U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2008–5228

In Cooperation with the Nebraska Platte River Cooperative Hydrology Study Group and Central 

Platte Natural Resources District

Evapotranspiration Rates of Riparian Forests, Platte River, 

Nebraska, 2002–06
Front cover: U.S. Geological Survey personnel maintaining sensors atop a 27.4-meter tower in a riparian forest near Gothenburg, 

Nebraska, July 2002.
Evapotranspiration Rates of Riparian 

Forests, Platte River, Nebraska, 2002–06

By Matthew K. Landon, David L. Rus, Benjamin J. Dietsch, Michaela R. Johnson, 

and Kathleen D. Eggemeyer

In cooperation with the Nebraska Platte River Cooperative Hydrology Study 

Group and Central Platte Natural Resources District

Scientific Investigations Report 2008–5228

U.S. Department of the Interior

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

KEN SALAZAR, SECRETARY

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Landon, M.K., Rus, D.L., Dietsch, B.J., Johnson, M.R., and Eggemeyer, K.D., 2009, Evapotranspiration rates of riparian 

forests, Platte River, Nebraska, 2002–06: U.S. Geological Survey Scientific Investigations Report 2008-5228, 65 p.
iii

Contents

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

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................2

Description of the Platte River Basin in Nebraska..........................................................................4

Previous studies....................................................................................................................................4

Acknowledgments................................................................................................................................4

Methods of Investigation..............................................................................................................................4

Site Selection and Characterization..................................................................................................4

Footprint and Forest Area Analysis ..........................................................................................5

Forest Surveys .............................................................................................................................5

Micrometeorological Measurements ...............................................................................................5

Eddy Covariance ..........................................................................................................................6

Energy Balance..........................................................................................................................12

Net Radiation.....................................................................................................................12

Soil-Heat Flux ....................................................................................................................12

Vegetation-Energy Storage.............................................................................................14

Energy Balance Adjustments .........................................................................................15

Meteorological Variables .........................................................................................................15

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

Filling of Data Gaps....................................................................................................................15

Water Balance ....................................................................................................................................16

Evapotranspiration ...........................................................................................................17

Precipitation and Canopy Interception ..................................................................................17

Precipitation and Throughfall Measurement...............................................................17

Accounting for Canopy Interception in the Water Balance......................................17

Soil-Water Content and Storage.............................................................................................18

Ground-Water Levels ................................................................................................................18

Leaf-Area Index...................................................................................................................................19

Evapotranspiration Modeling ...........................................................................................................19

Sap Flux ................................................................................................................................................19

Statistical Analysis .............................................................................................................................21

Evapotranspiration Rates ...........................................................................................................................21

Site Characteristics............................................................................................................................22

Footprint Areas...........................................................................................................................22

Vegetation Characteristics.......................................................................................................23

Hydrogeologic Characteristics................................................................................................27

Precipitation and Canopy Interception ...........................................................................................28

Measured Evapotranspiration Rates...............................................................................................31

Energy Balances.................................................................................................................................34

Adjusted Net Radiation.............................................................................................................34

Seasonal and Diurnal Energy-Flux Cycles.............................................................................34

Water Balances ..................................................................................................................................42
iv

Simulation of Evapotranspiration Rates..........................................................................................43

Environmental Factors Affecting Evapotranspiration Rates .......................................................44

Simple Linear Regressions.......................................................................................................46

Multiple Linear Regressions ....................................................................................................49

Evapotranspiration Responses to Seasonal Energy and Hydrologic Fluctuations.........50

Transpiration in Individual Plants.....................................................................................................51

Extrapolating Evapotranspiration Rates Beyond the Study Sites...............................................54

Summary........................................................................................................................................................55

References Cited..........................................................................................................................................57

Appendixes ...................................................................................................................................................64

Figures

1. Map showing location of riparian forest evapotranspiration study sites near Odessa 

and Gothenburg, Nebraska, and distribution of riparian forests along 

the Platte River..............................................................................................................................3

2–3. Aerial photographs showing—

2. Data collection locations and probable flux source areas for micrometeorological instrumentation on towers at study sites near Odessa 

and Gothenburg, Nebraska................................................................................................7

3. Areal layout of instrumentation near the towers at study sites near 

Odessa and Gothenburg, Nebraska .................................................................................8

4–5. Photographs showing—

4. Selected micrometeorological instrumentation used at study sites ..........................9

5. Instrumentation used for measurements of surface or subsurface 

conditions at study sites...................................................................................................13

6. Graph showing relation of source strength with distance upwind from a

measurement point 26.1 meters above land surface for end-member 

atmospheric conditions .............................................................................................................24

7. Map showing distribution of land cover and fraction of total area in each 

category at study sites near Odessa and Gothenburg, Nebraska .....................................24

8–21. Graphs showing—

8. Tree diameter and sapwood thickness for selected trees and

shrubs at study site near Odessa, Nebraska ................................................................26

9. Seasonality of average leaf-area index of riparian forests at study sites 

near Odessa and Gothenburg, Nebraska, 2002–04......................................................26

10. Vertical profile of grain-size distribution of study sites near Odessa and

Gothenburg, Nebraska......................................................................................................27

11. Daily, monthly measured and normal, and annual measured and normal

precipitation for study sites near Odessa and Gothenburg, Nebraska, 

2002–06 ................................................................................................................................29

12. Relations between precipitation and throughfall at study sites near 

Odessa and Gothenburg, Nebraska, 2002–06 ...............................................................30
v

13. Daily, monthly, and annual evapotranspiration rates for study sites near 

Odessa and Gothenburg, Nebraska, 2002–06 ...............................................................33

14. Relations between net radiation measured with different net radiometers 

at study site near Odessa, Nebraska, March 2004–May 2005 ...................................39

15. Monthly mean energy fluxes for study sites near Odessa and Gothenburg,

Nebraska, 2002–06.............................................................................................................40

16. Diurnal patterns of median energy fluxes based on March 2002 to 

December 2004 data for June–August and December–February at 

study sites near Odessa and Gothenburg, Nebraska..................................................41

17. Relations among annual water-balance terms for study sites near 

Odessa and Gothenburg, Nebraska, 2002–06 ...............................................................44

18. Average crop coefficients for riparian forests at study sites near Odessa 

and Gothenburg, Nebraska, 2002–05..............................................................................46

19. Simple linear regressions of daily evapotranspiration rates with vaporpressure deficit, solar radiation, air temperature, and leaf-area index at 

study sites near Odessa and Gothenburg, Nebraska, 2002–06..................................48

20. Daily average transpiration rates for monitored trees and shrubs and 

aggregate evapotranspiration measured by eddy-covariance method, 

April through August 2004, at study site near Odessa, Nebraska.............................52

21. Relation of daily transpiration rates for monitored trees and shrubs with 

evapotranspiration determined from eddy covariance, April through 

August 2004, at study site near Odessa, Nebraska......................................................53
vi

Tables

1. Summary of instrumentation used and placement of sensors at study sites

 near Odessa and Gothenburg, Nebraska ..............................................................................10

2. Tree and shrub species instrumented with sap-flow devices during April 

 through August 2004, at the study site near Odessa, Nebraska.........................................20

3. Summary of vegetation and hydrogeologic characteristics of study sites 

 near Odessa and Gothenburg, Nebraska ...............................................................................25

4. Summary of annual precipitation and interception at study sites near 

 Odessa and Gothenburg, Nebraska, 2002–06 ........................................................................32

5. Summary of monthly mean energy fluxes for April 2002 to April 2006 at 

 riparian forest study site near Odessa, Nebraska ................................................................35

6. Summary of monthly mean energy fluxes for April 2002 to April 2006 at 

 riparian forest study site near Gothenburg, Nebraska.........................................................38

7. Annual water-balance results for riparian forest study sites near Odessa 

 and Gothenburg, Nebraska, April 1, 2002, to March 31, 2006..............................................43

8. Average crop coefficients for riparian forest study sites near Odessa and 

 Gothenburg, Nebraska, 2002–05 ..............................................................................................45

9. Comparison of evapotranspiration simulated with 15-day average and 

 monthly crop coefficient models to actual evapotranspiration at riparian 

 forest study sites near Odessa and Gothenburg, Nebraska ...............................................46

10. Results of simple linear regressions of daily evapotranspiration with 

 potentially explanatory independent variables for study sites near Odessa 

 and Gothenburg, Nebraska, 2002–06.......................................................................................47

11. Results for multiple linear-regression models of daily evapotranspiration 

 as a function of potentially explanatory independent variables for study 

 sites near Odessa and Gothenburg, Nebraska, 2002–06 .....................................................49

12. Transpiration rates in individual plants determined from thermal dissipation 

 probes and aggregate evapotranspiration rates determined from eddy- 

 covariance methods for days with complete transpiration data, April to 

 August 2004, riparian forest study site near Odessa, Nebraska.........................................51
vii

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (m) 0.6215 mile (mi) 

Area

square meter (m2) 0.0002471 acre 

hectare (ha) 2.471 acre

square centimeter (cm2) 0.001076 square foot (ft2)

square meter (m2) 10.76 square foot (ft2) 

Volume

milliliter (mL) 0.03382 ounce, fluid (fl. oz)

liter (L) 33.82 ounce, fluid (fl. oz)

cubic meter (m3) 264.2 gallon (gal) 

cubic centimeter (cm3) 0.06102 cubic inch (in3) 

cubic meter (m3) 0.0008107 acre-foot (acre-ft) 

Flow rate

cubic centimeter per second (cm3/s) 0.06102 cubic inch per second (in3/s)

centimeter per second (cm/s) 0.3937 inch per second (in/s) 

millimeter per day (mm/d) 0.03937 inch per day (in/d) 

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Mass

kilogram (kg) 2.205 pound avoirdupois (lb)

Pressure

kilopascal (kPa) 0.1450 pound per square inch (lb/ft2

) 

Density

gram per cubic centimeter (g/cm3) 62.4220 pound per cubic foot (lb/ft3) 

kilogram per cubic meter (kg/m3) 0.06243 pound per cubic foot (lb/ft3) 

Energy

Joule (J) 0.239 Calorie (cal)

Power

Watt (W) 0.239 Calorie per second (cal/s)

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

°F=(1.8×°C)+32

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

°C=(°F-32)/1.8

Temperature in Kelvin (K) may be converted to degrees Celsius (°C) as follows:

°C=K-273.15

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

Altitude, as used in this report, refers to distance above an arbitrary datum.
viii

Symbols 

α Priestley-Taylor coefficient

Acanopy Area of the ground underneath the tree canopy

Asapwood Sapwood area

Bi Unit volume of component i

Ci Specific-heat capacity of component i

Cm Volumetric specific-heat capacity of the mineral component of the soil

Co Volumetric specific-heat capacity of the organic component of the soil

Cs Volumetric specific-heat capacity of the soil layer

Cw Volumetric-specific-heat capacity of water

d Thickness of the soil for which calculations are being performed

D Vapor-pressure deficit

∆ Ts Change in soil temperature in the upper 8 cm of soil

∆GW Change in ground-water storage

∆PS Change in the storage of energy in the biomass

∆PSi Change in energy storage of component i, where i represents tree trunks, branches, or 

leaves

∆PW Change in plant-water storage

∆S Change in storage of heat in the upper 8 cm of soil

∆SW Change in soil-water storage

∆Ti Change in temperature of component i

∆Tsap Sap-temperature difference

Abbreviations 

COHYST Platte River Cooperative Hydrology Study

DTW Depth to the ground-water table

EBC Energy balance closure

EBR Energy balance ratio

ET Evapotranspiration

GWET ET derived directly from ground water

LAI Leaf-area index

MLR multiple-linear regression

NSE Coefficient of efficiency

PET Potential evapotranspiration

R2 Coefficient of determination

Rn-2 Two-component net radiometer

Rn-4 Four-component net radiometer

RSE Standard error of the residuals

SLR Simple-linear regression

TDP Thermal-dissipation probe

TF Canopy throughfall gutter

USGS U.S. Geological Survey

UZ Unsaturated-zone vertical profile
ix

∆Tsap,max Maximum sap-temperature difference recorded between 2 and 4 am

EI Evaporation of canopy interception

Eo Evapotranspiration rate for a well-watered reference crop

ET Evapotranspiration

ETGWSW Evapotranspiration from ground-water and soil-water sources

ETsim Daily ET simulated using a crop coefficient

G Soil heat flux at the land surface

γ Psychrometric constant

G8 Soil heat flux measured at a depth of 8 cm 

GWin Ground-water inflow

GWout Ground-water outflow

H Sensible heat flux

I Canopy interception of precipitation

kc Crop coefficient

λE Latent heat flux

λEp Potential latent heat flux

P Precipitation

p Significance level

Pexcess Precipitation less ET

θm Gravimetric fraction of the mineral component of the soil

θo Gravimetric fraction of the organic component of the soil

Qsap Sap flux

θw Gravimetric fraction of the water in the soil

θw,v Volumetric fraction of the water in the soil

ρb Dry-bulk density

ρi Density of component i

rm Density of the mineral component of the soil

Rn Net radiation

ro Density of the organic component of the soil

RO Overland runoff

Rs Solar-radiation

rtree Tree radius, less the bark thickness

rw Density of water

s Slope of the relation of saturation-vapor pressure to air temperature at a given air 

temperature

SWin Lateral soil-water inflow

SWout Lateral soil-water outflow

t Duration of the measurement period

TF Canopy-throughfall precipitation

Tsap Sap-flux-derived measure of transpiration

Vsap Sap velocity

wang Zenith angle of the wind above the horizontal plane

wd Horizontal azimuth angle of the wind measured by the sonic anemometer

wsapwood Sapwood thickness
x
Abstract 

Evapotranspiration (ET) in riparian areas is a poorly 

understood component of the regional water balance in the 

Platte River Basin, where competing demands have resulted 

in water shortages in the ground-water/surface-water system. 

From April 2002 through March 2006, the U.S. Geological Survey, Nebraska Platte River Cooperative Hydrology 

Study Group, and Central Platte Natural Resources District 

conducted a micrometeorological study of water and energy 

balances at two sites in central Nebraska near Odessa and 

Gothenburg to improve understanding of ET rates and factors 

affecting them in Platte River riparian forests. A secondary 

objective of the study was to constrain estimates of groundwater use by riparian vegetation to satisfy ET consumptive 

demands, a useful input to regional ground-water flow models. 

Both study sites are located on large islands within the 

Platte River characterized by a cottonwood-dominated forest 

canopy on primarily sandy alluvium. Although both sites are 

typical of riparian forests along the Platte River in Nebraska, 

the Odessa understory is dominated by deciduous shrubs, 

whereas the Gothenburg understory is dominated by eastern 

redcedars. Additionally, seasonal ground-water levels fluctuated more at Odessa than at Gothenburg. The study period of 

April 2002 through March 2006 encompassed precipitation 

conditions ranging from dry to wet. 

This study characterized the components of the water balance in the riparian zone of each site. ET was evaluated from 

eddy-covariance sensors installed on towers above the forest 

canopy at a height of 26.1 meters. Precipitation was measured 

both above and below the forest canopy. A series of sensors 

measured soil-moisture availability within the unsaturated 

zone in two different vertical profiles at each site. Changes in 

ground-water altitude were evaluated from piezometers. The 

areal footprint represented in the water balance extended up to 

800 meters from each tower.

During the study, ET was less variable than precipitation. 

Annual ET fluctuated about 7 percent from the 4-year mean, 

ranging from about 514 to 586 millimeters per year (551 on 

average) at the Odessa site and 535 to 616 millimeters per year 

(575 on average) at the Gothenburg site. Conversely, annual 

precipitation fluctuated by about 35 percent from the 4-year 

mean, ranging from 429 to 844 millimeters per year at Odessa 

and 359 to 791 millimeters per year at Gothenburg. Of this 

precipitation, 14 to 15 percent was intercepted by the forest 

canopy before it could infiltrate into the soil.

For the 4-year period, annual ground-water recharge from 

the riparian measurement zone averaged 76 and 13 millimeters at Odessa and Gothenburg, respectively, to satisfy the 

water balance at each site. This indicates that, from an annual 

perspective, ground-water reductions caused by ET may be 

minimal. This effect varied somewhat and primarily was 

affected by fluctuations in precipitation. Ground-water discharge occurred during the driest study year (2002), whereas 

ground-water recharge occurred from 2003 to 2005. These 

results do not exclude ground water as an important source of 

water to riparian vegetation—especially to phreatophytes that 

have the capability of directly using water from the saturated 

zone—during periods of high ET in the summer, particularly 

during periods of lower than normal precipitation. However, 

the calculations indicate that, on an annual (or longer) netflux basis, ground-water use by riparian forests is likely to be 

balanced by periods of recharge from excess precipitation at 

other times of the year. In contrast to more arid settings, where 

scientific literature indicates that ground water may supply a 

large fraction of the water used for ET by riparian vegetation, 

precipitation along the Platte River of Nebraska was great 

enough—and generally greater than ET—that most or all of 

the annual ET demand was satisfied by available precipitation. 

Crop coefficients developed for 15-day and monthly periods from the measured data predicted ET within 3.5 percent 

of actual annual ET; however, daily ET was underpredicted 

on days of increased ET and overpredicted on days of low ET. 

These crop coefficients can be used to extrapolate riparian-forest ET along the Platte River in conjunction with atmospheric 

data from other climate stations in central Nebraska.

Regression models of simple and multiple-linear relations 

between explanatory variables and ET indicated that the relation of ET to environmental factors was different on days with 

precipitation than on dry days. At Odessa, ET was affected 

by vapor-pressure deficit, solar radiation, leaf-area index, and 

depth to water regardless of precipitation conditions, but was 

also affected by air temperature on days without precipitation, 

suggesting energy limitations on ET on days without precipitation. At Gothenburg, ET was always a function of vaporpressure deficit, solar radiation, and leaf-area index, but, as 

Evapotranspiration Rates of Riparian Forests, Platte River, 

Nebraska, 2002–06 

By Matthew K. Landon, David L. Rus, Benjamin J. Dietsch, Michaela R. Johnson, and Kathleen D. Eggemeyer
2 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

with Odessa, air temperature also became important on days 

without precipitation. 

Despite depths to ground water of less than 2 meters and 

phreatophytic vegetation, measured ET was substantially less 

than potential ET (based on the modified Penman method), 

consistent with plant-stomatal regulation of ET in response to 

environmental and meteorological factors. Although annual 

ET rates generally were similar, the two sites exhibited different intraannual soil-moisture regimes that had a corresponding 

effect on ET and vegetation vigor. Smaller seasonal declines 

in ground-water levels and a lack of understory shrubs at the 

Gothenburg site as compared to the Odessa site may explain 

why Gothenburg ET was comparatively greater later in the 

summer and was not dependent on depth to water (as identified by the multiple-linear regression model). These differences also may explain why, during years of increased precipitation, ET rates increased at Odessa but not at Gothenburg.

Introduction

An agreement between the Governors of Nebraska, Colorado, and Wyoming, and the U.S. Department of the Interior 

has established guidelines to increase in-stream flows within 

the Platte River in an effort to enhance the habitat of threatened and endangered species such as whooping cranes, piping 

plovers, and least terns (Platte River Endangered Species 

Partnership, 1997). In response to this agreement, a group of 

state agencies, local governmental units, and private organizations in Nebraska called the Platte River Cooperative Hydrology Study (COHYST) has been conducting hydrologic studies 

along the Platte River to develop scientifically supportable 

hydrologic data bases, analyses, and models. One of the goals 

of the COHYST effort is to better understand the ground-water 

resources and stream/aquifer interaction to fulfill a need to 

quantify river gains and losses from ground-water interaction. 

To quantify this interaction, COHYST constructed groundwater models of an area encompassing the Platte River in central Nebraska (fig. 1). A second phase of the study, begun in 

2001, included studies to fill gaps in hydrologic understanding 

for the models. One of the top priorities for COHYST in the 

second phase was to better quantify evapotranspiration (ET) 

from riparian forest areas along the Platte River and its effects 

on the hydrologic system. In response, the U.S. Geological 

Survey (USGS), in cooperation with the COHYST Group and 

the Central Platte Natural Resources District, characterized ET 

rates at two riparian forest sites along the Platte River. 

Rates of riparian forest ET are important to quantify 

because they have the potential to affect the regional water balance (Goodrich and others, 2000; Szilagyi and others, 2005). 

First, the area of riparian forest along the Platte River has 

expanded substantially from the 1930s to the 1960s (Johnson, 

1994, 1997). Second, riparian forests are suspected to have 

increased ET rates because of the relatively shallow depth to 

ground water near stream channels in conjunction with the 

presence of phreatophytes—primarily cottonwood with some 

willow trees along the Platte River—that can directly utilize 

ground water for transpiration processes (Busch and others, 

1992). In the context of this report, Platte River riparian forests 

represent the forest ecosystem located along the margins of 

the river as well as on the islands contained within the braided 

system.

Measurements of total ET rates and rates of ground-water 

use by riparian vegetation to satisfy ET consumptive demands 

are lacking in the region. Generally, ET is a key component 

in the water balance in Nebraska, second only to precipitation in magnitude (Szilagyi and others, 2005). Normal annual 

precipitation ranges from about 385 to 920 millimeters (mm) 

from west to east across the state (Gutentag and others, 1984; 

Szilagyi and others, 2003), whereas mean annual ET has been 

estimated to range from about 360 to 630 mm (Szilagyi and 

others, 2003). Despite the importance of riparian ET in the 

hydrologic cycle, little is known about its magnitude, seasonal 

and daily distributions, and the relation of ET to environmental variables. 

ET derived directly from ground water (GWET) is a 

process important to include in regional ground-water flow 

models of the Platte River Basin in Nebraska (Luckey and 

Cannia, 2006; Luckey and others, 2006). Estimates of GWET 

rates are not as precise as total ET measurements because of 

the difficulty in distinguishing if water utilized by plants for 

transpiration or evaporating from soil is derived from precipitation or ground water. Poorly constrained estimates could 

produce errors or uncertainties in model results. However, estimates of GWET can be improved and constrained by accurate 

measurements of ET, precipitation, evaporation of intercepted 

precipitation, soil-water balance, and ground-water levels. 

The objectives of the research reported herein were to 

estimate total ET and the GWET component in riparian forests 

along the Platte River. To do so, the study involved investigations of ET at two research sites in representative riparian 

settings. The investigations at each site included using eddycovariance and energy-balance techniques to determine total 

ET and determining relations of ET to selected environmental 

variables that commonly control ET rates. Measurements of 

precipitation through the canopy were used to estimate how 

much of total ET is comprised of evaporation of intercepted 

precipitation, thus providing an upper boundary of GWET 

estimates by subtracting interception evaporation from total 

ET. Additional constraints on GWET estimates were provided 

by calculations of annual-water balance in a representative 

volume enclosing the riparian system. Using these site-specific 

measurements, a crop-coefficient model was calibrated for use 

in estimating riparian ET elsewhere in the COHYST study 

area.

Purpose and Scope 

The purpose of this report is to describe total ET rates 

measured using micrometeorological techniques at two ripar-
Introduction 

3

CUSTER

LINCOLN

CLAY

BUFFALO HALL DAWSON

YORK

FRONTIER

PLATTE BOONE

POLK

FURNAS

LOGAN VALLEY

THAYER

PHELPS ADAMS

HARLAN

GREELEY

NANCE

HOWARD

FILLMORE

WEBSTER

GOSPER

FRANKLIN

RED WILLOW

KEARNEY

SHERMAN

NUCKOLLS

MERRICK

HAMILTON

Odessa Kearney

Gothenburg

Central City

Grand Island

North Platte

Platte

River

Republican River

100° 100°30’ 99° 30’ 30’ 98°

41°30'

40°30'

41°

EXPLANATION

0 5 10 15 20 25 30 MILES Base from U.S. Geological Survey digital data, 1:100,000, 1989, 

Lambert Conformal Conic projectionStandard parallels 40 ̊N and 43 ̊N, central meridian 100 ̊WHorizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83)

0 5 10 15 20 25 30 KILOMETERS

KANSAS

IOWA

COLORADO

SOUTH DAKOTA

MISSOURI

WYOMING

Enlarged area

Index map

Cooperative Hydrology Study (COHYST) study areaPlatte River 1997 woodland in the  riparian corridor—From Center for Advanced Land Management Information Technologies (2000)Platte River Basin within NebraskaStudy site

Boundary of

Platte River Basin

Boundary of COHYST

study area

Boundary of COHYSTstudy area

NEBRASKA

Platte River South Platte

River

Republican River

North Platte River

Figure 1. Location of riparian forest evapotranspiration study sites near Odessa and Gothenburg, Nebraska, and distribution of riparian forests along the Platte River.
4 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

ian forest sites along the Platte River in central Nebraska 

during 2002–06. The ET rates were used as part of the annual 

water-balance calculations and in calibrating a crop-coefficient 

model used for simulating ET rates. The report also documents canopy interception at the two sites and how it was used 

to constrain the amount of transpiration utilizing ground water 

rather than precipitation sources. An analysis of environmental 

variables affecting ET rates is reported, followed by a special 

section detailing transpiration rates in individual plants. The 

report closes with a discussion of uncertainties, limitations, 

and extrapolation of the results to other locations. 

Description of the Platte River Basin in 

Nebraska 

The North and South Platte Rivers originate in the 

Rocky Mountains of Wyoming and Colorado and merge at 

North Platte, Nebraska, where the main stem of the Platte 

River flows east from that point. Flows in the Platte River are 

derived mainly from snowmelt runoff originating in the Rocky 

Mountains and surface runoff and ground-water discharge 

from the remaining areas of the basin, and are affected by storage, diversion, and irrigation return flows (Bentall and Shaffer, 

1979; Gutentag and others, 1984). 

Land use in the Platte River Basin varies longitudinally. 

The eastern and central parts of Nebraska predominantly are 

cultivated-agricultural land with increasing amounts of rangeland to the west (Center for Advanced Land Management 

Information Technologies, 2000; U.S. Geological Survey, 

1999-2000). Riparian vegetation is restricted to lowlands 

located along the Platte River and its tributaries. 

Forests and grasslands (including wet meadows) in the 

riparian zone are the predominant communities, but other 

categories include herbaceous, shrub, and emergent wetlands 

(Currier and others, 1985). Previous investigations have 

indicated that the area of riparian forest expanded substantially from the 1930s to the 1960s and stabilized thereafter 

(Johnson, 1994, 1997). As a result, the formerly wide, shallow, 

and braided channel of the Platte River changed to mostly 

wooded islands separated by a few relatively narrow, deepened 

channels. The increases in riparian forest cover have resulted 

at least in part from widespread changes in environmental 

conditions including fire suppression, bison removal, and 

water-resources development (Currier, 1982; Johnson, 1994). 

Water-resources development has included the construction of 

storage reservoirs that regulate river flows and utilize the river 

channel for water delivery to locations downstream (Currier, 

1982; Johnson, 1994; Sidle and Faanes, 1997). As a result, 

Platte River flows can vary by location with respect to water 

diversion or return structures.

Previous Studies

Previous studies in and near the COHYST study area 

generally have estimated riparian ET from empirical formulas 

using meteorological data or from intermittent experiments; 

estimates have ranged widely (Tomanek and Ziegler, 1960; 

Weeks and Sorey, 1973; Nagel and Dart, 1980; Bureau of Reclamation, 1985; Eddy, 1995). The magnitude of the estimated 

riparian ET rates reported in previous studies in the region, 

ranging from about 500 to 1,300 millimeters per year (mm/yr), 

is poorly constrained. 

Techniques exist for accurately measuring riparian ET 

using micrometeorological approaches. The eddy-covariance 

method has been used to successfully measure ET in numerous studies (Businger and others, 1967; Moore, 1986; Baldocchi and others, 1988; Stannard and others, 1994). This 

micrometeorological method has several advantages in 

comparison to other approaches such as lysimeters or regional 

water balances, by providing more areal integration and less 

site disruption than lysimeters, by eliminating the need to 

estimate other terms such as runoff in the water balance, and 

by allowing fine temporal resolution of less than 1 hour of ET 

patterns. These data and the models calibrated from those data 

provide insight into the relations between ET and environmental variables (Stewart, 1988; Stannard, 1993). Models also 

provide a tool for extrapolating results to other locations. For 

example, Jensen and others (1990) developed crop coefficients 

that reflect the ratio of a given crop ET with that of a reference 

ET estimated from nearby weather stations. 

Acknowledgments 

The authors thank the landowners, Gerald and Keith 

Trampe and Steve Potter, who provided access to their property, and Jim Gaskill for logistical assistance. The COHYST 

technical committee also is acknowledged for extensive input 

and support of this project. The authors also thank USGS 

researcher David Stannard for providing guidance and expertise from the project’s conception to its conclusion.

Methods of Investigation

The sections below provide an overview of the 

approaches used in this study and provide references for more 

detailed descriptions. Methods used in selecting and characterizing study sites, collecting micrometeorological, waterbalance, leaf-area-index, and sap-flux data, and developing 

models of ET are described. 

Site Selection and Characterization

The study sites were chosen on the basis of several 

selection criteria. Candidate selections were based upon large 

north-to-south riparian forest extent along the Platte River, 

a forest community generally representative of Platte River 

riparian forests, and physical site characteristics. After analysis 

of land-cover data to select candidate riparian forest sites, field 
Methods of Investigation 5

reconnaissance verified that vegetation and site characteristics 

were within the range of those encountered in Platte River 

riparian areas. Thereafter, study sites were selected based upon 

landowner permission, site access, plans for future vegetation 

management, and representative vegetation characteristics. 

Site selection was completed during May-August, 2001, culminating in the final selection of study sites near Odessa and 

Gothenburg, Nebraska (fig. 1). 

Footprint and Forest Area Analysis 

To use micrometeorological techniques to measure fluxes 

from the targeted riparian areas rather than adjacent land uses, 

an adequately sized area of homogeneous riparian forest was 

necessary. Calculations of the footprint, or upwind area most 

likely to affect a flux measurement at a given height, were 

made using methods developed by Schuepp and others (1990). 

For computing preliminary footprint requirements, an average tree height of 18.3 m (meters)—based on riparian tree 

height data from the nearby Republican River in south-central 

Nebraska (Rand, 1973)—and hypothetical sensor height of 

27.4 m were used. The footprint boundaries were calculated 

by assuming neutral conditions, in which the relative effect 

of buoyancy on fluxes is negligible as compared to the effect 

of wind. The footprint calculations also were dependent on 

the ratio of roughness length (a measure of the aerodynamic 

roughness of the canopy) to canopy height and the ratio 

of displacement height (or the height within the canopy at 

which aerodynamic drag cancels out wind momentum) to 

canopy height, which were assumed initially to be 0.08 and 

0.75, respectively, based on the mean of six forest ET studies (Stanhill, 1969; Leonard and Federer, 1973; Thompson, 

1979; De Bruin and Moore, 1985; Gash, 1986; Bergen, 1987). 

The results of the preliminary calculations indicated that for 

neutral conditions, about 85 percent of the flux measured at a 

27.4 m height originated within an upwind distance of 800 m. 

The actual footprint was likely to be less than that because the 

largest ET rates occur during unstable conditions, when the 

footprint is smaller than during neutral conditions; however, to 

maintain a margin of safety, the initial estimate of the riparian 

forest area necessary to reflect fluxes from that vegetation was 

a circle with a radius of 800 m. 

Analysis of digital data for land cover (Center for 

Advanced Land Management Information Technologies, 

2000), riparian vegetation types (Currier and others, 1985; 

Bureau of Reclamation, 1998), and infrared reflectance (aerial 

photographs) covering parts of the Platte River Basin in 

Nebraska (Bureau of Reclamation, 1999), indicated that 20 

sites met the footprint criterion. Subsequently, nine candidate 

sites with the greatest densities of woodland vegetation (as 

compared to meadows or open water) were selected for further 

investigation. 

Basic reconnaissance visits were made to the nine 

candidate sites to survey tree heights and species from roads 

bordering the sites. Based upon the reconnaissance data, the 

assumption of an average canopy height of about 18 m was 

considered justified; however, the reconnaissance data also 

indicated large variability, with tree canopy height ranging 

from about 14 to 23 m. The canopy height variability indicated 

that the forests had a greater roughness length than previously 

assumed, thereby decreasing the footprint area. As a result, 

the preliminary selection criterion requiring at least 800 m of 

upwind riparian forest was conservative as compared to actual 

upwind source areas. As a result of this reconnaissance, as 

well as access limitations, four of the nine sites remained for 

further consideration.

Forest Surveys 

Forest surveys of the four remaining candidate sites, 

including the two study sites selected, were completed during 

July and August 2001. Vegetation data were collected at 12 to 

14 points along generally north-south transects spanning the 

entire width of the riparian forest area at each site. Along each 

transect, measurement points were spaced 100 m apart, and 

the point-centered quarter sampling method (Mueller-Dombois 

and Ellenberg, 1974; Fitzpatrick and others, 1998) was used to 

select the trees sampled. From the sampling point, the closest 

live tree in each of four quadrants was selected for description. Described trees were at least 2 m tall and had a diameter 

at breast height (1.5 m) of at least 3 cm (centimeters). For 

each quadrant, canopy closure was measured with a concave 

spherical densiometer following methods described by Platts 

and others (1987) and Fitzpatrick and others (1998); pointto-tree distance was measured with a cloth tape; the species 

of the tree was identified; the circumference of each sampled 

tree was measured using a cloth tape; the diameter at breast 

height and basal area were calculated from the field-measured 

circumference; tree heights were calculated using angles 

and distances measured with a clinometer and range finder 

(Rand, 1973); and any additional ancillary notes on understory 

vegetation composition and other observable features of note 

were recorded. In addition to the characteristics listed above, 

the vegetation data collected were used to compute the average 

canopy area per tree, density (number of individuals per area), 

species frequency, and relative densities and frequencies using 

formulas provided by Fitzpatrick and others (1998) and Rand 

(1973). 

Micrometeorological Measurements

At each of the study sites, micrometeorological instrumentation was installed at a height of 26.1 m on a 27.4-m tall 

tower, at the land surface, or in the subsurface. Because the 

cottonwood-dominated canopy was approximately 18.3-m tall 

on average at both sites, the tower allowed for instrumentation 

to be placed at a height of about 1.5 times the canopy height,

a typical design height for energy flux measurements (David 

I. Stannard, U.S. Geological Survey, oral commun., May 15, 

2001). The instrumentation was installed in March 2002 and 

measurements continued until May 2006 (figs. 2, 3, 4; table 1). 
6 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

Instruments recorded continuously through the year to monitor 

dormant-season ET as well as growing-season ET. 

Micrometeorological data were compiled every 30 

minutes, transmitted daily by cellular-telephone telemetry, 

and reviewed at least once per week. Monthly site visits were 

made to clean and repair sensors as necessary, to collect additional samples such as soil cores for determining soil-water 

content and bulk density, to measure leaf-area index (LAI) 

using a hemispherical-photographic system, and to download 

data loggers recording ground-water levels, unsaturated-zone 

temperature profiles, and plant sap flux. Details of the data 

collection and analysis are described in the sections that follow. 

Eddy Covariance

Total ET was determined from micrometeorological 

measurements using the eddy-covariance technique (Businger 

and others, 1967; Moore, 1986; Baldocchi and others, 1988; 

Stannard and others, 1994). Eddy covariance measures fluxes 

along all three Cartesian axes (vertical, or z, and horizontal, 

or x and y), with a focus on the vertical fluxes in scenarios 

where the footprint area is homogeneous. This technique was 

selected because it has been used successfully to measure 

actual ET for forest and shrub canopies (Stewart, 1988; Stannard, 1993; Nagler and others, 2005), which usually have considerable roughness. Because of the large fluctuations in vertical velocity associated with increased roughness in the forest 

canopy, eddy covariance is expected to produce more accurate 

results than techniques that rely on vertical gradients (Kanemasu and others, 1979), such as the Bowen ratio method, a 

commonly applied and less expensive micrometeorological 

technique used for vegetation of more uniform height. 

Sensors pertinent to evaluating ET from the eddy-covariance technique (sonic anemometer and krypton hygrometer; 

table 1) were deployed. These sensors took measurements 

every 0.1 seconds that were then compiled for 30-minute periods to record mean fluxes of water (in the form of latent heat) 

and energy (in the form of sensible heat) from the vegetated 

land surface. Water fluxes were direct measures of ET. Energy 

fluxes were used in conjunction with other sensors to determine how closely the energy balance approached closure, thus 

providing a quality-assurance check on the data.

Before the data could be used to evaluate energy-balance 

closure or to fill gaps in the data record, coordinate-rotation 

adjustments and atmospheric corrections had to be applied to 

the raw eddy-covariance data. To achieve the ideal conditions 

of the eddy-covariance method—where the mean verticalturbulent flux is the result of advective diffusion from the 

underlying surface—the mean vertical wind velocity must be 

zero (Baldocchi and others, 1988); however, apparent non-zero 

vertical wind-velocity components can result from imperfectly leveled eddy-covariance sensors or from non-horizontal 

upwind surfaces. Therefore, coordinate rotation typically 

is performed to transform the data into a natural coordinate 

system (Kaimal and Finnigan, 1994) in which each 30-minute 

mean wind velocity is parallel to the underlying surface. In 

more complex terrain, the planar-fit coordinate system (Paw U 

and others, 2000; Wilczak and others, 2001) may be preferable to natural coordinates because it reduces errors and biases 

associated with over-rotation (Massman and Lee, 2002; Lee 

and others, 2004). Although the land surfaces at both sites 

were comparatively flat, the intermittent pattern of trees and 

open areas resulted in an aerodynamically complex terrain 

that warranted the use of planar-fit coordinates. Planar-fit 

techniques correct the mean wind velocity for much longer 

periods (in this case, the 4-year study period) to be parallel to 

the underlying surface. For this study, this was accomplished 

by manually fitting a sinusoidal curve to the raw data (Wilczak 

and others, 2001). The derived equations were:

Near Odessa, Nebraska: 

 



 



    75

180

0.56 0.57 cos wang wd

�

(1)

Near Gothenburg, Nebraska: 

 



 



    220

180

0.05 1.94 cos wang wd

�

(2)

where

wang is the zenith angle of the wind above the 

horizontal plane, in degrees; and 

wd is the horizontal azimuth angle of the wind 

measured by the sonic anemometer, in 

degrees.

The resulting equation for w

ang

 at each site was then 

used in equations described by Baldocchi and others (1988) 

for calculating rotated turbulent-flux covariances between 

wind and krypton hygrometer voltage and between wind and 

air temperature. Covariances necessary to calculate rotated 

eddy-covariance measurements were not stored before February 6, 2003; therefore, coordinate rotations were not made to 

data collected before this date. The omission of coordinate 

rotations to flux data collected before February 6, 2003, 

is expected to have negligible impact because the rotation 

adjustment for later periods had a small (less than a 2-percent 

change in annual energy balance closure) effect on the estimated turbulent fluxes. 

Adjustments were made to the rotated latent- and sensible-heat fluxes to account for atmospheric effects. Corrections 

for air-density fluctuations caused by heat and water-vapor 

transfer followed the techniques of Webb and others (1980). 

The sensitivity of the krypton hygrometer to oxygen and the 

resulting effect on latent-heat-flux measurements (Tanner 

and Greene, 1989; Sumner, 1996) were taken into account. 

Because air temperature was determined from sonic measure-
Methods of Investigation 7

80

80

99°14' 45" 30" 15" 13' 45" 99°12'30"

40'

40°40'30”

40°39'45”

15”

0 100 200 300 400

0 100 200 300 400 500 METERS

500 600 YARDS

Wind rose - The values around the exterior are wind directions

in degrees. The interior values represent the percentage of time 

that the wind was blowing from that 10-degree sector.

The time period examined was March 2002 through March 2004.

(A) Odessa study site

100°12'15” 12' 45" 30" 15" 11' 100°10'45”

40°55'

30”

15”

40°55'45”

0 100 200 300 400 500

0 100 200 300 400 500 METERS

600 YARDS

5.00

OSC

ONC

OW2

OW1

OW4

EXPLANATION

OSC

OW4

Theoretical upwind source area

 (circular) projected in all

 directions from tower

Micrometeorological tower

Surface-water stage gage and 

 identifier

Ground-water piezometer and

 identifier

GNC

GSL

GW2 GW1

GW4

Wind rose - The values around the exterior are wind directions

in degrees. The interior values represent the percentage of time 

that the wind was blowing from that 10-degree sector.

The time period examined was March 2002 through March 2004.

(B) Gothenburg study site

Base from U.S. Department of Agriculture, Farm Service Agency,

Aerial Photography Field Office, digital data, 1:12,000, 2003, 

Lambert Conformal Conic projection

Standard parallels 40 ̊N and 43 ̊N, central meridian 100 ̊W

Horizontal coordinate information is referenced to the North American

 Datum of 1983 (NAD 83)

Base from U.S. Department of Agriculture, Farm Service Agency,

Aerial Photography Field Office, digital data, 1:12,000, 2003, 

Lambert Conformal Conic projection

Standard parallels 40 ̊N and 43 ̊N, central meridian 100 ̊W

Horizontal coordinate information is referenced to the North American

 Datum of 1983 (NAD 83)

Figure 2. Data collection locations and probable flux source areas for micrometeorological instrumentation on towers at study sites 

near Odessa and Gothenburg, Nebraska.
8 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

UZ2

UZ2

UZ1

TF3

TF2 2

1

1

5

3

6

4 TF1

TF1

99°13'13" 12" 11" 10" 9" 8" 7" 6" '5" 99°13'4"

40°40'1"

2"

3"

4"

5"

6"

40°40'7"

(A) Odessa study site

100°11'41" 40" 39" 38" 37" 36" 35" 34" 100°11'33"

40°55'17"

18"

19"

20"

21"

40°55'22"

(B) Gothenburg study site

UZ2

GW1

GW4 UZ1

TF3

TF2

TF1

EXPLANATION

OW4

OW1

OW1

Micrometeorological tower

Leaf-area-index measurement site

Ground-water piezometer and identifier

Throughfall gage and identifier

Tree-trunk temperature measurements

Sap flux measurements and plant

 number

Unsaturated zone vertical profile (soil-water

 content, heat flux, temperature) and

 identifier

0 15 30 45

0 15 30 45 METERS

60 YARDS

0 15 30 45

0 15 30 45 METERS

60 YARDS

Base from U.S. Department of Agriculture, Farm Service Agency,

Aerial Photography Field Office, digital data, 1:12,000, 2003, 

Lambert Conformal Conic projection

Standard parallels 40 ̊N and 43 ̊N, central meridian 100 ̊W

Horizontal coordinate information is referenced to the North American

 Datum of 1983 (NAD 83)

Base from U.S. Department of Agriculture, Farm Service Agency,

Aerial Photography Field Office, digital data, 1:12,000, 2003, 

Lambert Conformal Conic projection

Standard parallels 40 ̊N and 43 ̊N, central meridian 100 ̊W

Horizontal coordinate information is referenced to the North American

 Datum of 1983 (NAD 83)

Figure 3. Areal layout of instrumentation near the towers at study sites near Odessa and Gothenburg, Nebraska.
Methods of Investigation 9

Sonic anemometer

Micrometeorological

instrument tower

Solar pyranometer

Net radiometer

Wind vane and cup anemometer Hydrometer and thermometer

Micrometeorological measuring instruments

Rain gage

Krypton hygrometer

Solar panel (power source)

Figure 4. Selected micrometeorological instrumentation used at study sites.
10 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 Table 1. Summary of instrumentation used and placement of sensors at study sites near Odessa and Gothenburg, Nebraska.—Continued [CSI, Campbell Scientific, Inc.; REBS, Radiation and Energy Balance Systems, Inc.; RMY, R.M. Young, Inc.; TE, Texas Instruments, Inc; HS, Hydrological Services Ltd.; Tower, CSI XT-CR23X-4M data logger in shelter near base of tower; additional CSI XT-CR10X-2M data loggers for recording data abbreviated as: RN-4, 4-component net radiometer data; DH21, Design Analysis, Inc., DH21 data logger and submersible pressure transducer; Soil temp, soil thermocouple data; Sap flux, thermal heat-dissipation probe data; na, not applicable] Type of measurement Instrument/sensor type (number of sensors when more than one) Data logger Odessa study site Gothenberg study site Height above land surface1 (meters) Data collection started2 Height above land surface1 (meters) Data collection started2 Environmental instruments on the tower Orthogonal wind speed components, air temperature3 CSI model CSAT3 three-dimensional sonic anemometer Tower 26.06 March 2002 26.06 March 2002 Atmospheric water-vapor density fluctuations CSI model KH2O krypton hygrometer Tower 26.06 March 2002 26.06 March 2002 Net radiation REBS model Q-7.1 two-component net radiometer, RN-2 Tower 25.14 March 2002 25.05 March 2002 Net radiation Kipp and Zonen, Inc., model CNR1 four-component net radiometer, RN-4 RN-4 25.11 March 2004 na none Solar radiation Licor, Inc., model LI200X pyranometer Tower 25.14 March 2002 25.05 March 2002 Wind speed and direction RMY model 03001 Wind Sentry cup aneomometer and wind vane Tower 24.84 March 2002 24.75 March 2002 Air temperature/relative humidity CSI model HMP45C thermometer and hydrometer probe Tower 24.54 March 2002 25.78 March 2002 Precipitation TE model TE525WS tipping-bucket rain gage Tower 20.42 March 2002 20.51 March 2002 Environmental instruments on, near, or beneath land surface Soil-heat flux CSI model HFT3 soil-heat-flux plate Tower -0.08 March 2002 -0.08 March 2002 Soil temperature CSI model TCAV averaging soil thermocouple Tower -.02 to -.06 March 2002 -.02 to -.06 March 2002

Tree-trunk temperature 24-gauge Chromega Constantan Teflon E-type thermocouple 

wire (eight thermocouples in four trees, combined into one channel so readings are averaged)

Tower 1.2-1.5 March 2002 1.2-1.5 March 2002

Soil-water content (volumetric) CSI model CS615 water content reflectometer (14 sensors in 

two unsaturated-zone vertical profiles of 7 sensors each)

Tower -.08, -.2, -.4, 

-.5, -.6, -.7, 

-.8

March 2002 -.08, -.2, -.4, 

-.6, -.7, -.8, 

-.9

March 2002

Precipitation HS Model TB4 tipping-bucket rain gage, measuring drainage 

from rain gutter in open area with no canopy

Tower 0 August 2002 0 August 2002

Throughfall HS Model TB4 tipping-bucket rain gage, measuring drainage 

from rain gutter beneath vegetation canopy (2)

Tower 0 March 2002 0 March 2002

Ground-water level Design Analysis, Inc. DH21 submersible pressure transducer 

and data logger (2)

DH21 0 to -2 October 2001 0 to -2 October 2001
Methods of Investigation 11

Table 1. Summary of instrumentation used and placement of sensors at study sites near Odessa and Gothenburg, Nebraska.—Continued[CSI, Campbell Scientific, Inc.; REBS, Radiation and Energy Balance Systems, Inc.; RMY, R.M. Young, Inc.; TE, Texas Instruments, Inc; HS, Hydrological Services Ltd.; Tower, CSI XT-CR23X-4M data logger in shelter near base of tower; additional CSI XT-CR10X-2M data loggers for recording data abbreviated as: RN-4, 4-component net radiometer data; DH21, Design Analysis, Inc., DH21 data logger and submersible pressure transducer; Soil temp, soil thermocouple data; Sap flux, thermal heat-dissipation probe data; na, not applicable]

Type of measurement Instrument/sensor type (number of sensors when more than one) Data logger

Odessa study site Gothenberg study site

Height above 

land surface1

(meters)

Data collection 

started2

Height above 

land surface1

(meters)

Data collection 

started2

Surface-water level Design Analysis Inc. DH21 submersible pressure transducer 

and data logger

DH21 0 to -2 October 2001 0 to -2 October 2001

Soil temperature 24-gauge Chromega Constantan Teflon E-type thermocouple 

wire; (14 thermocouples in two unsaturated-zone vertical profiles of 7 sensors each)

Soil 

temp

-.05, -.2, -.4, 

-.5, -.6, -.7, 

-.8

March 2002 -.05, -.2, -.4, 

-.6, -.7, -.8, 

-.9

March 2002

Sap flux Dynamax thermal heat dissipation probes measuring sap-flux 

velocity (see table 2)

Sap flux 1.2–1.5 April–September 

2004

na none

Data logging, telemetry, power systems

na CSI XT-CR23X-4M data logger Tower 1 March 2002 1 March 2002na Power supply solar panels (2) Tower 18–20 March 2002 18–20 March 2002na CSI XT-COM200 9600 phone modem, cellular phone, and 

antenna for telemetry

Tower 1 March 2002 1 March 2002

na CSI XT-AM16/32 32-channel (2-wire) relay multiplexor (for 

linking 14 soil-water content sensors to a single datalogger channel)

Tower 1 March 2002 1 March 2002

na CSI XT-CR10X-2M data logger for net radiometer data CNR1 1 March 2004 na nonena CSI XT-CR10X-2M data logger for soil-temperature data Soil 

Temp

1 October 2003 1 October 2003

na CSI AM25T 25-channel relay multiplexor for thermocouples 

(for linking 14 soil thermocouples to a single data-logger channel)

Soil 

Temp

1 March 2002 1 March 2002

na CSI XT-CR10X-2M data logger for heat-dissipation-probe 

data

Sap flux 1 April–September 

2004

na none

Data logger temperatures Thermistor all 1 March 2002 1 March 2002

1Negative height is depth below land surface.2Data collected for this report extends through April 2006 unless otherwise noted.3A fine-wire thermocouple was deployed for high-frequency air-temperature measurements, but data collection was intermittent throughout the study, and thus was not included.
12 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

ments, the resulting effect on sensible-heat-flux measurements 

was adjusted following Schotanus and others (1983). 

Energy Balance

ET determined using eddy covariance was combined with 

data on other energy-balance variables to calculate energybalance closure (Nichols, 1993; Stannard and others, 1994; 

Unland and others, 1998; Laczniak and others, 1999). Assessment of energy-balance closure (EBC) was used to evaluate 

the accuracy of the ET calculations and was determined from 

the energy-balance equation:

Rn – G – ∆PS = λE + H (3)

where

Rn is net radiation;

G is the soil-heat flux;

∆PS is the change in the storage of energy in the 

biomass; 

λE is the latent-heat flux; and

H is the sensible-heat flux. 

All terms are in watts per square meter (W/m2). Inequality in 

the equation above is referred to as the EBC. The energy-balance equation was solved for 30-minute, daily, monthly, and 

annual periods. In addition, equation 3 was used to calculate 

the unitless ratio of turbulent flux to available energy, or the 

energy balance ratio (EBR): 

Rn G PS

E H

EBR − − ∆

+ = λ

(4)

The EBR provided a relative measure of data quality by 

comparing to unity (or an EBR of one), and also was used 

in adjusting energy terms for energy-balance closure during 

gap-filling procedures described in the “Filling of Data Gaps” 

section in this report. λE and H were determined from the 

eddy-covariance system previously described. The remaining terms were estimated from additional sensor arrays as 

described later in this report.

Net Radiation

Net radiation (Rn) primarily was measured using a twocomponent net radiometer (Rn-2) (table 1) deployed throughout the study period at each tower. The windshields of the net 

radiometer were inspected on every site visit and replaced as 

needed. Adjustment for the effects of wind speed on Rn were 

made concurrently with the data collection (Campbell Scientific, Inc., 1996a). 

A four-component net radiometer (Rn-4) (table 1) was 

installed at the Odessa site beginning in March 2004 for the 

duration of the study. The Rn-4 includes additional sensors 

to separately measure upward and downward short-wave and 

long-wave radiation, and may measure long-wave radiation 

components more accurately (Campbell Scientific, Inc., 2007) 

than the model Rn-2. Because of resource limitations, only 

one Rn-4 was purchased and was deployed at the Odessa site 

at the same height and about 0.88 m away from the Rn-2 on 

the south side of the tower. The data from the two net radiometers at the Odessa site after March 2004 were compared 

to investigate the effects of long-wave radiation on the energy 

balance. After determining that EBC was improved with the 

Rn-4, the net radiation values from the Rn-2 recorded before 

March 2004 at Odessa, and for the entire period at Gothenburg, were corrected to estimate the Rn that would have 

been measured from a Rn-4. These corrected Rn values were 

estimated by developing linear regression models of Rn measurements from the Rn-4 with those from the Rn-2 for the data 

collected after March 2004. Separate regression models were 

computed for daytime and nighttime conditions. 

Soil-Heat Flux

Soil-heat flux (G) was calculated as the weighted average 

of soil-heat-flux measurements from two unsaturated-zone 

vertical profiles—UZ1 and UZ2 (fig. 3)—at each site. At 

Odessa, UZ1 was beneath a cottonwood canopy, and UZ2 was 

beneath a shrub canopy with no forest overstory. At Gothenburg, UZ1 was located beneath a cottonwood and eastern redcedar canopy, and UZ2 was located in a grassy meadow with 

no forest overstory. Weighting of each profile into a site average was determined by estimating the relative extent of vegetation corresponding to each profile within the theoretical source 

area (footprint) for the tower (using aerial photographs). At 

each location, soil-heat flux was calculated following the techniques of Campbell Scientific, Inc. (1999):

G  G  ∆S 8 (5)

t

T d C

S

 s   s ∆ 

(6)

where

G is the soil-heat flux at the land surface, in 

W/m2;

G8 is the heat flux measured at a depth of 8 cm 

using a Campbell Scientific, Inc., model 

HFT3 soil-heat-flux plate (table 1; fig. 5), 

in W/m2; 

∆S is the change in storage of heat in the upper 

8 cm of soil, in W/m2;

∆Ts is the change in soil temperature in the upper 

8 cm of soil during the measurement 

period, in Kelvin (K); 

d is the thickness of the soil for which the 

calculations are being performed, 0.08 m;
Methods of Investigation 13

Surface/subsurface measurements

Sap flux

Leaf-area index

Soil-heat flux/soil temperature

Soil-water content

Throughfall/interception

Figure 5. Instrumentation used for measurements of surface or subsurface conditions at study sites.
14 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

Cs is the volumetric specific-heat capacity of the 

soil layer, in Joules per cubic meter per 

Kelvin (J/(m3 K)); and

t is the duration of the measurement period, 

1,800 seconds.

A Campbell Scientific (Logan, Utah) model TCAV averaging 

soil thermocouple (table 1) array was used to measure ΔTs and 

consisted of two sets of thermocouples installed 2 and 6 cm 

deep on either side of the soil-heat-flux plate (fig. 5) installed 

horizontally at 8 cm depth. Values of the volumetric specificheat capacity (Cs

) were calculated using the DeVries equation 

(Hillel, 1980):















   

w

w

w

o

o

o

m

m Cs b Cm C C

ρ

θ

ρ

θ

ρ

θ

ρ

(7)

where

ρ b is the median dry-bulk density determined 

from 11 to 13 soil samples per profile, in 

kilograms per cubic meter (kg/m3);

Cm is the volumetric specific-heat capacity of the 

mineral component of the soil, 2.13 x 106

J/(m3 K) (Hillel, 1980); 

θm is the gravimetric fraction of the mineral 

component of the soil determined from 

three samples per profile, in kg of minerals 

per kg of soil;

ρm is the assumed density of the mineral 

component of the soil, 2,650 kg/m3 (Hillel, 

1980);

Co is the volumetric specific-heat capacity of the 

organic component of the soil, 2.5 x 106 J/

(m3 K) (Hillel, 1980); 

θo is the gravimetric fraction of the organic 

component of the soil determined from 

three samples per profile, in kg of organics 

per kg of soil;

ρo is the assumed density of the organic 

component of the soil, 1,300 kg/m3 (Hillel, 

1980);

Cw is the volumetric-specific-heat capacity of 

water, 4.19 x 106 J/(m3 K) (Hillel, 1980); 

θw is the gravimetric fraction of the water in 

the soil, in kg of water per kg of soil, and 

calculated from

b

w v

w ρ

θ θ ,  ;

ρw is the density of water, 1,000 kg/m3 (Hillel, 

1980); and

θw,v is the volumetric fraction of the water in the 

soil measured continuously at each profile 

using a Campbell Scientific model CS615 

water-content reflectometer installed to 

provide an integrated measurement for 

0–8 cm deep, in cubic meters of water per 

cubic meter of soil.

Vegetation-Energy Storage 

The changes in energy storage in tree trunks, tree 

branches, and leaves (∆PS) were expected to be small relative to the other terms in eq. 3, but it was still included in the 

energy-balance analysis. Previous studies determined that 

∆PS had a negligible effect on the energy balance, especially 

at timescales longer than a day (Bossung and others, 2003), 

and the term is not always included in energy-balance studies (Sumner, 1996; Bastiaanssen and others, 1998). However, 

some practitioners have determined it to be important at daily 

timescales (Samson and Lemeur, 2001; Meyers and Hollinger, 

2004; Gu and others, 2007). ∆PS was calculated from air and 

tree-trunk temperature measurements for each 30-minute 

period. Air temperature was assumed to represent leaf temperature. Tree-trunk temperature was measured as the mean temperature measured at eight points located at depths one-half 

the radii of four representative trees. Tree-branch temperatures 

were estimated as the mean of the air and tree-trunk temperatures. Changes in these temperatures were used to estimate the 

changes in vegetation-energy storage using a summation of the 

definite integral defined by McCaughey and Saxton (1988):

  ∆ 

∆  ∆  t

T C

PS PS i i i i

i

ρ

(8)

where

∆PSi is the change in energy storage of component 

i, where i represents tree trunks, branches, 

or leaves, in W/ m2;

∆Ti is the change in temperature of component i, 

in K; 

Ci is the specific-heat capacity of component i, in 

J/(kg K) ; 

ρi is the density of component i, in kg/m3;

Bi is the unit volume of component i, in 

cubimeters per square meter (m3/m2); and

t is the duration of the measurement period, 

1,800 seconds.

Heat capacities and densities of the trunks were estimated by 

measuring or estimating the heartwood and sapwood fractions 

of the trunk; assigning specific-heat capacities and densities 

to wood (Cwood = 1,760 J/(kg K), ρwood = 700 kg/m3) and water 

(Cwater = 4,190 J/(kg K), ρwater = 1,000 kg/m3) using the values 

of Hodgman and Holmes (1941); and assuming volumetricmoisture contents for heartwood, sapwood, and leaves of 

0.05, 0.35, and 0.50 cubic meters of water per cubic meter of 

vegetation, respectively. Trunk volume was determined from 
Methods of Investigation 15

forest-survey measurements of tree height, diameter, and areal 

density (trees per hectare) for each site. Branch volume was 

assumed to be one-half of the trunk volume. Leaf volume at 

maximum leaf out was assumed to be one-half of the trunk 

volume, and the seasonal cycle of leaf volume was represented by applying a ratio of measured-leaf area (integrating 

several individual plants and species) for a given period with 

the maximum-measured-leaf area. Simple approaches were 

used to quantify many of these terms, producing considerable 

uncertainty with these assumptions. However, because ∆PS

is typically small relative to the other variables in an open 

system such as a riparian forest (Samson and Lemeur, 2001), 

this uncertainty probably has a negligible effect on the energy 

balance at seasonal or annual timescales. 

Energy-Balance Adjustments 

Previous investigations have described that long-term 

average energy-balance ratio values typically are in the range 

of 0.7 to 1.0 for energy balance investigations (Moore, 1986; 

Sumner, 1996; Aubinet and others, 2000; Twine and others, 

2000). For this study, initial analyses of 2002 to 2003 data 

collected using the 2-component net radiometer indicated EBR 

values around 0.83. Possible explanations for the discrepancy 

between measured turbulent fluxes and measured available 

energy include several factors (Sumner, 2001) but can be 

summarized as resulting from underestimation of the turbulent fluxes using the eddy-covariance method (Moore, 1986; 

Twine and others, 2000; Barr and others, 2006), or overestimation of Rn using a Rn-2 (David I. Stannard, U.S. Geological 

Survey, written commun., 2004), as a result of underestimated 

long-wave radiation components (Field and others, 1992). 

In the case of the latter mechanism, better long-wave radiation measurement has the effect of improving the daily EBC 

by decreasing daily Rn; therefore, a four-component netradiometer equipped to more accurately measure long-wave 

radiation was deployed at the Odessa site after March 2004 

(table 1). Rn values from this sensor systematically were less 

than those from the existing Rn-2; therefore, it was evident 

that Rn had been systematically overestimated, thereby resulting in a long-term average EBR less than 1. After correcting 

all Rn data in relation to the Rn-4 values, the long-term (April 

1, 2002–March 31, 2005) EBR was 0.89 at both sites, an 

improvement in energy balance closure. 

Adjustments to energy-balance terms routinely are made 

as part of micrometeorological investigations to correct for the 

effects of this inherent lack of energy balance closure (Twine 

and others, 2000). To account for the assumed overestimation of Rn by the Rn-2, the Rn data were adjusted downward 

by multiplying by long-term EBR (0.89 at both sites). The 

adjusted Rn data were considered the final values that were 

used in the analyses and reported in the appendixes. 

It should be noted that a long-term EBR less than 1 could 

be the result of a combination of underestimated-turbulent 

fluxes and overestimated Rn. Because the relative contribution 

of each was unknown, upper bounds of λE and H were estimated by dividing the measured values by the long-term mean 

EBR; however, these upper bounds served only as a reference 

and were not used in the analyses.

Meteorological Variables

Additional meteorological variables were either measured 

or calculated to relate environmental conditions to ET rates 

at the study sites. Air temperature, relative humidity, solar 

radiation, wind speed, and wind direction (table 1; fig. 4) were 

measured every 10 seconds and averaged for each 30-minute 

period. The saturation-vapor pressure was calculated from air 

temperature using the equations of Lowe (1977). The vapor 

pressure was calculated from the relative-humidity measurements and saturation-vapor-pressure calculations (Campbell 

and Norman, 1998). Vapor-pressure deficit (D) is a good 

measure of the drying potential of air and often is an important 

variable affecting ET rates (Jarvis, 1976; Stewart, 1988; Stannard, 1993). D was calculated as the difference between the 

saturation-vapor pressure and vapor pressure.

Potential Evapotranspiration 

Potential evapotranspiration (PET) — the vapor flux that 

would occur from a well-watered short green crop (Rosenberg 

and others, 1983)—was calculated at the study sites. Winter 

and others (1995) demonstrated that the Priestley-Taylor equation (Priestley and Taylor, 1972) produced similar results as 

the Penman-Monteith equation, but was computationally less 

demanding. The equation took the form: 

Rn G PS

s

s Ep   ∆ 















  γ

λ α

(9)

where

λEp is the potential latent-heat flux, in W/m2; 

α is the Priestley-Taylor coefficient, 1.26 

(Priestley and Taylor, 1972); 

s is the slope of the relation of saturation-vapor 

pressure to air temperature at a given air 

temperature, computed using the equations 

of Jensen and others (1990), in kilopascals 

per degree Celsius (kPa/oC); 

γ is the psychrometric constant, computed using 

Jensen and others (1990), in kPa/oC; and

 Rn, G, and ∆PS are previously defined.

Filling of Data Gaps 

Eddy-covariance data were either missing or discarded 

for approximately 11 percent of the study period. Gaps in 

these data can occur for a number of reasons, including condensation on either the windows of the krypton hygrometer or 

the transducers of the sonic anemometer; sensor or data-logger 
16 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

failure; other problems such as debris on sensors; and data not 

meeting quality standards, such as data spikes. Gaps in eddycovariance data are common (Falge and others, 2001), and had 

to be filled to develop daily, monthly, and annual values of ET 

and other micrometeorological variables. Typically, these gaps 

were filled using either linear interpolation or a form of the 

energy equation (eq. 3) modified to account for the long-term 

mean EBR (EBRmean) (eq. 4):

EBRmean(Rn – G – ∆PS) = λE + H (10)

where all terms are previously defined.

Most of the gaps (66 percent) were caused by condensation on the krypton hygrometer or sonic anemometer that 

produced over-range values in λE and H. In these situations, 

λE typically was estimated as the product of λEp and EBRmean. 

H was then estimated to be the residual needed to balance 

the energy equation (eq. 10). For a small subset of gaps corresponding to short (less than 2 hours) and small (less than 

1 mm) precipitation events, linear interpolation between 

measured data was used to estimate data for the missing time 

periods. 

Some gaps were caused by instrumentation failure or 

maintenance-related down time. In these instances, gaps less 

than 6 hours typically were filled using linear interpolation. In 

some cases, perceived trends in the data—such as the nonlinearity between nighttime values and daytime peaks—were 

applied to estimate data within missing periods. For extended 

periods of missing data, other approaches were used. For 

example, a sensor malfunction at the Odessa site caused the 

removal of λE values from September to December 2002. 

For this and similar periods, λE was estimated as the residual 

needed to balance the energy equation (eq. 10). During some 

of these extended λE gaps, values of H also were missing for 

shorter periods typically corresponding to mid-day periods of 

rising λE and H. In these situations, H was linearly interpolated before λE could be calculated from the energy equation. 

For periods where none of the energy-equation variables were 

available at a site (less than 1 percent of the study period), 

missing data for a given variable were estimated using linearregression equations of one site with the other that were developed from periods of measured data.

Water Balance

A water-balance approach was used to estimate the part 

of ET derived from ground water. In addition to the measured 

ET rates, measurements of precipitation, soil-water storage, 

and ground-water storage were included. These were used to 

estimate the remaining term in the water balance, net-groundwater flow to or from the riparian forest area, which served as 

a surrogate for the presence of aquifer recharge or GWET. In 

addition to their application to the water balance, precipitation, 

soil-water content, and ground-water level were explored as 

explanatory variables for predicting ET rates. 

The annual water balance was estimated for a representative volume extending vertically from a height of 26.1 m 

above land surface—corresponding to the eddy-covariance 

sensors—to about 3 m below land surface (into the saturated 

zone). Horizontally, the representative volume is considered 

to represent the riparian island where the tower is located, 

between flowing river channels to the north and south, and 

extending out 800 m from the tower to the east and west 

along the island, corresponding to the calculated footprint for 

the tower. The water-balance equation was a function of the 

inputs, the outputs, and the changes in storage (Healy and others, 2007) within the representative volume:

P + SWin + GWin = ET + SWout + GWout + RO

+ ∆SW + ∆GW + ∆PW + error (11)

where

P is the precipitation; 

SWin is the lateral soil-water inflow;

GWin is the ground-water inflow;

ET is the evapotranspiration;

SWout is the lateral soil-water outflow;

GWout is the ground-water outflow;

RO is the overland runoff;

∆SW is the change in soil-water storage;

∆GW is the change in ground-water storage; 

∆PW is the change in plant-water storage; and

error is the residual term including errors in other 

terms in the equation and other sources of 

error.

All terms are expressed in mm/yr corresponding to a water 

volume on a unit-area basis.

For these study sites, several of the terms in equation 11 

were relatively unimportant, and were thus omitted. Surfacewater flow (RO) into or out of the representative volume was 

assumed to be negligible based on the sandy soils and flat 

topography. Lateral flow of soil water into (SWin) or out of 

(SWout) the representative volume and changes in plant water 

storage (∆PW) also were assumed to be negligible. As a matter 

of practicality, the error also was assumed to be insignificant. 

The resulting water-balance equation was then rearranged to 

isolate ground-water inflow and outflow:

GWin – GWout = ET – P + ∆SW + ∆GW (12)

where all terms are previously defined.

This approach was applied annually, with annual periods 

defined as April 1 of a given calendar year to March 31 of the 

following calendar year. 
Methods of Investigation 17

Evapotranspiration

To include ET in the water balance, the latent-heat flux 

(λE) measured using micrometeorological techniques was 

converted from units of power for a given area (W/m2) to units 

of water flux for a given area (cubic millimeters of water per 

square millimeter per second, reducing to millimeters of water 

per second). This was done by dividing λE by the latent-heat 

of vaporization (λ)—calculated from air temperature following 

Jensen and others (1990)—and by the density of water. The 

resulting value represented an ET rate associated with vegetation within the footprint area.

Precipitation and Canopy Interception 

Because of the importance of precipitation to the water 

balance, total precipitation and throughfall precipitation (the 

precipitation not intercepted by canopy vegetation) were both 

measured. Canopy interception was then calculated as the difference between total and throughfall precipitation, or, when 

rearranged:

P = I + TF (13)

where

I is the canopy interception of precipitation, in 

mm; and

TF is the canopy-throughfall precipitation, in 

mm. 

Precipitation and Throughfall Measurement

Throughfall precipitation was measured underneath 

the canopy using gutter systems that drained into rain gages 

(Davie, 2003). At each site, a series of three gutters were 

installed (fig. 3) beneath a relatively dense canopy (TF1), 

beneath a relatively less dense canopy (TF2), and in an open 

meadow with no overlying canopy (TF3). Each gutter was 9.1 

m long, 2.5 cm wide, 6.4 cm deep (fig. 5), and was installed 

at a 4-percent slope that drained into a tipping-bucket rain 

gage (Hydrological Services, Sydney, Australia, model TB4, 

table 1) installed below ground surface in an 18.9-L (liter) 

bucket. 

For comparison to the gutter systems, a fourth tippingbucket rain gage (Model TE525WS; Campbell Scientific, 

Inc., 2008) was installed at a height of approximately 20.5 m 

on each tower. Precipitation measured in all three throughfall gages generally was greater than precipitation measured 

in the tower gage. It was suspected that precipitation in the 

tower gage was negatively biased with respect to the throughfall gages for two reasons: Wind speed and turbulence were 

greater at the tower gage, thereby reducing the effective area 

of the gage, and the throughfall gages had a collection area 

approximately 7.1 times larger than the tower gage so that the 

design of the throughfall gages permitted them to measure 

small amounts of precipitation more accurately. As a result, 

the tower gages were operated throughout the study to have an 

additional source of precipitation data, but generally were not 

used for estimating precipitation totals. 

Field tests of the throughfall and tower rain gages were 

done annually to verify that the rain gages accurately measured precipitation. Tests were conducted by delivering a 

known volume of water to the rain gage for 20 to 70 minutes. 

For 31 rain-gage tests conducted during 2002 to 2005, the rain 

gages recorded 90 to 110 percent of the volume of water added 

with a median of 102 percent. 

Precipitation data generally were excluded if any of the 

following criteria were met: The air temperature was below 

freezing; the recorded precipitation appeared to be associated 

with snow or ice melt; the recorded precipitation was associated with gage maintenance during the time of a site visit; 

or the precipitation was recorded in a gage that was partially 

clogged. All rain gages and throughfall gutters were inspected 

on monthly site visits. Observations of clogged gages were 

recorded in field notes, and then the debris was removed. In 

addition, periods of clogging generally were evident from 

the recorded data as reduced totals or substantially delayed 

responses compared to gages that were operating normally. 

Once anomalous data were removed, the maximum daily 

value from the four gages at each site was used as the best 

estimate of total precipitation at each site for periods without 

freezing conditions. The daily-total precipitation for each 

site was compared to daily precipitation measured at nearby 

weather stations at Gothenburg and Kearney (High Plains 

Regional Climate Center, 2006). These weather stations were 

located approximately 4 to 19 kilometers (km) away from the 

Gothenburg and Odessa study sites, respectively, leading to 

differences in the amounts and timing of precipitation. During 

non-winter months, monthly precipitation values at the study 

sites were approximately 27 and 30 percent larger than at 

the weather stations at Gothenburg and Odessa, respectively. 

Annual and monthly precipitation data for each site also were 

compared to 30-year normal precipitation data for 1961 to 

1990 at the weather stations. Because the weather stations also 

recorded precipitation during freezing conditions, the daily 

precipitation from the weather stations was used as the daily 

estimate of precipitation for days in which the air temperature 

was below freezing. Although spatial differences were likely 

between the study sites and the weather stations, the rain gages 

at the study sites were not equipped to accurately monitor frozen precipitation. The effect of these differences on the annual 

water balance is expected to be lessened because a relatively 

small part (less than 15 percent; High Plains Regional Climate Center, 2006) of the annual precipitation occurs during 

November through February. 

Accounting for Canopy Interception in the Water Balance 

Using measurements of canopy interception, distinctions between water fluxes associated with the evaporation 

of intercepted water and fluxes associated with ET processes 

utilizing soil-water and ground-water sources could be made. 
18 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

By making the assumption that all intercepted precipitation 

returned to the atmosphere by evaporation, the ET term could 

be expanded by:

ET = EI + ETGWSW (14)

where

EI is the evaporation of canopy interception—

assumed to equal I, in mm; and

ETGWSW is the evapotranspiration from ground-water 

and soil-water sources, in mm. 

By substituting equations 13 and 14 into equation 12 and 

relying on the assumption that I and EI fluxes were of equal 

magnitude but in the opposite direction, the water balance was 

modified to constrain the ET flux:

GWin – GWout = ETGWSW – TF + ∆SW + ∆GW (15)

where all terms are previously defined.

This equation resulted in the same value for (GWin – GWout) as 

equation 12, but was used to exclude fractions of precipitation 

and ET affected by canopy interception. 

Soil-Water Content and Storage

Soil-water contents were monitored to determine changes 

in soil-water storage and to relate those changes to ET. Volumetric soil-water content (θw,v) was measured every 30 minutes 

using model CS615 water-content reflectometers (Campbell 

Scientific, 1996b) at seven depths in unsaturated-zone vertical 

profiles at each site (table 1; figs. 3, 5). At Odessa, reflectometers were installed at 8, 20, 40, 50, 60, 70, and 80 cm below 

land surface. At Gothenburg, reflectometers were installed at 

8, 20, 40, 60, 70, 80, and 90 cm below land surface. One profile (UZ1) was located close to a cottonwood tree to characterize θw,v within a tree-root system. The other profile (UZ2) was 

located away from trees to characterize θw,v in non-forested 

areas. At Odessa, the UZ2 profile was located in an area of 

mostly dogwood shrub cover with grass and forb ground 

cover. At Gothenburg, the UZ2 profile was located in an open 

meadow with moderate grass cover. The two profile settings 

were intended to represent the primary vegetation conditions 

at each site. Soil-water storage was calculated at each UZ profile by integrating the measured soil-moisture contents from 

all seven depths. Soil-water storage was then estimated across 

the entire footprint by assuming a uniform soil thickness 

above the aquifer materials and assigning UZ-derived values 

to corresponding vegetation types (forested and nonforested) 

distributed throughout the footprint area. 

Several soil samples were collected for particle-size analysis, ρb (bulk density), and θw (gravimetric-soil-water content) 

from each of the profiles. Particle-size samples were collected 

at each reflectometer depth in March 2002. Within 5 m of 

each profile, a series of soil-core samples were collected from 

each reflectometer depth between July 2002 and September 

2004 for ρb and θw analyses. Three soil samples from the upper 

10 cm were collected at each of the profiles and analyzed 

for organic carbon content. All soil analyses were performed 

using standard techniques (Natural Resources Conservation 

Service, 2004) by the University of Nebraska-Lincoln Soil and 

Plant Analytical Laboratory. 

The reflectometers report θw,v using default-calibration 

curves developed for typical soils having clay contents less 

than 30 percent (Campbell Scientific, 1996b). Attempts were 

made to develop custom-calibration curves for each reflectometer. The laboratory θw results from the soil samples of each 

profile were converted to θw,v values, and then compared to the 

corresponding reflectometer measurements. Although results 

varied for different reflectometers, these comparisons generally indicated that the default-calibration curves performed as 

well as customized calibrations. As a result, the default curves 

were used.

Ground-Water Levels 

Ground-water levels were measured to determine changes 

in ground-water storage and for the purpose of evaluating 

depth to water as an explanatory factor affecting ET. The 

change in annual ground-water storage was determined as 

the product of the change in the height of the saturated zone 

within the control volume and the specific yield of the aquifer. 

The specific yield was estimated as the porosity (calculated 

from the average ρb of the sediment below 80 cm at each site, 

which was observed to represent shallow aquifer sediment). 

At each site, three shallow piezometers (wells) and one 

surface-water stage gage were installed along a transect from 

near the center of the vegetated island to the northern river 

channel (fig. 2). The river gage primarily was installed so 

that the effects of fluctuations in river levels on ground-water 

levels could be identified. 

The wells were temporary shallow piezometers with 

slotted 7.6-cm-diameter polyvinyl chloride that were installed 

using a hand auger to about 2 m deep during the fall of 2001. 

Submersible-pressure transducers and data loggers recorded 

water levels every 30 minutes in the river-stage gage, in 

one well close to the tower, and in one well approximately 

equidistant between the tower and the river. Water levels 

were measured manually during site visits to determine if the 

recorded water levels were accurate. The median deviation of 

manual and recorded water levels was 0.57 cm. The maximum 

deviation between measured and recorded water levels was 

6.6 cm. On rare occasions when deviations between manual 

and recorded water levels were more than 1.5 cm, the water 

level recorded by the pressure transducer was reset to reflect 

the measured value. Typically, water levels are adjusted by 

prorating the measured deviation from one site visit to the next 

and shifting the recorded water level accordingly (Freeman 
Methods of Investigation 19

and others, 2004). However, these shifts were only deemed 

necessary if seasonal changes in ET were sensitive to changes 

in depth to water on the scale of these deviations. As described 

later in the Environmental Factors Affecting Evapotranspiration Rates section, the depth to ground water was often an 

inconclusive explanatory factor, and visual inspection of the 

data suggested that changes in ground-water depth of 20 cm 

or less had little effect on ET. Therefore, the recorded water 

levels for the preceding period were not shifted to reflect the 

measured water levels. 

Leaf-Area Index 

Leaf-area index (LAI) is a key forest characteristic that 

is important to measure for ET studies because the abundance 

of green leaves in the canopy affects transpiration rates (Chen 

and others, 1997; Hall and others, 2003). At each site, LAI 

data were collected at 16 fixed stations spaced 20 m apart 

in two 8-station transects and oriented approximately north 

to south (fig. 3). Measurements were made at least six times 

each year between April and October of 2002 to 2004. LAI 

was calculated from analysis of hemispheric-digital photographs (Ringold and others, 2003; Kelley and Krueger, 2005). 

Hemispheric images were collected with automatic exposure 

and high contrast using a wide-angle (fisheye) lens mounted 

on a leveled camera oriented to magnetic north (fig. 5). The 

photos were collected from a height of 0.8 m above land 

surface at each LAI-measurement station. LAI estimates were 

derived from the images using standard techniques offered 

in Hemiview (version 2.1) software (Delta-T Devices, Ltd., 

1999). 

The LAI data were considered a proxy for seasonal variations in live vegetation for use as an explanatory variable for 

ET rates. Considerable research has been done on methods for 

determining LAI, of which hemispheric photos represent one 

of the preferred techniques (Chen and others, 1997; Hall and 

others, 2003). 

Approximately monthly measurements of LAI during 2002 to 2004 were used to interpolate daily LAI values 

between measurements. For 2005 to 2006, when LAI was not 

measured, an average of the 2002 to 2004 values for each day 

of the year was used.

Evapotranspiration Modeling 

The ET data collected were used to simulate riparian 

forest ET for the purposes of extrapolating the results beyond 

the two sites and improving the understanding of factors upon 

which ET is dependent. Daily crop coefficients (kc) for riparian 

forests were determined so that riparian ET can be estimated 

in similar forests elsewhere in the COHYST study area using 

climatic data from weather stations. Although the crop-coefficient technique is commonly associated with agricultural 

production, kc values have been developed for more natural 

cover such as wetlands (Allen and others, 1998) and fallowed 

fields (Bidlake, 2002). Nonetheless, a kc for a riparian forest may include additional uncertainty beyond that typically 

associated with the technique. Measured ET values were also 

regressed with explanatory variables to identify factors that 

may be affecting ET. 

Measured ET rates at the study sites were compared to 

reference ET rates calculated from data at nearby weather 

stations to estimate kc for riparian forests. For daily periods, 

riparian forest kc was calculated following the techniques of 

Jensen and others (1990): 

c

Eo

ET

k 

(16)

where

kc is the empirical-crop coefficient for riparian 

forests in the study area, dimensionless;

ET is the evapotranspiration rate for riparian 

forests in the study area, in millimeters of 

water per day (mm/d); and

Eo is the evapotranspiration rate for a wellwatered reference crop, in mm/d.

Eo provides an ET standard that incorporates climate effects 

and ET from surfaces other than the reference crop in the 

same area. Eo values were downloaded from the High Plains 

Regional Climate Center (2006) and are calculated using a 

modified Penman-evapotranspiration method with a wind function for a reference crop of alfalfa. The Odessa ET data were 

compared to Eo values from the Kearney weather station (High 

Plains Regional Climate Center, 2006), located about 19 km 

northeast of the site. The Gothenburg ET data were compared 

to Eo from the Gothenburg weather station, located about 4 km 

north of the site. 

Daily mean kc values were computed for three complete 

years of data (April 1, 2002, to March 31, 2005). Because 

daily mean kc values varied considerably, 15-day and monthly 

mean kc values also were computed. ET data from the fourth 

year of study (April 1, 2005, to March 31, 2006) were compared to ET rates simulated from kc to evaluate the crop-coeffi- to evaluate the crop-coefficient models. 

Sap Flux 

To characterize the transpiration rates of the major tree 

and shrub species, sap-flux measurements were made on 

a subset of individual trees and shrubs (table 2). Thermaldissipation probes (TDP) were used to estimate sap velocities 

from sap-temperature differences following the techniques of 

Granier (1987). Sap velocity refers to the speed of sap measured at a point in the stem (Clearwater and others, 1999). The 

sap flux is then calculated as the product of the sap velocity 

and the cross-sectional area of the stem that can transmit sap 

(sapwood). Because of limited resources, sap-flux measure-
20 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

ments were done only at the Odessa site during 2004. Several 

assumptions are implicit in using this method, including: sap 

velocity is uniform in sapwood of known area; plant-water 

storage is negligible relative to transpiration; there is an instantaneous response of sap velocity to the environmental factors 

driving transpiration; measured-temperature differences are 

a function of sap flux and not of thermal gradients caused 

by solar or atmospheric heating in the stem; the temperature 

is evenly distributed along the length of the probe; and the 

empirical relation of Granier (1987) between sap velocity and 

temperature difference applies to tree species measured in this 

study (Granier, 1987; Granier and others, 1994; Clearwater 

and others, 1999).

Fourteen TDP probes were installed in four trees and two 

shrubs at the Odessa site in September 2003 (fig. 3). Although 

the system was powered by three 60-watt solar panels, power 

limitations during relatively low solar input conditions during September 2003 to March 2004 resulted in collection of 

few continuous data during this period. Nearly continuous 

data were collected during April to August 2004; thereafter, 

the sap-velocity measurement system was largely inoperable 

because of power limitations and undetermined electrical 

malfunctions.

The TDP probes were installed at breast height on 1 or 

2 sides of the monitored trees, depending upon trunk size, so 

that average-sap velocities could be calculated from measurements in different parts of the trunk (Clearwater and others, 

1999; Ewers and Oren, 2000). Three different lengths of TDP 

probes were installed, ranging from 10 to 50 mm long (table 

2). Sap velocity was calculated from the expression of Granier 

(1987): 

0.0119 















∆

∆  ∆ =

sap

sap,mas sap

sap

T

T T

V

1.231

(17)

where

Vsap is the sap velocity, in centimeters per second, 

for a 30-minute period;

∆Tsap,max is the maximum sap-temperature difference 

recorded between 2 and 4 am—used to 

represent conditions of no sap flux, in oC; 

and 

∆Tsap is the sap-temperature difference for an 

individual probe measured every minute 

and averaged during a 30-minute period, in

o

C.

When probes extended into non-conducting xylem, 

values of ∆Tsap were corrected using the method of Clearwater 

and others (1999). In cases where two sides of a tree were 

monitored (table 2), a composite Vsap value was calculated as 

the average of velocities computed from both probes. 

Table 2. Tree and shrub species instrumented with sap-flow devices during April through August 2004, at the study site near Odessa, 

Nebraska.

[TDP-XX, Thermal dissipation probe -XX millimeters long]

Plant

number

(fig. 3)

Tree or

shrub

Distance

from

data logger

(meters)

Direction

from

data 

logger

Tree diameter

(centimeters) Instrument

Side of 

tree

where

installed

Depth of 

thermocouple

in tree

(millimeters)

Thickness 

of

sapwood

(millimeters)

1 Dogwood 1.5 northeast 2.5 TDP-10 south 5 1.5-4.6a

2b Dogwood 23.5 northeast 2.5 TDP-10 south 5 1.5-4.6a

3 Eastern

redcedar

6.1 south 15.2 TDP-30 north 15 19-27c

Eastern

redcedar

6.1 south 15.2 TDP-30 south 15 19-27c

4 Green ash 19.8 northeast 11.0 TDP-30 north 15 23

Green ash 19.8 northeast 11.0 TDP-30 south 15 26

5 Cottonwood 3.0 south 22.9 TDP-30 east 15 28d

Cottonwood 3.0 south 22.9 TDP-30 west 15 28d

6 Cottonwood 7.9 south 61.0 TDP-50 east 25 37d

Cottonwood 7.9 south 61.0 TDP-50 west 25 37d

a

 Sapwood thickness determined from three nearby dogwoods.

b

 Because of unrealistic variability in measured sap velocity, data from this dogwood were considered erroneous. 

c

 Sapwood thickness determined from two nearby eastern redcedars of similar diameter.

d

 Sapwood thickness determined as the average of two cores.
Evapotranspiration Rates 21

The depths and thickness of the bark, cambium, sapwood, 

and heartwood (non-conducting xylem) were identified from 

color differences in tree cores collected following sap-velocity 

monitoring. Cores were collected from monitored trees and 

additional trees and shrubs selected to characterize the full 

range of trunk diameters. Twenty-three cores were collected 

from 16 individuals. The sapwood area was calculated by 

assuming that the plant stem was perfectly cylindrical, and 

that the sapwood thickness (an average of 2 to 4 measurements 

around the tree) was uniform:

( )    ) 2 2

sapwood tree tree wsapwood A  �r  � r  (18)

where

Asapwood is the sapwood area, in square centimeters 

(cm2);

π is 3.14; 

rtree is the tree radius, less the bark thickness, in 

cm; and 

wsapwood is the sapwood thickness, in cm.

Sap flux was then calculated from:

Qsap = Vsap Asapwood (19)

where

Qsap is the sap flux, in cubic centimeters per 

second (cm3/s).

The sap flux is a direct measure of the volumetric transpiration 

rate of an individual plant. To facilitate comparison to the ET

rates measured on a unit-area basis from the micrometeorological measurements, the sap-flux values were scaled up to 

the canopy areas of the monitored plants using:

canopy

sap

sap A

Q T =

(20)

where

Tsap is the sap-flux-derived measure of 

transpiration, in centimeters per second 

(cm/s); and

Acanopy is the area of the ground underneath the tree 

canopy, in cm2.

This equation spreads the volumetric sap flux measured in the 

sapwood area uniformly to the area of the canopy of the tree 

measured. It is assumed that the rooted area, from which water 

is withdrawn from the subsurface to satisfy transpiration, was 

similar to the canopy area (Ewers and others, 2002).

Whereas several investigators have used Tsap to scale 

transpiration rates to the stand scale, this scaling was not 

performed for this study because of limited resources to devote 

to measuring transpiration rates of additional tree species and 

sizes. Rather, the Tsap data were used to estimate transpiration 

rates in a subset of important species to assess their possible 

relative contribution to ET from the riparian forest as a whole. 

Statistical Analysis

Simple-linear regression (SLR) and multiple-linear 

regression (MLR) analysis of ET with potential explanatory variables used methods described by Helsel and Hirsch 

(2002). Statistical analyses were performed using the S-PLUS 

statistical package, version 6.1 (Insightful Corporation, 2002). 

The SLR models were used to identify potential explanatory variables that may have affected ET. A t-test was used to 

evaluate whether the regression coefficients were significantly 

different from zero. For hypothesis testing, the significance 

level (p) was compared to a threshold value of 5 percent to 

evaluate whether the relation was statistically significant (p < 

0.05). Significant relations were further evaluated using the 

coefficient of determination (R2). All investigated variables 

and their regression residuals were graphed as scatter plots and 

probability plots—to evaluate the normality of residuals—to 

check for obvious violation of the assumptions underlying 

ordinary least-square linear regressions. 

Once identified by SLR analysis, the significant variables were included in two sets of MLR models. The first 

model was developed for comparison between study sites and 

constrained the MLR analysis to a consistent set of variables. 

The second model set was developed to determine the array of 

variables that best explain ET without sacrificing model significance through over-specification of parameters. The array of 

variables was identified using step-wise MLR analysis (Helsel 

and Hirsch, 2002). The performance of the MLR model was 

evaluated using the F-statistic and corresponding probability, 

the standard error of the residuals (RSE), and the coefficient 

of efficiency (NSE) (Nash and Sutcliffe, 1970). Because ET

is likely affected by several factors, SLR and MLR models 

explaining a large part of the ET response were not expected. 

Rather, the diagnostic values were used for relative comparisons between various models. 

Evapotranspiration Rates 

To provide perspective when evaluating the ET rates and 

annual water balances at each study site, the footprint, vegetation, and hydrogeologic attributes of the sites are characterized. In the sections that follow, measured precipitation and 

canopy interception are described to define moisture inputs 

to the study sites and to estimate possible evaporation from 

canopy interception as a component of total ET. Subsequently,

ET rates determined from micrometeorological measurements 

are discussed. Energy balances are then described to assess 

the quality of the ET measurements and the seasonal dynam-
22 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

ics of energy fluxes. Annual water balances are discussed to 

constrain estimates of GWET. A crop-coefficient model for 

simulating ET rates, comparison of simulated and measured

ET rates, and relations of measured ET rates to environmental factors are described. Comparisons of transpiration rates 

measured in individual plants using sap-flux techniques are 

then compared to ET rates determined from micrometeorological techniques to assess the possible relative contribution of 

transpiration from different plants to total ET. The final section 

of this report describes factors to consider in extrapolating the 

results of the study.

The period of data collection from April 2002 through 

March 2006 constituted four complete annual periods, and 

is hereinafter referred to as the study period. For ease of 

understanding and because energy and water fluxes were 

expected to be relatively small from January through March, 

annual periods are referred to by the calendar year associated 

with April through December. Daily mean or total values for 

selected micrometeorological variables are shown in appendix 1 at the back of this report. Although most variables were 

recorded every 30 minutes, these data were processed and 

aggregated into daily averages or totals for the purposes of 

understanding ET rates on daily to annual time scales and 

the factors affecting them. Given that 4 years of record are 

included in the study, analyzing daily data greatly simplified 

the analyses compared to 30-minute data. Daily data are presented in appendixes 1 and 2.

Site Characteristics 

Although both sites included small patches of nontargeted land covers, greater than 85 percent of the measured 

fluxes were being produced from riparian forests at most 

times. The canopy at both sites was characterized by cottonwoods of similar height, but the Gothenburg understory was 

dominated by eastern redcedars, whereas the Odessa understory was more diverse and included dogwood shrubs. Soils 

at both sites coarsened with depth, and typically consisted of 

sand and fine gravel interbedded with thin silt horizons. The 

presence of irrigation-supply water in the Gothenburg river 

channel resulted in a fundamental difference in the groundwater conditions between the two sites.

Footprint Areas 

Footprint calculations and analysis of land-cover data 

indicated that at least 85 percent of the fluxes measured by the 

eddy-covariance sensors originated from areas of riparian forest. In the following paragraphs, the land-cover characteristics 

within each footprint area measured by the eddy-covariance 

sensors are described. 

During neutral conditions—associated with negligible 

buoyant heat flux relative to wind effects—85 percent of the 

cumulative energy fluxes measured at the height of eddycovariance sensors (26.1 m above land surface) originated 

within 800 m of the tower (fig. 6). During unstable conditions—in which buoyant heat flux is substantial relative to 

wind effects—more than 95 percent of the cumulative fluxes 

measured at a height of 26.1 m originated from within 800 m 

of the tower (fig. 6). Refined-footprint calculations that estimated atmospheric stability as a function of measured fluxes, 

wind speed, and roughness using equations of Brutsaert (1982) 

indicated that even under very unstable conditions, footprints 

were intermediate between the neutral and unstable curves; 

therefore, most measured energy fluxes are likely to originate 

from intermediate upwind distances within the riparian forest 

zone. 

At both study sites, land cover in the theoretical-footprint 

area within 800 m of the towers was dominated by woody 

vegetation but interspersed with open areas (figs. 2 and 7). 

The open areas had no tree canopy and primarily were grasses 

and forbs with or without shrubs. These relatively low-density 

riparian forests intermixed with open areas are characteristic 

of the forests along the Platte River (Currier and others, 1985; 

Bureau of Reclamation, 1998). 

At Odessa, woody vegetation (55.8 percent) and open 

areas (21.0 percent) accounted for 76.8 percent of the area 

within 800 m of the tower (fig. 7A). Although the edge of the 

riparian forests was only 500 m north of the tower, northern 

winds occurred (fig. 2A) primarily during winter periods, 

when minimal ET was expected or measured. For this reason, 

the close proximity of the northern riparian-forest boundary 

was expected to have minimal effect on fluxes and energy 

balances measured at the tower. Besides the woody vegetation 

and open areas, there is cropland located in the northern part 

of the footprint area and several Platte River channels lying 

along easterly paths in the footprint area (fig. 7A). The largest 

channel was about 100 m wide—one-half of which was wetted 

through most of the year—and was located about 600 m south 

and east of the tower. Other channels generally were less than 

50 m wide, were mostly dry except for narrow channels with 

small flows and wetland vegetation, and were bounded by 

riparian forests. Winds from the southeast were dominant (fig. 

2A) particularly during the summer when most ET occurred. 

It is recognized that the river channels may have had minor 

effects on measured fluxes and energy balances at the towers 

because of the presence of considerable river-channel area 

upwind of the tower to the southeast. When winds were from 

the southeast, between 6 and 20 percent of the cumulative 

normalized flux (fig. 6) was predicted to originate from the 

river channel 600 m away. Although it is expected that the 

river channels had a minor effect on measured fluxes at the 

tower, their presence within the footprint is a minor source of 

uncertainty in the data at Odessa. 

At the Gothenburg study site, the combination of woody 

vegetation (40.6 percent) and open areas (49.9 percent) 

accounted for 90.5 percent of the area within 800 m of the 

tower (fig. 7B). The remaining 9.5 percent was predominantly 

a thin sliver of cropland in the northeastern part of the footprint, and two river channels. Because the closest edge of 

the riparian area was approximately 650 m northeast of the 
Evapotranspiration Rates 23

tower and winds blew infrequently from the northeast (fig. 

2B), effects of the cropland to the northeast were expected to 

be insignificant. At less than 100 m wide, the northern river 

channel was the largest of the channels, was 400 m from the 

tower at its closest, and was bordered by forest. Moreover, 

the predominant wind directions at the Gothenburg site (fig. 

2B) generally followed the riparian corridor of the Platte 

River. Consequently, effects of other land-cover types on the 

measurements at the Gothenburg tower were expected to be 

insignificant.

Because the land cover at both study sites consists of 

woody vegetation interspersed with open areas and some river 

channels, the water- and energy-balance measurements for this 

study characterize the riparian-vegetation community rather 

than just riparian trees. Riparian trees are a dominant component of the riparian-vegetation community, but forests in this 

study area have low density in comparison with many forests, 

and are characterized by relatively widely spaced trees with 

intervening open areas. 

Some investigations have incorporated corrections for the 

estimated effects of non-target land uses within and adjacent to 

the flux-measurement footprint (Sumner, 2001). These corrections were not implemented for this study because non-target 

land uses (river channel, cropland) accounted for a small proportion of the estimated footprint, and ET rates associated with 

cropland were expected to be similar to those of riparian forests. For example, irrigated corn, which bordered the theoretical footprint at both sites, has an average ET rate of 690 mm/

year in western Nebraska (Younts, 2002); this rate is similar to

ET rates determined at the study sites (see Measured Evapotranspiration Rates section of this report). Open-water evaporation rates, approximated by calculated modified-Penman

ET data available for weather stations (High Plains Regional 

Climate Center, 2006) were approximately twice that of riparian forests as determined in this study; however, because many 

of the river channels were dry, the area of open water in the 

channels was so small that even an evaporation rate double 

that of the riparian forests has an insignificant effect on total 

fluxes. For these reasons, corrections for estimated effects of 

non-target land covers were not made. 

Vegetation Characteristics 

Both study sites have a forest canopy, or overstory, dominated by cottonwoods (table 3). The canopy closure, meantree spacing, and density and height of cottonwoods were 

similar between the two sites. Canopy-closure measurements 

indicated that a vegetative canopy overlies slightly more than 

one-half of the landscape. In order of abundance, tree species 

with densities greater than 5 trees per hectare at the Odessa 

site were eastern cottonwood, green ash, eastern redcedar, red 

mulberry, willow, and Russian olive. At the Gothenburg site, 

tree species with densities greater than 5 trees per hectare were 

eastern redcedar, eastern cottonwood, and green ash. 

The primary differences between the vegetation at the 

two sites was the greater abundance of eastern redcedars at the 

Gothenburg site and the presence of a shrub understory at the 

Odessa site that was absent at the Gothenburg site. Generally, 

the understory forest at Odessa was more diverse than at Gothenburg, with green ash, eastern redcedar, red mulberry, willow, 

and Russian olive composing a moderately dispersed secondary canopy between the top-level cottonwood canopy and the 

underlying shrub vegetation. The mean tree heights of green 

ash, eastern redcedar, mulberry, and willow were 7.5 to 8.5 

m, and 4.7 m for Russian olive, compared with a median tree 

height for cottonwood of 16.9 m (table 3). The shrub understory, which was dominated by roughleaf dogwoods (Cornus 

drummondii), was approximately 2 to 4 m tall and often was 

present in areas with tree cover and open areas with no tree 

cover. The shrubs were present across more than one-half of 

the landscape, but were not continuous. At the Gothenburg 

site, a shrub understory was absent, but eastern redcedars having a mean height of 7.2 m were present at a density approximately three times greater than the Odessa site (table 3). The 

Gothenburg eastern redcedars often were present in patches 

in which individual trees were densely spaced. These dense 

patches occurred in areas with and without overlying cottonwood canopy, but may prevent understory shrubs from gaining 

establishment. At both sites, the ground cover consisted of 

grasses and forbs in open areas and included some wetland 

plants in shallow depressions where the depth to water was 

less than for most of the landscape. 

Qualitative comparisons of riparian-vegetation characteristics of the study sites with those from the regional 

literature (Rand, 1973; Johnson and others, 1976; Sedgewick 

and Knopf, 1989; Good, 1999) indicated that the sites were 

representative of regional riparian forests. Because of differences in characterization methods, quantitative comparisons 

were not possible.

0

10

20

30

40

50

60

70

80

90

100

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

DISTANCE FROM MEASUREMENT LOCATION, IN METERS

CUMULATIVE NORMALIZED FLUX, IN PERCENT

26.1-meter sensor height—unstable conditions

26.1-meter sensor height—neutral conditions

Figure 6. Relation of source strength with distance upwind from 

a measurement point 26.1 meters above land surface for endmember atmospheric conditions.
24 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

(B) Gothenburg study site

EXPLANATION

Open—49.9 percent

Woody vegetation—40.6 percent

Other—9.5 percent

Gothenburg land cover

99°14' 45" 30" 15" 13' 45" 99°12'30"

40'

40°40'30"

40°39'45"

15"

0 100 200 300 400

0 100 200 300 400 500 METERS

500 600 YARDS

(A) Odessa study site

EXPLANATION

Open—21 percent

Woody vegetation—55.8 percent

Other—23.2 percent

Odessa land cover

100°12'15" 12' 45" 30" 15" 11' 100°10'45"

40°55'

30"

15"

40°55'45"

0 100 200 300 400 500

0 100 200 300 400 500 METERS

600 YARDS

Land cover by Nebraska Department of Natural Resources, 2000 

Base from U.S. Department of Agriculture, Farm Service Agency,

Aerial Photography Field Office, digital data, 1:12,000, 2003, 

Lambert Conformal conic projection

Standard parallels 40 ̊N and 43 ̊N, central meridian 100 ̊W

Horizontal coordinate information is referenced to the North American

 Datum of 1983 (NAD 83)

Base from U.S. Department of Agriculture, Farm Service Agency,

Aerial Photography Field Office, digital data, 1:12,000, 2003, 

Lambert Conformal conic projection

Standard parallels 40 ̊N and 43 ̊N, central meridian 100 ̊W

Horizontal coordinate information is referenced to the North American

 Datum of 1983 (NAD 83)

Land cover by Nebraska Department of Natural Resources, 2000 

Figure 7. Distribution of land cover and fraction of total area in each category at study sites near Odessa and Gothenburg, Nebraska.
Evapotranspiration Rates 25

At Odessa, sapwood thickness determined from 23 tree 

cores in 4 species of trees and 1 species of shrub indicated that 

sapwood thickness generally increased with trunk diameter 

(fig. 8), as was generally expected on the basis of previous 

investigations (Meinzer and others, 2001). However, considering the large diameters of the four cottonwoods cored in 

comparison with the other species of trees cored, the sapwood 

thickness was less for cottonwoods than would have been 

expected from general relations between trunk diameter and 

sapwood thickness. Similar deviations from general relations 

between diameter at breast height and sapwood thickness have 

been noted in some other studies (Ewers and others, 2002). 

Although the data set is limited, the sapwood thickness in cottonwoods does not appear to increase with trunk diameter as 

much as in the green ash, eastern redcedar, and Russian olive, 

which had substantial increases in sapwood thickness for small 

increases in trunk diameter. These results imply that cottonwoods, despite their larger diameter compared to other tree 

species at the study sites, have relatively smaller proportions 

of sapwood as a percentage of total cross-sectional trunk area. 

At Odessa, LAI values were about 1.03 to 1.40 during 

May through September (fig. 9A). The lowest LAI values of 

0.36 occurred before leaf growth and reflected the presence 

of tree branches and conifers (primarily eastern redcedars). 

This baseline value is subtracted from growing-season values 

to represent seasonal vegetation growth. Late-April LAIs of 

0.42 and 0.45 reflects partial leaf-out conditions by some species. Peak values occurred in July or August of each year. The 

seasonal pattern of LAI was characterized by large increases 

in LAI between late April and late May, transitioning to more 

Table 3. Summary of vegetation and hydrogeologic characteristics of study sites near Odessa and 

Gothenburg, Nebraska.

[cm, centimeters]

Site characteristics Odessa Gothenburg

Overstory dominated by: cottonwood cottonwood

Understory dominated by: shrubs and grass eastern redcedars and grass

Mean canopy closure, percent 56 52

Mean tree spacing, meters 9.0 8.3

Density, trees per hectarea

 All 125 145

 Eastern cottonwood (Populus deltoides) 39 37

 Eastern redcedar (Juniperus virginiana) 27 80

 Green ash (Fraxinus pennsylvanica) 28 9

 Willow (Salix spp.) 7 4

 Russian olive (Elaeagnus angustifolia) 6 5

 Red mulberry (Morus rubra) 16 2

 American elm (Ulmus americana) 3 2

 Ashleaf maple (boxelder) (Acer negundo) 0 4

Number of tree species measurements 184 144

Cottonwood tree height, median, meters 16.9 16.9

Cottonwood height, number of measurements 47 45

Typical early spring depth to water, cm 90 110

2002–04 water-level fluctuation, cm 70 40

Stratigraphy 10–20 cm sandy loam 

overlying 

40–50 cm fine sand 

with silt layers overlying poorly sorted 

sand and fine gravel

10–20 cm sandy loam overlying

40–50 cm fine sand with silt 

layers overlying poorly sorted 

sand and fine gravel

a

 Density was derived from the point-centered quarter sampling method, which may have a negative bias in clustered 

populations (Batcheler, 1973) such as the riparian forests along the Platte River.
26 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

gradual increases until peak values were reached in July or 

August, followed by gradual declines to values measured in 

late September or early October, slightly before leaf drop. 

Monthly variations in LAI at the Odessa site appear to 

have been affected by moisture availability. In 2002, LAI 

decreased from June to July, corresponding with dry conditions that caused visible water stress and dormancy in understory shrubs. Rains in August caused noticeable improvements in vegetation vigor that may have been reflected in the 

increased LAI in August. In 2003, conditions during May 

to July were wetter than those in 2002 and LAI values were 

larger; however, dry conditions in August and September 

corresponded with lower monthly LAI in 2003 than during 

2002. Rainfall in 2004 was closer to normal during the growing season, except for August, and LAI values generally were 

intermediate between the 2002 and 2003 values. 

At Gothenburg, values of LAI were about 1.26 to 1.53 

during May through September (fig. 9B). LAI values of 

0.87 to 0.94 were measured in March and late April, probably reflecting pre-leaf-out conditions during dormancy. The 

larger dormant-LAI values at Gothenburg relative to Odessa 

probably were because of the greater abundance of coniferous eastern redcedars at the Gothenburg site. Annual peaks in 

Gothenburg LAI values occurred in June in 2002 and 2003 and 

in early August in 2004. Generally, the timing of seasonal rises 

and declines in LAI were similar at Gothenburg and Odessa; 

however, monthly variations in LAI were somewhat smaller at 

Gothenburg than at Odessa, particularly those associated with 

wet and dry periods during the summers of 2002 and 2003.

The increase in LAI during the growing season in 

comparison with dormant-season baseline values was less at 

Gothenburg, about 0.6, than at Odessa, about 1.0. This result 

implies that seasonal-leaf growth was greater at the Odessa 

site than at the Gothenburg site, consistent with the greater 

density of deciduous tree and shrub species at the Odessa site. 

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

DIAMETER AT BREAST HEIGHT, IN CENTIMETERS

SAPWOOD THICKNESS, IN MILLIMETERS

Green ash

Cottonwood

Eastern redcedar

Russian olive

Dogwood

Figure 8. Tree diameter and sapwood thickness for selected 

trees and shrubs at study site near Odessa, Nebraska.

(A) Odessa (B) Gothenburg

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

LEAF-AREA INDEX, DIMENSIONLESS

EXPLANATION

Average leaf-area index from

 2002 through 2004

 2002

 2003

 2004

Figure 9. Seasonality of average leaf-area index of riparian forests at study sites near Odessa and Gothenburg, Nebraska, 2002–04.
Evapotranspiration Rates 27

Hydrogeologic Characteristics 

At both study sites, the stratigraphy within 1 m of the surface coarsened with depth. Generally, the UZ vertical profiles 

consisted of a 10- to 20-cm-thick layer of sandy loam overlying a 40- to 50-cm-thick layer of fine sand interbedded with 

silt layers of variable thickness overlying a bottom layer of 

poorly sorted sand and fine gravel. Classified particle-size distributions from the UZ1 and UZ2 profiles are shown for each 

site in figure 10. At three of the four profiles (Odessa UZ1 and 

UZ2, Gothenburg UZ1), silt and clay contents were greater 

than 30 percent in the upper 20 cm but less than 1 percent at 

depths of 40 cm or more. The Gothenburg UZ2 profile was an 

exception, with less than 10 percent silt and clay in the upper 

20 cm. This profile was located in an open area with thin grass 

cover, and typically had lower soil-moisture contents than the 

other three profiles. At the time of sensor installation (in walls 

of trenches), silt horizons 5 to 10 cm thick were visible within 

sandy loam and fine-sand layers ranging from 12 to 50 cm 

deep in the different profiles. These silt horizons explained the 

silt and clay contents greater than 50 percent in soil samples 

collected from depths of 40 cm in the Gothenburg UZ2 profile 

and 20 cm in the Odessa UZ2 profile. Coarsening became 

particularly apparent at depths greater than 40 cm. Samples 

from 40 to 90-cm depths consisted of as much as 60 percent 

medium sand. Because of this coarser material, capillary rise 

of water into the unsaturated zone was expected to be small in 

comparison to soils having higher silt and clay contents (Hillel, 1980). 

At Odessa, average values of ρb—based on 13 samples at 

each depth—generally increased with depth from 1.05 grams 

per cubic centimeter (g/cm3) at 5 cm to 1.74 g/cm3 at 80 cm, 

with similar values for both profiles. At the Gothenburg UZ1 

profile, average values of ρb generally increased with depth 

from 1.00 g/cm3 at 5 cm to 1.88 g/cm3 at 90 cm. The surficial 

ρb was comparatively larger at the Gothenburg UZ2 profile, 

with ρb of 1.52 g/cm3 at 5 cm increasing to 1.74 g/cm3 at 90 

cm. Because organic matter has a lower density than mineral 

soils, the larger surficial ρb at Gothenburg UZ2 most likely 

was the result of lower organic content in comparison to the 

other profiles. Based on three samples from the upper 10 cm at 

each profile, the average organic-carbon content was 6.3 percent at Odessa UZ1, 7.3 percent at Odessa UZ2, 12.1 percent 

at Gothenburg UZ1, and 3.0 percent at Gothenburg UZ2. 

On average, the depth to water from the land surface 

(DTW) was slightly greater at the Gothenburg site than at the 

Odessa site. Typical DTW in early spring at the Odessa site 

was 90 cm compared to 110 cm at the Gothenburg site. 

Ground-water levels typically peaked in April or May and 

then declined through September at both sites. Annual groundwater-level fluctuations of about 70 cm at the Odessa site were 

about 30 cm larger than fluctuations at the Gothenburg site. 

Silt and clay

Fine sand

Medium sand

Coarse sand

Silt and clay

Silt and clay

Silt and clay

Fine sand

Fine sand

Fine sand

Medium sand

Medium sand

Medium sand

Coarse sand

Coarse sand

Coarse sand

(D) Gothenberg UZ2

(A) Odessa UZ1 0

20

40

60

80

100

DEPTH, IN CENTIMETERS

(C) Gothenberg UZ1

CUMULATIVE PERCENTAGE OF SOIL-SEPARATE CLASS AND FINER

(B) Odessa UZ2

0

20

40

60

80

1000 20 40 60 80 100 0 20 40 60 80 100

Figure 10. Vertical profile of grain-size distribution of study sites near Odessa and Gothenburg, Nebraska.
28 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

The smaller declines at Gothenburg probably are the result of 

increased aquifer recharge from surface water as compared 

to Odessa. Because the north channel of the Platte River is 

used to route irrigation-supply water past the Gothenburg site, 

surface-water levels are more consistently maintained than at 

Odessa. During most of the year, water-level altitudes were 

highest in the GW1 piezometer, intermediate in the GW2 

piezometer, and lowest in the river channel (GNC), indicating that the gradient for ground-water flow was toward the 

river channel. However, during the period of irrigation-water 

routing through the river channel from late June through early 

August, water-level altitudes were greater at GNC than in 

GW1 or GW2. At Odessa, ground-water levels fell by as much 

as 70 cm during the growing season, and water-level altitudes 

consistently were higher at the OW1 and OW2 piezometers 

than at the north channel of the Platte River (ONC) (fig. 2A). 

Unlike Gothenburg, the Platte River at Odessa is not used for 

irrigation-water routing and, as a result, no gradient reversal 

was observed that was similar to that at Gothenburg. Because 

of its presumed effect of artificially maintaining local groundwater levels, the presence of routed irrigation water in the 

river resulted in a fundamental difference in the ground-water 

conditions between the two sites. 

Precipitation and Canopy Interception 

At both sites, a wide range in daily, monthly, and annual 

precipitation occurred during the study period (fig. 11). This 

period encompassed a range of hydrologic conditions, including some periods of normal precipitation. Daily precipitation 

was characterized by many rainstorms of 25 mm or less with a 

few rainstorms greater than 25 mm accounting for a relatively 

large fraction of annual totals. The maximum daily precipitation occurred at Odessa, and was nearly 74 mm (fig. 11A, D). 

Thirty-year average monthly precipitation for 1961 to 

1990 (hereinafter referred to as normal monthly precipitation) 

(High Plains Regional Climate Center, 2006) varies seasonally. 

Normal monthly precipitation generally was similar between 

the study sites and varied from a minimum of about 10 mm in 

January to a maximum of about 100 mm in June (fig. 11B, E). 

Additionally, 66 percent and 69 percent of the normal annual 

precipitation occurred from May through September at Odessa 

and Gothenburg, respectively. 

Monthly precipitation during the study period was more 

variable than normal (fig. 11B, E), resulting in relatively wet 

and relatively dry seasonal periods of 2 to 3 months long. 

Monthly departures from normal were calculated by subtracting the normal precipitation from the measured precipitation, 

with positive departures indicating relatively wet months 

and negative departures indicating relatively dry months. Of 

greatest hydrologic significance were consecutive months of 

substantial positive or negative departures. At both sites, the 

largest negative departures for consecutive months occurred 

during April through July 2002, and July through August 2003 

(fig. 11B, E). The largest positive departures for consecutive 

months occurred during April through June of 2003, and May 

through June 2005 at the Odessa site (fig. 11B) and during 

June through July 2004 at the Gothenburg site. 

For the four annual periods between 2002 and 2005, precipitation at both sites varied substantially from below normal 

in 2002 to substantially above normal in 2004, and approximately normal during 2003 and 2005 (fig. 11C, F). Annual 

precipitation ranged from 429 to 844 mm at the Odessa site, 

compared to a 30-year average of 630 mm. Annual precipitation at the Gothenburg site ranged from 359 to 791 mm, 

compared to a 30-year average of about 560 mm. Annual 

precipitation at Odessa always was greater than at Gothenburg, 

consistent with a general west-to-east increase in precipitation that occurs across Nebraska (Gutentag and others, 1984; 

Szilagyi and others, 2003). During the study period, annual 

precipitation at both sites varied by about 35 percent from its 

4-year average.

Variations in annual precipitation primarily reflected wet 

and dry periods during May through September, when most 

precipitation occurred. These months also encompass most of 

the growing season that usually occurred between late April 

and mid-October. The substantial negative departure from 

normal in 2002 primarily reflected drier than normal conditions during April through July 2002. The substantial positive departure from normal in 2004 primarily reflected wetter 

than normal conditions during July through November 2004. 

The 2003 annual period had wetter than normal precipitation 

from May through July 2003, and drier than normal precipitation from July through August 2003. During the 2005 annual 

period, monthly departures from normal were not systematic. 

Throughfall through the canopy at each site ranged from 

about 67 to 89 percent of precipitation (fig. 12; table 4). These 

percentages were derived from the ratio of mean throughfall 

and precipitation values at each gage when paired measurements were available. Simple linear-regression (SLR) models relating throughfall to precipitation during non-freezing 

conditions had R2 of 0.97 or greater, indicating a relatively 

strong relation between precipitation and throughfall over 

the measured range of rainfall intensities and amounts. The 

SLR models for the throughfall gages all had small, negative 

intercepts, which may reflect either uncertainty in the data or 

physical factors such as the storage capacity of the canopy, 

which can result in zero throughfall for small precipitation 

events. Because the SLR intercepts were relatively small in 

comparison to the measured data, comparisons between sites 

were made using the SLR slopes. All of the slopes were less 

than one, indicating throughfall was less than precipitation; 

however, there were a few rainstorms (fig. 12) where throughfall exceeded precipitation. These rainstorms could reflect 

scenarios where vegetation channeled raindrops toward the 

gutters or may be undetected rain-gage malfunctions. At the 

Odessa site, greater amounts of throughfall occurred in the 

shrub-dominated gage two—as indicated by a slope of 0.878 

(fig. 12B)—than gage one—as indicated by a slope of 0.773 

(fig. 12A)—located beneath the canopy of a large cottonwood 

tree. At the Gothenburg site, greater amounts of throughfall 
Evapotranspiration Rates 29

(D) Daily precipitation rates, Gothenburg

(B) Monthly precipitation rates, Odessa (E) Monthly precipitation rates, Gothenburg

(C) Annual precipitation rates, Odessa (F) Annual precipitation rates, Gothenburg

measured

30-year average

measured

30-year average

30-year average

30-year average

Jan. July

2002

July

2003

July

2004

July

2005 2006

Jan. Jan. Jan. Jan.

(A) Daily precipitation rates, Odessa

Jan. July

2002

July

2003

July

2004

July

2005 2006

Jan. Jan. Jan. Jan.

0

0

300

350

400

450

500

550

600

650

700

750

800

850

900

50

100

150

200

25

50

75

PRECIPITATION RATE, IN 

MILLIMETERS PER DAY PRECIPITATION RATE, IN 

MILLIMETERS PER MONTH PRECIPITATION RATE, IN 

MILLIMETERS PER YEAR

April 2002–March 2003 April 2004–March 2005

April 2003–March 2004 April 2005–March 2006

April 2002–March 2003

April 2003–March 2004

April 2004–March 2005

April 2005–March 2006

Figure 11. Daily, monthly measured and normal, and annual measured and normal precipitation for study sites near Odessa and 

Gothenburg, Nebraska, 2002–06. (Normal precipitation from High Plains Regional Climate Center, 2006)
30 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

y = 0.773x - 0.32R

2

= 0.98

y = 0.899x - 0.70

R

2

= 0.98

y = 0.878x + 0.11

R

2

= 0.97

y = 0.794x - 1.27R

2

= 0.98

755025

00 25 0 50 75 25 0 50 75

755025

, 2 EGAGLL AF HGUORHT NI DERUSAE MLL AF HGUORHT

MROTS NI AR REP SRETE MI LLI M IN 

, 1 EGAGLL AF HGUORHT NI DERUSAE MLL AF HGUORHT

MROTS NI AR REP SRETE MI LLI M IN 

(A) Throughfall in gage 1 at site near Odessa(B) Throughfall in gage 2 at site near Odessa

(C) Throughfall in gage 1 at site near Gothenburg(D) Throughfall in gage 2 at site near Gothenburg

PRECIPITATION MEASURED IN THROUGHFALL GAGE 3 (NO CANOPY), IN 

MILLIMETERS PER RAIN STORM

Linear for individual rain stroms

Linear for individual rain stroms

1:1 line

1:1 line

Individual rain storm

Linear for individual rain stroms

Linear for individual rain stroms

1:1 line

1:1 line

Individual rain storm

Individual rain storm

Individual rain storm

Figure 12. Relations between precipitation and throughfall at study sites near Odessa and Gothenburg, Nebraska, 2002–06.
Evapotranspiration Rates 31

occurred through the cottonwood and Russian olive trees at 

gage one—as indicated by a slope of 0.899 (fig. 12C)—than 

the relatively low-hanging canopy of several eastern redcedar 

trees at gage two—as indicated by a slope of 0.794 (fig. 12D). 

Interception of precipitation by the plant canopy (hereinafter referred to as interception) was calculated as the difference between precipitation and throughfall. This calculation 

is a maximum estimate of interception because some water 

could reach the ground surface through stemflow, the flow of 

precipitation down plant surfaces to the ground. Although not 

measured in this study, the fraction of stemflow for forests 

varies widely as a function of many factors, and has been 

determined to account for 5 to 15 percent of precipitation 

(Levia and Frost, 2003). Thus, these estimates of evaporation 

of intercepted water represent an upper boundary of possible 

values.

Estimates of area-weighted interception—calculated 

by allocating the interception determined at the two canopyaffected throughfall gages to the distribution of vegetation 

cover in the theoretical footprint—were both 13 percent of 

precipitation at the two sites (table 4). These calculations were 

based on the assumptions that interception determined from 

SLR for throughfall gages 1 and 2 (fig. 12) each represented 

one-half of the woody vegetation areas shown in figure 7; 

interception in areas of open or other land cover (fig. 7) was 5 

percent of precipitation—one-half of the minimum interception of 10 percent measured in throughfall gages beneath tree 

or shrub canopies; and the vegetation cover within 800 m of 

the tower, shown in figure 7, was representative of the study 

sites. Sensitivity analyses, done by varying the assumed interception for open vegetation cover and changing area weights 

within reasonable boundaries, indicated that uncertainties in 

these parameters resulted in estimates of area-weighted interception ranging from about 10 to 16 percent. It is likely that 

area-weighted interception lies within this range. 

Most intercepted water is likely to evaporate into the 

atmosphere on time scales of one day or less (Savenije, 2004). 

For the purposes of this study, it was assumed that all intercepted water evaporated from the canopy. 

The estimated interception values for Platte River riparian 

forests appear to be reasonable in comparison with literature 

values. Studies of interception indicate it is typically 10 to 30 

percent of precipitation, and can be as much as 50 percent of 

the rainfall (Calder, 1990; Liu, 1997). Evaporation of intercepted water in the forest canopy has been determined to be a 

substantial component of total ET in some studies (Savenije, 

2004). For example, Grelle and others (1997) determined that 

interception evaporation composed 20 percent of total ET in a 

coniferous forest in Scandinavia. Because the riparian forests 

along the Platte River have a relatively low areal density of 

deciduous plants (table 3), it is reasonable that interception 

and evaporation of intercepted water are in the lower range of 

values appearing in the literature. 

Measured Evapotranspiration Rates 

Measured ET rates were compiled on daily, monthly, and 

annual timescales (fig. 13). The ET values indicated a clear 

seasonal trend at both sites but exhibited subtle differences 

with respect to annual maximums and the dependence on 

precipitation. 

Daily ET rates determined from eddy-covariance data 

ranged from -0.7 to about 7.5 mm/d (appendix 1; appendix 

2). From June through August, daily ET rates typically varied 

between 2.5 and 5.0 mm/d. Daily ET rates from December 

through February often were less than 0.5 mm/d. On average, 

the daily ET rates for the study period were 1.51 mm/d at the 

Odessa site, and 1.56 mm/d at the Gothenburg site. 

Monthly ET rates ranged from less than 20 mm per 

month from November through February to a maximum of 

about 130 mm per month during summer (June, July, August) 

months (fig. 13B, E). In 3 (2002, 2003, 2005) of the 4 study 

years, maximum monthly ET occurred 1-2 months later at 

the Gothenburg site than at the Odessa site. During 2004, the 

wettest of the study years, maximum ET occurred during July 

at the Odessa site and during June at the Gothenburg site, a 

reversal of the pattern from other study years with dry to near 

normal precipitation. 

Annual ET ranged from 514 to 586 mm/yr at the Odessa 

site and 535 to 616 mm/yr at the Gothenburg site (fig. 13C, F). 

The average annual ET during the study period was 551 mm 

at the Odessa site and 575 mm at the Gothenburg site. At both 

sites, the maximum and minimum annual ET values differed 

from the 4-year average by about 7 percent. 

The ET response to annual precipitation was different 

between the two study sites. Annual ET at Gothenburg was 

greater than at Odessa (fig. 13C, F) during the first 2 years 

of the study, corresponding to substantially below normal to 

nearly normal precipitation (fig. 11C, F). Conversely, annual

ET at the Odessa site was greater than at the Gothenburg site 

during the last 2 years of the study, corresponding to substantially above normal to slightly above normal precipitation. Thus, at the Odessa site, annual ET was larger during 

relatively wet years and smaller during relatively dry years. 

The opposite occurred at the Gothenburg site, where annual

ET was smaller during relatively wet years and larger during 

relatively dry years. These results indicate that the dependence 

of ET on precipitation was different between the two sites. 

This important distinction is discussed further in the “Environmental Factors Affecting Evapotranspiration Rates” section of 

the report.

Based on an analysis of the EBRmean, the ET rates determined using eddy covariance are estimated to be within about 

15 percent of the actual rates. The largest source of uncertainty 

arises from the method used to adjust measured-energy fluxes 

to close the energy balance. Based only on days of complete 

data during March 12, 2002, through June 27, 2005, before 

gap-filling, the long-term EBRmean (eq. 4) was about 0.89 for 

both sites, indicating that either turbulent fluxes (λE + H) were 
32 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

Table 4. Summary of annual precipitation and interception at study sites near Odessa and Gothenburg, Nebraska, 

2002–06.

[P, total precipitation; mm, millimeters; I1, interception in throughfall gage 1 underlying a full tree canopy; I2, interception in throughfall 

gage 2 underlying shrubs and a light tree canopy; I0, interception associated with no tree or shrub canopy; I/P, interception as a fraction 

of precipitation; WA1, weighted areal fraction represented by a full tree canopy; WA2, weighted areal fraction represented by shrubs and 

light tree canopy; WA0, weighted areal fraction represented by areas other than tree or shrub canopy; Iwa, area-weighted interception]

April 1, 2002–

March 30, 2003

April 1, 2003–

March 30, 2004

April 1, 2004–

March 30, 2005

April 1, 2005–

March 30, 2006

Odessa

P, mm 429 633 844 679

Mean I1/P, gage 1, full tree canopy1 .27 .27 .27 .27

Mean I2/P, gage 2, shrub and light tree canopy1 .11 .11 .11 .11

Mean Io/P, no tree or shrub cover2 .05 .05 .05 .05

WA1, gage 1, full tree canopy3 .28 .28 .28 .28

WA2, gage 2, shrub and light tree canopy3 .28 .28 .28 .28

WAo, no tree or shrub canopy, open and other3 .44 .44 .44 .44

Area-weighted interception (Iwa), mm 4 55 81 107 86

Iwa/P .13 .13 .13 .13

Gothenburg

P, mm 359 610 791 589

Mean I1/P, gage 1, full tree canopy1 .18 .18 .18 .18

Mean I2/P, gage 2, shrub and light tree canopy1 .33 .33 .33 .33

Mean Io/P, no tree or shrub cover2 .05 .05 .05 .05

WA1, gage 1, full tree canopy3 .20 .20 .20 .20

WA2, gage 2, shrub and light tree canopy3 .20 .20 .20 .20

WAo, no tree or shrub canopy, open and other3 .59 .59 .59 .59

Area-weighted interception (I

wa), mm4 48 82 106 79

I

wa/P .13 .13 .13 .13

1

Estimated from linear regression of throughfall against precipitation (fig. 12) subtracted from 1.0 to estimate interception. 

2

 Interception fraction for open and other areas without tree and shrub canopy cover assumed to be 0.05, slightly less than one-half of 

the lowest interception determined from throughfall gages. 

3

 Areal weights calculated using percentages of vegetation cover shown in figure 7. Areas of woody vegetation were assumed to be 

equally represented by gages 1 and 2. The relevant representative area is assumed to be the circular area of radius of 800 meters shown 

on figure 7.

4 Iwa = P x ((I1

/P) x WA1) + ((I2/P) x WA2) + ((Io/P) x WAo))
Evapotranspiration Rates 33

(D) Daily evapotranspiration rates, Gothenburg

(B) Monthly evapotranspiration rates, Odessa (E) Monthly evapotranspiration rates, Gothenburg

(C) Annual evapotranspiration rates, Odessa (F) Annual evapotranspiration rates, Gothenburg

Jan. July

2002

July

2003

July

2004

July

2005 2006

Jan. Jan. Jan. Jan.

(A) Daily evapotranspiration rates, Odessa

Jan. July

2002

July

2003

July

2004

July

2005 2006

Jan. Jan. Jan. Jan. 0

0

500

550

600

650

50

100

150

2.5

5.0

7.5

10.0

EVAPOTRANSPIRATION RATE, IN 

MILLIMETERS PER DAY EVAPOTRANSPIRATION RATE, IN 

MILLIMETERS PER MONTH EVAPOTRANSPIRATION RATE, IN 

MILLIMETERS PER YEAR

2002

April 2002–March 2003 April 2004–March 2005

April 2003–March 2004 April 2005–March 2006

April 2002–March 2003

April 2003–March 2004

April 2004–March 2005

April 2005–March 2006

2003 2004 2005 2006 2002 2003 2004 2005 2006

Figure 13. Daily, monthly, and annual evapotranspiration rates for study sites near Odessa and Gothenburg, Nebraska, 2002–06.
34 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

being underestimated or available energy (Rn – ∆PS – G) was 

being overestimated. This study assumed that available energy, 

which was dominated by the Rn term, was being overestimated. Therefore, to provide energy-balance closure, Rn data 

were adjusted downward by multiplying by EBRmean. If instead 

the energy balance had been closed by dividing the turbulent 

fluxes by 0.89 and doing the same with λEp during gaps when 

the atmosphere was saturated, then the resulting ET would 

have been about 13 percent greater than the reported values. 

Actual ET values may lie between these estimates. For this 

reason, the reported ET values are estimated to represent minimum values with maximum values up to 13 percent greater. 

Procedures used in filling data gaps probably had little 

effect on annual ET estimates. On average, gap-filled ET

accounted for 2.9 percent of the total ET at the two sites, of 

which 2.4 percent was associated with data gaps filled by multiplying λEp by 0.89, and 0.5 percent was associated with data 

gaps filled with linear interpolation or regression procedures. 

It is expected that utilizing alternative gap-filling procedures 

would have had relatively little effect on annual ET estimates. 

Energy Balances 

Energy imbalances were used to evaluate the data quality. 

For the entire study period, energy imbalances as a percentage of available energy were 9 percent at Odessa (table 5) 

and 13 percent at Gothenburg (table 6). These imbalances 

are within the range of 0 to 30 percent typically present in 

energy-balance studies (Twine and others, 2000), indicating 

that the energy-flux data are reasonable. The energy balance 

was improved by adjusting net-radiation values to account for 

an observed underestimation of long-wave radiation presumed 

to be emanating from the land surface. 

Adjusted Net Radiation

As stated, Rn values were adjusted to better account for 

long-wave radiation emanating from the land surface, which 

predominates at night. Separate adjustments were made for 

nighttime—operationally defined as periods with solar-radiation (Rs

) values less than 1 W/m2—and daytime periods. SLR 

techniques were used to relate Rn values measured from the 

conventional two-component net radiometer (Rn-2) with those 

from the four-component net radiometer (Rn-4) at Odessa to 

produce an adjusted Rn value (Rnadj). 

Comparison of Rn-2 and Rn-4 data for Odessa indicated 

that the Rn-4 included more long-wave radiation, resulting in 

smaller daily Rn values and subsequently better EBC than for 

the Rn-2 measurements. SLR relations between the Rn-2 and 

Rn-4 were developed using 30-minute data from March 2004 

through May 2005. Because nighttime values from the Rn-4 

were less than the Rn-2 (fig. 14B), they were considered to better represent Rn. Differences between the net radiometers were 

largest at more negative values from the Rn-2 (fig. 14B). The 

daytime slope coefficient for values from the Rn-2 compared 

to those from the Rn-4 was smaller than the nighttime slope, 

indicating that the differences between the two radiometers 

were less important as values became increasingly positive 

(fig. 14). During the growing season, daily Rn-4 measurements were typically 0 to 20 percent less than those from the 

Rn-2, with a median difference of about 10 percent. During 

the dormant season, there was more variability in the differences between the two sensors, but the Rn-4 measurements 

generally were less than those from the Rn-2 by a greater 

amount than during the summer.

At Odessa, the SLR models of the relations between the 

Rn-4 and the Rn-2 measurements for daytime and nighttime 

conditions (fig. 14) were the basis for estimating Rnadj for 

periods before the installation of the Rn-4. The SLR models 

were validated by comparing Rnadj to the Rn-4 measurements 

for March 2004 through May 2005 with the expectation of 

a one-to-one relation (or a slope coefficient of one). The 

regression-estimated slope coefficient was 0.9941, which was 

not significantly different from one. The calculation for Rnadj

subsequently was applied to the entire Odessa data set. 

At Gothenburg, the Rn-4 was not deployed at any time 

during the study; however, SLR comparisons were made 

between Gothenburg and Odessa for Rn-2 measurements as 

well as for Rs. Though Odessa data typically were greater 

(albeit only slightly), relations for both data types were well 

constrained (R2 > 0.90). Based on the comparability of Rn-2 

measurements and Rs between the two sites, the SLR models 

of relations between the Rn-4 and the Rn-2 for daytime and 

nighttime conditions at Odessa also were applied to the Rn-2 

data at the Gothenburg site to adjust for the underestimation 

of long-wave radiation. Consequently, Rn values referred to 

hereinafter in this report are synonymous with Rnadj.

Seasonal and Diurnal Energy-Flux Cycles 

Within the study period, seasonal changes in the EBC and 

energy flux were evident. Energy fluxes generally were largest 

during the summer months (June through August) and smallest during the winter months (November through February), 

reflecting seasonal variations in Rn (fig. 15; tables 5 and 6). 

Monthly mean Rn values during summer months were roughly 

an order of magnitude greater than winter values, and generally were similar between years and between sites. Monthly 

means of λE, H, G, and ∆PS generally were largest in summer 

and smallest in winter, roughly tracking summertime increases 

in Rn.

During the growing season (May through September), λE

accounted for the most energy leaving the system at both sites, 

whereas H was the dominant energy outflow during the dormant season (October through April). Monthly mean values 

of λE usually were greater than H (meaning H/λE, the Bowen 

ratio, was less than unity) during the growing season with 

a mean Bowen ratio of 0.65 at Odessa (table 5) and 0.60 at 

Gothenburg (table 6). During the dormant season, H typically 

was the largest energy outflow term at both sites, with mean 

Bowen ratios of 1.72 at Odessa and 1.90 at Gothenburg. 
Evapotranspiration Rates 35

Table 5. Summary of monthly mean energy fluxes for April 2002 to April 2006 at riparian forest study site near Odessa, Nebraska.—Continued[Rn, net radiation; λE, latent heat flux, H, sensible heat flux, G, soil heat flux, ∆PS, change in vegetation energy storage, EBC, energy balance closure, EBR, energy balance ratio (equation 4); H/ λE, Bowen ratio; W/m2, watts per square meter; italicized values identify periods in which the EBR or H/λE was less than unity; unshaded rows designate the May–September growing season; grey shading designates the October–April dormant season]

Month Year

Monthly

mean

Rn

W/m2

Monthly

mean

λE

W/m2

Monthly

mean

H

W/m2

Monthly

mean

G

W/m2

Monthly

mean

∆PS

W/m2

Monthly

mean

EBC

W/m2

Monthly

mean

(EBC/(Rn-G-∆PS))

in percent

Monthly

mean

EBR

(dimensionless)

Monthly

mean

H/ λE

(dimensionless)

April 2002 110 28.5 28.5 28.5 -0.01 -11.30 -10.9 1.11 3.04

May 2002 142 66.8 66.8 66.8 .01 -8.4 -6.3 1.06 1.13June 2002 173 90.4 90.4 90.4 .01 26.8 16.6 .83 .49July 2002 186 73.0 73.0 73.0 .00 36.9 20.9 .79 .91August 2002 146 71.0 71.0 71.0 -.01 27.6 19.6 .80 .59September 2002 101 66.8 66.8 66.8 -.02 12.7 12.5 .87 .34October 2002 51.0 26.8 26.8 26.8 -.07 4.2 7.0 .93 1.09November 2002 27.2 12.1 12.1 12.1 .01 3.9 11.1 .89 1.58December 2002 10.1 4.15 4.15 4.15 -.01 3.9 20.4 .79 2.67January 2003 24.2 7.32 7.32 7.32 .01 3.5 11.5 .88 2.67February 2003 41.6 14.2 14.2 14.2 -.02 -1.2 -2.6 1.03 2.33

March 2003 77.3 19.9 19.9 19.9 .05 -1.1 -1.5 1.02 2.83

April 2003 100 40.5 40.5 40.5 -.01 3.4 3.6 .96 1.27May 2003 150 72.3 72.3 72.3 .02 14.1 9.9 .90 .79June 2003 166 104 104 104 .01 23.9 14.9 .85 .31July 2003 182 83.7 83.7 83.7 .01 24.9 14.3 .86 .77August 2003 159 52.8 52.8 52.8 -.02 20.8 13.5 .86 1.52September 2003 118 45.5 45.5 45.5 -.06 18.4 15.4 .85 1.22October 2003 71.8 29.8 29.8 29.8 -.01 13.2 17.8 .82 1.04November 2003 21.4 12.3 12.3 12.3 .00 -.7 -2.5 1.03 1.33December 2003 -0.756 6.85 6.85 6.85 -.01 -7.1 * 2.23 .89

January 2004 16.7 5.52 5.52 5.52 -.03 -2.7 -11.6 1.12 3.70February 2004 37.3 9.83 9.83 9.83 .04 4.5 11.4 .89 2.54March 2004 80.0 23.2 23.2 23.2 .02 -1.7 -2.2 1.02 2.40

April 2004 114 41.5 41.5 41.5 .00 1.5 1.4 .99 1.61May 2004 152 77.2 77.2 77.2 .03 11.9 8.1 .92 .74June 2004 164 78.4 56 56 .02 23.6 14.9 .85 .71
36 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 Table 5. Summary of monthly mean energy fluxes for April 2002 to April 2006 at riparian forest study site near Odessa, Nebraska.—Continued

[Rn, net radiation; λE, latent heat flux, 

H, sensible heat flux, 

G, soil heat flux, 

∆PS, change in vegetation energy storage, EBC, energy balance closure, EBR, energy balance ratio (equation 4); H/ λE, Bowen 

ratio; W/m2, watts per square meter; italicized values identify periods in which the EBR or H/λE was less than unity; unshaded rows designate the May–September growing season; grey shading designates the October–April dormant season]

Month Year

Monthly

mean

Rn

W/m2

Monthly

mean

λ

E

W/m2

Monthly

mean

H

W/m2

Monthly

mean

G

W/m2

Monthly

mean

∆PS

W/m2

Monthly

mean

EBC

W/m2

Monthly

mean

(EBC/(Rn-G-∆PS))

in percent

Monthly

mean

EBR

(dimensionless)

Monthly

mean

H/ 

λ

E

(dimensionless)

July 2004 172 96.2 37.7 7.35 .01 30.8 18.7 .81 .39August 2004 162 72.5 57.9 3.49 -.01 28.1 17.7 .82 .80September 2004 108 66.4 30.7 0.799 -.02 10.5 9.8 .90 .46October 2004 51.4 33.8 19.4 -3.78 -.01 2.0 3.6 .96 .57November 2004 22.2 14.5 18.5 -8.76 -.05 -2.1 -6.8 1.07 1.28December 2004 7.08 8.09 7.58 -8.94 .01 .3 1.9 .98 .94January 2005 16.3 10.6 17.2 -6.13 .01 -5.4 -24.1 1.24 1.63

February 2005 45.7 16.4 30.4 -1.99 -.01 .9 1.9 .98 1.85March 2005 71.6 23.5 49.1 2.04 .03 -3.1 -4.5 1.04 2.09April 2005 112 41.4 67.2 6.12 .00 -2.5 -2.4 1.02 1.62

May 2005 147 67.2 59.9 8.97 .02 11.3 8.2 .92 .89June 2005 182 116 26.2 11.1 .02 28.7 16.8 .83 .23July 2005 180 102 44.3 6.91 .02 26.3 15.2 .85 .43August 2005 142 72.7 43.5 3.00 -.03 23.3 16.8 .83 .60September 2005 123 59.6 39.7 1.14 .00 22.6 18.5 .81 .67October 2005 69.1 26.2 36.5 -4.01 -.03 10.4 14.2 .86 1.39November 2005 28.5 11.8 18.3 -6.6 -.04 5.0 14.2 .86 1.55December 2005 12.7 9.21 -1.39 -6.93 .02 11.8 60.2 .40 -.15January 2006 24.3 10.3 8.44 -3.77 .01 9.3 33.1 .67 .82February 2006 56.0 10.2 40.8 -4.12 .02 9.0 15.0 .85 3.98March 2006 71.8 16.7 45.7 0.331 .00 9.2 12.9 .87 2.74April 2006 108 47.5 66.5 5.82 .00 -11.6 -11.4 1.11 1.40

April 2002–March 2006

Mean 92.5 42.8 39.7 0.645 .00 9.4 10.2 .90 .93Mean (October–April) 49.0 18.4 31.6 -3.12 .00 2.0 3.8 .96 1.72Mean (May–September) 153 76.8 49.6 5.66 .00 20.7 14.0 .86 .65

* Poor energy balance closure was obtained for December 2003 because of low measured net radiation. This value was excluded from calculation of 4-year mean EBC/(Rn-G-

∆PS) percentage.
Evapotranspiration Rates 37

Table 6. Summary of monthly mean energy fluxes for April 2002 to April 2006 at riparian forest study site near Gothenburg, Nebraska.—Continued[Rn, net radiation; λE, latent heat flux, H, sensible heat flux, G, soil heat flux, ∆PS, change in vegetation energy storage, EBC, energy balance closure, EBR, energy balance ratio (equation 4); H/ λE, Bowen ratio; W/m2, watts per square meter; talicized values identify periods in which the EBR or H/λE was less than unity; unshaded rows designate the May–September growing season; grey shading designates the October–April dormant season]

Month Year

Monthly

mean

Rn

W/m2

Monthly

mean

λE

W/m2

Monthly

mean

H

W/m2

Monthly

mean

G

W/m2

Monthly

mean

∆PS

W/m2

Monthly

mean

EBC

W/m2

Monthly

mean

(EBC/(Rn-G-∆PS ))

in percent

Monthly

mean

EBR

(dimensionless)

Monthly

mean

H/ λ E

(dimensionless)

April 2002 116 34.4 75.1 3.94 0.03 2.4 2.1 0.98 2.18May 2002 151 60.6 81.5 6.72 .03 1.7 1.2 .99 1.35June 2002 173 96.3 60.1 9.97 .02 7.0 4.3 .96 .62July 2002 177 104 59.0 7.36 .00 6.1 3.6 .96 .57August 2002 140 85.2 27.7 3.77 -.01 23.4 17.2 .83 .33September 2002 101 71.4 22.2 1.03 -.03 6.8 6.8 .93 .31October 2002 46.6 28 26.5 -6.64 -.07 -1.2 -2.3 1.02 .95

November 2002 27.7 16.2 19.1 -6.45 .01 -1.2 -3.5 1.04 1.18December 2002 8.38 4.2 12.1 -7.71 .01 -0.2 -1.2 1.01 2.88January 2003 21.7 6.63 21.0 -5.17 .01 -0.7 -2.6 1.03 3.16February 2003 47.2 14.3 40.8 -4.08 -.01 -3.9 -7.6 1.08 2.85

March 2003 81.4 22.0 52.0 2.81 .05 4.5 5.7 .94 2.37April 2003 102 50.6 47.6 4.43 -.01 -0.3 -0.3 1.00 .94

May 2003 159 56.8 68.0 10.7 .03 23.9 16.1 .84 1.20June 2003 169 70.1 44.2 8.12 .01 46.6 29.0 .71 .63July 2003 180 106 45.5 9.51 .01 19.0 11.1 .89 .43August 2003 164 122 32.9 5.99 -.02 4.0 2.5 .97 .27September 2003 119 72.1 38.0 -0.202 -.04 8.9 7.5 .92 .53October 2003 74.3 33.2 34.5 -1.65 -.02 8.3 10.9 .89 1.04November 2003 26.8 12.1 14.7 -7.68 .01 7.7 22.3 .78 1.21December 2003 12.3 5.68 7.06 -6.86 -.01 6.4 33.4 .67 1.24January 2004 33.8 5.39 25.6 -6.80 -.02 9.6 23.6 .76 4.76February 2004 47.7 9.15 29.8 -1.98 .03 10.8 21.8 .78 3.25March 2004 96.4 28.2 52.3 3.24 .03 12.7 13.6 .86 1.85April 2004 103 46.7 59.8 3.91 .00 -7.0 -7.1 1.07 1.28May 2004 165 70.8 65.2 8.69 .03 20.2 12.9 .87 .92June 2004 162 91.5 47.1 8.09 .01 14.9 9.7 .90 .51
38 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 Table 6. Summary of monthly mean energy fluxes for April 2002 to April 2006 at riparian forest study site near Gothenburg, Nebraska.—Continued

[Rn, net radiation; λE, latent heat flux, 

H, sensible heat flux, 

G, soil heat flux, 

∆PS, change in vegetation energy storage, EBC, energy balance closure, EBR, energy balance ratio (equation 4); H/ λE, Bowen 

ratio; W/m2, watts per square meter; talicized values identify periods in which the EBR or H/λE was less than unity; unshaded rows designate the May–September growing season; grey shading designates the October–April dormant season]

Month Year

Monthly

mean

Rn

W/m2

Monthly

mean

λ

E

W/m2

Monthly

mean

H

W/m2

Monthly

mean

G

W/m2

Monthly

mean

∆PS

W/m2

Monthly

mean

EBC

W/m2

Monthly

mean

(EBC/(Rn-G-∆PS ))

in percent

Monthly

mean

EBR

(dimensionless)

Monthly

mean

H/ λ 

E

(dimensionless)

July 2004 184 80.0 37.1 10.3 .01 56.4 32.5 .68 .46August 2004 149 62.2 36.2 5.63 -.01 45.2 31.5 .69 .58September 2004 111 78.2 15.3 1.64 -.02 15.8 14.4 .86 .20October 2004 61.6 30.4 18.8 -3.57 -.02 16.0 24.5 .75 .62November 2004 25.2 13.9 13.4 -8.41 -.05 6.4 19.0 .81 .96December 2004 13.4 6.21 5.18 -9.01 .01 11.0 49.1 .51 .83January 2005 24.2 8.04 19.6 -5.16 .01 1.7 5.8 .94 2.44February 2005 55.4 13.6 33.5 -2.58 -.01 10.9 18.8 .81 2.46March 2005 75.3 23.0 48.4 1.45 .02 2.5 3.4 .97 2.10April 2005 64.4 31.7 54.1 4.60 .00 -26.0 -43.5 1.44 1.70May 2005 148 57.8 67.4 10.1 .03 12.2 8.8 .91 1.17June 2005 172 79.7 37.1 13.0 .03 42.7 26.9 .73 .47July 2005 183 103 33.8 11.4 .02 34.5 20.1 .80 .33August 2005 150 75.0 31.8 5.93 -.03 36.8 25.5 .74 .42September 2005 124 54.8 35.7 3.74 .00 29.3 24.4 .76 .65October 2005 73.2 28.0 32.3 -4.32 -.03 17.3 22.3 .78 1.15November 2005 36.2 14.7 11.6 -6.90 -.05 16.9 39.2 .61 .79December 2005 21.6 8.57 8.54 -7.42 .03 11.9 41.0 .59 1.00January 2006 33.2 10.0 9.14 -4.78 .01 18.8 49.5 .50 .91February 2006 62.0 10.1 49.4 -4.35 .02 6.8 10.3 .90 4.87March 2006 76.3 22.3 49.6 0.441 .01 3.9 5.1 .95 2.22April 2006 110 45.7 62.9 6.83 .00 -5.6 -5.4 1.05 1.38

April 2002–March 2006

Mean 94.8 44.5 36.6 1.14 .00 12.5 13.3 .87 .82Mean (October–April) 52.4 19.2 31.1 -3.10 .00 5.2 9.4 .91 1.90Mean (May–September) 154 79.9 44.3 7.08 .00 22.8 15.5 .84 .60
Evapotranspiration Rates 39

Within annual cycles, there were variations in values of 

λE, H, and soil-heat flux (G) within seasons and between sites. 

The maximum monthly mean λE typically occurred 1 to 2 

months later at the Gothenburg site as compared to the Odessa 

site, with the exception of 2004 (fig. 15). The greatest monthly 

mean values of H typically occurred earlier in the growing season than values of λE. At Odessa, the greatest monthly mean 

values of H occurred during April in 2002, 2004, and 2005 

(fig. 15A; table 5). The exception occurred during 2003, when 

the greatest monthly mean H for the year occurred during 

August. At the Gothenburg site, the greatest values of monthly 

mean H occurred during May of each of the 4 study years, 1 to 

3 months earlier than peak values of λE (fig. 15B; table 6).

Annually and monthly, G typically was a small energy 

term relative to λE and H (fig. 15; tables 5 and 6). The largest positive values of G (indicating downward flow of heat 

into the soil from the land surface, or soil heating) typically 

occurred during June or July and the largest negative values 

(indicating upward flow of heat from soil to the land surface, 

or soil cooling) occurred during December. Calculated values 

of monthly average ∆PS were negligible at all times of the 

year at both sites. 

Monthly EBCs were largest during the growing season 

because all energy terms were larger during these periods than 

during the dormant seasons (fig. 15). Values of EBC were 

nearly always positive reflecting that Rn - G - ∆PS was greater 

than H + λE during most months. At the Odessa site, monthly 

EBC/(Rn – G – ∆PS) percentages for the growing season were 

less than 21 percent with a mean value of about 14 percent. 

At the Gothenburg site, monthly EBC/(Rn – G – ∆PS) percentages for the growing season were more variable than at 

Odessa, ranging from 1 to 32 percent with a mean of about 15 

percent. The dormant-season EBC/(Rn – G – ∆PS) percentages were much more variable than during the growing season 

(fig. 15), although mean values were 3.8 percent at Odessa and 

9.4 percent at Gothenburg. The largest EBC/(Rn – G – ∆PS) 

imbalances at both sites occurred during December and January when the lowest energy fluxes occurred. 

Diurnal cycles in energy fluxes were examined using a 

subset of data to represent typical conditions. Median energy 

flux for every 30-minute period in a diurnal cycle is shown in 

figure 16 for summer (276 days of data for each 30-minute 

period) and for winter (212 days of data for each 30-minute 

period). The median energy fluxes for each 30-minute period 

were calculated from data for 2002 through 2004. 

Median diurnal cycles of λE and H during summer 

approximately followed those of Rn. Peak summer values of 

Rn, H, and λE occurred in early afternoon and were nearly 

synchronous (fig. 16A, C). Diurnal cycles of H closely followed those of Rn during summer and winter (fig. 16). Diurnal 

cycles of λE during summer had slightly broader and flatter 

afternoon peaks and a more gradual transition from daytime 

to nighttime values than did cycles for H. The looser relation 

between Rn and λE values than between Rn and H values may 

indicate that a broader range of conditions such as humidity, water availability, and plant health, may be affecting λE. 

At the Odessa site, peak values of λE occurred slightly after 

those of Rn and H in the afternoon and peak values were less 

than those of H (fig. 16A). At the Gothenburg site, median λE

values exceeded H values throughout the summer daily cycle; 

however, median diurnal values of λE and H were fairly similar during summer at both sites. At Odessa during summer, 

median G values peaked at 10:30 am and declined thereafter. 

In contrast, at the Gothenburg site during summer, median G

(A) Daytime (B) Nighttime

y = 1.0297x - 15.6

R2 = 0.9947

y = 1.4639x - 1.4

R2 = 0.9676

-200

0

200

400

600

800

-200 0 200 400 600 800

NET RADIATION MEASURED WITH TWO-COMPONENT NET RADIOMETER, IN WATTS PER SQUARE METER 

NET RADIATION MEASURED WITH FOUR-COMPONENT NET

RADIOMETER, IN WATTS PER SQUARE METER 

-120

-100

-80

-60

-40

-20

0

20

-120 -100 -80 -60 -40 -20 0 20

1:1 line

1:1 line

Individual 30-minute average valuesIndividual 30-minute average values

Linear for individual 30-minute

average values

Linear for individual 30-minute

average values

Figure 14. Relations between net radiation measured with different net radiometers at study site near Odessa, Nebraska, March 

2004–May 2005.
40 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

-50

0

50

100

150

200

-50

0

50

100

150

200

Jan. July

2002 2003 2004 2005 2006

Jan. July Jan. July Jan. July Jan.

W

ATTS PER SQUARE METER

-50

0

50

100

150

200

-50

0

50

100

150

200

EBC/(Rn − G − 

∆PS) x 100, IN PERCENT

(A) Odessa

(B) Gothenburg

EXPLANATION

Latent heat flux (λE)

Change in vegetation

 energy storage (∆PS)

Sensible heat flux (H)

Net radiation (Rn)

Soil heat flux (G)

Energy balance

 closure (EBC)

EBC/(Rn – G – ∆PS)

Figure 15. Monthly mean energy fluxes for study sites near Odessa and Gothenburg, Nebraska, 2002–06.
Evapotranspiration Rates 41 12:00 a.m. 6:00 a.m. 12:00 p.m. 6:00 p.m. 12:00 a.m. 12:00 a.m. 6:00 a.m. 12:00 p.m. 6:00 p.m. 12:00 a.m. TIME OF DAY

TNECREP IN ,001 x ) PS∆ − G − nR(/CBE

-10

0

0

100200300400500600700

-10

0

0

100200300400500600700

RETEMERAUQSREPSTTAW

-10

0

0

100200300400500600700

-10

0

0

100200300400500600700

10

0

20304050607080

10

0

2030405060708010

0

20304050607080

10

0

20304050607080

(A) Odessa, June-August (C) Gothenburg, June-August(B) Odessa, December-February (D) Gothenburg, December-February

EXPLANATION

Latent heat flux (λE)

Change in vegetation energy storage (∆PS)

Sensible heat flux (H)

Net radiation (Rn)Soil heat flux (G)Energy balance closure (EBC)

EBC/(Rn – G – ∆PS)

Figure 16. Diurnal patterns of median energy fluxes based on March 2002 to December 2004 data for June–August and December–February at study sites near Odessa and Gothenburg, Nebraska.
42 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

values peaked in the early afternoon and the shape of the diurnal cycle was approximately similar to that of λE and H.

During summer, all median nighttime-energy fluxes but 

λE were negative (fig. 16A). Median values of λE declined 

from about 20 W/m2 at 8:00 p.m. to about 4 W/m2 at 6:00 

a.m., but were always greater than zero. Thus, a small latent 

heat flux occurred at night during the summer. During winter, 

nighttime mean λE values generally were less than 1 W/m2. 

For 30-minute periods during the diurnal cycle, the 

smallest median EBC/(Rn – G – ∆PS) imbalances, usually less 

than 20 percent, occurred between 8:00 a.m. and 6:00 p.m. 

during the summer when the greatest energy fluxes and ET 

occurred (fig. 16A, C). At both sites, approximately 70 percent 

of the total λE during the study period occurred under daytime 

conditions during the growing season. EBC/(Rn – G – ∆PS) 

imbalances typically were larger for nighttime and transitional 

periods from daytime to nighttime conditions when conditions were changing relatively quickly (fig. 16); however, 

λE was relatively small under these nighttime and transition 

conditions. Thus, the measurements of energy fluxes were of 

greatest accuracy for periods when most of the energy fluxes 

occurred.

Water Balances 

The measured components of the water balance (precipitation, ET, soil-water and ground-water storage, eq. 12) 

were evaluated to determine that net ground-water recharge 

occurred during the study at both sites. Precipitation and ET

were the dominant components of the annual water balance, 

with changes in soil-water and ground-water storage being 

comparatively minor terms (table 7). Annual precipitation 

fluctuated more than annual ET (fig. 17A). Annual precipitation varied by about 35 percent from the 4-year mean whereas 

annual ET varied by about 7 percent from the 4-year mean. 

Because precipitation less ET (Pexcess) largely determined the 

calculated net ground-water inflow or outflow, the presence or 

absence of aquifer recharge was sensitive, though not strictly 

dependent, on these precipitation fluctuations at each study 

site (fig. 17B). 

Positive values of (GWin – GWout) from equation 12 

indicated that net ground-water inflow to the riparian forests 

was necessary to satisfy the water balance (ground-water 

discharge occurs in the riparian forest). Negative values of 

(GWin – GWout) indicated that net ground-water outflow from 

the riparian forests was necessary to satisfy the water balance 

(ground-water recharge occurs in the riparian forest). 

Net ground-water discharge from the riparian forest to 

the atmosphere occurred at both sites in 2002 (fig. 17B), when 

annual ET exceeded annual precipitation. Because Pexcess was 

more negative at Gothenburg in 2002, ground-water discharge 

was larger at Gothenburg than at Odessa. Net ground-water 

recharge occurred at both sites in 2003, 2004, and 2005 (fig. 

17B). At the Odessa site, annual net ground-water flux was 

almost linearly related to Pexcess, with increases in net groundwater recharge as Pexcess increased. At Gothenburg, the relation 

between annual net ground-water flux and Pexcess was less well 

defined, reflecting the greater variability in annual changes in 

Gothenburg soil-water storage.

The 4-year-average values of (GWin – GWout) were 

negative, indicating that the study sites were a net source of 

ground-water recharge during the study period (table 7; fig. 

17B). The 4-year-average net ground-water recharge at Odessa 

(76 mm/yr) was larger than that at Gothenburg (13 mm/yr), 

consistent with larger Pexcess values at Odessa than Gothenburg. 

After adjusting the annual water-balance calculations to 

account for canopy interception (eq. 13), evaporation of intercepted water (EI

) accounted for an average of about 14 percent 

of total ET at Odessa and 9 percent at Gothenburg (table 7). 

The fractions of ET attributable to EI at the study sites are 

within the lower range of values determined for other forests 

(Grelle and others, 1997; Savenije, 2004), most likely because 

of the relatively low tree density and windy, semi-arid climate.

There are several sources of uncertainty in the annual 

water-balance results. As discussed earlier, annual precipitation probably was measured with a precision of about 5 

percent and ET probably was estimated with an uncertainty of 

10 percent. Uncertainties in ∆GW primarily reflect uncertainties in the estimate of specific yield. The porosity is an upper 

limit of the specific yield, but generally has been determined 

to be a reasonable estimate for sandy sediments (Loheide 

and others, 2005). When the specific yield was reduced to 50 

percent of the porosity (Meyboom, 1964), values of (GWin – 

GWout) increased by an average of 9 percent at Odessa and 19 

percent at Gothenburg. Thus, if specific yield actually is less 

than porosity, the net ground-water discharge could be larger 

and net ground-water recharge smaller than the values reported 

in table 7. Uncertainties in determining ∆SW primarily reflect 

uncertainties in the accuracy of soil-water content measurements, areal weights assigned to UZ1 and UZ2 profiles, and 

the degree to which spatial heterogeneity in soil-water content 

can be represented by two profiles of 7 probes each. These 

uncertainties are difficult to quantify but are not expected to 

fundamentally change the results of the water-balance calculations, as changes in soil-water storage were a small term in the 

calculations. 

The water-balance results can be used to estimate upper 

and lower boundaries on the values of GWET (ET derived 

from ground water rather than precipitation sources). The 

upper boundary of possible values of GWET is ETGWSW (eq. 

14). ETGWSW ranged from 448 to 504 mm/yr with an average 

value of 473 mm/yr at the Odessa site, or roughly 86 percent 

of total ET (table 7). ETGWSW ranged from 480 to 559 mm/yr 

with an average value of 520 mm/yr at the Gothenburg site, 

or roughly 90 percent of total ET. The lower boundary of possible values of GWET, annually, is (GWin – GWout) or the net 

ground-water inflow into the riparian area to satisfy the annual 

water balance. The negative values of (GWin – GWout) indicate 

that the lower bound of GWET was effectively zero during 

2003-2005. During the driest study year, 2002, (GWin – GWout) 

was 53 mm (10 percent of total ET of 514 mm) at Odessa and 
Evapotranspiration Rates 43

161 mm (28 percent of total ET of 585 mm) at Gothenburg. 

Thus, on an annual basis, ground water becomes an important, albeit minor, water source for satisfying riparian-forest 

ET demands during dry years, with the remainder of the ET 

demand satisfied by available precipitation. During wet years, 

all of the ET demand could be satisfied by available precipitation. These bounding limits do not imply that ground water 

is not an important source of water to riparian vegetation—

especially to phreatophytes—during increased ET periods in 

the summer. On summer days of increased ET and no precipitation, GWET demand could be considerable; however, the 

calculations imply that annually, ground-water use by riparian 

forests is likely to be balanced by periods of recharge from 

excess precipitation at other times of the year. In more arid settings, studies have determined that ground water may supply a 

large fraction of the water used for ET by riparian vegetation 

(Williams and others, 2006); however, along the Platte River 

in Nebraska, precipitation was great enough that annually, 

most or all of the ET demand can be satisfied by available 

precipitation. 

Simulation of Evapotranspiration Rates

Crop coefficients were developed from the measured 

data that could be used to simulate ET rates in other riparian 

forests along the Platte River. Daily mean kc values developed 

from the study-period data—calculated by dividing measured 

ET by reference ET from nearby weather stations—indicated 

considerable variation, reflecting short-term variations in sitespecific conditions. As these conditions would not necessarily 

occur at other sites or in subsequent years, mean kc values 

for 15-day and monthly periods were computed to produce 

a less erratic kc distribution (table 8; fig. 18). By rearranging 

equation 16, the 15-day and monthly kc values were multiplied 

by Eo to simulate daily ET (ETsim). ETsim was compared with 

measured ET to evaluate the model performance. 

The 15-day and monthly mean kc values (table 8; fig. 18) 

ranged from 0.07 to 0.51, followed the same seasonal patterns, 

and generally were similar at Odessa and Gothenburg. The 

primary differences between the two sites were that kc values 

were greater at Odessa during parts of May, and greater at 

Gothenburg during August and parts of July and September. 

The summer-peak kc values occurred in June at Odessa as 

compared to August at Gothenburg. Although unexplained, kc

values were greater in early February than in January or March 

Table 7. Annual water-balance results for riparian forest study sites near Odessa and Gothenburg, Nebraska, April 1, 2002, to 

March 31, 2006.

[ET, total evapotranspiration; P, precipitation; P-ET, precipitation less total evapotranspiration; ∆SW, change in soil-water storage; ∆GW, change in groundwater storage; GWin - GWout, difference between ground-water inflow and ground-water outflow to representative control volume; TF, canopy throughfall; I, 

canopy interception, computed from P-TF (equation 13); EI, evaporation of canopy interception, assumed to be of equal magnitude as I; ETGWSW, evapotranspiration from ground- and soil-water sources, computed from ET-EI

 (equation 14); EI /ET, evaporation of canopy interception as a percentage of total 

evapotranspiration; mm, millimeters]

Water balance neglecting canopy interception (equation 12)

GWin - GWout = ET – P + ∆SW + ∆GW

Water balance including canopy interception 

(equation 15)

GWin - GWout = ETGWSW – TF + ∆SW + ∆GW

Year ET

(mm)

P 

(mm)

P - ET

(mm)

∆SW 

(mm)

∆GW

(mm)

GWin-GWout

(mm)

TF 

(mm)

I

(mm)

EI

(mm)

ETgwsw

(mm)

EI/ET

(percent)

Odessa

2002 514 429 -85 -18 -14 53 377 51 51 463 10

2003 524 633 109 5 -28 -132 557 76 76 448 14

2004 580 844 264 40 60 -164 743 101 101 478 17

2005 586 679 94 -8 39 -63 598 82 82 504 14

4-year 

average 551 646 95 5 14 -76 569 78 78 473 14

Gothenburg

2002 585 359 -226 -61 -4 161 326 33 33 552 6

2003 616 610 -6 -54 -2 -50 554 57 57 559 9

2004 563 791 227 147 60 -21 717 73 73 490 13

2005 535 589 55 -52 -34 -141 535 55 55 480 10

4-year 

average 575 587 13 -5 5 -13 533 54 54 520 9
44 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

at both sites. Otherwise, the kc values generally followed a seasonal curve from a winter minimum to a summer maximum. 

For annual simulation periods or longer, the simple cropcoefficient model using either monthly or 15-day average kc

reasonably simulated measured ET. For the Odessa site, deviations of ETsim from annual ET ranged from -11.7 percent to 

+7.5 percent with a 4-year average of -3.5 percent for monthly 

coefficients and -3.4 percent for 15-day coefficients (table 9). 

For the Gothenburg site, annual deviations of ETsim from ET

ranged from -6.7 percent to +9.0 percent, with a 4-year average of +1.8 percent for monthly coefficients and +1.6 percent 

for 15-day coefficients. Annually, deviations of ETsim from ET

were similar whether modeled using the 15-day average or 

monthly mean kc values.

Generally, the simple-crop-coefficient models underpredicted ET for days of increased ET and overpredicted ET

for days of low ET. As a result, ETsim was less variable than 

measured ET. For annual or longer periods, these short-term 

discrepancies balanced out, and ETsim using simple crop-coefficient models was within 12 percent of measured ET (table 9). 

The kc models were further tested by comparing the SLR 

relations between measured daily ET and ETsim determined 

for the 2002 to 2004 calibration period with those determined 

for the 2005 validation period. The pattern of reduced variability in daily ETsim as compared to measured ET occurred for 

both 2002 to 2004, and 2005. Coefficients of determination 

between daily ETsim and measured ET were greater for 2005 

than for 2002 to 2004. These results indicate that the kc models 

approximated measured ET for a nearly normal period other 

than the calibration period, but that the kc models are likely 

to be more accurate in predicting ET for monthly or annual 

periods, rather than as daily values. 

Environmental Factors Affecting 

Evapotranspiration Rates 

The simple crop-coefficient models approximated measured ET at the riparian-forest sites, particularly for longer 

periods, but these simple models do not contribute to an understanding of the environmental factors that affect ET rates. 

SLR and MLR models of the relations between explanatory 

variables and ET were developed to better understand the factors affecting ET rates in riparian forests. 

Numerous studies investigating the factors that affect 

ET rates commonly have determined ET rates to be strongly 

dependent on vapor-pressure deficit (D) (Monteith, 1965; 

Mackay and others, 2007), solar radiation (Rs) (Schulz and 

Jarvis, 2004), air temperature (Ta)(Rosenberry and others, 

2004), moisture availability (Williams and others, 2006), and 

measures of vegetation state such as LAI (Kristensen, 1974; 

Ewers and others, 2002). For this study, daily values of ET

were compared to daily averages of environmental factors D, 

Rs, Ta, moisture availability, and LAI. 

D, Rs, and Ta followed seasonal patterns with the greatest values during the summer and the lowest values during 

the winter. ET rates are expected to increase as D increases 

because D is considered a proxy for the vapor-pressure difference between the inside of leaves and the atmosphere. Water 

availability does not necessarily follow seasonal patterns 

because of the variability of precipitation and ground-water 

levels, and thus will not always be strongly correlated with ET. 

Moisture availability was evaluated using several variables, 

including the average soil-water content in the upper 8 cm 

(from the area-weighted average of soil-water contents at UZ1 

and UZ2); the daily change in soil-water content; the deficit 

in the soil-water content below the 4-year median (assigned 

a value of zero for soil-water contents above or equal to the 

median); the depth to the ground-water table (DTW) in the 

well closest to the tower site; the daily change in ground-water 

level; the depth to ground water below the 4-year median 

DTW (assigned a value of zero for DTWs less than the median 

DTW); and the daily precipitation. Daily interpolated LAI was 

-200 -100 0 100 200 300

1:1 line

ANNUAL EVAPOTRANSPIRATION, IN MILLIMETERS

350

450

550

650

750

850

350 450 550 650 750 850

ANNUAL PRECIPITATION, IN MILLIMETERS

ANNUAL PRECIPITATION MINUS EVAPOTRANSPIRATION, Pexcess, IN MILLIMETERS

-200

-100

0

100

200

-300 ANNUAL NET GROUND-WATER INFLOW (POSITIVE)

OR OUTFLOW (NEGATIVE), IN MILLIMETERS

(A)

(B)

EXPLANATION

Odessa site annual precipitation

 measurement 

Gothenburg site annual precipitation

 measurement

4-year average precipitation from

 measurements onsite

Year of annual precipitation

 2002

 2003

 2004

 2005

Figure 17. Relations among annual water-balance terms for 

study sites near Odessa and Gothenburg, Nebraska, 2002–06.
Evapotranspiration Rates 45

Table 8. Average crop coefficients for riparian forest study sites near Odessa and Gothenburg, Nebraska, 

2002–05.

[ET, measured evapotranspiration at riparian study site; Eo, evapotranspiration for well-watered alfalfa reference crop; kc, crop coefficient determined as ET/ETo

; mm/d, millimeters per day]

Odessa Gothenburg

Evaluation

Period

Mean

ET

(mm/d)

Mean

Eo

1

(mm/d)

Mean

kc

(dimensionless)

Mean

ET

(mm/d)

Mean

Eo

1

(mm/d)

Mean

kc

(dimensionless)

Values averaged for monthly evaluation periods

January 0.27 1.41 0.19 0.23 1.61 0.14

February .47 1.87 .25 .43 2.19 .20

March .77 4.27 .18 .85 4.50 .19

April 1.28 5.91 .22 1.54 5.97 .26

May 2.51 6.60 .38 2.21 6.91 .32

June 3.22 7.66 .42 3.04 7.42 .41

July 2.99 7.79 .38 3.43 7.83 .44

August 2.32 6.95 .33 3.18 6.92 .46

September 2.10 6.34 .33 2.61 6.45 .40

October 1.06 3.59 .29 1.07 3.82 .28

November .45 2.45 .18 .49 2.49 .20

December .22 2.01 .11 .19 2.08 .09

Values averaged for 15-day evaluation periods (Beginning date of period reported)

January 1 .23 1.52 .15 .22 1.60 .14

January 16 .30 1.32 .23 .23 1.61 .14

January 31 .46 1.58 .29 .44 1.83 .24

February 15 .47 2.08 .23 .41 2.47 .17

March 1 .63 4.29 .15 .70 4.60 .15

March 16 .91 4.38 .21 1.00 4.55 .22

March 31 1.00 6.16 .16 1.33 6.26 .21

April 15 1.56 5.66 .27 1.74 5.68 .31

April 30 2.05 6.34 .32 2.06 6.81 .30

May 15 2.90 6.72 .43 2.26 6.86 .33

May 30 3.21 8.16 .39 3.28 8.04 .41

June 14 3.26 7.16 .45 2.86 6.88 .42

June 29 3.24 7.80 .42 3.06 7.65 .40

July 14 2.73 7.72 .35 3.55 7.94 .45

July 29 2.40 7.27 .33 3.12 7.29 .43

August 13 2.41 6.92 .35 3.47 6.87 .51

August 28 2.32 7.10 .33 3.24 7.17 .45

September 12 1.98 6.04 .33 2.31 6.18 .37

September 27 1.49 4.02 .37 1.54 4.25 .36

October 12 .82 3.39 .24 .77 3.58 .21

October 27 .59 2.58 .23 .61 2.75 .22

November 11 .39 2.50 .16 .46 2.48 .18

November 26 .30 1.82 .16 .28 1.91 .15

December 11 .21 2.12 .10 .16 2.15 .07

1 Eo

 values, calculated using a modified Penman-evapotranspiration method, were obtained from High Plains Regional Climate 

Center (2006). Data from the Kearney station were used for the Odessa site and data from the Gothenburg station were used for the 

Gothenburg site.
46 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

used as an indicator of vegetation state. 

Because LAI data were available only for 

2002-04, average LAI values from those 

years were used to estimate LAI in 2005. 

Simple Linear Regressions

Initial plots of daily average Rs and 

Ta against daily ET indicated that these 

variables were nonlinearly related to

ET. Quadratic transformations of Rs and 

Ta data improved the linearity with ET; 

therefore, SLRs of ET as a function of 

the transformed variables Rs

2

 and Ta

2 were 

performed (table 10; fig. 19), and these 

transformed variables also were used in the 

MLR models. 

At both sites, ET increased with 

increasing D, Rs

2

, Ta

2

, and LAI, as identified by nonzero slope coefficients (p < 

0.001). With R2 ranging from 0.50 to 0.64 

for regressions that used data from every 

day in the study period (table 10; fig. 19), 

the relation of ET to these variables seemed 

reasonably well fit by SLR. Daily ET was 

either unrelated (R2 < 0.10) or was related 

only weakly (0.10 < R2 < 0.30) to all of the predefined indicators of moisture availability. 

By separating the daily data into two groups according 

to the presence or lack of precipitation, the resulting SLR 

models indicated that ET was larger on days with precipitation 

than days without precipitation (fig. 19). SLR relations of ET

with D, Rs

2

, and Ta

2

 all had similar slopes for both groups, but 

the intercepts of the SLR equations always were greater for 

days with precipitation than days without precipitation. D, Rs

2

, 

Ta

2

, and LAI using the SLR models had better fits (as evaluated by R2

 values and residual standard errors) of ET for days 

without precipitation than days with precipitation (table 10). 

SLR goodness-of-fit statistics (R2, RSE) also were better for 

days without precipitation than for models fit using the entire 

data set. These results indicate that ET was more strongly 

controlled by atmospheric and vegetation conditions on days 

without precipitation than days with precipitation, when the 

evaporation of wet surfaces affects ET rates. Thus, although 

the selected moisture-availability indicators were not directly 

related to ET rates, the relation of ET to environmental factors 

was different on days with precipitation—when additional 

moisture availability resulted in ET that was probably limited 

only by energy-balance conditions—than on dry days when 

ET may have been water-limited. These comparisons indicated 

that to understand the factors controlling ET rates, it was necessary to consider different relations for days with and without 

precipitation. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROP COEFFICIENT (kc)

(A) Monthly average crop coefficients

(B) 15-day average crop coefficients

Jan.

January 1

April 1

July 1

September 30

December 31

Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.

DATE

Gothenburg

Odessa

Gothenburg

Odessa

Figure 18. Average crop coefficients for riparian forests at study 

sites near Odessa and Gothenburg, Nebraska, 2002–05.

Table 9. Comparison of evapotranspiration simulated with 15-day average and 

monthly crop coefficient models to actual evapotranspiration at riparian forest study 

sites near Odessa and Gothenburg, Nebraska.

[ET, evapotranspiration; mm, millimeters; kc, crop coefficient; %, percent]

Study year1 4-year 

mean 2002 2003 2004 2005

Odessa

Measured annual ET (mm) 514 524 580 586 551

Simulated annual ET compute from:

Monthly kc (mm) 551 513 513 548 531

15-day kc (mm) 553 512 512 551 532

Deviation of simulated from measured ET:

Monthly kc (%) 7.2 -2.0 -11.5 -6.4 -3.5

15-day kc (%) 7.5 -2.3 -11.7 -5.9 -3.4

Gothenburg

Measured annual ET (mm) 585 616 563 535 575

Simulated annual ET computed from:

Monthly kc (mm) 638 601 526 575 585

15-day kc (mm) 637 598 528 573 584

Deviation of simulated from measured ET:

Monthly kc (%) 9.0 -2.4 -6.7 7.6 1.8

15-day kc (%) 9.0 -2.9 -6.3 7.2 1.6

1

 For this report, study year is from April 1 of the reported year to March 31 of the following year.
Evapotranspiration Rates 47

Table 10. Results of simple linear regressions of daily evapotranspiration with potentially explanatory independent variables for study sites near Odessa and Gothenburg, Nebraska, 2002–06.[All slope and intercept coefficients shown are significantly different from zero (t test with significance threshold value of 5 percent), unless italicized; RSE, residual standard error; mm/d, millimeters per day; R2, coefficient of determination (dimensionless); No. of days, number of days used in the regression analysis; kPa, kilopascals; W/m2, watts per square meter; oC, degrees Celsius; cm, centimeters; NR, regression results not reported because R2 was less than 0.10; <, less than]

Independent variable

Regression of independent variable with daily evapotranspiration

Odessa Gothenburg

2Slope Intercept (mm/d) RSE (mm/d) R2 No. of days 2 Slope Intercept (mm/d) RSE (mm/d) R2 No. of days

Regressions computed from all days 

Vapor pressure deficit (D), in kPa 1.78 0.34 0.91 0.50 1,467 1.76 0.34 0.92 0.55 1,497Solar radiation squared (Rs2), in (W/m2)2 .0000279 .29 .79 .63 1,496 .0000293 .34 .92 .55 1,500Air temperature squared (Ta2), in oC2 .0043 .52 .81 .61 1,494 .0046 .52 .82 .64 1,509Leaf Area Index (LAI), dimensionless 2.62 -.42 .75 .64 1,015 5.03 -3.93 .93 .58 1,023Depth to water, in cm .0335 -2.19 1.21 .13 1,450 NR NR NR < .10 NRDepth of water below median depth to water1, in cm .048 1.17 1.20 .14 1,450 NR NR NR < .10 NR

Regressions computed from days without precipitation

Vapor pressure deficit (D), in kPa 1.83 .08 .81 .59 1,074 1.80 .07 .78 .66 1,104Solar radiation squared (Rs2), in (W/m2)2 .0000284 .06 .70 .70 1,089 .0000297 .13 .84 .61 1,107Air temperature squared (Ta2), in oC2 .0042 .41 .73 .66 1,086 .0046 .42 .71 .72 1,115Leaf Area Index (LAI), dimensionless 2.68 -.50 .70 .68 738 5.26 -4.23 .83 .64 758Depth to water, in cm .0342 -2.79 1.20 .16 1,058 NR NR NR < .10 NRDepth of water below median depth to water1, in cm .0532 .99 1.10 .17 1,058 NR NR NR < .10 NR

Regressions computed from days with precipitation

Vapor pressure deficit (D), in kPa 2.08 .81 .88 .53 393 2.04 .84 .97 .51 393Solar radiation squared (Rs2), in (W/m2)2 .0000294 .79 .73 .67 407 .0000299 .86 .92 .56 393Air temperature squared (Ta2), in oC2 .0042 .85 .91 .49 408 .0045 .89 1.00 .47 394Leaf Area Index (LAI), dimensionless 2.32 -.05 .87 .49 277 4.06 -2.66 1.10 .37 265Depth to water, in cm NR NR NR < .10 NR NR NR NR < .10 NRDepth of water below median depth to water1, in cm NR NR NR < .10 NR NR NR NR < .10 NR

1 Median depth to water was 120 cm at Odessa and 137 cm at Gothenburg.2 Slope units differ for each independent variable.
48 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

0

20,00040,00060,00080,000

100,000120,000140,000160,000

0 100 200 300 400 500 600 700 800 900 1,000

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

DAILY AVERAGE VAPOR PRESSURE DEFICIT (D), IN KILOPASCALS

-1

0

1

2

3

4

5

6

7

8

-1

0

1

2

3

4

5

6

7

8

-1

0

1

2

3

4

5

6

7

8

-1

0

1

2

3

4

5

6

7

8

DAILY EVAPOTRANSPIRATION (ET), IN MILLIMETERS

Days with precipitation

ET = 2.08D + 0.81

R

2

= 0.53

Days with precipitation

ET = 2.94x10-5 Rs

2

+ 0.79

R

2

= 0.67

Days with precipitation

ET = 0.0042Ta

2

+ 0.85

R

2

= 0.49

Days with precipitation

ET = 2.32LAI - 0.05

R

2

= 0.49

Days without precipitation

ET = 2.84x10-5 Rs

2

+ 0.06

R

2

= 0.70

Days without precipitation

ET = 0.0042Ta

2

+ 0.41

R

2

= 0.66

Days without precipitation

ET = 2.68LAI - 0.50

R

2

= 0.68

Days without precipitation

ET = 1.83D + 0.08

R

2

= 0.59

(A) Daily average vapor-pressure deficit at study site near Odessa

(B) Daily average solar radiation at study site near Odessa

(C) Daily average air temparature at study site near Odessa

(D) Daily interpolated leaf-area index at study site near Odessa (H) Daily interpolated leaf-area index at study site near Odessa

DAILYAVERAGESOLAR RADIATIONSQUARED(Rs

2

),IN (WATTSPERSQUAREMETER)

2

0

20,00040,00060,00080,000

100,000120,000140,000160,000

DAILYAVERAGEAIR TEMPERATURESQUARED(Ta

2

),IN (DEGREESCELSIUS)

2

0 100 200 300 400 500 600 700 800 900 1,000

DAILYINTERPOLATEDLEAF-AREAINDEX(LAI),DIMENSIONLESS

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

EXPLANATION

Linear regression line for days with precipitation

Linear regression line for days without precipitation

Day with precipitation

Day without precipitation

Days with precipitation

ET = 2.04D + 0.84

R

2

= 0.51

Days without precipitation

ET = 1.80D + 0.07

R

2

= 0.66

Days with precipitation

ET = 2.99x10-5 Rs

2

+ 0.86

R

2

= 0.56

Days without precipitation

ET = 2.97x10-5 Rs

2

+ 0.13

R

2

= 0.61

Days with precipitation

ET = 0.0045Ta

2

+ 0.89

R

2

= 0.47

Days without precipitation

ET = 0.0046Ta

2

+ 0.42

R

2

= 0.72

Days with precipitation

ET = 4.06LAI - 2.66

R

2

= 0.37

Days without precipitation

ET = 5.26LAI - 4.23

R

2

= 0.64

(E) Daily average vapor-pressure deficit at study site near Gothenburg

(F) Daily average solar radiation at study site near Gothenburg

(G) Daily average air temparature at study site near Gothenburg

Figure 19. Simple linear regressions of daily evapotranspiration rates with vapor-pressure deficit, solar radiation, air temperature, and 

leaf-area index at study sites near Odessa and Gothenburg, Nebraska, 2002–06.
Evapotranspiration Rates 49

Coefficients of determination for SLRs relating ET to D, Rs

2

, or Ta

2

 varied no more than 16 

percent between Odessa and Gothenburg. On days 

without precipitation, D, Rs

2

, Ta

2

, and LAI were all 

strongly related to ET at both sites (R2 ranging from 

0.59 to 0.72), with Gothenburg having stronger 

relations to D and Ta

2 and Odessa having stronger 

relations to Rs

2

 and LAI (table 10). For days with 

precipitation, D and Rs

2

 were most strongly related 

to ET (R2 ranging from 0.51 to 0.67), whereas Ta

2

(R2 of 0.47 to 0.49) and LAI (R2 of 0.37 to 0.49) 

were less strongly related to ET. Relations of ET to 

LAI varied between days with and without precipitation and were dissimilar between sites (table 10). 

Surprisingly, on days without precipitation, 

Odessa ET increased weakly as DTW (R2 = 0.16) 

and depth of water below median DTW (R2 = 0.17) 

increased. These relations were somewhat counterintuitive because they indicated that ET increased 

as the water table declined, whereas the opposite 

was expected. Because the relations are weak, they 

may be the result of variation in extraneous environmental factors; however, it is possible that this 

pattern reflects seasonal increases in ET that result 

in increases in the DTW during the summer. 

Multiple Linear Regressions

Based upon the SLR relations between ET and 

D, Rs

2

, Ta

2

, LAI, and DTW (at Odessa only) for days 

with and without precipitation, multiple linearregression models of ET as a function of these 

variables were constructed (table 11). The goals 

of the MLR analysis were to compare the relative 

effect that each variable had on ET between sites 

and to identify the assemblage of variables that 

most strongly affects ET rates at each site. To allow 

for direct comparison between sites, initial models 

were constructed that included D, Rs

2

, Ta

2

, and LAI, 

even if some of those variables were not determined 

to be significant at the 5-percent significance level. 

In some cases, a second MLR model was developed 

that improved on the regression for a given site and 

precipitation condition by omitting some of those 

variables or by including DTW.

At Odessa for days without precipitation, two 

MLR models are listed in table 11. Stepwise MLR 

procedures identified D, Rs

2

, Ta

2

, LAI, and DTW 

as significant variables for inclusion in the model 

(model 2). All of the coefficients were significantly 

different (at the 0.05 significance level) from zero 

based on partial t-tests (Helsel and Hirsch, 2002), 

indicating that all of the variables were relevant to 

explaining ET. Positive coefficients for D, Rs

2

, Ta

2

,

and LAI indicate that ET increases with increases 

Table 11. Results for multiple linear-regression models of daily evapotranspiration as a function of potentially explanatory independent variables for study sites near Odessa and Gothenburg, Nebraska, 2002–06.[ET, evapotranspiration, in millimeters per day; 

Ci , multiple linear-regression coefficient for independent variable i; D, vapor-pressure deficit, in kilopascals; 

Rs

2, solar radiation squared, in (watts per 

square meter) squared; 

Ta

2, air temperature squared, in (degrees Celsius) squared; LAI, leaf-area index, dimensionless; DTW, depth to water, in centimeters; No. of days, number of days used in the 

regression analysis; F, value of the F-statistic along the F-distribution; p-value, probability that the null hypothesis is rejected; RSE, residual standard error; NSE, coefficient of efficiency; NA, not included in the regression model; model 1, an initial model developed for intrasite comparison purposes that may include nonsignificant variables; model 2, a refined model developed using stepwise procedures to identify the assemblage of variables that most strongly affect ET; all partial-slope and intercept coefficients shown are significantly different from zero (t-test with 5-percent significance level), unless italicized]

No. of days

Partial-slope coefficient for the independent variable indicated1 Comparison to measured ET

Intercept

(mm/d)

D

Rs

2 Ta

2 LAI DTW F p-value RSE (mm/d) NSE

Odessa

Days without precipitation, model 1 1,068 -0.435 0.199 0.0000148 0.000524 1.29 NA 1252 0 0.530 0.825Days without precipitation, model 2 1,028 1.35 .366 .0000115 .000523 1.82 -0.0174 1122 0 .504 .846Days with precipitation, model 1 391 .0116 .279 .0000223 -.00046 1.24 NA 354 0 .599 .786Days with precipitation, model 2 371 1.17 .24 .0000208 NA 1.42 -.011 380 0 .575 .806

Gothenburg

Days without precipitation, model 1 1,104 -1.87 .602 .0000088 .000987 2.08 NA 1221 0 .572 .816Days with precipitation, model 1 393 -1.6 .86 .0000148 -.000402 2.25 NA 209 0 .778 .683Days with precipitation, model 2 393 -1.42 .742 .0000151 NA 2.06 NA 278 0 .778 .682

1 Multiple linear-regression equation is in the form of: predicted ET = 

C0 + 

C1 D + 

C2 Rs

2 + 

C3 Ta

2 + 

C4 LAI + 

C5 DTW
50 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

affected by a broader range of environmental factors including D, LAI, and water availability—which also affect stomatal 

regulation of plant water exchange with the atmosphere—than 

is H, which is largely a function of available energy. 

With annual values differing by a maximum of 17 

percent, and on average by only 4 percent, ET rates generally 

were similar between the two sites; however, the sites exhibited different patterns with respect to moisture availability and 

its effect on measured ET and vegetation health. The differences were reflected in seasonal and annual ET responses to 

water availability at each site. During the dry periods of April 

through July 2002 and August through September 2003, differences in vegetation characteristics and DTW between the 

sites appeared to affect relative ET rates between the sites. At 

the Odessa site, decreases in area-weighted soil-water storage of up to 50 percent and declines in ground-water levels of 

0.3 to 0.7 m (appendix 1) were coincident with wilting of the 

shrub understory and decreases in ET values as compared to 

maximum values in June of each year (fig. 13A, B). The summer drought conditions resulted in many understory shrubs at 

the Odessa site going into dormancy during the growing season in July 2002 and August 2003. This was consistent with 

decreases, as compared to preceding months, in Odessa LAI

in July 2002 and August 2003. Partial dormancy or mortality 

continued in understory shrubs through the 2004 growing season. During the same period, there were no visually obvious 

declines in the vigor of overstory cottonwoods. Presumably, 

the shrubs are relatively shallow rooted and more dependent 

upon soil-water storage than the deeper-rooted trees. 

At the Gothenburg site, with a similar cottonwood-dominated overstory, but an eastern redcedar-dominated understory 

without shrubs, no wilting of vegetation was evident during 

the summer drought conditions of 2002 and 2003. Seasonal 

declines in soil-water storage were similar between the 

Odessa and Gothenburg sites; however, in contrast to Odessa, 

ground-water levels declined only about 15 to 25 cm during 

May through June in 2002 and 2003 at the Gothenburg site 

(appendix 2) before water levels rose again following routing of irrigation water through the north channel of the Platte 

River beginning in July. During increased flows in the north 

channel, water-level altitudes were higher in the channel (fig. 

2; GNC) than in wells GW1 and GW2, a reversal of hydraulichead gradients as compared to the rest of the year. Thus, 

ground-water level declines were less at Gothenburg than at 

Odessa. The smaller seasonal declines in ground-water levels 

and lack of understory shrubs at the Gothenburg site compared 

to the Odessa site may explain why high ET rates persisted 

later into the summer at Gothenburg, and why DTW had a significant effect on ET (as identified in the MLR model results) 

at Odessa but not at Gothenburg. It is likely that the larger 

summer declines in DTW at Odessa produced greater water 

stress in relatively shallow-rooted understory shrubs, resulting 

in moderate declines in ET. 

These differences in the ET responses to seasonal 

drought conditions between the sites also may help to 

explain why, during years of increased precipitation, ET rates 

in these variables. Although the SLR relation of ET to DTW 

was not significant, DTW was significant in the MLR model, 

and resulted in a lower RSE and greater model efficiency as 

indicated by NSE values. Unlike the unexpectedly positive 

SLR relation between ET and DTW (table 10), the coefficient 

for DTW was negative in the MLR model (table 11), indicating that ET should decline as DTW increases, as would be 

expected. 

At Odessa on days with precipitation, MLR model 1 indicated that the coefficients for D and Ta

2

 were not significantly 

different from zero, indicating that these variables contributed 

little to the explanatory power of the MLR model. However, 

stepwise techniques that included a larger potential array of 

variables for MLR model 2 found ET to be positively correlated to D, Rs

2

, and LAI, and negatively correlated to DTW. 

The results from the stepwise MLR models indicated that, 

regardless of precipitation conditions, Odessa ET increased 

with increases in D, Rs

2

, and LAI and decreases in DTW. On 

days without precipitation, Ta

2

, also had a positive relation to 

ET. The uncertainty associated with MLR models, as indicated 

by RSE, was greater on days with precipitation than on days 

without precipitation. 

At Gothenburg, stepwise MLR models indicated that, 

regardless of precipitation conditions, ET increased as D, Rs

2

, 

and LAI increased. Similar to the Odessa ET response, Ta

2

, had 

a positive relation to Gothenburg ET on days without precipitation, but not on days with it. The absence of DTW as a controlling variable at Gothenburg probably reflects the previously 

noted fundamental difference of reduced ground-water fluctuations as compared to Odessa. 

The SLR and MLR analyses quantified relations that were 

either expected or were identified from previous studies. D, 

Rs

2

, and LAI were important variables at both sites for days 

with and without precipitation. The slope coefficient of D was 

considerably greater at Gothenburg than Odessa, which may be 

explained by average daily values of D being 7 percent greater 

at Gothenburg. At both sites, Ta

2

 was significantly related to ET

on days without precipitation, but did not significantly contribute to explaining ET on days with precipitation. This may 

indicate that ET was limited by the amount of available energy 

on days without precipitation and by the amount of available 

water on days with precipitation. 

Evapotranspiration Responses to Seasonal 

Energy and Hydrologic Fluctuations

Although ground-water depths were less than 2 m in the 

presence of phreatophytic vegetation, measured ET was substantially less than potential ET (calculated using the modifiedPenman method). The limited ET rates are consistent with 

plant limitations on ET through stomatal regulation in response 

to seasonal environmental and meteorological factors, as has 

been described in previous studies (Jarvis, 1976; Roberts, 

1999; Ewers and others, 2002). Moreover, λE showed less of 

a seasonal dependence on Rn than did H, indicating that λE is 
Evapotranspiration Rates 51

increased at Odessa, but decreased at Gothenburg. In addition 

to the vegetation response, average D was 9 percent greater 

at Gothenburg than Odessa during 2002 and 2003 but only 5 

percent greater in 2004 and 2005. The differences in responses 

of ET to environmental factors between the sites, although 

relatively subtle, highlight a greater sensitivity of ET rates to 

soil-moisture availability at the Odessa site than at Gothenburg.

Transpiration in Individual Plants

Transpiration was determined for five individual plants 

from May through August 2004, including two cottonwoods, 

a dogwood shrub, a green ash, and an eastern redcedar (table 

12), using sap-flux techniques. Measured transpiration from a 

second dogwood was extremely variable and was not reported. 

Using SLR analysis, individual transpiration rates were compared to the aggregate ET rates measured for the entire footprint. Preliminary comparisons of these individual transpiration rates indicate that larger cottonwoods transpire at a greater 

rate than the aggregate ET of the riparian-forest community.

Because transpiration rates were measured in only four 

trees and one shrub for one growing season, these sap-flow 

analyses represent preliminary reconnaissance results. Much 

more extensive monitoring is necessary to develop quantitative 

understanding of transpiration rates for individual tree species 

in the riparian forests of Nebraska. 

The two cottonwoods represented a wide range in tree 

sizes in the riparian forest. Based on forest surveys of 47 cottonwood trees at the Odessa site, the diameter of cottonwood 

trees varied from 17.8 to 110 cm, with a median of 39.6 cm, 

and an average of 45.6 cm. For this distribution, approximately 

95 percent of the cottonwoods in the forest were expected 

to have diameters larger than the 22.9-cm diameter cottonwood, but only 15 percent were expected to be larger than the 

61.0-cm diameter cottonwood. 

The 61.0-cm diameter cottonwood had the greatest average transpiration rate at 3.8 mm/d during May through August 

2004 (figs. 20 and 21; table 12). These transpiration rates were 

greater than the aggregate ET rate measured for the entire 

riparian-forest footprint, which was an average of 2.8 mm/d 

for the same period. If the measurements are considered to be 

comparable, the results imply that other vegetation is contributing less to the aggregate ET than large cottonwoods. Conversely, the smaller diameter (22.9 cm) cottonwood had lower 

transpiration rates than the green ash, the dogwood shrub, or 

the aggregate ET rate (table 12). The average transpiration rate 

of the smaller cottonwood (1.4 mm/d) was approximately 50 

percent of the aggregate ET rate, and only about 37 percent of 

the larger cottonwood’s rate (table 12). The transpiration rate 

of the smaller cottonwood was correlated to the aggregate ET

rate (fig. 21) but typically was much smaller in magnitude than 

the aggregate ET rate. 

Before leaf out in April 2004, the eastern redcedar had 

the greatest average transpiration rate (0.98 mm/d; table 12) of 

any of the monitored trees. The transpiration rate for eastern 

redcedar was similar to the aggregate ET rate of 1.3 mm/d 

measured from the tower during the same period (table 12; 

fig. 20). In contrast to deciduous trees and shrubs, for which 

transpiration rates from May through August 2004 were substantially greater than those during April 2004, transpiration 

rates for the eastern redcedar were slightly greater in April 

than in May through August (fig. 20; table 12). As a result, 

eastern redcedar transpiration was less correlated to aggregate

ET rates from May through August 2004 (fig. 21E) as compared to that for the cottonwoods. 

Table 12. Transpiration rates in individual plants determined from thermal dissipation probes and aggregate 

evapotranspiration rates determined from eddy-covariance methods for days with complete transpiration data, April to 

August 2004, riparian forest study site near Odessa, Nebraska.

[ET, evapotranspiration rate; T, transpiration rate; mm/d, millimeters per day; %, percent]

Aggregate

ET

(mm/d)

Plant species and diameter, in centimeters

Eastern 

cottonwood, 

61.0

Eastern 

cottonwood, 

22.9

Roughleaf 

dogwood, 

2.5

Green ash, 

11.0

Eastern 

redcedar, 

15.2

T

(mm/d)

T/ET

(%)

T

(mm/d)

T/ET

(%)

T

(mm/d)

T/ET

(%)

T

(mm/d)

T/ET

(%)

T

(mm/d)

T/ET

(%)

April–August

2.52 3.06 121 1.15 46 1.54 61 1.70 68 0.81 32

April (dormant season month)

1.34 .26 19 .05 3 .04 3 .40 30 .98 73

May–August (growing season months)

2.82 3.78 134 1.44 51 1.93 68 2.04 72 .76 27
52 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

0 April May June July August

3

4

5

2

1

6

7

8

9

TRANSPIRATION, IN MILLIMETERS PER DAY

2004

2.50-centimeter diameter Dogwood shrub

Aggregate evapotranspiration

0

3

4

5

2

1

6

7

8

9

15.2-centimeter diameter Eastern redcedar tree

Aggregate evapotranspiration

0

3

4

5

2

1

6

7

8

9

11.0-centimeter diameter Green ash tree

Aggregate evapotranspiration

0

3

4

5

2

1

6

7

8

9

22.9-centimeter diameter Cottonwood tree

Aggregate evapotranspiration

0

3

4

5

2

1

6

7

8

9

61.0-centimeter diameter Cottonwood tree

Aggregate evapotranspiration

Figure 20. Daily average transpiration rates for monitored trees and shrubs and aggregate evapotranspiration measured by 

eddy-covariance method, April through August 2004, at study site near Odessa, Nebraska.
Evapotranspiration Rates 53

(C) Dogwood shrub, 2.5-centimeter diameter

y = 1.150x - 1.32

R2 = 0.37

(A) Cottonwood tree, 61.0-centimeter diameter

y = 0.909x + 1.22

R2 = 0.50

(B) Cottonwood tree, 22.9-centimeter diameter

y = 0.444x + 0.19

R2 = 0.58

(E) Eastern redcedar tree, 15.2-centimeter diameter

y = 0.156x + 0.30

R2 = 0.25

0

2.5

5.0

7.5

TRANSPIRATION, IN MILLIMETERS PER DAY

0

2.5

5.0

7.5

0

2.5

5.0

7.5

0 2.5 5 7.5

0 2.5 5 7.5

STUDY-SITE AGGREGATE EVAPOTRANSPIRATION,

IN MILLIMETERS PER DAY

STUDY-SITE AGGREGATE EVAPOTRANSPIRATION,

IN MILLIMETERS PER DAY

EXPLANATION

(D) Green ash tree, 11.0-centimeter diameter

y = 0.264x + 1.29

R2 = 0.19

Daily transpiration rates for April 2004

Daily transpiration rates for May

 through August 2004

Figure 21. Relation of daily transpiration rates for monitored trees and shrubs with evapotranspiration determined from eddy 

covariance, April through August 2004, at study site near Odessa, Nebraska.
54 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

If the measured transpiration rate of the eastern redcedar 

before leaf out is representative of those of most other conifers, the results imply that transpiration from conifers was a 

primary contributor to aggregate ET before leaf out of deciduous vegetation. Based on micrometeorologic data from both 

sites, dormant-season (October through April) ET accounted 

for approximately 25 percent of the annual total, whereas 

growing season (May through September) ET accounted for 

75 percent of the total. Although there is more uncertainty 

in estimating dormant-season ET than in growing-season 

estimates, these results indicate that dormant-season ET needs 

to be included in assessments of the water balance to produce 

reliable results. 

Dogwood transpiration rates were more variable than 

those of the other monitored plants (fig. 20). For example, 

dogwood transpiration rates spiked in mid-May and late 

July to a much greater degree than either the monitored trees 

or the aggregate ET. It is not known whether or not this is 

a real effect or an artifact of increased sensitivity to thermal gradients in the small diameter of the dogwood trunk 

(2.5-cm). Average transpiration rates for the dogwood shrub 

from May through August 2004 were similar to those for the 

green ash (table 12). The monitored dogwood (plant 1; table 

2) was located among shrubs that were partially shaded by 

tree canopy. A second dogwood (plant 2; table 2) located in 

a comparatively wet area with shallower depth to water was 

characterized by extremely variable transpiration and was not 

considered reliable enough to report. 

Daily transpiration rates for green ash were intermediate 

in comparison with the other monitored plants, and were about 

72 percent of aggregate ET rates during May through August 

(table 12). Similar to the eastern redcedar, transpiration rates 

for the green ash were less well correlated with aggregate ET

rates (fig. 21D) than were the cottonwood rates. Additionally, transpiration rates for green ash increased slightly during 

August 2004 at a time when transpiration rates for cottonwoods generally were decreasing. 

Fluctuations in daily transpiration rates generally were 

similar to those of aggregate daily ET, but the magnitude of 

the fluctuations often differed (fig. 20). Sap-flow-derived 

transpiration rates and aggregate ET rates both indicated 

considerable daily variations, reflecting daily variations in the 

environmental factors controlling ET. 

Extrapolating Evapotranspiration Rates Beyond 

the Study Sites 

The importance of this study lies not in the site-specific 

water- and energy-balance measurements, but in the ability to 

estimate the ET from similar forests throughout the riparian 

zone of the Platte River by extrapolating those measurements 

to different spatial and temporal scales. It should be noted, 

however, that extrapolated-ET estimates may deviate from the 

actual ET rates in other forests as the vegetation communities 

and environmental conditions deviate further from those of 

this study. These study results could be generally similar to 

those for zones of riparian forests in the northern and central Great Plains that are narrower than 800 m; however, as 

riparian zones become narrower, energy balances may change 

because advective energy transfers from adjacent land covers 

may become more important in these settings than along the 

Platte River. 

Specifically, riparian-forest ET rates along the Platte 

River can be extrapolated by using the kc values determined 

for this study in conjunction with atmospheric data from other 

climate stations in central Nebraska. Generally, kc values from 

the Odessa site probably are best suited for locations east of 

the midway point between Odessa and Gothenburg, whereas 

kc values from the Gothenburg site probably are best suited for 

locations west of this point. 

In light of recent (1999 to 2003) drought conditions in 

Nebraska (Hayes and others, 2005), the ET from riparian 

forests, and more specifically riparian phreatophytes such 

as cottonwoods and saltcedar (Tamarix spp), has received 

increased scrutiny in terms of its effect on ground-water 

resources and stream depletion. This partly is because of the 

concept of ET salvage, which is loosely defined as ground 

water that is made available for other uses by reducing the ET 

of riparian phreatophytes either through vegetation management (Cleverly and others, 2006) or by lowering the water 

table by pumping ground-water wells (Cooper and others, 

2006). Nebraska Legislative Bill (LB) 701 became law in 2007 

and established riparian-vegetation-management programs in 

areas where available water resources were fully appropriated 

or over appropriated (State of Nebraska, 2007). 

Although few, if any, studies have focused on cottonwoods, considerable uncertainty is associated with the effects 

of ET-salvage programs in the American Southwest involving 

saltcedar (Shafroth and others, 2005), and there has been little 

evidence of streamflow increases following saltcedar removal 

there (Hart and others, 2005). Although the effects of vegetation management on riparian-forest ET are not the subject of 

this study, the results indicate that ET rates are constrained 

by vegetation, moisture-availability, and climatic conditions. 

As a result, ET rates in riparian forests were considerably 

lower than potential ET rates and were similar to ET rates of 

irrigated corn (Younts, 2002). Additionally, removing cottonwoods from riparian areas may not reduce ET in the long 

term, because vegetation communities adapt dynamically to 

changes in environmental variables. It is possible that the ET 

of replacement vegetation in the altered riparian system may 

be similar to cottonwoods or other existing (as of 2005) riparian vegetation. Changes in vegetation cover alter the partitioning of energy between latent and sensible heat (Ewers and 

others, 2002). Annual ET by forests may be relatively stable 

because of factors such as understory vegetation and climate 

coupling (Roberts, 1999; Ewers and others, 2002). Additional 

studies are needed to quantify the effects of natural or managed changes in riparian vegetation on water availability and 

to consider the economic and environmental effects of sustainable vegetation removal or alternative management plans.
Summary 55

Summary 

Evapotranspiration (ET) in riparian areas is a poorly 

understood component of the regional water balance in the 

Platte River Basin, where competing demands have resulted 

in water shortages in the coupled ground-water/surface-water 

system. In 2002, the U.S. Geological Survey in cooperation 

with the Nebraska Platte River Cooperative Hydrology Study 

Group and Central Platte Natural Resources District began a 

micrometeorological study of water and energy balances at 

two sites in central Nebraska to improve understanding of ET 

rates and factors affecting them in Platte River riparian forests. 

This report includes descriptions of the micrometeorological 

methods and data analysis, interpretations of amounts of canopy interception and evaporation of intercepted water, calibration of a crop-coefficient model used for simulating riparianforest ET rates, analysis of environmental variables affecting 

ET rates, transpiration rates in individual plants (measured 

using sap flux techniques), and a discussion of extrapolating 

the results to other locations.

Source-area, or footprint, calculations and analysis of 

land-cover data indicated that at least 85 percent of the fluxes 

measured by the eddy-covariance sensors originated from 

areas of riparian forest. Both study sites have a forest canopy 

or overstory dominated by cottonwoods and characteristics 

generally representative of riparian forests in the region. 

Because the land cover at both study sites consists of woody 

vegetation interspersed with open areas and some river channels, the water- and energy-balance measurements for this 

study characterize the riparian-vegetation community rather 

than just riparian trees. Riparian trees are a dominant component of the riparian-vegetation community, but forests in this 

study have low tree density in comparison with many forests, 

and are characterized by relatively widely spaced trees with 

intervening open areas. 

At both sites, the stratigraphy of the unsaturated zone and 

upper-saturated zone coarsened with depth, consisting of 10 to 

20 centimeters of sandy loam overlying 40 to 50 centimeters 

of fine sand with interbedded silt layers of variable thickness 

overlying poorly sorted sand and fine gravel. As a result, capillary rise into the unsaturated zone was minimal. Typical depth 

to water in early spring at the Odessa site was 90 centimeters 

compared to 110 centimeters at the Gothenburg site. However, the greater fluctuation of depth to water at Odessa than 

Gothenburg throughout the study proved to be a fundamental 

difference between the sites. 

The study period of April 2002 through March 2006 

encompassed conditions ranging from wet to dry. Variations 

in annual precipitation primarily reflected wet and dry periods 

during May through September, when most precipitation 

occurred. Precipitation ranged from 429 to 844 millimeters per 

year at Odessa and 359 to 791 millimeters per year at Gothenburg. During the study period, annual precipitation at both 

sites varied by about 35 percent from its 4-year mean, and 

was much more variable than annual ET, which stayed within 

about 7 percent of its 4-year mean. 

Average throughfall in two gages beneath the canopy at 

each site ranged from about 67 to 89 percent of total precipitation, indicating a relatively strong relation between precipitation and throughfall for the measured range of rainfall intensities and amounts. Interception of precipitation by the plant 

canopy was calculated as the difference between precipitation 

and throughfall. Area-weighted interception, based on interception determined from the two throughfall gages and the 

distribution of vegetation cover, was 13 percent of precipitation at each site. 

Daily ET rates determined using eddy-covariance methods ranged from about -0.7 to 7.5 millimeters per day and 

commonly were greater than 2.5 millimeters per day during 

June through August, but only rarely were greater than 5.0 

millimeters per day. Daily ET rates during December, January, and February often were less than 0.5 millimeter per day. 

Annual ET ranged from about 514 to 586 millimeters per year 

(average 551 millimeters per year) at the Odessa site and 535 

to 616 millimeters per year (average 575 millimeters per year) 

at the Gothenburg site. These values were within the range 

of annual precipitation. Based on an analysis of the energybalance closure, the ET rates determined using eddy covariance are estimated to be within about 13 percent of the actual 

rates; the reported ET rates are thought to represent minimum 

values.

For the study period, energy imbalances as a percentage 

of available energy were 9 percent at Odessa and 13 percent 

at Gothenburg. These imbalances are within the range of 0 to 

30 percent typically present in energy balance studies. Energy 

fluxes generally were largest during June through August, 

and smallest during November through February, reflecting 

seasonal variations in net radiation. During the growing season 

(May through September), latent-heat flux was the largest 

energy outflow at both sites, whereas sensible-heat flux was 

the dominant energy outflow during the dormant season (October through April). At both sites, approximately 70 percent 

of the total latent-heat flux during the study period occurred 

during daytime conditions during the growing season. For 

30-minute periods during the diurnal cycle, the smallest mean 

energy imbalances, usually less than 20 percent of available 

energy, occurred between 8:00 am and 6:00 pm during the 

summer when the greatest energy fluxes and ET occurred. 

Thus, the measurements of energy fluxes were of greatest 

accuracy for periods when most of the energy fluxes occurred. 

Precipitation and ET were the dominant components 

of the annual water balance, with changes in soil-water and 

ground-water storage being comparatively minor terms. Net 

ground-water discharge to both sites (ground-water inflow 

to the riparian forests necessary to satisfy the water balance) 

occurred in 2002, when annual ET exceeded annual precipitation. Net ground-water recharge occurred from both sites in 

2003, 2004, and 2005 (ground-water outflow from the riparian 

forests necessary to satisfy the water balance). The 4-year-
56 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

average water balance indicated that the study sites were a net 

source of ground-water recharge for the study period (table 

7; fig. 17B). The 4-year-average net ground-water recharge 

at Odessa (76 millimeters per year) was larger than that at 

Gothenburg (13 millimeters per year), consistent with larger 

precipitation surpluses at Odessa than Gothenburg. After 

adjusting the annual water-balance calculations to account 

for canopy interception, evaporation of intercepted water 

accounted for an average of about 14 percent of total ET at 

Odessa and 9 percent at Gothenburg. 

The upper boundary of possible values of ground-water 

ET ranged from 448 to 504 millimeters per year with an average value of 473 millimeters per year at the Odessa site, or 

roughly 86 percent of total ET, and ranged from 480 to 559 

millimeters per year with an average value of 520 millimeters 

per year at the Gothenburg site, or roughly 90 percent of total 

ET. This upper boundary is met if none of the throughfall precipitation infiltrates into the soil, which probably is unrealistic 

given the relatively flat and sandy terrain of the study sites. 

The lower boundary of possible values of annual ground-water 

ET is the net ground-water inflow into the riparian area to satisfy the annual water balance. Net ground-water outflow from 

the riparian zone occurred during 2003 to 2005, indicating that 

the lower boundary of annual ground-water ET effectively was 

zero during 2003 to 2005. During the driest study year, 2002, 

net ground-water inflow was 53 millimeters (10 percent of 

total ET of 514 millimeters) at Odessa and 161 millimeters (28 

percent of total ET of 585 millimeters) at Gothenburg. Thus, 

annually, ground water becomes an important water source, 

albeit minor compared to precipitation, during dry years for 

satisfying riparian-forest ET demands. During wet years, all 

of the ET demand could be satisfied by available precipitation. These results do not imply that ground water is not an 

important source of water to riparian vegetation—especially to 

phreatophytes—during increased ET periods in the summer, 

particularly during periods of lower than normal precipitation. 

However, annual ground-water use by these riparian forests 

likely is balanced by periods of recharge of excess precipitation at other times of the year. In contrast to more arid settings, 

where literature indicates that ground water may supply a 

large fraction of the water used for ET by riparian vegetation, 

along the Platte River of Nebraska, precipitation was large 

enough—and generally larger than ET—that for annual or 

longer periods most or all of the ET demand can be satisfied 

by available precipitation. 

The 15-day and monthly average crop coefficients—

calculated by dividing measured ET by reference ET from 

nearby weather stations—had values that ranged from 0.07 

to 0.51, and generally were similar between sites. The simple 

crop-coefficient models using either monthly or 15-day average crop coefficients reasonably simulated annual ET. For the 

Odessa site, annual ET simulations deviated from measured 

ET by -11.7 percent to +7.5 percent with a 4-year average 

of -3.5 percent for monthly coefficients and -3.4 percent 

for 15-day coefficients. For the Gothenburg site, annual ET 

simulations deviated from measured ET by -6.7 percent to 

+9.0 percent with a 4-year average of +1.8 percent for monthly 

coefficients and +1.6 percent for 15-day coefficients. Generally, the simple crop-coefficient models underpredicted large 

daily ET and overpredicted small daily ET. For annual or 

longer periods, these short-term discrepancies averaged out, 

and simulated ET was close to measured ET.

Simple linear-regression and multiple linear-regression 

models of ET were developed to better understand the factors 

affecting ET rates in riparian forests. At both sites, simple 

linear regression indicated that ET increased with increasing 

vapor-pressure deficit, solar radiation, air temperature, and 

leaf-area index, all of which followed seasonal patterns with 

summer peaks and winter lows. When analyzing all of the 

days in the data set, ET was statistically unrelated to soil-water 

content and precipitation and depth to water was only significant at Odessa; however, by separating the daily data into 

two groups according to the presence or lack of precipitation, 

ET was larger on days with precipitation than days without. 

These results indicate that ET was more strongly controlled by 

atmospheric and vegetation conditions on days without precipitation than days with precipitation, when the evaporation 

from wet surfaces affects ET rates. Thus, although moisture 

availability was not directly related to ET rates, the relation 

of ET to environmental factors was different on days with 

precipitation—when additional moisture availability resulted 

in increased ET —than on dry days. These comparisons 

indicated that to understand the factors affecting ET rates, it 

was necessary to consider different relations for days with and 

days without precipitation. 

Based upon the simple linear-regression relations 

between ET and vapor-pressure deficit, solar radiation, air 

temperature, leaf-area index, and depth to water (at Odessa 

only) for days with and without precipitation, multiple linearregression models of ET as a function of these variables were 

constructed using stepwise techniques to identify the assemblage of variables that most strongly affect ET rates.

At Odessa, on days without precipitation, ET rates were 

best explained by a multiple linear-regression model including vapor-pressure deficit, solar radiation, air temperature,

leaf-area index, and depth to water. The model indicated that 

ET rates increase with increasing vapor-pressure deficit, solar 

radiation, air temperature, and leaf-area index, and decreasing 

depth to water. At Odessa on days with precipitation, air temperature became insignificant in the model, but the assemblage 

and trend of variables were otherwise similar to days without 

precipitation, suggesting that ET was limited by the amount 

of available energy on days without precipitation but, on days 

with precipitation, ET was limited by the amount of available 

water.

 At Gothenburg on days without precipitation, ET was a 

function of vapor-pressure deficit, solar radiation, air temperature, and leaf-area index but not depth to water. As with 

Odessa, the best Gothenburg ET model on days with precipitation was similar to the model on days without precipitation 

except that air temperature became insignificant, thereby 

lending further evidence that the system was energy-limited on 
References Cited 57

days without precipitation. The absence of depth to water as a 

controlling variable at Gothenburg probably reflects reduced 

ground-water fluctuations as compared to Odessa. 

The simple and multiple linear-regression analyses quantified relations that were either expected or were identified 

from previous studies. Solar radiation, vapor-pressure deficit, 

and leaf-area index were important variables at both sites for 

days with and without precipitation. 

Despite the presence of phreatophytic vegetation in areas 

of shallow ground water, measured ET was substantially 

less than potential ET. The limited-ET rates are consistent 

with plant limitations on ET through stomatal regulation in 

response to environmental and meteorological factors, as has 

been described in previous studies. 

With annual values differing by a maximum of 17 percent 

and an average of 4 percent, ET rates generally were similar 

between the two sites; however, the sites exhibited different 

patterns with respect to moisture availability and its effect on 

ET and vegetation health. The smaller seasonal declines in 

ground-water levels and lack of understory shrubs at the Gothenburg site, as compared to the Odessa site, may explain why 

high ET rates persisted later in the summer at Gothenburg and 

why depth to water had a significant effect on ET (as identified by the multiple linear-regression model) at Odessa but 

not at Gothenburg. It is likely that the larger summer declines 

in depth to water at Odessa provided more opportunity for 

water stress to develop in relatively shallow-rooted understory 

shrubs, resulting in moderate declines in ET. These differences 

between the sites in ET responses to seasonal drought conditions also may help to explain why, during years of increased 

precipitation, ET rates increased at Odessa but decreased at 

Gothenburg. 

Transpiration rates calculated from sap-flow measurements in five individual trees and shrubs during the summer 

of 2004 at Odessa gave preliminary indication that a large 

cottonwood transpired more than the aggregate ET of the 

riparian-forest community. Because transpiration rates were 

measured in four trees and one shrub only for one growing 

season, these sap-flow analyses represent preliminary reconnaissance results. Much more extensive monitoring is necessary to develop quantitative understanding of transpiration 

rates in individual tree species in the riparian forests. The 

61.0-centimeter diameter cottonwood had the greatest average transpiration rate at 3.8 millimeters per day during May 

through August 2004, greater than the aggregate ET rate 

measured for the entire riparian-forest footprint, which was 

an average of 2.8 millimeters per day for the same period. 

Conversely, the smaller diameter (22.9 centimeter) cottonwood 

had lower transpiration rates than the green ash, the dogwood 

shrub, or the aggregate ET rate. 

Before leaf out in April 2004, the eastern redcedar had 

the greatest average transpiration rate (0.98 millimeters per 

day) of any of the monitored trees, similar to the aggregate ET 

rate of 1.3 millimeters per day measured for the same period. 

If the measured transpiration rate of the eastern redcedar 

before leaf out is representative of those of most other conifers, the results imply that transpiration from conifers was a 

primary contributor to aggregate ET before leaf out of deciduous vegetation. Based on micrometeorologic data from both 

sites, dormant-season (October through April) ET accounted 

for approximately 25 percent of the annual total, whereas 

growing season (May through September) ET accounted for 

75 percent of the total. Although there is more uncertainty in 

estimating dormant-season ET than in growing-season estimates, these results indicate that dormant-season ET needs to 

be included in reliable assessments of the water balance.

The importance of this study lies not in the site-specific 

results, but in the ability to estimate the ET from similar 

forests throughout the riparian zone of the Platte River by 

extrapolating those measurements, and in the understanding 

of processes and factors affecting ET. Specifically, riparianforest ET rates along the Platte River can be extrapolated by 

using the crop-coefficient values determined for this study in 

conjunction with atmospheric data from other climate stations 

in central Nebraska, keeping in mind that extrapolated ET 

estimates may deviate from the actual ET rates in other forests 

as the vegetation communities and environmental conditions 

deviate further from those of this study. 

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Appendixes 65

Appendixes

Appendix 1. Appendix1. xls—Daily values of micrometeorological variables 

from riparian forest study site near Odessa, Nebraska, March 23, 2002, to May 10, 

2006.

Excel spreadsheet

Appendix 2. Appendix2.xls—Daily values of micrometeorological variables 

from riparian forest study site near Gothenburg, Nebraska, March 23, 2002, to May 10, 

2006.

Excel spreadsheet 
66 Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06 

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Or visit the Nebraska Water Science Center Web site at:

http://ne.water.usgs.gov
Appendixes 67
Landon and others—Evapotranspiration Rates of Riparian Forests, Platte River, Nebraska, 2002–06—Scientific Investigations Report 2008–5228