U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2005–5200

Prepared in cooperation with the American Samoa Environmental Protection Agency

Potential Evapotranspiration on Tutuila,

American Samoa

14o 20'

170o 50'

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170o 40'

14o 15'

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Potential Evapotranspiration on Tutuila, 

American Samoa

By Scot K. Izuka, Thomas W. Giambelluca, and Michael A. Nullet

Prepared in cooperation with the American Samoa Environmental Protection Agency

Scientific Investigations Report 2005-5200

U.S. Department of the Interior

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

Gale A. Norton, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2005

For sale by U.S. Geological Survey, Information Services

Box 25286, Denver Federal Center

Denver, CO 80225

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endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Izuka, S.K., Giambelluca, T.W., and Nullet, M.A., 2005, Potential evapotranspiration on Tutuila, American Samoa: 

U.S. Geological Survey Scientific Investigations Report 2005-5200, 40 p.
iii

Contents

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

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

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

Acknowledgments................................................................................................................................3

Physiographic Setting of Tutuila..................................................................................................................3

Study Methods ...............................................................................................................................................5

Estimating Potential Evapotranspiration from Climate Data .........................................................6

Data Collection......................................................................................................................................6

Climate Data....................................................................................................................................................9

Potential Evapotranspiration .....................................................................................................................15

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

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

Appendix 1 – Description of Climate Stations and Methods for Computation of 

Potential Evapotranspiration........................................................................................................36

Figures

Figure 1. Map showing location and physical features of Tutuila, American Samoa ....................2

Figure 2. Graph showing rainfall at the National Weather Service office at the Pago Pago 

Airport and the U.S. Geological Survey Aasufou rain gage, Tutuila, 

American Samoa........................................................................................................................4

Figure 3. Graph showing temperature, pan evaporation, wind speed, and relative humidity at 

the National Weather Service office at the Pago Pago Airport, Tutuila, 

American Samoa........................................................................................................................5

Figure 4. Generalized diagram of instrument setup at climate stations on Tutuila, 

American Samoa................................................................................................................................7

Figure 5. Map showing climate stations on Tutuila, American Samoa .............................................8

Figure 6. Graph showing mean hourly and mean monthly rainfall at the climate stations on 

Tutuila, American Samoa........................................................................................................10

Figure 7. Graph showing mean hourly and mean monthly net radiation at the 

climate stations on Tutuila, American Samoa.....................................................................11

Figure 8. Graph showing mean hourly and mean monthly soil heat flux at the climate stations on 

Tutuila, American Samoa............................................................................................................... 12

Figure 9. Graph showing mean hourly and mean monthly air temperature at the 

climate stations on Tutuila, American Samoa.....................................................................13

Figure 10. Graph showing mean hourly and mean monthly soil temperature at the 

climate stations on Tutuila, American Samoa.....................................................................14

Figure 11. Graph showing mean hourly and mean monthly relative humidity at the 

climate stations on Tutuila, American Samoa.....................................................................16

Figure 12. Graph showing mean hourly and mean monthly wind speed at the 

climate stations on Tutuila, American Samoa.....................................................................17

Figure 13. Graph showing mean hourly potential evapotranspiration at the 

climate stations on Tutuila, American Samoa.....................................................................19
iv

Figures—Continued

Figure 14. Graph showing unadjusted mean monthly potential evapotranspiration at the 

climate stations on Tutuila, American Samoa.....................................................................21

Figure 15. Map showing distribution of mean annual potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................22

Figure 16. Map showing distribution of mean January potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................23

Figure 17. Map showing distribution of mean February potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................24

Figure 18. Map showing distribution of mean March potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................25

Figure 19. Map showing distribution of mean April potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................26

Figure 20. Map showing distribution of mean May potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................27

Figure 21. Map showing distribution of mean June potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................28

Figure 22. Map showing distribution of mean July potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................29

Figure 23. Map showing distribution of mean August potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................30

Figure 24. Map showing distribution of mean September potential evapotranspiration on 

Tutuila, American Samoa........................................................................................................31

Figure 25. Map showing distribution of mean October potential evapotranspiration on Tutuila, 

American Samoa......................................................................................................................32

Figure 26. Map showing distribution of mean November and mean December potential 

evapotranspiration on Tutuila, American Samoa ...............................................................33

Tables

Table 1. Locations and monitoring periods of climate stations used in the study of potential

evapotranspiration on Tutuila, American Samoa. ..............................................................9

Table 2. Mean hourly potential evapotranspiration for stations on Tutuila, 

American Samoa......................................................................................................................18

Table 3. Unadjusted mean monthly and mean annual potential evapotranspiration for

stations on Tutuila, American Samoa. ................................................................................20

Table 4. Adjusted mean monthly and mean annual potential evapotranspiration for stations

on Tutuila, American Samoa. .................................................................................................21
Abstract

Data from nine widely distributed climate stations were 

used to assess the distribution of potential evapotranspiration on the tropical South Pacific island of Tutuila, American 

Samoa. Seasonal patterns of climate data in this study differed 

in detail from available long-term data because the monitoring 

period of each station in this study was only 1 to 5 years, but 

overall climate conditions during the monitoring period (1999-

2004) are representative of normal conditions.

Potential evapotranspiration shows a diurnal pattern. On 

average, potential evapotranspiration in the daytime, when 

net radiation is the dominant controlling factor, constitutes 90 

percent or more of the total daily potential evapotranspiration 

at each station. Positive heat advection from the ocean contributes to potential evapotranspiration at at least one station, and 

possibly other stations, in this study.

Seasonal variation of potential evapotranspiration is 

linked to seasonal daylight duration. Spatial variation of 

potential evapotranspiration, however, is linked primarily 

to orographic cloud cover. Potential evapotranspiration on 

Tutuila is lowest in the interior of the island, where rainfall is 

higher, cloud cover is more frequent, and net radiation is lower 

than along the coasts. Potential evapotranspiration is highest 

along the southern and eastern coasts of the island, where rainfall is lower and cloud cover less frequent. The gradient from 

areas of high to low potential evapotranspiration is steepest in 

November and December, when island-wide potential evapotranspiration is highest, and less steep in June and July, when 

island-wide potential evapotranspiration is lowest.

Comparison of potential evapotranspiration to rainfall 

indicates that evapotranspiration processes on Tutuila have the 

potential to remove from 23 to 61 percent of the water brought 

by rainfall. In lower-rainfall coastal locations, potential evapotranspiration can be 50 percent or more of rainfall, whereas in 

higher-rainfall interior locations potential evapotranspiration is 

less than 30 percent of rainfall.

Introduction

Tutuila is a part of the Samoa Islands, a volcanic archipelago in the tropical South Pacific (fig. 1). The archipelago is 

divided into two political entities: (1) the independent nation 

of Samoa in the west and (2) the U.S. Territory of American 

Samoa in the east. Tutuila is the largest (53 mi2) and most 

populated (55,000 people [U.S. Census Bureau, 2004]) island 

in American Samoa and is the seat of government and site of 

most industry in the Territory. The need to assess, manage, 

and protect Tutuila’s water resources has become increasingly 

important as population and ground-water use increase and 

awareness about potential threats to the quality and quantity 

of ground-water resources is raised. Nearly all of the public 

drinking water supply on Tutuila comes from ground-water 

resources.

Hydrologic investigations (e.g. Stearns, 1944; Davis, 

1963; Bentley, 1975; Matsuoka 1978; Wong, 1996; Izuka, 

1999) have advanced understanding of water resources on 

Tutuila, but without data on the island’s freshwater budget, 

it has not been possible to accurately quantify the rate at 

which Tutuila’s aquifers are recharged. In the water budget of 

mid-ocean islands such as Tutuila, recharge of ground-water 

resources is ultimately derived from precipitation (mostly 

rainfall but probably also fog drip). The rest of the water from 

precipitation runs directly off the land surface via streams, 

evaporates, or is transpired by plants. Ground-water recharge 

can be computed if losses due to stream runoff and evapotranspiration (the combined effects of evaporation and plant 

transpiration) are known or can be estimated. Assessment of 

ground-water resources requires accurate estimates for rainfall, 

runoff, and evapotranspiration. Whereas decades of rainfall 

and streamflow data have been collected at various locations 

on Tutuila, existing data on evapotranspiration rates are insufficient for the needs of ground-water resource assessment.

To quantify the amount of water being evaporated and 

transpired from the land surface of Tutuila, the U.S. Geological Survey (USGS), in cooperation with the American Samoa 

Environmental Protection Agency (ASEPA), conducted a 

study to monitor climate and assess island-wide distribution of 

evapotranspiration. The method developed by Penman (1948) 

for estimating potential evapotranspiration was considered 

most suitable for the purposes of this study, given the high 

rainfall, heterogeneous vegetation, and high relief of Tutuila. 

Potential Evapotranspiration on Tutuila, American Samoa

By Scot K. Izuka1, Thomas W. Giambelluca2, and Michael A. Nullet2

__________________ 1

U.S. Geological Survey, Honolulu, Hawaii, 96813

2

Department of Geography, University of Hawaii at Manoa, Honolulu, 

Hawaii, 96822
14o 20'

170o 50'

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EXPLANATION

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

USGS Aasufou 

Rain Gage

141952170460201

Climate Monitoring and 

Diagnostics Laboratory

Cape Matatula 

National Weather Service, 

Pago Pago Airport

Mount Alava 

(1,610)

Mount Alava 

(1,610)

Masefau

Malaeimi Valley

Iliili

Maloata

Fagaitua

Matafao Peak

(2,142)

Olotele

Mountain

(1,617) MOUNTAIN PEAK NAME AND ELEVATION, IN FEET

 ABOVE MEAN SEA LEVELFACILITY WITH LONG-TERM CLIMATE DATA VILLAGE OR OTHER SIGNIFICANT SITE

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20o

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S

140o E 160o E 180o 160o W

500 1,000 MILES 0

0 500 1,000 KILOMETERS

HAWAII

INDONESIA AMERICAN

 SAMOA

FEDERATED STATES OF MICRONESIA

PAPUA

NEW GUINEA FIJI

TAHITI AUSTRALIA

GUAM

REPUBLICOF PALAU

COMMONWEALTH

OF THE NORTHERN

MARIANA ISLANDS

REPUBLIC OF THE

MARSHALL ISLANDS

PACIFIC OCEAN

Pago Pago Harbor

Tafuna-Leone P al ni

Figure 1. Location and physical features of Tutuila, American Samoa.

 Potential Evapotranspiration on Tutuila, American Samoa
Potential evapotranspiration (PE) is the evapotranspiration 

rate that would exist, under given atmospheric conditions and 

vegetation, if the availability of water is not a limiting factor.

Purpose and Scope

This report describes the methods and results of the study 

of PE distribution on Tutuila. This report includes summaries 

of climate data collected between 1999 and 2004 from nine 

sites (stations) on Tutuila, as well as computed values of PE 

for each station, and maps showing the distribution of mean 

monthly and annual PE. To facilitate discussions in this report, 

stations will be referred to by an abbreviated name consisting 

of the station number and a single word describing its location 

(for example, station 331, which was at the summit of Mount 

Alava, will be referred to as “331 Alava”).

Acknowledgments

The authors are grateful to Peter Peshut, Director of the 

ASEPA, and Togipa Tausaga, former Director of the ASEPA, 

for their cooperation and assistance. The authors are also 

grateful to Fa’amao Asalele of the ASEPA, who assumed the 

primary responsibility for maintaining the stations and retrieving and transmitting data to the USGS. Additional assistance 

was provided by Sheila Wiegman and Francis Huber of the 

ASEPA. Land-owner permission for the deployment of the 

climate-monitoring stations was granted by Maatifa Eleasario, 

Peter Gurr, the American Samoa Power Authority (ASPA), the 

National Oceanic and Atmospheric Administration (NOAA) 

Climate Monitoring and Diagnostics Laboratory (CMDL) 

and National Weather Service (NWS), and the National Park 

Service. These individuals and agencies also provided field 

assistance to the authors during the setup and removal of the 

stations.

Physiographic Setting of Tutuila

The island of Tutuila was built by several mid-oceanic, 

hot-spot shield volcanoes during the Pliocene to Holocene 

(Stearns, 1944). Erosion has transformed most of the island 

into a ridge of steep mountains trending generally in the eastnortheast direction (fig. 1). The mountains rise abruptly from 

sea level to elevations as high as 2,142 ft. Juxtaposition of the 

deeply eroded mountains with the sea creates a sinuous coastline, much of which has little or no coastal plain. The TafunaLeone Plain on the southwest of Tutuila is an area of gentler 

topography, but has many hills and more than 600 ft of relief. 

The plain was formed by Holocene-age volcanic eruptions that 

covered a preexisting barrier reef (Stearns, 1944). Most of the 

island’s industry and much of the population is located on the 

relatively extensive flat areas of the Tafuna-Leone Plain; maritime industries and a large tuna-packing complex are located 

in the Pago Pago Harbor area. Elsewhere, rugged topography 

limits most of the population to narrow coastal areas, the 

floors of small valleys, or the narrow plateau at Aasufou.

The Samoa Islands lie in the warm humid tropics of the 

South Pacific, within an area influenced by the South Pacific 

Convergence Zone (SPCZ) (Wright, 1963; Giambelluca 

and others, 1988). Because of the seasonal movement of the 

SPCZ and its associated storms, rainfall is relatively high and 

seasonally variable. In most places on Tutuila, rainfall exceeds 

100 in/yr. On average, rainfall is higher from about October 

through May and lower from about June through September 

(fig. 2). Rainfall on Tutuila also varies substantially with 

location and topography relative to the prevailing winds as 

a result of the orographic effect. As winds blowing onshore 

from the ocean encounter the steep mountains of Tutuila, 

the moisture-laden air is forced upward and cools, causing 

precipitation in the high-elevation interior of the island to be 

greater than at the coast. At the NWS office at the Pago Pago 

Airport, which is near sea level in the southeastern lowlands 

of the Tafuna-Leone Plain (fig. 1), rainfall averages 118 in. per 

year (National Climatic Data Center, 2002). At Cape Matatula, 

on the eastern tip of Tutuila, rainfall averages 71 in. per year 

(Mefford, 2004). In contrast, rainfall at the former USGS rain 

gage in Aasufou Village, at an elevation of 1,340 ft above sea 

level, averaged about 200 in. per year (Izuka, 1999). Longterm data indicate that rainfall on Tutuila also varies interannually (fig. 2). During the period from 1970 through 2004, 

annual rainfall at the Pago Pago Airport ranged from a high of 

46 in. above average in 1981 to a low of 61 in. below average 

during the El Niño event of 1998.

Most of the island, particularly the mountainous interior, 

is covered with dense tropical forest. Sites for homes, livestock enclosures, community areas, and small farms have been 

cleared in the villages, most of which lie along the coastal 

plain, valley floors, or on the flat areas of the Tafuna-Leone 

Plain. Urbanized areas consisting of scattered low buildings and paved roads exist primarily in the eastern part of the 

Tafuna-Leone Plain near the airport, and along the shoreline 

of Pago Pago Harbor. The total area of cleared and urbanized 

land on Tutuila is small, however, relative to the large area 

covered by tropical forest.

Normal daily low and high temperatures at the NWS 

office at the Airport (based on the period 1971–2000) are 24 

and 30°C (76 and 86°F), respectively, and each varied seasonally by a few degrees (fig. 3). Relative humidity averages 86 

percent in the morning and 75 percent in the afternoon, with 

a seasonal variation of a few percent (National Climatic Data 

Center, 2004). Prevailing winds are southeasterly for most of 

the year, but winds are generally more variable and weaker 

from about late December to early April (Mefford, 2002, 

2004; Climate Monitoring and Diagnostics Laboratory, 2004; 

National Climatic Data Center, 2004). Wind speed averages 

11 mi/hr at both the NWS office at the Pago Pago Airport 

(National Climatic Data Center, 2004) and the CMDL facility 

at Cape Matatula on the eastern tip of the island (Mefford, 

Physiographic Setting of Tutuila 
USGS Aasufou rain gage 141952170460201 1980-2001

0

5

10

15

20

25

National Weather Service Pago Pago Airport rain gage 1970-2001

0

2

4

6

8

10

12

14

16

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

MONTH

YEAR

MEAN MONTHLY RAINFALL, IN INCHES

National Weather Service Pago Pago Airport rain gage

0

20

40

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80

100

120

140

160

180

200

mean

197019721974197619781980198219841986198819901992199419961998200020022004

ANNUAL RAINFALL, IN INCHES

Figure 2. Rainfall at the National Weather Service (NWS) office at the Pago Pago Airport and the U.S. Geological Survey 

Aasufou rain gage, Tutuila, American Samoa (Data for NWS facility from National Climatic Data Center, 2002, 

2001-05).

 Potential Evapotranspiration on Tutuila, American Samoa
2004). Both locations are exposed to the prevailing southeasterly winds.

Prior to this study, the only existing measurements of 

evapotranspiration on Tutuila were from an evaporation pan 

monitored daily by the NWS at the Pago Pago Airport. Mean 

monthly pan-evaporation rate (for the period 1978 to 2001) at 

this site is lowest in July and highest in December (fig. 3); the 

long-term average evaporation rate for the period was 91 in/yr. 

This single monitoring location is not sufficient, however, to 

assess the island-wide distribution of evapotranspiration on 

Tutuila, where climate varies substantially from one location 

to another as a result of the orographic effect. Quantification 

of island-wide evapotranspiration requires monitoring many 

sites spatially distributed over the island.

Study Methods

A variety of methods have been used to estimate evapotranspiration. The Penman (1948) method is best suited for 

the purposes of this study because of limitations imposed by 

the steep topography, dense and variable vegetation, and high 

rainfall on Tutuila. In moist, heavily vegetated areas such as 

Tutuila, PE can closely approximate actual evapotranspiration 

(Bidlake and others 1996); in other areas, PE can be used in 

a soil water-balance model to estimate actual evapotranspiration provided adequate data on precipitation, runoff, vegetation, and soil characteristics are available or can be estimated 

(Thornthwaite and Mather, 1955). Soil water-budget models 

can also be used to estimate ground-water recharge.

22

24

26

28

30

32

J F M A M J J A S O N D J F M A M J J A S O N D

Maximum

Mean

Minimum

NORMAL DAILY TEMPERATURE,

 IN DEGREES CELSIUS

0

2

4

6

8

10

12

14

MEAN MONTHLY WIND SPEED,

 IN MILES PER HOUR

0.1

0.2

0.3

0.4

MONTH

MEAN MONTHLY PAN EVAPORATION,

 IN INCHES PER DAY

50

55

60

65

70

75

80

85

90

95

MONTH

Afternoon (13:00)

MEAN MONTHLY RELATIVE HUMIDITY,

Morning (07:00)

 IN PERCENT

Figure 3. Temperature, pan evaporation, wind speed, and relative humidity at the National Weather Service (NWS) office at 

the Pago Pago Airport, Tutuila, American Samoa. Normal daily temperature is a 30-year average based on the period 1971-2000. 

Mean monthly pan evaporation is based on the period 1978-2001 (data from National Climatic Data Center, 2002). Mean monthly 

wind speed is based on 36-year period of record ending in 2003, and mean monthly relative humidity is based on a 35-year period 

of record ending 2003 (data from National Climatic Data Center, 2004). 

Study Methods 
Energy-balance approaches that measure actual evapotranspiration directly, such as the Bowen-ratio and the eddycovariance methods, have been used with success by many 

researchers, but these methods are unsuitable for Tutuila 

because they require sites surrounded by large areas of homogeneous land cover and low relief. Methods using atmometers 

(such as evaporation pans) to measure PE also were considered unsuitable for this study because the devices are difficult 

to operate in high-rainfall climates (Ekern, 1983).

Estimating Potential Evapotranspiration from 

Climate Data

Penman’s (1948) equation for estimating PE from climate 

data may be written:

 γ

γ

∆ +

∆ + = H Ea PE (1)

where 

	 PE	 is potential evapotranspiration [L/T],

H is the energy term, expressed in terms 

of evaporation equivalent units [L/T],

Ea is the aerodynamic term [L/T],

∆ is the slope of the saturation vapor 

pressure versus temperature curve 

[F/L2/K], 

and

γ is the psychrometric constant 

[F/L2/K].

Penman’s original formulation was calibrated with respect to 

an evaporation pan and hence did not have a soil heat conduction term. In applications to land-surface evaporation, the Penman energy term (H) is a function of net radiation, soil heat 

flux, soil temperature, and soil moisture. The aerodynamic 

term (Ea) is a function of wind speed and vapor-pressure 

deficit (a function of air temperature and relative humidity). In 

this study, sets of microclimate-monitoring instruments were 

deployed at various locations (stations) on Tutuila to collect 

the data required by the Penman method. The procedure used 

to compute PE from the measured climate data is described in 

detail in the appendix.

Data Collection

The instruments deployed at each station included 

above-ground and subsurface sensors to measure net radiation, relative humidity, air and soil temperature, soil heat flux, 

soil moisture, wind speed, and rainfall (fig. 4). All sensors 

were placed within 7.5 ft (2.3 m) of the ground surface. An 

automatic data logger controlled the sensors, made simple 

computations, and stored data. For most sensors, the logger 

took measurements every 10 seconds. Every hour on the hour, 

the logger computed and stored the average of the 10-second 

measurements made over the preceding hour. Exceptions to 

this procedure were the measurements for soil moisture and 

rainfall. The logger took measurements from the soil-moisture 

probe once every hour. To measure rainfall, a tipping-bucket 

rain gage was used; each tip represented 0.01 in. of rain. The 

data logger converted the tip counts to rainfall, and summed 

the total rainfall for each minute as well as for each hour. The 

net radiation and soil heat-flux sensors were oriented such 

that downward conduction of energy (daytime) is positive and 

upward conduction (nighttime) is negative. A 12-volt battery 

and a 10-watt solar panel provided power. Where possible, 

visits for inspection, maintenance, and data retrieval were 

made twice each month. Data retrieved during field visits were 

subject to quality-control analysis as described in Giambelluca 

and Nullet (2000). All data were plotted as time series and 

visually examined to identify periods of sensor or data-logger 

malfunction or calibration drift. All data suspected of being 

inaccurate were flagged and excluded from analysis.

Practical factors limited the number of instrument sets 

used in this study to four, but over the 5-year monitoring 

period of this study, instruments were deployed at nine different locations representing the range of climate conditions on 

Tutuila (fig. 5, table 1). To increase the number of locations 

that could be monitored with the limited number of instrument sets, three of the four sets were mobile, that is, each of 

the three instrument sets was deployed at a site for a year, and 

then was moved to another location. The single non-mobile 

instrument set remained at the Pago Pago Airport (station 

111 Airport) for the entire period of this study to monitor 

year-to-year climate variations. The proximity of the site 

to the NWS Pago Pago Airport office allowed comparison 

of climate data from this project with data (including panevaporation data) from the NWS. Two other stations were 

established near existing climate-monitoring facilities: station 

442 Matatula on the grounds of the CMDL facility, and station 221 Aasufou near the USGS Aasufou rain gage (USGS 

number 141952170460201). In some cases, it was possible to 

use climate data from the CMDL and NWS when instruments 

installed as part of this study failed to collect data due to malfunction or calibration drift.

Vegetation at the climate stations differed from that of the 

dense tropical forest that covers most of Tutuila. For practical 

purposes, the climate stations were installed in areas that were 

cleared of trees and other tall vegetation, and ground surfaces 

were covered with grass that was kept at a height of a few 

inches either by the landowner or by personnel servicing the 

climate station. In general, albedo is lower and roughness is 

higher over forest than over grass, therefore PE over the forest 

is probably slightly higher than over grass. For the purpose 

of broadly describing PE on Tutuila, it was not necessary to 

adjust the PE estimates to account for differences between 

grass and forest. For applications requiring more precise PE 

measurements, the PE estimates in this study may require 

adjustment to account for the differences between albedo and 

roughness for various types of land cover present on Tutuila. 

Additional information, including descriptions of the land 

cover at each station, is provided in the Appendix.

 Potential Evapotranspiration on Tutuila, American Samoa
Wind Speed (7 feet) Rainfall (7 feet)

Air Temperature and

Relative Humidity (7 feet)

Net Radiation

(6.25 feet)

Solar Panel (5.75 feet)

Data Logger

Soil Moisture

(inserted to 

depth of 0.98 feet)

Battery

Soil Temperature

(buried at depths of 

0.07 and 0.20 feet)

Soil Temperature

(buried at depths of 

0.07 and 0.20 feet)

Soil Heat Flux

(buried at depth of 0.26 feet)

Soil Heat Flux

(buried at depth of 0.26 feet)

Not to scale

Figure 4. Generalized diagram of instrument setup at climate stations on Tutuila, American Samoa. 

Approximate heights and depths of instruments, relative to ground surface, are given in parentheses.

Study Methods 
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

EXPLANATION

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

CLIMATE STATION AND NAME - Elevation, in  feet above sea level, in parentheses. 221 Aasufou

(1,320)

222 Masefau

(730)

222 Masefau

(730)

111 Airport

(10)

441 Maloata

(120)

223 Iliili

(200)

333 Malaeimi

(150)

331 Alava

(1,600) 444 Fagaitua

(280)

442 Matatula

(240)

Figure 5. Climate stations on Tutuila, American Samoa.

 Potential Evapotranspiration on Tutuila, American Samoa
Climate Data

Mean hourly statistics were computed for each climate 

parameter. Daily mean values were computed from the hourly 

values and used as a basis for computing mean monthly and 

mean annual statistics. Because many climate parameters have 

a regular pattern of diurnal variation, daily mean values based 

on incomplete days of data may result in significant bias. For 

this reason, only the daily means from complete days of data 

were used in the computation of mean monthly and mean 

annual statistics.

Available long-term climate data such as rainfall indicate 

that climate on Tutuila can vary from year to year (fig. 2). 

The period of climate monitoring for this study (1999-2004) 

followed a period of relatively dry weather concurrent with 

the El-Niño event of 1997-98. Rainfall during the period of 

monitoring for this study was closer to normal, with some 

years that had above-average rainfall and some years that had 

below-average rainfall. The overall annual average rainfall 

during the monitoring period of this study was 113 in., which 

agrees within a few percent with the long-term annual average of 118 in. (National Climatic Data Center, 2002). This 

close agreement indicates that the climate during the monitoring period of this study was not substantially different from 

long-term average conditions. For the purposes of this study, 

climate conditions during the monitoring period for this study 

are considered representative of normal conditions.

The climate during a given 12-month period can, however, be substantially different from other 12-month periods or 

the average for the entire monitoring period. Because stations 

in this study were operated for different periods and some stations have periods of missing data due to instrument malfunction or calibration drift, the data from one station may not 

be directly comparable to the data from other stations. Some 

general characteristics in the climate data are common to all or 

most stations, however, and some broad comparisons between 

stations can be made.

Plots of mean hourly rainfall indicate that rainfall at most 

stations occurs with nearly equal frequency at any hour of the 

day, although some stations (333 Malaeimi and 441 Maloata, 

for example) show slightly lower rainfall in the late afternoon 

and early evening, and slightly higher rainfall in the morning 

(fig. 6). The pattern in mean monthly rainfall varies substantially from station to station, which may be a reflection of the 

short periods over which most stations operated. Stations 111 

Airport, 221 Aasufou, 223 Iliili, 331 Alava, 441 Maloata, and 

442 Matatula show lower rainfall in July and August than for 

the rest of the year, which is consistent with the seasonal patterns apparent in the long-term data from the NWS office at 

the Pago Pago Airport and CMDL facility at Cape Matatula. 

Not all of these stations, however, show a distinct high-rainfall 

season in October to May, as seen in the long-term data. The 

average rainfall for the period of record of 442 Matatula (96 

in/yr) is higher than the long-term average rainfall (71 in/yr 

[Mefford, 2004]) reported from the nearby CMDL facility, 

whereas the average rainfall recorded at 221 Aasufou (178 

in/yr) is lower than the long-term average (more than 200 in/yr 

[Izuka, 1999]) for the USGS Aasufou rain gage. Average 

rainfall for the period of record of 111 Airport was 125 in/yr, 

which is slightly higher than the long-term average rainfall 

(118 in/yr [National Climatic Data Center, 2002]) reported 

from the NWS office at the Pago Pago Airport.

Mean hourly net radiation, soil heat flux, and air and soil 

temperatures are generally consistent with the diurnal cycle 

of daylight and darkness (figs. 7-10). Most of the differences 

between these parameters can be attributed to the contrast in 

heat capacity of soil (and the water it contains) relative to air. 

The peak in mean hourly air temperature is only slightly later 

than the peak in mean hourly net radiation, whereas the peak 

in mean hourly soil temperature lags the peak in mean hourly 

net radiation by 2 to 4 hours because soil and the water within 

Table 1. Locations and monitoring periods of climate stations used in the study of potential evapotranspiration on Tutuila, American 

Samoa. 

[Coordinates: World Geodetic System 1984 (WGS 84), determined by hand-held global positioning system (GPS). Elevation: determined by barometric 

altimeter.] 

Station name U.S. Geological

Survey number

Coordinates Elevation

(feet above sea 

level)

Monitoring period Latitude Longitude

111 Airport 141954170425101 14° 19’ 54” 170° 42’ 51” 10 July 1, 1999 to April 29, 2004

221 Aasufou 141909170461201 14° 18’ 51” 170° 46’ 16” 1,320 July 1, 1999 to June 7, 2000

222 Masefau 141538170371201 14° 15’ 38” 170° 37’ 12” 730 December 12, 2001 to April 27, 2004

223 Iliili 142030170444901 14° 20’ 31” 170° 44’ 49” 200 August 2, 2000 to December 11, 2001

331 Alava 141614170411601 14° 15’ 55” 170° 41’ 16” 1,600 March 2, 2000 to June 5, 2002

333 Malaeimi 141842170435801 14° 18’ 42” 170° 43’ 58” 150 February 26, 2003 to April 28, 2004

441 Maloata 141844170484101 14° 18’ 27” 170° 48’ 44” 120 March 3, 2000 to May 1, 2001

442 Matatula 141507170334701 14° 14’ 50” 170° 33’ 52” 240 May 3, 2001 to June 4, 2002

444 Fagaitua 141602170370201 14° 16’ 02” 170° 37’ 02” 280 June 5, 2002 to April 28, 2004

Climate Data 
MEAN HOURLY RAINFALL, IN HUNDREDTHS OF INCHES

MEAN MONTHLY RAINFALL, IN INCHES

43210

43210

43210

43210

43210 0

40302010

0

40302010

0

40302010

0

40302010

0

40302010

MONTH

MONTH

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

J F M A M J J A S O N D

J F M A M J J A S O N D

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

Figure 6. Mean hourly and mean monthly rainfall at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

10 Potential Evapotranspiration on Tutuila, American Samoa
MEAN HOURLY NET RADIATION, IN WATTS PER SQUARE METER

MEAN MONTHLY NET RADIATION, IN WATTS PER SQUARE METER

MONTH

MONTH

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

80604020

160140120100

80604020

160140120100

80604020

160140120100

80604020

160140120100

80604020

160140120100

-100

0

-100

500400300200100

0

-100

500400300200100

0

-100

500400300200100

0

-100

500400300200100

0

500400300200100

J F M A M J J A S O N D

J F M A M J J A S O N D

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

331 ALAVA

223 ILIILI

221 AASUFOU

222 MASEFAU

111 AIRPORT

444 FAGAITUA

441 MALOATA

442 MATATULA

333 MALAEIMI 111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

Figure 7. Mean hourly and mean monthly net radiation at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

Climate Data 11
MEAN HOURLY SOIL HEAT FLUX, IN WATTS PER SQUARE METER

MEAN MONTHLY SOIL HEAT FLUX, IN WATTS PER SQUARE METER

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR MONTH

MONTH

-1-2-3

3210

-1-2-3

3210

-1-2-3

3210

3210

-1-2-3

3210

-1-2-3

-10-15

25201510

50

-5

-10-15

25201510

50

-5

-10-15

25201510

50

-5

-10-15

25201510

50

-5

-10-15

25201510

50

-5

J F M A M J J A S O N D

J F M A M J J A S O N D

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

Figure 8. Mean hourly and mean monthly soil heat flux at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

12  Potential Evapotranspiration on Tutuila, American Samoa
MEAN HOURLY AIR TEMPERATURE, IN DEGREES CELSIUS

MEAN MONTHLY AIR TEMPERATURE, IN DEGREES CELSIUS

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR MONTH

MONTH

2928272625242322

2928272625242322

29282726252423222928272625242322

2928272625242322

32302826242220

32302826242220

32302826242220

32302826242220

32302826242220

J F M A M J J A S O N D

J F M A M J J A S O N D

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

331 ALAVA

223 ILIILI

221 AASUFOU

222 MASEFAU

111 AIRPORT 333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

Figure 9. Mean hourly and mean monthly air temperature at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

Climate Data 13
MEAN HOURLY SOIL TEMPERATURE, IN DEGREES CELSIUS

MEAN MONTHLY SOIL TEMPERATURE, IN DEGREES CELSIUS

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR MONTH

MONTH

403836343230282624

403836343230282624403836343230282624403836343230282624403836343230282624

383634323028262422

383634323028262422

383634323028262422

383634323028262422

383634323028262422

J F M A M J J A S O N D

J F M A M J J A S O N D

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

444 FAGAITUA

442 MATATULA

333 MALAEIMI

441 MALOATA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

Figure 10. Mean hourly and mean monthly soil temperature at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

14  Potential Evapotranspiration on Tutuila, American Samoa
it has a higher heat capacity than air. At station 222 Masefau, 

a lack of symmetry in the mean hourly net-radiation curve 

(fig. 7) indicates that the net radiation at this site was consistently depressed in the late morning of every day, possibly by 

the shadow of nearby trees. Variations in mean monthly values 

of net radiation, soil heat flux, and air and soil temperature 

are consistent with seasonal changes in daylight duration, 

with higher values in the summer (December to February) and 

lower values in the winter (May to July).

Mean hourly relative humidity at all stations was higher 

at night than during the day (fig. 11). The mean-hourly relative humidity curves decline steeply beginning about 07:00 to 

08:00, are lowest at about 14:00 to 15:00, rise steeply in the 

late afternoon until about 19:00 to 20:00, then rise gradually 

overnight. The range of the mean hourly relative humidity 

values at each station was about 10 to 20 percent, depending 

on location. Plots of mean monthly relative humidity indicate 

that this parameter varies only by a few percent throughout the 

year.

Wind speed at most stations shows an increase during 

daylight hours (fig. 12). The higher daytime winds are probably generated by more rapid daytime heating of air over the 

island relative to the air over the ocean (Wright, 1963). The 

diurnal pattern is not apparent, however, in the data from 

222 Masefau, 331 Alava, and 442 Matatula. Plots of mean 

monthly wind speed at 111 Airport and 221 Aasufou show 

a pattern of lower wind speeds from December to April and 

higher wind speeds the rest of the year, which is consistent 

with seasonal wind patterns reported from the CMDL facility 

at Cape Matatula and the NWS office at the Pago Pago Airport 

(Mefford, 2002, 2004; Climate Monitoring and Diagnostics 

Laboratory, 2004; National Climatic Data Center, 2004). The 

seasonal pattern is not as readily apparent in the wind speed 

data from other stations in this study.

Potential Evapotranspiration

The Penman (1948) equation was used to compute hourly 

PE from the hourly climate data. Mean hourly PE values for 

each station were computed from the hourly PE values (table 

2). Despite representing non-concurrent monitoring periods, 

the plots of mean hourly potential evapotranspiration show 

some characteristics common to all stations (fig. 13). Mean 

hourly PE at all stations closely parallels mean hourly net 

radiation, the primary source of energy for evaporation (Allen 

and others 1998). For both net radiation and PE, the mean 

hourly curves rise steeply in the morning, peak at 13:00, and 

decline steeply in the afternoon. Higher temperatures and wind 

speeds and lower relative humidity during the day also correspond approximately with the higher daytime PE. At night, 

the PE curves depart from the net radiation curves. Following 

the steep decline in PE in the afternoon, PE curves for stations 111 Airport, 222 Masefau, and 223 Iliili show a gradual 

decline, whereas the PE curves at the other stations show a 

small secondary rise peaking between 20:00 and 22:00, and 

a gradual decline in PE through the remainder of the night. 

Thus, net radiation is the dominant factor controlling PE in the 

daytime, whereas other factors control PE at night. Even so, 

PE between 07:00 and 19:00 on average constitutes 90 percent 

or more of the daily PE at each station (table 2).

It is possible that heat is transported to the stations by 

advection from surrounding land or, especially in the case 

of islands, the ocean (Ekern and Chang, 1985; Nullet, 1987; 

Nullet and Giambelluca 1990; Ekern, 1993). The Penman 

(1948) equation is not generally sensitive to advection, except 

that advection can affect the aerodynamic term in the equation 

through the vapor-pressure deficit. Thus, where wind speeds 

are high, advection can affect computed PE. A cursory evaluation of the effect of heat advection can be made by comparing the mean daily PE values computed using the Penman 

equation to the mean daily net radiation values converted to 

equivalent PE using the latent heat of vaporization for water 

at 20°C (table 2). At station 111 Airport, mean daily PE 

exceeded net radiation, which indicates positive heat advection 

at this site. The excess heat may be coming from the surrounding land, or more likely, the ocean, which is upwind (relative 

to the prevailing wind) from this site and has the potential to 

store and release large amounts of heat. The relatively high 

average wind speed at station 111 Airport probably accounts 

for the fact that advection is apparent in the Penman calculation for this station. In contrast, average PE at all other stations 

was less than net radiation, thus advection is not conclusively 

indicated. It is still possible that positive advection from the 

ocean occurs at these sites, but because wind speed at these 

sites is significantly lower than at station 111 Airport, the 

effect of advection is not apparent in PE computed by the 

Penman equation. In any case, the data indicate that advection 

patterns on a small island such as Tutuila, where the ocean and 

its effects are not far from any location, differ somewhat from 

those observed on larger and taller islands in the trade-wind 

belt such as Hawaii (Ekern, 1983, 1993; Nullet, 1987).

Spatial distribution of PE. – Assessing the spatial 

distribution of PE on Tutuila is a key step in quantifying 

water losses in the water budget of Tutuila. Each location has 

a unique climate, and hence PE, related to such geographic 

variables as elevation, exposure to sunlight and prevailing 

winds, proximity to the ocean, or topographic relief of the surrounding area. In the foregoing discussion, the differences in 

PE from one station to the next are due not only to differences 

in location, but also to each station having a different period 

of record. To facilitate assessment of the spatial distribution of 

PE, the effects related to non-concurrent monitoring periods 

must be minimized. For the following discussion, daily mean 

PE was computed from the hourly PE values and used to compute mean monthly and mean annual PE. Days having fewer 

than a complete set of 24 hourly values were excluded from 

computations of mean monthly and mean annual PE. To allow 

comparisons between stations having different periods of 

record, the daily PE values were adjusted to a common (base) 

period represented by the 4.5-year record of station 111 AirPotential Evapotranspiration 15
MEAN HOURLY RELATIVE HUMIDITY, IN PERCENT

MEAN MONTHLY RELATIVE HUMIDITY, IN PERCENT

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR MONTH

MONTH

90807060

100

90807060

100

90807060

100

90807060

100

90807060

100

908070

100

908070

100

908070

100

908070

100

908070

100

J F M A M J J A S O N D

J F M A M J J A S O N D

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

331 ALAVA

223 ILIILI

221 AASUFOU

222 MASEFAU

111 AIRPORT 333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

Figure 11. Mean hourly and mean monthly relative humidity at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

16  Potential Evapotranspiration on Tutuila, American Samoa
MEAN HOURLY WIND SPEED, IN MILES PER HOUR

MEAN MONTHLY WIND SPEED, IN MILES PER HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR MONTH

J F M A M J J A S O N D

J F M A M J J A S O N D

MONTH

86420

10

86420

10

86420

10

86420

10

86420

10

86420

10

86420

10

86420

10

86420

10

86420

10

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

333 MALAEIMI

441 MALOATA

442 MATATULA

444 FAGAITUA

111 AIRPORT

221 AASUFOU

222 MASEFAU

223 ILIILI

331 ALAVA

Figure 12. Mean hourly and mean monthly wind speed at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

Potential Evapotranspiration 17
port. The adjustment was based on linear regressions between 

daily mean PE at each of the stations versus PE at station 111 

Airport. The correlation coefficients in all cases were higher 

than 0.70 except for the correlation coefficient between 111 

Airport and 221 Aasufou, which was 0.56.

The unadjusted mean monthly PE rates range from 0.066 

in/d (in July at 333 Malaeimi) to 0.212 in/d (in February at 

442 Matatula) (table 3). Unadjusted mean annual PE ranged 

from 40 in. at 333 Malaeimi to 62 in. at 111 Airport. A plot of 

unadjusted mean monthly PE shows that despite having nonconcurrent monitoring periods, PE at all stations shows a similar seasonal pattern in which PE is lower from April to August 

and higher from September to March (fig. 14). This pattern 

corresponds with the pattern of lower net radiation during the 

winter and higher net radiation in the summer (fig. 7).

The mean monthly pan evaporation computed from the 

NWS data (fig. 3) is higher than the PE values computed for 

any stations in this study, including station 111 Airport which 

was within a few feet of the NWS pan. The difference is large 

enough that it cannot be wholly attributed to the non-concurrent periods of record between the NWS pan and 111 Airport 

data. The difference in PE probably results from the difference 

in the physical process of evaporation from an open metal pan 

versus evaporation from a vegetated soil surface. Because 

pans can absorb heat through the bottom and the sides, they 

Table 2. Mean hourly potential evapotranspiration for stations on Tutuila, American Samoa.

[Data have not been adjusted to a common base period. PE, potential evapotranspiration]

Hour 

(local standard 

time)

Mean hourly PE, in inches per day

111 

Airport

221 

Aasufou

222

Masefau

223

Iliili

331 

Alava

333

Malaeimi

441 

Maloata

442

Matatula

444

Fagaitua

01:00 0.0312 0.0080 0.0210 0.0152 0.0069 0.0222 0.0146 0.0203 0.0225

02:00 0.0301 0.0071 0.0197 0.0145 0.0068 0.0208 0.0142 0.0190 0.0211

03:00 0.0299 0.0070 0.0185 0.0142 0.0062 0.0199 0.0133 0.0182 0.0203

04:00 0.0299 0.0074 0.0180 0.0139 0.0063 0.0191 0.0132 0.0179 0.0202

05:00 0.0297 0.0069 0.0171 0.0140 0.0059 0.0184 0.0129 0.0173 0.0197

06:00 0.0306 0.0073 0.0166 0.0148 0.0058 0.0187 0.0135 0.0181 0.0197

07:00 0.0509 0.0281 0.0285 0.0345 0.0203 0.0287 0.0244 0.0363 0.0300

08:00 0.1302 0.1135 0.0649 0.1138 0.0880 0.0609 0.0587 0.1018 0.0749

09:00 0.2449 0.2111 0.1356 0.2197 0.1748 0.1303 0.1798 0.2030 0.1582

10:00 0.3524 0.2943 0.2416 0.3138 0.2434 0.2377 0.3476 0.3088 0.2427

11:00 0.4382 0.3477 0.2805 0.3838 0.2985 0.2804 0.4137 0.3972 0.3400

12:00 0.4915 0.3777 0.3477 0.4224 0.3361 0.3029 0.4503 0.4307 0.3880

13:00 0.5019 0.3850 0.4127 0.4291 0.3520 0.3203 0.4727 0.4564 0.4044

14:00 0.4726 0.3669 0.3974 0.4099 0.3408 0.3118 0.4510 0.4376 0.3863

15:00 0.4010 0.3210 0.3341 0.3485 0.2966 0.2704 0.3873 0.3930 0.3330

16:00 0.2991 0.2523 0.2519 0.2712 0.2343 0.2082 0.2779 0.3048 0.2541

17:00 0.1914 0.1611 0.1201 0.1408 0.1483 0.1144 0.1250 0.1977 0.1542

18:00 0.0964 0.0656 0.0542 0.0621 0.0587 0.0583 0.0331 0.0880 0.0664

19:00 0.0441 0.0041 0.0475 0.0323 0.0112 0.0303 0.0140 0.0404 0.0358

20:00 0.0384 0.0067 0.0453 0.0264 0.0167 0.0320 0.0241 0.0417 0.0383

21:00 0.0394 0.0152 0.0389 0.0253 0.0201 0.0350 0.0262 0.0375 0.0363

22:00 0.0371 0.0182 0.0317 0.0218 0.0136 0.0310 0.0218 0.0301 0.0303

23:00 0.0344 0.0137 0.0262 0.0191 0.0094 0.0266 0.0178 0.0255 0.0263

24:00 0.0329 0.0100 0.0231 0.0167 0.0078 0.0244 0.0157 0.0224 0.0241

Mean daily PE 

(inches per day)

0.1699 0.1265 0.1247 0.1407 0.1129 0.1093 0.1426 0.1527 0.1311

Mean daily net 

radiation converted 

to equivalent PE1

(inches per day)

0.1510 0.1413 0.1399 0.1535 0.1392 0.1163 0.1602 0.1664 0.1341

1

 At 20°C, 720.344 watts of net radiation equals 1.0000 inch per day of evaporation. 

18  Potential Evapotranspiration on Tutuila, American Samoa
111 AIRPORT

0.600.400.200.00

221 AASUFOU 222 MASEFAU

331 ALAVA 223 ILIILI

0.600.400.200.00

333 MALAEIMI

442 MATATULA 441 MALOATA

0.600.400.200.00

444 FAGAITUA

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

1 3 5 7 9 11 13 15 17 19 21 23

HOUR

MEAN HOURLY POTENTIAL EVAPOTRANSPIRATION, IN INCHES PER DAY

Figure 13. Mean hourly potential evapotranspiration at the climate stations on Tutuila, American Samoa (periods of record given in table 1).

Potential Evapotranspiration 19
Table 3. Unadjusted mean monthly and mean annual potential evapotranspiration for stations on Tutuila, American Samoa. 

[Mean annual rainfall measured at each station is provided for comparison. Values are computed on the basis of daily potential evapotranspiration and rainfall 

values that have not been adjusted to a common base period. –, no data; PE, potential evapotranspiration] 

Month

Mean monthly PE, in inches per day

111 

Airport

221 

Aasufou

222

Masefau

223

Iliili

331 

Alava

333

Malaeimi

441 

Maloata

442

Matatula

444

Fagaitua

January 0.187 0.144 0.147 0.186 0.156 0.138 0.171 0.201 0.122

February 0.177 0.155 0.147 0.139 0.135 0.135 0.137 0.212 –

March 0.165 0.108 0.151 0.127 0.114 0.121 0.136 0.172 0.143

April 0.148 0.117 0.117 0.117 0.112 0.107 0.148 0.135 0.137

May 0.151 0.111 0.090 0.121 0.109 0.085 0.132 0.132 0.113

June 0.145 0.127 0.095 0.103 0.089 0.084 0.116 0.116 0.123

July 0.146 0.116 0.080 0.116 0.111 0.066 0.131 0.139 0.105

August 0.166 0.121 0.102 0.122 0.099 0.086 0.146 0.157 0.125

September 0.191 0.113 0.134 0.126 0.125 0.118 0.165 0.161 0.153

October 0.186 0.120 0.148 0.150 0.104 0.127 0.137 0.172 0.151

November 0.194 0.137 0.112 0.160 0.126 0.103 0.141 0.167 0.118

December 0.194 0.152 0.132 0.157 0.145 0.131 0.158 0.169 0.145

Mean annual PE

(inches per day)

0.171 0.127 0.121 0.135 0.119 0.108 0.143 0.161 0.130

Mean annual PE

(inches)

62.4 46.3 44.3 49.5 43.4 39.6 52.2 58.8 47.6

Mean annual rainfall

(inches)

125 178 124 129 145 175 156 96 127

Mean annual PE as

percent of mean 

annual rainfall

50 26 36 38 30 23 33 61 38

are susceptible to heat advection (Ficke, 1972; Ekern and 

Chang, 1985; Allen and others, 1998).

The adjusted mean monthly and mean annual PE values are given in table 4. These adjusted values were used to 

construct maps showing the monthly and annual distribution 

of PE on Tutuila. The distribution of mean annual PE, like 

the distribution of rainfall, is affected by topography and 

exposure to the southeasterly prevailing winds (fig. 15). Mean 

annual PE on Tutuila is highest along the southern and eastern 

coasts of the island, and decreases toward the higher-elevation 

interior of the island. This pattern indicates an inverse relation 

between the orographic rainfall distribution and PE. In the 

interior of the island, where rainfall is higher and cloud cover 

is more frequent, PE is lower because net radiation is lower. At 

the coast, where rainfall is lower and cloud cover less frequent, 

PE and net radiation are higher. A similar pattern has been 

described in Hawaii (Ekern and Chang, 1985).

The distributions of mean PE for individual months (figs. 

16-26) also show patterns consistent with the orographic 

distribution of climate on Tutuila. In all months, PE is lowest 

in the mountains and highest at the coast. The PE distribution 

pattern for November and December, when mean monthly 

PE is the highest, are virtually identical, with a steep gradient 

from PE at the coast to PE in the mountains (fig. 26). The PE 

distribution pattern for June and July (figs. 21, 22), when mean 

monthly PE is lowest, are also nearly identical, but the gradient from the coast to the mountains is less steep than in the 

November-December distribution. The distribution patterns for 

the other months are transitional between these high- and lowPE end-member months.

Whereas the spatial variation of annual and mean 

monthly PE is linked to orographic cloud cover, the seasonal 

variation of mean monthly PE more closely parallels the 

seasonal variation in daylight duration (figs. 7, 14). Even 

though the seasonal rainfall pattern (fig. 2) on Tutuila would 

indicate greater cloud cover during the wet summer and less 

cloud cover during the dry winter, PE is highest in the summer 

because days are longer and total net radiation is higher than 

in the winter.

In high-rainfall areas such as Tutuila, where actual evapotranspiration may be close to PE, comparing PE to the rainfall 

recorded at each station can be instructive in illustrating what 

percentage of water brought to the site by rainfall can be 

potentially lost back to the atmosphere by evapotranspiration. 

Comparison of PE to rainfall indicates that evapotranspiration 

processes on Tutuila have the potential to remove 23 to 61 percent of the water brought by rainfall (table 3). This percentage 

depends on location. In coastal locations where rainfall is low, 

such as stations 111 Airport and 442 Matatula, PE can be 50 

percent or more of rainfall. In wetter interior locations, such 

as stations 221 Aasufou and 333 Malaeimi, PE is less than 30 

percent of rainfall.

20 Potential Evapotranspiration on Tutuila, American Samoa
0.05

0.10

0.15

0.20

0.25

MEAN MONTLY POTENTIAL EVAPOTRANSPIRATION,

IN INCHES PER DAY

111 Airport

221 Aasufou

222 Masefau

223 Iliili

331 Alava

333 Malaeimi

441 Maloata

442 Matatula

444 Fagaitua

 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

EXPLANATION

Figure 14. Unadjusted mean monthly potential evapotranspiration at the climate stations on Tutuila, 

American Samoa (periods of record given in table 1).

Table 4. Adjusted mean monthly and mean annual potential evapotranspiration for stations on Tutuila, American Samoa.

[Values are computed on the basis of daily potential evapotranspiration values that have been adjusted to a common base period; in/d, inches per day; 

in/yr, inches per year] 

Month

Mean monthly potential evapotranspiration, in inches per day

111 

Airport

221 

Aasufou

222

Masefau

223

Iliili

331 

Alava

333

Malaeimi

441 

Maloata

442

Matatula

444

Fagaitua

January 0.187 0.131 0.132 0.146 0.125 0.117 0.150 0.168 0.139

February 0.177 0.126 0.125 0.137 0.118 0.111 0.143 0.160 0.134

March 0.165 0.121 0.118 0.127 0.112 0.105 0.137 0.151 0.128

April 0.148 0.113 0.108 0.112 0.102 0.096 0.127 0.138 0.119

May 0.151 0.115 0.109 0.115 0.103 0.097 0.129 0.140 0.120

June 0.145 0.112 0.105 0.110 0.100 0.094 0.125 0.136 0.117

July 0.146 0.112 0.106 0.110 0.100 0.094 0.125 0.136 0.118

August 0.166 0.121 0.119 0.128 0.112 0.106 0.137 0.152 0.128

September 0.191 0.132 0.134 0.149 0.127 0.119 0.152 0.170 0.141

October 0.186 0.130 0.131 0.145 0.124 0.116 0.149 0.167 0.138

November 0.194 0.134 0.136 0.152 0.129 0.121 0.154 0.173 0.143

December 0.194 0.134 0.136 0.152 0.129 0.121 0.154 0.173 0.143

Mean annual

(in/d)

0.171 0.123 0.122 0.132 0.115 0.108 0.140 0.155 0.131

Mean annual 

(in/yr)

62.44 45.09 44.42 48.19 42.04 39.49 51.15 56.71 47.70

Potential Evapotranspiration  21
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

EXPLANATION

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

55 50

45

40

35

55

60

50

50

45

55

LINE OF EQUAL MEAN ANNUAL  POTENTIAL EVAPOTRANSPIRATION,  IN INCHESCLIMATE-MONITORING STATION

50

MEAN ANNUAL POTENTIALEVAPOTRANSPIRATION

Figure 15. Distribution of mean annual potential evapotranspiration on Tutuila, American Samoa.

22  Potential Evapotranspiration on Tutuila, American Samoa
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

14

13

16

15 12

11

1617

18

19 15

14 13

13

15 16

MEAN JANUARY

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 16. Distribution of mean January potential evapotranspiration on Tutuila, American Samoa.

Potential Evapotranspiration  23
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

15

14

14

15

13

12

11

12

16

17

18

14

13

15

16

MEAN FEBRUARY

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 17. Distribution of mean February potential evapotranspiration on Tutuila, American Samoa.

24  Potential Evapotranspiration on Tutuila, American Samoa
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

15

14 14

13

12

11

16

13 12

12 12

14

15

MEAN MARCH

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 18. Distribution of mean March potential evapotranspiration on Tutuila, American Samoa.

Potential Evapotranspiration  25
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

14

13

13

11

12

10

13 12

11

11

MEAN APRIL

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 19. Distribution of mean April potential evapotranspiration on Tutuila, American Samoa.

26  Potential Evapotranspiration on Tutuila, American Samoa
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

15

13

14

12

13

11

10

13

14

12 11

11

MEAN MAY

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 20. Distribution of mean May potential evapotranspiration on Tutuila, American Samoa.

Potential Evapotranspiration  27
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

13

14

12

10

11

13 12

12 13

11

11 MEAN JUNE

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 21. Distribution of mean June potential evapotranspiration on Tutuila, American Samoa.

28  Potential Evapotranspiration on Tutuila, American Samoa
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

14

11

12

13

10

13 12 11

11

12 13

MEAN JULY

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 22. Distribution of mean July potential evapotranspiration on Tutuila, American Samoa.

Potential Evapotranspiration  29
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

15

14

14

13

12

11

16

14 13

12

15

MEAN AUGUST

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 23. Distribution of mean August potential evapotranspiration on Tutuila, American Samoa.

30 Potential Evapotranspiration on Tutuila, American Samoa
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

14

15

13 16 15 12

11

16

17

18

19

14

15

16

MEAN SEPTEMBER

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 24. Distribution of mean September potential evapotranspiration on Tutuila, American Samoa.

Potential Evapotranspiration  31
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

15

14

13

16

15 12

11

16

17

1819

14 13 15

16

MEAN OCTOBER

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 25. Distribution of mean October potential evapotranspiration on Tutuila, American Samoa.

32  Potential Evapotranspiration on Tutuila, American Samoa
14o 20'

170o 50'

170o 45'

170o 40'

14o 15'

170o 35'

SCALE

00 4 KILOMETERS

4 MILES Base modified from U.S. Geological Survey, Tutuila East, Tutuila West, 1:24,000 scale,

Universal Transverse Mercator, zone 2, south, World Geodetic System 1984,Digital Line Graph and Digital Elevation Model data.

Pacific Ocean

15

16

14

13

13

12

15 16

17

18

19

15

14

16 17

MEAN NOVEMBER-DECEMBER

12

POTENTIAL EVAPOTRANSPIRATION

LINE OF EQUAL MEAN MONTHLY  POTENTIAL EVAPOTRANSPIRATION,  IN HUNDREDTHS OF INCHES PER DAYCLIMATE-MONITORING STATION

EXPLANATION

Figure 26. Distribution of mean November and mean December potential evapotranspiration on Tutuila, American Samoa. 

Potential Evapotranspiration  33
Summary and Conclusions

Analysis of climate data collected at nine stations on 

Tutuila, American Samoa between 1999 and 2004 indicate that 

rainfall at most stations occurs with nearly equal frequency 

at any hour of the day, although some stations show slightly 

lower rainfall in the afternoon and early evening, and slightly 

higher rainfall in the morning. Rainfall at most stations was 

lower in July and August than for the rest of the year, which 

is consistent with the seasonal patterns apparent in available 

long-term climate data, but most stations did not show the 

distinct October-May wet season apparent in the long-term 

data. These differences may be attributed to the relatively short 

monitoring periods of stations in this study.

Net radiation, soil heat flux, and air and soil temperatures 

show diurnal variations that are consistent with the cycle of 

daylight and darkness. These parameters also show monthto-month variations that are consistent with seasonal changes 

in daylight duration. Relative humidity was about 10 to 20 

percent higher at night than during the day, and varied by only 

a few percent from month to month throughout the year. Wind 

speed at most stations was higher during the day, probably as 

a result of differential rates of warming of air over the island 

relative to the air over the ocean. Some stations show a pattern 

of lower wind speeds from December to April and higher wind 

speeds the rest of the year, which is consistent with seasonal 

wind patterns indicated from long-term data collected by the 

National Weather Service and Climate Monitoring and Diagnostics Laboratory.

Potential evapotranspiration shows a diurnal pattern that 

closely parallels net radiation, the primary source of energy for 

evaporation on islands. Higher temperatures and wind speeds 

and lower relative humidity during the day also correspond 

approximately with the higher daytime potential evapotranspiration, but net radiation is the dominant factor controlling 

potential evapotranspiration in the daytime, whereas other 

factors control potential evapotranspiration at night. On average, potential evapotranspiration in the daytime constitutes 90 

percent or more of the total daily potential evapotranspiration 

at each station. Potential evapotranspiration at all stations is 

lower from April to August and higher from September to 

March. This pattern corresponds with the pattern of lower 

net radiation during the winter and higher net radiation in the 

summer.

Whereas seasonal variation of mean monthly potential evapotranspiration is linked to the seasonal variation in 

daylight duration, spatial variation of potential evapotranspiration is linked to orographic cloud cover. The distribution of 

potential evapotranspiration reflects topography and exposure 

to the southeasterly prevailing winds. Potential evapotranspiration on Tutuila is highest along the southern and eastern 

coasts, and decreases toward the higher elevation interior. This 

pattern indicates an inverse relation between the orographic 

rainfall distribution and potential evapotranspiration. In the 

interior of the island, where rainfall is higher and cloud cover 

is more frequent, potential evapotranspiration is lower because 

net radiation is lower. At the coast, where rainfall is lower and 

cloud cover less frequent, potential evapotranspiration and net 

radiation are higher. The gradient from high potential evapotranspiration at the coast to low potential evapotranspiration in 

the interior of the island is steepest in November and December, when island-wide potential evapotranspiration is highest, 

and less steep in June and July, when island-wide potential 

evapotranspiration is lowest

Positive heat advection contributes to potential evapotranspiration at at least one station in this study—111 Airport. 

The excess heat is probably coming from the ocean, which lies 

only a few hundred feet upwind from this station. Potential 

evapotranspiration at other stations also may be enhanced by 

advection, but the Penman method used in this study does not 

yield strong evidence of this possibility. On a small island 

such as Tutuila, the ocean and its effects are not far from any 

location.

Pan evaporation monitored by the NWS was much higher 

than the Penman potential evapotranspiration values computed 

in this study for station 111 Airport. The disparity probably 

reflects the difference in the physical process of evaporation 

from an open metal pan versus evaporation from a vegetated 

soil surface.

Comparison of potential evapotranspiration to rainfall 

indicates that evapotranspiration processes on Tutuila have the 

potential to remove 23 to 61 percent of the water brought by 

rainfall. In lower-rainfall coastal locations, potential evapotranspiration can be 50 percent or more of rainfall, whereas in 

higher-rainfall interior locations potential evapotranspiration is 

less than 30 percent of rainfall.

Comparison of annual rainfall for the period of this study 

(1999-2004) against the long-term average indicates that climate during the monitoring period of this study was not substantially different from long-term average climate conditions. 

Climate conditions during the monitoring period for this study 

are therefore considered representative of normal conditions.

The potential-evapotranspiration estimates in this study 

can be used in water-budget methods to estimate actual evapotranspiration and ground-water recharge, provided adequate 

data on precipitation, runoff, vegetation, and soil characteristics also exist or can be estimated.

References Cited

Allen, R.G., Pereira, L.S., Raes, Dirk, and Smith, Martin, 

1998, Crop evapotranspiration: guidelines for computing 

crop water requirements: Food and Agriculture Organization of the United Nations, FAO Irrigation and Drainage 

Paper 56, 300 p.

Bentley, C.B., 1975, Ground-water resources of American 

Samoa with emphasis on the Tafuna-Leone Plain, Tutuila 

Island: U.S. Geological Survey Water-Resources Investigations Report 29-75, 32 p.

34  Potential Evapotranspiration on Tutuila, American Samoa
Bidlake, W.R., Woodham, W.M., and Lopez, M.A., 1996, 

Evapotranspiration from areas of native vegetation in westcentral Florida: U.S. Geological Survey Water-Supply Paper 

2430, 35 p.

Climate Monitoring and Diagnostics Laboratory, 2004, Station 

meteorology for Samoa Observatory: world-wide website 

at http://www.cmdl.noaa.gov/obop/met/smo/, accessed 

November, 18, 2004.

Davis, D.A., 1963, Ground-water reconnaissance of American 

Samoa: U.S. Geological Survey Water-Supply Paper 1608-

C, 21 p.

Ekern, P., 1983, Measured evaporation in high rainfall areas, 

leeward Koolau Range, Oahu, Hawaii: University of Hawaii 

Water Resources Research Center, Technical Report 156, 

60 p.

Ekern, P.,1993, Evaporation along a transect across southern 

O’ahu, Hawai’i: University of Hawaii Water Resources 

Research Center, Project Report PR-94-01, 23 p.

Ekern, P., and Chang, J.H., 1985, Pan evaporation: State of 

Hawaii, 1894-1983: State of Hawaii Department of Land 

and Natural Resources, Division of Water and Land Development, Report R74, 172 p.

Ficke, J.F., 1972, Comparison of evaporation computation 

methods, Petty Lake, Lagrange County, Northeastern 

Indiana: U.S. Geological Survey Professional Paper 686-A, 

49 p.

Giambelluca, T.W., Nullet, D., and Nullet, M.A, 1988, Agricultural drought on south-central Pacific Islands: Professional Geographer, v. 40, no. 4, p. 404-415.

Giambelluca, T.W., and Nullet, M.A., 2000, HaleNet Haleakala Climate Network: Data-analysis procedures: worldwide website at http://webdata.soc.hawaii.edu/climate/

HaleNet/Index.htm accessed August 21, 2003.

Izuka, S. K., 1999, Hydrogeologic interpretations from available ground-water data, Tutuila, American Samoa: U.S. 

Geological Survey Water-Resources Investigations Report 

99-4064 (atlas format), 2 sheets.

Matsuoka, Iwao, 1978, Flow characteristics of streams in 

Tutuila, American Samoa: U.S. Geological Survey WaterResources Investigations Report 78-103, 34 p.

Mefford, Thomas, 2002, Meteorological measurements, in

King, D.B., Schnell, R.C., Rosson, R.M., and Sweet, C. 

(eds.): Climate Monitoring and Diagnostics Laboratory 

Summary report No. 26, 2000-2001, p. 20-31.

Mefford, Thomas, 2004, Meteorological measurements, in

Schnell, R.C., Buggle, A., and Rosson, R.M. (eds.): Climate 

Monitoring and Diagnostics Laboratory Summary report 

No. 27, 2002-2003, p. 20-31.

National Climatic Data Center, 2002, Cooperative summary of 

the day, TD3200, period of record through 2001, Western 

United States and Pacific Islands: CD-ROM.

National Climatic Data Center, 2004, Comparative climate 

data: world-wide website at http://www1.ncdc.noaa.gov/

pub/data/ccd-data/ accessed December 15, 2004.

National Climatic Data Center, 2001-05, Locate weather 

observation station record: world-wide website at http://

www.ncdc.noaa.gov/oa/climate/stationlocator.html/ 

accessed February 1, 2001, April 11, 2001, August 7, 2001, 

October 19, 2001, November 19, 2001, January 29, 2002, 

April 26, 2002, June 25, 2002, October 18, 2003, September 15, 2004, and June 20, 2005.

Nullet, D., 1987, Energy sources for evaporation on tropical 

islands: Physical Geography, v. 8, p. 36-45.

Nullet, D., and Giambelluca T.W., 1990, Winter evaporation 

on a mountain slope, Hawaii: Journal of Hydrology, v. 112, 

p. 257-265.

Penman, H.L., 1948, Natural evaporation from open water, 

bare soil, and grass: Proceedings of the Royal Society of 

London, A193, p. 120-146.

Stearns, H.T., 1944, Geology of the Samoan Islands: Geological Society of America Bulletin, v. 55, p. 1279-1332.

Thornthwaite, C.W., and Mather, J.R. 1955, The water balance: Publications in Climatology v. 8, p. 1-104.

U.S. Census Bureau, 2004, Population and housing profile: 

2000, American Samoa, 2000 Census of population and 

housing (revised): Document obtained from http://www.

census.gov/prod/cen2000/island/ASprofile.pdf, November 

18, 2004.

Wong, M.F., 1996, Analysis of streamflow characteristics for 

streams on the island of Tutuila, American Samoa: U.S. 

Geological Survey Water-Resources Investigations Report 

95-4185, 168 p.

Wright, W.C.S., 1963, Soils and land use of Western Samoa: 

New Zealand Department of Scientific and Industrial 

Research, Soil Bureau Bulletin 22.

References Cited  35
Appendix 1 – Description of Climate 

Stations and Methods for Computation 

of Potential Evapotranspiration

Station Descriptions

111 Airport. – Station 111 Airport (U.S. Geological Survey [USGS] number 141954170425101) was on the grounds 

of the National Weather Service (NWS) office at the Pago 

Pago Airport at the eastern end of the Tafuna-Leone Plain. 

The site was about 10 ft above sea level on a relatively flat, 

open area covered with short grass. This station was operated 

from July 1, 1999, to April 29, 2004. Except for occasional 

power-related problems, particularly in the first 5 months of 

operation, the station operated continuously during most of 

this period. The station sustained minor damage from Tropical Cyclone Heta, which passed over the Samoa Islands on 

January 5, 2004, resulting in the collection of no rainfall data 

between January 5 and February 20, 2004. Instrument calibration drift resulted in the collection of no air-temperature data 

from July 14 to December 12, 2001; from November 11 to 

December 19, 2002; and from January 5, 2004 through the 

end of the monitoring period. Instrument calibration drift also 

resulted in the collection of no relative-humidity data from 

January 5 through February 20, 2004.

221 Aasufou. – Station 221 Aasufou (USGS number 

141909170461201) was in Aasufou Village on a grassy lawn 

fronting a private residence about 1,320 ft above sea level, 

near the crest of the mountains that form western Tutuila. The 

station was a few feet north of a USGS rain gage that had 

been in operation since January 1980. The climate station at 

Aasufou was operated from July 1, 1999, to June 7, 2000, with 

only minor logistical problems that resulted in an abbreviated 

monitoring period (0.94 yr) and the collection of no soil-moisture data from May 19, 2000 through the end of the monitoring period.

222 Masefau. – Station 222 Masefau (USGS number 

141538170371201) was in Masefau Village, on the northern 

slope of Tutuila. The site was about 590 ft above sea level on 

a grassy clearing on the northeast side of a water tank. The 

tank clearing was covered with short grass, but surrounded 

by trees as much as 20-ft tall, some of which were within ten 

feet of the station. The station was operated from December 

12, 2001 to April 27, 2004. Between May 2002 and February 2003, instruments frequently malfunctioned as a result 

of power-related problems. To make up for the resulting data 

gaps, the monitoring period was extended beyond 1 year. 

Because of persistent problems with the soil-moisture probe, 

this parameter was not monitored at this site for most of the 

period. Instrument calibration drift resulted in the collection 

of no air-temperature data from February 9, 2004 to the end of 

the monitoring period.

223 Iliili. – Station 223 Iliili (USGS number 

142030170444901) was in Iliili Village at about 200 ft above 

sea level, on the Tafuna-Leone Plain. The station was enclosed 

within the fenced compound of a water tank and was operated 

from August 2, 2000, to December 11, 2001. The ground at 

the site was covered by short grass. The monitoring period was 

extended beyond 1 year to make up for gaps in data resulting 

from data-logger malfunction between August 10 and November 7, 2000, March 15 and April 26, 2001, and June 6 and 

June 21, 2001.

331 Alava. – Station 331 Alva (USGS number 

141614170411601) was located at about 1,600 ft above sea 

level, on the crest of Mount Alava in the American Samoa 

National Park. South of the monitoring site, the land surface 

drops precipitously to Pago Pago Harbor, forming a cliff that 

faces the prevailing southeasterly winds. The station was operated from March 2, 2000 to June 5, 2002. Before the climate 

station was installed, the area was covered with grass about 

4 ft high, but the grass was cut down to a few inches to accommodate the climate station. During monitoring, the grass was 

kept short. The monitoring period was extended beyond 1 year 

to make up for a gap in the rainfall data from January 5 to 

April 12, 2001. Problems with the soil-moisture probe, possibly due to the highly weathered and fractured rock into which 

it was inserted, resulted in collection of no soil-moisture data 

for this station for most of the monitoring period.

333 Malaeimi. – Station 333 Malaeimi (USGS number 

141842170435801) was on the floor of Malaeimi Valley. 

Although it is one of the largest on Tutuila, the valley is only 

about 1 mi wide from ridge crest to ridge crest, about 1.5 mi 

long, and is tucked among the tallest and most massive mountains on the island. Station 333 Malaeimi was located about 

150 ft above sea level on a lawn fronting a private residence. 

The ground surface was covered with short grass. The station 

operated from February 26, 2003 to April 28, 2004, with no 

technical difficulties or loss of data.

441 Maloata. – Station 441 Maloata (USGS number 

141844170484101) was in Maloata, a small valley on the 

northern side and near the western end of Tutuila. The station 

was about 120 ft above sea level in a clearing on the heavily 

vegetated slope that forms the southwest side of the valley. 

The ground was covered with short grass, but vegetation in 

the surrounding areas within a few tens of feet of the climate 

station included tall grass (about 4 ft high) and one tall tree 

(about 20 ft high). The station operated from March 3, 2000 

to May 1, 2001 and successfully gathered data for most of the 

13 months it was deployed. The temperature/relative-humidity sensor drifted in the last few weeks of monitoring, but a 

full year of continuous data was obtained before the problem 

arose.

442 Matatula. – Station 442 Matatula (USGS number 

141507170334701) was on Cape Matatula on the eastern tip of 

Tutuila. The site was about 240 ft above sea level on a wellmaintained lawn of short grass on the grounds of the Climate 

Monitoring and Diagnostics Laboratory (CMDL) observatory. Station 442 Matatula was operated from May 3, 2001 to 

36  Potential Evapotranspiration on Tutuila, American Samoa
June 4, 2002 and all instruments except the relative-humidity 

sensor operated successfully during this period. The relativehumidity sensor showed signs of drift from about October 2, 

2001, but the problem was not discovered until after the monitoring period was complete and the station was dismantled; 

therefore, about 7 months of relative-humidity data were not 

collected. In the potential-evapotranspiration (PE) computations for this station, missing relative-humidity data was 

replaced by estimated relative-humidity values computed on 

the basis of dew-point temperatures reported by the CMDL.

444 Fagaitua. – Station 444 Fagaitua (USGS number 

141602170370201) was in Fagaitua Village on the south side 

of Tutuila. The site for the station was on a hill about 280 ft 

above sea level within the fenced compound of a water tank. 

The station was located to the west of the tank, on ground 

covered with short grass. The station operated from June 5, 

2002 to April 28, 2004. The monitoring period was extended 

beyond 1 year to make up for numerous incidences of instrument malfunction and calibration drift between June 7 and 

September 25, 2002, and between November 16, 2002 and 

March 28, 2003. Wind-speed data were not collected during 

the period from January 10 and March 7, 2004 because of 

apparent instrument malfunction. The wind-speed sensor was 

also vandalized sometime between September 9 and 

October 3, 2003 and repairs were made on October 24, 2003, 

but the vandalism had little apparent effect on the data collected.

Computation of Potential Evapotranspiration

The following section details the computation of PE from 

the measured climate data collected at each station. In the 

main text of the report, some parameters were converted to 

English-system units for the convenience of the reader. The 

data collection and computation of potential evapotranspiration in this study, however, were done using metric-system 

units. The following formulations, therefore, use constants 

that are specific to the metric system, and dimensions for each 

term specify which metric units are involved.

The Penman (1948) equation can be expressed as:

 γ

γ

∆ +

∆ + = H Ea PE , (1)

where

	 PE is potential evapotranspiration [mm/d],

∆ is the slope of saturation vapor pressure versus 

temperature curve [mb/K],

H is net radiation minus soil heat conduction, 

expressed in evaporation equivalent units 

[mm/d],

γ is the psychrometric constant [mb/K], and

Ea is the aerodynamic term of the Penman 

equation [mm/d].

The energy term (H) is a function of net radiation, soil 

heat flux, soil temperature, and soil moisture:

 H 0.0353 (R G ) net = − ,

 (2)

where

Rnet is net radiation [W/m2]

G is soil heat conduction [W/m2], and

0.0353 is the factor to convert energy flux in watts per 

square meter to evaporation equivalent units 

in millimeters per day [(mm/day)/(W/m2)].

The term G is based on measurements from soil heat flux 

plates (SHF, in W/m2) positioned at a depth of 8 cm, plus flux 

into and out of the upper 8-cm layer (S, in W/m2), computed 

from changes in soil temperature in the layer with time:

 G = SHF + S, (3)

where

[( ) ( )]

1

1

b s w w i i

i i D C C

t t

Tsoil Tsoil

S ρ + θ ρ −

− = −

−

,

where

S is the change in sensible energy stored in the 

upper 8-cm soil layer during one time step 

[W/m2],

Tsoil is the mean temperature in upper 8-cm soil 

layer [K] (subscript indicates time step),

t is time [s],

D is the depth of soil layer [m] (0.08),

ρb is dry soil bulk density 

[assumed ρb = 1,052 kg/m3], 

Cs is the mass specific heat of dry soil 

[assumed Cs = 840 J/kg/K],

θ is the volumetric soil-moisture content 

[m3/m3],

ρw is the density of water 

[ρw = 1,000 kg/m3], and

Cw is the mass specific heat of water 

[Cw = 4,190 J/kg/K].

The volumetric soil-moisture content was monitored with 

a soil-moisture probe, the results from which were checked 

against laboratory analysis of soil samples from each station.

The aerodynamic term (Ea) is a function of wind speed 

and vapor-pressure deficit:

	 	 Ea = 0.263 + 0.138U (es - e), (4)

Appendix 1  37
where

U is wind speed [m/s], and 

es – e is vapor pressure deficit, the difference between 

saturation vapor pressure (es) and ambient 

vapor pressure(e) [mb].

The saturation vapor pressure (es) is estimated as a function of air temperature (T [K]) using the relationship of Goff 

and Gratch (1946):

 x

s e = 10 , (5)

where

log 1,013 246 .

8 1328 10 10 1

1 3816 10 10 1

373 .16

5 02808 log

1

373 .16

7 90298

1 373.16 3.49149 3

373.16

11 344 1

7

.

.

.

T .

− )

T

x . (

T

T .

+

















+ ⋅ −

















− ⋅ −

+ ⋅

= −





 



 − − −





 



 − −

The ambient air vapor pressure (e) is a function of es and relative humidity (RH):

100

RH

e = es .

 (6)

The slope of the saturation vapor-pressure versus temperature curve (∆) is a function of air temperature and es

:

 (1.9335 ) ( 0.5196 ),

373.15 13.3185 ( 3.952 )

2 3

2

R R

R

s

t t

t

T

e

− −

∆ = −

(7)

where

t

R = T

373.15

1 − , and

T is air temperature [K].

The psychrometric constant (γ) is not actually constant, 

but varies with temperature and atmospheric pressure. This 

parameter was calculated using the formula of Storr and Den 

Hartog (1975) as:

λ λ γ

T

T

atm RH

P +

= +

237.3

7.5

10 0.3863 0.02694

 (8)

where

Patm is atmospheric pressure [mb],

λ is the latent heat of vaporization 

[cal/g] (λ=597.3 – 0.5653T),

RH is relative humidity [percent], and

T is air temperature [oC].

For all stations except station 442 Matatula, atmospheric 

pressure used in the PE computation was estimated using the 

following equation:

 Patm = Pnws× (1.0 – (As – Anws) / 44,307.69) 5.25, (9)

where

Patm is the atmospheric pressure at the PE station, 

[mb], 

Pnws is the atmospheric pressure reported at the 

NWS office at Pago Pago Airport (National 

Climatic Data Center, 2001-05), [mb],

As is the elevation at the PE station [meters above 

mean sea level], and 

Anws is the elevation at the NWS office at Pago 

Pago Airport [given as 3 m above sea level 

(National Climatic Data Center, 2001-05)].

Where Pnws was not available, a standard atmosphere of 

1,013.246 mb was used:

 Patm = 1,013.25 × (1.0 – As / 44307.69) 5.25. (10)

For the PE computation at station 442 Matatula, the atmospheric pressure reported by the CMDL facility (Climate 

Monitoring and Diagnostics Laboratory, 2003) was used.

Estimates substituted for missing data in the PE computation. – At some stations, periods of missing data resulted 

from instrument malfunction and calibration drift. For the 

purposes of computing PE, some of the missing data were substituted by estimates. These substitutions were used only for 

the purposes of computing PE, not in the summary of climate 

statistics.

Missing air-temperature data from station 111 Airport 

were replaced by estimates using a regression relation between 

hourly air temperature reported by the NWS office (National 

Climatic Data Center 2001-05) and air temperature recorded at 

station 111 Airport:

 T111 = 0.87 × Tnws + 2.69, (11)

38  Potential Evapotranspiration on Tutuila, American Samoa
where

T111 is the air temperature at station 111 Airport 

[°C], and 

Tnws is the air temperature at the NWS office at Pago 

Pago Airport [°C]. 

Missing relative-humidity data resulting from calibration drift at station 441 Matatula were replaced by estimates 

derived from air temperature and dew-point data recorded at 

the nearby CMDL facility (Climate Monitoring and Diagnostics Laboratory, 2003) using the following equation from 

Rosenberg and others (1983):

 















 =

s

dp

e

e

RH 100 , (12)

where

RH is relative humidity [percent],

edp is the saturation vapor pressure [mb] at the dew 

point temperature, and

es is the saturation vapor pressure [mb] at the 

ambient temperature.

The saturation vapor pressures at the dew-point temperature 

(edp) and ambient temperature (es) are given by:

 















+ = ×273.30

17.269

0.61078 exp

dp

dp

dp T

T

e and

 















+ = ×273.30

17.269 0.61078 exp

a

a

s T

T

e , (13)

where

Tdp is dew-point temperature in Celsius, and

Ta is air temperature in Celsius.

Malfunction of the soil-moisture probe resulted in 

occasional missing hourly soil-moisture data. In these cases, 

default soil-moisture values were used in place of missing data 

for the purposes of computing PE. For all stations except 222 

Masefau, the default soil-moisture value was equal to the average of the existing soil-moisture readings. At 222 Masefau, the 

soil-moisture probe failed to give any accurate soil-moisture 

values. For this station, a default soil-moisture value of 65 percent (by volume) was used, which is close to the average (57 

percent by volume) of soil-moisture values determined from 

laboratory analysis of soil samples collected at the station. 

Use of this single default value of soil moisture was considered reasonable because tests using other default soil-moisture 

values on the data from 222 Masefau indicate that the resulting 

PE is affected little by variations in soil moisture between 50 

and 80 percent.

References Cited

Climate Monitoring and Diagnostics Laboratory, 2003, file 

transfer protocol site at ftp://140.172.192.211/met/hourlymet/smo/, accessed through Climate Monitoring and 

Diagnostics Laboratory data archive: world-wide website at 

http://www.cmdl.noaa.gov/infodata/ftpdata.html, August 14, 

2003.

Goff, J.A., and Gratch, S., 1946, Low-pressure properties of 

water from -160 ̊ to 212 ̊F, in Transactions of the American 

Society of Heating and Ventilating Engineers, p. 95-122, 

presented at the 52nd annual meeting of the American society of heating and ventilating engineers, New York.

National Climatic Data Center, 2001-05, Locate weather 

observation station record: world-wide website at http://

www.ncdc.noaa.gov/oa/climate/stationlocator.html/ 

accessed February 1, 2001, April 11, 2001, August 7, 2001, 

October 19, 2001, November 19, 2001, January 29, 2002, 

April 26, 2002, June 25, 2002, October 18, 2003, September 15, 2004, and June 20, 2005.

Penman, H.L., 1948, Natural evaporation from open water, 

bare soil, and grass: Proceedings of the Royal Society of 

London, A193, p. 120-146.

Rosenberg, N.J., Blad, B.L., and Verma, S.B., 1983, Microclimate, the biological environment: 2nd ed., John Wiley & 

Sons, Inc., New York, p. 170-171.

Storr, D., and Den Hartog, G., 1975, Gamma—The psychrometer non-constant: Journal of Applied Meteorology 

v. 14, p. 1397–1398.

Appendix 1  39
This page is intentionally blank.

40 Potential Evapotranspiration on Tutuila, American Samoa
Prepared by the U.S. Geological Survey Publishing staff, 

Menlo Park Publishing Service Center

Luis Menoyo

Tacoma Publishing Service Center

Debra Grillo

For more information concerning the research in this report, contact the 

Director, Pacific Islands Water Science Center 

U.S. Geological Survey, 677 Ala Moana Blvd., Suite 415 

Honolulu, Hawaii 96813

http://hi.water.usgs.gov/
Izuka and others—Potential Evapotranspiration on Tutuila, American Samoa—SIR 2005–5200

Printed on recycled paper