Prepared in cooperation with the 

New Hampshire Department of Environmental Services and the 

New Hampshire Department of Health and Human Services

Simulated Hydrologic Response to Climate Change 

During the 21st Century in New Hampshire

Scientific Investigations Report 2017–5143

U.S. Department of the Interior

U.S. Geological Survey
B

D

F

C

E

A

Front cover. View looking at the western side of Mount Washington as seen from Bretton Woods, New 

Hampshire, including the track of the Mount Washington Cog Railway to the left of the summit; photograph by 

user:wwoods, Wikimedia Commons, CC-BY-SA-3.0 / GFDL.

Back cover. A, View of the Suncook River in Epsom, N.H., where a flood in 2006 changed the course of the river; 

photograph by Robert H. Flynn, U.S. Geological Survey (USGS). 

B, View of Lake Winnipesaukee from Lake Shore Park, looking north toward Rattlesnake Mountain; photograph 

courtesy of user SteveA, used with permission by Don Zimmer from www.winnipesaukee.com. 

C, Communications hut at the summit of Mount Washington; photograph by David M. Bjerklie, USGS. 

D, Flooded USGS streamgage at Lamprey River near Newmarket, N.H.; photograph by Robert H. Flynn, USGS. 

E, Ellis River near Pinkham Notch, N.H.; photograph by David M. Bjerklie, USGS. 

F, Headwaters of the Ammonoosuc River in Sargents Purchase, N.H.; photograph by David M. Bjerklie, USGS.
Simulated Hydrologic Response to 

Climate Change During the 21st Century 

in New Hampshire

By David M. Bjerklie and Luke Sturtevant

Prepared in cooperation with the 

New Hampshire Department of Environmental Services and the 

New Hampshire Department of Health and Human Services

Scientific Investigations Report 2017–5143

U.S. Department of the Interior

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

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Deputy Director

exercising the authority of the Director

U.S. Geological Survey, Reston, Virginia: 2018

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Suggested citation:

Bjerklie, D.M., and Sturtevant, Luke, 2018, Simulated hydrologic response to climate change during the 21st century 

in New Hampshire: U.S. Geological Survey Scientific Investigations Report 2017–5143, 53 p., https://doi.org/10.3133/

sir20175143.

ISSN 2328-0328 (online)
iii

Acknowledgments

The authors would like to thank the project cooperators represented by Steven Couture of 

the New Hampshire Department of Environmental Services and Matthew Cahillane of the 

New Hampshire Department of Health and Human Services. In addition, valuable input and 

consultation were provided by Cameron Wake of the Sustainability Institute, University of 

New Hampshire; Fay Rubin of the Earth Systems Research Center at the University of New 

Hampshire; and the entire stakeholder group assembled for this project. The stakeholder group 

is acknowledged for supporting the project, for providing helpful guidance and suggestions 

regarding data and information needs, and for feedback. This publication was supported by 

Centers for Disease Control and Prevention (CDC) grant number #NUE1EH001332-01 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. The authors would also like to thank 

Jacob LaFontaine, Robert Dudley, Sara Levin, Gregory Stewart, Gardner Bent, and Thomas Mack 

of the U.S. Geological Survey for valuable review comments and suggestions during the course 

of this study.

v

Contents

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

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

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

Previous Investigations........................................................................................................................2

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

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

Methods of Study...........................................................................................................................................3

Precipitation Runoff Modeling System .............................................................................................5

Justification and Model Applicability.......................................................................................6

PRMS Parameterization .............................................................................................................7

Climate Data...........................................................................................................................................7

Streamflow Data ...................................................................................................................................8

Modeling Considerations and Calibration Objectives ....................................................................8

Calibration of the New Hampshire PRMS Model..........................................................................10

Evaluation of the New Hampshire PRMS Model....................................................................................10

Simulated Hydrologic Response to Climate Change .............................................................................18

Model Sensitivity to Land-Use Change and Effects Relative to Climate Change ....................19

Model Limitations................................................................................................................................34

Related USGS Datasets ..............................................................................................................................34

Summary and Conclusions.........................................................................................................................36

References Cited..........................................................................................................................................36

Appendix 1. Evaluation of the Jensen-Haise Method of Estimating Potential 

Evapotranspiration in New England Using the Precipitation Runoff Modeling System.....41

Figures

1. Map showing the location of hydrologic response units and the calibration

streamgages used in the Precipitation Runoff Modeling System model of

New Hampshire ............................................................................................................................4

2. Graphs showing A, simulated mean daily streamflow in relation to measured

mean daily streamflow and B, standard deviation of simulated streamflow in

relation to the standard deviation of measured mean daily streamflow at

27 calibration streamgages in New Hampshire for the period of record for each

streamgage..................................................................................................................................11

3. Graph showing the simulated monthly mean streamflow in relation to the

measured monthly mean streamflow at the 27 calibration streamgages in

New Hampshire for the period of record at each streamgage...........................................12

4. Map showing the spatial distribution of Nash-Sutcliffe efficiency statistic for

27 streamgages in New Hampshire at A, the daily time step and B, the monthly

time step.......................................................................................................................................14

5. Map showing the spatial distribution of the mean of the log residuals for

27 streamgages in New Hampshire at A, the daily time step, and B, the monthly

time step.......................................................................................................................................16
vi

6. Maps showing the spatial distribution of changes in simulated mean streamflow 

at the 27 calibration streamgages in New Hampshire between the current period 

and the end-of-century period using two general circulation models and the two 

emission scenarios: A, coupled general circulation model (CGCM) RCP 4.5, 

B, CGCM RCP 8.5, C, Geophysical Fluid Dynamics Laboratory (GFDL) model 

RCP 4.5, and D, GFDL RCP 8.5 ...................................................................................................20

7. Maps showing the spatial distribution of change in simulated mean groundwater 

recharge at hydrologic response units in New Hampshire between the current 

period and the end-of-century period, using two general circulation models (GCMs) 

and the two emission scenarios: A, combined general circulation model (CGCM) 

RCP 4.5, B, CGCM RCP 8.5, C, Geophysical Fluid Dynamics Laboratory (GFDL) 

RCP 4.5, and D, GFDL RCP 8.5 ...................................................................................................25

8. Maps showing the spatial distribution of percent changes in simulated monthly 

snowfall at hydrologic response units in New Hampshire between the current 

period and the midcentury and end-of-century period, using two general circulation 

models (GCMs) and the two emission scenarios: A, March, CGCM RCP 4.5, 2046 to 

2065; B, March, CGCM RCP 4.5, 2081 to 2100; C, November, GFDL RCP 8.5, 2046 to 

2065; D, February, GFDL RCP 4.5, 2046 to 2065; and E, February, GFDL RCP 4.5, 

2081 to 2100..................................................................................................................................29

Tables

1. General circulation models used for climate change scenarios in New Hampshire .......5

2. Calibrated parameters for the Precipitation Runoff Modeling System ...............................9

3. Description of 27 selected streamgages with streamflow calibration statistics in 

New Hampshire ..........................................................................................................................12

4. Simulated mean change in daily streamflow and standard deviation using two 

emission scenarios and five general circulation models, plus a composite, for 

27 streamgages in New Hampshire, by 20-year periods .....................................................18

5. Simulated change in mean monthly streamflow, in percent, for 27 selected 

streamgages in New Hampshire, composited from simulations made from all five 

general circulation models and two scenarios from 2046 to 2065 and 2081 to 2100 .......18

6. Standard deviation of the percent change in the mean of the monthly streamflows 

at 27 selected streamgages in New Hampshire, composited from simulations 

made from all five general circulation models and two scenarios from 2046 to 

2065 and 2081 to 2100 .................................................................................................................18

7. Simulated percent changes in average recharge in New Hampshire for 20-year 

periods using simulations made from five general circulation models and two 

emission scenarios.....................................................................................................................24

8. Simulated percent change in average snowfall in New Hampshire for 20-year 

periods using two emission scenarios and five general circulation models, in 

inches of snow............................................................................................................................24

9. Description of datasets of simulated hydrologic data from 1976 to 2005 and 

2010 to 2099..................................................................................................................................35
vii

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

square mile (mi2) 2.590 square kilometer (km2)

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

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

°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).

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

Abbreviations

CGCM coupled general circulation model

CMIP3 Coupled Model Intercomparison Project phase 3

CMIP5 Coupled Model Intercomparison Project phase 5

CNRM Centre National de Recherches Météorologiques

ESM Earth system model

GCM general circulation model

GFDL Geophysical Fluid Dynamics Laboratory

GIS geographic information system

HRU hydrologic response unit

IPCC Intergovernmental Panel on Climate Change

NHDES New Hampshire Department of Environmental Services

NHDHHS New Hampshire Department of Health and Human Services

NLCD National Land Cover Data

NRCC Northeast Regional Climate Center

NRMSE normalized root mean square error
viii

NSE Nash-Sutcliffe efficiency

PBIAS percent bias

PET potential evapotranspiration

PRMS Precipitation-Runoff Modeling System

RCP representative concentration pathway

RMSE root mean square error

RSR ratio of root mean square error to the standard deviation

SRES Special Report on Emissions Scenarios

USGS U.S. Geological Survey
Simulated Hydrologic Response to Climate Change During 

the 21st Century in New Hampshire

By David M. Bjerklie and Luke Sturtevant

Abstract

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

New Hampshire Department of Environmental Services and 

the Department of Health and Human Services, has developed a hydrologic model to assess the effects of short- and 

long-term climate change on hydrology in New Hampshire. 

This report documents the model and datasets developed by 

using the model to predict how climate change will affect the 

hydrologic cycle and provide data that can be used by State 

and local agencies to identify locations that are vulnerable to 

the effects of climate change in areas across New Hampshire.

Future hydrologic projections were developed from 

the output of five general circulation models for two future 

climate scenarios. The scenarios are based on projected 

future greenhouse gas emissions and estimates of land-use 

and land-cover change within a projected global economic 

framework. An evaluation of the possible effect of projected 

future temperature on modeling of evapotranspiration is summarized to address concerns regarding the implications of the 

future climate on model parameters that are based on climate 

variables. The results of the model simulations are hydrologic 

projections indicating increasing streamflow across the State 

with large increases in streamflow during winter and early 

spring and general decreases during late spring and summer. 

Wide spatial variability in changes to groundwater recharge 

is projected, with general decreases in the Connecticut River 

Valley and at high elevations in the northern part of the State 

and general increases in coastal and lowland areas of the State. 

In general, total winter snowfall is projected to decrease across 

the State, but there is a possibility of increasing snow in some 

locations, particularly during November, February, and March. 

The simulated future changes in recharge and snowfall vary 

by watershed across the State. This means that each area of the 

State could experience very different changes, depending on 

topography or other factors. Therefore, planning for infrastructure and public safety needs to be flexible in order to address 

the range of possible outcomes indicated by the various model 

simulations. The absolute magnitude and timing of the daily 

streamflows, especially the larger floods, are not considered 

to be reliably simulated compared to changes in frequency 

and duration of daily streamflows and changes in accumulated 

monthly and seasonal streamflow volumes.

Simulated current and future streamflow, groundwater 

recharge, and snowfall datasets include simulated data derived 

from the five general circulation models used in this study for 

a current reference time period and two future time periods. 

Average monthly streamflow time series datasets are provided for 27 streamgages in New Hampshire. Fourteen of the 

27 streamgages associated with daily streamflow time series 

showed a good calibration. Average monthly groundwater 

recharge and snowfall time series for the same reference time 

period and two future time periods are also provided for each 

of the 467 hydrologic response units that compose the model.

Introduction

A coordinated effort is underway in New Hampshire to 

proactively prepare for the effects of climate change using 

several action plans (New Hampshire Climate Change Policy 

Task Force, 2009). A key component of the action plans is a 

vulnerability assessment of hydrologic responses to climate 

change. The U.S. Geological Survey (USGS) is a partner with 

the New Hampshire Department of Environmental Services 

(NHDES) and the New Hampshire Department of Health and 

Human Services (NHDHHS) in the development of models 

and maps to assess the effects of short- and long-term climate 

change on hydrology in New Hampshire. For this effort, 

the USGS, in cooperation with the NHDES and NHDHHS, 

conducted an investigation to predict how climate change will 

affect the hydrologic cycle and provide data that can be used 

by State and local agencies to identify locations across the 

State that are vulnerable to the effects of climate change.

Hydrologic models can be used to map and project 

climate change effects for areas that otherwise could 

be observed only from measurements made at discrete 

locations, often in somewhat rural watersheds. The USGS 

watershed model Precipitation Runoff Modeling System 

(PRMS) provides a method to extend trends observed at 

specific locations spatially and temporarily across a region. 

Additionally, hydrologic systems may no longer be considered 

stable and predictable (Milly and others, 2008), indicating that 

possible future hydrologic conditions need to be simulated and 

evaluated so that these future conditions can be considered in 

the planning and design of infrastructure and other projects.
2 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Through the application of the USGS PRMS for the State 

of New Hampshire, this study is designed to assess potential 

changes in the hydrologic cycle that result from projected 

climate change in New Hampshire. In addition, a limited sensitivity analysis was completed to compare changes associated 

with climate change to potential changes that may result from 

increases in impervious surface. The New Hampshire PRMS 

rainfall-runoff model is the method used to simulate the 

spatial and temporal distribution of streamflow, groundwater 

recharge, and snowfall across New Hampshire.

Researchers have used 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, 2010a). Various types of hydrologic models have been used to couple simulated watershed 

processes with input data derived (downscaled) from general 

circulation models (GCMs; Hayhoe and others, 2006, 2007). 

As previously mentioned, this investigation uses the USGS 

PRMS to develop the New Hampshire regional rainfall-runoff 

simulations (Leavesley and others, 1983; Markstrom and others, 2015). The PRMS lends itself to the goals of this study 

because it can be parameterized at a wide range of scales with 

any scheme for subdividing the modeled area, often using subdivisions that correspond to watershed boundaries. 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 runoff, 

groundwater storage flux, snow cover, snowmelt, and streamflow, as well as other hydrologic variables. The PRMS model 

computes these water balance variables for all subdivisions 

that compose the watershed of interest, referred to as hydrologic response units (HRUs).

Previous Investigations

Hydrologic vulnerability from future climate scenarios 

has increasingly become a major point of research in New 

Hampshire and the Northeast (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 the seacoast region of the State, 

Mack (2009) found that potential effects of climate change 

on groundwater by 2025 are likely to be more important 

for groundwater recharge than for water demand. Using a 

watershed runoff model to simulate groundwater recharge 

and snowfall in the Connecticut River Basin (western New 

Hampshire), Bjerklie and others (2010a) found that, from 

1960 to 2007, in general, snowfall decreased and base flow 

(discharge to streams from groundwater) increased. These 

changes have not been distributed uniformly in time or space, 

and snowfall and base flow have marginally decreased or 

increased in some places in response to local conditions. 

Similarly, for the 57-year study from 1950 to 2006, Hodgkins 

and Dudley (2011) found increased base flow and stormflow 

in New England, and Dudley and Hodgkins (2013) found 

increased groundwater levels.

Hamilton and others (2010) reported on climate trends in 

New Hampshire; air temperature and precipitation increased 

throughout the 20th century, accelerating since 1970. Hamilton and others (2010) found indications of decreased 

snowfall from 1970 to 2009; however, they acknowledge 

that the trends in snowfall are complicated by inconsistencies. Jacobs and others (2014) found evidence of consistently 

decreasing snowfall across New Hampshire from 1955 to 

2012, using data from NHDES and from the Hubbard Brook 

Experimental Forest.

Wake and others (2014a,b) indicated that the climate 

model consensus shows increasing air temperature and 

precipitation for New Hampshire in the future, at least until 

the year 2100. Bjerklie and others (2010a, 2015) projected 

that, in response to the climate change, groundwater recharge 

(and subsequently base flow) will continue to increase and 

snowfall will decrease in parts of New Hampshire and the 

Connecticut River Basin. These effects do not occur everywhere but depend on the results of the GCM (often referred 

to as a “global climate model”) greenhouse gas emission 

scenarios that are coupled with land use and land cover within 

a projected global economic framework (Solomon and others, 2007; Stocker and others, 2013). Similar projections are 

reported by the U.S. Geological Survey (2016) on the basis of 

the Intergovernmental Panel on Climate Change (IPCC) fifth 

climate change assessment report (Stocker and others, 2013) 

and the National Climate Assessment (Melillo and others, 

2014). Campbell and others (2011) report that, in the White 

Mountains of New Hampshire, precipitation has increased 

during the latter half of the 20th century, resulting in increased 

water yield, and the upward trend in precipitation is expected 

to continue into the future. Researchers also report a decrease 

in evapotranspiration (ET) during the same period (Campbell 

and others, 2011; Melillo and others, 2014); however, they 

expect that ET will increase in the future in response to rising 

air temperatures (projected by GCMs) that possibly keep pace 

with increasing precipitation, offsetting continued increases in 

water yield.

Past trends show precipitation increases across much 

of 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 in streamflow 

that occurred around 1970 coincides with changes in the 

timing of snowmelt peaks in New England (Hodgkins and 

others, 2003). Projections from GCM simulations consistently 

indicate that the trends in precipitation, temperature, and total 

runoff will continue to increase in the northeastern United 

States (Hayhoe and others, 2006). Additionally, Demaria 

and others (2016) project increases in 3-day peak flows and 

decreases in 7-day low flows in the Northeast on the basis of 

future climate simulations from 16 GCMs.
Methods of Study 3

Changes in land and water use also could affect hydrologic conditions in the future. This is particularly true in areas 

with high populations, including areas within the Merrimack 

River and Piscataqua River Basins in New Hampshire. Claessens and others (2006) suggest that land-use change is not as 

effective as climate change and water use in causing changes 

in the water budget for an urbanizing watershed in Massachusetts; however, the balance of these effects likely will vary 

depending on the spatial distribution of climate change factors 

and degree of urbanization. In contrast, LaFontaine and others 

(2015) found that land-use change (characterized as increasing 

development and impervious surface) can have a great effect 

on total runoff generated from a watershed; however, stormwater management practices can largely mitigate this effect.

Purpose and Scope

A watershed scale hydrologic model using the PRMS 

was developed to assess the effects of climate change on 

hydrologic responses and vulnerability. The New Hampshire 

rainfall-runoff model developed in this study using the USGS 

PRMS provides information that will inform users on the 

potential effects of climate change on aspects of the hydrologic cycle in New Hampshire. The purpose of this report is to 

document the model, datasets, and application of PRMS that 

simulates future hydrologic conditions in the State. Projected 

changes in snowfall, mean streamflow, and mean groundwater recharge are shown in illustrations and tables. The study 

makes available hydrologic simulation data that reflect the 

projected future conditions so that State and local agencies can 

address specific vulnerabilities for specific locations across 

the State that might be susceptible to those vulnerabilities. A 

number of output datasets developed in this study are available 

from Bjerklie and Sturtevant (2017).

Description of Study Area

The physiography and general climate of New Hampshire 

have been described by Flanagan and others (1999), and that 

description is summarized in this report (fig. 1). New Hampshire can be divided into three broad physiographic regions: 

the White Mountains, the Connecticut River Valley and New 

England Uplands, and the Seaboard Lowlands. The White 

Mountain region is dominated by mountain landscapes with 

elevations (relative to the North American Vertical Datum of 

1988 [NAVD 88]) ranging from approximately 1,500 feet (ft) 

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 river flows through the White Mountain region and the New England Uplands, which are south 

of the White Mountains in New Hampshire. The New England Uplands region consists of undulating, hilly topography, 

ranging in elevation from less than 1,000 ft to greater than 

2,000 ft. The Seaboard Lowlands region is lower in elevation, at less than 500 ft, and is less hilly than the New England 

Upland region.

The climate in the study area is 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. The 

climate varies between the physiographic regions. 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, 

elevation, and proximity to the ocean. The statewide average 

amount of precipitation 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 

elevation, terrain, and latitude. Snowfall amounts (reported 

here as inches of snow that has fallen as opposed to depth of 

snowpack or snow as water equivalent) 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 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 (Bjerklie 

and others 2010a; Wake and others 2014a,b). 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 of Study

A statewide hydrologic model was developed for the 

river watersheds of New Hampshire (fig. 1) by using the 

PRMS (Leavesley 1983; Markstrom and others, 2015). The 

calibration of the New Hampshire regional model is based 

on daily streamflow records from USGS streamgages for 

the period of record from 1981 to 2000. Future hydrologic 

conditions are simulated using daily simulated air temperature 

and precipitation from GCMs (Solomon and others, 2007; 

Stocker and others, 2013). 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; Hay and others, 2014). The 

future time periods are compared with the current reference 

(baseline) time period so that projected change can be evaluated in a self-consistent hydrologic framework described by 

the PRMS model. The output data include estimates of daily 

base flow, total river streamflow, and snowfall for each HRU 

in the model.
4 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

01052500

01064300

01064400

01064500

01065000

01073000

01073587

01074520

01075800

01078000

01087000

01089000

01089100

01092000

01093800

010965852

01127880

01130000

01137500

01141800

01150500

01152500

01160350

01161000

01064801

01076500

01085800

72° 70°

45°

30'

Base from U.S. Geological Survey

The National Map digital data

Albers equal area conic projection

North American Datum 1983

EXPLANATION

Streamgages used for PRMS model, with

 station numbers—Derived from U.S. Geological 

 Survey (2017b)

Hydrologic response unit (HRU) boundaries—

 Derived from U.S. Geological Survey (2008b)

010965852

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

MOUNT WASHINGTON

Connecticut River

Piscataqua River

Merrimack River

Figure 1. The location of hydrologic response units and the calibration streamgages used in the Precipitation Runoff Modeling 

System (PRMS) model of New Hampshire. USGS, U.S. Geological Survey.
Methods of Study 5

The PRMS generates daily runoff from input of daily 

air temperature and precipitation data. The New Hampshire 

PRMS model is subdivided into small subwatersheds (catchments) that can be aggregated into larger river watersheds 

of interest. As previously stated, these subwatersheds are the 

basic computational unit of the model and are referred to as 

HRUs. The statewide model also includes several hundred 

river reaches that constitute the simulated river network that 

routes runoff generated from the HRUs downstream. The 

statewide model consists of 467 HRUs (fig. 1).

The streamgages used for calibration have at least 5 years 

of daily data, are widely distributed across the State, and are 

not significantly affected by lake and reservoir storage or 

water diversions because these are not explicitly modeled 

in the PRMS. The parameters for each HRU in the model 

were defined using a GIS (Steve Markstrom and Lauren Hay, 

USGS, written commun., 2013) or were adjusted on the basis 

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

The adjusted parameter is the monthly Jensen-Haise potential 

evapotranspiration (PET) coefficient (Bjerklie and others, 

2010a), which limits the amount of daily PET available each 

month. The model calibration was evaluated on the basis of 

a 20-year mean (1981–2000) of simulated daily streamflows 

compared to measured streamflows from a set of 27 USGS 

calibration streamgages (fig. 1).

In this study, five GCMs were used to develop input datasets for the PRMS that represent potential future climate conditions (table 1). Each GCM simulates a representative current 

reference conditions (1981–2000) dataset and two future 

scenarios, including IPCC scenarios RCP 4.5 and RCP 8.5 

(RCP stands for representative concentration pathway). The 

scenarios were developed taking into consideration various 

greenhouse gas emission levels and changes in socioeconomic 

conditions (land use and land cover within a projected economic framework) over time. For the RCP 4.5 scenario, it was 

assumed that total radiative forcing (that is, carbon emissions 

and other greenhouse gases) is stabilized shortly after 2100 at 

current target levels (Smith and Wigley, 2006; Clarke and others, 2007; Wise and others, 2009). For the RCP 8.5 scenario, 

it was assumed that greenhouse gas emissions continue over 

time leading to relatively high greenhouse gas concentrations 

in the atmosphere (Riahi and others, 2007). In this report, the 

RCP 4.5 scenario (representing a low emissions scenario) is 

comparable to the previous Coupled Model Intercomparison 

Project phase 3 (CMIP3; U.S. Department of Energy, Lawrence Livermore National Laboratory, 2006), the IPCC special 

report on emissions scenario (SRES) B1 (Nakićenović and 

Swart, 2000), and the RCP 8.5 scenario (representing a high 

emission scenario) is comparable, but with somewhat higher 

emissions, to SRES A2 used by Bjerklie and others (2015).

The time frame for the projections includes two 20-year 

periods in the 21st century, from 2046 to 2065 and from 2081 

to 2100. These are referred to as midcentury and end-ofcentury periods, respectively. As previously stated, the PRMS 

model simulates hydrologic variables for a current reference 

period spanning the 20 years from 1981 to 2000. The 20-year 

periods were chosen to provide an adequate length of time 

to derive mean statistics but a short enough length of time so 

that the differences between periods are easily discerned from 

the means and standard deviations. All the GCMs selected 

were tested for their ability to simulate the sample statistics 

of weather and hydrologic variables using monthly water 

balances (LaFontaine and Hay, 2013; LaFontaine and others, 

2013; Lauren Hay, USGS, written comm., 2016).

Precipitation Runoff Modeling System

The PRMS is a USGS public access watershed 

runoff model (Leavesley and others, 1983; Markstrom and 

others, 2015) that has been updated by several versions 

(U.S. Geological Survey, 2017a). The most recent available 

Table 1. General circulation models used for climate change scenarios in New Hampshire.

Model designation Model identifier 

in this report Research group and country of origin Years of data

CNRM–CM5 CNRM Centre National de Recherches Météorologiques, Centre Européen de Recherche et Formation, Avancée en Calcul Scientifique - France1981–2000 (current)

2046–2065 (midcentury)

2081–2100 (end-of-century)

CSIRO–Mk3.6.0 CSIRO Commonwealth Scientific and Industrial Research Organisation 

in collaboration with Queensland Climate Change Centre of 

Excellence - Australia

1981–2000 (current)

2081–2100 (end-of-century)

GFDL–ESM2G GFDL National Oceanic and Atmospheric Administration - United States 1981–2000 (current)

2046–2065 (midcentury)

2081–2100 (end-of-century)

MPI–ESM–MR ESM Max-Planck-Institut für Meteorologie - Germany 1981–2000 (current)

2081–2100 (end-of-century)

MRI–CGCM3 CGCM Meteorological Research Institute - Japan 1981–2000 (current)

2081–2100 (end-of-century)
6 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

model structure was used for this study (version 4.0, 2015). 

The PRMS simulates the hydrologic cycle using the spatial 

variation in measurable physical characteristics, including land 

cover, topography, soil, and geology, that can be quantified 

using a GIS. The data and an automated method developed by 

the USGS (Viger, 2014; Viger and Bock, 2014) were used to 

develop the model spatial domain and to derive many of the 

parameters used in the model. Data derived from the National 

Land Cover Data (NLCD; Vogelmann and others, 2001) were 

used to set many of the initial parameter values.

The modeled region is divided into subwatersheds, or 

HRUs. The daily water balance is simulated for each of the 

HRUs on the basis of precipitation and temperature input 

data. The HRUs and the 12-digit hydrologic unit code (HUC) 

subwatersheds (Seaber and others, 1994), although approximately the same scale, are not the same. The HRUs in the 

PRMS model include a wider range of sizes and could divide 

some HUC–12s into smaller subwatersheds. In some cases, 

more than one HUC was aggregated. Although PRMS does 

not require HRUs to be delineated along watershed boundaries, the HRUs in the model developed in this study represent 

subwatersheds so that each HRU can represent a streamflow at 

the HRU outlet.

The total runoff simulated by PRMS is aggregated from 

the HRUs draining to stream segments that represent the 

stream channel network in New Hampshire. Each stream segment defined in PRMS represents a channel reach bounded 

on each side by an HRU and, with the exception of the most 

upstream segment, is fed by an upstream segment and the two 

bounding HRUs. The accumulated discharge in the segment is 

then routed to the next downstream segment using a Muskingum routing scheme (Markstrom and others, 2015).

Justification and Model Applicability

The PRMS model uses physically based algorithms to 

simulate various hydrologic processes (Markstrom and others, 

2015). The procedure employed in this study first calibrates 

the coefficients of the PET to match the annual water balance 

(Bjerklie and others, 2010a, b), then varies the value of various routing coefficients and rain/snow temperature threshold 

to match the magnitude and timing of streamflow records at 

USGS streamgages. The method of assigning values to the 

routing coefficients consists of an algorithm that is tied to 

measured physical characteristics of the geospatial domain. 

With this strategy, model uncertainty is limited to the accuracy 

of the input data, how well the process algorithms represent 

the various hydrologic pathways, and the effects of spatial 

scaling. Uncertainty of the model is understood from the 

calibration statistics and is then assumed to apply to future 

conditions. This assumption is valid for the various algorithms 

that are physically based and not subject to changing parameter values that would be associated with climate change—

meaning those parameters that are arbitrarily calibrated to a 

particular set of climatic characteristics.

Three processes are represented in the model that may 

be altered for the future climate; these are (1) the temperature 

lapse rate, which is important in the snowmelt simulation; 

(2) the relation between the occurrence of precipitation and 

assumed cloudiness, which the model incorporates to estimate 

incident solar radiation; and (3) the assumed relation between 

the daily maximum and minimum air temperatures and the 

vapor pressure gradient. The latter two processes are critical to 

the estimation of PET.

The lapse rate of temperature with elevation is an important driver for both snow accumulation and snowmelt in HRUs 

with high topographic relief. Although the dry adiabatic lapse 

rate would not be expected to change in a changing climate 

because it is a function of the specific heat of dry air (Wallace and Hobbs, 1977), the lapse rate of moist air varies as a 

function of relative humidity (Wallace and Hobbs, 1977) and 

as such will be affected by changes in atmospheric moisture 

resulting from climate change, generally decreasing with 

increasing moisture. The decrease in lapse rate would result in 

more uniform accumulation and melting of snow as a function of elevation. However, the actual lapse rate may vary 

considerably because of local variations in topography and 

humidity not captured by the GCM input data or at the HRU 

scale of the PRMS model; the greatest potential for variation during the snowmelt phase is because of the potential for 

greater variation in humidity during periods of no precipitation. On a monthly or seasonal time frame, we would expect 

that local variations in lapse rate, spatially and temporally, 

would average out and reflect the mean temperature input to 

the model in a consistent manner across the modeled region. 

Thus, the relative change in snow accumulation and melt 

between simulations of current and future climate conditions 

would be expected to be comparable on the HRU scale and 

on longer time frames under the assumption that changes in 

humidity would not alter the monthly and seasonal balance 

between snowfall and snowmelt. Consequently, we do not 

place confidence in the simulated timing and magnitude of the 

daily snowfall and snowmelt and report only monthly snowfall 

amounts in order to avoid the inherent complexities associated with smaller spatial and temporal scales, particularly with 

regard to the snowmelt process.

The model simulation of PET is critical to the annual, 

seasonal, and monthly daily water balance outcomes; therefore, the potential effects of the cloudiness and vapor pressure 

gradient assumptions are critical to assessing the application 

of the calibrated model to future conditions. It is assumed that 

cloudiness associated with precipitation events and incident 

solar radiation will not change. Thus, the model calibration for the baseline conditions is expected to be applicable to future conditions, and the calibrated model, in this 

regard, should perform similarly for future conditions as for 

baseline conditions.

With respect to the assumption that the relation between 

daily air temperature range and the vapor pressure gradient will remain constant for the current baseline and future 

conditions, a detailed analysis of the physical process of PET 
Methods of Study 7

and its relation to atmospheric variables is provided in the 

appendix, along with a comparison of model representations 

of PET and sensitivity of the PET estimates to temperature 

change. On the basis of the analysis presented in the appendix, 

PET is expected to show only minor effects (less than 10 percent) from changes in temperature over the expected range of 

climate change in the 21st century. Thus, on the basis of the 

analysis, in humid regions that are expected to remain humid 

during the next 100 years (2000–2100), as in the northeastern 

United States, we expect that the assumption remains valid; 

therefore, the calibrated model can be used to represent future 

conditions with changed air temperatures.

PRMS Parameterization

The New England regional PRMS model was initially 

parameterized using general GIS-based strategies (Steve 

Markstrom and Lauren Hay, USGS, written commun., 2013) 

and provided the starting point for the parameterization of the 

New Hampshire model (Bjerklie and others, 2015). Bjerklie 

and others (2010a, b) determined several other parameters 

pertaining to groundwater fluxes that were based on surficial 

geologic characteristics of several watersheds in Connecticut. 

These methods have been applied in New Hampshire for the 

model developed in this study.

The PRMS model used for this study was parameterized at the approximate HUC–12 scale using methods that 

compute parameter values from GIS data or set default values 

within a range defined by physical limits (Markstrom and 

others, 2015). 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 from national datasets that are used to derive 

spatial information about the HRU including the coordinates 

of the HRU centroid, topography (slope, aspect, and elevation; 

U.S. Geological Survey, 2008a), soils (U.S. Department of 

Agriculture, 2008), land cover (Vogelmann and others, 2001; 

U.S. Geological Survey, 2008c), and hydrography (U.S. Geological Survey, 2008b). Snow parameters are primarily derived 

from an analysis of the temperature input data, which controls 

when all snow, mixed rain and snow, and all rain occur. These 

parameters were globally adjusted for all HRUs in the model 

to attain an optimal representation for snow accumulation 

across the State.

Climate Data

The PRMS model uses daily precipitation accumulation 

and maximum and minimum daily air temperatures as input. 

The daily precipitation and air temperature data used for this 

model were obtained through the USGS Geo Data Portal 

(U.S. Geological Survey, undated) for a 20-year calibration 

period (1981–2000) and for future climate datasets consisting of 20-year periods (2046–2065, 2081–2100). The 20-year 

calibration and the 20-year simulation periods provide sufficient length of time series data to yield representative mean 

values and variability so that inferential statistics are meaningful and are of sufficient length for comparison with regional 

projections of atmospheric climate change studies (Wake and 

others, 2014a,b), as well as other regional climate change 

projects (U.S. Geological Survey, 2016).

The input time series of daily precipitation and minimum and maximum temperatures used to calibrate the model 

was derived by Maurer and others (2002; referred to as “the 

Maurer dataset”) and was obtained through the USGS Geo 

Data Portal (U.S. Geological Survey, undated). The Maurer 

dataset consists of a gridded product available on a daily time 

step at the 0.125-degree (°) spatial resolution (approximately 

7.46 miles [mi] or 12 kilometers [km]) for historical periods 

of record for the continental United States (1949–2010). The 

Maurer dataset includes daily precipitation and maximum 

and minimum air temperatures. The data are modeled from 

streamgage data using physical algorithms to distribute the 

streamgage data to the 0.125° grid spacing. These data provide 

a consistent input dataset, although smoothing of streamgage 

data over the grid could result in damping of extreme and 

more intense precipitation events. The Maurer dataset also 

provides the basis for statistically downscaling the future 

GCM input data. The Maurer dataset was used to correct bias 

and to downscale the GCM input from large 1° grids in the 

GCM to the 0.125° grid and then to the HRU through the Geo 

Data Portal (U.S. Geological Survey, undated).

The future climate datasets were also obtained through 

the Geo Data Portal and are derived from downscaled GCM 

simulation data that are stored there, as previously discussed. 

Previous studies of climate change in New Hampshire (Wake 

and others, 2014a,b; Bjerklie and others, 2015) used data from 

the CMIP3 (U.S. Department of Energy, Lawrence Livermore National Laboratory, 2006) set of experiments with 

associated SRESs (Nakićenović and Swart, 2000). In 2015, 

Climate Model Intercomparison Project phase 5 (CMIP5; 

U.S. Department of Energy, Lawrence Livermore National 

Laboratory, 2011) experimental data that employ revised emission scenarios of the RCPs became available. This study uses 

the updated CMIP5 RCP simulations. 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 1. 

All five of the GCMs used in this study have been shown to 

provide water balance results for New England that are statistically comparable to those generated using the Maurer dataset 

for 1950 to 2000 (Hay and others, 2014; Lauren Hay, USGS, 

written comm., 2016); the GCMs are considered appropriate 

for use in New England (Intergovernmental Panel on Climate 

Change, 2007).

All the GCM projections show substantial temperature 

and precipitation increases during the 21st century for watersheds in New Hampshire. Compatible datasets for the current 

(1981–2000) period and two future (2046–65 and 2081–2100) 
8 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

periods were derived for all GCMs. These time frames represent average climate conditions projected to occur for each 

20-year climate period. Two RCPs were chosen for each of the 

two periods (four datasets). One RCP incorporates the assumption of greater reductions in future greenhouse gas emissions 

(RCP 4.5) than the other RCP (RCP 8.5). RCP 4.5 and RCP 

8.5 are comparable to SRES B1 and SRES A2, respectively, 

used in previous studies in New Hampshire (Bjerklie and 

others, 2015); the RCP 8.5 is somewhat less optimistic than 

SRES A2. The RCP scenarios 4.5 and 8.5 provide the upper 

and lower reasonable bounds of possible climate changes in 

New Hampshire for the 21st century. The smallest projected 

changes in precipitation and temperature are associated with 

the RCP 4.5, and the largest projected changes and the largest uncertainties are associated with RCP 8.5. In general, the 

largest variability and uncertainty in the GCM projections are 

for precipitation. The future GCM input datasets represent one 

specific climate realization and, therefore, may not represent 

the “expected” future conditions, but rather one equally possible condition. It is anticipated that the hydrologic simulations 

are primarily driven by the input data from the GCMs and will 

reflect the specific climate realization that the GCMs provide. 

In lieu of multiple realizations, we looked for consistency 

between the five GCMs to provide a level of confidence in the 

direction (increasing or decreasing) and spatial variability of 

future change.

The three periods of downscaled data (1981–2000, 

2046–2065, and 2081–2100, representing the current, midcentury, and the end-of-century conditions, respectively) were 

used to calculate the difference between the simulated current 

and future conditions under each of the two carbon emission 

scenarios. The changes in daily precipitation and maximum 

and minimum air temperatures associated with each HRU in 

the PRMS model and each GCM and scenario show ranges 

similar to those reported by Bjerklie and others (2010b), Wake 

(2014a,b), and Bjerklie and others (2015) for the midcentury 

and end-of-century periods.

Streamflow Data

USGS streamflow records were used for model calibration (U.S. Geological Survey, 2017b). 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 the effects of flow regulation 

by dams and diversions and storage effects from lakes were 

not important in streamflow characteristics. The calibration 

points consisted of 27 streamgages in New Hampshire (fig. 1). 

The parameters that were selected for calibration are shown 

on table 2. These were selected for calibration on the basis of 

the above criteria and had at least 5 years of record during the 

calibration period (1981–2000).

Modeling Considerations and Calibration 

Objectives

This study compared possible future hydrologic changes 

for various climate change scenarios in New Hampshire. The 

scenarios were generated using different GCMs and different 

carbon emission projections. Hydrologic effects of climate 

scenarios were simulated by use of the self-consistent regional 

hydrologic model for New England, including New Hampshire, regional PRMS model. Hydrologic self-consistency such 

that all parameters are calibrated in the same way for all areas 

of the state and each model run (thus avoiding local differences in calibration that might weight certain processes more 

than others depending on the input data) is critical for enabling 

direct comparisons between the different scenarios in time and 

space. 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 responses to 

future scenarios would not be directly comparable.

As mentioned previously, historical climate data (Maurer 

and others, 2002) were obtained from the USGS Geo Data 

Portal. The data were interpolated from streamgage data by 

using physically based algorithms to a grid spacing of approximately 7.46 mi (12 km) and are consistent with other national 

gridded weather datasets, including the Daymet dataset, which 

is a gridded daily weather dataset for the continental United 

States (Oak Ridge National Laboratory, undated), as evidenced by similar calibration results. Daymet input data were 

used in the New Hampshire PRMS pilot study (Bjerklie and 

others, 2015). 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 

larger grids, and the issues of smoothing and a loss of detail 

resulting from the resolution of the GCM data are likely to be 

inherent in future simulations.

Data and calibration consistency are key to the regional 

model development 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 consist of streamflow statistics measured at USGS 

streamgages, streamflow hydrograph characteristics, and the 

annual and seasonal water balance. To avoid the potential for 

variable and (or) exaggerated response to future input datasets 

between HRUs, the following modeling and calibration objectives were adopted for this study.

• Use of regionally distributed input datasets for calibration and for future comparisons. The input datasets are 

derived from nationally consistent sources and developed using consistent methods. The same GCMs used to 

derive future scenario input datasets are used to derive 

the current scenario input climate datasets so that comparisons are not confounded by inherent data biases.
Methods of Study 9

Table 2. Calibrated parameters for the Precipitation Runoff Modeling System.

[Parameter calibrations based on defined physical relations that can be applied in any hydrologic response unit (HRU) using a geographic information system 

(GIS). Elevation datum is National Geodetic Vertical Datum of 1929]

Parameter Function Calibration method

Water balance

jh_coef Controls monthly variation in potential evaporation demand Adjusted based on calibration method in Bjerklie and others 

(2010b)

jh_coef_hru Controls spatial variation in potential evaporation demand 

as a function of HRU elevation and latitude

Adjusted based on calibration method in Bjerklie and others 

(2010b) and physical characteristics of the HRU

Storage and routing of water through the watershed

gwflow_coef Controls daily outflow from the groundwater reservoir 

associated with each HRU

Value determined from regression relation using groundwater base-flow index and landcover as independent 

variables

fastcoef_lin Controls daily outflow from the upper part of the subsurface 

(between the spoil and the saturated groundwater) reservoir associated with each HRU

Varies as a function of the amount of coarse stratified drift 

in the HRU, parameters set by trial and error

slowcoef_lin Controls daily outflow from the lower part of the subsurface 

(between the spoil and the saturated groundwater) reservoir associated with each HRU

Set as one half of the value of the fastcoef_lin, in lieu of no 

specific information available to define its value in relation to G. Other fractions were tested and found to have 

minimal effect on calibration

smidx_coef Controls the rate of growth of the variable source area of 

saturated soil around streams and wetlands that contribute 

direct runoff to water bodies

Varies as a function of the fraction of area draining to 

depression storage

carea_max Maximum area of variable saturated source in an HRU that 

can contribute directly to water bodies

Varies as a function of the soil type and the amount of 

depression storage in the HRU

soil2gw_max Controls the amount of infiltrated water percolating directly 

to the saturated groundwater from the soil layer, bypassing the subsurface zone

Varies as a function of the amount of coarse stratified drift 

in the HRU

Snow accumulation and storage

tmax_allrain Temperature at which precipitation is considered all rain, 

with no snow accumulation

Adjusted by trial and error

• Use of consistent methods to assign parameter values 

for all regions in the model.

• Parameterization of the model with methods based on 

physical features of watersheds. Parameters that cannot 

be assigned a value from data are left as default values 

that represent a typical condition to ensure that the 

values are consistent across the State.

• Calibration objectives include water balances and 

comparison of streamflow statistics at calibration 

streamgages.

The primary objective function includes goodness of 

fit as measured with the Nash-Sutcliffe efficiency (NSE) 

statistic, which weighs the timing and magnitude of the time 

series of simulated streamflow estimates (Nash and Sutcliffe, 

1970; Moriasi and others, 2007). The NSE is used to compare 

the difference between the paired measured and simulated 

streamflows with the corresponding difference between the 

measured and the mean of the measured streamflows (see 

Bjerklie and others, 2015). Additionally, the residual variance 

(difference between measured and simulated) is compared 

with the variance of the measured values from its own mean, 

thus providing a measure of how well the simulated values fit 

the observations relative to a constant mean value. Following Moriasi and others (2007), an NSE of 0.5 or greater is 

considered a satisfactory or good fit between the simulated and 

measured hydrographs, and an NSE of 0.4 to 0.5 is considered marginally satisfactory. Values of NSE less than 0.4 are 

considered unsatisfactory.

Additional measures of goodness of fit, addressing 

systematic error (bias) and comparability of overall sample 

variability include the percent bias (PBIAS; Moriasi and others, 2007), the log residual of error between the simulated and 

measured daily streamflows, the ratio of the root mean square 

error to the standard deviation (RSR; Moriasi and others, 

2007), and the normalized root mean square error (NRMSE) 

expressed as a percent. The NRMSE represents the sample 

standard deviation of the differences between simulated values 

and measured values (Hyndman and Koehler, 2006) and 

aggregates the magnitudes of the errors between the paired 

simulated and measured streamflows in the time series into 

a single measure of predictive power. The NRMSE is thus 

a measure of overall accuracy. Normalizing the root mean 

square error (RMSE) facilitates the comparison of streamflow 
10 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

estimates at streamgage locations with different scales. The 

RMSE is normalized in this study on the full range of measured streamflow values, computed as the square root of the 

summed squared differences between the paired measured 

streamflow divided by the number of paired values and normalized (divided) by the difference between the maximum 

measured value and the minimum measured value.

Calibration of the New Hampshire PRMS Model

The model was calibrated to measured USGS streamgage 

records at the regional scale for the period 1981 to 2000. 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 27 USGS streamgages. This approach 

derives coefficients directly from the temperature records and 

HRU elevation (Markstrom and others, 2015). The primary 

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

with local variability in streamflow in individual rivers, on the 

whole, being within acceptable calibration standards for daily 

and monthly streamflows.

The PRMS model simulates unregulated natural streamflow and, as applied in this study, does not simulate the effects 

of dams, lake and reservoir storage, and groundwater and 

surface-water diversions; therefore, these were not considered 

in the calibration. A method to adjust the monthly evapotranspiration coefficient in the model developed by Bjerklie 

and others (2010a) was used to calibrate the model-simulated 

evapotranspiration and general water balance for New Hampshire HRUs. Other parameters that affect snow, snowmelt, and 

interception were adjusted to be comparable to Bjerklie and 

others (2010a) or were kept at their GIS derived values. The 

groundwater base-flow coefficient, which controls the rate of 

outflow from the groundwater reservoir associated with each 

HRU, was estimated using a method that predicts the value 

from the land cover and hydrologic characteristics of each 

HRU (R.J. Viger, USGS, written commun., 2016). The default 

values for the subsurface routing coefficients, which represent 

the interflow component of groundwater runoff, were not 

adjusted in the model because there was insufficient regional 

information to assign values on the basis of the GIS data. The 

subsurface runoff coefficient affects groundwater recharge and 

the shape of the hydrograph recession (see calibrated parameter list; table 2).

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 values 

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

the data. Simulation errors (difference between the USGS 

streamflow data and the simulated streamflows) for the 

calibrated model, therefore, will be consistently carried over 

to future time frames, 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. The default 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 equaled 

the mean annual streamflow (total runoff) for the calibration 

period (1981–2000) at the 27 streamgages. It was assumed that 

matching the long-term mean does not seriously bias the daily 

and seasonal water balance estimates.

Evaluation of the New Hampshire 

PRMS Model

The calibrated model simulations are evaluated by statistically comparing the population of simulated streamflows and 

the USGS measured streamflow. The results are evaluated with 

the NSE statistic, the PBIAS, the RSR, the NRMSE expressed 

as a percent, and the log residual of the simulated minus the 

measured streamflow. The mean and standard deviation of 

the log residual reduce the weight (influence) of the low end 

and high end errors, providing a better picture of the typical 

residual error of the time series when the daily and monthly 

residual errors are averaged. The log residual, the PBIAS, the 

NRMSE, and the RSR all characterize the overall error and 

bias in the simulated hydrographs compared to the measured 

hydrographs. The NSE statistic is a measure of how well a 

simulated dynamic time series (streamflow hydrograph) fits 

the measured time series during the period of record relative to 

the mean, accounting for timing and quantity. 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 PRMS model calibration was evaluated on the daily 

time step, and the aggregated monthly and annual time steps, 

by comparing the simulated streamflows with measured 

streamflows at USGS streamgages in the State. The New 

Hampshire model showed very good simulation of the mean 

streamflow at all the streamgages after the statewide calibration (fig. 2; table 3). The error for the mean annual simulated 

daily streamflows for the 27 streamgages is 6 percent with an 

absolute error of 0.15 in. of runoff per unit area of watershed, 

which indicates that the regional annual water balance, on 

average, is well simulated. The error in the mean of the standard deviation of the simulated daily streamflows is –4 percent 

with an absolute error of –0.13 in. of runoff per unit area of 
Evaluation of the New Hampshire PRMS Model 11

0

1,000

2,000

3,000

4,000

5,000

6,000

0 1,000 2,000 3,000 4,000 5,000 6,000

Simulated mean daily streamflow, in cubic feet per second

Measured mean daily steamflow, in cubic feet per second

0

1,000

2,000

3,000

4,000

5,000

6,000

0 1,000 2,000 3,000 4,000 5,000 6,000

Standard deviation of simulated streamflow, in cubic feet per second

Standard deviation of measured mean daily streamflow, in cubic feet per second

A

B

Figure 2. A, Simulated mean daily streamflow in relation to measured mean daily streamflow and 

B, standard deviation of simulated streamflow in relation to the standard deviation of measured mean 

daily streamflow at 27 calibration streamgages in New Hampshire for the period of record for each 

streamgage.
12 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

watershed, which indicates that the variation in mean streamflows around the State was also well simulated. The mean 

percent differences and ranges of differences in this study 

are similar to the differences found in other calibrated PRMS 

models developed for complex terrain where the data were 

used to assess hydrologic change and to represent hydrologic 

conditions when comparative data were not available (Koczot, 

2005; Chase, 2011). The simulated monthly mean streamflows 

for the period of record for all the calibration streamgages 

plotted in relation to the measured monthly mean streamflows 

are shown in figure 3.

Table 3. Description of 27 selected streamgages with 

streamflow calibration statistics in New Hampshire.

[This table is available for download at https://doi.org/10.3133/sir20175143]

The 27 streamgages used for the evaluation are described, 

and the associated calibration statistics on the daily and 

monthly time steps for each gage are listed, in table 3. An 

NSE statistic of 0.5 or larger indicates a satisfactory or 

“good” calibration; 0.4 to less than 0.5, a marginal or “fair” 

calibration; and less than 0.4, a relatively “poor” calibration 

(Moriasi and others, 2007). For this study, a mean log residual 

less than 0.1 (overall error of approximately 10 percent or 

less) is considered a good (satisfactory) calibration; greater 

than 0.1 and less than or equal to 0.2, fair (marginal); and 

greater than 0.2, poor. Also in this study, an NRMSE of greater 

than 10 percent is considered unsatisfactory. According to 

Moriasi and others (2007), a PBIAS greater than 20 percent 

and an RSR greater than 0.7 are considered to represent a fair 

or poor simulation.

The daily calibration statistics show that 14 of the 

27 streamgages have good calibration on the basis of the NSE 

statistic, 5 have a fair calibration, and 8 have a poor calibration. The RSR statistic is similar to the NSE statistic, with 

13 streamgages having a satisfactory simulation (13 included 

in the 14 with satisfactory NSE). The NRMSE statistics show 

that 26 of 27 streamgages have an overall error of 10 percent 

or less. The mean log residual of the daily estimates shows 

that 19 of the streamgages have an error less than or equal 

to ± 10 percent, and 22 have an error less than or equal to 

± 20 percent, indicating that most of the streamgages have 

an acceptable calibration. Only two simulations show PBIAS 

greater than 20 percent. The streamgages that show poor 

calibration indicate that some aspect of the simulation is not 

being accurately represented at the daily time step. This could 

be because of nonrepresentative input data resulting from 

orographic effects and effects of storage or another physical 

feature of the HRU that is not represented by the GIS data for 

the HRUs composing the watershed.

0

5,000

10,000

15,000

20,000

25,000

0 5,000 10,000 15,000 20,000 25,000

Simulated monhtly mean streamflow, in cubic feet per second 

Measured monthly mean streamflow, in cubic feet per second

Figure 3. The simulated monthly mean streamflow in relation to the measured monthly mean 

streamflow at the 27 calibration streamgages in New Hampshire for the period of record at each 

streamgage.
Evaluation of the New Hampshire PRMS Model 13

The statistics for the distribution of the error across the 

model are shown on figure 4. Figure 4A (daily NSE statistic) 

and 4B (monthly NSE statistic) shows that the poor calibrations occur in the vicinity of the White Mountains, in the 

extreme north of the State, and in one small coastal basin, the 

Oyster River (streamgage 1073000), which includes some 

diversions supplying water to the University of New Hampshire. The poor daily calibration in the White Mountain area 

may reflect situations where the climate input data are affected 

by the resolution of that data, particularly in headwater areas 

and where large parts of the watersheds are in the White 

Mountains. The 7.45-mi (12-km) grid spacing of the Maurer 

dataset, as well as the downscaled GCM data, likely do not 

capture the magnitude of the orographic effects of this mountain terrain (Bjerklie and others, 2015). Additionally, the input 

data to the PRMS model associated with the simulation at the 

Big Brook near Pittsburg streamgage (1127880) are likely 

affected by data limitations resulting from the proximity to 

Canada. The poor calibration for the Oyster River streamgage 

in coastal New Hampshire may also be a function of the 

resolution of the input data near areas of GIS data discontinuity—in this case, the proximity to Canada and the Atlantic 

Ocean, which do not have the same GIS and weather data grid 

as New Hampshire.

For the monthly time step, the calibration statistics are 

much better overall (figs. 4B and 5B). On the basis of the NSE 

(fig. 4B), only five streamgages exhibit poor calibration, and 

one exhibits fair calibration. All the others (21 streamgages) 

show good calibration (>0.5). The log residual of the monthly 

streamflows shows the same geographic pattern, with only 

three streamgages exhibiting poor calibration (>0.2, or approximately 20 percent error (fig. 5B). Those streamgages with 

poor monthly calibration are in the extreme north in the White 

Mountains, locations that are similar to those for streamgages 

with poor daily calibration statistics. The fact that the monthly 

calibration is much better than the daily calibration further 

indicates that the timing of daily precipitation events in the 

input data may be the most important driver in producing the 

simulation errors instead of the general water balance.

The range of errors and NSE statistics exhibited 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) and in PRMS studies designed to simulate 

streamflow in other regional and local areas (Viger and others, 

2010; Dudley and Nielsen, 2011; LaFontaine and Hay, 2013; 

LaFontaine and others, 2013). However, the errors indicate 

that the daily streamflows need to be used with more caution 

than the monthly streamflows, particularly in regions of higher 

elevations in the State and near the dataset boundary. Given 

that the general water balance seems to be well simulated for 

most of the streamgages used in model calibration, the calibration would be most applicable when considering changes in 

water balance rather than timing of streamflows. Similarly, 

considering the overall water balance on the monthly time 

step, groundwater recharge and snow accumulation can be 

used with reasonable confidence at the HRU spatial scale.

Future simulations using the PRMS model are expected 

to provide hydrologic responses to the future climate input 

datasets with similar characteristics for the daily, monthly, and 

seasonal streamflow variations. The focus of this study is to 

look at differences between current and future conditions. The 

future model results were compared only with the current conditions using the same model parameters for each streamgage. 

Therefore, the comparison of current and future conditions 

will evaluate change that is based on the same watershed 

characterization; these comparisons are considered sufficiently 

valid as long as the water balance is represented well.

Considering all 27 streamgages, there is a weak negative 

correlation (90-percent confidence interval) between the elevation of the streamgage and the daily NSE statistic, indicating 

that the streamgages in and around the higher elevations in the 

White Mountains are subject to the greatest calibration error 

and, therefore, the greatest uncertainty in interpreting future 

simulations. There is also a weak negative correlation between 

the elevation of the streamgage and the daily log residuals 

with the residual being a larger positive value (overprediction; 

simulated streamflow greater than measured streamflow) at 

lower elevations and a larger negative value (underprediction; 

simulated streamflow less than measured streamflow) at higher 

elevations. Also, the largest absolute errors indicated by the 

daily log residuals occurred as underpredictions. These observations indicate that the largest errors are because of underrepresentation of the larger precipitation events in the mountains 

where orographic effects are important. The daily and monthly 

patterns of error may result from underprediction of precipitation at higher elevations but also may be driven by the issue at 

USGS station 01127880 (Big Brook), resulting from incomplete data because of the proximity to the boundary of the 

climate dataset (which does not extend into Canada).
14 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

M A I N E

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V E R M O N T

CANADA

UNITED STATES

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Base from U.S. Geological Survey

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

EXPLANATION

USGS streamgage locations, and 

 numbers—Derived from U.S. Geological

 Survey (2017b) 

Daily Nash-Sutcliffe efficiency statistic 

GREEN MOUNTAINS

WHITE MOUNTAINS

Oyster River

Connecticut River

Poor: less than 0.4

Marginal: 0.4 to to less than 0.5

Good: greater than 0.5

Figure 4. The spatial distribution of Nash-Sutcliffe efficiency statistic for 27 streamgages in New Hampshire at A, the daily time 

step and B, the monthly time step.
Evaluation of the New Hampshire PRMS Model 15

M A I N E

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Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

EXPLANATION

Monthly Nash-Sutcliffe efficiency statistic 

USGS streamgage locations, and 

 numbers—Derived from U.S. Geological

 Survey (2017b) 

GREEN MOUNTAINS

WHITE MOUNTAINS

Oyster River

Connecticut River

Poor: less than 0.4

Fair: 0.4 to 0.5

Good: greater than 0.5

Figure 4. The spatial distribution of Nash-Sutcliffe efficiency statistic for 27 streamgages in New Hampshire at A, the daily time 

step and B, the monthly time step.—Continued
16 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

M A I N E

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V E R M O N T

CANADA

UNITED STATES

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The National Map digital data

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North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

Mean of log residuals at daily timestep

USGS streamgage locations, and 

 numbers—Derived from U.S. Geological

 Survey (2017b) 

EXPLANATION

Good: –0.1 to less than 0.1

Poor: greater than 0.2

Fair: –0.2 to less than –0.1

Poor: less than –0.2 

Marginal: 0.1 to 0.2 

GREEN MOUNTAINS

WHITE MOUNTAINS

Figure 5. The spatial distribution of the mean of the log residuals for 27 streamgages in New Hampshire at A, the daily time step, 

and B, the monthly time step.
Evaluation of the New Hampshire PRMS Model 17

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

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Base from U.S. Geological Survey

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

USGS streamgage locations, and 

 numbers—Derived from U.S. Geological

 Survey (2017b) 

EXPLANATION

Mean of log residuals at monthly timestep

Good: –0.1 to less than 0.1

Poor: greater than 0.2

Fair: –0.2 to less than –0.1

Poor: less than –0.2 

Marginal: 0.1 to 0.2 

GREEN MOUNTAINS

WHITE MOUNTAINS

Figure 5. The spatial distribution of the mean of the log residuals for 27 streamgages in New Hampshire at A, the daily time step, 

and B, the monthly time step.—Continued
18 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Simulated Hydrologic Response to 

Climate Change

The parameterized and calibrated New Hampshire PRMS 

model was used to generate simulated daily streamflow values 

for a 20-year period representing current hydrologic conditions (1981–2000) and two 20-year periods representing future 

hydrologic conditions (2046–65 and 2081–2100). The five 

GCMs chosen for this study were used to generate individual 

20-year time series of daily streamflow, groundwater recharge, 

and snowfall for the current period (1981‒2000) for each 

of two climate change scenarios (RCP 4.5 and RCP 8.5). 

The future time series (four time series for each GCM input, 

including two scenarios for midcentury and end of century) 

were compared with the historical time series generated by 

each GCM to insure self-consistency. The changes that may 

occur in streamflow, groundwater recharge, and snowfall 

across the State as a result of climate change were evaluated.

Mean daily streamflow and the standard deviation of 

daily streamflow (indicating a greater range of flows with 

more highs and lows) are forecast to generally increase at 

all 27 streamgages across the State. The mean and standard 

deviation of the changes in average daily streamflow for the 

27 streamgages, in percent, between the future periods and the 

historical period for both emissions scenarios and all GCMs, 

and the composite (average) of all five GCMs are listed in 

table 4. Most streamgages show an increase in the standard 

deviation of daily streamflows; however, simulations from the 

GCM designated as CNRM–CM5 from the Centre National de 

Recherches Météorologiques (CNRM, France; see table 1) are 

the exception, in general, showing a decrease in the standard 

deviation of daily streamflows. The general tendency for the 

GCMs to produce increasingly greater standard deviations of 

daily streamflows indicates that there will be higher high flows 

and lower low flows. The patterns of change in mean streamflow are shown in figures 6A and B for both scenarios and two 

of the GCMs: the coupled general circulation model (CGCM), 

which is the GCM designated MRI–CGCM3 from Yukimoto 

and others (2012), and the GFDL model, which is the GCM 

designated GFDL–ESM2G from the National Oceanic and 

Aeronautics Administration Geophysical Fluid Dynamics Laboratory (2016)—as examples. The composite mean 

percentage changes for all five GCMs and both scenarios, by 

month, are listed in table 5. The changes in standard deviation 

of the composited means are listed in table 6.

Table 4. Simulated mean change in daily streamflow and 

standard deviation using two emission scenarios and five general 

circulation models, plus a composite, for 27 streamgages in New 

Hampshire, by 20-year periods.

[This table is available for download at https://doi.org/10.3133/sir20175143]

Table 5. Simulated change in mean monthly streamflow, 

in percent, for 27 selected streamgages in New Hampshire, 

composited from simulations made from all five general 

circulation models and two scenarios from 2046 to 2065 and 2081 

to 2100.

[This table is available for download at https://doi.org/10.3133/sir20175143]

Table 6. Standard deviation of the percent change in the mean 

of the monthly streamflows at 27 selected streamgages in New 

Hampshire, composited from simulations made from all five 

general circulation models and two scenarios from 2046 to 2065 

and 2081 to 2100.

[This table is available for download at https://doi.org/10.3133/sir20175143]

By midcentury (2046–2065), all five GCMs show 

increased mean streamflow for both scenarios by various 

percentages, except for the streamflows generated by the Earth 

system model (ESM) with the RCP 8.5 emissions scenario, 

which shows decreases at most of the streamgages (table 4). 

Additionally, for a few streamgages, CGCM and CNRM show 

a decrease. By the end of the century (2081–2100), all the 

GCMs for both scenarios show increased streamflow, except 

CNRM RCP 4.5. The seasonal pattern of change in streamflow is relatively consistent for all the GCMs with the largest increases in streamflow occurring in the winter and early 

spring and the largest decreases in the late spring and summer. 

The spatial pattern of change is less consistent, varying by 

GCM, emissions scenario, and time period. However, the largest increases generally occur in the eastern part of the State. 

The standard deviation of the composited means provides an 

indication of the uncertainty in GCM projections because the 

composited mean represents the difference between simulations using the different GCM input data rather than the standard deviation of the simulated streamflow time series from an 

individual model run.

The daily streamflow data are expected to have an 

accuracy that is reflected in the calibration. Therefore, those 

streams with relatively poor NSE values and high log residuals 

may not be representative of the general hydrologic response 

at the time step of the time series; hence, the NSE values and 

log residuals are not provided for more detailed analysis. 

Specifically, the nonrepresentativeness includes conditions 

that control volume and timing at the daily time step, including routing through the groundwater and subsurface reservoirs, routing in the stream channel, storage in the watershed, 

and snowmelt processes. Smoothing and damping of large 

precipitation events in the gridded climate data and lack of 

detail in the model regarding storage and routing effects on 

daily streamflow restrict the validity of using the daily data to 

interpret magnitude and timing of floods. However, change 

in streamflow, based on differences between the GCM input 

for current and future time periods, can be considered selfconsistent and controlled primarily by the GCM input rather 
Simulated Hydrologic Response to Climate Change 19

than the PRMS calibration. Streamflow on the monthly time 

step (mean of the daily values for the month) is expected to 

be more accurate and reflective of the processes that control 

streamflow at that temporal scale.

The statewide changes in groundwater recharge and 

snowfall, in percent (tables 7 and 8, respectively), represent 

the unweighted composited means from the GCM future 

simulations. The unweighted means are derived by averaging 

the linear estimates of recharge and snowfall, in inches, for 

each HRU without adjusting for HRU area. The snowfall is 

reported as snow-water equivalent times 10 (Bjerklie and 

others, 2015) to provide a value consistent with a typical snow 

depth. Table 7 indicates that the largest quantitative variability 

between models (as evidenced by the standard deviation) was 

measured in the simulated groundwater recharge using the 

RCP 4.5 scenario.

Figures 7 and 8 show that there is significant spatial 

variability in the simulated change in mean groundwater 

recharge and snowfall across the State. However, the change 

in groundwater recharge shows relatively consistent patterns 

of spatial variability between models but with different 

magnitudes. The simulated changes in groundwater recharge 

that are based on the RCP 4.5 scenario show decreased 

recharge in the HRUs adjacent to the Connecticut River with 

increases in most other parts of New Hampshire, and the 

simulated changes that are based on the RCP 8.5 show an 

expansion of areas with decreases. All the models generally 

simulated decreased groundwater recharge for the end-ofcentury period (2081–2100) for both scenarios. This indicates 

that by the end of the century, increases in evapotranspiration 

may outpace summer and fall increases in precipitation.

The mean changes in groundwater recharge over the 

periods representing current, midcentury, and end-of-century 

conditions are reflective of the mean water balance, which 

has been shown to be accurately represented in PRMS. Thus, 

we anticipate groundwater recharge change estimates to be 

primarily the result of the input data and not the result of 

arbitrary variation in infiltration and movement through the 

subsurface; thus, the estimates reflect the specific climate 

realizations that the GCMs provide.

Generally, snowfall is projected to decrease through 

the 21st century across the State for both RCP scenarios for 

all months, with one exception. The exception is the CGCM 

RCP 4.5 simulation, in which March snow first increases 

by the midcentury (2046–65) period, then decreases by the 

end-of-century (2081–2100) period (table 8). However, the 

decreases are not uniform across the State, and in fact, snow 

is projected to increase in some locations for some months 

(fig. 8), depending on the GCM and the RCP scenario used. 

For example, using the CGCM GCM for RCP 4.5, March 

snow is projected to increase in southern New Hampshire 

generally by midcentury and continue to increase by the end 

of the century in several locations in the same part of the State 

(fig. 8A, B). Similarly, simulations using the GFDL GCM 

with RCP 8.5 for November for the midcentury period show 

some areas of increasing snow in southern New Hampshire 

(fig. 8C). However, the GFDL simulations for midcentury with 

RCP 4.5 show increased snow for February in northern New 

Hampshire (fig. 8D); for the same GCM, this area of increase 

is greatly reduced across the northern part of the State during 

the end-of-century period (fig. 8E).

The difference between the total winter trends and the 

monthly trends in snowfall indicate that, even with warming 

temperatures, snowfall will remain an important feature of 

winter in New Hampshire, and snowfall is likely to be more 

variable with the possibility that some months may experience 

relatively large snowfalls during early and late winter relative 

to current conditions, particularly in midcentury. Relatively 

large snowfalls could occur because, for the future climate, 

the possibility of months with below freezing temperatures 

still remains (winter is a function of the seasonal variation 

in the declination of the sun); below freezing temperatures 

combined with increased atmospheric moisture can result in 

high snowfall.

Spatial variability in the monthly changes (percent) for 

increased and decreased snowfall may be significant. The 

locations of the increases and decreases vary and are likely the 

result of the particular dynamics associated with each GCM 

run; if many simulations of snowfall for each GCM were 

run, it would be possible to derive a statistical probability of 

the different realizations. Snow generally will decrease but 

may be highly variable, depending on location, GCM, and 

climate RCP scenario. The change in snowfall directly reflects 

changes in the atmospheric inputs to the model via the selected 

temperature for all snow, rain and snow mix, and all rain 

thresholds. Consequently, the change in snow amounts would 

be consistent with the defined parameter values and would 

also be expected to primarily reflect the input data rather than 

the PRMS model calibration.

Model Sensitivity to Land-Use Change and 

Effects Relative to Climate Change

The sensitivity of the PRMS model to land-use change, 

as reflected in the percent impervious surface in each HRU, 

was evaluated in order to compare the relative magnitudes 

of climate change and land-use change. It was assumed that 

land-use change resulting from increased development can be 

directly indexed by increasing impervious surface. In PRMS, 

increases in the percent impervious surface will uniformly 

drive decreases in recharge and base flows and increases in 

peak streamflows, unless storm retention structures are considered. The degree of change would be highly local, depending 

on the spatial distribution of development; however, assuming reasonable estimates of increased impervious surface 

uniformly distributed in an HRU, the resulting hydrologic 

changes could be on par with those predicted over longer time 

frames as a result of climate change.
20 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Figure 6. The spatial distribution of changes in simulated mean streamflow at the 27 calibration streamgages in New Hampshire 

between the current (1981–2000) period and the end-of-century (2081–2100) period using two general circulation models and the two 

emission scenarios: A, coupled general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical Fluid Dynamics Laboratory 

(GFDL) model RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is 

the model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory

(2016). GCM, general circulation model; RCP, representative concentration pathway; USGS, U.S. Geological Survey.

M A I N E

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North American Datum 1983

0 25 50 75 100 MILES

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Change in mean streamflow from CGCM 

and RCP 4.5, in percent

0 to less than 5

5 to less than 10

10 to less than 15

15 to less than 20

20 to less than 25

EXPLANATION

USGS streamgage locations and 

 numbers—Derived from U.S. Geological

 Survey (2017b) 

A

GREEN MOUNTAINS

WHITE MOUNTAINS
Simulated Hydrologic Response to Climate Change 21

Figure 6. The spatial distribution of changes in simulated mean streamflow at the 27 calibration streamgages in New Hampshire between 

the current (1981–2000) period and the end-of-century (2081–2100) period using two general circulation models and the two emission 

scenarios: A, coupled general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical Fluid Dynamics Laboratory (GFDL) 

model RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is the 

model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory 

(2016). GCM, general circulation model; RCP, representative concentration pathway; USGS, U.S. Geological Survey.—Continued

M A I N E

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The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

Change in mean streamflow from CGCM 

and RCP 8.5, in percent

EXPLANATION

USGS streamgage locations and 

 numbers—Derived from U.S. Geological

 Survey (2017b) 

B

0 to less than 5

5 to less than 10

10 to less than 15

15 to less than 20

20 to less than 25

GREEN MOUNTAINS

WHITE MOUNTAINS
22 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Figure 6. The spatial distribution of changes in simulated mean streamflow at the 27 calibration streamgages in New Hampshire 

between the current (1981–2000) period and the end-of-century (2081–2100) period using two general circulation models and the two 

emission scenarios: A, coupled general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical Fluid Dynamics Laboratory 

(GFDL) model RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is 

the model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory

(2016). GCM, general circulation model; RCP, representative concentration pathway; USGS, U.S. Geological Survey.—Continued

M A I N E

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NEW HA M P S H I R E

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CANADA

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The National Map digital data 

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

Change in mean streamflow from GFDL

RCP 4.5, in percent

EXPLANATION

USGS streamgage locations and 

 numbers—Derived from U.S. Geological

 Survey (2017b)

C

0 to less than 5

5 to less than 10

10 to less than 15

15 to less than 20

20 to less than 25

GREEN MOUNTAINS

WHITE MOUNTAINS
Simulated Hydrologic Response to Climate Change 23

Figure 6. The spatial distribution of changes in simulated mean streamflow at the 27 calibration streamgages in New Hampshire 

between the current (1981–2000) period and the end-of-century (2081–2100) period using two general circulation models and the two 

emission scenarios: A, coupled general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical Fluid Dynamics Laboratory 

(GFDL) model RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is 

the model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory

(2016). GCM, general circulation model; RCP, representative concentration pathway; USGS, U.S. Geological Survey.—Continued

M A I N E

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NEW HA M P S H I R E

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CANADA

UNITED STATES

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01127880

01130000

01137500

01141800

01150500

01152500

01160350

01161000

01064801

01076500

01085800

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

EXPLANATION

USGS streamgage locations and 

 numbers—Derived from U.S. Geological

 Survey (2017b) 

D

Change in mean streamflow from GFDL

RCP 8.5, in percent

–5 to less than 0

0 to less than 5

5 to less than 10

10 to less than 15

15 to less than 25

GREEN MOUNTAINS

WHITE MOUNTAINS
24 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Table 7. Simulated percent changes in average recharge in New Hampshire for 20-year periods using simulations 

made from five general circulation models and two emission scenarios.

[Values are for unweighted averages of statewide hydrologic response units (HRUs), in inches per year. CGCM, coupled general 

circulation model; CNRM, Centre National de Recherches Météorologiques; CSIRO, Commonwealth Scientific and Industrial Research 

Organisation; ESM, Earth system modeling; GFDL, Geophysical Fluid Dynamics Laboratory coupled model; Stdev, standard deviation; 

RCP, representative concentration pathway]

Scenario Period Statistic

General circulation model Composite

CGCM CNRM CSIRO ESM GFDL Mean Stdev

RCP 4.5 2046–2065 Mean –2.8 –8.4 2.9 –3.8 –0.4 –2.5 4.2

Stdev –1.9 –10.8 0.0 –8.6 –5.5 –5.3 4.5

RCP 4.5 2081–2100 Mean 5.2 –15.6 –6.1 –8.3 –0.6 –5.1 7.9

Stdev 1.4 –18.2 –6.6 –7.9 –3.7 –7.0 7.2

RCP 8.5 2046–2065 Mean 1.1 –12.8 –8.7 –11.9 –2.6 –7.0 6.0

Stdev –1.5 –16.0 –10.0 –10.0 –6.7 –8.8 5.3

RCP 8.5 2081–2100 Mean 3.6 –19.4 –20.6 –14.1 –15.5 –13.2 9.8

Stdev 0.0 –22.3 –19.1 –14.8 –16.9 –14.6 8.6

Table 8. Simulated percent change in average snowfall in New Hampshire for 20-year periods using two emission 

scenarios and five general circulation models, in inches of snow.

[Values are for unweighted averages of statewide hydrologic response units (HRUs), in inches of snow, calculated as inches of snowwater equivalent (SWE) times 10. CGCM, coupled general circulation model; CNRM, Centre National de Recherches Météorologiques; 

CSIRO, Commonwealth Scientific and Industrial Research Organisation; ESM, Earth system model; GFDL, Geophysical Fluid Dynamics Laboratory coupled model; Stdev, standard deviation; RCP, representative concentration pathway]

Month Scenario Time period

General circulation model Composite

CGCM CNRM CSIRO ESM GFDL Mean Stdev

November RCP 4.5 2046–2065 –59.0 –71.5 –3.2 –71.1 –85.1 –58.0 32.0

December –51.4 –50.1 –45.5 –51.4 –37.0 –47.1 6.1

January –19.5 –50.7 –19.0 –49.8 –48.2 –37.4 16.6

February –18.5 –11.5 –35.8 –28.5 –15.0 –21.8 10.1

March 8.8 –50.4 –60.5 –71.0 –56.2 –45.9 31.5

November RCP 8.5 2046–2065 –67.6 –85.0 –80.0 –94.1 –44.1 –74.2 19.3

December –54.8 –68.2 –71.7 –43.8 –52.8 –58.3 11.5

January –34.9 –53.0 –47.6 –54.4 –50.4 –48.0 7.8

February –24.2 –45.5 –42.8 –38.6 –41.2 –38.5 8.4

March –33.4 –66.8 –76.2 –77.6 –72.8 –65.4 18.4

November RCP 4.5 2081–2100 –79.7 –53.0 –73.6 –90.7 –81.5 –75.7 14.1

December –67.2 –59.0 –60.3 –57.9 –54.1 –59.7 4.8

January –1.1 –62.2 –51.3 –44.5 –26.4 –37.1 24.0

February –25.2 –27.6 –45.2 –35.3 –12.7 –29.2 12.1

March –13.6 –63.9 –80.1 –69.6 –60.0 –57.4 25.7

November RCP 8.5 2081–2100 –98.7 –92.6 –93.4 –98.9 –90.0 –94.7 3.9

December –84.5 –79.9 –79.4 –83.2 –71.3 –79.7 5.1

January –47.7 –88.4 –77.4 –70.3 –72.2 –71.2 14.9

February –55.3 –86.4 –79.4 –73.8 –69.1 –72.8 11.7

March –71.4 –89.7 –84.6 –86.1 –82.2 –82.8 6.9
Simulated Hydrologic Response to Climate Change 25

Figure 7. The spatial distribution of change in simulated mean groundwater recharge at hydrologic response units in New 

Hampshire between the current period (1981–2000) and the end-of-century period (2081–2100), using two general circulation models 

(GCMs) and the two emission scenarios: A, combined general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical 

Fluid Dynamics Laboratory (GFDL) RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto 

and others (2012); the GFDL is the model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, 

Geophysical Fluid Dynamics Laboratory (2016). RCP, representative concentration pathway.

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

Hydrologic response unit and change in potential 

mean groundwater recharge using CGCM and 

RCP 4.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

A

Less than −10

−10 to less than 0

0 to less than 10

10 to 20

Greater than 20
26 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Figure 7. The spatial distribution of change in simulated mean groundwater recharge at hydrologic response units in New 

Hampshire between the current period (1981–2000) and the end-of-century period (2081–2100), using two general circulation models 

(GCMs) and the two emission scenarios: A, combined general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical 

Fluid Dynamics Laboratory (GFDL) RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto 

and others (2012); the GFDL is the model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, 

Geophysical Fluid Dynamics Laboratory (2016). RCP, representative concentration pathway.—Continued

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

B

Hydrologic response unit and change in potential 

mean groundwater recharge using CGCM and 

RCP 8.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −10

−10 to less than 0

0 to less than 10

10 to 20

Greater than 20
Simulated Hydrologic Response to Climate Change 27

Figure 7. The spatial distribution of change in simulated mean groundwater recharge at hydrologic response units in New 

Hampshire between the current period (1981–2000) and the end-of-century period (2081–2100), using two general circulation models 

(GCMs) and the two emission scenarios: A, combined general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical 

Fluid Dynamics Laboratory (GFDL) RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto 

and others (2012); the GFDL is the model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, 

Geophysical Fluid Dynamics Laboratory (2016). RCP, representative concentration pathway.—Continued

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

C

Hydrologic response unit and change in potential 

mean groundwater recharge using GFDL 2081–

2100 and RCP 4.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −10

−10 to less than 0

0 to less than 10

10 to 20

Greater than 20
28 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

D

Hydrologic response unit and change in potential 

mean groundwater recharge using GFDL 2081–

2100 and RCP 8.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −10

−10 to less than 0

0 to less than 10

10 to 20

Greater than 20

Figure 7. The spatial distribution of change in simulated mean groundwater recharge at hydrologic response units in New 

Hampshire between the current period (1981–2000) and the end-of-century period (2081–2100), using two general circulation models 

(GCMs) and the two emission scenarios: A, combined general circulation model (CGCM) RCP 4.5, B, CGCM RCP 8.5, C, Geophysical 

Fluid Dynamics Laboratory (GFDL) RCP 4.5, and D, GFDL RCP 8.5. The CGCM is the model designated MRI–CGCM3 from Yukimoto 

and others (2012); the GFDL is the model designated GFDL–ESM2G from the National Oceanic and Atmospheric Administration, 

Geophysical Fluid Dynamics Laboratory (2016). RCP, representative concentration pathway.—Continued
Simulated Hydrologic Response to Climate Change 29

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

A

Hydrologic response unit and change in March

potential snowfall between 2046 and 2065 using 

CGCM and RCP 4.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −40

−40 to less than –20

–20 to less than 0

0 to 20

Greater than 20

Figure 8. The spatial distribution of percent changes in simulated monthly snowfall at hydrologic response units in New Hampshire 

between the current (1981–2000) period and the midcentury (2046–2065) and end-of-century (2081–2100) period, using two general 

circulation models (GCMs) and the two emission scenarios: A, March, CGCM RCP 4.5, 2046 to 2065; B, March, CGCM RCP 4.5, 2081 to 2100; 

C, November, GFDL RCP 8.5, 2046 to 2065; D, February, GFDL RCP 4.5, 2046 to 2065; and E, February, GFDL RCP 4.5, 2081 to 2100. The CGCM 

is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is the model designated GFDL–ESM2G from the National 

Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory (2016). RCP, representative concentration pathway.
30 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

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UNITED STATES

B

Hydrologic response unit and change in March

potential snowfall between 2081 and 2100 using 

CGCM and RCP 4.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −40

−40 to less than –20

–20 to less than 0

0 to 20

Greater than 20

Figure 8. The spatial distribution of percent changes in simulated monthly snowfall at hydrologic response units in New Hampshire 

between the current (1981–2000) period and the midcentury (2046–2065) and end-of-century (2081–2100) period, using two general 

circulation models (GCMs) and the two emission scenarios: A, March, CGCM RCP 4.5, 2046 to 2065; B, March, CGCM RCP 4.5, 2081 to 

2100; C, November, GFDL RCP 8.5, 2046 to 2065; D, February, GFDL RCP 4.5, 2046 to 2065; and E, February, GFDL RCP 4.5, 2081 to 2100. 

The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is the model designated GFDL–ESM2G 

from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory (2016). RCP, representative 

concentration pathway.—Continued
Simulated Hydrologic Response to Climate Change 31

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

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UNITED STATES

C

Hydrologic response unit and change in November

potential snowfall between 2046 and 2065 using 

GFDL and RCP 8.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −40

−40 to less than –20

–20 to less than 0

0 to 20

Greater than 20

Figure 8. The spatial distribution of percent changes in simulated monthly snowfall at hydrologic response units in New Hampshire 

between the current (1981–2000) period and the midcentury (2046–2065) and end-of-century (2081–2100) period, using two general 

circulation models (GCMs) and the two emission scenarios: A, March, CGCM RCP 4.5, 2046 to 2065; B, March, CGCM RCP 4.5, 2081 to 

2100; C, November, GFDL RCP 8.5, 2046 to 2065; D, February, GFDL RCP 4.5, 2046 to 2065; and E, February, GFDL RCP 4.5, 2081 to 2100. 

The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is the model designated GFDL–ESM2G 

from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory (2016). RCP, representative 

concentration pathway.—Continued
32 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

72° 70°

45°

30'

Base from U.S. Geological Survey

The National Map digital data

Albers equal area conic projection

North American Datum 1983

Derived from U.S. Geological

Survey (2008b)

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

Hydrologic response unit and change in February 

potential snowfall between 2046 and 2065 using 

GFDL and RCP 8.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −40

−40 to less than –20

–20 to less than 0

0 to 20

Greater than 20

D

Figure 8. The spatial distribution of percent changes in simulated monthly snowfall at hydrologic response units in New Hampshire 

between the current (1981–2000) period and the midcentury (2046–2065) and end-of-century (2081–2100) period, using two general 

circulation models (GCMs) and the two emission scenarios: A, March, CGCM RCP 4.5, 2046 to 2065; B, March, CGCM RCP 4.5, 2081 to 

2100; C, November, GFDL RCP 8.5, 2046 to 2065; D, February, GFDL RCP 4.5, 2046 to 2065; and E, February, GFDL RCP 4.5, 2081 to 2100. 

The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is the model designated GFDL–ESM2G 

from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory (2016). RCP, representative 

concentration pathway.—Continued
Simulated Hydrologic Response to Climate Change 33

72° 70°

45°

30'

Base from U.S. Geological Survey 

The National Map digital data

Albers equal area conic projection

North American Datum 1983

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

M A I N E

M A S S A C H U S E T T S

NEW HA M P S H I R E

V E R M O N T

CANADA

UNITED STATES

E

Hydrologic response unit and change in February 

potential snowfall between 2081 and 2100 using 

GFDL and RCP 8.5, in percent—Derived from 

U.S. Geological Survey (2008b)

EXPLANATION

Less than −40

−40 to less than –20

–20 to less than 0

0 to 20

Greater than 20

Figure 8. The spatial distribution of percent changes in simulated monthly snowfall at hydrologic response units in New Hampshire 

between the current (1981–2000) period and the midcentury (2046–2065) and end-of-century (2081–2100) period, using two general 

circulation models (GCMs) and the two emission scenarios: A, March, CGCM RCP 4.5, 2046 to 2065; B, March, CGCM RCP 4.5, 2081 to 

2100; C, November, GFDL RCP 8.5, 2046 to 2065; D, February, GFDL RCP 4.5, 2046 to 2065; and E, February, GFDL RCP 4.5, 2081 to 2100. 

The CGCM is the model designated MRI–CGCM3 from Yukimoto and others (2012); the GFDL is the model designated GFDL–ESM2G 

from the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory (2016). RCP, representative 

concentration pathway.—Continued
34 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

The potential effects of increased impervious surface 

were explored via a sensitivity analysis for the coastal region 

of New Hampshire, an area that is projected to increase in 

population in the coming years. By doubling the impervious surface in two watersheds in south coastal New Hampshire including streamgage 01073587 (the Exeter River) and 

streamgage 0107300 (the Oyster River), the PRMS model 

simulated a 30-percent increase in mean streamflow, with the 

increased mean driven by larger high streamflows. Along with 

the larger high streamflows, there was a reduction in recharge 

by 5 to 10 percent, which then would result in lower base 

flows. These effects of changing streamflow and recharge have 

been documented in previous studies in New England (Ahearn 

and Bjerklie, 2010; Bjerklie and others, 2010a) using PRMS.

Given the variability of future groundwater recharge 

across the State, particularly the observation that increased 

recharge is expected primarily in the southeastern and coastal 

areas of the State, the interaction with changing land use is an 

important consideration for further analysis. For example, the 

southeastern and coastal areas of the State are expected to be 

areas of future growth and development (Bjerklie and others, 

2015). These areas then would be subject to potential reductions in recharge because of increased impervious surface, 

while at the same time experiencing increased recharge resulting from climate change. Similarly, this region of the State 

is expected to experience increases in streamflow resulting 

from climate change, which will be compounded by increased 

streamflow from increased development and impervious 

surface.

Model Limitations

This study emphasizes the importance of understanding 

predicted trends in streamflow, snowmelt, and base flow from 

potential climate change through the hydrologic responses of 

models. Episodic events such as floods, which are affected by 

hourly and finer time-scale precipitation, are not addressed in 

this study. Pursuant to the discussion of the model calibration, 

simulated daily and monthly time series are likely subject to 

greater uncertainty where the calibration statistics are unsatisfactory. Other limitations include the following circumstances.

• Water withdrawals and returns, interbasin transfers, and 

changes in land use and land cover are not simulated.

• The effect of frozen ground on runoff is not simulated 

(the version of PRMS used in this study does not 

account for this process).

• The future GCM input datasets represent one specific 

climate realization and, therefore, may not represent 

the expected future conditions, but rather one of a 

population of equally likely possible conditions. There 

are many uncertainties in the GCM model simulations, 

which need to be understood (see Wooten, 2016).

• The scale of the model (approximate HUC–12 scale) is 

such that more localized hydrologic conditions, such 

as impervious surface, soil and geologic heterogeneity, 

small scale wetlands, and surface ponding that may 

affect specific areas, may not be well represented in the 

model. Conditions and processes that may be important locally may be smoothed or overwhelmed at the 

larger scale.

• Similar to the effects of model scale, downscaling of 

weather data from the historical datasets and from the 

GCMs may not capture the local scale variability of 

weather that may be associated with specific physiographic features.

Hydrologic effects associated with land-use and landcover change could be important in trends of possible future 

change and could be a more important driver of hydrologic 

change than climate change in some areas. The climate scenarios simulated in this study are possibilities only. The reality 

could be 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 last few decades of the 20th century.

Related USGS Datasets

A number of output datasets developed in this study 

(table 9) are available (Bjerklie and Sturtevant, 2017). The 

input data and the PRMS model used to generate the output 

data have been archived within the USGS model archive 

system. Additionally, the 20-year calibration and simulation 

datasets used for the study are archived in Bjerklie and Sturtevant (2017). The datasets include simulated data derived from 

the five GCMs used in this study for a 30-year reference time 

frame (1976–2005) and for a 90-year future period (2010–

2099) in order to provide continuous data for the 21st century. 

The data are presented in monthly time steps for each HRU in 

the model and in monthly and daily time steps for calibration 

streamgages where the NSE statistic is greater than 0.5. The 

data deliverable will provide the individual climate realizations for each of the five models and two emission scenarios.
Related USGS Datasets 35

Table 9. Description of datasets of simulated hydrologic data from 1976 to 2005 and 2010 to 2099.[GCM, general circulation model; CGCM, Meteorological Research Institute, Japan; CNRM, Centre National de Recherches Météorologiques, Centre Européen de Recherche et Formation, Avancée en Calcul Scientifique, France; CSIRO, Commonwealth Scientific and Industrial Research Organisation, Australia; ESM, Max-Planck-Institut für Meteorologie, Germany; GFDL, National Oceanic and Atmospheric Administration Fluid Dynamics Laboratory, United States; RCP, representative concentration pathway; NSE, Nash-Surtliffe efficiency coefficient; PRMS, Precipitation Runoff Modeling System]

Source and variable of interest Period of record Data type Number of datasets and dimensions Limitations and recommended applications

Daily and monthly streamflow 

simulations using input from five GCMs: CGCM, CNRM, CSIRO, ESM, GFDL for a reference period and two RCP scenarios: RCP 4.5 and RCP 8.5 for 19 streamgages with NSE statistic greater than 0.4. Only one time-series dataset was generated for the simulation period from 1980 to 2009 because no future emission scenario is associated with this period.

Calendar year: 

Reference period 1976–2005, future period 2010–2099

Daily streamflow, in 

cubic feet per second, for 14 calibration streamgages, and monthly average streamflow, in cubic feet per second, for 21 calibration streamgages.

Thirty datasets including daily and 

monthly time series for 19 streamgages, 10 with reference time frame, 10 with future time frame using RCP 4.5, and 10 using future time frame using RCP 8.5.

Streamflow quantity and timing reflects the expected accuracy of 

the model representation of hydrologic processes in the watershed upstream from the streamgage. The accuracy is evaluated in the calibration results discussion in this report and varies for each streamgage. The input data from the GCM likely do not capture extreme precipitation events well, especially at time scales less than 1 day. Additionally, the GCM input data represent for each GCM a single possible weather record realization and therefore cannot be considered to represent an average or “typical” condition, but only one possible and equally likely condition. Compositing the results from the five GCM input simulations provides a mean value of five uniquely different realizations, indicating a more robust representation of future hydrologic conditions. Because of these limitations, it is recommended that the absolute magnitude and timing of the daily flows, especially the larger flood events, be used with caution. Evaluation of changes in frequency and duration of daily flow events may be subject to greater misrepresentation relative to mean monthly events.

Monthly groundwater recharge and 

snowfall simulations using input from five GCMs: CGCM, CNRM, CSIRO, ESM, GFDL for the two RCP scenarios: RCP 4.5 and RCP 8.5

Calendar year: 

Reference period 1976–2005, future period 2010–2099

Monthly total recharge 

and snowfall, in inches, for each of the 467 HRUs in the statewide PRMS model.

Thirty monthly time 

series datasets of groundwater recharge and snowfall for 467 HRUs, 10 with reference period, 10 with future period using RCP 4.5, and 10 using period using RCP 8.5.

The accuracy of the groundwater recharge in the PRMS model is 

not quantified other than secondarily using the calibration at the streamgages. As such, it is recommended that change statistics associated with the population of monthly values composing the 30-year periods be the focus of analysis.
36 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Summary and Conclusions

The State of New Hampshire had need of hydrologic 

simulations to meet planning requirements for infrastructure and other projects, and in response, the U.S. Geological 

Survey, in cooperation with the New Hampshire Departments 

of Environmental Services and Health and Human Services, 

developed a statewide Precipitation Runoff Modeling System 

(PRMS) hydrologic model designed to provide simulations 

of projected change in streamflow, groundwater recharge, and 

snowfall in response to climate change projections. Two emissions scenarios were used with five general circulation models 

(GCMs). In general, the statewide hydrologic model is considered to be well calibrated across the State; however, a number 

of streams, primarily at higher elevations, had relatively poor 

daily simulations. The model calibration at the monthly time 

step is considerably better across the State than the calibration 

at the daily time step. The statistics for change in streamflow, 

groundwater recharge, and snowfall may be considered more 

independent of the PRMS model calibration than of data input 

when comparing model-derived current (1981‒2000) and 

future (2046‒65 and 2081‒2100) conditions. Additionally, 

concerns that the PRMS model calibration may not be representative of future conditions because some of the parameter 

values are dependent on air temperature were investigated. 

Parameter values dependent on air temperature, especially the 

parameters that control potential evapotranspiration, have been 

shown through sensitivity analysis to have only minor possible effects (less than 10 percent) over the range of expected 

changes in temperature.

Results of hydrologic simulations using the GCM projections of climate indicate increased streamflow across the State 

with large increases in streamflow during the late fall, winter, 

and early spring, and general decreases during late spring, 

summer, and early fall. Simulations project general decreases 

in groundwater recharge in the Connecticut River Valley and 

at high elevations and increases in coastal and lowland areas 

of the State. Total winter snowfall is projected to steadily 

decrease across the State as a whole, but there is a possibility 

of increased snowfall in some locations, particularly during November, February, and March. The simulated future 

changes in recharge and snowfall are predicted to vary across 

the State because of such factors as topography and elevation. 

In response to potential changes in streamflow, groundwater 

recharge, and snowfall amounts and variability, planning for 

infrastructure needs and public safety may need to be flexible 

and will depend on the location and the range of possible outcomes indicated by the various model simulations. Additionally, hydrologic systems may no longer be considered stable 

and predictable, thus possible future hydrologic conditions 

may need to be considered in planning and design of infrastructure and other projects.

Compositing the results from the five GCM input simulations for each of the two emissions scenarios provides mean 

values for five uniquely different future climate realizations 

per scenario, indicating a robust representation of future 

hydrologic conditions. The absolute magnitude and timing 

of the daily flows, especially the larger floods, are best used 

with caution and only for streamgages that had satisfactory 

simulations. Evaluation of changes in accumulated monthly 

and seasonal flow volumes are considered to be less subject to misrepresentations and therefore more meaningful 

to understanding and planning for possible changes in the 

hydrologic conditions.

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Appendix 1. Evaluation of the Jensen-Haise Method of Estimating Potential 

Evapotranspiration in New England Using the Precipitation Runoff Modeling 

System

Introduction

Modeling the land-surface water cycle, as is done in 

all watershed scale rainfall-runoff models, consists of three 

fundamental hydrologic processes—precipitation, evapotranspiration, and land-surface storage and runoff of water. The first 

two processes, precipitation and evapotranspiration, can be 

considered to be primarily driven by atmospheric conditions 

(although not entirely). The latter process is driven by conditions on the land surface and in the long term is the residual 

of the balance between precipitation and evapotranspiration. 

Generally, the precipitation process is determined by measured 

values and therefore is not subject to modeling or estimation, 

although distributing the measured values across the modeled 

watershed may be. Evapotranspiration in most cases is not 

measured; therefore, it is a simulated process that depends on 

measurements of other atmospheric and land-surface variables. Evapotranspiration is critical to understanding the water 

balance, thus the method used to simulate its value is key to 

the accuracy of the model.

The evapotranspiration process, as previously mentioned, is driven primarily by atmospheric conditions. More 

specifically, it is driven by atmospheric conditions near the 

ground and by the availability of water that can be evaporated 

or transpired (evapotranspiration). The water that is evapotranspired is collectively termed “actual evapotranspiration” 

(AET). In most watershed scale Precipitation Runoff models, 

including the U.S. Geological Survey Precipitation Runoff 

Modeling System (PRMS), the AET is simulated by first determining an evaporative demand, then determining how much 

water (soil water, open water, or water on open surfaces) is 

available to meet that demand. As such, the AET would equal 

the evapotranspirative demand unless the water available 

is less than the demand, in which case AET is less than the 

evapotranspirative demand.

The evapotranspirative demand is generally termed 

“potential evapotranspiration” (PET), which can be thought of 

as purely a function of atmospheric conditions or as a function of atmospheric conditions coupled with resistances to 

water movement offered by the soil and vegetation and the 

energy state of the stored water that is available for evapotranspiration. The atmospheric variables that drive PET are 

net energy inputs and mass transfer of water vapor away from 

the evapotranspirative surface. Methods used to estimate 

PET from temperature alone (such as the Hamon method; 

Lu and others, 2005) make the implicit assumption that there 

are general correlations between near surface air temperature 

and the temperature of the evaporating body and evaporation 

surface, the humidity, and the vapor pressure gradient. Generally, these methods ignore the effect of wind. The general 

relation between air temperature and the temperature of the 

evaporating body is probably consistent from one region to 

another, which is a reasonable assumption. However, in areas 

where shortwave solar radiation heats surfaces to temperatures 

significantly greater than the air temperature, greater evaporation rates can occur. Thus, inclusion of solar radiation in the 

PET relation can account for differences that result from solar 

radiation inputs to the near surface energy balance. Additionally, the general relation between air temperature, humidity, 

and vapor pressure gradient varies regionally and even within 

local areas, depending on the proximity to water bodies and 

elevation. Therefore, a method that includes relative humidity and (or) vapor pressure gradient data for the ground-level 

region would also be able to provide greater estimation accuracy on the basis of local conditions.

Past applications of the PRMS (Leavesley and others, 

1983; Markstrom and others, 2015) typically employed the 

Jensen-Haise approach to modeling PET. In this appendix, two 

methods to estimate evapotranspiration are compared with the 

Jensen-Haise method. The key issue raised in a paper by Milly 

and Dunne (2011) indicates that successful hydrologic modeling of future conditions requires that the method for estimating PET not be unduly sensitive to changes in climate input 

variables, primarily air temperature.

At the scale of the hydrologic response unit (HRU), localized processes of heat transfer and convective/advective moisture transport will tend to balance. Also, the data necessary to 

assess the specific local conditions associated with advective 

transport generally would not be available. The data typically 

available from weather stations to model the evapotranspiration process include air temperature, humidity (or an estimate 

of dew point temperature), and solar radiation. Thus, it is clear 

that the methods to be assessed should include a representation 

of these three variables, preferably on a daily time step. These 

variables can also be obtained directly from downscaled general circulation model (GCM) output for any HRU and thus 

are ideal variables for use in the PET algorithm. The incident 

solar radiation at the surface in some cases may be difficult to 

obtain from weather stations as well as from GCMs because of 

a lack of data collection sites (in the case of weather stations) 

and poor representation of cloud cover dynamics (in the case 

of the GCMs). However, solar radiation can be estimated from 

calculated incoming solar radiation at the top of the atmosphere if cloud cover observations are available or from temperature and precipitation data (Markstrom and others, 2015).
42 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Approach and Methods

We evaluated the Jensen-Haise method used in PRMS by conducting a sensitivity study, 

along with the Penman method of estimating PET (Thom and Oliver, 1977; Fennessey and 

Vogel, 1996; Villa Nova and others, 2006). The comparison with Penman indicates how well 

Jensen-Haise compares with other more complete methods for the current climate over a 

specific time period, and the sensitivity of both methods to changing air temperature provides a 

means to test how the methods might respond to a changing climate.

The Penman method is considered to be one of the more complete physically based methods used in modeling applications (Allen and others, 1998). The Penman method requires daily 

incident solar radiation, outgoing longwave radiation, daily air temperature, daily wind speed, 

daily vapor pressure, and vapor pressure gradient. In comparison to the Penman method, the 

Jensen-Haise method can be viewed as somewhat simplified, requiring incident solar radiation, 

daily air temperature, and a climatic index to the vapor pressure, based on the saturation vapor 

pressures associated with the maximum and minimum air temperatures during the warmest 

month of the year.

Penman Method

The Penman method is a physically based approach to estimating evapotranspiration that 

combines the energy balance at the land surface with an aerodynamic term that accounts for 

mass transfer of water vapor away from the evaporating surface. The aerodynamic term is not 

dimensionally homogeneous and uses the empirical wind speed as a modifier to the vapor pressure deficit, which drives aerodynamic mixing. A recent form of the equation for evaporation 

(E) from Yates and Strzepek (1994), is used in this study, as follows:

E R( ) n s G U 15.36 1( ) 0.0062 ( ) a e e γ

γ γ

    = − + + −       ∆ +  + ∆

∆

 , (1–1)

where

Δ is the slope of the saturation vapor pressure curve at the mean daily 

temperature (Tmean), calculated by 

2 504

17 27

273 3

273 3 2 ,

.

.

.

exp

T

T

T

mean

mean

mean

+





 





( ) +

γ is the psychrometric constant, equal to 0.000655P,

Rn is net radiation calculated as the incident solar radiation (Rs) at the surface 

minus the outgoing long wave radiation (Rlw),

P calculated as P z =  −



 



 101 3 293 0 0065

293

5 26

. . .

,

z is the weather station elevation,

G is the sensible heat flux to or from the ground,

U is the wind velocity at 2 meters above the evaporating surface, and

(es – ea) is the vapor pressure deficit computed as the saturation vapor pressure at the 

mean daily air temperature minus the actual mean daily vapor pressure of 

the atmosphere above the evaporating surface.

Generally, this equation is not subject to calibration.

Evaporation (E) is in units of radiation energy and can be converted to evaporated water 

equivalent by dividing by the latent heat (energy) of vaporization (λ). It is important to keep 

the units consistent; the left term in equation 1–1 is in units of energy, and the right term mixes 

units of vapor pressure and velocity that must be converted to units of energy. The coefficient in 

equation 1–1 associated with the right term (15.36) provides the specific conversion, assuming that the units of energy are langleys and vapor pressure are millibars. The coefficient of 

the velocity variable in the right term is empirically derived and assumes that velocity is in 
Appendix 1 43

kilometers per day. Units for Δ and γ need to be the same, but because they occur as a ratio, the 

units do not need to be consistent with the other terms in equation 1–1.

The outgoing longwave radiation is computed from air temperature according to methods 

outlined in Environmental and Water Resources Institute (2005) and is given by the following 

equation:

( ) ( ) ( ) ( )

4 9 0.5 1.35 4.903 10 273 0.34 0.14 0.35

0.75 0.00002

s

lw mean a

a

R R T e

R h

−   = × + −   − +  

, (1–2)

where

ea is the actual vapor pressure,

Ra is the extraterrestrial solar radiation, and

h is the site elevation.

The sensible heat flux to or from the ground (G) is computed for this study from temperature, assuming that air temperature is equivalent to surface ground temperature using the 

following equation:

1 1 ( – )

2

K Tt mean i Tmean i G + − = , (1–3)

where

Kt is a conversion factor,

i+1 is the next day’s temperature, and

i–1 is the previous day’s temperature.

Jensen-Haise Method

The most commonly used PET module in PRMS is the Jensen-Haise method (Jensen 

and Haise, 1963; Leavesley and others, 1983; Igbadun and others, 2006). The method is 

semiempirical and uses mean near-surface air temperature and diurnal saturation vapor pressure differences (estimated from air temperature), elevation, and mean incident solar radiation. 

The mean incident solar radiation is calculated for latitude and day of the year and adjusted for 

atmospheric attenuation and reflectance (albedo) using a method based on precipitation and 

temperature. The variability of the daily evaporative demand is driven by the daily variation in 

average near-surface air temperature and the daily incident solar radiation. The Jensen-Haise 

method, as developed for PRMS, uses an average monthly maximum temperature index to 

compute the mean maximum and minimum saturation vapor pressures that are applied to the 

entire modeled area to account for climatic setting. The Jensen-Haise model is given by the following equations:

E C= − t m ( ) T T ean x Rs (1–4)

1

650 68

278 (

t

smax smin

C

h

e e

=

      − +      −

(1–5)

27.5 0.25( ) 1,000 x smax smin

h T e e   = − − − −     (1–6)

where

E and Rs are potential water evaporation,

Ct and Tx are coefficients,

Tmean is the mean daily temperature, calculated by the average of maximum and 

minimum daily air temperatures,

h is the mean elevation of the representative station (or in the case of the model, 

the hydrologic response unit), and
44 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

(esmax – esmin) is the saturation vapor pressure deficit 

between the daily maximum and the daily 

minimum temperatures for the warmest 

month of the year.

The vapor pressure function is an index for the actual vapor 

pressure deficit and is used in lieu of input data for relative 

humidity or dew point temperature. Given that all coefficients 

used in the model are calculated directly, the model does not 

require calibration in the form presented in this study. However, in the original form of the method, the coefficients were 

not precisely defined and thus are subject to calibration. Therefore, the coefficients of the Jensen-Haise model (Ct

 and Tx) are 

considered to be primarily empirical in nature and thus subject 

to calibration. In this study, we did not use any calibration; 

therefore, the method was evaluated as presented.

Data and Data Requirements

The data needed to estimate PET using the methods 

outlined previously include incident solar radiation, air temperature, vapor pressure deficit (calculated from dew point 

temperature or relative humidity), station elevation, station 

latitude, and wind velocity. All of these data are available 

from some weather stations but are not available from most 

stations. Typically, weather stations report precipitation and 

air temperature along with elevation, latitude, and longitude. 

Cloud cover, as a percentage of the clear sky, is also available from some stations. The extent to which all of these data 

can be used improves the coverage of information necessary 

to estimate PET over large areas. Comparison of the different methods provides insight into the key variables that are 

necessary to calculate a reasonable value for PET. Cloud cover 

and precipitation can be used to estimate incident solar radiation from calculated extraterrestrial radiation where measured 

incident solar radiation is not available.

For this study, data were acquired from the Northeast 

Regional Climate Center (undated). Datasets were selected to 

provide the data necessary to evaluate the methods described 

above and to provide independent estimates of PET, either calculated or from pan evaporation. The datasets were not quality 

controlled as part of this study; thus, the data were used 

directly under the assumption that they are quality controlled 

at the source. This is generally true of the Northeast Regional 

Climate Center (NRCC) data (DeGaetano and others, 1994).

Independently Estimated PET

Some weather stations report an independently estimated 

value for PET or AET. These estimates are based on the 

weather data collected at the site and may use directly measured solar radiation or estimated solar radiation determined 

from cloud cover and calculated extraterrestrial solar radiation. 

The estimates of PET reported by the NRCC were determined 

using a calibrated Penman-Monteith method (DeGaetano 

and others, 1994). The estimates are calibrated to individual 

stations and therefore include site specific information about 

the surroundings, including cover characteristics and setting. As such, these data are considered more accurate for the 

individual station than the estimates developed for a model 

application that computes a generalized PET for the region 

being modeled.

Solar Radiation

The pertinent solar radiation data consist of the incident shortwave radiation and the net longwave radiation (the 

longwave out and the longwave reflected back or absorbed 

and radiated back to Earth from clouds and other greenhouse 

gases). These radiation quantities are generally estimated 

or calculated from a calculated value of the extraterrestrial 

radiation (incoming radiation at the top of the atmosphere) and 

from weather variables, including air temperature, precipitation, and humidity, and cloud cover. Some locations measure 

incident solar radiation. Longwave radiation is not measured 

and must be estimated from the temperature. Values used here 

are obtained from the NRCC (DeGaetano and others, 1993).

Temperature and Precipitation

Near-surface air temperatures, including maximum, minimum, and average daily values, and daily precipitation totals 

are widely available from numerous sources and therefore provide the foundation for input data to watershed models. Given 

the close relation between minimum air temperature and the 

dew point temperature, the minimum temperature may be 

used in lieu of dew point temperature to calculate actual vapor 

pressure. With this recommendation, the Penman based equation can be used for comparison to the Jensen-Haise method 

of estimating PET. In addition, the relation of minimum air 

temperature to dew point temperature indicates the basis of 

validity for its application in the Jensen-Haise method.

Wind

Wind speed is available from many weather stations 

across the country but generally is not available for regional 

coverage from the National Oceanographic and Atmospheric 

Administration Cooperative Observer Program stations. Additionally, wind is generally not available from gridded weather 

datasets for the continental United States or from GCM output 

obtained through the Geo Data Portal (U.S. Geological Survey, 

undated). Consequently, wind speed is not generally available 

for regional modeling. Wind may be an important factor in 

local evapotranspiration. It is not clear how important wind 

is on larger scales, as would be the case if estimating PET for 

an HRU that is 10 or 20 square miles in area, for example. To 

test the importance of wind, three key questions are addressed 

in this analysis. What is the average wind speed for this area? 

What role does it play in the mass transfer of moisture at that 

scale? How important is wind in the PET estimates?
Appendix 1 45

Comparison of Methods

The calculation of the slope of the saturation vapor 

pressure curve, the vapor pressure gradient, the longwave 

radiation, and the sensible heat flux are all dependent on 

temperature; by inspection of equations 1–1 and 1–4, it is 

evident that the Penman and Jensen-Haise methods rely on air 

temperature and solar radiation to estimate PET. The Penman 

method also accounts for wind speed, sensible heat flux to or 

from the ground, and net longwave radiation. Thus, the differences between the two methods are a function of the magnitude and arrangement of the modifiers. We would expect that 

the methods would predict similar PET values, given similar 

temperature and solar radiation inputs. The key question is 

whether the two methods produce linear or nonlinear estimates 

relative to each other over the range of temperature values 

expected in future climate scenarios. Assuming incident solar 

radiation does not change appreciably in the future climate, 

the change in air temperature over the 21st century is expected 

to range from 4 to 8 degrees Fahrenheit (°F) in New England 

with minimum daily air temperature changing at a different 

rate than maximum daily air temperature (Bjerklie and others, 

2015; Wake and others, 2014).

Input datasets were compiled for the Lebanon and the 

Mount Washington weather stations in New Hampshire for 

comparison (Northeast Regional Climate Center, undated). 

PET estimates for 2 years from the Penman and Jensen-Haise 

methods were compared with PET estimates from the Northeast Regional Climate Center for the same weather stations. 

Neither the Penman nor Jensen-Haise estimates were calibrated. The Jensen-Haise method tends to produce the most 

accurate estimates compared to independent estimates. JensenHaise consistently estimated values lower than the Penman 

based methods (figs. 1–1 and 1–2). The Jensen-Haise method 

can produce negative values when temperature is below freezing. When water is frozen, evaporation occurs by sublimation, 

and neither the Penman nor the Jensen-Haise method models 

the sublimation process, which is much slower than evaporation from an open water surface. Although Jensen-Haise may 

produce negative values of PET when temperatures are below 

freezing, Penman typically will produce a positive value when 

the temperature is below freezing. In either case the PET estimate for temperatures below freezing are either unrealistic or 

at least methodologically suspect and have been removed from 

the record analyzed in this appendix. In PRMS, evapotranspiration is assumed to be zero if PET is negative and generally 

is not considered to be operating when leaves are off the trees 

during the cold winter months.

Figures 1–3 through 1–6 show the estimated PET from 

Penman and Jensen-Haise methods, and the NRCC method 

using measured data, in relation to the input variables of 

incident solar radiation and average daily air temperature 

in order to evaluate the relative importance of these two 

variables. Solar radiation is more highly correlated with PET 

than air temperature at both weather stations. The relations 

for the three methods are similar with Jensen-Haise method 

resulting in nearly the same relation for the two variables as 

the NRCC method.

As stated previously, the Penman method accounts for the 

effects of wind and sensible heat loss or gain from the ground. 

The national gridded input weather datasets accessed for the 

PRMS modeling in this report do not include wind or ground 

temperatures, thus these contributions to PET could not be 

modeled by Penman or any other method. Therefore, the Jensen-Haise method does not fall short in this regard. However, 

as part of this sensitivity analysis, wind and an estimate of the 

sensible heat flux to the ground, based on air temperature in 

the Penman method, were included for comparison. At both 

the Mount Washington and Lebanon stations, wind is nearly 

always present. The wind speed averages 3.8 miles per hour at 

Mount Washington and 4.5 miles per hour at Lebanon. Inclusion of wind in the PET estimates made using Penman resulted 

in an increase of the mean PET by 12 percent at Mount Washington and 17 percent at Lebanon; the standard deviations of 

PET increased by 6 and 9 percent, at the Mount Washington 

and Lebanon stations, respectively. Inclusion of sensible 

heat flux resulted in negligible change. Although this would 

indicate that wind is an important contributor to PET, because 

Penman tended to overpredict the measured values at both 

stations even without the inclusion of wind, it would seem that 

there would be no imperative to include wind. Also, because 

the effects of wind at the station scale are difficult to compare 

to the effects of wind at the PRMS HRU scale as a result of 

the highly variable nature of wind in varied terrain, the exclusion of wind in Jensen-Haise method is not considered to be a 

substantial limitation for the PET estimates in PRMS.

Climate Change Sensitivity Analysis

The sensitivity of the Penman and Jensen-Haise methods 

was tested by increasing the maximum and minimum daily 

air temperatures that are input to the estimates. Two mean temperature change datasets were tested: a dataset where the mean 

value of random change in the temperature data is increased 

by 4 °F for the maximum daily and 5 °F for the minimum 

daily and a dataset where the mean value of random change 

in the temperature data is increased by 7 °F for the maximum 

daily and 8 °F for minimum daily. The two methods responded 

consistently and similarly (figs. 1–7 through 1–12). In these 

figures, the Jensen-Haise estimate is plotted against the Penman estimate, and the slope of the relation is evaluated. The 

character of the relation between the two methods remains 

consistent, including similar slope, intercept, and spread of the 

points, indicating that the changing air temperature does not 

alter the comparative predictive qualities of the methods. This 

is likely because temperature is controlling the vapor pressure 

gradient, and if the maximum and minimum temperatures 

vary similarly, the vapor pressure difference is not radically 

altered. Assuming that solar radiation is adequately simulated 

for future conditions, the two methods likely respond to the 

changing climate similarly and therefore can be expected to be 

stable in a future simulation.
46 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

7-day moving average, NRCC PET 7 percent, Jensen-Haise method 7 percent, Penman method

EXPLANATION

Date

Moving average of potential evapotranspiration 

0

0.05

0.1

0.15

0.2

0.25

0.3

10/1/2004

10/16/200410/31/200411/15/200411/30/200412/15/2004

12/30/2004

1/14/20051/29/2005

2/13/2005

2/28/20053/15/20053/30/20054/14/20054/29/20055/14/20055/29/20056/13/20056/28/20057/13/20057/28/20058/12/20058/27/20059/11/20059/26/2005

10/11/200510/26/200511/10/200511/25/200512/10/200512/25/2005

1/9/2006

1/24/2006

2/8/2006

2/23/20063/10/20063/25/2006

4/9/2006

4/24/2006

5/9/2006

5/24/2006

6/8/2006

6/23/2006

7/8/2006

7/23/2006

8/7/2006

8/22/2006

9/6/2006

9/21/2006

PET, in inches

Figure 1–1. Time series of 2 years of estimated potential evapotranspiration for the Mount Washington weather station in New 

Hampshire using Northeast Regional Climate Center measured data and Penman and Jensen-Haise methods with a 7-day moving 

average. NRCC, Northeast Regional Climate Center; PET, potential evapotranspiration.

0

0.05

0.1

0.15

0.2

0.25

0.3

0 100 200 300 400 500 600 700

PET, in inches

Incident solar radiation, in langleys

EXPLANATION

NRCC PET Penman method Jensen-Haise method

Figure 1–2. Time series of 2 years of estimated potential evapotranspiration for the Lebanon weather station 

in New Hampshire from the Northeast Regional Climate Center measured data and Penman and JensenHaise methods with the 7-day moving average. NCRR, Northeast Regional Climate Center; PET, potential 

evapotranspiration.
Appendix 1 47

0

0.05

0.1

0.15

0.2

0.25

0.3

-20 0 20 40 60 80 100

PET, in inches

Daily average temperature, in degrees Fahrenheit

NRCC PET Penman method Jensen-Haise method

EXPLANATION

Figure 1–3. Estimated daily potential evapotranspiration using Northeast Regional Climate Center method with 

measured data and Penman and Jensen-Haise methods in relation to average daily incident solar radiation 

for the Mount Washington, New Hampshire, weather station. NCRR, Northeast Regional Climate Center; PET, 

potentiometric evapotranspiration.

Date

7 -day moving average, NRCC PET 7 percent, Jensen-Haise method 7 percent, Penman method

EXPLANATION

Moving average of potential evapotranspiration 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

PET, in inches

10/1/200411/1/200412/1/2004

1/1/20052/1/20053/1/20054/1/20055/1/20056/1/20057/1/20058/1/20059/1/2005

10/1/2005

11/1/200512/1/2005

1/1/20062/1/20063/1/20064/1/20065/1/20066/1/20067/1/20068/1/20069/1/2006

10/1/2006

Figure 1–4. Estimated daily potential evapotranspiration using Northeast Regional Climate Center method with measured data 

and Penman and Jensen-Haise methods in relation to daily average air temperature for the Mount Washington, New Hampshire, 

weather station. NCRR, Northeast Regional Climate Center; PET, potentiometric evapotranspiration.
48 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

NRCC PET Penman method Jensen-Haise method

EXPLANATION

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 100 200 300 400 500 600 700

PET, in inches

Incident solar radiation, in langleys

Figure 1–5. Estimated daily potential evapotranspiration using Northeast Regional Climate Center method with 

measured data and Penman and Jensen-Haise methods in relation to average daily incident solar radiation 

for the Lebanon, New Hampshire, weather station. NCRR, Northeast Regional Climate Center; PET, potential 

evapotranspiration.

NRCC PET Penman method Jensen-Haise method

EXPLANATION

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

-20 0 20 40 60 80 100

PET, in inches

Daily average temperature, in degrees Fahrenheit

Figure 1–6. Estimated daily potential evapotranspiration using Northeast Regional Climate Center method with 

measured data and Penman and Jensen-Haise methods in relation to daily average air temperature for the Lebanon, 

New Hampshire, weather station. NCRR, Northeast Regional Climate Center; PET, potential evapotranspiration.
Appendix 1 49

-0.05

0

0.05

0.1

0.15

0.2

0.25

0 0.05 0.1 0.15 0.2 0.25 0.3

Jensen-Haise method PET, in inches

Penman method PET, in inches

y = 0.7964x − 0.0225 

R 2 = 0.9568

Figure 1–7. Estimated daily potential evapotranspiration for a 2-year period using Jensen-Haise method in relation 

to estimates using the Penman method for the Mount Washington weather station in New Hampshire with the best 

fit linear correlation with slope and intercept. PET, potential evapotranspiration.

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Jensen-Haise method PET, in inches

Penman method PET, in inches

y = 0.8565x − 0.0258 

R 2 = 0.9692

Figure 1–8. Estimated daily potential evapotranspiration for a 2-year period using Jensen-Haise method in 

relation to estimates using the Penman method for the Mount Washington weather station in New Hampshire with 

the best fit linear correlation with slope and intercept after adjusting the mean maximum daily temperature by 

4 degrees Fahrenheit and the minimum by 5 degrees Fahrenheit. PET, potential evapotranspiration.
50 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Jensen-Haise method PET, in inches

Penman method PET, in inches

y = 0.9037x − 0.0284 

R 2 = 0.9754

Figure 1–9. Estimated daily potential evapotranspiration for a 2-year period using Jensen-Haise method in relation 

to estimates using the Penman method for the Mount Washington weather station in New Hampshire with the best 

fit linear correlation with slope and intercept after adjusting the mean maximum daily temperature by 7 degrees 

Fahrenheit and the minimum by 8 degrees Fahrenheit. PET, potential evapotranspiration.

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Jensen-Haise method PET, in inches

Penman method PET, in inches

y = 0.8368x − 0.0239 

R 2 = 0.9643

Figure 1–10. Estimated daily potential evapotranspiration for a 2-year period using the Jensen-Haise method in 

relation to estimates using the Penman method for the Lebanon weather station in New Hampshire with the best fit 

linear correlation with slope and intercept. PET, potential evapotranspiration.
Appendix 1 51

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Jensen-Haise method PET, in inches

Penman method PET, in inches

y = 0.8936x − 0.0275 

R 2 = 0.9739

Figure 1–11. Estimated daily potential evapotranspiration for a 2-year period using the Jensen-Haise method in 

relation to estimates using the Penman method for the Lebanon weather station in New Hampshire with the best 

fit linear correlation with slope and intercept after adjusting the mean maximum daily temperature by 4 degrees 

Fahrenheit and the minimum by 5 degrees Fahrenheit. PET, potential evapotranspiration.

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Jensen-Haise method PET, in inches

Penman method PET, in inches

y = 0.9395x – 0.0304 

R 2 = 0.9788

Figure 1–12. Estimated daily potential evapotranspiration for a 2-year period using the Jensen-Haise method in 

relation to estimates using the Penman method for the Lebanon weather station in New Hampshire with the best 

fit linear correlation with slope and intercept after adjusting the mean maximum daily temperature by 7 degrees 

Fahrenheit and the minimum by 8 degrees Fahrenheit. PET, potential evapotranspiration.
52 Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire

Summary

The sensitivity of the Jensen-Haise method potential 

evapotranspiration (PET) algorithm used in the PRMS model to 

changes in temperature were tested by comparison of those PET 

estimates with the best estimates of PET from weather station 

data and with estimates from the Penman PET algorithm. On the 

basis of these comparisons, the Jensen-Haise method is expected 

to provide changes in air temperature comparable in magnitude 

and response to those from the Penman method. We conclude that 

over the range of expected climate change addressed in this study, 

the Jensen-Haise method for estimating PET provides results 

similar to those from the more physically based Penman method. 

The Penman method includes the effects of wind and sensible 

heat exchange with the Earth, which Jensen-Haise method does 

not. Including wind in the PET estimates tends to increase the 

PET estimate for the weather station, and inclusion of sensible 

heat flux to the ground tends to decrease the PET. Neither wind 

nor sensible heat flux information is readily available as input to 

regional watershed runoff models, so inclusion of these data is not 

practical. Consequently, the Jenson-Haise method is considered 

appropriate for application to future climate modeling because its 

equation reduces to an equation similar to that of Penman, if wind 

and sensible heat flux are ignored.

Selected References

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Webb, R.M.T., Payn, R.A., and LaFontaine, J.H., 2015, 

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book 6, chap. B7, 158 p., accessed September 2015 at 

https://doi.org/10.3133/tm6B7.

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adjustment of climate-model projections—The potential 

pitfall of potential evapotranspiration: Earth Interactions, 

v. 15, no. 1, 15 p.

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The New Hampshire climate action plan—A plan for 

New Hampshire’s energy, environmental, and economic 

development future: Concord, N.H., New Hampshire 

Department of Environmental Services, 74 p. [Also 

available at https://www.des.nh.gov/organization/divisions/

air/tsb/tps/climate/action_plan/documents/nhcap_final.pdf.]

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2016 at http://www.nrcc.cornell.edu/services/access/

access.html.

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estimating regional evaporation: Quarterly Journal of the 

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For more information about this report, contact:

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Bjerklie and Sturtevant—Simulated Hydrologic Response to Climate Change During the 21st Century in New Hampshire—SIR 2017–5143

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20175143