defined as initiatives and
    measures to reduce the vulnerability of natural and human systems against actual and expected climate
    change effects. A wide range of options for adaption exist and some of the more common ones are
    listed in Table 5.2. Adaptation strategies for water management under potential climate change have to
    be developed while considering scenarios for future regional population and economic development. As

    discussed at multiple places within this chapter, population growth, urbanization and other land cover
    change, and pollution of water bodies could be equal or even more important stressors than climate
    change at least in the near future. A holistic approach to developing adaptation strategies will be
    required, while the existing uncertainty in current projections of climate change impacts suggests that
    “no regret” strategies might be the best option for now. tSetrgiea s are classified as “no regret” if they
    lead to societal benefits regardless of the degree of climate change. Examples of such strategies include
    water conservation and better monitoring of hydrological and other environmental variables.
    Water-use sector
    Supply-side measure
    Demand side-measure
    Municipal water supply
    Increase reservoir capacity
    Incentives to use less (e.g.
    through pricing or rebates)
    Extract more water from rivers
    and groundwater
    Legally enforced wateru se
    standards (e.g. foapr pliances)
    Alter system operating rules
    Increase use of grey water
    Inter-basin water transfer
    Reduce leakage
    Capture more rain water
    Increase use of recycled water
    Development of
    non-water-based sanitation
    Seasonal forecasting
    Increase irrigation source
    Increase irrigation-use efficiency
    Increase use of drought tolerant
    Alter cropping patterns
    Industrial and power station
    Increase source capacity
    Increase water-use efficiency
    and water recycling
    use of low-grade water
    Hydropower generation
    Increase reservoir capacity
    Increase efficiency of turbines,
    encourage energy efficiency
    Build weirs and locks
    Alter ship size and frequency
    Pollution control
    Enhance treatment works
    Reduce volume of effluents to
    treat (e.g. by charging for
    Watershed management to
    reduce polluting runoff
    Flood management
    Increase flood protection
    (levees, reservoirs)
    Improve flood warning and
    Watershed source control to
    reduce peak discharges
    Curb floodplain development
    Table 5.2.
    List of examples for supp- lyand demand-side adaptation strategies. (Cooley, 2009)
    Barriers and Opportunities
    Main barriers to understanding the potential implications of climate change on Pennsylvania freshwater
    are mainly twofold: [1] insufficient monitoring of hydrological variables, and [2] lack of -swtaide
    modeling studies to interpret past observations and future projections of climate. Both aspects will be
    discussed in more detail below.

    Any scientifically valid assessment of current and past conditions of the water cycle in Pennsylvania has
    to be based on observations of the main hydrological variables (streamflow, soil moisture, snow water
    equivalent,gro undwater, water quality) and their meteorological drivers. The river networks of many
    drainage basins in the eastern U.(Sin. cluding Pennsylvania) are characterized by a large fraction of small
    (low order) streams. It is increasingly recognized that ste heheadwater streams often control water
    quantity and quality through much of the river network. However, contuins usotreamflow gauging
    stations are heavily biased towards larger streams, meaning that for much of the basin we only have
    observations of the large-scale integrated streamflow response. This issue limits our ability to provide
    reliable benchmarks of past and current hydrological conditions in most headwater streams. This
    problem is exacerbated by a lack of soil moisture and snow measurements as well as a lack of
    coordinated assessments of large scale groundwater dynamics. The monitoring of hydrological variables
    has to go hand in hand with observations of changes to land cover and population size. The former is
    important since changes to land coevr, (in particular, the extension of impervious areas), is likely to have
    significant impacts on water flow paths. Population size and residenwatialter use behavior arelik ely to
    impact water demand, an important stress on current and future freshwater soreurces that should be
    The lack of appropriate spatially-distributed data means that hydrological models have to be edu sto
    extrapolate hydrological characteristics in space and time. Scientific studies of past and future
    hydrologic conditions across the state have so far been limited to snapshots ofteinn g utshe output of
    only a few GCMs, aggregating over large areas,n oot r providing estimates of confidence in the
    streamflow (or other) simulations provided. Continuus oadvancement in watershed-scale hydrologic
    models and increasing availability of hig-pherformance computing continuously reduces these
    limitations though. Another barrier related to the
    use of hydrological models, currently of great interest
    to the research community, lies in the problem that the hydrological system itself is not stationary. For
    example, changes in climatic conditions such as temperature and frequency of rainfall impact vegetation
    and soil characteristics, which in turn alter hydrological flow paths. These changes are often gradual and
    the evolution of the hydrological system has uts hfar been ignored, tuhs assuming that non-stationarity
    only occurs in the boundary conditions, i.e. the climate. New approaches to include the evolution of the
    hydrological system itself in our models have to be developed to address this issue.
    The redistribution of freshwater resources due to a change in wintreer cippitation and due to a general
    warming trend in Pennsylvania might provide opportunities for improved water management strategies
    (see Table 5.2). Increased availability of precipitation in liquid form during winter and spring months
    might enable improved groundwater recharge (naturally or artificial) and increased storage of water in
    reservoirs or the sub-surface to support summer water demands. These water management strategies
    will in particular have to consider competing summer demands under climaptoep, ulation and economic
    change scenarios while considering environmental constraints.
    Information Needs
    Information needs are strongly connected to the barriers duisssced in the previous section. Improved
    monitoring and more detailed modeling studieare
    s essential to overcome said barriers. Improved
    monitoring is necessary to enable better quantification of magnitude and trends in major hydrological
    variables. In particular the lack of continuus osnow and soil moisture measurements limit the direct
    assessment of climate change impacts and the evaluation of hydrological models. In addition to
    hydrological variables it is crucial to understand water demand patterns and trends across the state,
    both agricultural and municipal inudstrial. The latter is needed since human imposed stresses on

    Pennsylvania’s freshwaterre sources are likely to be very important to understand overall water cycle
    dynamics. The value of some historical data to support the assessment of potential future conditions
    (using for example statistical analyse) sis likely to decrease, though trends in the data might be very
    important. Statistical approaches –o ften the basis of current water resources engineering w– ill have to
    be replaced with new model-based strategies that allow for the inucslion of the non-stationarity of the
    system. Regional models have to be implemented and tested to interpret trends in historical
    hydrological data and to extrapolate hydrologic conditions in space and time. Such modeling studies
    need to be performed especially to provide better information regarding potential future flooding
    (rainon snow) and recharge to groundwater. Current studies are generally too coarse in their spatial
    and temporal resolution. Information about the uncertainty in climate change projections needs to be
    included in these modeling studies and these uncertainties have to be propagated into ecological and
    water resources endpoints (e.g. flood frequens cieor water temperature ranges).
    Management of Pennsylvania freshwater resources requires a balance between the competing societal
    and environmental needs placed on the basin’s freshwater resources. Humans and ecosystems are
    embedded in the watershed systems, which exhibit a wide range of characteristics depending on their
    location and their degree of human activity. Watersheds internal heterogeneity means that we are
    dealing with complex and uncertain systems. An important task is to support the development of
    sustainable integrated resource management strategies through environmental models, which enable
    U.S. to understand these complex systems and to predict their response to future environmental
    change. This predictive capability is necessary to achieve water security for people (both current and
    future generations) and the environment in an increasingly n-ostnationary world (Falkenmark, 2001;
    Milly et al., 2002; 2005), for which water security can be defined as protection from both water excess
    and water scarcity (Gleick, 2002). Models of watderriv-en environmental systems play an important role
    in understanding human and climate impacts. There is a growing recognition within the scientific
    community (Reedet al., 2006; National Research Council, 2008; Wagener, 2007) that the management
    of large scale water resoucres under uncertainty requires community level advances for developing and
    evaluating predictive models as well as new frameworks fori nug sthese models to enhance monitoring
    systems. As noted by Dooge (1986) our ability to understand, predict and manyadgre olohgic systems is
    dependent on our ability to characterize both the natural and human systems that shape their
    evolution. The relative uncertainty of these projections in regards to both the results presented in this
    chapter and to climate change impas ctshould be noted. Our ability to make such projections at higher
    spatial and temporal resolutions should not be mistaken for a reduction in uncertainty. It is important to
    keep this in mind whenu sing the results for actual decisio-mn aking. Estimating the uncertainty in
    projections at decision-making scales is an open research question.
    This chapter discussed the current understanding regarding the implications of climate change on
    Pennsylvania water resources. Throughout this diusscsion, it is imperativeto stress that the IPCC
    scenarios present potential futures of our world— none of whichm ay actually occur, though all are
    possible. The process of assessing the implications of these scenarios, through global climate models
    and local hydrological models fo varying complexity, contains uncertainties that haveu ts hfar not been
    quantified. The confidencewi th which we can make statements about prospective impacts therefore
    differ for the various elements of the water cycle. Table 5.3 summarizes the main lcuosniocns of this
    section and also provides a statement of confidence associated with each property.

    Century Projection
    Increase in winter precipitation. Small to no increase in summer precipitation.
    Potential increase in heavyp recipitation events [High confidence for winter,
    lower for summer]
    Snow pack
    Substantial decrease in snow cover extend and duration [High confidence]
    Overall increase, but mainly due to higher winter runoff. Decrease in summer
    runoff due to higher evapotranspiration [moderate confidence]
    Soil moisture
    Decrease in summer and fall soil moisture. Increased frequency of short and
    medium term soil moisture droughts [Moderate confidence]
    Increase in temperature throughout the eyar. Increase in actual
    evapotranspiration during spring, summer and fall [High confidence]
    Potential increase in recharge due to reduced frozen soil and higher winter
    precipitation when plants are not active and evapotranspiration is low
    [Moderate confidence]
    Stream temperature
    Increase in stream temperature for most streams likely. Some spring fed
    headwater streams less affected [High confidence]
    Potential decrease of rain on snow events, but more summer floods and
    higher flowvariabilit y [Moderate confidence]
    Increase in soil moisture drought frequency [Moderate confidence]
    Water quality
    Flashier runoff, urbanization and increasing water temperatures might
    negatively impact water quality [Moderate confidence]
    Salt water intrusion
    Increase in salt water inustr ion (in estuaries) due to rising sea levels
    [Moderate confidence]
    Table 5.3.
    Summary of general projections for Pennsylvania water resources.

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    Aquatic Ecosystems and Fisheries
    This section updates Chapter 8 of the 2009
    Pennsylvania Climate ImpaActsses ssment
    , which foucsed
    on climate change’s impact on ecosystems. The 20P0C9 IA came to the following conuclsions:
    1. Wetlands represent an important ecological part of Pennsylvania’s resources and their numbers
    are depleting and their depletion can be attributed to direct human intervention. However,
    measuring individual negative impacts are difficult due to the number anomd pclexity of factors
    affecting wetland survival.
    2. The case study for Little Juniatua nder climate change conditions forecasted decreased water
    levels, rising (near head water streams) and lowering (throughout the watershed) of the water
    table, and increased discharge throughout the basin. Buesce a of the lack of research and
    complexity, ti’s difficult to predict with high certainty the exact overall effect of these combined
    conditions. However, individually these impacts (with the exception of rising water tables near
    head water streams) are expected to have negative consequences, which wereu sdsised
    c in the
    2009 PCIA.
    Overall, the same concluiosns are reached in this update and the 20P09 CIA. This update foucses on
    expanding the case study for Little Juniatba y comparing climate changes impacs tto the Little Juniata to
    Young Woman’s Cree. k After looking at (past and future) stream flow and groundwater levels for these
    areas, negative effects were found for both watersheds. These include: increased erosion, loss of native
    habit (e.g., eastern brook trout), and an increase in invasive ssp. ecie
    Pennsylvania’s Aquatic Resources
    Pennsylvania’s aquatic resources are primarily freshwatewr, hich represent a significant natural
    resource. While inventory accounts do not precisely agree, the Pennsylvania State Water Plan (2009)
    presents the following census:
    • About 138,000 kilometers (8; 6,000 miles) of streams
    • Nearly 4000 lakes, re; servoirs and ponds
    • About 303 trillion liters (8; 0 trillion gallons) of groundwater
    • Over 163,000 hectares (404,000 acres) of wetlands;
    • 90 kilometerof sco as(5t al6 ong
    mithel Dees)
    laware Estuary and 103 kilometers (64 miles)
    along Lake Erie.
    While lakes and coastlines present significant and important habitat resources, this udssisiocn will cover
    the signature resources of the commonwealth (e.., g streams and ewtlands). These resources are
    intertwined and dependent upon one another for ecological integrity. For example, the trout population
    of a headwater stream is dependent upon wetland habitat along its edge. For that reason, this
    discussion is based on: the mipacts of climate change on wetlands and headwater streams as a riparian
    ecosystem, and as representative of the majority of the aquatic ecosystems of the commonwealth.
    Pennsylvania’s streams and rivers are classified into 124,181 segments by PennsylvanDepia artment of
    Environmental Protection and Department ofranT sportation (data from PASDAa) nd are second only to
    Alaska in total stream kilometers in any state. The largest area of stream kilometers can be found in the
    Ridge and Valley eco-region (34,770 km; 21,605 miles), Allegheny High Plateau e-creogion (26,596 km;

    16,526 miles) and Pittsburgh Low Plateau (23,477 km; 14,588 miles), as reported by the Department of
    Conservation and Natural
    Resources (
    Nationally, the United States has destroyed over half of its original wetlands throughout the past
    200 years, leaving approximately 40 million hectares (100 millioacn res), while Pennsylvania has lost an
    estimated two-thirds hectares of its original wetland area. Estimates of the total amount of current
    wetland area in the commonwealth vary, and are due either to the usiinocn l of lakes, ponds, and
    estuarine habitat under the definition of wetlands, or their placement in a separate category. The
    National Wetland Inventory data, as reported by the Pennsylvania Game Commission, includes this
    aquatic habitat under the definition of wetland, and reports a total of 295,2e3tl2 anwd hectares
    (729,535 acres) found in more than 160,000 wetlands across the
    state (
    These occur in two major categories: [1] a total of 59,414 hectares (146,816 acres) are defined as
    lacustrine (lakes and ponds primarily), [2] and 165924 hectares (410,009 acres) are defined ast rinpale us
    habitat (marshes, etc. .)An additional 260 hectares (643 acres) of estuarine habitat are located in the
    southeastern region along the Delaware River. Most of Pennsylvania’s wetlands p(er9c7 ent) are
    palustrine (bogs, fens, swamps, shallow pools). Emergent wetlands (marshes, meadows) and shrub
    swamps comprise 10-20 percent of state wetlands. Generally, natural wetlands are concentrated in
    northeast and northwestern counties, with more than 5p0 ercent of the wetlands in the state occurring
    in these areas (Tiner19, 90).
    Definition and Description of Ecosystem Services
    Wetlands and streams are diverse and productive, and provide a number of tangible and intangible
    benefits to society and the environment. These goods and services have recently been termed
    “ecosystem services,” and the realization that they are critical for human health and wbeeilln-g
    (Millennium Ecosystem Assessment, 2005) has heightened the need for assessments that can estimate
    the level of service provided, detect the impact of human activities (including climate change) on these
    ecosystem services, and guide
    S. restoration of these services (Zedler, 2003). The MEA defines four
    types of ecosystem services: regulating, provisioning, cultural and supporting. These are provided by, or
    derived from, wetlands and headwater streams (Table 6.1). Many of the ecosystem services most highly
    valued by society are regulating services, including water quality improvement and flood contr, oalnd
    provisioning services such as production of fish and game are also valuable and are more commonly
    recognized as “habitat.” The freshwater wetlands of Pennsylvania represent critical areas of aquatic
    ecosystem function, serving as nursery areas, sources of dissolved organic carbon, critical habitat, and
    stabilizers of available nitrogen, atmospheric sulfur, carbon dioxide and methane
    (Mitsch and Gosselink, 2000).

    Table 6.1.
    Ecosystem services provided by wetlands, as per the Millennium syEcsotem Assessment
    Pennsylvania’s streams provide productive and diverse habitats for fish, shellfish and other wildlife. For
    instance,up stream freshwater reaches provide critical habitat for eastern brook trout and other
    resident species, and downstream reaches provide spawning and nursery habitats for migratory fish
    species such as alewife, Atlantic sturgeon, and the federally endangered s-nhoosrte sturgeon. Wetlands
    also are spawning and nursery grounds for fish. In fact, most freshwater fish feed in wetlands or upon
    food produced in wetlands. Pennsylvania wetland habitat statistics for other types of warilde life
    significant; 84 percent (32 of the 38 amphibian species) find a home in wetlands the majority of the
    time. 25 percent (11 of the 41 reptile species) spend nearly p9er9 cent of their life in wetlands.
    Approximately 122 species of shore and wading birds, waterfowl and some soirdngbs perform most of
    their activities in, on or around water.
    While stream ecosystem services (primarily “regulating and supporting”) have been described on a
    regional basis (e.g., Rotet
    h al., 2004; U.S. Environmental Protection Agency 2006), the same nis ot true
    for wetlands. For example, in the M-Aidtlantic, wetland functional assessments have generally been
    limited to specific functions and/or a limited number of sites. Habitat functions in wetlands have been
    described in West Virginia for amphibians and macroinvertebrates (Snyderal.
    , 2006; Balcombe etal. ,
    2005a, Balcombe et al., 2005b), southeastern Virginia for bog turtles (Cartera
    , 1999), and West
    Virginia and North Carolina for vascular flora (Warret
    enal. , 2004; Rossell & Kesgen 2004). Hydrologic

    functions are even more rarely described (Moorhead, 2001), but the high level of resources necessary to
    perform studies of this magnitude make them rare. In addition, characterization of specific ecosystem
    services (or functions) provided by wlanet ds has only recently advanced to larg-secale surveys (for a
    review, see Kentula, 2007).
    While all wetland types serve valuable roles, headwater wetlanand d stream systems may contribute a
    disproportionate share to watershed functioning and the largerainr d age areas and regional watersheds
    into which they drain. Brinson (1993) described how headwater streams tend to set the biogeochemical
    state of downstream river networks. These loowrd- er headwater streams account for 6-0to-75 percent
    of the nation’s ttoal stream and river lengths, making their riparian communities extremely important
    for overall water quality (Leopold etal. , 1964). Lowrance et al.(1 997) emphasized the importance of
    riparian ecosystems along firs-t, second-, and third-order streams forn utrient abatement, pollution
    reduction of overland flow, and other ecosyste-lmevel processes in the Bay watershed.
    In these systems, the connectivity of the floodplain to the adjacent stream is especially important to the
    functioning of both communitiesan d all associated downstream systems. Natural patterns of channel
    and floodplain connectivity usstain resident biota and ecosystem processes such as organic matter
    accumulation, decomposition, and nutrient cycling (Bayley, 1995; Sheldet onal. , 2002). Tis
    h lateral and
    longitudinal connectivity is extremely important for the maintenance of viable populations of aquatic
    organisms in headwater streams. The loss of stream connectivity to the floodplain can lead to the
    isolation of populations, failed recruitemnt and even local extinctions (Bunn & Arthington 2002).
    While the ecosystems services that are potentially derived directly from wetlands in good condition are
    obvious, it is important to note that wetlands form the ecotone and interface between humtiavn ities
    in uplands and the streams and rivers of large watersheds, resulting in important indirect services. Due
    to this unique landscape position, pollutants and fertilizers from managed portions of the landscape
    accumulate in these systems, impacting dan often impairing their condition, and preventing them from
    functioning at their highest possible level; this has implications for the condition of streams and rivers.
    Direct modification of wetlands and streams also occur frequently in the context of agriculture or
    development, altering habitat structure. Direct appropriation of freshwater for human consumption or
    agriculture displaces the water on which these systems depend; this is likely to become a larger problem
    as populations increase (Postel, 2000; Vörösmartet
    y al., 2000). Current and future activity associated
    with the extraction of natural gas from shales will place additional demand on freshwater, from both
    surface and groundwater sources. While the full extent of potential activity is yet cto
    onbfie dently
    estimated, the uSsquehanna River Basin Commission has estimated an additional consumptive water
    usage in the Basin to be 106 mld (28 mgd) on an annualized basis (SRBC, 2009). This equates to a
    19 percent increase in demand attributable to the neergy sector. Also, it should be noted that the
    Susquehanna Basin drains 71,250 km
    (27,510 mi
    ), which covers half the land area of the
    Commonwealth. SRBC is partnering with state and federal environmentals oreurce agencies, the UASCE
    and the Nature Conservancy to develop flow management recommendations. Unlike regulatory flow
    thresholds in past policies, the collaboratively recommended flows will offer protection of critical
    aquatic life and habitat conditions, and have seasonal and aquatic life stage liciatmiop ns.
    Chapter 5 of this report presents the impacts of climate change on the provisioning and regulating
    ecosystem services of floods, drought, and saltwater intioruns. To be complementary, this chapter
    focuses on changes in hydrological flows at thsce ale of headwater streams and wetland and stream
    habitat functions.

    Major Drivers of Aquatic Ecosystem Response to Climate Change
    In order to understand the potential impact of climate change on the production of ecosystem services
    by streams and wetlands, it is imperative to recognize the major drivers in the production of such
    services. Watersheds and their freshwater elements are defined by a set of inherent physical fa, ctors
    including climate, soils, geomorphology, topographyn, da hydrology (Myers et al., 2006; Griscom et al.,
    2007). Hydrologic processes and patterns, as delivered by regional climate forces and modified by the
    underlying physical features, fundamentally define and usstain wetlands, streams and lakes. Either
    directly or indirectly, the ecosystem services provided by these freshwater ecosystems are derived from
    how water is delivered to and maintained in each type of aquatic resource, ast ratilleusd in Figure 6.1.
    While temperature and carbon dioxide levels have direct effects hoef irt own, the clear driver in
    wetlands and streams are the combined effects of temperature, carbon dioxide, and precipitation on
    the resulting flow regime (for streams) and hydroperiod (for wetlands). Flow regime and hydroperiod
    are the defining factors int he structure and function of these systems. The amount of water, its rate of
    flow, and the timing of delivery all significantly determine the type of organisms present, the cycling and
    removal of nutrients, the occurrence of flooding, the amount of rregceh, aand the growth and survival
    of plants and animals. A change in the timing, seasonality, and magnitude of water delivery can severely
    alter these systems.
    Figure 6.1.
    Linkages between atmospheric increases in CO2 and environmental drivers of tempuere
    and precipitation that regulate many ecological processes and patterns in inland freshwater and coastal
    wetland ecosystems. Solid arrows indicate direct responses and dashed arrows indicate direct effects of
    lesser known importance (Poff etal. , 2002).
    Situated at the interface of terrestrial and aquatic systems, wetlands are especially vulnerable to
    changes in soil moisture regime. Alterations in water sources (ground and surface), along with changes

    in evapotranspiration, affect wetlands. Most wetland processes are dependent on catchmenlet-vel
    hydrology (Gitay etal. , 2001). Potential impacts range from extirpation to enhancemen, and include
    alterations in community structure and changes in ecological function (BurketKt u& sler, 2000).
    Evidence suggests that wetlands depending primarily on precipitation for their water supply may be
    more vulnerable to climate change than those relying on regional groundwater (Winter, 2000). The
    number and complexity of factors that influence wetland occurrence and e tympake it difficult to predict
    the fate of wetlands directly from temperature and precipitation changes alone. Predictions of
    hydrologic shifts induced by both climate and land cover changes are needed to mitigate the difficulty of
    analyzing the effect chnages have on wetlands. However, as preuvsioly stated this can be problematic.
    For example, hydrologic impacts due to changes in rainfall patterns will depend on the amount and
    location of impervious surfaces in the watershed. This is information can be cumbersome to collect, but
    is necessary to describe wetlands as a function of temperature and precipitat. ion
    While hydrology is paramount, the existing condition (i.e., health) of these systemes cois na
    d smajor
    driver of their ability to provide ecosystem services. The link between the delivery of ecosystem services
    and condition lies in the assumption that measures of condition reflect wetland ecosystem processes,
    which in turn drive the delivery of services. For instance, if condition is excellent (i.e-d., istleuasrbted, or
    equal to reference condition), then the ecological integrity of the wetland is intact and the provision of
    services characteristic of that wetland type should occur at reference levels. Climate induced impacts to
    wetlands will be layered onto an already compromised resource. An assessment of wetland condition in
    the upper Juniata River watershed in Pennsylvania (Wardropal.
    et , 2007a) reported that over
    68 percent of the total wetanl d area was in medium or low condition, correlating with increased
    agricultural and urban land ue s in the watershed. Two regional assessments of wetland condition found
    that the ability of wetlands in both the Upper Juniata (Pennsylvania) and Nanticoeklae w(areD )
    watersheds to perform valuable functions, such as removal of inorganic nitrogen and retention of
    inorganic particulates, is already significantly reduced (Wardroet p al., 2007a; Whigham et al., 2007).
    The majority of these wetlands are functioninbg elow standard reference levels. These impacts are
    expressed primarily by modification of supporting hydrology (Brooet
    ksal. , 2004). Climat-einduced
    hydrologic regime changes may additionally stress these systems, further decreasing their capacity to
    serve important ecotone functions. The condition of streams shows similar patterns; -an
    depinth stream
    assessment conducted through most of Pennsylvania by EPAin ug s a systematic statistical sampling in
    1993 and 1994 revealed that 27 percent of streams were ni poor condition based on fish and insect
    populations (Mid-Atlantic Highlands Stream Assessment 2000). Additional baseline assessment of
    stream condition will be forthcoming for the usSquehanna River Basin; in 2010, the uSsquehanna River
    Basin Commission S(RBC) initiated a network designed to remotely monitor water quality conditions
    within smaller rivers and streams throughout the portion of the basin experiencing natural gas
    development (SRBC previously operated and maintained such a system only on the inmstaem of the
    ). The network consists o5f 0 monitoring
    stations in the Pennsylvania and New York portions of thue sqSuehanna basin whichc ontinuously
    monitor and record the following five parameters: temperature, pH, conductance, dissolved oxygen, and
    turbidity. In addition, water depths are recorded to establish a relationship with stream flows at select
    Potential Climate Change Impacts to Pennsylvania Aquatic Ecosystems
    While future scenarios related to climate change remain uncertain, the most significant effects
    predicted for stream and wetland communities are increased water temperature and increased

    hydrological variability. The latter of which may be reflected by changing seasonal patterns of water
    levels, reduced stream flows during dry periods, larger floods and longer droughts (Met , 1997;
    Rogers & McCarty 2000). Some surfac-ewater wetlands, which are believedto be the most vulnerable to
    these changes, may disappear completely. This loss of water to the system will stem mainly from greater
    runoff during severe storm events, longer drought periods, and increased evaporation and transpiration,
    rather than decreased precipitation (Moore eal.t , 1997). More severe storm events and extensive dry
    periods will create substantially altered flow patterns, essentially eliminating the flow pulse (below
    bankfull flood events) and resulting in major changes in channel moorlopghy and aquatic habitat (Poff
    et al., 1996; Tockner etal. , 2000; Amoros & Bornette 2002). In addition, water quality in streams is
    expected to decline due to increasedu fslhing of contaminantsfr om adjacent lands during ruonff and
    production of higher esdiment loads to downstream reaches through runoff and erosion of stream
    banks during more intense storm flows (Moore , 1997; Rogers & McCarty 2000).
    Such changes in temperature, water quantity and water quality will most certainly affect stream and
    wetland biological communities. Climate change impacts across a number of natural systems at the
    global scale have shown significant range shifts averaging 6.1 km per decade (3.8 miles per decade)
    towards the poles; (Parmesan & Yohe 2003) this includess hf. i The largest negative impact may be in lost
    biodiversity (Fisher 2000; Tockneret al., 2000), the effects of which are exacerbated by human
    disturbance (Moore etal. , 1997; Rogers & McCarty 2000). Habitat fragmentation from agriculture and
    urban development creates migration barriers that will prevent many species from moving to colder
    climates to offset warming temperature trends (Rogers & McCarty 2000). Although typically considered
    within terrestrial settings (e.g., forest patch sizes), fragmentation applies to aquatic habitats, as well.
    Hydrologic modification and stream-bank erosion isolate streams from their floodplains and nearby
    riparian wetlands, effectively reducing areas for flood refuge, larval development, and oviposition sites
    (Sedell et al., 1990; Tockner et al., 2000). This loss of hydrological connectivity not only reduces aquatic
    biodiversity, it also makes it more difficult for species to adapt to altered precipitation and temperature
    patterns. The predictability of timing and durationf hoigh flow events has been shown to be important
    in determining the ue s of floodplain habitats by some fish species (Humphries
    , 1999).
    Temperature is a critical component in aquatic systems, executing both physiological and behavioral
    influence onthe survival and growth of nearly all macroinvertebrate and fish species (Sweeet
    neal.y ,
    1991; Ward 1992; Mountain, 2002; Harper & Peckarsky 2006). For example, emergence of mayfly
    populations is initiated primarily by increases in water temperature (Sewneey et al., 1991; Watanabe
    et al., 1999; Harper & Peckarsky 2006). Consistently warmer temperatures earlier in the year can have
    negative consequences for the longte-rm health of mayfly populations, since early emergence coincides
    with reduced growth during the larval period, which reduces the size and fecundity of the adult mayfly
    (Peckarsky et al., 2001; Harper & Peckarsky 2006). Pennsylvania contains a vast muultdite of headwater
    streams that provide high quality habitat for numeurs ocold-water species, including the brook trout
    Salvelinus fontinalis
    ) and the majority of intolerant mayfly, stonefly, and caddisfly species. Increased
    stream temperatures can negatively impact these organisms by exceeding their thermal tolerance
    levels, lowering dissolved oxygen concentrations, and biomagnifying toxins (Mountain, 2002; Moore
    et al., 1997). Unlike intolerant species that typically cannot withstand high temperatures,y mtoanlerant
    species respond to warmer temperatures through increased growth rates and fecundity (Sweeneal.y ,e t
    1991). In addition, the general tolerance and opportunistic nature of these species will enable them to
    adjust to shorter and unpredictable hyodrperiods. As a result, the commonwealth may see a decline in
    some of our most valued co-ldwater communities and a simultaneuos increase in the abundance of less
    desirable biological assemblages, especially invasive species that outcompete and often dece
    populations (Rogers & McCarty 2000; Dukes & Mooney 1999).

    Of special concern is the impact of higher temperatures and altered flow regimes asotn ernE Brook
    Trout, not only becuase of its status as a recreationally and culturally important specie, sbut becuase it is
    an indicator of high water quality and may be an early casualty of climate change. A populatiuos n stat
    assessment of eastern brook trout was performed by the Eastern Brook Trout Joint Venture (Hual.dy , et
    2008; Hudy et al., 2005) and tuilized known and predicted brook trout stuas tto classify eastern U.S.
    subwatersheds according to the percentage of historical brook trout habitat that still maintained
    self-sustaining populations. The data for Pennsylvania (among all eastUern. S. states in the native range)
    identified 143 subwatersheds (10 percent) in which over 5p0 ercent of brook trout habitat was intact;
    550 subwatersheds (40 percent) in which less than 5p0 ercent of brook trout habitat was intact; 612
    subwatersheds (44 percent) from which self-sustaining populations were extirpated; and 72
    subwatersheds (5p ercent) where brook trout were absent but the explanation for the absence was
    unknown (i.e., either extirpation from or a lack of historical occurrenceo sin
    e tshubwatersheds). Huy d
    et al.(20 08 ) utilized this data to assess whether classification of subwatersheds could be reasonably
    well-predicted by utilizing the five factors of percent total forest, sulfate and nitrate deposition, percent
    mixed forest in the water corridor, percnt eagriculture, and road density; the classification was correct
    71 percent of the time. The classification model was corroborated by a ranking of threats by resource
    managers; EBTJV (2006) interviewed regional fishery managers and asked them to rank prbertatuions
    and threats for all subwatersheds that historically supported reproducing brook trout populations,
    according to three categories of severity: [el1]im inates brook trout life cycle component; [2] reduces
    brook trout population; and [3] potentially impacts brook trout population. Across the entire study area
    of eastern U.S. states supporting brook trout, the top five perturbations listed as a category 1 or 2
    severity for streams were high water temperature, agriculture, riparian condition, one or more
    non-native fish species, and urbanization; increased stream temperatures were ranked by biologists as
    the top threat to Appalachian brook trout (EBTJV, 2006). Climate change will exacerbate all of these
    perturbations, either alone or synergistically witch ontinued land cover change. Increased stream
    temperature may be the first and most direct impact.
    Increases in hydrological variability (larger floods and longer droughts) could have severe -tloermn g
    effects on both stream and wetland communities (Harper & Peckarsky 2006; Humphries & Baldwin
    2003). Larger peak flows will result in higher rates of sedimentation and increased scouring of stream
    banks and floodplains, both of which decrease survival and reproductive success for fish and
    macroinvertebrates (Chapman, 1988; Fisher, 2000). Fine sediment reduces stream insect and salmonid
    spawning habitats, and lowers survival rates of many insect species and salmonid embryos (Chapman
    1988; Roy etal. , 2003). Large flood events reduce survival rates for eggs laid alongside stream banks and
    floodprone areas and cursh species lacking flood refugia (Karr & Chu 1999; Sedet
    ell al., 1990). The
    greatest impacts will occur in urban areas with a high percentage of impues rvsiuorfaces where runoff is
    quickly routed to streams (Rogers & McCarty 2000). Furthermore, loss of seasonally predictable flood
    events and reduced groundwater recharge would affect many species that have adapted their life cycles
    to coincide withti mes of high water (Tockner etal. , 2000; Amoros & Bornette 2002; Suen 2008). Climate
    change can negatively impact these populations in a multitude of ways, including mismatched timing of
    life cycle stages and aquatic habitat availability (e.g., aestivating eggs that rely on inundation to initiate
    hatching in seasonal wetlands), insufficient duration of inundation (e.g., aquatic life cycle stages
    dependent on longer hydroperiods), and lack of sufficient habitat refugia (e.g., young insect larvae and
    fish fry that depend on seasonal backwater areas to escape predation and ensure adequate food supply)
    (Poff & Ward 1989; Sedell et al., 1990; Firth & Fisher 1991; Sweeney et al., 1991; Bunn & Arthington
    2002; Suen, 2008). Hydrological factors are significant variables in structuring fish assemblages;
    alterations in the hydrology could greatly modify fish assemblage structure (Poff & Allan 1995).

    At a larger spatial scale, climate change is likely to alter the biogeochemistry of the Chesapeake
    watershed via the lrage contribution of the uSsquehanna River to its total freshwater input (p5e1 rcent).
    The direction of change is not well constrained given the uncertainty in flow projections (et ,
    2008), as well as the lack of a mechanistic understanding of watershed processes. For example, two
    studies summarized in Najar etal. (2008 ) for the Susquehanna River BasinC ommission, present
    estimates of percent change in annual streamflow of p2er4 cent. Nutrient and sediment loading during
    winter and spring will likely rise due to the anticipated increase in flow during this time and is due to
    increased runoff and erosion of stream banks. In addition, elarg
    concentrations of nutrients (nitrogen
    and phosphorus) are stored by benthic biofilms (mostly algae) in the bed of streams through
    Pennsylvania (Godwin et al., 2009). Once dislodged, this material is transported downstream (Godwin &
    Carrick 2007; Godwin etal. , 2009). Over a longer time frame, the aimct pof development and other land
    cover changes could control fluxes of both nitrogen and phosphuos roby further altering both hydrology
    (through an increase in impervuios surface) and nutrients and contaminants (contained in runoff).
    A Case Study for Climate Change Impacts to Hydrology: Comparison of the Little
    Juniata River and Young Woman’s Creek Watersheds
    In order to provide context for the consideration of potential climate change impacts on aquatic
    ecosystems, we present estimates and predictios nof ecologically-relevant streamflow characteristics for
    two watersheds, onee ach in two major physiographic regions of Pennsylvania, during present
    (1979-1998) and future time periods (204-26065). While the reporting of general statewide estimates is
    informative, impacts at the local scale often
    provide greater understanding of the issue. It is important
    to note that fine spatial scale assessment of potential impacts is generally lacking for aquatic resources,
    due to the enormous amount of complexity and sitesp- ecificity when predicting hydrologic change
    resulting from projected climate. Tuhs, we present this current research as a general example of the
    potential small-scale variability in effects and not as a general example of future conditions stawteid-e.
    The Little Juniata watershed is a small mesoscale (845
    i) subwatershed of the Juniata River,
    the second largest tributary to the usSquehanna River, which, in turn, is the lart getribs utary of
    Chesapeake Bay (McIlnay, 2002). The Little Juniata watershed is located primarily within the Ridge and
    Valley physiographic province of central Pennsylvania. However, its headwaters are found in the
    Allegheny Front, which is the watershedv didie and transitional region between the Ridge and Valley
    Province and the Allegheny Plateau.
    Most of the bedrock found in the watershed is sedimentary siliclastic and carbonate rock of alternating
    layers of sandstone, shale, and limestone. Valley floors can be either shale or limestone. The climate of
    the region is moderate, with an annual average temperature of 10° C (50°F) and monthly averages
    ranging from 3-°C (26.6°F) in January to 22°C (72.3° F) in July. Average annual precipitation is 102 cm
    (40 in) and is evenly distributed throughout the year, with substantial amounts of frozen precipitation in
    winter. It is representative of a Ridge and Valley subwatershed, and provides a relevant window into
    potential effects of climate change on headwater streams and wetlands.
    In contrast, Young Woman’s Creek is a southw-eflostwing tributary of the West Branch of the
    Susquehanna River. The Young Woman’s Creek Basin is in the Appalachian Pulas tpehaysiographic
    province in north-central Pennsylvania in an area characterized by high, -flattopped uplands dissected
    by steep- sided stream valleys. The basin drains 120 k
    i) of forested terrain. The basin is
    unglaciated, and the bedrock that underlies it includes primarily sedimentary rocks. The most common

    formation in the basin (the Pocono Group) is highly permeable gray sandstone with layers of
    conglomerate and shale.
    Climate of the area is characterized as a humid continental type with cold winters and warm summers;
    average daily air temperatures range from 3.-3°C (26.1°F) in January to 22.5°C (72.5°F) in July (Kohler,
    1986). Precipitation averages 105 cm (41 in) annually and is fairly evenly distributed throughout the year
    (Kohler, 1986). Frontal storms are the most common source of precipitation, althouugnh dethrshowers
    are prevalent in summer. Average seasonal snowfall is 120 cm (47 in); however, a seasonal snowpack
    rarely persists through the winter.
    Establishing the quantitative relationships among surface water, soil water, and groundwater conditions
    for dynamic climate scenarios, represents an essential step to understanding the problem of wetland
    and small stream hydrologic dynamics. For this example of two case studies, a fully coupled and
    distributed modeling system was applied, which simulates sure fawc ater (overland, channel, lake), soil
    moisture, and groundwater dynamics. The model is referred to as the PIHM (Penn State Integrated
    Hydrologic Model; Qu & Duffy 2007). The model has shown dynamic interaction between groundwater
    level and evapotranspiration and local topographic and stream morphology effects on stream aquifer
    interactions. A simple future climate scenario was constructed by applying a daily temperature and
    precipitation change, obtained from monthly changes predicted by the model meatn he of2 1 GCMs
    under the A2 scenario. The model presented estimates of selected hydrologic metrics for two time
    periods: present conditions (197-91998) and predicted conditions under the climate change scenario
    (2046-2065). Metrics are presented as annual eravages over the 19-year run period.
    Because of the high level of uncertainty associated with forecasting hydrologic variables with climate
    change in general and in the M-idAtlantic in particular, it is important to ufos con changes that could be
    ecologically relevant. Our approach is to e usregional models of climate change and to feed the
    scenarios into an integrated, physically based hydrologic model and generate a range of possible
    conditions. The questions that we arte
    hen investigating as ecologists are: what hydrological changes
    can we forecast with the most confidence? What potential and pilablue s hydrologic changes due to
    climate change could caue s changes in the ability of wetlands and streams to provide ecologic services?
    What can be done to prevent these pulsaible changes? We answer the first two questions by examining
    the ecologically-relevant hydrologic metrics associated with stream flows (mean, maximum, and
    minimum flows, and flow variability), and groundwr atleevels (average depth to water, time in the
    growing zone). Management actions to prevent these changes are udsissed
    c in Section 6.6.
    6.5.1 Stream Flow
    Various measures of flow magnitude provide a general measure of aquatic habitat availability and
    suitability, with monthly means describing daily monthly conditions, and similarity between monthly
    means describing hydrologic constancy throughout the year. In-tanernual variation for any given month
    describes contingency, or the extent to which flows vary thwiin any given month from ye-ator -year. For
    many aquatic organisms, this predictability in conditions and timing is critical for successful
    reproduction. Extremes in daily to seasonal water conditions provide measures of environmental stress.
    For both watersheds there were increases in the magnitude of mean flows under the future scenario,
    with accompanying increases in the magnitude of maximum flows but a decrease in the magnitude of
    minimum flows (Figures 6.2a and 6.2b). Forecasted seasonal differences inflo w are not evenly
    distributed: the largest increases occur during the typically wet winters and springs while the summers
    show slight decreases in mean flows. Overall, an increase in the mean magnitude flows would indicate

    an increase in flooding event, swith a concomitant increase in the duration of inundation in habitats in
    the floodplain. Additionally, it would indicate an increase in stream power, or the amount of work a
    stream can do in terms of moving materials. Under these conditions, increased erosion and deposition is
    likely to occur, especially in areas where stream banks are compromised with little vegetation to hold
    the soils in place. Increasing stream power can translate into increasing incision of streams and mean
    less of the small over bank flooding events, which are typically not highly detrimental to humans but
    very important in forming streamside habitats. Furthermore, an increase in sediment deposition can
    cause a decrease in the rate of native plant species germination and can tfill
    ghs and hummocks
    within a wetland that act as important habitat.
    Figure 6.2a.
    Seasonal stream flow for the Little Juniata and Young Woman's Creek waters, havederaged
    over present (1979-1998) and future (2046-2065) time periods.

    Figure 6.2b.
    Average stream flow for the Little Juniata and Young Woman’s Creweaterk sheds, averaged
    over present (1979-1998) and future (2046-2065) time periods.
    Another ecologically-relevant metric of stream flow is flashiness. Flashiness has no set definition but is
    generally associated with dramatic fluctuations in flow, such as high flows immediately following wet
    weather and a rapid return to p-rrain
    conditions shortly after the end of the precipitation. This rapidity
    in response is often the result of fasteru rfsace runoff, with a sudden and intense peak flow in the
    receiving stream, which represents a loss of water storage in soils and vegetation, i.e., water that
    precipitates will make its way quickly from the land into the stream and bhe efld utshrough the ysstem.
    Two estimates of flashiness are presented in Figure 6.3: the baseflow index (proportion of baseflow to
    the total flow) (Dunne & Leopold 1979; Chapman & Maxwell 1996) and the Rich-aBakrdser flashiness
    index (increases with increasing flashiness) (Beakr, Richards etal. , 2004). The baseflow index is given
    here because a stream with a lower baseflow index will be more prone to flashiness due to a relatively
    higher amount of surface water contributing to the overall flow. For both watersheds, the pioron poortf
    baseflow goes down in the future scenario. This could have potentially significant consequences for the
    thermal sensitivity, defined as the sensitivity of stream temperature of a given site to change in air
    temperature, as disucssed in Section 5. Bsaeflow index is inversely related to thermal sensitivity in
    smaller streams (Kelleher eal.t , 2011), but does not appear to influence thermal sensitivity in large
    streams. Thus, small srteams could exhibit an increased thermal sensitivity that will furthexaer cerbate
    the impact of higher air temperatures, resulting in higher stream temperatures. In addition, the
    flashiness increases with the future scenario, with a higher probability of lowering stream levels during
    the critical summer months. All three factors (increased thermal sensitivity, higher air temperatures, and
    lower stream levels during summer periods) could present significant challenges for cold water fish
    species such as brook trout.

    Figure 6.3
    Baseflow Index and Richard-sBaker Flashiness nI dex for the Little Juniata and the Young
    Woman’s Creek watersheds, averaged over present (19-197998) and future(204620- 65) time periods.
    6.5.2 Groundwater Levels
    Average groundwater levels, expressed as mean dep-toth-water (zero is interpreted as ground surface),
    increase in both watersheds for the future scenario, meaning that average groundwater levels are closer
    to the surface, resulting in "wetter" conditions (Figure 6.4). An increase in groundwater would influence
    different types of wetlands differetnly; wetlands along headwater streams could see possible increases
    in groundwater driven microhabitats, and there could be a general expansion of these and other
    groundwater-supported wetlands. In contrast, an increase in inundation can change the vegeont aatni d
    habitat conditions of other wetlands, with a resulting shift in aquatic communities. Though the overall
    mean groundwater levels increase in the future scenarios, seasonally there are increases in the winter
    and spring but decreases during the dry msumer months. This may be a critical change in aquatic habitat
    for macro invertebrates, as suitable habitat in floodplains disappears.

    Figure 6.4.
    Average and seasonal average groundwater levlse, expressed as mean depth-to-Water, for
    the Little Juniata and Young Woman’s Creek watersheds, averaged over present (-11997998) and future
    (2046-2065) time periods.
    Another ecologically-relevant metric for groundwater hydrology is the time that the water table is
    present in the upper 30 cm (12 in) of soil, whis icch ommonly held to be the average rooting zone for

    wetland vegetation. This metric has been shown to be related to the wetland type et( Cal.ole , 2000), as
    well as the general type of wetland vegetation. Similar to overall groundwater levels, the future
    scenarios as shown in Figure 6.5 show a marked increase in the percent of time groundwater is in the
    growing zone (upper 30c m; 12 in). Seasonally, the increases in time in the growing zone occur in the
    winter and spring, while there is a decrease in tuhme ms er months. While an increase in the percent of
    time groundwater is in the upper 30 cm (12 in) is generally correlated with a higher quality plant
    community due to a more stable and constant state of soil moisture, the increased seasonal extremes
    (wet springs and drier summers) may instead lead to a higher presence of aggressive and invasive
    species (i.e., more tolerant).
    Summary of Impacts
    The most significant climate change effects predicted for stream and wetland communities are
    increased water temperature and increased hydrological variability (high agreement, much
    evidence; high confidence).
    Pennsylvania may see a decline in some of its most valued -cwoaldter communities and a
    simultaneous increase in the abundance of less desirable biological seasmblages, especially
    invasive species. Eastern Brook Trout will continue to decline as a result of higher water
    temperatures (high agreement, much evidence; high confidence).
    Wetlands may experience a similar change in habitat conditions, as hydrolvoariabgic ility
    changes habitat structure (high agreement, limited evidence). Potential impacts on other
    ecosystem services cannot be predicted at this time.
    Wetlands and headwater streams in Pennsylvania are already compromised in their ability to
    provide ecosystem services, due to degraded conditions resulting from modification of
    hydrology and nutrient enrichment (high agreement, much evidence; very high confidence).
    These stressors primarily arise from human activities associated with agriculture and
    Impacts of climate change on aquatic ecosystems will be difficult to detect bee caousf the
    continuation of primary stressors to their condition such as development and invasive species
    (high agreement, much evidence; high confidence).

    Figure 6.5.
    Average and seasonal average groundwater levels, expressed as the percent of time
    groundwater levels are present in the upper 30 cm (12 in) of othil e susrface, for the Little Juniata and
    Young Woman’s Creek watersheds, averaged over present (197-91998) and future (2046-2065) time

    Adaptation Strategies
    Strategies to avoid the above impacts from climate change need to center around maintaining and
    improving the resiliency of aquatic systems through minimization of increased stream teramtpuere,
    nutrient enrichment, hydrologic modification, habitat fragmentation and degradation, and species loss.
    Such actions would include:
    Protection of existing stream and wetland habitat, especially intact habitat for identified species
    of interest, sucha s Eastern Brook Trout (EBTJV, 2008).
    Consideration of hydrological connectivity within and between stream and wetland habitats.
    Maintenance of riparian forests for moderation of streame mtperature and treatment of ruonff
    from adjoining lands
    Implementation of Best Management Practices to reduce nutrient loading
    Restoration of aquatic ecosystems such as streams and wetlands wherever possible
    Minimize groundwater pumping for irrigation, human consumption, etc., that removes water
    from aquatic and wetland ceosystems.
    Informational needs for Aquatic Ecosystems
    What are the projected increases in temperature in streams of the commonwealth, especially in
    cold-water habitats?
    What is the projected change in flow rates and hydroperiods in watersheds acroe ss th
    What controls the retention of nutrients verus stheir export to aquatic systems once they are
    deposited onto the landscape?
    What is the existing condition of streams, lakes, and wetlands across the commonwealth, how
    will that affect their ability to respond to additional climate change impacts, and how will that
    affect the production of ecosystem services?
    How will humans continue to interact with aquatic ecosystems under scenarios of climate
    change, e.g., how will changing patterns of wr aretesourceus e affect wetlands and streams?

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    Zedler, Joy B. 2003. Wetlands at Your Service: Reducing Impacts of Agriculture at the Watershed Scale.
    Frontiers in Ecology and the Environment. 1(2):- 7265.

    Energy Impacts of Pennsylvania’s Climate Futures
    This section updates Chapter 10 of th20e 09
    Pennsylvania Climate ImpactsA
    , focusing on
    possible impacts of climate change on energy production and utilization in Pennsylv. aThniae 2009 PCIA
    suggested a few broad implications:
    1. Warming in Pennsylvania is likely to increase demand feonr ergy, particularly electric power,
    during the summer month. sThis increase is likely to be larger than any decline in wintertime
    energy consumption. Thus, overall energy utilization in Pennsylvanis
    ia likely to increase as a
    result of climate change.
    2. Impacts of climate change on Pennsylvania’s energy infrastructure are likely to bue sefod con
    the electric power production and delivery system.
    3. Some opportunities exist forP ennsylvania to facilitate the adaptation to climate change as well
    as mitigation off urther greenhouse gas emissions, particularly in the areas of
    and energy efficiency.
    These conclusions have not changed significantly since th20e 09 PCIA, nor has Pennsylvania’s stuats as a
    major energy-producing state. This section updates some information from the 2009 PCaIA
    nd highlights
    a few areas where additional information is needed to assess climate imp. acFirtsst, the research
    literature has increasingly foucsed on location decisions for lo-wemissions power generation as an
    important factor in the contribution to reduced greenuhsoe-gas emissions. Second, there is significant
    uncertainty regarding likely shifts in the transportation sectoA r. shift away from gasoline and diesel fuel
    towards electrified transportation or natural gas transportation is likely to reduce greenuhse o gas
    emissions from this sector, but the rate and direction of transformation is unce. rtThainird, we highlight
    the dependence of Pennsylvania’s energy sector on water supp. liIenscreased seasonal variations on
    freshwater supplies may impact the ability of Pennsylvania’s energy sector to produce reliable supplies
    under some scenarios.
    Energy Supply in Pennsylvania
    Pennsylvania continues to be a major ener-gpyroducing state. Based on 2009 production data, the
    Commonwealth ranks sixth nationally in total energy producti. oInt ranks third in the nation in electric
    power production, fourth in the nation in coal production, an
    nation in crud-eoil extraction
    (EIA, 2010). Natural gas from the MarcellSuhs ale has represented the largest energy growth area for
    Pennsylvania since 2009; gas production in the Commonwealth has increased from un1 detr rillion BTU
    per day 1( billion cubic feet per day) to more th3 an trillion BTUp er day 3( billion cubic feet per day) in
    the past two years
    the Commonwealth continues to be the nation’s largest exporter of electric
    energy and a major exporter of coal, it is largely ssuefficlf- ient in natural gas. Pennsylvania continues to
    import the majority of its crude oil and petroleum products. Overall, the Commonwealth continues to
    be an energy importer – total energy production in 2009 amounted to 2.6 quadrillion B
    energy consumption for all purposes in 2009 was 3.6 quadrillion BTU.
    Natural gas production data after 2009 is from the Pennsylvania Oil & Gas Production Reporting System;
    One quadrillion BTU (British Thermal Units) is commonly referred to as a “quad;” offifgiciuaresl after 2009 are
    not available from theU. S. Energy Information Administration, but the increase in Pennsylvania’s natural gas
    production itself is not enough to make Pennsylvania a ne
    exporter in 2010 or 2011.

    Prices for many energy commodities in Pennsylvania have fallen since 2009, driven in part by the
    recessionary economic environmen. t Increased supplies of natural gas are estimated to have played a
    smaller but still significant role in reducing consumer energy prices in Pennsylvania (Considinal.e,, et
    2011), although natural gas costs in Pennsylvania have remained strong, aboupt e3r0 cent higher than
    the national average (as of summer 2011, the citygate price for natural gas in Pennsylvania was around
    $8 per million BTU(tho usand cubic feet) while the natioal n average was around $6 per million BTU
    (thousand cubic feet). )Presently, virtually all of Pennsylvania is part of a regional electricity market
    known as the PJM Interconnection, which covers all or poaf rt 13 states puls the District of Columbia.
    While coal is a major fuel eusd to supply electricity within the PJM region, electricity prices (particularly
    during peak periods) are sensitive to the price of natural . Pgasetroleum is not a major contributor to
    electricity generation in Pennsylvania, so the transportation energy sector in Pennsylvania is essentially
    separated from the electric power, inudstrial and building energy sectors.
    Coal and nuclear power remain the predominant fuels ed
    usfor generating electricity in Pennsylvania.
    Pennsylvania’s installed capacity mix as of 2010 is shown in Figure 7.1, while utilization of fuels for
    electric generation is shown in Figure 7. .P2ennsylvania’s generation capacity mix is similar to the mix of
    the U.S. as awhole . The cost of fuels, capital and maintenance all influence how often generating units
    are used. Thus, there a substantial difference between Pennsylvania’s installed generation capacity and
    the intensity with which generating units or technologiese aursed to produce electricit. yOne significant
    change in Pennsylvania’s utilization of fuels for electricity generation is the decline in output from
    coal-fired power plants to less than 5p0 ercent of the Commonwealth’s electric energy mix; this decline
    has been matched with increases in the utilization of natural gas and renewable energy (primarily wind
    Figure 7.1.
    Installed capacity mix for electric generation in Pennsylvania, 2. 00S9ource: U.S. Energy
    Information Administration.
    Natural Gas, 22%
    Coal , 41%
    Nuclear, 20%
    Other Renewables,
    Hydroelectric, 4%
    Petroleum, 10%
    Natural Gas
    Other Renewables

    Figure 7.2.
    Fuel mix for electric production in Pennsylvania, 20. 1S1ource: U.S. Energy Information
    Energy consumption and pricing in Pennsylvania
    Total energy consumption over all sectors and for uall
    ses in Pennsylvania has declined by apopxrimately
    10 percent since 2007, to 3.6 quadrillion BTU in 20. 0F9igure 7.3 shows a breakdown of total energy
    consumption in Pennsylvania by secto. rThe industrial and transportation sectors consumed the largest
    amount of total energy, although inudstrial energy use declined by the largest amount.
    Natural Gas, 19%
    Coal , 44%
    Nuclear, 34%
    Other Renewables,
    Hydroelectric, 1%
    Petroleum, .002%
    Natural Gas
    Other Renewables

    Figure 7.3.
    Sectoral energy consumption in Pennsylvania, 200. 9Total energy consumption in the
    Commonwealth was 3.6 quad. sSource: U.S. Energy Information Administration.
    Natural gas consumption in Pennslvyania has largely mirrored national trends, as shown in Figure . 7.4
    The rate of increase in natural gas aguse for electric power generation has increased more rapidly in
    Pennsylvania than in the U.S. as a whole; the share of Pennsylvania natural gas comnspution
    represented by power generation has risen to 2p5
    ercent in 2009 from 10 percent in the late 1990s. At
    the same time, inudstrial use of natural gas has declined in Pennsylvania, particularly with the onset of
    recession beginning in 2008.
    Figure 7.4.
    Sectoral natural gas consumption in Pennsylvania, 19-927009. Source: U.S. Energy
    Information Administration.
    Wholesale electricity pricing in Pennsylvania is primarily determined by market outcomes in the
    PJM electricity market, whose footprint encompasses nearly the entire state (Pike County in
    northeastern Pennsylvania participates in markets run by the New York Independent System Oper. ator)
    Prices in the PJM electricity market have been falling steadily since 2008, in part due to the recession
    and in part due to declining natural gas prices (Kleit, 2011).
    As of 2011, virtually all electricityu cstomers in Pennsylvania are free to choose their own electric
    generation supplier, at prices that are largely deregula. teCdonsumers who do not make aexpn licit
    choice of electric generation supplier are assigned to a “default” supplier,u ally
    us the regulated
    distribution utility. The prices charged for s-ocalled “default service” are determined by a series of
    auctions overseen by the Public Utility Comissim on. Since the auctions determine longte-rm contract
    prices for electricity, consumers in Pennsylvania choosing default electric service may not pay prices that
    are representative of current wholesale market conditions, as shown in Figure 7.5 ( , et20 11).
    T rans portation

    Figure 7.5.
    Contract prices for default electric service in PennsylvanSiao. urce: Kleit etal. , (2011) from
    data provided by the Pennsylvania Public Utility Commission and PJM.
    Natural gas prices have remained high in Pennsylvania relative to the national average, owing to the
    amount of pipeline capacity able to transport natural gas from the MarceSllhuas le to markets in New

    York and New England. In gas-producing states with lower demand and less pipeline infrastructure, such
    as Wyoming, natural gas prices have fallen to levels not seen since the 1990s (Blumsack, . 2Th01e 0)
    emergence of regional electricity markets, such as PJM, in the wake of electricity deregulation in the
    1990s has strengthened the link between natural gas and electricity pric. Tehse build-out in natural gas
    generation that occurred during the m-id1990s increased the utilization of natural gas as a power
    generation fuel in the U., S.followed by natural gas price increases and increased volatility (Figure 7.6).
    Figure 7.6.
    The increase in the ue s of natural gas for power generation has been accompanied by
    increased price levels and volatility inP ennsylvania gas markets.T his trend is similar for the
    S. a
    whole (Blumsack, 2010).
    The future price of natural gas in Pennsylvania is uncertain, and regional price projections are sensitive
    to the assumed balance between supply and dema. nBlud msack (2010) suggests a significant difference
    between scenarios where the rate of growth of Marcellgaus s production is halted (i.e., there are few or
    no new Marcelul s wells drilled within the next decade) and scenarios where Marcedlluevs elopment
    continues, even if the pace of development is not rapid. These price projections are shown in Figu. re 7.7
    In both of the latteras ces, natural gas prices in the M-Aidtlantic fall through the 10- year projection
    period even if the demand for natural gas increases due to increased reliance on gas for power
    generation. Even moderate growth in the development of Marucs elslhale gas will result in significant
    quantities of “stranded” gas in the M-idAtlantic if pipeline projects do not proceed as currently

    Figure 7.7.
    Price projections for natural gas in the M-Aidtlantic region. The figure assumes a moderate
    rate of growth in natural gas demand of 0p.e3 rcent per year. Blumsack (2010).
    Greenhouse-gas impacts of energy production and consumption in
    The primary sources of energ-yrelated greenhouse gas emissions in Pennsylvania continue to be
    associated with the electric power, transportation and uinsdtrial sectors. The burning of fossil fuels for
    space conditioning in homes or commercial buildings also contributes, but these effects are small by
    comparison, particularly since the majority of omh es in Pennsylvania ue s natural gas for heatin. g
    Table 7.1 shows average and total carbon dioxide emissions from the burning of fossil fuels for us vario
    consumptive uses, including the generation of electricit. Tyhe figures for electricity generation are based
    on data specific to Pennsylvania, from the UE.nS. ergy Information Administration and the Emissions and
    Generation Resource Integrated Database (eGRID) available through the UE.nS.
    vironmental Protection
    The figures for home heating from ful eoil or natural gas are taken from Blumsack
    et al. (2009).

    Table 7.1.
    Average and annual CO
    emissions from energy use in Pennsylvania. Annual figures are based
    on 2009 data. Blumsack (2009), from U.ESn. ergy Information Administration data.
    *Electric Generation includes consumption for residential heating and cooling.
    **Natural Gas includes cooking fuel.
    The electric generation sector continues to be the largest source of greenushe ogas emissions in the
    Pennsylvania economy. As Table 7.1 demonstratePse, nnsylvania’s coal plants emit on average more
    than one ton of C
    megawatt-hour generated, while natural gas emits half as much
    . CThO e
    burning of refined petroleum for electricity is more carb-ionntensive than burning coal, but -ofireil d
    generation accounts for only a small portion of the Commonwealth’s ele-cstecrictor emissions.
    The emissions figures in Table 7.1 are limited to greeunsheo-gas emissions from the actual production of
    electric power. Viewed from a lif-ecycle perspective, the role ocof al-fired power generation is even
    more apparent, as shown in Figure 7.8 (taken from Blumsackal., ,et 2010). The figure shows how
    different stages of the life-cycle of power generation from different fuels contributes to overall
    greenhouse gas emissions from Pennsylvania’s electric generation sectoWr. hile no fuel is “carbo-nfree”
    from a life-cycle perspective (even renewables, as in Fthenakis & Kim, 2006), the cuosmtiobn of coal
    accounts for nearly 85 percent of Pennsylvania’s total life-cycle greenhouse gas emissions associated
    with electricity generation. More broadly, any assessment of the greenuhsoe-gas implications of energy
    utilization in Pennsylvania (or other U.sS.
    tates) is driven largely by the comusbtion of fossil fuels.
    Changes in upstream parctices (resource extraction, processing and transport) may be environmentally
    beneficial but are likely to do relatively little to reduce ene-rerglaty ed greenhouse gas emissions
    (Jaramillo et al., 2007).

    Figure 7.8.
    Contribution of different life-cycle phases of fossil fuels to the overall greenuhsoe-gas impact
    of Pennsylvania’s electricity sector (Blumsacet
    k al., 2010).
    Pennsylvania’s role as the nation’s largest exporter of electricity to other states suggests that some
    portion of greenhouse-gas emissions produced by the power sector in Pennsylvania effectively serve
    electricity consumers in other states. Emissions leakagaec, ross state borders has been
    an important
    governance issues in regional emissions compacts, particularly involving bordstaer tes that lie outside
    the emissions management region. Pennsylvania, for example, adjoins several states that participate in
    the northeastern Regional Greenhouse Gas Initiative (RGGI) but is not itself bound by RGGI’s
    greenhouse-gas reduction targets. Pennsylvania’s role as an exporter of fosfiresil- d electric generation
    to states participating in the RGGI agreement uths creates some accounting issues in evaluating the
    impacts of greenhouse-gas management policies. Blumsack et al. (2010) attempt to bound
    Pennsylvania’s exports of greenhuose gases based on generator performance, location and transmission
    data. Their analysis, summarized in Figure 7.9a and 7.9b, suggests that 25 pto
    erc4e0 nt of total
    greenhouse-gas emissions from Pennsylvania power plants are produced to satisfy electric demands in
    Maryland (and the Washingto, nD.C. metropolitan area) and New Jerse. yWeber et al., (2010) have also
    noted that the measured carbo-nintensiveness of an electric power system (and uths mitigation or
    adaptation policy recommendations) is highly sensitive to the choice of system boundary (state,
    regional, or broader) and the correct choice for analysis is not clear.
    Coal Mining, 5%
    Coal Combustion,
    Natural Gas
    Combustion, 4%
    Oil Combustion,
    Coal Mining
    Coal Transport
    Coal Combustion
    Natural Gas Production
    Natural Gas Processing
    Natural Gas Transport
    Natural Gas Combustion
    Oil Production, Transport and
    Oil Combustion
    Upstreams from Other Fuels is
    Fossil-Fuel Power Plants

    Figure 7.9a.
    Estimated carbon dioxide exports from Pennsylvania, based on 2009 data (English Un. its)
    The figure is suggestive of how fossfireil- d generation in Pennsylvania is utilized to satisfy electric
    demands in other states. Blumsack, (2010), based on data from PJM Interconnection.
    Figure 7.9b.
    Estimated carbon dioxide exports from Pennsylvania, based on 2009 data (Metric Un. its)
    The figure is suggestive of how fossfireil- d generation in Pennsylvania is utilized to satisfy electric
    demands in other states. Blumsack, (2010), based on data from PJM Interconnection.

    Extraction of natural gas from deep shale– s such as the Mrca ellus and Utica represents –a longside
    growth in the wind energy inudstry, probably the most significant change in Pennsylvania’s energy
    sector over the past few years. The size of the resource and proximity to nearby markets suggest that
    the role of natural gas as an energy source Pein nnsylvania is likely to increas. eThe reduction in air
    emissions of conventional pollutants (oxides of sulfur and nitrogen; mercury; ticpuarlate matter)
    achievable through a shift away from the comusbtion of coal and petroleum and towards natural gas
    can be substantial, although Katzenstein and Apt (2009) suggest that the magnitude of the emissions
    reduction is sensitive to the efficiencoy f the combustion process; the inefficient utilization of gas
    combustion engines may
    emissions under some scenarios.
    The combustion of natural gas releases approximately half of the carbon dioxide as the cuostmiobn of
    an equivalent amount ofc oal or petroleum. However, this may be offset by methane produced by the
    natural gas extraction process as methane is a more powerful greeunsheo-gas than carbon dioxid. eThe
    direct atmospheric venting of methane (as opposed to flaring) at the wellheadsig, noificr ant leakage of
    methane from natural gas infrastructure, may reduce the overall greeunsheo-gas reduction potential of
    substituting natural gas for other fossil fue. lsVery little data from actual well or infrastructure
    operations is available that suggests the rate at which methane is vented into the atmosphere or
    escapes from pipelines, compressor stations or other infrastructu. Mreeasuring the greenhouse-gas
    impacts of Marcellus or Utica shale development, as well as the potential greeunsheo-gas reductions,
    involves significant uncertainties. Under scenarios where large amounts of methane are vented, or
    fugitive methane emissions from the gas transportation system are h(igah
    s has been found for one area
    of Colorado, as described in Tollefson (20))12, the lif-ecycle climate impacts of natural gas power
    generation may be on par with cofireal- d power generation (Howarth eal.t , 2011). This conclusion also
    rests on assumptions regarding the timing of climate impacts over which there is additional uncer. tainty
    Three other studies (Jiang et al., 2011; NETL, 2011; Cathles, 2011) question the assumptions eud s by
    Howarth et al. (2011) and collectively draw three broad conucslions regarding the greenhouse-gas
    impacts of gas-shale development:
    The reduction in life-cycle greenhouse-gas emissions from the increased utilization of shal-gas
    for power generation are sensitive to the efficiency of comtbiousn and the operational scenario. s
    Natural gas base-load generation reduced greenhouse-gas emissions by approximately 40 to
    50 percent compared to base-load coal generation. Generating electricity with natural gas in
    older or less-efficient plants may decrease this benefit to as little aspe
    2r0 cent.
    Shale-gas does have a slightly higher greenhouse-gas footprint than conventional gas
    production, though the literature suggests less than pe5 rcent higher. The differences in
    greenhouse-gas footprint can be traced to methane venting or flaring; and differences in
    transportation requirements. The greenhouse-gas footprint of Marcelus
    l Shale production and
    utilization is similar to that of liquefied natural gas (Jiang al.e, t 2011; Jaramillo et al., 2007).
    Where direct capture is technologically infeasible or economically unattractive, policies to
    encourage flaring of natural gas rather than venting can reduce greenhouse-gas emissions
    associated with gas shale development.
    Climate-related policy drivers affecting Pennsylvania’s energy sector
    Most economic research has foucsed on regulating greenhouse-gas emissions through price-based or
    market mechanisms, such as taxes on greenuhsoe gases or establishing a system of tradable permits for
    greenhouse-gas emissions. To date, Pennsylvania has not adopted these types of policies, though it acts

    as an “observing state” in the Regional Greenuhsoe Gas Initiative for trading of carbon dioxide credits in
    the northeastern U.S. Pennsylvania has adopted different types of policies that are relevant to the
    reduction of greenhouse gas emissions.
    7.4.1 Pennsylvania’s Alternative Energy Portfolio Standard
    Like a large number of U.sS.
    tates, Pennsylvania has adopted a “portfolio standard” that sets
    quantity-based targets for specific alternative energy technologie. Psennsylvania’s portfolio
    standard, the Alternative Energy Portfolio Standard (AEPS), defines quantity targets for a suite of electric
    generation technologies that have the potential to reduce air emissions of greeunshe ogases or
    conventional pollutants, or solve other environmental problems (such as the monitoring
    and remediation of waste coal piles. )The specified technologies generally have higher costs than
    existing power generation facilities, but may be relatively undperro-vided by the market since
    many environmental costs of conventional power generation technologies are not directly borne
    or “internalized” by generation facility owners.
    Policies such as AEPS typically provide subsidies for generation resources that have high costs on an
    average-cost basis. In other words, the levelized cost of energy mfro subsidized resources is generally
    higher than from conventional power plan. tHs owever, many technologies subsidized through AEPS
    have high capital costs but very low marginal or operating cois.ets., (fuel from the wind and sun is free
    at the margin. )The subsidies are ujstified economically if the integration of technologies covered under
    the portfolio standard provides the desired level of emissions reduction when integrated into the
    electrical system.
    Determining the level of avoided emissions assocted
    ia with renewable electricity
    generation technologies is difficult, due to the complexity in electrical system operat. ioOnne
    s megawatt
    of an alternative resource, for example, may not exactly displace one megawatt of a conventional
    (higher-emissions) resource (Apt et al., 2007; Katzenstein & Apt 2009).
    The importance of matching subsidy levels to efficient levels of emissions avoidance is not fully sded
    although it is easy to ov-esrubsidize technologies when multiple regulatroy authorities are involved. Blumsack et
    al. (2011) discuss this issue using ground-source heat pumps as an example.

    Figure 7.10.
    In some cases, locational decisions for renewable electricity generation may increase overall
    system emissions. The figure illustrates the impacts of location a large amouonf t wind energy in the
    Pacific Northwest on annual emissions of: (a) carbon dioxide; (b
    ; aSnOd (c) N
    Oemissions under
    California’s RECLAIM program.
    Location decisions appear to be a crucial part of utilizing portfo-stliano dard type policies to achieve
    environmental goals. Choudhary et al.(20 11) finds that locating wind energy facilities in the most
    profitable areas is not the same decision as locating facilities in areas that are best for the system as a
    whole. Ruiz and Rudkevich (2010) and Rudkevich and Ruiz (2011) have devised a method of screening
    the emissions impacts of incremental generation location decisio. nPserhaps surprisingly, they find that
    locating zero-emissions generation assets in some areas may increase emissions in other areas at the
    margin. This finding is echoed by Blumsack and Xu (2011), who studied wind energy location choices in
    the Western U.S. As shown in Figure 7.10, they confirmed that emission of carbon dioxide and some
    criteria pollutants in the Western power grid as a whole may increase slightly based on location
    The impacts of renewable portfolio standards on energy prices are also unce. rtWainhile the direct
    subsidy costs for technologies covered under AEPS are transferred to Pennsylvania ratepayers via
    electricity bills, the integration of larg-secale alternative energy resources may serve to
    es on
    wholesale electricity markets (such as PJM) if sufficient resources with higher marginal costs can be
    displaced (Fischer, 2010). Coupling conventional and renewable electricity generation may also lower
    average costs of producing energy (Richardson & Blumsack, 2011).

    7.4.2 Energy conservation through Pennsylvania’s Act 129
    Pennsylvania’s Act 129, passed in 2008, requires electric retailersP ein
    nnsylvania to reduce annual and
    peak-time electricity demand. The reduction targets originally laid out in Act 129 specify a performance
    year of 2013 for meeting those target. sWhether Act 129 will be extended (and in what form) past 2013
    is still uncertain. Moreovre, the Act does have implications for greenuhsoe-gas emissions from
    Pennsylvania power generators, even over the short performance perio. Kled it et al. (2010) and
    Sahraei-Ardakani et al.(20 11) have modeled the impact of Act 129 on electricity pricing, fuels utilization
    and greenhouse-gas emissions in Pennsylvani. aThey find that successful implementation of Act 129 will
    primarily affect the so-called marginal fuels or generation technogloies – those that serve incremental
    electricity demand. Base-load generators (which operate more or less continusuoly) will see their
    operations affected less than other generation technologi. eSsince natural gas is often the marginal fuel
    during periods of peak electricity demand, and since greenuhsoe-gas emissions from natural gas are
    lower than from coal, the emissions impacts of A12c9 t are estimated to be smaller than might be
    expected. The influence of Act 129 on co-afilred generation is estimated to be relatively small, although
    coal is estimated to play a larger role in electricity price formation in Pennsylvania.
    Uncertainties and Informational Needs in Assessing Clima-tCehange Impacts on
    Pennsylvania’s Energy Sector
    Separating mitigation from adaptation in the energy sector is inherently difficult, as many strategies
    aimed at allowing individuals to adapt to climate change (such as increasee d ouf sairc-onditioning) may
    be coupled with shifts in energy systems or the e uosf higher-efficiency technologies that also provide
    mitigation services. The impacts of climate change on the energy sector, or impacts of en-serecgtoy r
    shifts on mitigation efforts, are highly uncertain in some are. Tashis section identifies and briefly
    discusses specific areas where significant further research is needed.
    7.5.1 Uncertainties Related to Natural Gas Impacts
    As discussed in Section 7.3, growth in the natural gas usintryd has the potential to induce substantial
    energy-sector change, bothin Pennsylvania and elsewhere in the U.TS.
    here is still uncertainty, however,
    in the speed and direction of the substitution of natural gas for other fuels across all sections of
    Pennsylvania’s economy. Two sources of uncertainty in particular deserve bto
    e highlighted here,
    representing areas where additional information and research are needed to address impacts with a
    higher degree of certainty.
    First, the manner in which natural gas replaces other fuels needs to be asseusssineg
    d a systems-level
    approach. The introduction of natural gas into energy utilization and delivery systems (e.g., electric
    power or transportation) is more complex than
    ustj making calculations based on replacing one BTU of
    another fuel with a BTU of natural g. asFor example, while it is true that burning a BTU of natural gas in a
    power plant releases less C
    burning a BTU of coal or fuel oil in a power plant, the toBT-BUTU
    comparison can be misleadin. gThe greenhouse-gas impacts of additional investments in natural gas
    fired power generation will depend on the efficiency with which nal
    atugras is utilized in the plantht,e
    costs of utilizing the natural gas plant veurss other technologies,an d the location of the natural gas
    plant (i.e., the ability of the electric stmranission system to bring the gas-fired power generation to
    market). A more valid comparison in this case would incorporate these system effects to compare the
    impacts of additional gas-fired power generation on a kW-bhasis, not a BT-Ubasis. Such an approach has

    been lacking in the existing literature, with a few exceptions (e.g.,, , 201et 1;
    Dowds, et al., 2012).
    Second, life-cycle comparisons of greenhouse-gas emissions from the natural gas sector are subject to
    uncertainties due primarily to lack of data, but also due to other modeling assump. tDioiffnesrences in
    greenhouse-gas implications of Marcellus shale development largely come down to assumptions made
    over vented and fugitive C
    and the relevant time frame for life-cycle analysis. As discussed
    in Section 7.3, Howartet
    h al.(201 1) assume high levels of vented C
    fugitive emissions; these
    estimates are viewed as unrealistically aggressive in other studies (Jiaet ng al., 2011; NETL, 2011;
    Cathles, 2011). Direct measurement of CH
    venting and fugitive emissions is rare and expens. ivThe e
    recent study described in Tollefson (2012) based on measurement ogf as a field in Colorado, finds that
    methane releases to the atmosphere are more in line with Howartet hal, .(20 11) than with other
    studies. While the Colorado study represents only a single data point, it is suggestive of the high degree
    of uncertainty that exists in current estimates of direct methane releases from natural gas drilling.
    7.5.2 Uncertainties Related to the Transportation Sector
    Pennsylvania currently has an energy sector dominated by the e uosf fossil fuels; even a relatively
    aggressive alternative energy policy is unlikely to change this characteristic of energy utilization in the
    Commonwealth. The largest potential shifts are likely to occur in the substitution of natural gas in place
    of other energy commodities, particularly coal (for power generation) and potentially petroleum
    (for power generation and transportation. )The use of natural gas as a transportation fuel can reduce
    greenhouse-gas emissions from the transportation sector and provide more local health benefits
    through reduction in other pollutants, such as particulate emissions from h-edauvty
    y diesel vehicles
    (buses and trucks. )Retrofitting Pennsylvania’s transportation energy infrastructure to utilize natural gas
    on a wide scale (i.e., for lig-dhutty and heavy-duty fleets) would involve substantial costs and would
    likely need large public investment. sAs Jiang etal. (2011) reports, natural gas transportation may be
    most socially beneficial if limited to fleets ouf sbes and some trucks, although the greenuhsoe-gas
    reduction impacts would not be that large.
    Electrified transportation represents another option to reduce greenuhsoe-gas emissions from
    Pennsylvania’s energy sector. Based on analysis of emissions factors from electric generation in
    Pennsylvania and the broader PJM region, powering li-ghdutty vehicles using grid electricity is likely to
    result in lower carbon emissions than fueling this same class of vehicles on gasoline or diesel (Samaras
    and Meisterling, 2008; Stephan and Sullivan, 2008). The rate of adoption of inp luhgy-brid electric
    vehicles (PHEV) is extremely uncertain and further research is necessary to understand factors likely to
    lead to widespread adoption. Previous research on hybrid electric vehicles (HEVs such as the Toyota
    Prius) over the past decade has suggested that consumer decisions to purchase inp luegle- ctric vehicles
    are likely to be made on the sbis a of factors besides economic. sFor example, Maclean and Lave (2001)
    reported that hybrid ga-selectric vehicles were unlikely to pay for themselves without a m-yueltiar
    period of sustained high gasoline prices (more than -$$5
    4 per 3.8 liters; 1 gallon) veen if the social
    benefit value of emissions reductions are incorporated into the c-obestnefit calculation. Yet, within
    three years after publication of that article, sales of the Toyota Palriouns e increased by nearly
    500 percent. As costs have declinedo ver the past five years, sales of the uPs rhiave again increased by a
    factor of four. Even in scenarios where the electricity stored in p-luin ghybrid electric vehicles could be
    sold to the grid during peak demand periods, virtually no scenario shows the fuel savings and “energy
    arbitrage” activity paying off the increased costs of plug in hy-belridectric vehicles (Peterson etal. , 2010;
    Lemoine et al., 2010). Experience with the market for HEVs suggests that any growth in sales of PHEVs

    and fully-electric vehicles will likely be spurred by no-enconomic factors (including early technology
    adopters) or by significant declines in vehicle prices.
    7.5.3 Uncertainties related to coupled energy and water systems
    Electric power generation is the largest eu sof water in Pennsylvania, primarily in steam turbines for
    cooling. More than 70p ercent of all withdrawals from major river basins in Pennsylvania are made to
    supply the water needs of a number of fo-sfirsiled and nuclear power plant. sNot all of this wateur se is
    purely consumptive, although the quality of return water may be an issue for maintaining downstream
    ecosystem health.
    While climate change does not have the same drought implications for Pennsylvania as for
    southwestern areas, as disucssed in Chapter 5, under some scenarios streamflows are projected to
    decline during the summer season. sIn some southeastern regions, increased population pressures have
    led to highly constrained freshwater systems during belo-nwormal (but still not uunsually low) drought
    years (Wishunt etal. , 2008). Whether similar constraints could arise during summer seasons in the
    future in Pennsylvania, and its implications for energy systems is highly unce. rtThaine potential for
    increased frequency of low-flow conditions during hte summer season represents a potential threat to
    electric reliability. If withdrawals are limited due to -lofloww conditions, elevated stream temperatures
    or other reasons having to do with maintaining watershed ecosystem health, forced curtailments could
    result at nuclear facilities or fossil energy plants that require water for cooling (i.e., those that utilize
    steam generators burning coal, oil or natural g. asRe)cent droughts in the southern and southeastern
    U.S. have strained the ability of power plants to operate normally (Associated Press, 2011; Averyt,
    Whether streamflow-induced plant curtailments could occur at Pennsylvania’s major generating
    facilities, and how often, is highly uncertain and represents a strong need for further re. seCarcurrhent
    planning practices for the electricity system in Pennsylvania do not currently consider wreatlater-ed
    curtailments that power plants could experience over longer time fram. Iest is also unclear how or
    whether incorporating hydrologic constraints (ocr limate-induced uncertainty in the hydrologic cycle)
    would lead to different system planning decisions than those currently made.
    Recent research suggests that increased demands on river systems could increase the cost of mitigating
    greenhouse-gas emissions from electricity production. Variable electricity generators (such as wind and
    solar energy) require some sort of system bac-up
    k to smooth out fluctuations in outp. uHtydroelectricity
    can be used as a source for providing fillin -power, as outpuct an be changed rapidly to accommodate
    fluctuations. Hydro power has been uesd as a “load-following” resource because of this operational
    flexibility. Fernandez et al. (2011) have investigated the financial implications (opportunictyo sts)
    associated withut ilizing hydroelectric dams to mitigate fluctuations in wind energy output over a range
    of hydrologic conditions (from wet to drought yea. rWs)hen river flows are unconstrained by other
    demands, the opportunity costs associated with providing -ifn ill power for intermittent wind energy is
    on par with providing load-following services (the costs of which are shared among uall
    stocmers in the
    PJM region, and are small relative to the cost of actually producing electri. Dcituyr)ing droughts, or when
    other policy objectives govern release patterns from the dam, the costs ionf g uhsydroelectricity to
    facilitate renewable energy integration increase dramatically. Kern et al. (2011) report that operating
    hydroelectric dams as “run-of-river” rather than tailoring oeprations to capture peak prices in
    deregulated electricity markets reduces poerating profits significantly.

    Broadly, the likely impacts of climate change on energy production and utilization in Pennsylvania have
    not changed significantlyf rom the 2009PCIA. Warming in Pennsylvania is likely to increase the demand
    for electricity for cooling in the summertime, and can be expected to decrease demand for heating fuels
    in Pennsylvania, the primary fuels eud s for heating are natural gas, fueil l oand electricity. The increase
    in cooling demand is likely to outweigh the decline in heating demand, implying that electricity
    consumption is likely to increase as a result of climate chan. Pgeerhaps more notably, peak-time
    electricity demand is likelyt o increase. Meeting peak-time electricity demand without sacrificing
    reliability is challenging and costly (Spees & Lave 2008), although recent policy initiatives to increase
    demand-side participation in regional electricity markets may help to reducet sc oasnd impacts on
    electric reliability (Walawalkar etal. , 2008; Blumsack & Fernandez 2011).

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    Chapter 7 of the 2009
    Pennsylvania Climate Impacts Assessment
    on climate change in Pennsylvania
    (Shortle et al.20 09) listed six key conculsions related to climate change and Pennsylvania’s forests:
    1. Suitable habitat for tree species is expected to shift to the n. oTrhthis will reduce the amount of
    suitable habitat in Pennsylvania for species that are at the southern extentt heoif r range in
    Pennsylvania, and the amount of habitat in the state that is suitable spfeocr ies that are at the
    northern extent of their range in Pennsylvania will increase.
    2. The warming climate will caue s species inhabiting decreasingly suitable habitat to become
    increasingly stressed. Mortality rates will increase and regeneration success will decline for
    these species, resulting in declining populations of those species in the state.
    3. Longer growing seasons, warmer temperatures, possibly higher rainfall, and a phenomenon
    termed “CO
    fertilization” may increase overall forest growth rates in tshtae te, but the
    increased growth rates may be offset by increased mortality (see coniocln us2 above).
    4. The state’s forest products inudstry will need to adujst to a changing forest resource. The
    industry could benefit from planting fastegrr-owing species and from salvaging dying stands of
    trees. Substantial investments in artificial regeneration may be needed if large areas of forests
    begin to die back due to clima-treelated stress.
    5. Forests can contribute to the mitigation of climate change by sequestering carbIt onw. ould be
    difficult to substantially increase the growth rates of Pennsylvania hardwoods, so the best
    opportunities most likely lie in preventing forest loss.
    6. Forests can also be a significant source of biomass to replace fossil fuels.
    Since the 2009 PCIA, climate scientists and forest biologists have continued to improve our
    understanding of how climate change is affecting and will likely affect Pennsylvania’s forested
    ecosystems. However, none of the above counscilons are contradicted by recent reseachr on the
    impacts of climate change on the forests of the eastern UA.s S. discussed below, research continues to
    support the expectation that suitable habitat for the tree species currently found in the state will shift
    northward, but recent research has omre accurately quantified expected habitat shift. sRecent research
    does not show observed increases in tree mortality in the eastern dUu.e S. to climate change, nor have
    growth rates been shown to increase as a result of climate chan. Ngeevertheless, thesee ffects are still
    expected to occur as climate change progress. esThere has been little, if any, new research on how the
    forest products industry of the northeastern U.Sis . likely to adapt to changed forests in the future, so no
    research on that topic isd iscussed in this updat. eThere is little doubt that forests play a key role in the
    Earth’s carbon cycle. Considerable research has been done in the past two years on how forests can be
    managed to sequester more carbo. nHowever, none of this research coradnt icts the basic concluios n of
    the 2009 PCIA that the best strategy for managing the carbon stored in the hardwood forests of the
    northeastern U.S. is to minimize forest los. sSome new research has raised doubts about the efficacy of
    replacing fossil fuels with forest biomas. Tshis literature is disucssed later in this chapter.

    The 2009 PCIA listed four strategies for managing Pennsylvania’s forests to help them adapt to climate
    change. They are:
    1. Management for healthy, resilient forests with a high dee greof biodiversity.
    2. Conduct research to better predict the impacts of climate change on the forests in the
    3. Monitor the health and productivity of the forest resource to identify and dethtee ct effects of
    climate change.
    4. Recognize potential climate-change inducedstr esses when planning forestm anagement
    This update supports the importance of each of these adaptation strateg. Wiesith regard to the first
    adaptation recommendation, this update emphasizes the importance of minimiziadng ditional
    fragmentation of the state’s forest resources.
    This chapter of the update is organized as follo. wThse rest of the introduction provides an overview of
    the key issues related to the impacts of climate change on Pennsylvania’s fo. rThese
    ts next section
    reviews various ways climate change is expected to affect forests, including shifts in tree species ranges,
    impacts on tree regeneration and mortality rates, changes in the timing of key biological events (called
    phenology) such as leafing out anfld owering, impacts on tree growth rates, interactions of climate and
    pollution and the resultant effects on trees, changes in insect and disease dynamics, and impacts on
    forest animals. The following section disucsses how forests can be managed to mitigate climate change.
    This section discusses strategies for managing forests to increase carbon storage and the debate about
    whether substitution of woody biomass for fossil fuels is a efuul s strategy for reducing carbon emissiosn.
    The final section disucsses ways to help forests adapt to climate changThe. e section focuses primarily
    on the so-called “assisted migration” debate, in which some have advocated actively relocating species
    that are most threatened by climate change.
    Current and projected changes in the state’s climate affect forests directly through increases in average,
    maximum and minimum temperatures, longer growing seasons, increased average rainfall, decreased
    winter snow cover, more intense weather events, and longer periods of drought o(He aet
    yhal., 2007,
    2008). These direct effects will change the suitability of areas within the state to support the species
    that are currently found there, ucsaing increased stress and mortality in mature trees, changes in the
    regeneration rates of tree species, and ultimately changes in stand structure and species composi. tion
    Changing atmospheric concentrations of various gases also directly influence plant chemistry and
    growth. These changes will also indirectly affect forests in many different and less predictable ways,
    including changes in soil chemistry (Campbell , 2009), changing population dynamics of pests and
    organisms that cause disease (Dukes etal. , 2009), changes in rates of growth and spread of invasive
    species (Dukes etal. 2009), and changes in both competitive and symbiotic relationships among species,
    both plant and animal. How all these changes will play out on the forested ecosystems of the state is
    difficult to predict, but evidence from around the world and from the region alrehaodw y ssome
    consistent signs of how these ecosystems are likely to respo. Snpdecies’ ranges are shifting poleward
    (Chen et al., 2011), in many areas (although not necessarily the northeUas.St . (Dietze & Moorcroft
    2011)) mortality is increasing (Allen etal. , 2010), and rates of regeneration are shifting (Woodall ,
    2009). By changing the relative competitiveness of different tree species, climate change is likely to shift
    the species composition of Pennsylvania’s fores. tsTrees and other plants are also responding to
    changing climate by leafing out earlier, flowering earlier, and through other phenological changes
    (Bertin 2008).

    In the long run, whether species thrive or decline under changing climate regimes depends othn eir
    ability to adapt to a wide range of conditions, [2] their ability to migrate to locations with more
    favorable climates and to compete with the other species that they encounter as they migrate, [3] their
    ability to compete with other species that migrate into their ranges, [a4n] gechs in the distribution and
    phenology of species with which they have symbiotic relationships, such as pollinators, and [5] changes
    in the distributions and vigor of the pest species that target them, such as insects and diseases
    (Aitkenet al., 2008). As a general rule, climate change will tend to favor generalist species with shorter
    reproductive cycles, greater mobility, and greater genetic divers. Citliymate change is a greater threat to
    species with small populations that occupy narrow, geographically isolated ecological niches, and with
    limited genetic diversity (Aitkenet al., 2008).
    As forests represent significant pools of carbon, they can play
    role a in mitigating climate chang. e
    Globally, loss of forestland is a significant contributor to climate change, but Pennsylvania’s forests have
    been growing and therefore represent a carbon s. inHkowever, this trend could be reversed by
    increasing lossesof forestland to development and through increased mortality rates.
    Modern forested landscapes have been fragmented by agriculture, development and transportation
    corridors, making them less capable of adapting to climate change today than in the . Tphasis tis
    primarily because fragmentation reduces the ability of speciesb – oth plant and animal –to migrate
    across the landscape. One of the most useful things humans can do to help forests adapt to climate
    change is by reducing and even reversing the trenod wtard greater fragmentation (Krosby et al., 2010).
    Nevertheless, it is unlikely that the majority of forest plant species will be able to migrate as fast as will
    be necessary to keep up with changes in the climate (Loet
    arial.e , 2009). Because of this, smo e authors
    have suggested that we should actively move some species to new habitats that are currently or are
    projected to be more suitable for those species (Hewitt al.e, t 2011). Others have argued against this
    notion, for a variety of reasons (Hewittal.
    et , 2011).
    Changes in forest ecosystems’ composition and health will also affect communities anud strinides that
    depend on these natural resource. sThese changes will influence the ability of the state’s forest
    ecosystems to provide forest products, can
    le water, carbon sequestration, recreational opportunities,
    wildlife habitat, and maple syrup productio. Fnurthermore, other trends, such as the development of
    the Marcellus shale for gas production, are also affecting these ecosyste. mNats ural gas development is
    bringing jobs and wealth to Pennsylvania communities, but at the same time this development may
    make the forest ecosystems of Pennsylvania less resilient to changes in clim. Iatn eaddition, the
    recession that started in 2008 has had a devastating ffece t on the state’s forest products iunsdtry. New
    wood products industries, such as those that would utilize woody biomass for energy production have
    not yet grown significantly, but have the potential to do so in the coming d. eMcaduceh has changed
    since the 2009 PCIA on potential impacts of climate change on the st. atTehis update reviews new
    literature relevant to understanding the potential impacts of climate change on Pennsylvania’s forests
    and wildlife.

    Climate Changes’ Effects on Pennsylvania Forests
    Anticipated changes in the climate of Pennsylvania are likely to alter Pennsylvania’s forest ecosystems
    through [1] range shifts, including expansions and contractions, of tree species, birds and mammals,
    [2] increased mortality and extinction raets, [3] changes in ecosystem productivity and phenology, and
    [4] increases in insects, pathogens and invasive species.
    8.1.2 Tree Species Range shifts
    Evidence continues to accumulate that climate change is altering forest ecosystems through shifts,
    including expansions and contractions, in the ranges of tree species and the other fauna and flora
    associated with those ecosystem. sIn a seminal study based on three data sets, (British birds, Swedish
    butterflies, and Swiss alpine plants) Parmesan and Yohe 0(32) 0estimated that species were migrating
    poleward at an average rate of 6.1 km per decad(3e
    .8 miles per decade. )A more recent study by Chen
    et al., (2011) updates these estimates based on 53 studies, which includedt a“x2o3 nomic group ×
    geographic region combinations for latitude, incorporating 764 individual species responses and N = 31
    taxonomic group × region combinations for elevation, representing 1367 species responses”
    (Chen et al., 2011, p. 1024. )Chen et al.(20 11) estimate that the median latitudinal migration rate is
    16.9 km/decade (10.5 miles/decade) and that the median altitudinal (upslope) migration rate is
    11.0 m/decade (36 feet/decade). Chen et al.(20 11) suggest that the primary reason they estimated
    faster migration rates is that their datasets cover more recent time periods than thoesd e buy s
    Parmesan and Yohe (2003. )This is consistent with the observation that temperatures have increased
    about four times as fast since 1970 than between 1900 and 1970 (IPC20C 07). The study found that the
    latitudinal shifts were consistent with expected shifts based on observed regional temperature changes
    but that elevation shifts were generally less than expec. teBed rtin (2008) also reviews numeruos studies
    showing poleward and upslope range shifts for dozens of plant species in response to climate change.
    In the northeastern U.S., Beckage etal. (2008) identified a 91-110 m (299-361 feet) upslope shift in the
    northern hardwood–boreal forest ecotone along elevation transects in the Green Mountains of Vermont
    over the riod between 1962 and 2005. This translates into a 21.-225.6 m(7 0-84 feet) decadal
    altitudinal migration rate, approximately double thae verage rate found by Chenet al.(2 011) . Based on
    climate data, Beckage et al.(20 08) estimated an expected shift of 208 (6m 82 feet), suggesting a lag in
    the rate of migration.
    Models of tree species envelope shifts project that boreal hardwood species such as birch and aspen are
    likely to decrease dramatically in abundance in Pennsylvania during the
    C21entury as their climate
    envelopes shift northward (McKenneyet al., 2007; Mohan etal. , 2009; Iverson et al., 2008). Similarly,
    northern hardwoods, such as American beech, red and sugar maples, black cherry, and American
    basswood, and common northern conifers, including hemlock and white pine, are also likely to decline
    in abundance in the state as the habitat in the state becomes increasingly less suitable for those s. pecies
    On the other hand, species that are currently in the northern extent of their climate envelopes in
    Pennsylvania, such as oaks, hickories, silver maple, eastern- creeddar, and loblolly and shortleaf pine,
    will find increasingly suitable habitat conditions In Pennsylvania in the coming century and are likely to
    increase in abundance (McKenney etal. ; 2007; Mohan et al., 2009; Iversone t al., 2008). In addition to a
    general northward shift of species’ ranges, species will likely shpift
    sloupe to higher elevations during
    the coming century.

    Models used to predict species shifts based on the predictions of atmosph-oecreean global circulation
    models (AOGCMs) are being refined and validat. eMdcKenney et al.(20 07) projected on average that
    the climate envelopes of 130 North American tree species would shift north by 700 km (435 miles) by
    the end of the century, and that the average climate envelope would shrink bpy er1c2
    ent. Under a
    scenario where tree species climate envelopes were assumed to not migrate northward, the average
    northward shift was only 330 km ( 205 miles), but future potential ranges were projected to be
    58 percent smaller, on average, than today ( , 2007). (Note, the centroid of the climate
    envelope moves north even though the northern boundary is not allowed to advanceub see caof
    shrinkage of the enveloe
    p along its southern extent.A ) more recent article by McKenney eal.
    t (2011)
    compares how different AOGCMs influence prediciotns of shifts in species’ climate envelopes over the
    course of the centur. yWhile McKenney et al.(2 011) found large differences between the predicted
    climate envelope shifts based on different AOCGMs, the predictions from more recent versions of the
    models (2008-2010) were more consistent than the predictions from older versions of the models
    (2003-2005), suggesting that improvements in the AOCGMs are reducing this source of uncertainty in
    climate envelope projections.
    8.1.2 Tree Regeneration
    Seed germination is affected by temperature and moisture (Walcet k al., 2011). In general, each species
    has an optimal temperature and moisture regime for seed germination, so deviations in either direction
    from this optimum can be expected to lead to lower regeneration r. aFtuesrthermore, increased CO
    levels have been found to increase (Farnsworth & Bazzaz 1995; LaDeau & Clark 2001, 2006) and
    decrease (Farnsworth & Bazzaz 1995; Thomas etal. , 1999) seed production, and tou csae it to occur
    earlier (Farnsworthe t al., 1996). Woodall et al.(2 009) found that for northern tree species, seedling
    density relative to tree biomass was nearly 10 times higher in northern latitudes compared to southern
    latitudes. However, no such relationship was found for souertn
    h tree species. For northern species, this
    suggests that conditions for regeneration are more favorable at the northern limits of their ranges than
    at the southern limits. Increases in regeneration rates in species’ northern ranges and decreases in
    regeneration rates in their southern ranges is one mechanism by which climate change shifts species
    composition and ultimately produces a northward shift in the species’ ranges.
    8.1.3 Tree Mortality
    As climactic factors such as temperature and rainfall shift, at least some trees that were upsrly
    well-adapted to the climate in their current location will become less well adap. Ttheis
    d maladaptation
    to the changed climate will result in physiological stress that could directly kill trees or increase their
    susceptibility to other cauess of mortality such as fire, insects and diseases (Alleal.n et, 2010).
    Furthermore, warmer temperatures and longer growing seasons will increase rates of
    evapotranspiration by plants, (Huntington etal. , 2009) exacerbating the effect of longer periods of
    drought as rainfall becomes more sporadic in the reg. ioHnigher mortality has been observed with
    higher temperatures and lower precipitation rates in many regions of the world (Alleal.n ,et 2010) and
    in the Western U.S.
    (van Mantegem et al., 2009). At least at present, however, there is little evidence
    that climate factors have significantly increased mortality rates in the eastern DUie.S.
    tze and Moorcroft
    (2011) evaluated four categories of tree mortality drivers in the eastern and central : U[1.] S.climate,
    [2] air pollutants, [3] topography, and [s4t] and characteristics. They found that air pollutants
    (e.g., acidification and nitrogen deposition and ozone) and stand characteristics (stand age and density)
    were the most important drivers of mortality. Climate factors were a distant third in significance, and
    varied with different species and portions of their ran. gFeor many eastern species, mortality declined

    with increased temperatures, and increased mortality was not alwayos re
    mpronounced in the southern
    parts of species’ range. sSimilarly, warmer winters were associated with increased mortality in some
    species but lower mortality rates in others (Dietze & Moorcroft 20. 1F1o) r some species, hemlock for
    example, higher mortality rates associated with warmer winter temperatures were likely linked to
    greater winter survival of insects and pathogens (Paradis , 2006).
    Increased tree mortality could also occur if climatean
    chge increases the intensity of storms in
    Pennsylvania. Although there is considerable uncertainty about how climate change will affect the
    intensity and frequency of major storms in the region, Huntingtonal. e(t 2008 ) and Emanuel (2005)
    derived an indexo f potential hurricane destructiveness and found that it has been increasing over the
    past 30 years. In addition, longer periods of drought can lead to more fires and associated tree mortality
    (Huntington et al., 2009).
    8.1.4 Phenological Mistiming
    Climate change is altering the timing of a variety of events in the life cycles of plants (i.e., their
    phenology). Spring events, including leafing out and flowering, have moved up on agve ebray 4-5 days
    for each degree Celsius of warming (Bertin, 2008). Fall eenvts are typically delayed, but less consistently
    compared to spring events (Bertin, 200. 8P)henological impacts are easier to observe than range shifts
    because it takes longer for plants to shift their ranges than tou sadt thj e timing of events in theiife
    r l
    cycles. As a result, there are
    an overwhelming number of studies documenting shifts in the life cycles of
    plants in response to climate change (Bertin, 20. 08P)lants rely on animals, such as bees and squirrels,
    for a variety of critical ecosystem services (i.e., pollination and seed dispersal). These animals also
    respond to climate change by shifting critical events in their life cycles, but not necessarily at the same
    rates as the plants with which they interac. Ats climate change shifts the timing of events such as when
    forest plants flower, disperse pollen, and produce seed, these events could become out of sync with the
    life cycles of the animals they depend on to support these functi. oSuncsh “de-coupling” of these events
    can reduce reproductive success of both plant and animal species (Bertin 2008; Mohet
    an al., 2009). In
    some cases, plants could benefit from mismatching of pest phenology with the timing of key events in
    the plants’ life cycles. (e.g., winter moths’ eggs hatching later than thng e otif mtihe oak buds on which
    they feed) (Bertin, 2008; Visser & Holleman 2001).
    8.1.5 Growth impacts
    Climate change could lead to increased growth rates due to [1] longer growing seasons (Hayet hal.oe,
    2007; Campbell et al., 2009), [2] increased C
    (Huang etal. , 2007), and [3] increases in soil
    nitrogen due to increased mineralization and nitrification (Bertin, 2008; Campbell
    , 2009). Growing
    seasons in Pennsylvania are projected to increase by -423 9 days during the 21st century (Hayhe oet al.,
    2007) . Many studies have looked at the impact of elevated
    on plant growth and reproductio. n
    Huang et al.(2 007) reviewed the literature on the hypothesis that
    plant growth. While
    many free-air CO
    enrichment (FACE) expermi ents have found that trees grow faster under elevated
    conditions, most of these studies are relatively sh-otertrm compared to the full life cycle of trees and
    have focused mostly on young trees (Ainsworth & Long 2005). Attempts to detect the effecelt evoaf ted
    on tree growth from tree ring analysis have been less cuosnicvle, and Huang et al.(2 007) concluded
    that the CO
    fertilization hypothesis is only well supported by tre-rine g analysis in semiarid
    or arid
    conditions where nitrogen is not liminitg. This is not surprising, since it is well established that
    increases water use efficiency (Huang etal. , 2007). In other cases, results are less clear becae uist is hard
    to separate the CO
    effect from the effects of warmer climate and anthropogc enati mospheric

    deposition (e.g., nitrogen) (Huang , 2007). However, in an analysis of 49 studies, Boisvenue and
    Running (2006) found that climate change has generally increased forest growth rates, except on
    water-limited sites, over the past 55 yea. rs
    8.1.6 Atmospheric Impacts
    The effects of increased atmospheric C
    Oare complicated by other man-made atmospheric changes,
    such as increased ozone (
    and deposition of nitrogen (N) and sulfates (S), whose negative effects
    could more than offset aniy ncrease in productivity due to the potential grow-ethnhancing effects of
    nitrogen deposition and enriched atmospheric C
    et al., 2002; Campbell et al., 2009; Mohan
    et al., 2009). Sulfate deposition causes soil acidification in Pennsylvania anod ther industrialized areas,
    but sulfate aerosols also scatter solar radiation and reduce temperatures (Campbeal.ll et, 2009).
    Nitrogen deposition can improve soil productivity, stimulating greater forest growth and carbon
    sequestration, or it can contribtue to acidification (Campbell eal.t , 2009)
    8.1.7 Insects, Pathogens and Invasive Species
    Climate change will indirectly affect forests by influencing the populations, ranges and activity ofu s vario
    “nuisance species,” including both native and exotic ecintss , tree diseases, and invasive species
    (Dukeset al., 2009). These species affect forests by stressing and killing trees and by interfering with key
    processes such as regeneration. Pennsylvania forests host a large number of forest insect pests,
    including elm spanworm, emerald ash borer, forest tent caterpillar, gypsy moth, hemlock woolly adelgid,
    and two-lined chestnut borer (Dukes et al., 2009). While we cannot say with certainty how climate
    change will affect these and other insect pests, a few genl erobaservations are possible. Insect metabolic
    rates tend to double with an increase of 10(1°C 8°F) (Clark & Fraser 2004). Like other species, insect
    ranges tend to shift northward with warming climate (Logan
    , 2003; Parmesan, 2006. )On the other
    hand, while insect ranges are often limited by minimum winter temperatures, lack of winter snow cover
    may reduce overwintering survival rates for some species (Ayres & Lombardero 20. 0L0o)nger warm
    seasons have allowed some insect species to go through more generational cycles within each season,
    greatly increasing potential population growth rates (Logan & Powell 200. 9W) etter climates are
    generally better for insects, but longer periods of drought can be detrimental to insect populat. In ions
    general, insects are likely to be more adaptable to changing ecological conditions bee cathuesir short
    reproductive cycles allow for faster rates of genetic adaptation. Dukesal. et
    (2009) discuss an example
    of how climate change can influence the impact of an insect pest that is important in Pennsylvania’s
    forest ecosystems: the hemlock woolly adelgid. The adelgid has had a devastating effect on one tree
    species in Pennsylvania, particularly in the eastern and southern part of the . sHtaotwee ver, the insect’s
    expansion into the northwestern part of the state has been limited by the region’s cold winters
    (Paradis et al., 2006). A warming climate could therefore allow the insect to expand its range within the
    Pennsylvania’s forests are also affected by a number of e trpeathogens, including: armillaria root rot,
    elm yellows, beech bark disease, chestnut blight, dogwood anthracnose, Dutch elm disease, and oak
    wilt, among others (Dukes
    et al., 2009). Given the wide variety of pathogens and their ecological
    characteristics, few generalizations are possib. leOn one hand, many diseases, such aus str fungi, will
    benefit from wetter conditions (Dukes etal. , 2009; Lonsdale & Gibbs 1994; Vanarsdel etal. , 1956), but
    others, such as powdery mildew, do not (Lonsdale & Gibbs 19. 9A4s )with insects, warmer winter
    temperatures tend to increase overwinter survival (Coakleet y al., 1999), but lack of snow cover can be
    detrimental to other species (Ayres & Lombardero 200. 0)Increased mechanical damage to trees

    resulting from more intense storms can create more opportunities for both diseases to infect trees
    (Shigo, 1964. )Like insects, pathogens are likely to be more adaptive to changing environmental
    conditions because of their short reproductive cycles relative to trees (Brasier, 2. 0I0n 1)general, as trees
    are stressed by climate change, they can become moure scseptible to insects and diseases. Armillaria
    root rot, for example, is a tree disease that is common in Pennsylvania. Be ecit autesnds to infect and kill
    mainly trees that are already stressed by some other factor, armillaria root rot is not currently a major
    cause of mortality in Pennsylvania’s fores. tHsowever, if a large number of trees are stressed by climate
    change, this currently minor disease could become a significant erd rivof tree mortality in the state
    (Dukes et al., 2009).
    Invasive plant species affect Pennsylvania’s forest ecosystems by competing with native trees and
    understory species for space, interfering with regeneration processes, and, in the case of vines, by
    climbing, breaking and killing tree. sSome non-native invasive species that affect Pennsylvania’s forests
    are oriental bittersweet, purple loosestrife, Japanese knotweed, Japanese stiltgrass, Japanese and
    European barberry, Russian olive, Japanese and Amuhr oneysuckle, multiflora rose, mile-a-minute vine,
    kudzu, Norway maple, and tre-oef-heaven. While changes in climate can potentially affect these species
    in both negative and positive ways, the fact that these species are invasive is an indication oabf tilithey ir
    to compete in new environments and to migrate quickly into new hab. itThatess e species tend to have
    potential for rapid evolutionary change (Maron et al., 2004; Schweitzer & Larson 1999) and broad
    environmental tolerances (Qian & Ricklefs 2006. )Thus, these species are likely to thrive under changing
    climatic conditions (Dukes etal. , 2009), and climate change will likely exacerbate problems cead usby
    these species.
    8.1.8 Fauna
    Climate change will affect the animals that inhabit forest ecosystems in many different . wAays ws ith
    plants, some species will benefit from these changes and others will be affected negat. Civliemlyate
    change affects animals through direct effects from changes in temperature and rainfall regimes and
    indirect effects through changes in habitat and interactions with other species, including predators,
    prey, competitors, parasites and diseases. Rodenuhsoe et al.(2 009) review the potential impacts of
    climate change on mammals, birds, amphibians and insects in the northeasUte.rS.
    n The 59 species of
    mammals in the northeastern U.Sv. ary in size from the smallest, the pipistrelle bat -(6 3.g5; .12.-21 oz),
    to the largest, the moose (3-16530 kg; 694-1,389 lbs). Smaller mammals tend to be moreu ssceptible to
    colder temperatures due to their relatively high surface aretoa--mass ratios. Smaller mammals,
    therefore, generally need to find cover during the winter. (e.g., underground in the case of small rodents
    such as mice and voles, or in caves or under tree bark in the case o. f In bagetsn)eral, one would expect
    these species to benefit from less harsh winter conditions, but this is not necessarily always the In case.
    the case of small rodents, snow cover provides additional insulation, so reduced snow cover can result in
    higher winter mortality. Bats are very sensitive to hibernation conditions, and warmer temperatures can
    decrease their survival due to more frequent aursoals even though the duration of the hibernation
    period is decreased (Rodenhuose et al., 2009). Large mammals find thermal cover under coniferous
    trees, so reductions in the number and distribution of hemlock and white pine can have a negative
    effect on their winter survivalS. horter, warmer winters with less snow cover can also result in greater
    parasite populations, such as ticks (Rodenhuose et al., 2009).
    Birds are affected by climate change in many w. aWysarmer weather has resulted in earlier arrival and
    breeding dates for migrants (Rodenhuose et al., 2009). Rodenhouse et al.(20 09) also found that the
    populations of 15 out of 25 bird species that are permanent residents of the North Atlantic Forest Bird

    Conservation (NAFBC) have increased in abundance, 5 species have decreased, and 5 species have
    shown no discernible trend. The ranges of most bird species found in the northeastern U(2.7
    S. out of
    38, Rahbek etal. , 2007) have shifted northwar. dRodenhouse et al.(2 008) model shifts in the bioclimatic
    envelopes for 150 bird specie. sThey projected declining bird species richnes sin Pennsylvania and
    western New York, but increasing species richness in Maine and New Hamp. shDiirfeferent groups of
    bird species were projected to be affected differently, with more temperate migrants declining than
    increasing, no net changes in neotproical migrants (declines approximately equaling gains), and with
    most resident species gaining from warming temperature. Isn addition to temperature changes, changes
    in rainfall can affect bird population. sIncreased rain can result in reduced survival eoggsf , nestlings and
    adults and less food for aerial insectivores (Rodenuhsoe et al., 2009). Furthermore, migrants are
    affected by changes in climate in their wintering areas and can experience greater mortality during
    migration as a result of storms (Rodheonuse et al., 2009).
    Amphibians are an important component of the fauna of temperate forests, often comprising a greater
    proportion of the faunal biomass than all other faunal species combined (Rodeunse
    hoet al., 2009).
    Furthermore, amphibian populations are already stressed, with 25 of the 32 species found in
    northeastern forests under some type of protected stat(uRs odenhouse et al., 2009). Amphibians are
    particularly susceptible to climate change becaue s they are sensitive to desiccation, their habitat is often
    dispersed, and they are ussceptible to disruption of phenological relationships with their prey
    (Rodenhouse et al., 2009). Warmer temperatures and higher rates of evapotranspiration will likely lead
    to faster desiccation and even loss of the verl npaools that are crucial habitat for some amphibian. s
    Amphibians are also likely to be negatively affected by the increased variation in streamflows and soil
    moisture in riparian areas that is projected under climate change.
    Forests play a significant role in the carbon cycle of the Eart. Whorldwide, they store about two times
    the amount of carbon in the atmosphere, and each year they sequester an amount of carbon
    approximately equal to 30 percent of all emissions from burning fossil fuels and deforestation (Canell ad
    & Raupach 2008). In the U.S., net CO
    sequestration in U.Sfo. rests and forest products was 790 million
    metric tons (Heath et al., 2011), offsetting 12-19 percent of the nation’s fossil fuel emissions (Ryanal.
    et ,
    2010) . As Pennsylvania’s forests have recovered from heavy exploitation around the beginning of the
    Century, they have increased in volume and acted as a net carbon sink (McWilliamal.s , et20 07).
    In spite of this important role, it is not always obuvs ihoow forests can be managed to best mitigate
    climate change. Ryan et al.(2010) discuss several strategies for [1] increasing forest carbon storage,
    [2] reducing the loss of carbon stored in forests, and/or [3] offsetting fossil fuel consum. pHtoiownever,
    each of the strategies has trad-eoffs and risks. The least risky strategy is reducing deforestatio. Wn hen
    forests are lost, much of the carbon stored in them is also lost. Moreover, it should be noted forests
    provide many ecosystem and social benefits in additioton carbon storage and sequestration, which is
    why forest management is imperativ. eThere are also no direct costs of mitigating deforestation, only
    the opportunity costs of not developing the forestla. nSdimilarly, afforestation is a relatively low risk
    strategy, but in this case, costs are dir– eclat nd must be acquired and planted. Decreasing harvests
    retains more of the carbon stored in the forest, but foregoes forest products and harvest revenue that
    could have been obtained, and may simply shift hasrts
    veto another location. Furthermore, harvested
    wood and its associated carbon that ends up in forest products may represent anothertel ornm g-carbon
    storage pool, and young (regenerating) forests grow faster and sequester more carbon per acre than
    older forests. Increasing growth rates of existing forests by management intensification sequesters more
    carbon and produces more forest products, but such treatments may be expensive and can lead to loss

    of biodiversity if natural forests are replaced with planed tforests. In Pennsylvania’s forests, few
    cost-effective management intensification options are availab. leUsing woody biomass from the forest
    for energy in place of fossil fuels can, in the long run, decrease carbon emissions, but in the short run
    this strategy reduces the amount of carbon stored in the forest and results in higher emis. sTihoins s
    option is discussed in more detail below. Using wood products in place of concrete and steel reduces net
    emissions, but reduces carbon stored in fore. stFsinally, increased planting and better management of
    urban forests can increase the amount of carbon stored in urban ecosystems (Ryaal.n ,et 2010). All of
    these options can potentially be applied to some degree in Pennsylv. aFnuiarther analysis of the costs
    and benefits of each should be done.
    Woody biomass is considered yb many to be “carbon neutral.H” owever, this is a complex issu. e
    Harvesting more wood biomass for energy production will, at least in the short run, inevitably lead to
    less carbon stored in forests and emission of this carbon to the atmosph. eFreurthermore, because the
    energy content per metric ton of carbon emitted by burning wood is less than for coal, and significantly
    less than for natural gas, replacing these fossil fuels with biomass energy will in the short run require
    emitting more carbon into the atmosphere per unit of energy produ. cAesdsuming that the harvested
    wood eventually grows back, this “carbon debt” will be offset over time by the regrowth of the forest,
    ultimately resulting in a net carbon bene. fitBut the time required achieving
    a net reduction in
    atmospheric carbon by substituting woody biomass for fossil fuels ranges from a few years rte
    o tmhoan
    a century (Manomet 2010;M cKechnie et al., 2011). The length of time needed too ffset this carbon debt
    varies with four variables.
    1. The efficiency of the process ued
    s to convert the wood to ener, gy
    2. The type of fossil fuel technology that is replaced,
    3. Whether the woodu sed is from standing trees (that presumably would not have beaern vesh ted
    or died anyway) or whether it is from harvest residues (which would have eventually released
    their carbon through decomposition, )
    4. The rate of regrowth of the harvested forest (McKechnie
    , 2011). Thus, each woody
    biomass bioenergy applications hould be analyzed carefully to determine the time profile of the
    carbon benefits, if indeed there are any.
    Whether or not tree populations successfully adapt to climate change will depend on [1] their ability to
    migrate to more suitable abh itats, [2] the genetic variation in populations and their ability to adapt and
    thrive in a variety of conditions, [3] their ability to compete with other species in the context of the
    changed climate (Clark etal ., 2011), and [4] their ussceptibility to existing and new mortality agents,
    such as fire, drought, insects and diseases (Aitket
    enal ., 2008). Several strategies have been disucssed in
    the literature for enhancing ecosystems’ ability to adapt to climate change, inclu[d1]in ing creasing
    landscape connectivity (Heller & Zavaleta 2009; Krosby et al,. 2010), [2] assisted migration (Hewital.t ,et
    2011) . The purpose of improving landscape connectivity is to facilitate the migration of populations of
    organisms across the landscape so they can colonize
    ewn areas that have more suitable habitat
    conditions as climate change makes the areas they currently inhabit less suitable (Kret
    osbal.y , 2010).
    This can be done by maintaining or restoring corridors of natural landscape, increasing the number and
    proximity of steppings-tone reserves, and by making the matrix between reserves more suitable for
    migration of species if not colonization (Krosbet y al., 2010). The diversity of habitat requirements for
    different species makes this difficult to accomplish in practice, but corridors of natural landscapes can be
    used by a wide variety of species (Gilb-eNrtorton et al., 2010). In Pennsylvania, a key challenge in the

    coming decades will be maintaining forest habitat connectivity in the more heavily forested partths e of
    Marcellus Shale region where natural gas development is likely to result in expansion of existing roads,
    development of new roads, and development of pipeline corridors, all of which will contribute to further
    fragmentation of the landscape.
    Loarie et al.(2 009) estimate that, on average, some plants and animals will have to migrate at a rate of
    420 m/year (1/4 mile/year) in response to climate changes projected under the IPCC A1B emissions
    scenario. This is consistent with Hughes (2000), who estimated
    that eastern North American trees will
    have to migrate at rates of 300-50000 m/decade . Fossil pollen studies suggest that when the Laurentide
    ice sheet melted in the late Quaterna-reay rly Holocene North American trees migrated at rates up to
    100 -1000 m/de cade(328 -3280 feet/decade) (Davis, 1981; Delcourt & Delcourt 1987; MacDonald ,
    1993). However, Dyer (1995) estimated ear-lHyolocene migration rates of only 136 m/decade
    (446 feet/decade) or les. sPetit et al.(20 08) suggest that past migration ratems ay have been even
    slower than this becuase the role of small refugia has been ignored in most pollen stu. dFuiersthermore,
    migration rates could be much slower in modern, fragmented landscap. Unesder current rates of climate
    change, mobile species have eenb observed to be migrating at rates of more than 1 km/yr
    (.62m iles/year) (Chenet al., 2011), and AOGCMs are predicting migration rates of 1 km/year
    (.62m iles/year) or higher (Malcom etal. , 2002). In contrast, Iversone t al.(2 004) estimate that most tree
    species may only be capable of migrating up to -120000 m/year (328-656 feet/year), which is less than
    one tenth of the rate that may be required to keep up with changing clim. Saptecesies with very specific
    habitat requirements, that tend to be found in small, isolated populations and that take longer to reach
    sexual maturity will be least able to adapt by migrating to new hab. itats
    To address the challenge that at least some species will not be able to migrate fast enough to keep up
    with changing habitat conditions, several authors (Appell, 2009; Hewet
    ittal., 2011) have proposed a
    strategy of assisted migration. Some are even putting the practice into place. For example, Marris (2009)
    reports on efforts in British Columbia to identify which Dougfir
    las seedlings, including those from seed
    sources from as much as 500 km( 311 miles)to the south, will grow best in varius olocations. Some
    researchers have opposed assisted migration (Hunter, 2007; Davidson & Simkanin, 2008) for a variety of
    reasons. Key concerns with assisted migration are [1] the huge logistical challenge of assisting more than
    a few key species, [2] the fact that one would ideally like to conserve entire communities, to the extent
    possible, rather than manage for the conservation odf ivinidual species, and [3] the bioethical issues
    related to replacing communities that are currently in a given location with communities from another
    location (Hewitt etal. , 2011). At a minimum, research is needed to identify species mousst ceps tible to
    extinction from climate change, and seed transfer guidelines should be developed for these species
    (Aitken et al., 2008).
    Climate is currently and will continue to affect Pennsylvania’s forests in the coming de. cKey
    changes that are likely to occur include species composition shifts, shifts in tree regeneration rates,
    greater tree stress, changes in the phenology of forest ecosystem species, changes in tree chemistry and
    growth rates, greater insect, disease and invasive species acyt, ivitand shifts in faunal populatio. nMs any
    of these shifts have already begun to occur, and while many may be expected to lead to greater tree
    mortality, at least for the present, increases in mortality that can be attributed to climate change have
    been minor. Furthermore, while one might expect longer growing seasons and the
    effect to increase tree growth rates, this has not yet been observed in Pennsylvania’s forests, and these
    effects could potentially be offset by the negative effectsf poollutants such as ozone and sulfate

    deposition. On the whole, it is important to keep in mind that all of these effects will interact in very
    complex ways, making highly specific projections of future forest conditions difficult.
    As a significant reservoir of carbon, Pennsylvania’s forests can contribute to mitigating future climate
    change, but these effects are not likely to be large, as the growth rate of Pennsylvania’s forests is
    relatively slow and difficult to accelerat. eThe most promising forest management strategies for
    mitigating climate change in Pennsylvania are to reduce rates of conversion of forestland to
    uses and to plant trees in areas where they are not currently found, for example, abandoned strip mines
    and some urban aeras.
    As climate change is already happening and is, to some extent, irreversible, forest managers need to
    think about how to help the state’s forests adapt to climate ch. aA
    ngekey strategy for accomplishing
    this is to maintain or increase forest connectiv. ityThis may be a significant challenge in areas where
    road and pipeline networks are being built and expanded to develop natural gas from the Marcellus
    Shale and other promising geological strataF. or some key species that are particularly vulnrabe le to
    climate change, assisted migration may be an option, but accomplishing this in practice for very many
    species will be difficult.

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