Pennsylvania Climate Impacts
The Pennsylvania State University
Commonwealth of Pennsylvania
Department of Environmental Protection
Pennsylvania Climate Impacts AssessmentU pdate
(Co-PI), Associate Professor
(Co-PI), 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
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
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.
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
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
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).
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.
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
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
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
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
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
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.
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  do -rehsigholution
regional models perform better than coars-ree solution global models ats imulating the
Pennsylvania-average climate; and  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
Differences between the 2
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
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
Bjerknes Centre for Climate Research
National Center for Atmospheric Research
Canadian Centre for Climate Modeling &
Météo-France /Cent re National de
CSIRO Atmospheric Research
CSIRO Atmospheric Research
Max Planck Institute for Meteorology
Meteorological Institute of the University of
Bonn, Meteorological Research Institute of
KMA, and Model and Data group.
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
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
OURANOS / UQAM
OURANOS / UQAM
UC San Diego / Scripps
Iowa State University
UC Santa Cruz
UC Santa Cruz
Pacific Northwest National Lab
Pacific Northwest National Lab
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
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.
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.
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
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 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.
Error index for each model and the mu-mltiodel ensemble averages for the regional and
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.
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).
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  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.
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.
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
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.
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).
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
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 enal.ne , 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
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
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.
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
F) and agrees well
with the mean of the UHSCN stations over Pennsylvania (0.6
F). These models include the
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
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.
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
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
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P.C., Ebisuzaki, W., Jovic, D., Woollen, J.,
Rogers, E., Berbery, E.H., 2006. North American Regional Resanis.aly
Bulletin of the American
Meteorological Society 87, 3433-60.
Nakićenović, N., Swart, R., 2000. Special Report on Emissions Scenarios. A Special Report of Working
Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge,
United Kingdom and New York, NY, AU, S599 pp.
Najjar, R.G., 2010. Analysis of climate simulations foe r inus the “Climat-eReady Adaptation Plan for the
Delaware Estuary”, Final report to the Partnership for the Delaware Estuary, 21 pp.
Najjar,R.G., Patterson, L., Graham, S., 2009. Climate simulations of major estuarine watersheds in the
Mid-Atlantic region of the United States. Climatic Change 95, -16138.
Nakićenović, N., Swart, R., 2000. Special ReRpepoortrt
rkinEg missions Scenarios. A Special
Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge,
United Kingdom and New York, NY, AU, S599 pp.
Reichler, T., Kim, J., 2008. How well do coupled models simulate today's climate? Buf llethte in o
American Meteorological Society 89, 30-3311.
Seager, R., Pederson, N.u, shKnir, Y., Nakamura, J., Jurburg, S., 2012. The 1960s drought and the
subsequent shift to a wetter climate in the Catskill Mountains region of the New York City watershed
Journal of Climate, in press.
Shortle, J., Abler, D., Blumsack, S., Crane, R., Kaufman, Z., McDill, M., Najjar, R., Ready, R., Wagener, T.,
Wardrop, D., 2009. Pennsylvania Climate Impact Assessment, Report to the Pennsylvania Department of
Environmental Protection, Environment and Natural Resources Institute, The Pennsylvania State
University, 350 pp.
Trenberth, K.E., Jones, P.D., Ambenje, P., Bojariu, R., Easterling, D., Klein Tank, A., Parker, D.,
Rahimzadeh, F., Renwick, J.A.u, stRicucci, M., Soden, B., Z, hPa.i , 2007. Observations: Surface and
atmospheric climate change. In: S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.
Tignor, H.L. Miller (Editors), Climate Change 2007: The Physical Science Basis. Contribution of Working
Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press, Cambridge, United Kingdom and New York, NYA, , UpSp. 23-5336.
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
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
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
FAO Food Price Inde. xPrice indices are inflation adujsted and scaled so that 200-22004 =
100. Food and Agriculture Organization (2011)
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):
the global economic recovery since 2009 and the declining value of the
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
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
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 al.et (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
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.
2007 Census of Agriculture
(USDA, National Agricultural Statistics Service, 2009).
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
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
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
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
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
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
, 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
, beyond which yields decline significant. lyThey
found a similar pattern for soybeans, with a threshold of about 30
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
, 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
should increase Pennsylvania corn
and soybean yields. Greater climate change (-56
) 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
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
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
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
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: h eat stress among livestock kept outdoors during much of the
year;  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 al.et , 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.
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 al.et , 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
Ainsworth, E.A., and J.M. McGrath (2010). “Direct Effects of Rising Atmospheric Carbon Dioxide and
Ozone on Crop Yields.”
Climate Change and Food Secur
. Lobell and M. Burke, eds., Springer,
New York, pp. 109-130.
Alexandratos, N. (2011. )“World Food and Agriculture to 2030/2050 Revisited. Highlights and Views Four
Looking Ahead in World Food and Agriculture: Perspectives to
Conforti, ed., FAO,
Rome, pp. 1-147
Babcock, B.A. (2010). “The Politics and Economics of the CUro.S.
p Insurance Program.” Working Paper,
National Bureau of Economic esR earch.
Boesch, D.F. (editor) (2008).
Global Warming and the Free State: Comprehensive Assessment of Climate
Change Impacts in Maryland
. Report of the Scientific and Technical Working Group of the Maryland
Commission on Climate Change. University of Maryland Center for Environmental Science, Cambridge,
Craine, J.M., A.J. Elmore, K.C. Olson, and D. Tolleson (2010).e“ CClimhanagte and Cattle Nutritional
Global Change Biology
Food and Agriculture Organization (2006)
orld Agriculture: Towards 2030/2050, Interim Report
Food and Agriculture Organization (2011). “FAO Food Price Index.”
Goodwin, B.K. (2001). “Problems with Market Insurance in Agricultu
erican Journal of Agricultural
Hecht, S. (2008). “Climate Change and the Transformation of Risk: Insurance Matters.”
UCLA Law Review
Huffman, W.E., and R.E. Evenson (2006).
Science for Agriculture: A TeLorm
, 2nd ed.
Blackwell Publishing, Ames, Iowa.
Irani, F. (2011). “Forecasted Farmland Loss in the Chesapeake Bay Watershed.” July 2011.
Martinez, S., MH. and, M. Da Pra, S. Pollack, K. Ralston, T. Smith, S. Vogel, S. Clark, L. Lohr, S. Low, and
C. Newman (2010).
Local Food Systems: Concepts, Impacts, and Iss
DA, Economic Research
Service, Economic Research Report No. 97.
Masnick, G.S., D. McCue, and E.S. Belsky (2010). “Updated202
2010 0-Household and New Home
Demand Projections.” Joint Center for Huosing Studies, Harvard University, Working Paper W-910.
Nardone, A., B. Ronchi, N. Lacetera, M.S. Ranieri, and U. Bernabucci (2010). “Effects of Climate Changes
on Animal Production and uSstainability of Livestock Systems.”
Newton, A.C., S.N. Johnson, and P.J. Gregory (2011). “Implications of Climate Change for Diseases,
Crop Yields and Food Security.”
Organic Trade Association (2011). “Inudstry Statistics and Projected Growth.”
Pennsylvania State Data Center (2008). “Pennsylvania County Population Projections, 2-2000030.”
Schimmelpfennig, D., and P. Heisey (2009). “The Evolving Public Agricultural Researcho lPioo.rt” f
,US DA, Economic Research Service, March 2009, p. 7.
Schlenker, W., and M.J. Roberts (2009). “Nonlinear Temperature Effects Indicate Severe DamaUges
Crop Yields under Climate Change.
roceedings of the National Academy of Science
Trethowan, R.M., M.A. Turner, and T.M. Chattha (2010). “Breeding Strategies to Adapt Crops to a
Climate Change and Food Secur
D. Lboell and M. Burke, eds., Springer, New York,
Trostle, R., D. Marti, S. Rosen, and P. Westcott (2011).
Why Have Food Commodity Prices Risen Again?
USDA, Economic Research Service, Report WR-11S03.
U.S. Census Bureau (2005). “State Interim Population Projections by Age and Sex: 2– 00204 30.”
USDA, Economic Research Service (2011). “Agcuriltural Baseline Projections.”
USDA, National Agricultural Statistics Service (2009
07 Census of Agriculture
Whelan, R. (2011).U “.S. Farmers Reclaim Land from Developers.
all Street Journal
, November 14,
Ziska, L.H., D.M. Blumenthal, G.B. Runion, E.R. Hunt, and -SHol. teDro
iaz(2011). “Invasive Spceies and
Climate Change: An Agronomic Perspective.”
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
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 al.et , 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
0.018 ˚F/decade), especially since about 1970 where rates have been even
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
1-4 week earlier peak streamflow due to
earlier warming-driven snowmelt
↑ U.S. West and U.S.
New England regions
↑ Increase in growing
Proportion of precipitation falling as snow
↓ U.S. West
Duration and extend of snow cover
↓ Most of North
↑ Most of North
↑ Up in winter months,
constant in summer
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;
↑ Most of North
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
Periods of droughts
↑ Western U.S.
↑ Increase ofbo th soil
decrease in summer
/ fall low flows
Observed changes to North American Water Resources during the
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.
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).
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 al.et , 2007).
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
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.
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
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
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.
Variability of soil moisture probability density functions with variability in rooting depth.
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
in air temperature will lead to about 0.7 to
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.
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.
. 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
. 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 al.et , 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
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.
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).
Flow chart visualizing the differences between meteorological, agricultural and hydrological
droughts (National Drought Mitigation Center, http://www.drought.unl.edu/whatis/concept.htm,
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 al.et
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 al.et
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 (1.to 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
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.
Municipal water supply
Increase reservoir capacity
Incentives to use less (e.g.
through pricing or rebates)
Extract more water from rivers
Legally enforced wateru se
standards (e.g. foapr pliances)
Alter system operating rules
Increase use of grey water
Inter-basin water transfer
Capture more rain water
Increase use of recycled water
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
Increase reservoir capacity
Increase efficiency of turbines,
encourage energy efficiency
Build weirs and locks
Alter ship size and frequency
Enhance treatment works
Reduce volume of effluents to
treat (e.g. by charging for
Watershed management to
reduce polluting runoff
Increase flood protection
Improve flood warning and
Watershed source control to
reduce peak discharges
Curb floodplain development
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:  insufficient monitoring of hydrological variables, and  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 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.
Increase in winter precipitation. Small to no increase in summer precipitation.
Potential increase in heavyp recipitation events [High confidence for winter,
lower for summer]
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]
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
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]
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
Summary of general projections for Pennsylvania water resources.
Barnett, T., D.W. Pierce, H. Hidalgo, C. BonfilsS, anB.teDr,
. T. Das, G. Bala, A.W. Wood, T. Nazawa,
A. Mirin, D. Cayan, M. Dettinger
2008. Hum-inanduced changes in the hydrology of the western United
, 319 (5863), 636b. DOI: 10.1126/science.319.5863.636b
Bates, B.C., Kundzewicz, Z.W., Wu, S. and Palutikof, J.P. (eds.) 2008. Climate change and water. Technical
Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva,p2 . 10p
Bernstein, L.e t al.2007. Summary for policymakers. Climate change 2007: Synthesis repo. rt
Intergovernmental Panel on Climate Change, Fourth Assessment Report, Accessed February 2009,
Boesch, D.F. (ed2.) 008. Global Warming and the Free State: Comprehensive Assessment of Climate
Change Impacts in Maryland. Report of the Scientific and Technical Working Group of the Maryland
Commission on Climate Change. University of Maryland Center for Environmental Science, Cambridge,
Maryland. This report is a component of the Plan of Action of the Maryland Commission on Climate
Change, submitted to the Governor and General Assembly pursuant to Executive Order 01.10.2007.07.
Budyko, M.I. 1974. Climate and . lifeAcademic Press, San Diego, CA, AU. S
Christidis, N., Stott, P.A., Brown, S., Karoly, D.J. and Caesar, J. 2007. Human contribution to the
lengthening of the growing season during 195-990 .
Journal of Climate
, 20, 5441-5454.
Church, J. A., N. J. White, R. ColeKm. aLnam, beck, and J. X. Mitrovica (2004), Estimates of the regional
distribution of sea level rise over the 19-5200 00 period,
Journal of Climat
Cook, E.R., P.J. Bartlein, N. Diffenbaugh, R. Seager, B.N. Shuman, R.S. Webb, J.W. dW Cilliam.
Woodhouse, 2008: Hydrological variability and change. In: Abrupt Climate Chan. gA e Report by the
U.S. Climate Change Science Program and the Subcommittee on Global Change Research. U.S.
Geological Survey, Reston, VA, pp. 1–4253 7.
Cooley, H. 2009. Water management in a changing climate. In Gleick, P.H., Cooley, H., Cohen, M.J.,
Morikawa, M., Morrison, J. and Palaniappan, M. 2009. The world’s wate-r 2020090. 8 The biennial report
and freshwater resources. Island Press, Washington, D.C., A, US39-56.
DeWalle, D.R., Swistock, B.R., Johnson, T.E. and McGuire, K.J. 2000. Potential effects of climate change
and urbanization on mean annual streamflow in the United States.
Water Resources Rese
Diffenbaugh, N.S., F. Giorgi, and J.S. Plimal, atCe change hotspots in the United States,
, L16709, doi:10.1029/2008GL035075, 2008.
Dooge, J.C.I. 2008. Looking for hydrologic laws. Water Resources Research, 1986. 22(9)-: 58p. s.4 6s
EPA (Environmental ProtectionA gency), Secondary Drinking Water Regulations: Guidance for Nuisance
Chemicals. Retrieved 2012 from www. water.epa.gov.
Falkenmark, M. 2001. The greatest water problem: The inability to link environmental security, water
security and food security. WateRr esources Development, 17(4): p. 5-35549 .
Field, C.B., L.D. Mortsch,, M. Brklacich, D.L. Forbes, P. Kovacs, J.A. Patz, S.W. Running and M.J. Scott,
2007. North America. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of
Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,
M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University
Press, Cambridge, UK, 61-657 2.
Frederick, K.D. and GleicPk,.H . 1999. Water and global climate change: Potential impactsU o.Sn . water
resources. Pew Center on Global Climate Change, Washington DU.CSA., .
Freeman, M.C., Pringle, C.M. and Jackson, C.R. 2007. Hydrologic connectivity and the contribution of
stream headwaters to ecological integrity at regional scales.
Journal of the American Water Resources
, 43(1), -514.
Gleick, P.H. 2002. Soft water paths.
18: p. 373.
Hayhoe, K., Wake, C.P., Anderson, B., Lian-Z.g, , MX.aurer, E., Zhu, J.a, dbBurry, J., DeGaetano, A.,
Stoner, A.M. and Wuebbles, D. 2008. Regional climate change projections for the NorthUSeaA. st
Mitigation and Adaptation Strategies for Global Chang
Hayhoe, K., Wake, C.P., Huntington, T.G., Luo, L., Schwa.r, tzS, hMef.fiDeld, J., Wood, E., Anderson, B.,
Bradbury, J., DeGaetano, A., Troy, T.J. and Wolfe, D. 2007. Past and future changes in climate and
hydrological indicators in the U.SN. ortheast.
, 28, 38-1407.
Hull, C. H. J., and R. C. Torto orie(19ll79), Se-alevel trend and salinity in the Delaware Estuary, Staff
Report, Delaware River Basin Commission, West Trenton, N. J.
Hull, C. H. J., M. L. Thatcher, and R. C. Tortoriello (1986), Salinity in the Delaware
effect, sea-level rise, and salinity in the Delaware Estua
dited by C. H. J. Hull and J. uGs. , Tpipt . -819,
United States Environmental Protection Agency and Delaware River Basin Commission, Washington,
D.C. and Trenton, N. J.
Huntington, T.G., Hodgkins, G.A.i, mK, eB.D. and Dudley, R.W. 2004. Changes in the proportion of
precipitation occurring as snow in the Northeast (1949 to 2000).
Journal of Clim
Huntington, T.G., Richardson, A.D., McGuire, K.J. and Hayhoe, K. 2009. Climate and hydrological changes
in the northeastern United States: recent trends and implications for forested and aquatic ecosystems.
Canadian Journal of Forest Resource
39, 1992-12 .
Kelleher, C., Wagener, T., Gooseff, M., McGlynn, B., McGuire, K. and Marshall, L. 2011.Investigating
controls on the thermal elasticity of Pennsylvania streams.
Kerr, R.A. 2011. Predicting climate chang– eV ital details of global warming are eluding forecasters.
, 334, 173-174.
Kundzewicz, Z. eal.
t 2007. Freshwater resources and their management. In Parryet al. (eds.) 2007.
Climate change 2007: Impacts, adaptation and vulnerabilit. yContribution of Working Group II thto e
Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University
Press, Cambridge, UK, 17-213 0.
Laio, F., Porporato, A., Ridolfi, L. and Rodrig-Ituurebze, I. 2001. Plants in watecro-ntrolled ecosystems:
active role in hydrologic processes and response to water stress. II. Probabilistic soil moisture dynamics.
Advances in Water Resource
Madsen, T. and Figdor. 2007. When it rains it pouorsb: al glwarming and the rising frequency of extreme
precipitation in the United States. Environment America Research & Policy Center, Washington, DC.
Mason, W. D., anPd ietsch, W. H.
Salinity movement and its caues
s in the Delaware River estua. ry
Transactions of the American Geophysical Union
, 21, 4-45763.
McCabe, G.J. and Wolock, D.M. 2002. A step increase in streamflow in the contues rmUninited
Geophysical Research Letters
Milly, P. C. ,D
.J. Betancourt, M . Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and
2008: Stationarity is dead: Whither wtear management?
, 319(5863), 57357- 4.
Milly, P.C.D., Dunne, K.A. and Vecchia, A.V. 2005. Global patterns of trends in streamflow and water
availability in a changing climate.
, al. 2002. Increasing risk of great floods in a changing climate. Nature, 415: p. -5517.
Milly, P.C.D., K.A. Dunne, and A.V. Vecchia, 2005. Global pattern of trends in streamflow and water
availability in a changing climate. Nature, 438: p. 34-3570.
Morrill, J.C., Bales, R.C. and Clinon, kM.H. 2005. Estimating stream temperature from air temperature:
Implications for future water quality.
ASCE Journal of Environmental Enginee
131(1), 139146- .
Namias, J. (1966). Nature and possible usces
a of the northeastern United States drought urding
Monthly Weather Review
, 94(9), 543-557.
National Research Council 2008. Integrating Multiscale Observations of UW.Sa. ters, ed. W.S.a.T. Board.
Washington, D.C.: The National Academies Press.
Niu, G. Y., & Yang, Z. L. (2006)
cts of frozen soil on snowmelt runoff and soil water storage
at a continental scal
NWS (National Weather Services) 2006D. rought – Public fact sheet.
Porporato, A., Daly, E. and Rodrig-uItuezrbe, I. 2004. Soil water balance and ecosystem response to
The American Naturalis
164(5), 62563- 2.
Rahmstorf, S. (2007), A se-memi pirical approach to projecting fuutre sea-level rise,
Reed, P., etal. 2006. Bridging River Basin Scales and Processes to Assess Hum-Canlimate Impacts and
the Terrestrial Hydrologic System. Water Resources Research, 42(W07418): p.
Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V. and Cox, D.R. 1999. Probabilistic modeling of
water balance at a point: the role of climate, soil and vegetation.
Proceedings of the Royal Society
, 455, 3789-3805.
Sankarasubramanian, A. and Vogel, R.M.
2003H. ydroclimatology of the Continental United State,
Geophysical Research Letters, 30(7), 136-133 66.
Sheffield, J. and Wood, E.F. 2008o. bGal l trends and variability in soil moisture and drought
characteristics, 1950-2000, from observation-driven simulations of the terrestrial hydrologic cycle.
Journal of Climate
, 21, 432-458.
Shuttleworth, W.J. 1993. Evaporation. Chapter 4 in Maidment,. D(e.dR.) Handbook of Hydrology.
Singh, R., Wagener, T., van Werkhoven, K., Mann, M. and Crane, R. 2011. Trading space for time:
A nonstationary uncertainty framework for hydrologic modeling in a changing climate.
Earth System Sciences
. (In Press)
SRBC 2006. Susquehanna River Basin. uSsquehanna River Basin Commission Fact Sheet,
http://www.srbc.net/pubinfo/docs/Susq% 20River % 20Basin % 20General % 20(11_06).PDF.
Swistock, B. 2007. A quick guide to groundwater in PennsylvaPneian. naylvania State University, College
of Agriculture, University Park, PA.
Trapp, R.J., N.S. Diffenbaugh and A. Gluhovsky, Projected trends in severet, roepxitcraa l thunderstorm
Geophysical Research Letters
, L01703, doi:10.1029/2008GL036203 , 2009.
Trenberth, K.E.e t al. 2007. Observations: Surface and atmospheric climate change. In Solomet
(eds.) 2007. Climate change 2007: The physical science bas. Cisontribution of Working Group I to the
Fourth Assessment Report of the Intergovernemntal Panel on Climate Change. Cambridge University
Press, Cambridge, U. K
U.S. Army Corps of Engineers (1997), Chapter 5: Hydrodynamic and salinity modeling, in
Main Channel Deepening Project: Supplemental Environmental Impact Statem
ited, pp. -51 to
UCS (Union of Concerned Scientists) 2008. Climate change in Pennsylvania. Report, Cambridge, MA.
Wagener, T. 2007. Can we model the hydrologic implications of environmental chan
, 21(23), 3233323- 6.
Wagener, T., Sivapalan, M. and McGlynn, B. 2008. Catchment classification and servTicowesa r– d a new
paradigm for catchment hydrology driven by societal needs. In Anderson, M.G. (ed.) Encyclopedia of
Hydrological Sciences. John Wiley & Sons Ltd.
Wagener, T., Sivapalan, M., Troch, P.A., McGlynn, B.L., Harman, C.J., Gupta, H.V., Kumar, P., Rao, P.S.C.,
Basu, N.B. and Wilson, J.S. 2010. The future of hydrology: An evolving science for a changing world.
Water Resources Research
, 46, W05301, doi:10.1029/2009WR008906.
World Water Assessment Programme. 2009Th. e United Nations World Water Development Report 3:
Water in a Changing World. Paris: UNESCO, and London: Earthscan.
Zervas, C. (2001),
Sea level variations of the United States, -11895949, NOAA Technical Report NOS
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
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
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
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
These occur in two major categories:  a total of 59,414 hectares (146,816 acres) are defined as
lacustrine (lakes and ponds primarily),  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 U.to
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).
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 l.et
, 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
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
), 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
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.
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
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
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
ooal.re , 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 al.et , 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 al.et
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
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
) 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;  reduces
brook trout population; and  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.
Seasonal stream flow for the Little Juniata and Young Woman's Creek waters, havederaged
over present (1979-1998) and future (2046-2065) time periods.
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.
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.
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).
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
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
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?
Amoros, C., and G. Bornette. 2002. Connectivity and biocomplexity in waterbodies of riverine
floodplains. Freshwater Biology 47:76-1776.
Baker, D. B., R. P. Richardet sal.
, (2004). A New Flashiness Index: Characteristics And Applications To
Midwestern Rivers And Streams. JAWRA (Journal of the American Water Resources Association)
40(2): 503 -522.
Balcombe, C.K., J.T. Anderson, J.S. Rentch, W.N. Grafton, R.H. Fortneyr, deWk. .S2. 00K5oa. A
comparison of plant communities in mitigation and reference wetlands in the -Ampidpalachians.
Wetlands. 25: 130-142.
Balcombe, C.K., J.T. Anderson, R.H. Fortney, and W.S. Kordek 2005b. Vegetation, invertebrate, and
wildlife community rankings and habitat analysis of mitigation wetlands in West Virginia. Wetlands
Ecology and Management. 13: 51753- 0.
Bayley, P.B. 1995. Understanding large rivfleoro- dplain ecosystems. Bioscience 45: 5-75.
Brinson, M.M. 1993. A Hydrogeomorphic Classification for Wetlands. Vicksburg, UMSAS. ,
Bunn, S. E., and A. H. Arthington. 2002. Basic principles and ecological consequences of altered flow
regimes for aquatic biodiversity. Environmental Management 30(4):4-95207.
Burkett, V.R., and J.u slKer. 2000. Climate change: potential impacts and interactions in wetlands of the
United States. JAWA 36(2): 3-13320.
Carter, S.L., Haas, C.A., and Mitchell, J.C. 1999. Home range and habitat selection of bog turtles in
southwestern Virginia. Journal of Wildlife Management. 63: 85860.
Chapman, T. G. and A. I. Maxwell (1996). Baseflow separatcoiomn p- arison of numerical methods with
tracer experiments. Institute Engineers uAstralia National Conference.
Chapman, D. W. 1988. Critical review of variablees d uto
s define effects of fines in redds of large
salmonids. Transactions of the American Fisheries Society 117-21.
Dukes, J. S., and H. A. Mooney. 1999. Does global change increase the success of biological invaders?
Trends in Ecology and Evolution 14:13-5139.
Dunne, T. and L. B. Leopold (1979). Water in environmental planning. San Francisco, California, Freeman.
EBTJV (Eastern Brook Trout Joint Venture), Conservation Strategy/Habitat Workgroup. 2008. Conserving
the Eastern Brook Trout: Action Strategies. Availab: lwww.e
easternbrooktrout.org. (May 2009).
EBTJV (Eastern Brook Trout Joint Venture). 2006. Eastern brook trout: staantud s threats. Prepared by
Trout Unlimited, Arlington, Virginia, for the Eastern Brook Trout Joint Venture. 36 p. Available:
www.easternbrooktrout.org. (May 2009).
Firth, P., and S. G. Fisher. 1991. Global climate change and freshwater ecosystems. SpVrinerlagger, -
New York, New YorkU, SA.
Fisher, A. 2000. Preliminary findings from the M-Aidtlantic regional assessment. Climate Research
14:26 1 -269.
Gitay, H., Brown, S., Easterling, W., Jallow, B., 2001. Ecosystems and their goods and services.
In: McCarthy, J., Canziani, O., Leary, N., Dokken, D., White, K. (Eds.), Climate Change 2001: Impacts,
Adaptation, and Vulnerability. Cambridge Univeity
rs Press, New York, pp. 2–33425 .
Godwin, C.M., Arthur, M.A., and Carrick, H.J. 2009. Periphyton nutriuens tin sta attemperate stream
with mixed land-uses: implications for watershed nitrogen storage. Hydrobiologia 623(1): -115412.
Godwin, C.M., and Crarick, H.J. 2007. Spat-itoemporal variation of periphyton biomass and production in
a temperate spring-fed stream. Aquatic Ecology 42: 58– 593 5.
Griscom, B., M. McKenn-eEyasterling, W.L. Myers, A. McQueen, J.A. Bishop, G. Rocco, J. French,
K.C.H ychka, A. aByard, T. O’Connell, G.P. Patil, C. Taillie, G. D. Constantz and R.P. Brooks. 2007.
Classifying and prioritizing watersheds for protection and restoration. Final report to EUn.vSiro. nmental
Protection Agency, Agreement No. R830593.
Harper, M. P., and B. L. Peckarsky. 2006. Emergence cues of a mayfly in- alta ithuigde h stream
ecosystem: potential response to climate change. Ecological Applications 16(2): -662121.
Hudy, M., T. M. Thieling, N. Gillespie, and E. P. Smith. 2008. Distribuutiso, n, ansd taltand use
characteristics of subwatersheds within the native range of brook trout in the eastern United States.
North American Journal of Fisheries Management 28: 106-109 85.
Hudy, M., T. M. Thieling, N. Gillespie, and E. P. Smith. 2005. Distrstibaututiso, n, and threats to brook
trout within the eastern United States. Final report to the steering committee of the Eastern Brook Trout
Joint Venture. 77 pp. Available: www.easternbrooktrout.org. (May 2009).
Keller, W. T. 1979. Management Humphries, P., and D. S. Baldwin. 2003. Drought and aquatic
ecosystems: an introduction. Freshwater Biology 48:114-11146.
Humphries, P., A.J. King, and J.D. Koehn. 1999. Fish, flows and flood plains: links between freshwater
fishes and their environment in the Murr-aDyarling River system, uAstralia. Environmental Biology of
Fishes 56: 129-151.
Intergovernmental Panel on Climate Change. 1998.
Karr, J. R., and E. W. Chu. 1999. Restoring life in running waters: better biological monitoring. Island
Press, Washington, D. C.
Kentula, M.E. 2007. Foreword: monitoring wetlands at the watershed scale. Wetlands. 27(3-41): 5.4 12
Kelleher, C., Wagener, T., Gooseff, M., McGlynn, GuB., ireM, cK. and Marshall, L. 2011 Investigating
controls on the thermal elasticity of Pennsylvania tresams. Hydrological Processes, doi:
Kohler, C.D., 1986, Soil survey of Lycoming County, PennsylvanU.ia:
S. Department of Agriculture Soil
Conservation Service, 209 p. Leopold, L.B., M.G. Wolman, and J.P. Miller. 1964. Fluvial processes in
geomorphology. San Francisco, California: W.H. Freeman.
Lowrance R, L.S. Altier, J.D. Newbold, R.R. Schnabel, P.M. Groffman, J.M. Denver, D.L. Correll,
J.W.Gilliam , and J.L. Robinson. 1997. Water quality functions of riparian forest buffers in Chakee sape
Bay watersheds. Environmental Management. 21:68–7712
McIlnay, D. P. 2002. Juniata, River of Sorrows. Seven Oaks Press, HollidaysburgUS, AP. A,
McKnight, D. 2001. Freshwater ecosystems and climate change: recent assessments and
recommendations. Limnool gy and Oceanography Bulletin 10:61-65.
Millennium Ecosystem Assessment, 2005. ECOSYSTEMS AND HUMAN WE-BLELING: WETLANDS AND
WATER Synthesis. World Resources Institute, Washington, DC.
Mitsch, W.J. and J.G. Gosselink. 1993. Wetlands. New York: Wiley & Sons.
Moore, M. V., M. L. Pace, J. R. Mather, P. S. Murdoch, R. W. Howarth, C. L. Folt, C. Y. Chen, H. F.
Hemond, P. A. Flebbe, and C. T. Driscoll. 1997. Potential effects of climate inc hfreangse hwater
ecosystems of the New England/Mi-dAtlantic Region. Hydrological Processes 11:92-5947.
Moorhead, K.K. 2001. Seasonal water table dynamics of a southern Appalachian floodplain and
associated fen. Journal of the American Water Resources Association. 37:- 114105.
Mountain, D. G. 2002. Potential consequences of climate change for the fish resources in the
Mid-Atlantic Region. American Fisheries Society 32:18-1594.
Myers, W.L., M. McKenn-Eeyasterling, K.C. Hychka, B. Griscom, J.A. Bishop, A. Bayard, G.L. Rocco,
R.P.Br ooks, G. Constantz, G. P. Patil and C. Taillie. 2006. Contextutal
erinclg usfor configuring
collaborative conservation of watersheds in the M-idAtlantic Highlands. Environmental and Ecological
Parmesan, C., and G. Yohe. 2003. A globally coherent fingerprint of climate change impacts across
natural systems. Nature 421 (2 January 2003): -342.
Peckarsky, B. L., B. W. Taylor, A. R. McIntosh, M. A. McPDee. kA, .a nLyd tle. 2001.Variation in mayfly size
at metamorphosis as a developmental response to risk of predation. Ecology 82-:7757.
Poff, N.L., and J.D. Allan. Functional organization of stream fish assemblages in relation to hydrological
variability. Ecology 76(2): 60662- 7.
Poff, N.L., M.M. Brinson, and J.W. Day. 2002. Aquatic Ecosystems and Global Climate Change: Potential
Impacts on Inland Freshwater and Coastal Wetland Ecosystems in the United States. Prepared for the
Pew Center on Global Climate Chaneg, January 2002. 56 pp.
Poff, N. L., S. Tokar, and P. Johnson. 1996. Stream hydrological and ecological responses to climate
change assessed with an artificial neural network. Limnology and Oceanography 41(5)-:86853.
Poff, N. L. and J. V. Wa19rd8. 9. Implications of streamflow variability and predictability for lotic
community structure: a regional analysis of streamflow patterns. Canadian Journal of Fisheries and
Aquatic Science 46:1805-1818.
Postel, S.L., and S.R. Carpenter. 1997. “FreshwEactoesr ystem Services.” In Nature’s Services: Societal
Dependence on Natural Ecosystems. G.D. Daily, ed. Island Press, Washington, DC, -p2p14.
Qu, Y. and C.J. Duffy. 2007. A semidiscrete finite volume formulation for multiprocess watershed
simulation. Water Res. Research. 43, W08419, doi:10.1029/2006WR005752
Rogers, C. E., and J. P. McCarty. 2000. Climate change and ecosystems of t-hAe tlanMtidic Region.
Climate Research 14(3):235-244.
Rossell, I.M. and Kesgen, J.M. 2003. The distribution and fruoif tinreg d and black chokecherry (Aronia
arbutifolia and A. melanocarpa) in a southern Appalachian fen. Journal of the Torrey Botanical Society.
Roth, N. E., M. T. Southerland, G. M. Rogers, and J. H. Volstad. 2004. Maryland Biologicalu rvSety ream S
200 -2004: Volume III. Ecological assessment of watersheds sampled in 2002. Maryland Department of
Natural Resources, Annapolis, MD, AU. S
Roy, A. H., A. D. Rosemond, D. S. Leigh, M. J. Paul, and J. B. Wallace. 2003. Habitatspecific responses of
stream insects to land cover disturbance: biological consequences and monitoring implications. Journal
of the North American Benthological Society 22(2): 2-3907.
Sedell, J. R., G. H. Reeves, F. R. Hauer, J. A. Stanford, and C. P. Hawkins. 1990. Ra oiln
e oref croevfeury
from disturbances: modern fragmented and disconnected river systems. Environmental Management
Sheldon, F., A.J. Boulton, and J.T. Puckr2id0ge02. . Conservation value of variable connectivity: aquatic
invertebrate assemblages of channel and floodplain habitats of a central
ustAralian arid-zone river,
Cooper Creek. Biol. Cons. 103:1-313 .
Snyder, C.D., J.A. Young, and B.M. Stout, III. 200at6. ic Ahqabu itats of Canaan Valley,
West Virginia: diversity and environmental threat. sNortheastern Naturalist. 1333: 3-352.
Sweeney, B. W., J. K. Jackson, J. D. Newbold, and D. H. Funk. 1991. Climate change and the life histories
and biogeography of aquatic insects in eastern North America. Pages-1 1746 3 in P. Firth and S. G. Fisher,
editors. Global climate change and freshwater ecosystems. SpringVeerrlag- , New York, New YorkU, SA.
Suen, J. P. 2008. Potential impacts to freshwater ecosystemus secd
a by flow egr ime alteration under
changing climate conditions in Taiwan. Hydrology and Earth Systems Sciences uDsissicons 5:3005-30032.
Susquehanna River Basin Commission, 2009. Accommodating a New Straw in the Water: Extracting
Natural Gas from the MarcelluSs hale in the Susquehanna River Basin. Prepared by Thomas W. Beauduy,
Deputy Director & Counsel, for presentation at the 27th Annual Water Law Conference, American Bar
Association, Section of Environment, Energy, and Resources, February, 2009, and the Environaml Leaw
Forum 2009, Pennsylvania Bar Institute and the Pennsylvania Bar Association, Environmental, Minerals
& Natural Resources Law Section, March, 2009.
Tiner, R.W. 1990. Pennsylvania's Wetlands: Current uSts atand Recent Trends. Pennsylvania Bureau of
Water Resources Management. Harrisburg, Pennsylvania. USA.
Tockner, K., F. Malard, and J. V. Ward. 2000. An extension of the flood pulse concept. Hydrological
United States Environmental Protection AgencyU (.S. EPA). 2006. Draft Wdaeable Streams Assessment:
A Collaborative Survey of the Nation's Streams. UE.nSv. ironmental Protection Agency, Office of Water,
Washington, DC, USA. EPA 8- 4B1-06 -002.
Vörösmarty, C.J., P. Green, J. Salisbury, and R.B. Lam2m00e0rs. .“ Global Water Resources: Vulnerability
from Climate Change and Population Growth.” Science 289:2-28884 .
Ward, J. V. 1992. Aquatic insect ecology. 1. Biology and habitat. John Wiley and Sons, New York,
New York, USA.
Wardrop, D. H., M. E. KentDula,
. L. Stevens Jr, S. F. Jensen, and R. P. Brooks. 2007b. Assessment of
wetland condition: an example from the Upper Juniata Watershed in Pennsylvania, USA. Wetlands
Warren, R.J., J.D. Pittillo, and I.M. Rossell. 2004. Vascular flora of arn soAuptphalace hian fen and
floodplain complex. Castanea. 69: 11-6124.
Watanabe, N. C., I. Mori, and I. Yoshitaka. 1999. Effect of water temperature on the mass emergence of
the mayfly, Ephoron shigae, in a Japanese river (Ephemeroptera: Polymitarcyidae).h wFreatser Biology
Winter, T.C. 2000. The vulnerability of wetlands to climate change: A hydrologic landscape perspective.
JAWA 36(2): 3053-11.
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
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
Installed capacity mix for electric generation in Pennsylvania, 2. 00S9ource: U.S. Energy
Natural Gas, 22%
Coal , 41%
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%
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.
Sectoral natural gas consumption in Pennsylvania, 19-927009. Source: U.S. Energy
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 (Kleal.it , et20 11).
T rans portation
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).
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 U.as
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
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.
The figures for home heating from ful eoil or natural gas are taken from Blumsack
et al. (2009).
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).
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
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%
Natural Gas Production
Natural Gas Processing
Natural Gas Transport
Natural Gas Combustion
Oil Production, Transport and
Upstreams from Other Fuels is
Fossil-Fuel Power Plants
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, al.et (2010), based on data from PJM Interconnection.
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, al.et (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
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
Determining the level of avoided emissions assocted
ia with renewable electricity
generation technologies is difficult, due to the complexity in electrical system operat. ioOnne
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.
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
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
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., Jiaal.ng, , 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
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).
Apt, J., S. Blumsack and L.B. Lave, . 2“0C0o7mpetitive Energy Options for Pennsylvania,” report prepared
for the Team Pennsylvania Foundation.
Associated Press, 2011. “Texas Drought 2011: Power Projects Endangered,” November 2, 20. 1L1as
accessed on 6/5/2012 through
Averyt, K., J. Fisher, A. Hu-Lbeeer, A. Lewis, J. Macknick, N. Madden, J. Rogers, S. Tellinghuise. n, 2011
“Freshwater use by U.SP. ower Plants,” report for the Union of Concerned Scientisatsvailab,
Blumsack, S., J. Brownson and L. Witmer, . 2“0E0ffic9 iency and Environmental Performance of
Ground-Source Heat Pumps in Central Pennsylvania,” Proc. 42nd Hawaii International Conference on
System Sciences, Waikoloa HI.
Blumsack, S., P. Jaramillo, W. M. Griffin and H.S. Matthew. s,“ L2ife00-9Cycle Greenhouse Gas Emissions
Inventory for Electricity Generation in Pennsylvania,” report to the Pennsylvania Department of
Blumsack, S., 201. 0“Implications of Marcellus Shale Natural Gas Development for the Economics of
Combined Heat and Power in the M-iAdtlantic U.S.,” report for the M-idAtlantic CleanEne rgy
Blumsack, S. and J. Xu, “Spatial Variation of Emissions Impacts due to Renewable Energy Siting Decisions
in the Western U.S.
Under High-Renewable Penetration Scenarios” Energy Policy 39:11, pp69. 62-6971.
Blumsack, S. and A. rFneandez, 2012. “Ready or Not, Here Comes the Smart Grid,” Energy 37:1, pp.
L.M. Cathles, L. Brown, M. Taamb, and A. Hunter. , “2A 01C1ommentary on ‘Methane and the
greenhouse gas footprint of natural gas from shale formations,’” Climate Change ersL ett
Choudhary, P., S. Blumsack and G. Young, . 2“0C11omparing Decision Rules for Interconnected
Wind Turbines,” Proc. Hawaii International Conference on System Sciences, Waikoloa HI.
Considine, T., B. Watson and S. Blumsack, “The Pennsylvania MarcNealltuus ral Gas Industry: Economic
Impacts and Prospects,” report prepared for the Marcues llShale Coalition.
Dowds, J., P. Hines and S. Blumsack, “Fuel Switching Under Carbon Constraints,” Proceedings of the IAEE
Annual Meeting, Perth uAstralia, June 2012.
Fthenakis, V. and H. Kim. 2006. “Greenuhseo-gas Emissions from Solar Electric- and Nuclear Power:
A Life-Cycle Study,” Energy Policy 35, pp. 254259 –57 .
Fischer, C., 201. 0“When do Renewable Portfolio Standards Lower Electricity Prices,” Energy Journal
31:1, pp. 101-120.
Howarth, R. W., R. Santoro, and A. Ingraffea, 2011.“Methane and the greusee nghas
o footprint of
natural gas from shale formations,” Climatic Change Letters, DOI:10.1007/s105-80411 -0061 -5
Jaramillo, P.; W. M. Griffin, and H. S. Matt, h2e0w0s7. “Comparative Life Cycle Air Emissions of Coal,
Domestic Natural Gas, LNG, and SNG for Electricity Generation.” Environmental Science & Technology
Jaramillo, P; Samaras, C; Meisterling, K; Weakley, uHsin. g“ Coal for Transporttiaon: Life Cycle
Assessment of Coalto- -Liquids, Plugi-n Hybrid, and Hydrogen Pathways.” Energy Policy. 2009; 37, 2689–
Jiang, M., W. M. Griffin, C. Hendrickson, P. Jaramillo, J. van Briesen and A. Venkat. e“sLhif, e 2C0y1c1le
Greenhouse Gas Emissions of Marcellus Shale Gas,” Environmental Research Letters
Katzenstien, W. and J. Apt, 20. 0“7Air Emissions from Wind and Solar Power,” Environmental Science
and Technology 43:2, pp. 25-253 8.
Kern, Jordan, Greg CharacklisM, artin Doyle, Seth Blumsack (3) and Richard Wishunt, “The Influence of
De-Regulated Electricity Markets on Hydropower Generation and Downstream Flow Regime,” in press,
Journal of Water Resources Planning and Mangeme. nAtccepted for publication in April 210.
Kleit, A., S. Blumsack, Z. Lei, M. SahAraerdaik-ani, L. Hutelmyer and S. Smith, 2. 0“1E0lectricity Prices in
Rural Pennsylvania in the Pos-Rt estructuring Era,” report to the Center for Rural Pennsylvani
Lemoine, D., 2010“. Valuing PHEV Battery Capacity using a Real Options Framework,” Energy Journal
31:2, pp. 1131- \\
Peterson, S., J.Whitacre and J. Apt, 2. 0“1E0conomics of using PHEV Battery Packs for Grid Storage,”
Journal of Power Sources 195:8, 237-237 84.
Rudkevich, A. and P. Ruiz, 20. 1“1Locational Carbon Footprint and Renewable Portfolio Policies: A
Theory and its Implications for the Eastern Interconnection of the ,U” .SP.roc. Hawaii International
Conference on System Sciences, Waikoloa HI.
Ruiz, P. and A. Rudkevich, 20. 1“0Analysis of Marginal Carbon Intensities in Constrained Power
Networks,” Proc. Hawaii International Conference on System Sciences, Waikoloa HI.
Sharaei-Ardakani, M. S. Blumsack and A. N. Kleit, “Estimating Supply Curves in Transm-Cisosnisotnrained
Electricity Markets,” working paper, Penn State Electricity Markets Initiative, 2. 011
Samaras, C. and K. Miesterling, 20. 0"8Life Cycle Assessment of Greenhuose Gas Emissions from Plu-gin
Hybrid Vehicles: Implications for Policy,” Environmental Science and Technology 42:9,31 p70p. – 3176.
Spees, K. and L.B. Lave, 2. 0“0I8mpacts of Responsive Load in PJM: Load Shifting and R-Teiaml e Pricing,”
Energy Journal 29:2, pp. 101 1– 21.
Tollefson, J., 201. 2“Air Sampling Reveals High Emissions from Gas Field,”
U.S. Energy Information Administration, 2010. “State Energy Profiles,” available at
http://www.eia.gov/state/stateen- ergy-profiles.cfm?sid=PA; last accessed 10 November 2011.
Walawalkar, R., S. Blumsack, J. Apt and S. Fernands,. “2A0n0a8lyzing PJM’s Economic Demand Response
Program,” Energy Policy, 36:10, pp. 3692
370- 2 .
Weber, C. L., P. Jaramillo, J. Marriott, and C. Samaras, 2010. “Life Cycle Assessment and Grid Electricity:
What Do We Know and What Can We Know?” Environmental Science & Technology 6,4 4p:p.
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
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
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
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 al.et , 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 al.et ,
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
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,  their ability to migrate to locations with more
favorable climates and to compete with the other species that they encounter as they migrate,  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  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
Climate Changes’ Effects on Pennsylvania Forests
Anticipated changes in the climate of Pennsylvania are likely to alter Pennsylvania’s forest ecosystems
through  range shifts, including expansions and contractions, of tree species, birds and mammals,
 increased mortality and extinction raets,  changes in ecosystem productivity and phenology, and
 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 43-yr.pe 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 (McKenneyal.et , 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.
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
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,
 air pollutants,  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 al.et , 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
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  longer growing seasons (Hayet hal.oe,
2007; Campbell et al., 2009),  increased C
(Huang etal. , 2007), and  increases in soil
nitrogen due to increased mineralization and nitrification (Bertin, 2008; Campbell al.et
, 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
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 al.et , 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 al.et
, 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
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.
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  increasing forest carbon storage,
 reducing the loss of carbon stored in forests, and/or  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
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 al.et
, 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  their ability to
migrate to more suitable abh itats,  the genetic variation in populations and their ability to adapt and
thrive in a variety of conditions,  their ability to compete with other species in the context of the
changed climate (Clark etal ., 2011), and  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),  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 al.et ,
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  the huge logistical challenge of assisting more than
a few key species,  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  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.
Ainsworth, E.A., and S.P. Long. 2005. What have we learned from 15 years -air
of freCOe2 enrichment
(FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant
production to rising CO2.
5: 351– 372.
Aitken, Sally N., Sam Yeaman, Jason A. Holliday, Tongli Wang, and Sierra- MCcuLartnise. 2008.
Adaptation, migrationo r extirpation: climate change outcomes for tree populatio. n
Allen, Craig D., Alison K. Macalady, Haroun Chenchouni, Dominique Bachelet, Nate McDowell, Michel
Vennetier, Thomas Kitzberger, Andreas Rigling, David D. Breshears, E.H. (Ted) Hogg, Patrick Gonzalez,
Rod Fensham, Zhen Zhangm, Jorge Castro, Natalia Demidova, -JHowngan Lim, Gillian Allard, Steven W.
Running, Akkin Semerci, and Neil Cobb. 2010. A global overview of drought and -inhdeuacted tree
mortality revealsem erging climate change risks for forests.
Forest Ecology and Managem
Appell, D., 2009C. an ‘‘Assisted Migration’’ Se avSpecies from Global Warming?
, March 2009.
Araujo, M.B., and C. Rahbek. 2006. How doesa te
climchange affect biodiversity?
Ayres, M.P., and M.J. Lombardero. 2000. Assessing the consequences of global change for forest
disturbance from herbivores and pathogens.
Sci. Total Envir
Beckage, Brian, Ben Osborne, Daniel G. Gavin, Carolyn Pucko, Thomas Siccama, and Timothy Perkins.
2008. Arapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of
105(11): 4197- 4202.
Beever, E.A., C. Ray, P.W. Mote, and ilkJ.Le. ninWg. 2010. Testing alternative models of
20:164 – 178.
Beever, E.A., C. Ray, C. Ray, J.L. Wilkening, uPss.Far. dB, rand P.W. Mote. 2011. Contemporary climate
change alters the pace and drivers of teinxction.
Global Change Biology
17: 2054– 2070.
Bertin, R.I. 2008. plant phenology and distribution in relation to recent climate change.
J. of the Torrey
135: 126– 146.
Birdsey, R., K. Pregitzer, and A. Lucier. 2006. Forest carbon managemene t Uin nitted
J. of Env. Qualit
Boisvenue, C., and S. W. Running. 2006. Impacts of climate change on natural forest prod–uctivity
evidence since the middle of the 20th centur
ob. Change Biol
:862 – 882.
Brasier, C.M. 2001.R apid evolution of introduced plant pathogens via interspecific hybridization.
Campbell, J.L., L.Eu. sRtad, E.W. Boyer, S.F. Christopher, C.T. Driscoll, I.J. Fernandez, P.M. Groffman,
D.Ho ule, J. Kiekubsch, A.H. Magill, M.J. Meitll,
ch and S.V. Ollinger. 2009. Consequences of climate
change for biogeochemical cycling in forests of northeastern North America.
Can. J. For
Canadell, J.G., and M.R. Raupach. 2008. Managing forests for climate change mitigation. 32
Clark, James S., David M. Bell , Miche. lle HeHrsh, and Lauren Nichols. 2011. Climate change vulnerability
of forest biodiversity: climate and competition tracking of demographic rates.
Coakley, S.M., H. Scherm, and S. Chakraborty. 1999. Climate change and plant disease management.
Annu. Rev. Phytopathol
Coté, I.M. and E.S. Darling. 2010. Rethinking ecosystem resilience in the face of climate c
Library of Science Biology
Chen, -IChing, Jane K. Hill, Ralf Ohlemüller, David B. Roy, and Chris D. Thomas. 2011. Rapid range shifts
of species associated with high levels of climate warmin
333 (6045): 1024-1026.
Clark, A. and Fraser, K.P.P. 2004. Why does metabolisale
m swcith temperature?
Davidson, I., and C. Simkanin. 2008. Skeptical of assisted colonizat
. 322, 1048– 1049.
Davis, M.B. 1983. Quaternary history of deciduus oforests of eastern North America and Europe.
Mo. Bot. Gard
Delcourt, P.A., and H.R. Delcourt. 1987.
LToengrm- Forest Dynamics of the Temperate Zo
Springer-Verlag, New York.
Dietze, Michael C., and Paul R. Moorcroft. 2011. Tree mortality in the eastern and central United States:
patterns and drivers.
Glob. Change Biol
: 3312– 3326.
Dukes, Jeffrey S., Jennifer Pounst,i David Orwig, Jeffrey R. Garnas, Vikki L. Rodgers, Nicholas Brazee,
Barry Cooke, Kathleen A. Theoharides, Erik E. Stange, Robin Harrington, Joan Ehrenfeld, Jessica
Gurevitch, Manuel Lerdau, Kristina Stinson, Robert Wick, and Matthew Ayres. 2009. Respoof nsiness ect
pests, pathogens, and invasive plant species to climate change in the forests of northeastern North
America: What can we predict?
Canadian Can. J. For.
Dyer, J.M. 1995. Assessment of climatic warmuinsg ing a model of forest species migratio
Emanuel, Kerry. 2005. Increasing destructiveness of tropical cyclones over the past 30 years.
436(4): 6866- 88.
Farnsworth, E.J., A.M. Ellison, and W.K. Gong. 1996. Elevat
anatomy, physiolog, y growth
and reproduction of red mangrove
zophora mangle L
Farnsworth, E.J., and F. A. Bazzaz. 1995.-I annted r intrageneric differences in growth, reproduction, and
fitness of nine hergaceous annual species grown at eleatv ed CO
Gilbert-Norton, L., R. Wilson, J. R. Stevens, and K.H. Beard. 2010. Corridors increase movement: a
24:660 – 668.
Hagerman, S., H. Dowlatabadi, T. Satterfield, T.an Md cDaniels. 2010. Expert views on biodiversity
conservation in an era of climate change.
Global Environmental Change
20: 192– 207.
Harmon, M.E., W.K. Ferrell, and J.F. Franklin. 1990. Effects on carbon storage of conversion of
old-growth forests to young forests
Hayhoe, K., C.P. Wake, T.G. Huntington, L. Luo, M.D. Schwartz, J. Sheffield, E. Wood, B. Anderson,
J.Bradb ury, A. DeGaetano, T.J. Troy, and D. Wolfe. 2007. Past and future changes in climate and
hydrological indicators in the U.S. Northeast.
Hayhoe, K., C.P. Wake, B. Ander, sXo. n Liang, E. Maurer, J. Zhu, J. Bradbury, A. DeGaetano, A. Stoner,
and D.W uebbles. 2008. Regional climate change projections for the northeUasSt A.
Strategies Glob. Change
Heath, L.S., J.E. Smith, K.E. Skog, D.J. Nowak, and C.W. Woodall. 2011. Managed Forest Carbon Estimates
for the U.S. Greenhouse Gas Inventory, 1990-2008. J. of Forestry. 109(3): 161737- .
Hewitt, N., N. Klenk, A.L. Smith, D.R. Bazely N. Yan, S. Wood, J.I. MacLellan-M, umC.m Leip, saignd I.
Henriques. 2011. Taking stock of the assisted migration debate.
: 2560– 2572
Heller, N.E., and E. S. Zavaleta. 2009. Biodiversity management in the face of climate chreanvgieew
: ao f
22 years of recommendations.
2:14 – 32.
Hobbs, R.J., S. Arico, J. Aronson, J.S. Baron, P. Bridgewater, V.A. Craner, P.R. Epstein, J.J. Ewel, C.A. Klink,
A.E.L ugo, D. Norton, D. Ojima, D.M. Richardson, E.W. Sanderson, F. Valladares, V. Montserrate, and M.
Zobel. 2006. Novel ecosystems: Theoretical and management aspects of the new ecological world order.
Global Ecology and Biogeography
15:1 – 7.
Huang, JG- ., Y. Bergeron, B. Denneler, F. Berninger and J. Tardif. 2007. Re esopf oFnosrest Trees to
Increased Atmospheric CO
Critical Reviews in Plant Science
Hughes, L. 2000. Biological consequences of global warming: is the signal already apparent.
Hunter, M.L., Jr. 2007. Climate change and moving species: Furthering the debate on assisted
21:1356 – 1358.
Huntington, T.G., A.D. Richardson, K.J. McGuire, and K Hayhoe. 2009. Climate and hydrological changes
in the northeastern U.S.: recent trends and implications for forested and aquatic ecosystem
. J. For.
Intergovernmental Panel on Climate Change (IPCC). 2007.
Fourth Assessment Report: Climate Change
2007. The Physical Science Basis
ds. Solomon, S. ealt ., Cambridge Univ. Press.
Iverson, L.R., A. Prasad, and S. Matthews. 200o8d. elMing the potential climate change impacts on the
trees of the northeastern United States.
Mitig. Adapt. Strategies Glob
. Change 13:- 54510. 7
Kelly, A. E., and M. L. Goulden. 2008. Rapid shifts in plant distribution with recent climate
Natl Acad. Sci.
USA 105, 11823 – 11826.
Krosby, Meade, Joshua Tewksbury, Nick M. Haddad, and Jonathan Hoekstra. 2010. Ecological
Connectivity for a Changing Climate.
Kuparinen A., O. Savolainen, and F.M. Schurr. 2009. asInecd remortality can promote evolutionary
adaptation of forest trees to climate change.
For. Ecol. Mgm
1003 – 1008.
LaDeau, S. and J. Clark. 2001. Rising
and the fecundity of forest trees.
LaDeau, S. and J. Clark20. 06. Elevated CO
and tree fecundity: the role of tree size, interannual
variability and population heterogeneity.
Glob. Change Bi
Lenoir, J., Gegout, J. C., Marquet, P. A., De Ruffray, P. & Brisse, H. 2008. A significant upward shift in
plant species optimum elevation during the 20th century.
320, 1768– 1771.
Loarie, S.R., P.B. Duffy, H. Hamilton, P.B. Asner, C.B. Field, and D.D. Ackerly. 2009. The velocity of climate
Logan, J.A., and J.A. Powell. 2009. Ecological consequences of climate c-haltanegreed forest insect
ddisturbance regimes. In
Climate Change in Western North America: Evidence and Environmental
. Edited by F.H. Wagner. Allen Press, Lawrence Kans. USA.
Lonsdale, D., and J.N. Gibbs. 1994. Effects of climate change on fungal diseases in trees. In
. Cambridge University Press, Cambridge.
MacDonald, G. M., T.W.D. Edwards, K.A. Moder, R. Pienitz, and J.P. Smol. 1993. Rapid response of
treeline vegetation and lakes due to past climate warming.
Malcolm, J. R., A. Markham, R. P. Neilson, and M. Garaci. 2002. Estimated migration rates under