Prepared in cooperation with the 

New Hampshire Department of Health and Human Services

Simulating Hydrologic Response to Climate Change 

Scenarios in Four Selected Watersheds of New Hampshire

Scientific Investigations Report 2015–5047

U.S. Department of the Interior

U.S. Geological Survey

EXPLANATION

nhru

SNOW DIFF SRESb1 − INCHES

-8.81 to -8.00

-7.99 to -6.00

-5.99 to -4.00

-3.99 to -2.00

-1.99 to 0.00

0 15 30 MILES

0 25 50 KILOMETERS

New Hampshire

Massachusetts

Vermont

Maine

CANADA

UNITED STATES
Front cover. Map showing projected pattern of change for snow melt, photograph of dry streambed in the upper Ammonoosuc River area, 

and photograph of U.S. Geological Survey streamgage on the Nonnewaug River during a flood. 
Simulating Hydrologic Response to 

Climate Change Scenarios in Four Selected 

Watersheds of New Hampshire

By David M. Bjerklie, Joseph D. Ayotte, and Matthew J. Cahillane

Prepared in cooperation with the 

New Hampshire Department of Health and Human Services

Scientific Investigations Report 2015–5047

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

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Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

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Suggested citation:

Bjerklie, D.M., Ayotte, J.D., and Cahillane, M.J., 2015, Simulating hydrologic response to climate change scenarios 

in four selected watersheds of New Hampshire: U.S. Geological Survey Scientific Investigations Report 2015–5047, 

53 p., http://dx.doi.org/10.3133/sir20155047.

ISSN 2328-0328 (online)
iii

Acknowledgments

The authors would like to thank Cameron Wake of the Sustainability Institute at the University 

of New Hampshire for helpful suggestions regarding data summary, analysis, and presentation. 

The authors would also like to thank John Mullaney of the U.S. Geological Survey for valuable 

assistance in preparing this manuscript.

This publication was supported by Centers for Disease Control and Prevention (CDC) grant 

number #5UE1EH001046-03 from the National Center for Environmental Health in Atlanta, 

Georgia. The contents of the report do not necessarily represent the official views of the CDC.

v

Contents

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

Introduction and Background......................................................................................................................1

Highlight 1 Hydrologic Hazards and Vulnerability ...........................................................................2

Purpose and Scope ..............................................................................................................................3

Description of Study Area ...................................................................................................................3

Methods and Data .........................................................................................................................................6

Precipitation Runoff Model .................................................................................................................6

Modeling Considerations and Calibration Objectives ....................................................................6

Input Data...............................................................................................................................................7

Parameterization, Calibration, and Evaluation of New Hampshire-Scale Model....................10

Selection and Evaluation of Small-Area Models...........................................................................11

Modeling of Future Streamflows...............................................................................................................17

Potential Implications of Modeled Streamflow Changes .....................................................................27

Model Limitations.........................................................................................................................................32

Summary and Conclusions.........................................................................................................................34

References Cited..........................................................................................................................................34

Figures

1. Map showing the major watersheds that encompass the State of New Hampshire.......4

2. Map showing watersheds in New Hampshire selected for detailed analysis of 

hydrologic change........................................................................................................................5

3. Maps showing A–H, distribution of predicted change in mean daily maximum air 

temperature and change in mean annual precipitation projected for New Hampshire 

from 2081 to 2100 between current conditions and future climate conditions for the 

CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1 ........................40

4. Graph showing simulated compared to mean annual streamflow at 45 calibration 

streamgages in New Hampshire..............................................................................................11

5. Monthly hydrographs for the calibration period from 1980 to 2011 for four pilot 

watersheds in New Hampshire................................................................................................14

6. Graphs showing USGS-measured and PRMS-simulated mean monthly hydrographs 

for four pilot watersheds from 1981 to 2011 ...........................................................................15

7. Hydrographs of USGS-measured and PRMS-simulated streamflow spanning 

1.5 years that show groundwater recession and rising limb characteristics for four 

pilot study rivers in New Hampshire .......................................................................................16

8. Mean monthly hydrographs for the simulated current and future streamflows in 

the Oyster River, New Hampshire, for the SRESa2 and SRESb1 scenarios, the 

midcentury and end-of-century simulations, respectively, using the CNRM and 

GFDL GCMs..................................................................................................................................22

9. Mean monthly hydrographs for the simulated current and future streamflows in 

the Pemigewasset River, New Hampshire, for the SRESa2 and SRESb1 scenarios, 

the midcentury and end-of-century simulations, respectively, using the CNRM and 

GFDL GCMs..................................................................................................................................23
vi

10. Mean monthly hydrographs for the simulated current and future streamflows in 

the Souhegan River, New Hampshire, for the SRESa2 and SRESb1 scenarios, the 

midcentury and end-of-century simulations, respectively, using the CNRM and 

GFDL GCMs..................................................................................................................................24

11. Mean monthly hydrographs for the simulated current and future streamflows in 

the Ashuelot River, New Hampshire, for the SRESa2 and SRESb1 scenarios, the 

midcentury and end-of-century simulations, using the CNRM and GFDL GCMs ............25

12. Graphs showing A–D, streamflow duration curves for the Oyster River and 

Pemigewasset River watersheds in New Hampshire for the two emissions 

scenarios for midcentury (SRESa2) and end-of-century (SRESb1) for the CNRM and 

GFDL models, two of the five GCMs; and E–F, average streamflow duration curves 

that average all five GCM predictions and the two emission scenarios...........................48

13. Maps of the simulated change in base flow between current (1981–2000) and 

end-of-century (2081–2100) periods using the CNRM model for the two scenarios 

mapped by hydrologic response unit in the four pilot river watersheds in New 

Hampshire....................................................................................................................................28

14. Maps of the simulated change in snowfall between current (1981–2000) and 

end-of-century (2081–2100) time periods using the CNRM model for the two 

scenarios mapped by hydrologic response unit in the four pilot river watersheds in 

New Hampshire ..........................................................................................................................30

Tables

1. Comparison of DAYMET and station precipitation data for four watersheds in 

New Hampshire from 1980 to 2008.............................................................................................8

2. General circulation models used in the study of effects of climate change in four 

watersheds in New Hampshire..................................................................................................9

3. Evaluation of daily statistics for four watersheds in New Hampshire ..............................12

4. Evaluation of monthly statistics for four watersheds in New Hampshire.........................13

5. Summary statistics for climate change simulations for the Oyster River watershed 

in New Hampshire ......................................................................................................................18

6. Summary statistics for climate change simulations for the Pemigewasset River 

watershed in New Hampshire..................................................................................................19

7. Summary statistics for climate change simulations for the Souhegan River 

watershed in New Hampshire..................................................................................................20

8. Summary statistics for climate change simulations for the Ashuelot River 

watershed in New Hampshire..................................................................................................21

9. Comparison of the mean of the largest 0.1 percent of streamflows and 0.1 percent 

exceedance probability statistics for four watersheds in New Hampshire.....................26

10. Mean change in base flow and snowfall in New Hampshire .............................................32

11. Regression statistics relating change in groundwater base flow and snowfall to 

geographic variables .................................................................................................................33
vii

Conversion Factors

Inch/Pound to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

section (640 acres or 1 square mile) 259.0 square hectometer (hm2

) 

Flow rate

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3/s)

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as 

°C = (°F – 32) / 1.8.

Datum

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

Abbreviations

CNRM Centre National de Recherches Meteorologiques

ECHAM5 European Centre Hamburg Model version 5

GCM general circulation model

GFDL Geophysical Fluid Dynamics Laboratory

GIS geographic information system

GW DIFF CNRM SRESa2 difference between current (1981–2000) and future (2081–2100) 

simulated base flow using the CNRM global circulation model 

input for the SRESa2 emission scenario

GW DIFF CNRM SRESb1 difference between current (1981–2000) and future (2081–2100) 

simulated base flow using the CNRM global circulation model 

input for the SRESb1 emission scenario

HRU hydrologic response unit

HUC hydrologic unit code
viii

IPCC Intergovernmental Panel on Climate Change

N number of observations

NRMSE normalized root mean square error

NSE Nash-Sutcliffe efficiency

PRCP DIFF CNRM SRESa2 difference between current (1981–2000) and future (2081–2100) 

daily precipitation output from the CNRM global circulation 

model for the SRESa2 emissions scenario

PRCP DIFF CNRM SRESb1 difference between current (1981–2000) and future (2081–2100) 

daily precipitation output from the CNRM global circulation 

model for the SRESb1 emissions scenario

PRCP DIFF ECHAM5 SRESa2 difference between current (1981–2000) and future (2081–2100) 

daily precipitation output from the ECHAM5 global circulation 

model for the SRESa2 emissions scenario

PRCP DIFF ECHAM5 SRESb1 difference between current (1981–2000) and future (2081–2100) 

daily precipitation output from the ECHAM5 global circulation 

model for the SRESb1 emissions scenario

PRMS Precipitation Runoff Model System

R2 coefficient of determination

SNOW DIFF CNRM SRESa2 difference between current (1981–2000) and future (2081–2100) 

simulated snowfall using the CNRM global circulation model 

input for the SRESa2 emissions scenario

SNOW DIFF SRESb1 difference between current (1981–2000) and future (2081–2100) 

simulated snowfall using the CNRM global circulation model 

input for the SRESb1 emissions scenario

SRES IPCC designation for Special Report on Emissions Scenarios

SRESa2 SRES scenario A2

SRESb1 SRES scenario B1

TMAX DIFF CNRM SRESa2 difference between current (1981–2000) and future (2081–2100) 

daily maximum air temperature output from the CNRM global 

circulation model for the SRESb1 emissions scenario

TMAX DIFF CNRM SRESb1 difference between current (1981–2000) and future (2081–2100) 

daily maximum air temperature output from the CNRM global 

circulation model for the SRESb1 emissions scenario

TMAX DIFF ECHAM5 SRESa2 difference between current (1981–2000) and future (2081–2100) 

daily maximum air temperature output from the ECHAM5 global 

circulation model for the SRESa2 emissions scenario

TMAX DIFF ECHAM5 SRESb1 difference between current (1981–2000) and future (2081–2100) 

daily maximum air temperature output from the ECHAM5 global 

circulation model for the SRESb1 emissions scenario

USGS U.S. Geological Survey
Simulating Hydrologic Response to Climate Change 

Scenarios in Four Selected Watersheds of 

New Hampshire

By David M. Bjerklie,1 Joseph D. Ayotte,1 and Matthew J. Cahillane2

Abstract

The State of New Hampshire has initiated a coordinated 

effort to proactively prepare for the effects of climate change 

on the natural and human resources of New Hampshire. An 

important aspect of this effort is to develop a vulnerability 

assessment of hydrologic response to climate change. 

The U.S. Geological Survey, in cooperation with the New 

Hampshire Department of Health and Human Services, 

is developing tools to predict how projected changes in 

temperature and precipitation will affect change in the 

hydrology of watersheds in the State. This study is a test case 

to assemble the information and create the tools to assess the 

hydrologic vulnerabilities in four specific watersheds.

The study uses output from general circulation models 

to drive hydrologic simulations of streamflow, groundwater 

base flow (hereafter referred to as base flow), and snowfall in 

four representative watersheds in New Hampshire during the 

21st century, including the watersheds of the Ashuelot, Oyster, 

Pemigewasset, and Souhegan Rivers. Simulations show that 

on average, relative to current conditions, streamflow is likely 

to increase and base flow is likely to decrease, although this 

change is highly variable by geographic location and season. 

Streamflow variability will likely increase, with more high 

streamflows and more low streamflows. The largest increases 

in streamflow are in the winter, with small decreases in summer. Change in base flow varies across the State with the 

largest change in the northern Pemigewasset River watershed. Changes in snowfall are consistently decreasing for all 

watersheds on average, with the largest change also in the 

Pemigewasset. However, monthly snowfall totals during any 

given winter could be higher in the future than expected under 

current conditions.

Increasing frequency of floods (the largest seven floods 

expected to occur in 20 years) could be more significant than 

the size of the floods, except in the northern high altitude 

watersheds. In other words, the projections indicate a pattern 

of multiple floods that might not breach the riverbanks, yet the 

increased frequency could put additional strain on the existing river banks, infrastructure, and nearby human settlements. 

There is also likely to be an increase in high flows during the 

winter and spring months, which could result in more uncertainty in planning for the design, operation, and maintenance 

of infrastructure, including roads and utilities. Similarly, it is 

expected that, on average, there will be less base flow available and a wider range of seasonal fluctuation in base flow 

than experienced historically. These issues could necessitate 

more attention to planning and management of the resource. 

Based on past experience, the most important effects of 

climate change could be less certain planning options and a 

greater need for planning that accounts for the effects of larger 

streamflows than are currently available.

The effects of hydrologic change on human health and 

well-being could be most readily apparent with respect to 

changes in streamflow and the subsequent increase in the 

frequency of minor flooding and the frequency of summer and 

fall low streamflows. These changes could require the development of plans to adapt, protect, and upgrade infrastructure, 

such as bridges, culverts, roads, and other structures. The precipitation runoff modeling shows that rivers and watersheds 

in New Hampshire will likely change in response to climate 

change, and that this response varies with season and latitude. 

Although four representative areas were simulated in this 

study, additional models could be used to predict the response 

over the entire State.

Introduction and Background

There is a coordinated effort in New Hampshire to 

proactively prepare for the effects of climate change with 

a practical action plan (New Hampshire Climate Change 

Policy Task Force, 2009). A key component of the action 

plan is a vulnerability assessment of hydrologic responses 

to climate change. The U.S. Geological Survey (USGS) has 

been identified as a partner along with the New Hampshire 

Department of Health and Human Services and the University 

of New Hampshire in the development of tools and maps to 

1

U.S. Geological Survey.

2

New Hampshire Department of Health and Human Services.
2 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

help assess the hydrologic effects from short- and long-term 

climate change. In this partnership, the USGS developed 

tools (1) to predict how climate change will affect streamflow, 

base flow, and snowfall and (2) to identify locations that 

are vulnerable to the effects of climate change in areas 

across the State. This report documents a test case to create 

the information and tools that will allow specific users to 

understand and assess the hydrologic vulnerabilities in four 

specific watersheds.

Vulnerability can be thought of as a measure of the 

degree to which a system is exposed to, susceptible to, and 

able to cope with and adapt to adverse effects (highlight 1). 

Hydrologic vulnerability as a result of climate change is 

defined for the purposes of this report as a measure of the 

degree to which a deleterious effect on the hydrologic system results from climate change. Deleterious effects include 

(1) depletion in reservoir storage, (2) depletion in groundwater 

storage, (3) decrease in snowpack, (4) decreases in base flow 

(the component of streamflow made entirely of base flow), 

(5) low streamflow, and (6) increases in flooding. Vulnerability 

can also incorporate an assessment of degraded conditions that 

result from quantitative changes in hydrology (O’Connor and 

others, 1999). These climatic effects could be exacerbated or 

alleviated by increased development through changes in land 

cover and water use. Each region will have differing vulnerabilities to climate change, and assessment of these vulnerabilities is the first step in considering potential adaptive strategies.

Hydrologic vulnerability from future climate scenarios 

has increasingly become a major point of research efforts in 

New Hampshire and the Northeastern United States (Neff and 

others, 2000). Mack (2009) assessed a future climate scenario 

for the seacoast region by simulating potential variations in 

groundwater recharge and water use. For this region of the 

State, Mack (2009) found that the potential effect of future 

climate change on groundwater by 2025 is likely to be more 

substantial on groundwater recharge than changes in water 

demand. Using a watershed runoff model to simulate groundwater recharge and snow in the Connecticut River Basin 

(western New Hampshire), Bjerklie and others (2010b) found 

that, during the more than five-decade period from 1960 to 

2007, snowfall has generally decreased and base flow has 

generally increased. These changes have not been distributed 

uniformly in time or space and have marginally decreased or 

increased in some places in response to local conditions. Similarly, for the 50-year study period from 1961 to 2010, Dudley 

and Hodgkins (2013) found increasing groundwater levels and 

streamflow in northern New England. Hamilton and others 

(2010) reported climate trends in New Hampshire showing air 

temperature and precipitation increasing throughout the 20th 

century, with the increases accelerating since 1970. Hamilton 

and others (2010) also found indications of decreasing snowfall; however, they acknowledge that the trends in snowfall 

are more complicated with inconsistent trends. Jennifer Jacobs 

(University of New Hampshire, Durham, oral commun., 

March 2014) found evidence of consistently decreasing snowfall in New Hampshire.

Wake and others (2014) indicate that the climate model 

consensus shows increasing air temperature and precipitation 

for New Hampshire in the future, at least into 2100. Bjerklie 

and others (2010b) project that, in response to this climate 

change, groundwater recharge (and subsequent base flow) 

will continue to increase and snowfall will decrease, but not 

everywhere, depending on the general circulation model 

(GCM, often referred to as a global climate model) emissions 

scenario. Similar projections are reported by the U.S. Geological Survey (2014) based on the Intergovernmental Panel on 

Climate Change (IPCC; 2014) and Melillo and others (2014).

Streamflow records for 400 sites in the conterminous 

United States, measured during a five-decade period from 

1941 to 1999, indicate an increase in annual minimum daily 

and median daily streamflow beginning around 1970 (Lins and 

Slack, 1999; McCabe and Wolock, 2002), although there is a 

mixed pattern of increases and decreases in annual maximum 

daily streamflow across the United States. Annual streamflow in the eastern United States appears to increase as a step 

change and coincides with an increase in precipitation.

Past trends and GCM projections indicate that precipitation will continue to increase in the conterminous United 

States (Karl and Knight, 1998; Groisman and others, 2005). 

These trends have also been noted in more localized studies 

in parts of New England (Miller and others, 2002) and the 

State of New York (Burns and others, 2007). The measured 

step increase from about 1970 also coincides with changes in 

the timing of snowmelt peaks in New England (Hodgkins and 

others, 2003).

Highlight 1

Hydrologic Hazards and Vulnerability

Hazard.—A hazard is a deleterious effect, source of 

potential danger, or condition that can cause harm, deprivation, and stress. The Federal Emergency Management 

Agency describes hazard identification as a process of 

“defining and describing a hazard, including its physical 

characteristics, magnitude and severity, probability and 

frequency, causative factors, and locations/areas affected”

(Schwab and others, 1998).

Vulnerability.—How exposed and susceptible the 

hydrologic system is to deleterious effects from climate 

change, and how well the human and natural system can 

cope with and adapt to the deleterious effects. Important 

deleterious effects include: (1) depletion of groundwater 

availability and flow to streams in summer, (2) decrease in 

snowpack, (3) increased frequency and magnitude of flooding, and (4) increasing stream channel erosion and water 

quality degradation as a result of changes in water flow. 

Vulnerability will vary across the State and in different 

watersheds in relation to altitude and topography, geologic 

setting, land use and land cover change, and water use.
Introduction and Background 3

Deleterious effects of climate change could be exacerbated by human activities. This is particularly true in areas of 

high population, including areas within the Merrimack and 

Piscataqua River Basins in New Hampshire (fig. 1). Hydrologic models can be used to map and project climate change 

effects for these highly vulnerable areas that otherwise could 

be observed only from measurements made at discrete locations, often in more rural watersheds. The model provides a 

tool to extend trends observed at specific locations spatially 

and temporarily across a region.

Through the application of a New England-wide regional 

precipitation runoff model, this study is designed to assess 

potential changes in the hydrologic cycle that result from 

projected climate change in New Hampshire. The model is 

used to simulate the spatial and temporal distribution of air 

temperature, precipitation, snowfall and snowmelt, streamflow, and groundwater recharge across New Hampshire, with 

a focus on local watersheds in different physiographic regions 

of the State. Four watersheds, located in the Piscataqua and 

Merrimack River Basins in the White Mountain region and 

the Connecticut River Valley, have been chosen as representative pilot study watersheds. These include the Ashuelot River, 

Oyster River, Pemigewasset River, and Souhegan River watersheds (fig. 2). The study will also assess the relative effects 

of changes in human activities in combination with climate 

change on the water cycle.

Researchers are applying regional hydrologic models 

to map and assess the past and potential future trends in 

hydrologic variability as a result of climate change over large 

areas (Bjerklie and others, 2010b). Various types of hydrologic 

models have been used to couple simulated watershed 

processes with input data derived (downscaled) from GCMs 

(Hayhoe and others, 2006; Hayhoe and others, 2007). This 

project uses the USGS Precipitation Runoff Modeling System 

(PRMS; Leavesley and others, 1983; Markstrom and others, 

2008). The PRMS lends itself to these goals because it can 

be parameterized at a wide range of scales with any scheme 

for subdividing the modeled area. The PRMS simulations 

are based on the spatial variation in measurable physical 

characteristics, including land cover, topography, soil, and 

geology, that can be quantified using geographic information 

systems (GISs). The PRMS simulates surface, soil, subsurface, 

groundwater storage flux, runoff, snow cover and snowmelt, 

and a large number of other hydrologic variables. The 

PRMS model computes these water balance variables for 

small watersheds (catchments) that comprise the watershed 

of interest. The catchment is referred to as the hydrologic 

response unit (HRU).

Purpose and Scope

The purpose of this report is to document a method 

that provides information and tools that will inform users 

on the potential effects of climate change on aspects of 

the hydrologic cycle in the State of New Hampshire. The 

report identifies large-scale hydrologic vulnerabilities of 

surface-water and groundwater systems of New Hampshire 

to potential climate change and presents development of a 

climate change hydrologic model in four selected areas of the 

State that can be used as a resource tool by State planners to 

assess near and distant future climate scenarios. The report 

documents a watershed scale hydrologic model that is used to 

assess the effects of climate change on hydrologic responses 

and vulnerability. The model simulates climate related 

watershed (hydrologic) changes in four sub-watersheds of 

New Hampshire that represent different physiographic and 

hydrologic areas of the State.

The model will provide information that can be used 

by the State of New Hampshire as adaptive management 

plans are developed for water resources. The report provides 

information on potential changes in streamflow, base flow to 

streams (and groundwater supply), and snowfall that serve as 

an initial indication of water resource management issues that 

the State could face in the future.

Description of Study Area

The State of New Hampshire can be divided into three 

broad physiographic regions, the White Mountains, the Connecticut River Valley and the New England Uplands, and the 

Seaboard Lowlands (Flanagan and others, 1999). The White 

Mountain region is dominated by mountain landscapes with 

altitudes (relative to the North American Vertical Datum of 

1988 [NAVD 88]) ranging from less than 1,000 ft near the 

lakes region of central New Hampshire, to 6,300 ft at the top 

of Mount Washington. The Connecticut River Valley is a relatively narrow incised valley on either side of the Connecticut 

River, which forms the western boundary of New Hampshire. 

The valley flows through the White Mountain region and the 

New England Uplands, which are found south of the White 

Mountains in New Hampshire. The New England Uplands 

region consists of an area of undulating, hilly topography, 

ranging in altitude from below 1,000 ft to above 2,000 ft. The 

Seaboard Lowlands region is lower in altitude, below 500 ft, 

and less hilly than the New England Upland region. The four 

selected small study watersheds include the Ashuelot River, 

Oyster River, Pemigewasset River, and Souhegan River 

watersheds. The Pemigewasset River watershed is within the 

White Mountain region, the Oyster River watershed is within 

the coastal Seaboard Lowlands, and the Ashuelot River and 

Souhegan River watersheds are in the New England Uplands 

region with the Ashuelot River watershed being also within 

the Connecticut River Valley.

The climate in the study area is classified as continental 

because of prevailing westerly winds and is characterized by 

changeable weather, wide ranges in diurnal and annual temperatures, distinct seasonal trends that vary from year to year, 

and a fairly uniform distribution of precipitation throughout 

the year. Important local influences on the climate are terrain, 

altitude, and proximity to the Atlantic Ocean. The climate 
4 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

St. Lawrence

River Basin

Hudson

River 

Basin

MASSACHUSETTS

NEW

YORK

MAINE

CANADA

Merrimack

River Basin Piscataqua

River Basin

Connecticut

River Basin

Saco

River Basin Merrimack River

Lake

Winnipesaukee

Lake

Champlain

Connecticut River

0

0

50 MILES

50 KILOMETERS

72o 73o

45o

44o

43o

71o

Basin map modified from http://nh.water.usgs.gov/WaterData/station_map.htm

UNITED STATES

EXPLANATION

State boundary

County boundary

Basin boundary

Androscoggin

River Basin

NEW HAMPSHIRE

VERMONT

Franklin

Orleans

Lamoille

Caledonia

Grafton

Carroll

Chittenden

Washington

Orange Addison

Rutland

Windsor

Bennington

Windham

Belknap

Sullivan Strafford

Rockingham

Merrimack

Cheshire

Hillsborough

Coos

Essex

Figure 1. The major watersheds that encompass the State of New Hampshire.
Introduction and Background 5

Figure 2. Watersheds in New Hampshire selected for detailed analysis of hydrologic change. Definitions of abbreviations are listed 

in the “Abbreviations” section, p. ix.

Pemigewasset 

Ashuelot

Souhegan 

Oyster 

-71°

-70°

-72°

-71°

-72°

-73°

45°

44°

44°

43°

43°

0 15 30 MILES

0 25 50 KILOMETERS

EXPLANATION

nhru

Study basins

Hydrologic Response Unit (HRU)

CANADA

VERMONT

MASSACHUSETTS

NEW HAMPSHIRE

MAINE
6 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

varies between the physiographic regions and the four small 

study areas. Generally, precipitation is evenly distributed 

throughout the year across New Hampshire, yet there is variation in the average annual amount of precipitation because 

of the effects of terrain, altitude, and proximity to the ocean. 

The average amount of precipitation the study area receives 

is 40 to 50 inches per year (in/yr); however, average annual 

precipitation ranges from 42 in/yr in low-lying areas to greater 

than 60 in/yr near the summits of the White Mountains. The 

amount of frozen precipitation is dependent on altitude, 

terrain, and latitude. Snowfall amounts are generally greatest in January or February and are spatially variable ranging 

from 20 to 50 in/yr near the coast in the Seaboard Lowlands, 

from 30 to 70 in/yr in the Connecticut River Valley and the 

New England Uplands, and from 50 to 110 in/yr in the White 

Mountains. Snowmelt is a major source of water in streams 

during late winter and early spring. Temperatures vary widely 

on an annual basis. Temperature data from 1961 to 1990 

indicate that the warmest month in the study area is July and 

the coldest month is January. On the basis of monthly average 

temperatures, winter temperatures are more variable across the 

study area than summer temperatures.

Methods and Data

An initial statewide hydrologic model was developed 

for the river basins of New Hampshire (fig. 1) using 

the Precipitation Runoff Modeling System (PRMS; 

U.S. Geological Survey, 2015). The PRMS generates daily 

runoff from daily input of air temperature and precipitation. 

The New Hampshire PRMS model is subdivided into small 

watersheds (catchments) that can be accumulated into river 

basins of interest. These catchments or HRUs are the basic 

computational unit of the model. The statewide model also 

includes several hundred river reaches that constitute the 

simulated river network that routes runoff generated from the 

HRUs downstream.

The calibration of the regional model that includes 

45 sites in New Hampshire is based on period of record 

from 1981 to 2011. The model parameters for each HRU 

in the model were defined from GIS, defined as a model 

default (Steve Markstrom and Lauren Hay, U.S. Geological 

Survey, written commun., August 2013), or adjusted based on 

considerations developed by Bjerklie and others (2010a,b). 

The adjusted parameter includes the monthly Jensen-Haise 

potential evapotranspiration (PET) coefficient (Bjerklie 

and others, 2010b) which limits the amount of daily PET 

available each month. During calibration, the 45 sites in 

New Hampshire were evaluated based on a 32-year mean 

(1981–2011) of simulated daily flows compared with the 

USGS measured flows for each site. A detailed hydrologic 

evaluation was completed on four selected areas of the State 

(watersheds), which included basins representing different 

areas of the State, in areas of high density population, and 

where development is expected.

Future hydrologic conditions are simulated using daily 

simulated air temperature and precipitation from GCMs 

(Intergovernmental Panel on Climate Change, 2007). Given 

the uncertainty in climate modeling, it is desirable to use more 

than one GCM to obtain a range of potential future climatic 

conditions (Hay and others, 2011; Bjerklie and others, 2012). 

In this study, five GCMs were used to develop input data sets 

for PRMS that represent potential future climate conditions. 

Each GCM simulates a representative current conditions 

(1981–2000) dataset and two future scenarios based on 

IPCC low-emissions scenario (SRES1b) and high-emissions 

scenario (SRES2a). The timeframe for the projections includes 

two 20-year periods in the 21st century, 2046–2065 and 2081–

2100. These are referred to as midcentury and end-of-century 

periods. The model also simulates a current reference period, 

spanning the 20-year period 1981–2000. The future time 

periods are compared with the current reference time period 

so that projected change can be evaluated in a self-consistent 

hydrologic framework described by the PRMS model. The 

output data include daily estimates of base flow, total river 

discharge, and snowfall for each HRU in the model.

Precipitation Runoff Model

The PRMS (http://wwwbrr.cr.usgs.gov/projects/

SW_MoWS/PRMS.html) is a USGS public access watershed 

runoff model (Leavesley and others, 1983; Markstrom and 

others, 2008) that has been developed into several updated 

versions. The model structure available as of August 2013 was 

used for the study in this report. The data and an automated 

method developed by Viger (2014) and Viger and Bock (2014) 

are used to develop the model spatial domain and to derive 

many of the parameters used in the model.

The daily water balance is simulated for each of the 

HRUs based on precipitation and temperature input data. The 

HRUs and the 12-digit hydrologic unit code (HUC) units, 

although at approximately the same scale, are not the same. 

The HRUs in the PRMS model include a wider range of sizes 

than the HUCs and could divide some HUCs into smaller 

subwatersheds. In some cases, more than one HUC could be 

lumped together. Although PRMS does not require HRUs to 

be delineated on watershed boundaries, the HRUs in the model 

developed in this study represent subwatersheds so that each 

HRU can represent a streamflow at its outlet. The parameterization, calibration, and evaluation of the PRMS model at the 

New Hampshire scale and detailed evaluation for four basins 

in New Hampshire are described in the following sections.

Modeling Considerations and Calibration 

Objectives

This modeling study compares possible future hydrologic changes for various climate change scenarios in 

New Hampshire. The scenarios were generated by different 

GCMs and different carbon emissions projections. Hydrologic 
Methods and Data 7

effects of climate scenarios were simulated by use of the 

self-consistent regional hydrologic model (New England, 

including New Hampshire) PRMS. Hydrologic selfconsistency is critical to enable direct comparisons between 

the different scenarios in time and space so that comparisons 

are “apples to apples,” so to speak. If different models were 

used or the model was calibrated using methods that are not 

consistent between different watersheds and for different 

locations, the model response to future scenarios would not be 

directly comparable.

Model calibration input data were obtained from 

DAYMET (Thornton and others, 2012; http://daac.ornl.

gov/cgi-bin/dsviewer.pl?ds_id=1219), which is a gridded 

digital dataset available on a daily time step for historical 

periods of record for the continental United States and 

includes daily precipitation and maximum and minimum air 

temperature. The DAYMET data are interpolated from station 

data, using physically based algorithms to a grid spacing 

of 1 kilometer (km) and are consistent with DAYMET data 

across the continental United States. Interpolation can cause 

smoothing of the data such that the timing and magnitude 

of local events are inaccurate; however, long-term averages 

are accurate (Thornton and others, 1997; Stahl and others, 

2006; Oubeidillah and others, 2013; Di Vittoria and Miller, 

2014). The future climate input data derived from GCMs 

are statistically downscaled from even larger grids, and the 

issues associated with the DAYMET data are also likely to be 

inherent in future simulations.

Data and calibration consistency are key to the regional 

model so that spatial comparisons can be made without differences in calibration and input that would complicate the 

comparison. The objective functions for the PRMS calibration 

include streamflow statistics measured at USGS streamgages, 

streamflow hydrograph characteristics, and the annual and seasonal water balance. To avoid the potential for comparatively 

variable and (or) exaggerated response to future input datasets 

between watersheds, the following modeling and calibration 

objectives are adopted for this study:

• Use of self-consistent input datasets for calibration 

and for future comparisons. The input datasets are 

derived from similar sources and developed using 

similar methods. The same GCMs used to derive future 

scenario input datasets are also used to derive the current scenario input datasets so that comparisons are not 

complicated by data differences.

• Use of consistent methods to assign parameter values 

for all regions in the model.

• Parameterization of the model uses methods based on 

physical features of watersheds. Parameters that cannot 

be assigned a value from data are left as default values 

to ensure that the values are physically realistic.

• Calibration is designed to achieve consistency 

and accuracy in reproducing water balances and 

hydrologic flow components (surface, subsurface, and 

groundwater) that result in representative hydrograph 

characteristics and flow regimes within the limitations 

of the input data.

• The model performance is evaluated by comparing 

streamflow statistics as measured by streamflow at the 

USGS streamgages of interest.

Input Data

The PRMS model uses mean daily precipitation and 

maximum and minimum daily air temperature as input. The 

daily precipitation and air temperature data used for this 

model were obtained through the USGS Geo Data Portal 

(http://cida.usgs.gov/gdp/) for a 32-year calibration period 

(1980–2011) and for future climate datasets consisting of 

20-year time periods. The 32-year calibration and the 20-year 

simulation time periods (1) provide sufficient length of time 

series data to yield representative mean values and variability 

so that inferential statistics are meaningful, and (2) are of 

sufficient length to compare with regional projections of 

atmospheric climate change studies (Wake and others, 2014), 

as well as other regional climate change projects (U.S. Geological Survey, 2014).

The input time series of daily precipitation and minimum 

and maximum temperature used to calibrate the model is 

derived from DAYMET (Thornton and others, 1997; Thornton 

and others, 2012; DiVittoria and Miller, 2014), obtained 

through the USGS Geo Data Portal. The DAYMET data 

provide a consistent input dataset; however, smoothing of 

station data over the grid could result in damping of extreme 

and more intense precipitation events.

The DAYMET input data used for model calibration 

does not match the high precipitation events well in the 

higher altitudes in the Pemigewasset River watershed as 

evidenced by comparing the DAYMET input with station 

data for Pinkham Notch, the Benton 5 SW (station name) 

National Weather Service Cooperative Observer Program 

network station, and the U.S. Forest Service precipitation gage 

network station at the Hubbard Brook Experimental Forest 

(Campbell and Bailey, 2014). The comparison data (table 1) 

show that the mean precipitation and the number of daily 

precipitation events greater than 0.5 inch estimated from the 

DAYMET gridded data for four high altitude PRMS HRUs 

in the Pemigewasset River watershed (HRU numbers 1065, 

1091, 1093, and 2337) are generally within 0 to 15 percent of 

the values from the data from the weather station for similar 

altitude, latitude, and longitude. However, the data from the 

weather stations show marked increases in the number of 

events greater than 1 inch (30–40 percent more days) and 

greater than 2 inches (60–80 percent more days). This is also 

expressed by a larger standard deviation of the daily data for 

the station data compared with that of the DAYMET data. 

Because of the differences between measured data from the 

weather stations and the gridded DAYMET data used as input 
8 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

to PRMS, the PRMS will underpredict the magnitude, but 

not necessarily the frequency, of the largest runoff events in 

the Pemigewasset River watershed because the high-altitude 

precipitation is underrepresented in the model input. This 

same issue is expected to be present in the other watersheds 

considered in this study (Ashuelot, Oyster, and Souhegan 

Rivers) but to a lesser degree because the altitude ranges are 

smaller in these other watersheds.

U.S. Geological Survey streamflow records are used for 

calibration, along with independent estimates of evapotranspiration (movement of water not related to streamflow) where 

available. The PRMS model does not account for storage 

in lakes and reservoirs and the effects of dams and water 

diversions; therefore, streamgages used for calibration were 

selected so that influences of flow regulation by dams and 

diversions and storage effects from lakes were not important 

in streamflow characteristics. There were 45 streamgages in 

New Hampshire that were selected and used for calibration.

Future climate datasets were also obtained through the 

Geo Data Portal and are derived from downscaled GCM 

simulation data that are stored there. The Geo Data Portal 

hosts 13 different climate-change model datasets for three 

different climate change scenarios. For this study, five GCM 

datasets were chosen to represent the possible range of future 

outcomes. The GCMs that were chosen are listed in table 2 

with their country of origin and the years of data used in this 

study. The future climate input data derived from GCMs are 

downscaled from large model grids, and the issues associated with the DAYMET data could be even greater in these 

datasets. Detailed information on the GCMs are available in 

Nakićenović and others (2000).

All the GCM projections show substantial temperature 

and precipitation increases during this century for watersheds 

in New Hampshire. Compatible datasets for the current (1981–

2000) timeframe and the future (2081–2100) timeframe were 

derived for all GCMs, and additional midcentury (2046–2065) 

datasets derived for two of the GCMs (Centre National de 

Recherches Meteorologiques [CNRM] and Geophysical Fluid 

Dynamics Laboratory [GFDL]). These timeframes represent 

average climate conditions projected to occur for each 20-year 

climate period. Two IPCC special report emission scenarios 

(SRES) were chosen for each of the two time periods (four 

datasets)—one is the most optimistic with regards to future 

greenhouse gas emissions (SRESb1) and the other is less 

optimistic (SRESa2). The smallest projected changes in 

precipitation and temperature are associated with the SRESb1, 

and the largest projected changes and the largest uncertainties 

are associated with SRESa2. In general, the largest variability 

and uncertainty in the GCM projections are with precipitation. The SRESa2 scenario assumes a heterogeneous world 

with slow adaptive change to new energy technologies and 

increasing population (Intergovernmental Panel on Climate 

Change, 2007). The SRESb1 assumes a convergent world (one 

in which economies become more similar; Intergovernmental 

Panel on Climate Change, 2007) with a global population that 

peaks in midcentury and then declines with rapid changes 

in economic structures toward a service and information 

economy, with reductions in material intensity, and with the 

introduction of clean and resource-efficient technologies.

The three periods of downscaled data (1981–2000, 

2046–2065, and 2081–2100, representing the current 

condition, midcentury, and the end-of-century, respectively) 

were used to calculate the difference between the simulated 

Table 1. Comparison of DAYMET and station precipitation data for four watersheds in New Hampshire from 1980 to 2008.

[Percent difference is the difference between the measurement at the station and the DAYMET average for the hydrologic response unit (HRU). PRMS, 

precipitation runoff modeling system; stdev, standard deviation]

HRU PRMS 

identifier 

number

Elevation, 

meters

Percent difference

Mean, 

inches

Stdev, 

inches

Number of days 

greater than 

2 inches

Number of days 

greater than 

1 inch

Number of days 

greater than 

0.5 inches

HUBBARD

1065 394 0.08 0.20 0.66 0.36 0.14

1091 578 0.08 0.23 0.73 0.41 0.14

1093 717 0.00 0.15 0.63 0.27 0.01

2337 708 0.06 0.21 0.81 0.42 0.12

Mean 0.05 0.20 0.71 0.37 0.10

NOAA

1065 394 -0.20 -0.03 -0.36 -0.02 -0.17

1091 578 0.13 0.33 0.82 0.48 0.14

Mean -0.03 0.15 0.23 0.23 -0.01
Methods and Data 9

current and future conditions under each of the two carbon 

emissions scenarios. The two midcentury future datasets 

(2046–2065) were obtained for the CNRM and GFDL GCMs 

for comparison with the end-of-century data. Figures 3A–H

(in back of report) shows the distribution of change, by HRU, 

of mean maximum daily air temperature and precipitation 

between the current time period (1981–2000) and the end-ofcentury future time period (2081–2100) for the CNRM and the 

European Centre Hamburg Model version 5 (ECHAM5) GCM 

input data. The CNRM and ECHAM5 models were chosen as 

representative of the range of projected changes from the five 

GCM chosen for comparison. The GFDL and CNRM input 

data were used as indicators of the midcentury, because these 

data are considered representative of the group as a whole. 

The ECHAM5 and CNRM input data are used as indicators 

of the expected upper and lower range for the end-of-century 

results. The CNRM and the GFDL model input data are fairly 

representative of the mean values for all five of the GCMs, 

with GFDL considered as more or less in the middle of the 

group. The ECHAM5 input data generally were the most 

different relative to the others. For comparative purposes in 

this report and to show a range of results, simulations that use 

the GFDL and CNRM models are focused on the midcentury, 

and simulations that use the CNRM and ECHAM5 are focused 

on the end-of-century results, although simulations using input 

from all five models are summarized in tables for the end-ofcentury results.

Figures 3A–H (in back of report) shows the general 

pattern of change that is expected, based on the input data 

from the two GCMs. Both GCMs show that change in mean 

maximum daily air temperature (figs. 3A–D, in back of report) 

for the SRESa2 scenario (high-emissions scenario) will 

increase by about 7 to 10 degrees Fahrenheit (°F) between 

the current time period (1981–2000) and the end-of-century 

(2081–2100). However, the two GCMs show different patterns 

for the distribution of change in New England. The CNRM 

model shows the largest change in southern New England, 

including much of New Hampshire, whereas the ECHMA5 

GCM shows the largest change in northern New England. In 

New Hampshire, however, both GCMs predict that maximum 

daily air temperature will increase about 8 to 9 °F. For the 

low-emissions scenario (SRESb1), the patterns of change for 

each of the GCMs shown are similar to the high-emissions 

scenario (SRESa2), however, the range of air temperature 

change is less. The CNRM model has a range of 3 to 5 °F, and 

the ECHAM5 GCM has a range of 5 to 7 °F.

The change maps of mean annual precipitation 

(figs. 3E–H, in back of report) show more difference 

(compared with changes in mean maximum daily air 

temperature) between the CNRM and ECHAM5 models, with 

the CNRM model showing an increase of precipitation across 

New England of 4 to 11 inches assuming the high-emissions 

(SRESa2) scenario and 2 to 9 inches assuming the lowemissions (SRESb1) scenario. The ECHAM5 model shows 

an increase of about 1 to 8 inches for both scenarios, with a 

more or less reverse spatial pattern compared with the CNRM 

model; the largest increases in precipitation were in northern 

New England, and the smallest increases were in southern 

New England. Both GCMs predict the full range of change 

in precipitation across New Hampshire, which indicates that 

the amount of change is dependent on location. Clearly, the 

choice of GCM and scenario used as input to the hydrologic 

is important to the hydrologic model predictions. The range of 

predictions for the five GCMs and two scenarios are presented 

as well as the averages for all of the models and scenarios.

Table 2. General circulation models used in the study of effects of climate change in four watersheds in 

New Hampshire.

[CNRM, Centre National de Recherches Meteorologiques; CSIRO, Commonwealth Scientific and Industrial Research Organisation; 

GFDL, Geophysical Fluid Dynamics Laboratory; ECHAM5, European Centre Hamburg Model version 5; CGCM, [Japan Meteorological 

Agency] coupled ocean-atmosphere general circulation model]

Model identifier Country of origin Years of data

Cnrm _cm3_01, 02, 03 (CNRM) France 1981–2000 (current)

2046–2065 (mid-century)

2081–2100 (end-of-century)

Csiro_mk3_0_01, 02, 03 (CSIRO) Australia 1981–2000 (current)

2081–2100 (end-of-century)

Gfdl_cm2_01, 02, 03 (GFDL) US National Oceanographic and 

Atmospheric Administration (NOAA)

1981–2000 (current)

2046–2065 (mid-century)

2081–2100 (end-of-century)

Mpi_echam5_01, 02, 03 (ECHAM5) Germany 1981–2000 (current)

2081–2100 (end-of-century)

Mri_cgcm2_3_01, 02, 03 (CGCM) Japan 1981–2000 (current)

2081–2100 (end-of-century)
10 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Parameterization, Calibration, and Evaluation of 

New Hampshire-Scale Model

The New England regional PRMS model was initially 

parameterized using general GIS-based strategies (Steve 

Markstrom and Lauren Hay, U.S. Geological Survey, written 

commun., August 2013) and provided the starting point for 

the parameterization of the New Hampshire model. The New 

England model has not been published but was built initially 

parameterized and provided to this study by the U.S. Geological Modeling of Watershed Systems group which is part of the 

U.S. Geological Survey National Research Program (written 

comm., Lauren Hay and Steve Markstrom, U.S. Geological Survey, August 2013). The initial model parameters were 

based on physical attributes of the HRUs that comprise the 

model. The New England model includes 2,462 HRUs that 

are delineated at the approximate scale of the 12-digit-HUC 

subwatershed distribution (Seaber and others, 1994). Bjerklie 

and others (2010a) derived several other parameters pertaining 

to groundwater fluxes based on surficial geologic characteristics for several watersheds in Connecticut. These methods 

were applied for watersheds in New Hampshire for the model 

developed in this study and the model calibration was evaluated at the New Hampshire scale. The PRMS model for New 

Hampshire consists of 467 HRUs. The PRMS model used 

for this study was parameterized at the New Hampshire scale 

using methods that compute parameter values from GIS data 

sources or set default values within a range defined by physical limits (Markstrom and others, 2008). The GIS characteristics of each HRU are used to derive initial model parameters. 

Most of these parameters are not subject to calibration and 

represent physical properties that determine the parameter 

value. These GIS characteristics are obtained in October 2008 

from national datasets that include the National Elevation 

Dataset (http://ned.usgs.gov/) for coordinates of the HRU 

centroid and topography, the Soil Survey Geographic Database 

(https://gdg.sc.egov.usda.gov/)for soils, the National Land 

Cover Database (http://www.mrlc.gov/nlcd2011.php) for 

land cover (Vogelmann and others, 2001) and the National 

Hydrography Dataset (http://nhd.usgs.gov/) for hydrography.

The model was calibrated to observed USGS streamgage 

records from 1980 to 2011. A previously developed PRMS 

model that included western New Hampshire (Bjerklie and 

others, 2010b) was used as the baseline calibration for this 

study. To achieve the best fit water balance for each HRU, the 

PET simulated by the model was optimized by comparing the 

long-term simulated streamflow against the long-term measured streamflow at the 45 USGS streamgages. This approach 

derives coefficients directly from the temperature records and 

basin altitude (Markstrom and others, 2008). The primary 

objective of the large area (statewide) calibration was to provide a reasonable representation of the annual water balance.

The PRMS model simulates unregulated natural streamflow and does not simulate the effects of dams, lake and reservoir storage, and groundwater and surface-water diversions. A 

method to adjust the monthly evapotranspiration coefficient in 

the model developed by Bjerklie and others (2010b) was used 

to calibrate the model-simulated evapotranspiration and general water balance at 45 New Hampshire basins. Other parameters that influence snow, snowmelt, and interception were 

adjusted to be comparable to Bjerklie and others (2010b) or 

were kept at their GIS derived values. The model default values for the groundwater base flow coefficient were adjusted by 

a method that links the surficial geologic characteristics of the 

model HRUs to the coefficient value that was used to adjust 

the groundwater routing (Bjerklie and others, 2010b). The 

default values for the subsurface routing coefficient, which 

represents the interflow component of groundwater runoff, 

were not adjusted in the model because there is insufficient 

regional information to assign values based on the GIS data. 

The value of the subsurface runoff coefficient affects groundwater recharge and the shape of the hydrograph recession.

The routing coefficients used in the model are linear 

functions, and the model would be expected to respond 

similarly to different input data of similar range. The current 

and future input datasets used for the simulations generally 

show a shift in the mean, maximum, and minimum that is in 

the same direction without a large change in the range of the 

data. As a consequence, the changes in model simulations will 

remain comparable between current and future timeframes as 

long as the input data sources and parameterization methods 

are self-consistent. Simulation errors (difference between the 

USGS streamflow data and the simulated streamflow) for the 

calibrated model, therefore, will be consistently carried over 

to future timeframes, and the magnitude and frequency of 

changes between simulated current and future conditions will 

be a product of the differences in the input datasets and the 

physical constraints of the modeled hydrologic system and 

will not be arbitrary.

New Hampshire exhibits a strong temperature gradient with latitude, and the PET coefficients were adjusted to 

account for this by uniquely fitting each coefficient to each 

HRU. The coefficient was adjusted up or down by a factor 

constant for all HRUs to match the long-term water balance 

so that precipitation minus actual evapotranspiration (AET) 

equals the mean annual streamflow (total runoff) for the 

calibration period (1980–2011) at each of the 45 streamgages. 

It is assumed that matching the long-term mean does not 

seriously bias the daily and seasonal water balance estimates. 

The effects of the long-term calibration on the seasonal and 

daily time scales are assessed for the four small-area (subset) 

models in the following section.

The New Hampshire model showed very good simulation of the mean streamflow at all of the streamgages after 

statewide calibration (fig. 4). The mean error for the simulated 

daily streamflows for all 45 streamgages is 2.7 percent, which 

indicates that the regional water balance, on average, is well 

simulated. The mean error in the standard deviation of the 

simulated daily streamflows was 2.2 percent, which indicates 

that the variation in mean streamflows around the State was 

also well simulated.
Methods and Data 11

Selection and Evaluation of Small-Area Models

Four small-area models were developed for more detailed 

analysis of the local effects of climate change on hydrology, (fig. 2) and to evaluate the performance of the calibrated 

New Hampshire model in more detail. The four watersheds 

(referred to as pilot study watersheds) chosen for detailed 

analysis, with HRUs at the same scale as the New Hampshire 

model, include the Ashuelot River watershed in southwestern 

New Hampshire in the Connecticut River Basin the Oyster 

River watershed in southeastern coastal New Hampshire in 

the Piscataqua River Basin, the Souhegan River watershed 

in south-central New Hampshire in the Merrimack River 

Basin, and the Pemigewasset River watershed in north-central 

New Hampshire in the southern White Mountain region. The 

Ashuelot River watershed is representative of a populated 

area of the State, but with less development than southern and 

coastal areas, and is also representative of the Connecticut 

Valley region. The population in the Oyster River and Souhegan River watersheds is rapidly increasing, and expansion of 

developed areas is likely to continue. The Pemigewasset River 

watershed is representative of an area of the State not expected 

to experience substantial increases in development. This 

watershed reflects the hydrology of high altitudes and mountainous terrain in the State.

The four pilot watersheds provide an initial look at the 

performance of the New Hampshire model in greater detail 

in four areas considered to be representative of physiographic 

environments in the State, including two areas (southeastern 

coastal and south-central) expected to grow in population 

during the coming decades. The evaluation results for the pilot 

watersheds are listed in tables 3 and 4 and shown on figure 5. 

The results are evaluated with the fractional percent error difference between the simulated and the measured streamflow 

(simulated minus measured divided by the measured) during 

the 31-year calibration period, the fractional percent difference 

between the standard deviation of the simulated and measured streamflow, the Nash-Sutcliffe efficiency (NSE) statistic 

(Nash and Sutcliffe, 1970), and the normalized root mean 

square error (NRMSE) between the simulated and measured 

streamflow (Goode and others, 2010). The percent difference 

provides information on how well the model simulates the 

mean and the standard deviation of measured data but does not 

account for the timing of the streamflow; differences can be 

overemphasized at the low end of the flow range and individual large errors can have a large affect even if the majority 

of the differences have small errors.

The NSE statistic and the NRMSE are measures of how 

well a simulated dynamic time series (streamflow hydrograph) 

fits the measured time series during the period of record. 

The NSE and the NRMSE statistics indicate how well the 

simulated hydrograph fits the dynamics of the measured 

hydrograph relative to the mean of the measured time series. 

A value of 1 for the NSE indicates that the simulated and 

measured time series are identical, and a value of 0 indicates 

that the simulated time series provides as much predictive 

information as the mean of the measured time series alone. 

Negative values for NSE indicate that the model simulation 

provides less predictive information than the mean value. 

The NRMSE provides a similar measure as the NSE with 

somewhat less emphasis on the highest streamflows, however, 

it is more difficult to interpret. An NRMSE value of 0 

indicates a perfect fit between the simulated and observed time 

series, and a value of 1 indicates that the model simulation 

has the same predictive power as the mean (reverse of the 

interpretation of the NSE).

The statistics for the daily values during entire period are 

listed in table 3, and the statistics for the monthly aggregated 

values are listed in table 4. The comparison of the monthly 

simulated hydrographs and the measured hydrographs are 

(Simulated streamflow) = 1.02X (USGS streamflow)

R2 = 0.99, N=45

1

10

100

1,000

10,000

1 10 100 1,000 10,000

Simulated mean annual streamflow 1980–2011,

in cubic feet per second

USGS mean annual streamflow 1980–2011, in cubic feet per second

Figure 4. Simulated compared to mean 

annual streamflow at 45 calibration 

streamgages in New Hampshire.
12 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

shown on figure 5. The results show that the mean simulated 

daily streamflow for the 31-year calibration period matches 

the USGS measured streamflows within -12 to 17 percent, 

with an average difference for all four basins of 6 percent. 

The difference in simulated standard deviations was less, 

from -15 to 0 percent for three rivers, but was -42 percent 

for the Pemigewasset River. The large difference for the 

Pemigewasset was a result of the poor representation of 

the highest precipitation events at the high altitudes in the 

input data and the resulting underprediction of the highest 

streamflow events. The large mean error (56 percent) for the 

Oyster River (table 3), could be associated with the large 

relative effect of groundwater pumping during low streamflow 

periods. Water use effects are not simulated in PRMS, and 

low streamflows are often not well simulated because of the 

simple linear reservoir representation of base flow that is 

used by PRMS. The mean percent differences and range of 

differences in this study are similar to the differences found in 

other calibrated PRMS models developed in complex terrain 

where the data were used to assess hydrologic change and to 

represent hydrologic conditions in unmeasured watersheds 

when comparative data were not available (Koczot, 2005; 

Chase, 2011).

The aggregated monthly differences (figs. 5 and 6; 

table 4) indicate that the largest percent errors occur during 

different months for each river; the largest overall error was in 

the Oyster River even though the absolute error (the difference between the measured and simulated discharge) in the 

Oyster River was less than 10 cubic feet per second (ft3

/s) for 

any given month. The range of percent errors are within those 

documented for monthly mean streamflow in other USGS 

PRMS studies designed to simulate streamflow in ungaged 

rivers as part of water resource planning efforts (Chase, 2011). 

Similar to the percent errors for the mean daily simulation 

during the calibration period, the errors associated with the 

standard deviation are less than those associated with only 

looking at the mean value. This indicates that the model is 

simulating the variability of monthly streamflow with greater 

accuracy than the mean. Inspection of the monthly and mean 

monthly hydrographs (figs. 5 and 6) demonstrates that the 

seasonal characteristics of the model simulation capture the 

seasonal water balance and unique hydrograph characteristics 

of each river reasonably well.

With the exception of the Pemigewasset River, the NSE 

values for the daily simulations are above typical values (0.5 

or better) for well calibrated hydrologic models (Moriasi and 

others, 2007). The NSE for the Pemigewasset, although not 

as high as the other rivers, is acceptable considering that the 

input data does not represent the high precipitation events 

well in this basin. The NSE values for monthly streamflow 

are better than the daily streamflow, which indicates that the 

aggregation of the daily streamflow to monthly streamflow 

improves the predictive power of the simulation and is well 

above the acceptable level, even in the Pemigewasset River. 

The range of NSE values are similar to those from other 

regional and local PRMS studies (Viger and others, 2010; 

Dudley and Nielsen, 2011; LaFontaine and others, 2013). The 

NRMSE values track similarly to the NSE values, showing a 

range for the daily simulations similar to other regional studies 

(Goode and others, 2010) with marked improvement for the 

monthly streamflow time series during the daily streamflow 

time series.

Table 3. Evaluation of daily statistics for four watersheds in New Hampshire.

[USGS, U.S. Geological Survey; No., number; ft3/s, cubic feet per second; stdev, standard deviation; PRMS, precipitation runoff modeling system]

USGS streamflow gaging station Data source Mean 

(ft3

/s)

Standard 

deviation 

(ft3

/s)

Percent 

difference 

in mean

Percent 

difference 

in stdev

Normalized 

root mean 

square error

NashSutcliffe 

coefficient

Ashuelot River at Hinsdale, N.H. 1161000 USGS Measured 772.0 831.5

Simulated 868.4 831.2 0.12 -0.00 0.61 0.63

Oyster River near Durham, N.H. 1073000 USGS Measured 21.6 37.1

Simulated 25.3 37.9 0.17 0.02 0.56 0.69

Pemigewasset River at Plymouth, N.H. 1076500 USGS Measured 1,464.9 2,027.6

Simulated 1,289.1 1,172.8 -0.12 -0.42 0.76 0.40

Souhegan River at Merrimack, N.H. 1094000 USGS Measured 373.2 471.7

Simulated 405.9 399.5 0.09 -0.15 0.58 0.67

Overall mean 0.06 -0.14 0.63 0.60
Methods and Data 13

Table 4. Evaluation of monthly statistics for four watersheds in New Hampshire.[USGS, U.S. Geological Survey; NSE, Nash-Sutcliffe efficiency coeeficient (dimesionless); NRMSE, normalized root mean square error; stdev, standard deviation]

USGS streamflow 

gaging station Difference Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Ashuelot RiverNSE = 0.82NRMSE = 0.42

Mean percent 0.27 0.29 0.07 0.01 0.16 0.05 0.02 -0.01 0.12 0.12 0.19 0.25Stdev percent -0.18 0.05 -0.19 -0.13 0.01 -0.12 -0.36 -0.14 0.23 0.18 -0.05 -0.14

Oyster RiverNSE = 0.81NRMSE = 0.43

Mean percent 0.55 0.35 -0.12 -0.09 0.10 0.13 0.70 0.61 0.78 0.43 0.34 0.21Stdev percent 0.15 -0.15 -0.18 -0.30 -0.06 -0.29 -0.11 0.11 0.58 0.35 0.11 -0.11

Pemigewasset RiverNSE = 0.79NRMSE = 0.46

Mean percent 0.11 0.07 -0.13 -0.22 -0.04 -0.09 -0.34 -0.46 -0.42 -0.17 -0.01 0.11Stdev percent -0.35 -0.18 -0.10 -0.35 -0.05 0.00 -0.20 -0.36 -0.15 -0.07 -0.01 -0.24

Souhegan RiverNSE = 0.82NRMSE = 0.42

Mean percent 0.22 0.20 -0.14 0.07 0.09 -0.01 0.13 0.31 0.20 0.37 0.18 0.04Stdev percent -0.15 0.04 -0.42 -0.23 -0.33 -0.44 -0.46 0.03 0.04 0.23 -0.32 -0.37
14 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

-500

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

May-79 Nov.-84 May-90 Oct.-95 Apr.-01 Oct.-06 Apr.-12

Ashuelot River–USGS, measured

Ashuelot River–PRMS, simulated

-20

0

20

40

60

80

100

120

140

160

May-79 Nov.-84 May-90 Oct.-95 Apr.-01 Oct.-06 Apr.-12

Oyster River–USGS, measured

Oyster River–PRMS, simulated

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

May-79 Nov.-84 May-90 Oct.-95 Apr.-01 Oct.-06 Apr.-12

Pemigewasset River–USGS, measured

Pemigewasset River–PRMS, simulated 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

Apr.-01 Sept.-02 Jan.-04 May-05 Oct.-06 Feb.-08 July-09 Nov.-10 Apr.-12 Aug.-13

Souhegan River–USGS, measured

Souhegan River–PRMS, simulated

Mean monthly streamflow, in cubic feet per second

Month-year

EXPLANATION

Figure 5. Monthly hydrographs for the calibration period from 1980 to 2011 for four pilot watersheds in New Hampshire. 

Note that the calibration period for the Souhegan River is from 2002 to 2011. Definitions of abbreviations are listed in the 

“Abbreviations” section, p. ix.
Methods and Data 15

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

USGS Ashuelot

PRMS Ashuelot

0

10

20

30

40

50

60

January

February

March

April

May

June

July

August

September

October

November

December

USGS Oyster

PRMS Oyster

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

USGS Pemigewasset

PRMS Pemigewasset

0

100

200

300

400

500

600

700

800

900

1,000

USGS Souhegan

PRMS Souhegan

Mean monthly streamflow, in cubic feet per second

Month

EXPLANATION 

Figure 6. USGS-measured and PRMS-simulated mean monthly hydrographs for four pilot 

watersheds from 1981 to 2011. Note that the calibration period for the Souhegan River is 

from 2002 to 2011. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.
16 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

The hydrologic simulations are generally adequate for 

analysis of potential future change from climate change, 

despite known issues with the input data in the Pemigewasset 

River watershed and groundwater withdrawals in the Oyster 

River watershed that are not accounted for in the model. This 

is evidenced by examining the hydrograph characteristics 

shown on figure 7. By inspection, the rate of rise for the rising 

limbs on the hydrographs and the rate of fall for the recessions 

on the hydrographs are similar for the measured and simulated 

hydrographs for all four basins, even if the magnitudes differ. 

This indicates that the routing processes are adequately 

simulated in the model. This is also evidenced by the NSE 

coefficients for the daily streamflows, which account for the 

flow timing and the magnitude. The comparisons between 

the simulated and measured streamflows for the calibration 

period support the usefulness of the models in simulating the 

hydrologic processes and the routing that occurs in the study 

watersheds even where the absolute magnitude could differ 

significantly. The future simulations using the PRMS model 

are expected to provide hydrologic responses to the future 

climate input datasets with similar behavior and accuracy for 

the daily streamflow and the seasonal variations. The focus 

of this study is to look at differences between current and 

future conditions, and as such, the future model results will 

only be compared with the current conditions using the same 

model parameters for each river. Therefore, the current and 

future comparisons will be evaluating change based on the 

same watershed characterization, and these comparisons are 

considered sufficiently valid as long as the water balance is 

represented well.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Streamflow, in cubic feet per second

USGS–Ashuelot

PRMS–Ashuelot

0

20

40

60

80

100

120

140

160

180

Streamflow, in cubic feet per second

USGS–Oyster

PRMS–Oyster

0

500

1,000

1,500

2,000

2,500

Streamflow, in cubic feet per second

USGS–Souhegan

PRMS–Souhegan

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Streamflow, in cubic feet per second

USGS–Pemigewasset

PRMS–Pemigewasset

11/05/2001

5/24/2002

12/10/2002

6/28/2003

1/14/2004

8/01/2004

11/05/2001

5/24/2002

12/10/2002

6/28/2003

1/14/2004

8/01/2004

11/05/2001

5/24/2002

12/10/2002

6/28/2003

1/14/2004

8/01/2004

11/05/2001

5/24/2002

12/10/2002

6/28/2003

1/14/2004

8/01/2004

11/05/2001

5/24/2002

12/10/2002

6/28/2003

1/14/2004

8/01/2004

11/05/2001

5/24/2002

12/10/2002

6/28/2003

1/14/2004

8/01/2004

EXPLANATION

EXPLANATION

EXPLANATION

EXPLANATION

Figure 7. USGS-measured and PRMS-simulated streamflow spanning 1.5 years that show groundwater recession and rising limb 

characteristics for four pilot study rivers in New Hampshire. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.
Modeling of Future Streamflows 17

Modeling of Future Streamflows

The parameterized and calibrated New Hampshire 

PRMS model was used to generate simulated daily estimates 

of streamflow for a 20-year period representing current 

hydrologic conditions (1981–2000) and future 20-year 

periods. The five GCMs chosen for this study each generated 

a 20-year end-of-century (2081–2100) time series of daily 

streamflow, snowfall, and base flow for each of two climate 

change scenarios (SRESa2 and SRESb1) and for the current 

conditions. Two of the models also generated midcentury time 

series (2046–2065) for comparison.

The comparative statistics for the results of the PRMS 

future simulations are provided in tables 5 through 8. The 

tables show the percent change in the mean and standard 

deviations for the simulated daily time series and the change 

in number of days greater than or less than the indicated 

streamflow exceedance threshold. In the tables, the number 

of days greater than 10 percent equals the number of daily 

streamflows during the 20-year record above the streamflow 

that exceeded 10 percent of the streamflows. The number 

of days less than 90 percent equals the number of days with 

streamflow less than the streamflow that exceeded 90 percent 

of all streamflows.

All the GCMs except the ECHAM5 model showed 

consistent increases in mean daily streamflow for each 

scenario and each river. In general, considering both emission 

scenarios, the CNRM and CGCM model input data estimated 

the largest increases in mean daily streamflow, and the 

ECHAM5 showed decreasing or very small increases in 

mean daily streamflow. All the rivers for each GCM showed 

increasing standard deviation of streamflow except the 

ECHAM5 SRESb1 scenario for the Oyster River. With few 

exceptions, all the GCMs for each of the four rivers showed 

an increasing number of high daily streamflows, exceeding the 

10- and 1-percent high-streamflow value, and low streamflows 

below the 90- and 99-percent low-streamflow value. The 

increases in variability were similar in all the watersheds, with 

the greatest in the southern and coastal watersheds (Oyster 

River and Souhegan River). Use of input data from the 

CNRM model shows that changes in mean daily streamflow 

for the midcentury simulations are less than the changes for 

the end-of-century simulations, which indicates a graduated 

change from the present until 2100. This is also true using 

the GFDL input data for the SRESb1 scenario, however, by 

using the SRESa2 scenario GFDL input for all four rivers, the 

end-of-century mean streamflows are lower than midcentury 

streamflows. This indicates that using the GFDL model input 

for this scenario causes increases in evapotranspiration to 

overtake increases in precipitation, which alters the water 

balance and reduces streamflow.

The changes in streamflow also have a pronounced 

seasonal trend. The mean monthly change in streamflow for 

the simulations made with CNRM, GFDL, and ECHAM5 

GCM scenarios are shown in figures 8 through 11. Most of 

the rivers show the largest increases in streamflow during 

the winter (December through February) and spring seasons 

(March through May) and the largest decreases in the summer 

(June through August), although the summer decreases are not 

large (less than 20 percent) for most of the GCM scenarios. 

A notable exception is that in the Ashuelot and Pemigewasset 

Rivers, using the ECHAM5 input data, the highest monthly 

streamflows occur earlier in the winter and spring with a 

smaller spring snowmelt volume. The climate change effects 

on streamflow are most pronounced relative to current conditions for the winter and spring months.

The change in the largest streamflows during the 20-year 

simulation period for all four rivers are compared by looking 

at the change in the number of days that the 0.1-percent 

exceedance streamflow occurs between current simulation 

periods and the future simulation periods. The 0.1-percent 

exceedance translates to the seven largest streamflows for the 

current (1981–2000) 20-year simulation period. Additionally, 

the average of the seven largest daily streamflow for the 

20-year periods of future simulation were also compared with 

the current period simulation. These statistics are shown in 

table 9, and the results varied by river, GCM, and scenario. 

Generally, the largest streamflows increased in frequency 

for all of the rivers. However, the magnitudes of the largest 

streamflows (average of the seven largest daily streamflows 

during the 20-year period) did not generally increase for all 

rivers, GCMs, scenarios, and time frames with the exception 

of the Pemigewasset River which increased in all of the 

simulations. This suggests that, in much of New Hampshire 

with the exception of the White Mountain region where 

the Pemigewasset River watershed is located, flooding will 

increase in frequency but not necessarily in magnitude. The 

magnitude and frequency of peak streamflows increased by 

a relatively large amount in the Pemigewasset River and to a 

lesser, but still large, degree in the Ashuelot River compared 

with the Oyster and Souhegan Rivers.

The projected change in streamflows can be difficult 

to assess by looking at the hydrographs (figs. 7 through 11) 

and summary tables (tables 5 through 9). Plotting the daily 

streamflow data as a streamflow duration organizes the 

information so that statistical trends can be readily seen. The 

streamflow duration curves for the Oyster and Pemigewasset 

Rivers, simulated from the CNRM and GFDL GCMs, are 

shown on figures 12A–D (in back of report), with simulated 

streamflows for the 20-year current and future time periods 

ranked highest to lowest and plotted as a probability of 

exceedance. The lowest streamflow in the simulation record 

is the streamflow that is exceeded 100 percent of the time. 

Thus, the lowest streamflows have the highest probability 

of exceedance, and the highest streamflows have the lowest 

probability of exceedance, with the highest streamflow in the 

simulation record having a zero-percent chance of exceedance. 

The largest changes in predicted streamflow are observed at 

the extremes—the highest and lowest probabilities.
18 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Table 5. Summary statistics for climate change simulations for the Oyster River watershed in New Hampshire.

[GCM, general circulation model; SRESa2, Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES; Nakićenović 

and others, 2000) scenario A2; SRESb1, IPCC SRES scenario B1; CNRM, Centre National de Recherches Meteorologiques model; GFDL, Geophysical Fluid 

Dynamics Laboratory model; CSIRO, Commonwealth Scientific and Industrial Research Organisation model; ECHAM5, European Centre Hamburg Model 

version 5; CGCM, [Japan Meteorological Agency] coupled ocean-atmosphere general circulation model; stdev, standard deviation]

GCM Mean_diff1 Stdev_diff2

∆Days 

greater than 

10 percent3

∆Days 

less than 

90 percent4

∆Days 

greater than 

1 percent5

∆Days 

less than 

99 percent6

Scenario SRESa2 Future simulation period 2081–2100

CNRM 0.18 0.37 194 68 69 95

GFDL 0.12 0.45 88 90 65 43

CSIRO 0.11 0.71 49 152 52 19

ECHAM5 0.02 0.09 47 461 19 168

CGCM 0.22 0.47 191 -182 54 -59

Mean 0.13 0.42 113.80 117.80 51.80 53.20

Stdev 0.08 0.22 73.69 230.27 19.69 84.84

Scenario SRESa2 Future simulation period 2046–2065

CNRM 0.12 0.22 121 -12 61 36

GFDL 0.14 0.51 146 -29 64 58

Scenario SRESb1 Future simulation period 2081–2100

CNRM 0.12 0.11 129 -51 26 49

GFDL 0.17 0.48 200 -114 63 9

CSIRO 0.07 0.39 11 352 44 128

ECHAM5 -0.03 -0.01 0 442 10 84

CGCM 0.03 0.20 3 178 29 10

Mean 0.07 0.23 68.60 161.40 34.40 56.00

Stdev 0.08 0.20 91.16 243.05 20.03 50.85

Scenario SRESb1 Future simulation period 2046–2065

CNRM 0.08 0.04 137 72 19 65

GFDL 0.06 0.42 35 124 55 106

1

Mean_diff is the difference between the mean of daily streamflows for the current period (1981–2000) and the future simulation period, in percent.

2

Stdev_diff is the difference between the standard deviation of daily streamflows for the current period (1981–2000) and the future simulation period, 

in percent.

3

∆Days greater than 10 percent is the number of days with streamflow greater than the streamflow exceeded 10 percent of the time (10th percentile).

4

∆Days greater than 90 percent is the number of days with streamflow less than the streamflow exceeded 90 percent of the time (90th percentile).

5

∆Days greater than 1 percent is the number of days with streamflow greater than the streamflow exceeded 1 percent of the time (1st percentile).

6

∆Days less than 99 percent is the number of days with streamflow less than the streamflow exceeded 99 percent of the time (99th percentile).
Modeling of Future Streamflows 19

Table 6. Summary statistics for climate change simulations for the Pemigewasset River watershed in New Hampshire.

[GCM, general circulation model; SRESa2, Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES; 

Nakićenović and others, 2000) scenario A2; SRESb1, IPCC SRES scenario B1; CNRM, Centre National de Recherches Meteorologiques model; 

GFDL, Geophysical Fluid Dynamics Laboratory model; CSIRO, Commonwealth Scientific and Industrial Research Organisation model; ECHAM5, 

European Centre Hamburg Model version 5; CGCM, [Japan Meteorological Agency] coupled ocean-atmosphere general circulation model; stdev, 

standard deviation]

GCM Mean_diff1 Stdev_diff2

∆Days 

greater than 

10 percent3

∆Days 

less than 

90 percent4

∆Days 

greater than 

1 percent5

∆Days 

less than 

99 percent6

Scenario SRESa2 Future simulation period 2081–2100

CNRM 0.14 0.32 190 358 84 147

GFDL 0.01 0.39 111 844 63 457

CSIRO 0.09 0.60 110 579 155 164

ECHAM5 -0.00 0.16 21 576 49 129

CGCM 0.15 0.23 180 123 67 117

Mean 0.08 0.34 122.40 496.00 83.60 202.80

Stdev 0.07 0.17 67.92 270.45 41.82 143.22

Scenario SRESa2 Future simulation period 2046–2065

CNRM 0.09 0.26 129 65 66 27

GFDL 0.05 0.26 136 445 66 212

Scenario SRESb1 Future simulation period 2081–2100

CNRM 0.09 0.14 117 111 29 -12

GFDL 0.10 0.40 224 283 70 129

CSIRO 0.06 0.59 -27 653 106 167

ECHAM5 -0.01 0.13 -25 459 25 71

CGCM 0.03 0.13 2 327 30 176

Mean 0.05 0.28 58.20 366.60 52.00 106.20

Stdev 0.05 0.21 109.87 202.76 35.29 77.94

Scenario SRESb1 Future simulation period 2046–2065

CNRM 0.06 0.11 147 210 24 40

GFDL -0.01 0.30 72 533 45 251

1

Mean_diff is the difference between the mean of daily streamflows for the current period (1981–2000) and the future simulation period, in percent.

2

Stdev_diff is the difference between the standard deviation of daily streamflows for the current period (1981–2000) and the future simulation period, 

in percent.

3

∆Days greater than 10 percent is the number of days with streamflow greater than the streamflow exceeded 10 percent of the time (10th percentile).

4

∆Days greater than 90 percent is the number of days with streamflow less than the streamflow exceeded 90 percent of the time (90th percentile).

5

∆Days greater than 1 percent is the number of days with streamflow greater than the streamflow exceeded 1 percent of the time (1st percentile).

6

∆Days less than 99 percent is the number of days with streamflow less than the streamflow exceeded 99 percent of the time (99th percentile).
20 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Table 7. Summary statistics for climate change simulations for the Souhegan River watershed in New Hampshire.

[GCM, general circulation model; SRESa2, Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES; Nakićenović 

and others, 2000) scenario A2; SRESb1, IPCC SRES scenario B1; CNRM, Centre National de Recherches Meteorologiques model; GFDL, Geophysical Fluid 

Dynamics Laboratory model; CSIRO, Commonwealth Scientific and Industrial Research Organisation model; ECHAM5, European Centre Hamburg Model 

version 5; CGCM, [Japan Meteorological Agency] coupled ocean-atmosphere general circulation model; stdev, standard deviation]

GCM Mean_diff1 Stdev_diff2

∆Days 

greater than 

10 percent

∆Days 

less than 

90 percent

∆Days 

greater than 

1 percent

∆Days 

less than 

99 percent

Scenario SRESa2 Future simulation period 2081–2100

CNRM 0.17 0.38 269 119 98 79

GFDL 0.08 0.44 108 233 85 118

CSIRO 0.06 0.69 34 303 86 4

ECHAM5 -0.02 0.11 96 665 5 356

CGCM 0.22 0.44 319 -66 82 -47

Mean 0.10 0.41 165.20 250.80 71.20 102.00

Stdev 0.10 0.21 122.17 270.34 37.51 155.81

SRESa2 Future simulation period 2046–2065

CNRM 0.11 0.27 159 -19 79 2

GFDL 0.11 0.39 139 50 59 17

Scenario SRESb1 Future simulation period 2081–2100

CNRM 0.09 0.12 141 -5 45 0

GFDL 0.16 0.52 221 -174 74 -5

CSIRO 0.04 0.40 2 420 75 125

ECHAM5 -0.01 0.09 45 529 8 187

CGCM 0.06 0.22 107 137 41 16

Mean 0.07 0.27 103.20 181.40 48.60 64.60

Stdev 0.06 0.19 85.07 291.88 27.66 86.62

SRESb1 Future simulation period 2046–2065

CNRM 0.06 0.03 173 137 23 50

GFDL 0.02 0.33 9 236 32 156

1

Mean_diff is the difference between the mean of daily streamflows for the current period (1981–2000) and the future simulation period, in percent.

2

Stdev_diff is the difference between the standard deviation of daily streamflows for the current period (1981–2000) and the future simulation period, 

in percent.

3

∆Days greater than 10 percent is the number of days with streamflow greater than the streamflow exceeded 10 percent of the time (10th percentile).

4

∆Days greater than 90 percent is the number of days with streamflow less than the streamflow exceeded 90 percent of the time (90th percentile).

5

∆Days greater than 1 percent is the number of days with streamflow greater than the streamflow exceeded 1 percent of the time (1st percentile).

6

∆Days less than 99 percent is the number of days with streamflow less than the streamflow exceeded 99 percent of the time (99th percentile).
Modeling of Future Streamflows 21

Table 8. Summary statistics for climate change simulations for the Ashuelot River watershed in New Hampshire.

[GCM, general circulation model; SRESa2, Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES; Nakićenović 

and others, 2000) scenario A2; SRESb1, IPCC SRES scenario B1; CNRM, Centre National de Recherches Meteorologiques model; GFDL, Geophysical Fluid 

Dynamics Laboratory model; CSIRO, Commonwealth Scientific and Industrial Research Organisation model; ECHAM5, European Centre Hamburg Model 

version 5; CGCM, [Japan Meteorological Agency] coupled ocean-atmosphere general circulation model; stdev, standard deviation]

GCM Mean_diff1 Stdev_diff2

∆Days 

greater than 

10 percent3

∆Days 

less than 

90 percent4

∆Days 

greater than 

1 percent5

∆Days 

less than 

99 percent6

Scenario SRESa2 Future simulation period 2081–2100

CNRM 0.16 0.39 263 109 122 74

GFDL 0.05 0.38 190 449 107 92

CSIRO 0.09 0.62 136 224 140 23

ECHAM5 -0.07 0.02 30 628 11 257

CGCM 0.19 0.34 334 12 83 -16

Mean 0.08 0.35 190.60 284.40 92.60 86.00

Stdev 0.10 0.21 116.81 251.88 50.17 104.64

Scenario SRESa2 Future simulation period 2046–2065

CNRM 0.11 0.29 157 -27 108 13

GFDL 0.10 0.29 227 109 93 0

Scenario SRESb1 Future simulation period 2081–2100

CNRM 0.08 0.22 72 82 54 15

GFDL 0.12 0.38 241 37 102 4

CSIRO 0.07 0.45 48 368 126 182

ECHAM5 -0.02 0.06 61 400 33 103

CGCM 0.06 0.22 149 165 38 7

Mean 0.06 0.26 114.20 210.40 70.60 62.20

Stdev 0.05 0.15 81.06 165.38 41.25 78.55

Scenario SRESb1 Future simulation period 2046–2065

CNRM 0.06 0.14 107 182 42 33

GFDL 0.03 0.24 105 323 65 89

1

Mean_diff is the difference between the mean of daily streamflows for the current period (1981–2000) and the future simulation period, in percent.

2

Stdev_diff is the difference between the standard deviation of daily streamflows for the current period (1981–2000) and the future simulation period, 

in percent.

3

∆Days greater than 10 percent is the number of days with streamflow greater than the streamflow exceeded 10 percent of the time (10th percentile).

4

∆Days greater than 90 percent is the number of days with streamflow less than the streamflow exceeded 90 percent of the time (90th percentile).

5

∆Days greater than 1 percent is the number of days with streamflow greater than the streamflow exceeded 1 percent of the time (1st percentile).

6

∆Days less than 99 percent is the number of days with streamflow less than the streamflow exceeded 99 percent of the time (99th percentile).
22 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

0

5

10

15

20

25

30

35

40

45

January

February

March

April

May

June

July

August

September

October

November

December

ECHAM5 1981–2000

ECHAM5 SRESa2 

2081–2100

ECHAM5 SRESb1 

2081–2100

Oyster River

0

5

10

15

20

25

30

35

40

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESa2 

2046–2065

CNRM SRESa2 

2081–2100

Oyster River

0

5

10

15

20

25

30

35

40

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESb1 

2046–2065

CNRM SRESb1 

2081–2100

Oyster River

0

5

10

15

20

25

30

35

40

45

January

February

March

April

May

June

July

August

September

October

November

December

January

February

March

April

May

June

July

August

September

October

November

December

GFDL 1981–2000

GFDL SRESa2 

2046–2065

GFDL SRESa2 

2081–2100

Oyster River

0

5

10

15

20

25

30

35

40

45

GFDL 1981–2000

GFDL SRESb1 

2046–2065

GFDL SRESb1 

2081–2100

Oyster River

EXPLANATION 

EXPLANATION 

EXPLANATION 

EXPLANATION 

EXPLANATION 

Month

Month

Streamflow, in cubic feet per second Streamflow, in cubic feet per second

Streamflow, in cubic feet per second

Streamflow, in cubic feet per secondStreamflow, in cubic feet per second

Figure 8. Mean monthly hydrographs for the simulated current and future streamflows in the Oyster River, New Hampshire, for 

the SRESa2 and SRESb1 scenarios, the midcentury and end-of-century simulations, respectively, using the CNRM and GFDL GCMs. 

The ECHAM5 simulations of end-of-century for the SRESa2 and SRESb1 scenarios are also shown for comparison. Definitions of 

abbreviations are listed in the “Abbreviations” section, p. ix.
Modeling of Future Streamflows 23

0

500

1,000

1,500

2,000

2,500

3,000

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESa2

2046–2065

CNRM SRESa2

2081–2100

Pemigewasset River

0

500

1,000

1,500

2,000

2,500

3,000

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESb1 

2046–2065

CNRM SRESb1 

2081–2100

Pemigewasset River

0

500

1,000

1,500

2,000

2,500

January

February

March

April

May

June

July

August

September

October

November

December

GFDL 1981–2000

GFDL SRESa2

2046–2065

GFDL SRESa2 

2081–2100

Pemigewasset River

0

500

1,000

1,500

2,000

2,500

3,000

January

February

March

April

May

June

July

August

September

October

November

December

GFDL 1981–2000

GFDL SRESb1

2046–2065

GFDL SRESb1

2081–2100

Pemigewasset River

0

500

1,000

1,500

2,000

2,500

3,000

January

February

March

April

May

June

July

August

September

October

November

December

ECHAM5 1981–2000

ECHAM5 SRESa2 

2081–2100

ECHAM5 SRESb1 

2081–2100

Pemigewasset River

EXPLANATION 

EXPLANATION 

EXPLANATION 

EXPLANATION 

EXPLANATION 

Month

Month

Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second

Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second

Figure 9. Mean monthly hydrographs for the simulated current and future streamflows in the Pemigewasset River, New Hampshire, 

for the SRESa2 and SRESb1 scenarios, the midcentury and end-of-century simulations, respectively, using the CNRM and GFDL GCMs. 

The ECHAM5 simulations of end-of-century for the SRESa2 and SRESb1 scenarios are also shown for comparison. Definitions of 

abbreviations are listed in the “Abbreviations” section, p. ix.
24 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

0

100

200

300

400

500

600

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESa2 

2046–2065

CNRM SRESa2 

2081–2100

Souhegan River

0

100

200

300

400

500

600

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESb1 

2046–2165

CNRM SRESb1 

2081–2100

0

100

200

300

400

500

600

700

January

February

March

April

May

June

July

August

September

October

November

December

GFDL 1981–2000

GFDL SRESa2 

2046–2065

GFDL SRESa2 

2081–2100

0

100

200

300

400

500

600

700

January

February

March

April

May

June

July

August

September

October

November

December

GFDL 1981–2000

GFDL SRESb1 

2046–2065

GFDL SRESb1 

2081–2100

0

100

200

300

400

500

600

700

January

February

March

April

May

June

July

August

September

October

November

December

ECHAM5 1981–2000

ECHAM5 SRESa2 

2081–2100

ECHAM5 SRESb1 

2081–2100

Month

Month

Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second

Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second

EXPLANATION 

Souhegan River

EXPLANATION 

Souhegan River

EXPLANATION 

Souhegan River

EXPLANATION 

Souhegan River

EXPLANATION 

Figure 10. Mean monthly hydrographs for the simulated current and future streamflows in the Souhegan River, New Hampshire, for 

the SRESa2 and SRESb1 scenarios, the midcentury and end-of-century simulations, respectively, using the CNRM and GFDL GCMs. 

The ECHAM5 simulations of end-of-century for the SRESa2 and SRESb1 scenarios are also shown for comparison. Definitions of 

abbreviations are listed in the “Abbreviations” section, p. ix.
Modeling of Future Streamflows 25

0

200

400

600

800

1,000

1,200

1,400

1,600

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESa2 

2046–2065

CNRM SRESa2 

2081–2100

Ashuelot River

0

200

400

600

800

1,000

1,200

1,400

January

February

March

April

May

June

July

August

September

October

November

December

CNRM 1981–2000

CNRM SRESb1 

2046–2065

CNRM SRESb1 

2081–2100

Ashuelot River

Ashuelot River

0

200

400

600

800

1,000

1,200

1,400

January

February

March

April

May

June

July

August

September

October

November

December

GFDL 1981–2000

GFDL SRESa2 

2046–2065

GFDL SRESa2 

2081–2100

Ashuelot River

0

200

400

600

800

1,000

1,200

1,400

1,600

January

February

March

April

May

June

July

August

September

October

November

December

GFDL 1981–2000

GFDL SRESb1 

2046–2065

GFDL SRESb1 

2081–2100

Ashuelot River

0

200

400

600

800

1,000

1,200

1,400

January

February

March

April

May

June

July

August

September

October

November

December

ECHAM5 1981–2000

ECHAM5 SRESa2 

2081–2100

ECHAM5 SRESb1 

2081–2100

Months

Months

Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second

Monthly streamflow, in cubic feet per second Monthly streamflow, in cubic feet per second

EXPLANATION EXPLANATION 

EXPLANATION 

EXPLANATION 

EXPLANATION 

Figure 11. Mean monthly hydrographs for the simulated current and future streamflows in the Ashuelot River, New Hampshire, for 

the SRESa2 and SRESb1 scenarios, the midcentury and end-of-century simulations, using the CNRM and GFDL GCMs. The ECHAM5 

simulations of end-of-century for the SRESa2 and b1 scenarios are also shown for comparison. Definitions of abbreviations are listed in 

the “Abbreviations” section, p. ix.
26 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

The GFDL and the CNRM models show a similar overall 

pattern of the curve, which indicates both models are simulating similar mean flow patterns, but the GFDL model shows 

greater differences at the high and low end than does the 

CNRM model. Also note that both models show increasing 

highs and decreasing lows for the SRESa2 (high-emissions) 

scenario compared with the current condition for both time 

periods, but the SRESb1 (low-emissions) scenario does not 

show the same degree of change at the low streamflow end. 

The Oyster River is expected to experience smaller change 

at the low streamflow end and greater change at the high 

streamflow end, whereas there are large changes at the high 

and low ends of the streamflow range in the Pemigewasset 

River. Figures 12E and F (in back of report) represents an 

average of all the future simulations and scenarios for all five 

GCMs for the current, midcentury, and end-of-century time 

periods (representing the mean of all of the possible outcomes modeled). The average curves show similar trends. The 

Pemigewasset River shows the largest change relative to the 

current time period at the high and low streamflow ends of the 

curve, and the Oyster River shows change mostly at the high 

end of the streamflow range. It is interesting to note, however, 

that the average curve for the Pemigewasset River shows more 

change during the entire range of streamflow compared with 

the curves for the individual GCM models.

The simulated future changes in base flow and snowfall 

are predicted to vary by watershed and HRU location 

(figs. 13 and 14). This means that each area of the State could 

Table 9. Comparison of the mean of the largest 0.1 percent of streamflows and 0.1 percent exceedance probability statistics for four 

watersheds in New Hampshire.

[Days > 0.1, the number of days with streamflow greater than the 0.1 percentile as defined by the current simulation period (1981–2000); the 0.1 percentile is 

that which is exceeded 0.1 percent of the time. Avg, average of seven largest streamflows in the model. ft3/s, cubic feet per second; SRESa2, Intergovernmental 

Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES; Nakićenović and others, 2000) scenario A2; SRESb1, IPCC SRES scenario B1]

Scenario, 

period of simulated record

Oyster River 

watershed

Pemigewasset River 

watershed

Souhegan River 

watershed

Ashuelot River 

watershed

Days > 0.1 Avg, 

in ft3

/s Days > 0.1 Avg, in ft3/s Days > 0.1 Avg, in ft3/s Days > 0.1 Avg, in ft3/s

Centre National de Recherches Meteorologiques general circulation model (GCM)

Current, 1981–2000 7 5 7 13,266 7 5,325 7 6,720

SRESa2, 2046–2065 26 501 19 21,475 14 6,265 38 8,185

SRESa2, 2081–2100 28 587 26 16,135 20 5,410 53 8,929

SRESb1, 2046–2065 11 452 11 14,691 5 4,315 29 7,152

SRESb1, 2081–2100 16 469 15 15,113 7 4,221 41 8,419

Geophysical Fluid Dynamics Laboratory GCM

Current, 1981–2000 7 323 7 11,695 7 3,038 7 6,334

SRESa2, 2046–2065 29 586 22 13,966 31 5,286 14 8,832

SRESa2, 2081–2100 31 463 29 18,912 36 4,804 26 9,066

SRESb1, 2046–2065 35 483 26 17,140 30 5,580 17 8,093

SRESb1, 2081–2100 32 528 36 19,374 46 5,583 28 9,477

[Japan Meteorological Agency] coupled ocean-atmosphere general circulation model

Current, 1981–2000 7 417 7 17,130 7 4,390 7 7,106

SRESa2, 2081–2100 28 635 9 17,144 41 6,269 30 9,114

SRESb1, 2081–2100 15 536 11 19,332 14 6,331 17 9,418

Commonwealth Scientific and Industrial Research Organisation GCM

Current, 1981–2000 7 346 7 12,583 7 3,527 7 5,619

SRESa2, 2081–2100 33 752 57 21,624 39 8,425 56 13,570

SRESb1, 2081–2100 15 520 58 23,752 29 5,177 56 9,393

European Centre Hamburg Model version 5 GCM

Current, 1981–2000 7 530 7 14,451 7 4,862 7 8,541

SRESa2, 2081–2100 11 524 6 17,514 11 5,197 4 7,625

SRESb1, 2081–2100 5 536 12 19,332 8 6,331 8 9,418
Potential Implications of Modeled Streamflow Changes 27

experience very different changes depending on topography or 

other factors. Therefore, different planning for infrastructure 

and public safety could need to be considered, depending on 

the location and the range of possible outcomes indicated by 

the various model inputs. The mean change in base flow and 

snowfall is computed by subtracting the 20-year mean for the 

future simulation from the 20-year mean for the current period 

simulation. The change statistics for each river’s watershed 

by GCM and scenario are shown in table 10. The simulated 

changes in the mean indicate that snowfall is expected to 

decrease in all locations for all GCMs and scenarios, with 

the greatest decrease associated with the SRESa2 scenario 

and located in the Pemigewasset River watershed and the 

least change located in the Oyster River watershed (note that 

the model simulation estimates snowfall in inches of snowwater equivalent). To put the snowfall in a more familiar 

scale, the inches of water equivalent are multiplied by 10 to 

estimate an approximate amount of snow for a temperature 

range from 28 to 34 °F (National Oceanic and Atmospheric 

Administration, n.d.). The change in base flow varies widely 

by GCM, scenario, and HRU location, ranging from an 

increase in base flow (reported in inches of water) of about an 

inch, to a decrease by as much as 2 inches.

Taking all the GCM scenarios and watershed HRUs as 

a single statistical population, the mean decrease in snowfall 

is expected to range between 1 and 2 ft by the end of this 

century. Base flow (normalized by watershed area) is expected 

to decrease about 0.5 inch in the Ashuelot River and Souhegan 

River watersheds, 0.1 inch in the Oyster River watershed, and 

nearly 1 inch in the Pemigewasset River watershed.

There is a strong relation between the changes in base 

flow and snowfall and in geographic information for each 

HRU. Table 11 shows regression statistics that relate the 

mean changes in base flow and snowfall for each HRU 

with the altitude (feet above NAVD 88) and the latitude and 

longitude (relative to the North American Datum of 1983) for 

each HRU. Baseflow decreases with elevation and increases 

with latitude. Decrease of snowfall is inversely related to 

altitude (ranging from approximately 50 to 900 ft in the HRU 

population for the four watersheds) and latitude (ranging from 

42 to 44 degrees north) and also inversely related to longitude 

(ranging from approximately 71.5 to 72.5 degrees west).

The simulated snowfall data, summarized by month and 

averaged during the 20-year simulation periods for all of the 

GCMs and scenarios (table 10), show decreases for each of 

the winter months. However, it is noted that the maximum 

monthly predicted snowfalls for the period of the simulations 

increase compared with the current condition. This indicates 

that, even though mean snowfall is decreasing from year 

to year, months of extreme snowfall can occur that exceed 

current conditions. Therefore, the snowfall from year to year 

and month to month is predicted to become more variable and 

the highs are predicted to be more extreme. This is because 

of the increased precipitation in months that remain below 

freezing in winter.

Potential Implications of Modeled 

Streamflow Changes

This study indicates a likelihood that climate change 

will alter flow regimes in rivers, snowfall amounts, and 

groundwater recharge both spatially and temporally. The 

direction of these changes, more frequent flooding and 

lower streamflows, increasing and decreasing groundwater 

recharge depending on location, and reduced snowfall but 

with continued chance of extreme snowfall during some years 

have implications for human and natural adaptations. For 

example, river channels will adjust to changing flow regimes 

over time (Leopold, 1994; Gibson and others, 2005), such that 

increasing flood frequency and magnitude would enlarge the 

river channel, and overbank flooding eventually will reach 

an equilibrium similar to the current condition. However, 

there will be a transition period that could be many years 

(Gibson and others, 2005). Similar to the natural environment, 

the developed environment—including roads, dams, river 

works, and recreational facilities—will also need to adapt 

to hydrologic change, which could take some time. It is 

recognized that hydrologic changes could not reach a static or 

even an equilibrium condition but could be in flux for a long 

period (Milly and others, 2008).

The implications of the hydrologic changes documented 

in this study for human health and well-being fall into multiple 

general categories (Backlund and others, 2008; Melillo and 

others, 2014). Although there will likely be economic effects 

resulting from climate change and effects on utilities including 

water and wastewater conveyance, and energy transmission, 

these are not directly assessed in this report.

The changes in hydrology discussed in this report are 

linked primarily to the potential for more rapid degradation 

of transportation infrastructure and attendant effects on 

public safety. Less predictable winter weather and more high 

streamflow events in the winter with attendant effects on 

water and transportation infrastructure could lead to adverse 

effects including increased frequency of road overtopping by 

high flows at stream crossings, less predictable winter driving 

conditions, and increased frequency of damage to culverts 

and bridges due to more frequent erosive high flows at stream 

crossings (Backlund and others, 2008; Melillo and others, 

2014). The increased frequency of floods can also have large 

effects in low-lying areas and could have an important effect 

on stream geomorphology and erosion.

Changes in engineering practices, design, operation, 

and maintenance could be needed in the future to ensure that 

public safety does not decline. Planning for these changes will 

be challenging when estimating priority needs and balancing 

those needs with increasing costs (Melillo and others, 2014).
28 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

nhru

GW DIFF CNRM SRESa2—INCHES

>-1.72 to -0.50

>-0.50 to 0.00

>0.00 to 0.50

>0.50 to 1.00

>1.00 to 1.50

>1.50 to 2.00

Pemigewasset 

Ashuelot

Souhegan 

Oyster

EXPLANATION

0 15 30 MILES

0 25 50 KILOMETERS

-71° -70°

-72° -71°

-72°

-73°

45°

44°

44°

43°

43°

CANADA

VERMONT

MASSACHUSETTS

NEW HAMPSHIRE

MAINE

Figure 13. The simulated change in base flow between current (1981–2000) and end-of-century (2081–2100) periods using the 

CNRM model for the two scenarios mapped by hydrologic response unit in the four pilot river watersheds in New Hampshire. 

Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.
Potential Implications of Modeled Streamflow Changes 29

0 15 30 MILES

0 25 50 KILOMETERS

EXPLANATION

nhru

GW DIFF CNRM SRESb1—INCHES

-0.33 to 0.00

>0.00 to 0.25

>0.25 to 0.50

>0.50 to 0.75

>0.75 to 1.00

>1.00 to 1.25

Pemigewasset 

Ashuelot

Souhegan 

Oyster

-71° -70°

-72° -71°

-72°

-73°

45°

44°

44°

43°

43°

CANADA

VERMONT

MASSACHUSETTS

NEW HAMPSHIRE

MAINE

Figure 13. The simulated change in base flow between current (1981–2000) and end-of-century (2081–2100) periods using the 

CNRM model for the two scenarios mapped by hydrologic response unit in the four pilot river watersheds in New Hampshire. 

Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.—Continued
30 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

0 15 30 MILES

0 25 50 KILOMETERS

-71° -70°

-72° -71°

-72°

-73°

45°

44°

44°

43°

43°

EXPLANATION

nhru

SNOW DIFF CNRM SRESa2—INCHES

-35.07 to -30.00

-29.99 to -25.00

-24.99 to -20.00

-19.99 to -15.00

-14.99 to -10.00

Pemigewasset 

Ashuelot

Souhegan 

Oyster

CANADA

VERMONT

MASSACHUSETTS

NEW HAMPSHIRE

MAINE

Figure 14. The simulated change in snowfall between current (1981–2000) and end-of-century (2081–2100) time periods using 

the CNRM model for the two scenarios mapped by hydrologic response unit in the four pilot river watersheds in New Hampshire. 

Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.
Potential Implications of Modeled Streamflow Changes 31

0 15 30 MILES

0 25 50 KILOMETERS

-71° -70°

-72° -71°

-72°

-73°

45°

44°

44°

43°

43°

EXPLANATION

nhru

SNOW DIFF SRESb1 - INCHES

-8.819 to -8.00

-7.99 to -6.00

-5.99 to -4.00

-3.99 to -2.00

-1.99 to 0.00

Pemigewasset 

Ashuelot

Souhegan 

Oyster

CANADA

VERMONT

MASSACHUSETTS

NEW HAMPSHIRE

MAINE

Figure 14. The simulated change in snowfall between current (1981–2000) and end-of-century (2081–2100) time periods using 

the CNRM model for the two scenarios mapped by hydrologic response unit in the four pilot river watersheds in New Hampshire. 

Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.—Continued
32 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Model Limitations

This study emphasizes the importance of understanding predicted trends in streamflow, snowmelt, soil moisture, 

and base flow through the hydrologic responses to models 

of climate change. Episodic events such as floods, which are 

affected by hourly and finer time-scale precipitation events, 

are not addressed in this study. Other limitations include the 

following:

• Increases in groundwater or reservoir withdrawals are 

not simulated;

• Interbasin transfers are not simulated from return flow 

from withdrawals;

• Land use changes are not simulated; and

• The effect of frozen ground on runoff is not explicitly 

simulated (the version of PRMS used in this study does 

not account for this process).

Complexities associated with land use, and change in 

land cover and vegetation could be important in trends of 

possible future change and could be a more important driver 

to hydrologic change than the climate in some areas. The 

climate scenarios simulated in this study are only possibilities; 

however, the reality could be expected within the range of 

GCMs and scenarios modeled. Although not a limitation, 

it is important to note that according to the IPCC report 

(Intergovernmental Panel on Climate Change, 2007), the 

modeled scenarios are more likely to occur than a return to the 

norm of our past experiences during the reference period of 

the past few decades.

Table 10. Mean change in base flow and snowfall in New Hampshire.

[Snowflow is estimated from the simulated snow-water equivalent times 10. GCM, global circulation model; ECHAM5, European Centre Hamburg Model 

version 5; SRESa2, Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES; Nakićenović and others, 2000) 

scenario A2; SRESb1, IPCC SRES scenario B1; CNRM, Centre National de Recherches Meteorologiques model; CGCM, [Japan Meteorological Agency] 

coupled ocean-atmosphere general circulation model; CSIRO, Commonwealth Scientific and Industrial Research Organisation model; GFDL, Geophysical 

Fluid Dynamics Laboratory model]

GCM and scenario

Groundwater baseflow change, inches Snowfall change, inches of snow-water equivalent times ten

Oyster 

River

Pemigewasset 

River

Souhegan 

River

Ashuelot 

River

Oyster 

River

Pemigewasset 

River

Souhegan 

River

Ashuelot 

River

ECHAM5SRESa2 -0.77 -1.89 -1.47 -1.92 -18.35 -30.92 -23.73 -20.38

ECHAM5SRESb1 -0.97 -1.47 -1.02 -1.02 -12.58 -21.14 -16.48 -12.55

CNRMSRESa2 0.64 0.17 0.57 0.61 -15.20 -22.56 -16.95 -15.17

CNRMSRESb1 0.85 0.67 0.66 0.34 -5.60 -5.60 -5.74 -5.37

CGCMSRESa2 0.97 0.03 1.21 0.65 -20.19 -33.42 -23.88 -21.12

CGCMSRESb1 -0.77 -1.10 -0.54 -0.35 -15.44 -28.75 -19.53 -15.79

CSIROSRESa2 -0.90 -1.88 -1.82 -1.19 -16.75 -32.77 -27.08 -20.95

CSIROSRESb1 -0.67 -1.31 -1.41 -0.95 -12.98 -17.64 -19.32 -14.83

GFDLSRESa2 -0.12 -1.98 -0.82 -0.94 -16.66 -28.13 -19.00 -17.42

GFDLSRESb1 0.97 -0.03 0.75 0.28 -11.43 -18.43 -10.78 -10.51

Mean for all GCMs 

and scenarios

-0.08 -0.88 -0.39 -0.45 -14.52 -23.94 -18.25 -15.41
Model Limitations 33

Table 11. Regression statistics relating change in groundwater base flow and snowfall to geographic variables.

[R, coefficient of multiple correlation; R2, coefficient of determination; t Stat, ratio of the departure of the parameter from its notional value and its standard 

error; P-value, threshold value]

Regression statistics for change in groundwater base flow

Multiple R 0.88

R2 0.78

Adjusted R2 0.77

Standard error 0.32

Observations 62

Coefficients Standard error t Stat P-value Lower 95 percent Upper 95 percent

Intercept -10.11 4.42 -2.29 0.03 -18.96 -1.26

HRU_ELEVATION -0.004 0.00 -12.42 0.00 -0.00 -0.00

HRU_LATITUDE 0.25 0.10 2.39 0.02 0.04 0.45

Regression statistics for change in snowfall

Multiple R 0.90

R2 0.81

Adjusted R2 0.80

Standard error 2.40

Observations 62

Coefficients Standard error t Stat P-value Lower 95 percent Upper 95 percent

Intercept -348.31 113.49 -3.07 0.00 -575.49 -121.14

HRU_ELEVATION -0.02 0.00 -7.71 0.00 -0.02 -0.01

HRU_LONGITUDE -6.04 1.23 -4.92 0.00 -8.50 -3.58

HRU_LATITUDE -2.30 0.91 -2.54 0.01 -4.12 -0.49

Regression equation:

∆SNOW is -0.177 × (HRU_ELEVATION/10) – 6.04 × (HRU_LONGITUDE) – 2.30 × (HRU_LATITUDE) – 348.31.

∆GW is mean annual change in groundwater base flow, inches.

∆SNOW is mean annual change in snowfall, snow-water equivalent, in inches times 10.

HRU_ELEVATION/10 is mean Hydrologic Response Unit (HRU) elevation, in feet above North American Vertical Datum 88 (NAVD 88).

HRU_LONGITUDE is longitude of the HRU centroid, in decimal degrees (Note that the longitude is negative for the Western Hemisphere).

HRU_LATITUDE is latitude of the HRU centroid, in decimal degrees.
34 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Summary and Conclusions

Based on evidence from the five models used to evaluate 

hydrologic change in four representative watersheds, streamflow is likely to increase in New Hampshire. Variability of 

streamflow is likely to increase, with an increased number 

of highs and lows. Streamflow will probably have the largest increases in winter and will decrease in summer, although 

summer decreases are small (less than 20 percent). The models 

demonstrate that the change in base flow and snowfall would 

vary by location. Base flow change ranges from reductions by 

as much as 2 inches to increases of a similar amount depending on the scenario and the global climate model (GCM). The 

change is highly dependent on altitude, latitude, and the GCM 

used for the future simulations. The largest change is in the 

northern Pemigewasset River watershed. Changes in snowfall 

are consistently decreasing for all models and all scenarios and 

are spatially variable with reductions as much as 2 feet in the 

Pemigewasset River watershed in northern New Hampshire 

and less change in the watersheds in southern New Hampshire. 

Similar to the change in groundwater, the change in snowfall 

is dependent on altitude and latitude and also on longitude.

The most significant effects of increasing flooding could 

be on the frequency of large events (the largest seven floods 

expected in 20 years) rather than the size of the floods, except 

in the watershed in northern New Hampshire. There is likely 

to be less snowfall on average, but large snow events could 

still occur in any given winter. There is likely to be an increase 

in high flows during winter and spring months that could 

result in more uncertainty in the planning of infrastructure 

design, operation, and maintenance (including roads and utilities). Similarly, less groundwater base flow is expected to be 

available on average in some areas, whereas in other areas, 

base flow may increase; additionally a wider range of seasonal 

fluctuation in base flow is expected than currently observed.

References Cited

Backlund, Peter, Janetos, Anthony, and Schimel, David, 2008, 

The effects of climate change on agriculture, land resources, 

water resources, and biodiversity; A report by the U.S. 

Climate Change Science Program and the Subcommittee 

on Global Change Research: Washington, D.C., U.S. 

Environmental Protection Agency, 362 p. [Also available at 

http://www.sap43.ucar.edu/documents/.]

Bjerklie, D.M., Hay, L.E., and Markstrom, S.L., 2012, Watershed scale response to climate change—Pomperaug River 

watershed, Connecticut: U.S. Geological Survey Fact Sheet 

2011–3122, 6 p. [Also available at http://pubs.usgs.gov/

fs/2011/3122/.]

Bjerklie, D.M., Starn, J.J., and Tamayo, Claudia, 2010a, 

Estimation of the effects of land-use and groundwater withdrawals on streamflow for the Pomperaug 

River, Connecticut: U.S. Geological Survey Scientific 

Investigations Report 2010–5114, 81 p. [Also available at 

http://pubs.usgs.gov/sir/2010/5114/pdf/sir2010-5114.pdf.]

Bjerklie, D.M., Trombley, T.J., and Viger, R.J., 2010b, 

Simulations of historical and future trends in snowfall and 

groundwater recharge for basins draining to Long Island 

Sound: Earth Interactions, v. 15, no. 34, p. 1–35.

Burns, D.A., Kraus, Julian, and McHale, M.R., 2007, Recent 

climate trends and implications for water resources in the 

Catskill Mountain region, New York: Journal of Hydrology, 

v. 366, p. 155–170.

Campbell, John, and Bailey, Amey, 2014, Hubbard Brook 

experimental forest—Daily precipitation standard rain gage 

measurements, 1956–2008: U.S. Forest Service Hubbard 

Brook experimental forest data archive, accessed July 7, 

2014, at http://hubbardbrook.org/data/dataset.php?id=81.

Chase, K.J., 2011, Development of a precipitation-runoff 

model to simulate unregulated streamflow in the South Fork 

Flathead River Basin, Montana: U.S. Geological Survey 

Scientific Investigations Report 2011–5095, 39 p. [Also 

available at http://pubs.usgs.gov/sir/2011/5095/.]

Di Vittoria, A.V., and Miller, N.L., 2014, Evaluating a 

modified point-based method to downscale cell-based 

climate variable data to high resolution grids: Earth 

Sciences Division, Lawrence Berkeley National 

Laboratory. [Also available at http://dx.doi.org/10.1007/

s00704-012-0740-9; http://escholarship.org/uc/

item/4d00w4nj.]

Dudley, R.W., and Hodgkins, G.A., 2013, Historical groundwater trends in northern New England and relations with 

streamflow and climatic variables: Journal of the American 

Water Resources Association, v. 49, no. 5, p. 1198–1212.

Dudley, R.W., and Nielsen, M.G., 2011, Simulation of streamflow in the Pleasant, Narraguagus, Sheepscot, and Royal 

Rivers, Maine, using watershed models: U.S. Geological 

Survey Scientific Investigations Report 2010–5221, 31 p. 

[Also available at http://pubs.usgs.gov/sir/2010/5221/.]

Flanagan, S.M., Nielsen, M.G., Robinson, K.W., and Coles, 

J.F., 1999, Water-quality assessment of the New England 

coastal basins in Maine, Massachusetts, New Hampshire, 

and Rhode Island—Environmental settings and implications 

for water quality and aquatic biota: U.S. Geological Survey 

Water-Resources Investigations Report 98–4249, 64 p. 

[Also available at http://pubs.usgs.gov/wri/wri984249/.]
References Cited 35

Gamble, J.L., Ebi, K.L., Grambsch, A.E., Sussman, F.G., 

and Wilbanks, T.J., 2008, Analyses of the effects of global 

change on human health and welfare and human systems: 

U.S. Climate Change Science Program Synthesis and 

Assessment Product 4.6, accessed March 31, 2015, at 

http://www.epa.gov/climatechange/impacts-adaptation/

health.html.

Gibson, C.A., Meyer, J.L., Poff, N.L., Hay, L.E., and 

Georgakakos, A., 2005, Flow regime alterations under 

changing climate in two river basins—Implications for 

freshwater ecosystems: River Research and Applications, 

v. 21, no. 8, p. 849–864.

Goode, D.J., Koerkle, E.H., Hoffman, S.A., Regan, R.S., Hay, 

L.E., and Markstrom, S.L., 2010, Simulation of runoff and 

reservoir inflow for use in a flood-analysis model for the 

Delaware River, Pennsylvania, New Jersey, and New York, 

2004–2006: U.S. Geological Survey Open-File Report 

2010–1014, 68 p. [Also available at http://pubs.usgs.gov/

of/2010/1014/.]

Groisman, P.Y., Knight, R.W., Easterling, D.R., Karl, T.R., 

Hegerl, T.C., and Razuvaev, V.N., 2005, Trends in intense 

precipitation in the climate record: Journal of Climate, v. 18, 

p. 1326–1350.

Hamilton, L.C., Keim, B.D., and Wake, C.P., 2010, Is New 

Hampshire’s climate warming?: University of New Hampshire, Carsey Institute New England Policy Brief no. 4, 8 p.

Hay, L.E., Markstrom, S.L., Regan, R.S., and Viger, R.L., 

2011, Watershed-scale response to climate change through 

the twenty-first century for selected basins across the United 

States: Earth Interactions, v. 15, no. 17, p. 1–37.

Hayhoe, Katharine, Wake, Cameron, Anderson, Bruce, Liang, 

Xin-Zhong, Maurer, Edwin, Zhu, Jinhong, Bradbury, James, 

DeGaetano, Art, Stoner, A.M., and Wuebbles, Donald, 2007, 

Regional climate change projections for the northeast U.S.: 

Mitigation and Adaptation Strategies for Global Change, 

v. 13, no. 5–6, p. 425–436.

Hayhoe, Katherine, Wake, C.P., Huntington, T.G., Luo, Lifeng, 

Schwartz, M.D., Sheffield, Justin, Wood, Eric, Anderson, 

Bruce, Bradbury, James, DeGaetano, Art, Troy, T.J., and 

Wolfe, David, 2006, Past and future changes in climate 

and hydrological indicators in the U.S. northeast: Climate 

Dynamics, v. 28, p. 381–407.

Hodgkins, Glenn, Dudley, R.W., and Huntington, T.G., 2003, 

Changes in the timing of high river flows in New England over the 20th century: Journal of Hydrology, v. 278, 

p. 244–252.

Intergovernmental Panel on Climate Change, 2007, Climate 

change 2007—Synthesis report; Contribution of Working 

Groups I, II, and III to the fourth assessment report of the 

Intergovernmental Panel on Climate Change: Geneva, 

Switzerland, Intergovernmental Panel on Climate Change, 

104 p. [Also available at https://www.ipcc.ch/

publications_and_data/ar4/syr/en/contents.html.]

Intergovernmental Panel on Climate Change, 2014, Fifth 

assessment report (AR5): Intergovernmental Panel on 

Climate Change Web page, accessed March 31, 2015, at 

http://www.ipcc.ch/report/ar5/.

Karl, T.R., and Knight, R.W., 1998, Secular trend of precipitation amount, frequency, and intensity in the United States: 

Bulletin of the American Meteorological Society, v. 79, 

p. 231–242.

Koczot, K.M., Jeton, A.E., McGurk, B.J., and Dettinger, M.D., 

2005, Precipitation-runoff processes in the Feather River 

Basin, northeastern California, and streamflow predictability, water years 1971–97: U.S. Geological Survey Scientific 

Investigations Report 2004–5202, 82 p.

LaFontaine, J.H., Hay, L.E., Viger, R.J., Markstrom, S.L., 

Regan, R.S., Elliott, C.M., and Jones, J.W., 2013, Application of the precipitation-runoff modeling system (PRMS) 

in the Apalachicola-Chattahoochee-Flint River Basin in 

the southeastern United States: U.S. Geological Survey 

Scientific Investigations Report 2013–5162, 132 p. [Also 

available at http://pubs.usgs.gov/sir/2013/5162/.]

Leavesley, G.H., Lichty, R.W., Troutman, B.M., and Saindon, 

L.G., 1983, Precipitation-runoff modeling system—User’s 

manual: U.S. Geological Survey Water-Resources Investigations Report 83–4238, 207 p. [Also available at 

http://pubs.usgs.gov/wri/1983/4238/report.pdf.]

Leopold, L.B., 1994, A view of the river: Cambridge, Mass., 

Harvard University Press, 293 p.

Lins, H.F., and Slack, J.R., 1999, Streamflow trends in the 

United States: Geophysical Research Letters, v. 26, p. 227–

230.

Lowe, Dianne, Ebi, K.L., and Forsberg, Bertil, 2013, Factors 

increasing vulnerability to health effects before, during 

and after floods: International Journal of Environmental Research Public Health, v. 10, no. 12, p. 7015–7067. 

[Available at http://www.ncbi.nlm.nih.gov/pmc/articles/

PMC3881153/.]

Mack, T.J., 2009, Assessment of ground-water resources in the 

Seacoast region of New Hampshire: U.S. Geological Survey 

Scientific Investigations Report 2008–5222, 188 p. [Also 

available at http://pubs.usgs.gov/sir/2008/5222.]
36 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Markstrom, S.L., Niswonger, R.G., Regan, R.S., Prudic, D.E., 

and Barlow, P.M., 2008, GSFLOW—Coupled ground-water 

and surface-water flow model based on the integration of 

the precipitation-runoff modeling system (PRMS) and the 

modular ground-water flow model (MODFLOW–2005): 

U.S. Geological Survey Techniques and Methods, book 6, 

chap. D1, 240 p. [Also available at http://pubs.usgs.gov/tm/

tm6d1/.]

McCabe, G.J., and Wolock, D.M., 2002, A step increase in 

streamflow in the conterminous United States: Geophysical 

Research Letters, v. 29, p. 2185–2188.

Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014: 

Climate change impacts in the United States—The third 

national climate assessment: Washington, D.C., U.S. Global 

Change Research Program, 829 p. [Also available at 

http://www.globalchange.gov/ncadac.]

Miller, D.R., Ogden, F.L., Warner, G.S., and DeGaetano, A.T., 

2002, Precipitation in Connecticut: Storrs, Conn., Connecticut Institute of Water Resources Special Reports, p. 1–65.

Milly, P.C.D., Betancourt, Julio, Falkenmark, Malin, Hirsch, 

R.M., Kundzewicz, Z.W., Lettenmaier, D.P., and Stouffer, 

R.J., 2008, Stationarity is dead—Whither water management?: Science, v. 319, p. 573–574.

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, 

R.L., Harmel, R.D., and Veith, T.L., 2007, Model evaluation guidelines for systematic quantification of accuracy 

in watershed simulations: Transactions of the American 

Society of Agriculture and Biology Engineers, v. 50, no. 3, 

p. 885–900.

Nakićenović, Nebojša, Alcamo, Joseph, Davis, Gerald, de 

Vries, Bert, Fenhann, Joergen, Gaffin, Stuart, Gregory, 

Kenneth, Grübler, Arnulf, Jung, T.Y., Kram, Tom, 

La Rovere, E.L., Michaelis, Laurie, Mori, Shunsuke, 

Morita, Tsuneyuki, Pepper, William, Pitcher, Hugh, Price, 

Lynn, Riahi, Keywan, Roehrl, Alexander, Rogner, H.H., 

Sankovski, Alexei, Schlesinger, Michael, Shukla, 

Priyararshi, Smith, Steven, Swart, Robert, van Rooijen, 

Sascha, Victor, Nadejda, and Zhou Dadi, 2000, Special 

report on emissions scenarios; A special report of Working 

Group III of the Intergovernmental Panel on Climate 

Change: Cambridge, United Kingdom, Cambridge 

University Press, 599 p. [Also available at http://www.grida.

no/publications/other/ipcc_sr/?src=/climate/ipcc/emission/

index.htm.]

Nash, J.E., and Sutcliffe, J.V., 1970, River flow forecasting 

through conceptual models; Part I—A discussion of principles: Journal of Hydrology, v. 10, no. 3, p. 282–290.

National Oceanic and Atmospheric Administration, [n.d.], 

Approximate snowfall amounts at specified temperature 

ranges: National Oceanic and Atmospheric Administration 

data, accessed March 31, 2015, at http://www.erh.noaa.gov/

box/tables/snowfall-meltwater.html.

Neff, Rob, Chang, Heejun, Knight, C.G., Najjar, R.G., Yarnal, 

Brent, and Walker, H.A., 2000, Impact of climate variation 

and change on mid-Atlantic region hydrology and water 

resources: Climate Research v. 14, no. 3, p. 207–218.

New Hampshire Climate Change Policy Task Force, 2009, 

The New Hampshire climate action plan—A plan for 

New Hampshire’s energy, environmental, and economic 

future: Concord, N.H., New Hampshire Department of 

Environmental Services, March, 74 p. [Also available at 

http://des.nh.gov/organization/divisions/air/tsb/tps/climate/

action_plan/documents/nhcap_final.pdf.]

O’Connor, R.E., Yarnal, Brent, Neff, Rob, Bord, Richard, 

Wiefek, Nancy, Reenock, Christopher, Shudak, Robin, 

Jocoy, C.L., Pascals, Peter, and Knight, C.G., 1999, Weather 

and climate extremes, climate change, and planning—Views 

of community water system managers in Pennsylvania’s 

Susquehanna River Basin: Journal of the American Water 

Resources Association, v. 35, no. 6, p. 1411–1419.

Oubeidillah, A.A., Kao, S.C., Ashfaq, M., Naz, B., and Tootle, 

G., 2013, A large-scale, high-resolution hydrological model 

parameter dataset for climate change impact assessment for 

the conterminous United States: Hydrology Earth Systems 

Sciences, v. 10, p. 9575–9613. [Also available at

http://www.hydrol-earth-syst-sci-discuss.net/10/9575/2013/

hessd-10-9575-2013.html.]

Schwab, J.C., ed., 1998, Hazard mitigation—Integrating best 

practices into planning: American Planning Association 

Planning Advisory Service Report, no. 560, p. 145.

Seaber, P.R., Kapinos, F.P., and Knapp, G.L.,1994, Hydrologic 

unit maps: U.S. Geological Survey Water-Supply Paper 

2294, 66 p. [Also available at http://pubs.usgs.gov/wsp/

wsp2294/.]

Stahl, K., Moore, R.D., Floyer, J.A., Asplin, M.G., and 

McKendry, I.G., 2006, Comparison of approaches for spatial interpolation of daily air temperature in a large region 

with complex topography and highly variable station density: Agricultural and Forest Meteorology, v. 139, no. 2006, 

p. 224–236.

Thornton, P.E., Running, S.W., and White, M.A., 1997, Generating surfaces of daily meteorological variables over large 

regions of complex terrain: Journal of Hydrology, v. 190, 

no. 1997, p. 214–251.
References Cited 37

Thornton, P.E., Thornton, M.M., Mayer, B.W., Wilhelmi, N., 

Wei, Y., and Cook, R.B., 2012, Daymet—Daily surface 

weather on a 1 km grid for North America, 1980–2011: Oak 

Ridge, Tennessee, Oak Ridge National Laboratory Distributed Active Archive Center, accessed September 2013, at 

http://dx.doi.org/10.3334/ORNLDAAC/Daymet_V2.

U.S. Geological Survey, 2014, National climate change viewer 

(NCCV) home: U.S. Geological Survey Climate and Land 

Use Change Research and Development Program Web 

page, accessed March 31, 2014, at http://www.usgs.gov/

climate_landuse/clu_rd/nccv.asp.

U.S. Geological Survey, 2015, Precipitation modeling runoff 

system (PRMS): U.S. Geological Survey watershed 

modeling Web page, accessed March 31, 2015, at 

http://wwwbrr.cr.usgs.gov/projects/SW_MoWS/PRMS.

html.

Viger, R.J., 2014, Preliminary spatial parameters for PRMS 

based on the Geospatial Fabric, NLCD2001 and SSURGO: 

U.S. Geological Survey data release, accessed March 31, 

2015, at http://dx.doi.org/10.5066/F7WM1BF7.

Viger, R.J., and Bock, Andrew, 2014, GIS features of the 

geospatial fabric for national hydrologic modeling: U.S. 

Geological Survey data release, accessed March 31, 2015, 

at http://dx.doi.org/10.5066/F7542KMD.

Viger, R.J., Hay, L.E., Jones, J.W., and Buell, G.R., 2010, 

Effects of including surface depressions in the application 

of the precipitation-runoff modeling system in the upper 

Flint River Basin, Georgia: U.S. Geological Survey 

Scientific Investigations Report 2010–5062, 36 p. [Also 

available at http://pubs.usgs.gov/sir/2010/5062/.]

Vogelmann, J.E., Howard, S.M., Yang, L.M., Larson, C.R., 

Wylie, B.K., and Van Driel, Nick, 2001, Completion of 

the 1990s national land cover dataset for the conterminous 

United States from Landsat thematic mapper data and 

ancillary data sources: Photogrammetry and Engineering 

and Remote Sensing, v. 67, no. 6, p. 650–662.

Wake, Cameron, Burakowski, Elizabeth, Hayhoe, 

Katharine, and Stoner, Anne, 2014, Climate change in 

southern New Hampshire—Past, present, and future: 

Durham, N.H., The Sustainability Institute at the 

University of New Hampshire, 86 p. [Also available at 

http://www.climatesolutionsne.org/sites/climatesolutionsne.

org/files/2014_southernnh_climate_assessment_unhsi_

csne_gsf.pdf.]

Figures 3 and 12
40 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean 

annual precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future 

climate conditions for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of 

abbreviations are listed in the “Abbreviations” section, p. ix.

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

TMAX DIFF CNRM SRESa2—DEG F

7.06 to 7.50

> 7.50 to 8.00

> 8.00 to 8.50

> 8.50 to 9.00

> 9.00 to 9.50

> 9.50 to 10.00

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

A
Figure 3 41

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean 

annual precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future 

climate conditions for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of 

abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

TMAX DIFF CNRM SRESb1—DEG F

3.46 to 4.00

>4.00 to 4.20

>4.20 to 4.40

>4.40 to 4.60

>4.60 to 4.80

>4.80 to 5.00

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

44°

42°

46°

72°

70°

68°

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

B
42 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean 

annual precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future climate 

conditions for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of abbreviations 

are listed in the “Abbreviations” section, p. ix.—Continued

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

TMAX DIFF ECHAM5 SRESa2—DEG F

7.44 to 8.10

>8.10 to 8.40

>8.40 to 8.70

>8.70 to 9.00

>9.00 to 9.30

>9.30 to 9.60

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

C
Figure 3 43

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean 

annual precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future climate 

conditions for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of abbreviations 

are listed in the “Abbreviations” section, p. ix.—Continued

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

TMAX DIFF ECHAM5 SRESb1—DEG F

5.23 to 5.30

>5.30 to 5.70

>5.70 to 6.10

>6.10 to 6.50

>6.50 to 6.90

>6.90 to 7.30

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

D
44 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean 

annual precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future 

climate conditions for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of 

abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

PRCP DIFF CNRM SRESa2—INCHES

3.97 to 5.00

>5.00 to 6.00

>6.00 to 7.00

>7.00 to 8.00

>8.00 to 9.00

>9.00 to 12.00

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

E
Figure 3 45

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean 

annual precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future 

climate conditions for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of 

abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

PRCP DIFF CNRM SRESb1—INCHES

1.61 to 3.00

>3.00 to 4.20

>4.20 to 5.40

>5.40 to 6.60

>6.60 to 7.80

>7.80 to 9.00

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

F
46 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean annual 

precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future climate conditions 

for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of abbreviations are listed in 

the “Abbreviations” section, p. ix.—Continued

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

PRCP DIFF ECHAM5 SRESa2—INCHES

0.37 to 2.20

>2.20 to 3.50

>3.50 to 4.80

>4.80 to 6.10

>6.10 to 7.40

>7.40 to 8.70

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

G
Figure 3 47

Figure 3. A–H, distribution of predicted change in mean daily maximum air temperature and change in mean annual 

precipitation projected for New Hampshire from 2081 to 2100 between current conditions and future climate conditions 

for the CNRM and ECHAM5 GCMs and the two scenarios SRESa2 and SRESb1. Definitions of abbreviations are listed in 

the “Abbreviations” section, p. ix.—Continued

Maine

t

New Hampshire

Massachusetts

Connecticut

Rhode Island

EXPLANATION

nhru

PRCP DIFF ECHAM5 SRESb1—INCHES

1.27 to 2.50

>2.50 to 3.70

>3.70 to 4.90

>4.90 to 6.10

>6.10 to 7.30

>7.30 to 8.50

44°

42°

0 40 80 MILES

0 40 80 KILOMETERS

46°

72°

70°

68°

Base from U.S. Geological Survey digital data, Albers Equal

Area Conic projection, 1992, Standard parallels 29o

30' and

45o

30', central meridian-96o

1:250,000 scale

MAINE

CANADA

CANADA

VERMONT

NEW YORK

CONNECTICUT

RHODE

ISLAND

MASSACHUSETTS

NEW HAMPSHIRE

ATLANTIC OCEAN

H
48 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Figure 12. A–D, streamflow duration curves for the Oyster River and Pemigewasset River watersheds in New Hampshire 

for the two emissions scenarios for midcentury (SRESa2) and end-of-century (SRESb1) for the CNRM and GFDL models, two 

of the five GCMs; and E–F, average streamflow duration curves that average all five GCM predictions and the two emission 

scenarios. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.

0 20 40 60 80 100

Probability, in percent

100

101

102

103

2

3

4

5

6

7

8

2

3

4

5

6

7

8

2

3

4

5

6

7

8

Oyster.River.Daily.1981.1999

Oyster.River.SRESa2.2046.2064

Oyster.River.SRESa2.2081.2099

Oyster.River.SRESb1.2046.2064

Oyster.River.SRESb1.2081.2099

Streamflow, in cubic feet per second

EXPLANATION

A

Data from the CNRM GCM
Figure 12 49

Figure 12. A–D, streamflow duration curves for the Oyster River and Pemigewasset River watersheds in New 

Hampshire for the two emissions scenarios for midcentury (SRESa2) and end-of-century (SRESb1) for the CNRM and 

GFDL models, two of the five GCMs; and E–F, average streamflow duration curves that average all five GCM predictions 

and the two emission scenarios. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

0 20 40 60 80 100

Probability, in percent

101

102

103

2

3

4

5

6

7

8

2

3

4

5

6

7

8

2

3

4

5

6

7

8

Oyster.River.Daily.1981.1999

Oyster.River.SRESa2.2046.2064

Oyster.River.SRESa2.2081.2099

Oyster.River.SRESb1.2046.2064

Oyster.River.SRESb1.2081.2099

Streamflow, in cubic feet per second

B

EXPLANATION

Data from the GFDL GCM
50 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Figure 12. A–D, streamflow duration curves for the Oyster River and Pemigewasset River watersheds in New Hampshire 

for the two emissions scenarios for midcentury (SRESa2) and end-of-century (SRESb1) for the CNRM and GFDL models, two 

of the five GCMs; and E–F, average streamflow duration curves that average all five GCM predictions and the two emission 

scenarios. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

0 20 40 60 80 100

Probability, in percent

102

103

104

3

4

5

6

7

8

2

3

4

5

6

7

8

2

3

4

5

6

7

8

2

3

4

5

Pemigewasset.River.Daily.1981.

Pemigewasset.River.SRESa2.2046

Pemigewasset.River.SRESa2.2081

Pemigewasset.River.SRESb1.2046

Pemigewasset.River.SRESb1.2081

Streamflow, in cubic feet per second

EXPLANATION

Data from the CNRM GCM

C
Figure 12 51

Figure 12. A–D, streamflow duration curves for the Oyster River and Pemigewasset River watersheds in New 

Hampshire for the two emissions scenarios for midcentury (SRESa2) and end-of-century (SRESb1) for the CNRM and 

GFDL models, two of the five GCMs; and E–F, average streamflow duration curves that average all five GCM predictions 

and the two emission scenarios. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

0 20 40 60 80 100

Probability, in percent

102

103

104

2

3

4

5

6

7

8

2

3

4

5

6

7

8

2

3

4

5

6

7

8

2

3

4

Pemigewasset.River.Daily.1981.

Pemigewasset.River.SRESa2.2046

Pemigewasset.River.SRESa2.2081

Pemigewasset.River.SRESb1.2046

Pemigewasset.River.SRESb1.2081

Streamflow, in cubic feet per second

D

EXPLANATION

Data from the GFDL GCM
52 Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire

Figure 12. A–D, streamflow duration curves for the Oyster River and Pemigewasset River watersheds in New 

Hampshire for the two emissions scenarios for midcentury (SRESa2) and end-of-century (SRESb1) for the CNRM and 

GFDL models, two of the five GCMs; and E–F, average streamflow duration curves that average all five GCM predictions 

and the two emission scenarios. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

E

0 20 40 60 80 100

Probability, in percent

101

102

3

4

5

6

7

8

9

2

3

4

5

6

7

8

9

2

3

Oyster.River.current

Oyster.River.2081

Oyster.River.2046

Streamflow, in cubic feet per second

EXPLANATION

Data from average of 

 all five GCMs
Figure 12 53

Figure 12. A–D, streamflow duration curves for the Oyster River and Pemigewasset River watersheds in New 

Hampshire for the two emissions scenarios for midcentury (SRESa2) and end-of-century (SRESb1) for the CNRM and 

GFDL models, two of the five GCMs; and E–F, average streamflow duration curves that average all five GCM predictions 

and the two emission scenarios. Definitions of abbreviations are listed in the “Abbreviations” section, p. ix.—Continued

F

0 20 40 60 80 100

Probability, in percent

102

103

104

2

3

4

5

6

7

8

9

2

3

4

5

6

7

8

9

Pemigewasset.River.current

Pemigewasset.River.2081

Pemigewasset.River.2046

Streamflow, in cubic feet per second

EXPLANATION

Data from average of 

 all five GCMs

For more information concerning this report, contact:

Director, New England Water Science Center

U.S. Geological Survey 

101 Pitkin Street

East Hartford, CT 06108

dc_nweng@usgs.gov

or visit our Web site at:

http://ct.water.usgs.gov

Publishing support by the: 

Pembroke and Rolla Publishing Service Centers
ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155047

Bjerklie and others—Simulating Hydrologic Response to Climate Change Scenarios in Four Selected Watersheds of New Hampshire—SIR 2015–5047