Prepared in cooperation with the WaterSMART Initiative

Estimating Irrigation Water Use in the Humid 

Eastern United States

Scientific Investigations Report 2013–5066

U.S. Department of the Interior

U.S. Geological Survey
Cover photographs. Turf farm in southern New England. Photographs by Gardner Bent, U.S. Geological Survey.
Estimating Irrigation Water Use in the 

Humid Eastern United States

By Sara B. Levin and Phillip J. Zarriello

Prepared in cooperation with the WaterSMART Initiative

Scientific Investigations Report 2013–5066

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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

resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

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

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Levin, S.B., and Zarriello, P.J., 2013, Estimating irrigation water use in the humid eastern United States: U.S. Geological Survey Scientific Investigations Report 2013–5066, 32 p., http://pubs.usgs.gov/sir/2013/5066/.
iii

Acknowledgments

The authors thank the Georgia Soil and Water Conservation Commission and the Virginia 

Department of Environmental Quality for allowing access to their irrigation water-use data. The 

authors also thank Lynn Torak of the U.S. Geological Survey for his assistance in accessing the 

Georgia irrigation data and in data compilation and interpretation.
THIS PAGE INTENTIONALLY LEFT BLANK
v

Contents

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

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

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

Previous Studies ...................................................................................................................................2

Irrigation Data Compilation ..........................................................................................................................3

Data Screening......................................................................................................................................3

Data Stratification by Type of Crop....................................................................................................5

Irrigation Estimation Methods .....................................................................................................................8

Logistic Regression ..............................................................................................................................8

Turf Irrigation in Rhode Island ...................................................................................................8

Agricultural Crop Irrigation in Georgia...................................................................................10

Crop-Water-Demand Models............................................................................................................16

Model Validation and Comparison............................................................................................................17

Georgia Irrigation................................................................................................................................17

Virginia Irrigation ................................................................................................................................17

Irrigation Model Transferability........................................................................................................25

Study Limitations and Areas of Further Study ........................................................................................25

Crop Coefficients.................................................................................................................................25

Application Rate..................................................................................................................................28

Irrigated Area ......................................................................................................................................28

Data Limitations...................................................................................................................................28

Summary and Conclusions.........................................................................................................................29

References Cited..........................................................................................................................................30
vi

Figures

1. Map showing locations of irrigation sites with daily and weekly irrigation data 

from 2009 and 2010 and National Climatic Data Center climate stations in southern 

Georgia ...........................................................................................................................................6

2. Map showing example overlay of meter locations with Cropland Data Layer and 

Georgia irrigated lands geospatial dataset..............................................................................7

3. Graphs showing monthly irrigation water use during 2009 and 2010 at A, 6 corn; 

B, 18 cotton; C, 6 peanut; and D, 6 soybean fields in southern Georgia..............................9

4. Graph showing reported data and model estimates of annual irrigation from 2000 to 

2004 at nine turf farms in Rhode Island...................................................................................10

5. Graphs showing crop coefficients for A, corn; B, cotton; C, peanuts; and 

D, soybeans for Georgia and E, turf for Rhode Island ..........................................................12

6. Graph showing reported data and model estimates of 2009 and 2010 seasonal 

irrigation (May through October) for corn, cotton, peanut, and soybean crops in 

Georgia .........................................................................................................................................15

7. Map showing metered irrigation sites with 2010 annual data and National Climatic 

Data Center climate stations in southwestern Georgia.......................................................18

8. Graphs showing reported data and model estimates of 2010 annual irrigation for 

A, corn; B, cotton; C, peanut; and D, soybean fields for selected counties in 

southwestern Georgia ...............................................................................................................19

9. Graphs showing reported data and model estimates of annual irrigation by county, 

for A, corn; B, cotton; C, soybeans; and D, turf crops in Virginia from 1999 to 2007........23

10. Graphs showing reported data and model estimates of mean monthly irrigation 

from 1999 to 2007 for A, corn; B, cotton; C, soybean; and D, turf farms in Virginia..........24

Tables

1. Irrigation data compiled from New England and mid-Atlantic states .................................4

2. Reported data and model estimates of annual irrigation for turf farms during 

2000 to 2004 in Rhode Island, and corn, cotton, peanut, and soybean farms during 

2009 and 2010 in southern Georgia ..........................................................................................11

3. Summary statistics for logistic regression equations developed for predicting 

weekly irrigation during the 2009 and 2010 growing season for corn, cotton, peanut, 

and soybean crops in Georgia..................................................................................................14

4. Seasonal median irrigation (May to October) during 2009 and 2010 for 6 corn, 

18 cotton, 6 peanut, and 6 soybean sites in Georgia ............................................................15

5. Reported data and model estimates of seasonal 2010 annual irrigation for corn, 

cotton, peanut, and soybean fields for selected counties in southwestern Georgia .....20

6. Reported and simulated mean annual irrigation from 1999 to 2007 for corn, cotton, 

soybean, and turf farms in Virginia..........................................................................................22

7. Primary irrigated crops in eastern states, by acreage, reported in the 2008 Farm 

and Ranch Irrigation Survey .....................................................................................................26
vii

Conversion Factors, Datum, and Abbreviations

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

Area

acre 4,047 square meter (m2)

Volume

gallon (gal) 231 cubic inches (in3)

million gallons (Mgal) 3,785 cubic meter (m3)

Flow rate

acre-foot per year (acre-ft/yr) 1.233 cubic meter per year (m3/yr)

acre-foot per year (acre-ft/yr) 0.001233 cubic hectometer per year (hm3/yr)

gallon per day (gal/d) 0.003785 cubic meter per day (m3/d)

inch per day (in/d) 25.4 millimeter per day (mm/d)

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

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3/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

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

(NAVD 88).

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

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

Abbreviations

AIC Akaike Information Criteria

CDL cropland data layer

CPET crop-specific potential evapotranspiration

FAO Food and Agriculture Organization of the United Nations

FRIS Farm and Ranch Irrigation Survey

MAD management allowed depletion

NCDC National Climatic Data Center

NDVI normalized difference vegetation index

NOAA National Oceanic and Atmospheric Administration

NWUIP National Water-Use Information Program

PET potential evapotranspiration

RMSE root mean squared error

Smax maximum soil water

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey
Estimating Irrigation Water Use in the Humid Eastern 

United States

By Sara B. Levin and Phillip J. Zarriello

Abstract

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use 

Information Program and the WaterSMART initiative to help 

maintain sustainable water resources in the Nation. Irrigation 

water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability 

associated with climate, soils, crops, and farming practices. 

To better understand irrigation water use in the eastern United 

States, two types of predictive models were developed and 

compared by using metered irrigation water-use data for corn, 

cotton, peanut, and soybean crops in Georgia and turf farms in 

Rhode Island. Reliable metered irrigation data were limited to 

these areas.

The first predictive model that was developed uses 

logistic regression to predict the occurrence of irrigation on 

the basis of antecedent climate conditions. Logistic regression 

equations were developed for corn, cotton, peanut, and 

soybean crops by using weekly irrigation water-use data 

from 36 metered sites in Georgia in 2009 and 2010 and turf 

farms in Rhode Island from 2000 to 2004. For the weeks 

when irrigation was predicted to take place, the irrigation 

water-use volume was estimated by multiplying the average 

metered irrigation application rate by the irrigated acreage for 

a given crop.

The second predictive model that was developed is a 

crop-water-demand model that uses a daily soil water balance 

to estimate the water needs of a crop on a given day based on 

climate, soil, and plant properties. Crop-water-demand models 

were developed independently of reported irrigation water-use 

practices and relied on knowledge of plant properties that are 

available in the literature. Both modeling approaches require 

accurate accounting of irrigated area and crop type to estimate 

total irrigation water use.

Water-use estimates from both modeling methods were 

compared to the metered irrigation data from Rhode Island 

and Georgia that were used to develop the models as well as 

two independent validation datasets from Georgia and Virginia 

that were not used in model development. Irrigation water-use 

estimates from the logistic regression method more closely 

matched mean reported irrigation rates than estimates from the 

crop-water-demand model when compared to the irrigation 

data used to develop the equations. The root mean squared 

errors (RMSEs) for the logistic regression estimates of mean 

annual irrigation ranged from 0.3 to 2.0 inches (in.) for the 

five crop types; RMSEs for the crop-water-demand models 

ranged from 1.4 to 3.9 in. However, when the models were 

applied and compared to the independent validation datasets 

from southwest Georgia from 2010, and from Virginia from 

1999 to 2007, the crop-water-demand model estimates were as 

good as or better at predicting the mean irrigation volume than 

the logistic regression models for most crop types. RMSEs 

for logistic regression estimates of mean annual irrigation 

ranged from 1.0 to 7.0 in. for validation data from Georgia and 

from 1.8 to 4.9 in. for validation data from Virginia; RMSEs 

for crop-water-demand model estimates ranged from 2.1 to 

5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia 

data. In general, regression-based models performed better 

in areas that had quality daily or weekly irrigation data from 

which the regression equations were developed; however, 

the regression models were less reliable than the crop-waterdemand models when applied outside the area for which they 

were developed. In most eastern coastal states that do not have 

quality irrigation data, the crop-water-demand model can be 

used more reliably.

The development of predictive models of irrigation 

water use in this study was hindered by a lack of quality 

irrigation data. Many mid-Atlantic and New England states 

do not require irrigation water use to be reported. A survey 

of irrigation data from 14 eastern coastal states from Maine 

to Georgia indicated that, with the exception of the data in 

Georgia, irrigation data in the states that do require reporting 

commonly did not contain requisite ancillary information such 

as irrigated area or crop type, lacked precision, or were at an 

aggregated temporal scale making them unsuitable for use 

in the development of predictive models. Confidence in the 

reliability of either modeling method is affected by uncertainty 

in the reported data from which the models were developed or 

validated. Only through additional collection of quality data 

and further study can the accuracy and uncertainty of irrigation water-use estimates be improved in the humid eastern 

United States.
2 Estimating Irrigation Water Use in the Humid Eastern United States

Introduction

Human water use, climate change, and the recognition 

of shared water resources for ecological needs are increasing the strain on freshwater resources in many regions of the 

United States. To manage water resources, particularly those at 

risk, sound estimates of human water demands and ecological 

needs are required. Irrigation is the largest consumptive use of 

water in the Nation. That is, unlike many other water uses that 

are returned or recycled back into the environment and can be 

reused, over half the water used for irrigation nationally is lost 

through evapotranspiration (Solly and others, 1998). Every 

5 years, the U.S. Geological Survey (USGS) National WaterUse Information Program (NWUIP) reports on water use 

across the country. The information is compiled from many 

sources at state and local levels and is aggregated to provide 

information on water uses, volumes, and sources by state. The 

2005 NWUIP compilation reported that irrigation water use 

totaled 128,000 million gallons per day (Mgal/d) and composed 37 percent of the total freshwater withdrawals in the 

country, second only to withdrawals for thermoelectric power 

(Kenny and others, 2009).

Irrigation water use is relatively small in the humid 

New England and mid-Atlantic States relative to the arid and 

semiarid western states, where most of the crop water demand 

is met by irrigation. In the humid east, farmers commonly 

rely heavily on frequent summer rainfall to meet crop water 

needs. According to the 2008 Farm and Ranch Irrigation 

Survey (FRIS) report, irrigated farms represented only 20 

and 30 percent of the total crop farms in New England and 

the mid-Atlantic regions, respectively (U.S. Department of 

Agriculture, National Agricultural Statistics Service, 2009a). 

However, there is evidence that irrigation is becoming more 

common in these regions. The acreage of irrigated land in New 

England increased by 3 percent from 2003 to 2008 despite a 

35 percent decrease in the total acreage of farmland. In the 

mid-Atlantic region, irrigated acreage increased by 33 percent 

during this time; whereas the total acreage of farmland 

increased by only 6 percent (U.S. Department of Agriculture, 

National Agricultural Statistics Service, 2009a).

The large consumptive use of water for irrigation underscores the importance of accurate monitoring and accounting 

of irrigation water use at the local, state, and national levels. 

However, estimating irrigation water use is difficult, especially 

in the eastern United States, because of its high variability 

and the lack of reporting standards. Irrigation water use varies 

widely due to climate conditions, crop and soil type, and differences in irrigation practices. This variability and the uncertainty in reported irrigation data result in inconsistent estimates of irrigation water use from different sources. The U.S. 

Department of Agriculture (USDA) Census of Agriculture 

and conjunctive FRIS provide major sources of supplemental 

information that support the development of NWUIP irrigation 

estimates, but this information is compiled on a 5-year cycle 

that is offset from the 5-year NWUIP compilation by 3 years. 

The 2008 FRIS reported irrigation water use of 91.3 million 

acre-feet per year (about 83,400 Mgal/d) of water in the 

United States. This is about 50 percent less than the irrigation 

water use reported in the 2005 NWUIP. Although irrigation 

can vary appreciably by year and region, differences of this 

magnitude may also be attributed to the quality of the irrigation water-use information used by the USGS and USDA and 

the methods used to analyze that information.

To better account for irrigation water use in the humid 

eastern part of the Nation, the USGS WaterSMART initiative 

is examining existing irrigation water-use data and is developing methods to better estimate irrigation water use. This study 

is part of the WaterSMART initiative to provide information 

and tools to those working in water-resource planning and for 

the management of sustainable water resources for the Nation.

Purpose and Scope

The purpose of this report was to present and compare 

models for estimating irrigation water use in humid New 

England and mid-Atlantic States and to assess the feasibility 

and data needs for implementing these estimation methods 

on a regional scale within the study area. Two methods of 

estimating crop irrigation water use were developed from 

existing metered data in Georgia and Rhode Island. The 

first method uses logistic regression equations to predict 

the occurrence of irrigation on a given week on the basis 

of antecedent climate variables and estimates of irrigation 

volume by using average water application rates. Logistic 

regression equations were developed for corn, cotton, peanut, 

and soybean crops in Georgia and turf farms in Rhode Island. 

The second method uses a daily water balance to predict 

irrigation on a given day based on climate, soil, and plant 

properties. The models were developed for the same five 

crops. Water-use estimates from both models were compared 

to independent datasets from Georgia and Virginia that were 

not used in model development.

Previous Studies

Zarriello and Bent (2004) developed logistic regression 

equations to predict the occurrence of daily irrigation for 

turf farms and golf courses in Rhode Island from antecedent 

precipitation and potential evapotranspiration (PET). These 

equations were revised with updated data by Bent and others 

(2009) and applied to a broader area of Rhode Island. The 

logistic regression equations accurately predicted daily irrigation 78 percent of the time for turf farms and 79 percent of the 

time for golf courses. On days when irrigation was predicted, 

water-use volumes were estimated by multiplying the average 

withdrawal rate per acre by the number of acres irrigated.

Fanning and others (2001) predicted irrigation water use 

in Georgia from five regression equations developed from 

withdrawal-permit information. The five equations predict 

(1) whether or not irrigation will be used at a given location, 

(2) the crop type, (3) the irrigated acreage, (4) the planting and 
Irrigation Data Compilation 3

harvesting dates, and (5) the volume of water applied. Model 

estimates of countywide irrigation in Georgia during the 

1996 growing season (March through October) were roughly 

15 percent higher than irrigation determined from a sample of 

metered irrigation data.

Although more commonly used in arid, water-scarce 

environments, physically based simulation models of crop 

water demand have been used in the eastern United States 

to estimate irrigation water requirements in Florida (Satti 

and others, 2004) and Georgia (Guerra and others, 2007), as 

well as potential changes in irrigation needs in the southeast 

United States under several climate-change scenarios (Peart 

and others, 1995). Additionally, the crop-water-demand 

method is incorporated into a more general soil water balance 

model that uses gridded climate and soil data to calculate 

spatially explicit groundwater-recharge estimates of soil water 

(Westenbroek and others, 2010).

Irrigation Data Compilation

Irrigation water-use data for this study were initially 

compiled for the coastal eastern United States from 13 states 

from North Carolina to Maine (table 1). The available irrigation data for these states were usually limited to the spatially 

and temporally aggregated information reported in the 5-year 

compilations for the NWUIP and the FRIS, which are not 

well suited to developing tools for estimating irrigation water 

use. About half the states in the study area have no reporting 

requirement. States that do have reporting requirements vary 

widely in the types of information that are required (irrigation volume, irrigated acreage, crop type, irrigation method, 

and water source), the reporting time scale (daily, monthly, or 

annual usage), and whether the data were metered, estimated 

from average pumping times, or determined by other means. 

These data are typically aggregated over a growing season and 

often lack quality assurance, which limited the use of this data. 

The study area was later expanded to include Georgia because 

of the availability of quality metered irrigation data (table 1).

The availability of irrigation water-use data varied 

by state. Site-specific irrigation water-use data were either 

not reported or were unavailable because of confidentiality 

agreements in Delaware, Maine, Massachusetts, New 

Hampshire, New York, and Vermont. In Rhode Island, metered 

irrigation data were collected by the USGS at nine turf farms 

and four golf courses from 2000 to 2004 for a detailed basin 

study. These data were used to develop predictive equations 

for irrigation use on turf farms and golf courses (Bent 

and others, 2009, Zarriello and Bent, 2004). Connecticut, 

Maryland, Pennsylvania and New Jersey required reporting 

of monthly irrigation volumes for agricultural crops and golf 

courses. These data were estimated from pumping rates or 

other non-metered methods, and ancillary information, such as 

irrigated acreage and crop type, were not reported. In Virginia, 

monthly irrigation volumes were available from 1999 to 2009 

at about 800 agricultural sites. Irrigated acreage and crop 

information were reported for roughly half of these sites. In 

North Carolina, estimated daily irrigation volumes for golf 

courses, nurseries, and farms were available from 1983 to 

2010; however, irrigated acreage was not available, and croptype information was limited. In Georgia since 2004, metered 

irrigation is required and is reported for all agricultural water 

use (Georgia General Assembly, 2003). Metered irrigation data 

from Georgia that were available for this study included daily 

volumes at 54 sites for the 2009 growing season and weekly 

irrigation volumes at 75 sites for the 2010 growing season. In 

addition, metered annual irrigation volumes were available 

for over 4,000 sites in southwestern Georgia during 2010. 

Irrigated acreage was reported at about half of the sites.

Data Screening

Irrigation data obtained from states within the study 

area were screened for use in developing predictive irrigation 

water-use models. Irrigation data from Connecticut, Maryland, 

and New Jersey were not suited for use in this study because 

the data did not include information regarding irrigated acreage and crop type, which are needed for model development. 

Irrigated acreage associated with a reported volume is required 

to normalize the water-use data to a volume-per-unit-area measurement to account for the large range in irrigation volumes 

associated with varying field sizes. Information concerning 

crop type is important because irrigation needs over the growing cycle vary for different crops. For example, in Georgia, 

corn is usually planted in March and harvested by July. Corn 

is most susceptible to water stress during the tasseling and 

silking phase of growth, which is roughly 60 to 70 days after 

planting, around May and June (Lee, 2012). Susceptibility to water stress declines after this period, and corn crops 

can require little or no irrigation during the hottest summer 

months when the corn is nearing harvest. Cotton is usually 

planted in May in Georgia, and in contrast to corn, cotton is 

fairly drought tolerant after germination through early growth. 

Consequently, cotton farmers with limited water sources may 

withhold irrigation after early growth so that water is available 

during the more critical fruiting phase in late July and August 

when the crop is more susceptible to drought. The large differences in crop-specific water use make this piece of information crucial for model development.

Reported irrigation water-use data were screened to 

identify potential problems with data quality or accuracy. 

Of the states in the study that require reporting of irrigation 

water use, only Georgia requires metering. Data from other 

states are unmetered and, at best, the water use is estimated 

by the pumping capacity of the irrigation system multiplied 

by the hours of operation, which is usually not well tracked. 

The uncertainty in these water-use estimates creates noise in 

the data that precludes the development of statistical models 

of acceptable significance or the validation of physical crop 

models with the reported irrigation water use.
4 Estimating Irrigation Water Use in the Humid Eastern United States

Table 1. Irrigation data compiled from New England and mid-Atlantic states.

[--, no information]

State Period of 

record Available data Water-use estimation method Notes

Connecticut 1997–2001 Monthly irrigation volumes for golf courses, nurseries, 

and farms.

 Average pumping 

rates

Data not georeferenced. 

Acreage and crop type not 

available.

Delaware Data not collected or unavailable. -- --

Georgia 2009–2010 Daily irrigation volumes for 2009 and weekly volumes 

for 2010. Annual volumes at a larger set of farms for 

2010. Irrigated acreage available at some sites.

Metered data Limited crop-type information.

Maine Data not collected or unavailable. -- --

Maryland 1980–2005 Monthly irrigation volumes and water source type. Unknown Acreage and crop type not 

available.

Massachusetts Data not collected or unavailable. -- --

New Hampshire Data not collected or unavailable. -- --

New Jersey 2003–2008 Monthly and annual irrigation volumes. Unknown Acreage and crop type not 

available.

New York Data unavailable due to confidentiality agreements. -- --

North Carolina 1983–2010 Daily irrigation volumes for golf courses, nurseries, 

and farms.

Estimated Acreage not available. Limited 

crop type information.

Pennsylvania 2003 Monthly irrigation volumes and water source type 

(groundwater or surface water).

Mostly estimated Acreage and crop type not 

available.

Rhode Island 2000–2007 Daily irrigation volumes and irrigated acreage for 

selected turf farms and golf courses.

Metered daily data --

Vermont Data not collected or unavailable. -- --

Virginia 1999–2009 Monthly irrigation volumes and water source type for 

golf courses, nurseries, and farms.

Mostly estimated, 

several metered

Limited crop-type and acreage.
Irrigation Data Compilation 5

Initial screening of the reported irrigation data revealed 

inconsistencies and quality issues associated with some 

reported water-use volumes. For example, two farms in North 

Carolina report daily irrigation during large tropical storms 

or hurricanes when they received about 7 to 10 inches (in.) of 

precipitation over a 2-day period. Even though farms could 

have automated irrigation systems that irrigate during short 

periods of rain, irrigation during an extended period of heavy 

rain is unlikely because it is uneconomical and could cause 

crop damage from excessive water. In other cases, reported 

daily irrigation volumes ranged over three orders of magnitude 

at a single location. At one farm, average daily applications 

of 2,000–3,000 gallons per day (gal/d) were interspersed 

with short application periods of 1,000,000 gal/d. Some sites 

reported irrigation only on the first or last day of each month. 

In these cases, it is likely that a monthly irrigation volume 

was estimated but reported on a single day for convenience. In 

Virginia, some sites reported irrigation rates of over 30 in. per 

month on a crop, even during months with average rainfall. In 

other locations, estimates of monthly irrigation water use are 

identical from one year to the next. Although these inconsistencies might be real, the data more likely reflect reporting 

errors that can be attributed to lack of metering or concern in 

what is reported. Accurate information about the area irrigated, 

crop type, and irrigation method could help determine the 

validity of reported irrigation water use in these cases.

In 2003, Georgia required all permitted irrigation water 

use to be metered (Georgia General Assembly, 2003), and 

meters were installed from 2004 to 2009. Since 2004, annual 

metered irrigation volumes are collected from all permitted 

irrigation sites in Georgia. In addition, daily metered data 

are collected at a subset of irrigation sites across the state. 

Although metered data are more accurate than estimated 

data, several conditions could cause incorrect readings. For 

example, after the pump is shut off, negative air pressure in 

the well or in the supply pipe can cause the meter impeller 

to operate in reverse and can artificially decrease the meter 

reading. Conversely, the recorded irrigation volume could be 

larger than the actual volume because of positive air pressure 

in the distribution line caused by rising groundwater levels 

after a pump has been shut off. A previous analysis of annual 

Georgia irrigation water-use data at over 4,000 metered sites 

determined that erroneous meter measurements were present 

at about 2.5 percent of the sites, resulting in a 38 percent 

overestimation of statewide mean irrigation volume (Torak 

and Painter, 2011). These sites were removed from the 

analysis, but less obvious metering errors could be a factor 

in the reported irrigation use. Despite potential uncertainties 

related to meter malfunctions, the daily and weekly metered 

data from Georgia are considered to be the most accurate of all 

the data available for this study. For this reason, the daily and 

weekly metered irrigation volume data from Georgia in 2009 

and 2010 were used to develop the logistic regression model 

and to assess the crop-water-demand models.

Data Stratification by Type of Crop

Irrigation data were stratified by crop type in order to 

develop predictive models for specific crops. The Georgia 

irrigation data consisted of daily metered water-use volumes at 

55 sites in southern and eastern Georgia from March 1, 2009, 

to December 31, 2009, and weekly metered water-use volumes at 76 sites in southwestern Georgia from May 9, 2010, 

to October 24, 2010 (fig. 1). Irrigation systems in Georgia are 

primarily center pivot, and each metered site provided water to 

only one field. It was assumed that only one crop was grown 

during the growing season; however, double cropping, in 

which two crops are grown in succession during one summer 

growing season, is a common practice in southern Georgia. 

It was unknown at which metered sites, if any, double cropping took place. In addition, irrigated acreage was not always 

reported, and crop type was typically not reported. This information then had to be determined from published geospatial 

data as described below.

Two geospatial datasets were used to supplement the 

Georgia irrigation data by providing additional information 

on the irrigated acreage and crop type. The Georgia irrigated 

lands spatial data layer maps the area of irrigated fields in 

Georgia from 2006 to 2008 (Hook and others, 2009). This 

data layer was used to confirm the reported irrigated acreage. 

When the reported irrigated area did not reasonably match 

the irrigated area from the spatial data layer, the data were 

removed from further analysis. The Georgia irrigated lands 

spatial data layer also provided some crop-type information 

such as pecans and turf; however, most fields are labeled 

only as rotation crops, which consist mostly of corn, cotton, 

peanuts, and soybeans in Georgia. Geospatial data from the 

Cropland Data Layer (CDL) (U.S. Department of Agriculture, 

National Agricultural Statistics Service, 2009b, 2010) were 

used extensively to determine the specific crop being grown 

in each irrigated field during the year the data were reported. 

The CDL has crop-specific and other land-cover information 

developed from composite satellite imagery at a 30-meter 

resolution. The CDL can identify crop types for doublecropped fields with a summer crop followed by a winter crop, 

but it does not identify both crop types in fields with two 

summer crops, as is common in southern Georgia. Fields with 

two or more summer crops are classified in the CDL as only 

having one crop type.

The Georgia irrigated lands spatial data layer and the 

CDL were overlain with the georeferenced position of the 

irrigation meters to match individual metered data from 2009 

and 2010 to specific irrigated fields and crop types (fig. 2). 

Center pivot systems are the most common type of irrigation systems used in Georgia. Fields irrigated by center pivot 

irrigation were easily identified in the CDL because of their 

round shape and most cases could be associated with the 

metered data because the meter location is typically in the 

center of the field (point A, fig. 2). In some cases, the meter 

was outside a field or between several nearby fields. In these 

cases, the irrigated acreage, if reported, was matched to the 
6 Estimating Irrigation Water Use in the Humid Eastern United States

0 25 50 MILES

0 25 50 KILOMETERS

Base maps from U.S. Geological Survey digital coverages, 

State Plane Coordinate System, NAD 83

GEORGIA

ALABAMA

FLORIDA

SOUTH CAROLINA

TENNESSEE NORTH CAROLINA

Enlarged Area

85° 84° 83° 82° 81°

33°

32°

31°

99194 National Climatic Data Center station and 

cooperative station number

County boundary

Weekly metered irrigation water-use data, 2010

Daily metered irrigation water-use data, 2009

EXPLANATION

#

99194

99186

98974

98703

98241

97528

97276

97171

96838

96650

96244

96126

95863

95811

95492

95401

95314

94862

94204

93754

92844

92839 92799

92783

91266

90670

90211

91463

99124

97201

97087

96087

95979

95061

94170

93578

93028

92966

92738

92450

92361

92266

92153 91500

90253

90140

90010

90006

Figure 1. Locations of irrigation sites with daily and weekly irrigation data from 2009 and 2010 and National Climatic Data Center 

climate stations in southern Georgia.
Irrigation Data Compilation 7

B D

A

C

84°23' 84°22' 84°21'

31°7'

31°6'

31°5'

0 0.25 0.5 MILES

0 0.25 0.5 KILOMETERS

EXPLANATION

Irrigation meter

Irrigated fields

Corn

Cotton

Peanuts

Georgia irrigated lands datalayer

Cropland Data Layer (2010) Types of metered conditions

Indistinguishable crop type

Meter not readily associated with an 

 irrigated field

Meter easily identified with a specific 

 area and crop

Meter associated with multiple crops

A

B

C

D

Figure 2. Example overlay of meter locations with Cropland Data Layer and Georgia irrigated lands geospatial dataset. 

Cropland data layer from U.S. Department of Agriculture, National Agricultural Statistics Service (2010).
8 Estimating Irrigation Water Use in the Humid Eastern United States

crop type in the nearby field(s) having the same acreage, or 

the data were removed from further analysis (point B, fig. 2). 

Roughly, half the reported irrigation data in Georgia did not 

report corresponding irrigated acreage. If no irrigated acreage was reported and the meter could not be associated with 

a particular field, data for that meter site were not used in the 

analysis. Data from 31 sites were removed from the analysis 

for this reason.

After matching each metered data point with a field, the 

CDL was used to identify the crop being grown for the appropriate year. In some cases, more than one crop is associated 

with a meter (points C and D, fig. 2). In these cases, the crop 

type cannot be identified (fig. 2, point C) or the field is split 

by different crops (fig. 2, point D) that may be irrigated at different rates or frequencies, or both. These sites were screened 

from further analysis.

A total of 36 sites with daily or weekly metered data from 

2009 and 2010 could be matched to an irrigated field and a 

single crop type. This screened data were composed of 6 corn, 

6 peanut, 6 soybean, and 18 cotton fields. Although climatic 

conditions were similar between the sites, the magnitude and 

frequency of monthly irrigation varied appreciably between 

crop types (fig. 3) because of different water demands during 

different parts of the growing cycle.

Turf grass is an important crop in many mid-Atlantic 

and northeastern states. In Rhode Island, turf farms represent 

over 60 percent of all irrigated farmland (U.S. Department of 

Agriculture, National Agricultural Statistics Service, 2009a). 

Daily metered turf-grass irrigation volumes and acreage were 

collected by the USGS at eight turf farms in Rhode Island 

during 2000 to 2004 for basin studies by Zarriello and Bent 

(2004) and Bent and others (2009).Turf-farm irrigation rates in 

Rhode Island ranged from 0.3 to 11 inches per year (in/yr); the 

average was 3.6 in/yr. Rates peaked in 2002 as result of low 

precipitation during the months of July and August.

Irrigation Estimation Methods

Irrigation water use was estimated by using two different methods. One method used irrigation data from Georgia 

for corn, cotton, peanut, and soybean crops to develop logistic 

regression models that predict the likelihood of irrigation for 

these crops. A logistic regression equation previously developed for turf farms and golf courses in Rhode Island (Zarriello 

and Bent, 2004; Bent and others, 2009) was also used in this 

study. The second method used a soil water balance and crop 

water demand to estimate irrigation needs and was applied to 

the same crops. Results from both modeling approaches were 

compared to each other and to independent reported data.

Logistic Regression

Logistic regression is a statistical technique that predicts 

the probability of an event occurring on the basis of one or 

more explanatory variables. Logistic regression models were 

first applied in a basin study in Rhode Island by Zarriello and 

Bent (2004) to estimate irrigation water use at unmetered sites 

and to estimate irrigation water use over time on the basis of 

past climate conditions. In this study, regression equations 

for daily turf irrigation in Rhode Island by Zarriello and Bent 

(2004) were reexamined with an expanded dataset, and new 

logistic regression equations were developed for Georgia corn, 

cotton, peanut, and soybean crops to predict the probability of 

irrigation taking place during a particular week on the basis 

of weekly antecedent climate conditions. Initial attempts to 

develop a daily irrigation model for Georgia crops produced 

poor results because of the reduced number of sites with daily 

data and because irrigation systems generally require more 

than a day to completely irrigate the full areal coverage of a 

field. Weekly equations were developed by using a suite of 

climate variables and the metered irrigation data from Georgia 

from 2009 and 2010 during the months of April through 

October. Weekly application rates were determined from the 

mean reported irrigation volumes by crop.

Turf Irrigation in Rhode Island

Zarriello and Bent (2004) developed the following 

logistic regression equation for predicting daily irrigation for 

turf farms and golf courses in Rhode Island by using metered 

irrigation data and climate data collected during the 2000 and 

2001 growing seasons:

P = +

− + + −

−

e

e

( . . . ×PET ×PET . ) ×PRCP

( .

5 6791 4 8271 2 1 5760 20 1 2070 10

5 1 6791+ + 4. . 8271×PET2 1 5760×PET20−1. ) 2070×PRCP10 , (1)

where

P = probability of daily irrigation;

e = base of the natural logarithm, equal to 

approximately 2.7183;

PET2 = total potential evaporation volume, in 

inches, during the previous 2 days;

PET20 = total potential evaporation volume, in 

inches, during the previous 20 days; and

PRCP10 = total precipitation, in inches, during the 

previous 10 days.

The equations apply to turf irrigation during May 1 through 

October 31 and golf-course irrigation during April 16 through 

November 15. The probability of irrigation predicted by the 

equations ranges from 0 to 1. A cutpoint is the probability 

threshold for predicting when irrigation is or is not used. 

Computed probabilities equal to and higher than the cutpoint 

represent days when irrigation is predicted and those less than 

the cutpoint predict days of no irrigation. The procedure for 

determining optimal cutpoints for a logistic regression equation is discussed later in this report. A cutpoint of 0.34 was 

determined by Zarriello and Bent (2004); however, with the 

additional turf-irrigation data from Bent and others (2009), a 

cutpoint of 0.50 was found to apply and was used in this study. 
Irrigation Estimation Methods 9

A. Corn B. Cotton

C. Peanuts D. Soybeans

May June July August September October

May June July August September October

May June July August September October

May June July August September October

Month Month

Month Month

12

10

8

6

4

2

0

12

10

8

6

4

2

0

Reported irrigation, in inches Reported irrigation, in inches

12

10

8

6

4

2

0

12

10

8

6

4

2

0

75th percentile

25th percentile

EXPLANATION

Outlier

75th percentile plus 

1.5 times the IQR

Median

75th percentile plus 

1.5 times the IQR

Interquartile

range (IQR)

Figure 3. Monthly irrigation water use during 2009 and 2010 at A, 6 corn; B, 18 cotton; C, 6 peanut; and D, 6 soybean 

fields in southern Georgia.
10 Estimating Irrigation Water Use in the Humid Eastern United States

The turf farms include the three metered turf farms used in the 

equation development by Zarriello and Bent (2004) and the 

six additional turf farms metered by Bent and others (2009). 

Equation 1 accurately predicted the days when irrigation 

took place 83 percent of the time at the nine turf farms with 

metered data from 2000 to 2004.

Total annual (May through October) irrigation depth was 

determined by multiplying the number of days when irrigation was predicted from the logistic regression equation by the 

mean application rate. The average irrigation application rate 

on Rhode Island turf farms was about 0.1 inch per day (Bent 

and others, 2009). Estimates of annual turf-farm irrigation 

by the regression equation matched average reported annual 

irrigation (fig. 4, table 2) reasonably well with a root mean 

squared error (RMSE) of 1.0 in. Model estimates do not vary 

spatially because only one central climate station was used in 

the analysis. Average precipitation during the growing season 

was 22.9 in.; the minimum was 20.1 in. in 2000 and the maximum was 25.6 in. in 2003. This equation was not evaluated 

for other areas in the northeast because of the lack of irrigation 

water-use data; however, the equation is considered a reasonable tool for estimating irrigation water use for turf farms and 

golf courses in the region on the basis of verification tests 

discussed later in the report.

Agricultural Crop Irrigation in Georgia

Logistic regression equations that predict irrigation for 

corn, cotton, peanut, and soybean crops were developed by 

using the metered irrigation data in Georgia. Precipitation, 

PET, and crop-specific potential evapotranspiration (CPET) 

during several antecedent periods were tested for use as 

explanatory variables in the logistic equations. Daily precipitation and temperature were obtained from the National 

Oceanic and Atmospheric Administration (NOAA) National 

Climatic Data Center (NCDC) for 24 stations for 2009 and 

2010 (fig. 1). PET was calculated from the temperature data 

by using the Hamon method (Hamon, 1961), which is based 

on minimum and maximum daily air temperature and potential 

hours of sunshine computed as a function of latitude. All daily 

precipitation and PET volumes were aggregated into 1-, 2-, 

and 3-week totals.

In addition to reference PET, CPET was calculated for 

each of the four crops in Georgia. Because equations for 

calculating PET are developed for a reference crop, usually a 

hay crop such as alfalfa, they can be unreliable when used for 

agricultural crops such as corn, cotton, peanuts, and soybeans, 

which have different growth patterns and water demands. 

CPET estimates the evapotranspiration of a specific crop and 

4 5 8 9 9

2000

12

10

8

6

4

2

0

2001 2002 2003 2004

Seasonal irrigation, in inches

Year

Logistic regression

Crop-water-demand model

4 Number of values 

Mean

Reported data

EXPLANATION

Model estimates

75th percentile

25th percentile

Outlier

75th percentile plus 

1.5 times the IQR

Median

75th percentile plus 

1.5 times the IQR

Interquartile

range (IQR)

Figure 4. Reported data and model estimates of annual irrigation from 2000 to 2004 at nine turf farms in Rhode 

Island.
Irrigation Estimation Methods 11

Table 2. Reported data and model estimates of annual irrigation for turf farms during 2000 to 2004 in 

Rhode Island, and corn, cotton, peanut, and soybean farms during 2009 and 2010 in southern Georgia.

[RMSE, root mean squared error]

Year Number of sites

Average irrigation, 

in inches per year

Observed Crop-water-demand model Logistic regression model

Corn

2009 3 6.2 5.5 8.6

2010 3 10.1 8.3 8.6

Mean 8.1 6.9 8.6

RMSE 1.4 2.0

Cotton

2009 5 3.6 4.8 3.2

2010 13 3.9 7.5 3.8

Mean 3.8 6.2 3.5

RMSE 2.7 0.3

Peanuts

2009 1 9.3 4.6 11.2

2010 5 12.8 9.8 13.4

Mean 11.0 7.2 12.3

RMSE 3.9 1.5

Soybeans

2009 5 9.7 8.3 10.7

2010 1 11.1 12.5 11.4

Mean 10.4 10.4 11.1

RMSE 1.4 0.7

Turf

2000 4 2.5 3.4 3.8

2001 5 4.6 6.7 5.2

2002 8 6.8 6.9 6.4

2003 9 1.1 3.9 2.7

2004 9 2.7 2.8 2.1

Mean 3.5 4.7 4.0

RMSE 1.6 1.0

is computed by multiplying the PET by a crop coefficient. The 

CPET assumes there is no water limitation, and it is not the 

actual crop evapotranspiration.

Crop coefficients have been determined for most crops 

and vary throughout the growing season. Coefficients are 

usually lowest in the early stages of growth when leaf area is 

small and highest in the middle of the growing season during 

fruit development and vary regionally because of differences 

in soil properties and climate. Crop coefficients for corn and 

cotton from the University of Georgia Cooperative Extension 

(Harrison, 2009a) vary seasonally from about 0.2 to 1.1 

(fig. 5A and B). Regional estimates of crop coefficients for 

soybeans and peanuts were not available for Georgia and were 

from the Food and Agriculture Organization of the United 

Nations (FAO) (Food and Agriculture Organization of the 

United Nations, Water Development and Management Unit, 

2011). Soybean and peanut crop coefficients varied seasonally 

from about 0.4 to 1.1 (figs. 5C and D). Crop coefficients 

are reported relative to the number of days after planting. 

Average planting dates for the four Georgia crops from the 

National Agricultural Statistics Service (U.S. Department of 

Agriculture, National Agricultural Statistics Service, 1997) 

were used to assign CPET values to specific calendar days. 

CPET volumes for each crop also were aggregated to 1-, 

2-, and 3-week totals for use as explanatory variables in the 

logistic equations.
12 Estimating Irrigation Water Use in the Humid Eastern United States

0

0.2

0.4

0.6

0.8

1.0

1.2

A. Corn

Apr. May June July Aug. Sep. Oct.

B. Cotton

C. Peanuts D. Soybeans

Apr. May June July Aug. Sep. Oct.

Apr. May June July Aug. Sep. Oct. Apr. May June July Aug. Sep. Oct.

Apr. May June July Aug. Sep. Oct.

E. Turf 

Crop coefficient, unitless

Crop coefficient, unitless

Month Month

Crop coefficient, unitless

Crop coefficient, unitless

Crop coefficient, unitless

Month Month

Month

0

0.2

0.4

0.6

0.8

1.0

1.2

0

0.2

0.4

0.6

0.8

1.0

1.2

0

0.2

0.4

0.6

0.8

1.0

1.2

0

0.2

0.4

0.6

0.8

1.0

1.2

Figure 5. Crop coefficients for A, corn; B, cotton; C, peanuts; and D, soybeans for Georgia and E, turf for Rhode 

Island.
Irrigation Estimation Methods 13

Logistic regression models were developed with the 

SAS software package (SAS Institute, Inc., 1995, p. 51–65). 

Model selection was performed with the best subsets method 

and avoided simultaneous use of highly correlated variables. 

This model-selection technique fits equations for all possible 

combinations of explanatory variables and chooses the final 

model based on goodness-of-fit statistics. PET and CPET 

variables of all antecedent periods were highly correlated with 

each other. In addition, precipitation variables of different 

antecedent periods were also highly correlated with each other 

but were not correlated (or inversely correlated) with PET or 

CPET variables. Highly correlated explanatory variables were 

not used together in any equation to avoid covariance, which 

can cause unreliable model coefficient estimates (Helsel and 

Hirsch, 1992). Candidate equations for each crop type were 

evaluated for goodness of fit by using the Akaike Information 

Criteria (AIC) and the Hosmer-Lemeshow test (Hosmer and 

Lemeshow, 2000). For a given dataset, the AIC indicator is 

used to compare candidate models; the lower values indicate 

better-fitting models. The Hosmer-Lemeshow test is specific to 

logistic regression and compares the predicted probabilities to 

observed responses to identify whether there is significant bias 

in the model. A Hosmer-Lemeshow test p-value less than 0.1 

indicates a lack of fit in the model. Final logistic models were 

chosen based on goodness-of-fit statistics and were all statistically significant at the 95-percent confidence level compared 

to the null model (table 3). The best-fit logistic regression 

models predicting the probability of irrigation during a week 

for corn, cotton, peanut, and soybean crops are:

• Corn

P = +

− +

− +

e

e

×CPET

×CPET

( . . )

( . . )

1 8569 1 1336 2

1 8569 1 1336 2 1

(2)

• Cotton

P = +

− + −

− +

e

e

×CPET ×PRCP

×C

( . . . )

( . .

1 3894 0 6428 2 0 3551 1

1 3894 0 6428 1 PET × 2 0− . ) 3551 PRCP1 (3)

• Peanuts

P = +

− + −

− +

e

e

×CPET ×PRCP

×C

( . . . )

( . .

1 9986 1 3443 2 0 4267 1

1 9986 1 3443 1 PET × 2 0− . ) 4267 PRCP1 (4)

• Soybeans

P = +

− +

− +

e

e

×CPET

×CPET

( . . )

( . . )

2 9786 2 4654 1

2 9786 2 4654 1 1

(5)

where

P = probability of irrigation during any week 

between April 1–October 31;

e = base of the natural logarithm, equal to 

approximately 2.7183;

CPET1 = total crop potential evapotranspiration, 

in inches, during the week for which 

irrigation is being predicted;

CPET2 = total crop potential evapotranspiration, 

in inches, during the week for which 

irrigation is being predicted and the 

previous week; and

PRCP1 = total precipitation, in inches, during 

the week for which irrigation is being 

predicted.

Each crop uses a measure of antecedent CPET, but the 

coefficients and antecedent period vary by crop. Cotton and 

soybean equations also use antecedent precipitation with different coefficients for each crop.

The predicted probability of irrigation (P) can range from 

near 0 to 1; values closer to 1 indicate a higher likelihood of 

irrigation. Each equation was analyzed to determine the most 

appropriate cutpoint for predicting irrigation use. The cutpoint 

is determined iteratively by comparing model outcomes 

with reported data over a range of potential cutpoint values 

(Hosmer and Lemeshow, 2000). For each cutpoint, the number 

of false positives and false negatives is determined. A false 

positive occurs when there is a prediction of irrigation when 

no irrigation was reported. A false negative occurs when 

there is a prediction of no irrigation when irrigation was 

reported. The optimal cutpoint for an equation balances the 

number of false negative and false positive values. Optimal 

cutpoints were about the same for corn and cotton (0.42 and 

0.41, respectively) but were higher for peanuts (0.56) and 

soybeans (0.46).

The overall accuracy of equations was determined by 

comparing weekly predictions of the occurrence of irrigation 

to reported data and determining the percentage of correct predictions. Equation accuracies were 70 percent for corn, 66 percent for cotton, and 72 percent for peanuts and soybeans; other 

metrics of the goodness of fit between predicted and reported 

weekly irrigation in Georgia are summarized in table 3. In 

general, the logistic regression equations (eqs. 2–5) predict 

the occurrence of weekly irrigation for these crops reasonably 

well with a balance of false positive and false negative predictions. False positives and negatives were predicted about 14 to 

17 percent of the time for corn and cotton irrigation and about 

13 to 14 percent of the time for peanut and soybean irrigation.

Total weekly irrigation water use was estimated from 

results of the logistic regression equations and the median 

weekly application rate determined for each month and crop 

type from the Georgia data (table 4). Mean annual irrigation 

was estimated by multiplying the weekly application rate by 

the number of weeks of irrigation during each month of the 

growing season. Annual irrigation water-use estimates from 

the regression equations had RMSEs of 2.0, 0.3, 1.5, and 

0.7 in. for corn, cotton, peanuts, and soybeans, respectively 

(table 2). Logistic regression estimates of annual irrigation 

match mean annual reported irrigation for the four crop types 

reasonably well; however, the variability between sites was 

not well represented with the logistic approach (fig. 6).
14 Estimating Irrigation Water Use in the Humid Eastern United States Table 3. Summary statistics for logistic regression equations developed for predicting weekly irrigation during the 2009 and 2010 growing season for corn, cotton, peanut, and soybean crops in Georgia. [CPET1, CPET2, 1-week and 2-week crop evapotranspiration volume, in inches; PRCP1, 1-week precipitation volume, in inches; AIC, Akaike Information Criterion; 

p-value, measure of significance; <, less 

than]

Corn Cotton Peanuts Soybeans

Intercept CPET2 Intercept CPET2 PRCP1 Intercept CPET2 PRCP1 Intercept CPET1

Best-fit model variables, coefficient values, and statistical significance

Model coefficients -1.857 1.134 -1.389 0.643 -0.355 -1.999 1.344 -0.427 -2.979 2.465Standard error 0.360 0.223 0.179 0.094 0.107 0.406 0.217 0.190 0.498 0.460p-value of equation cofficient estimates < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0009 < 0.0001 < 0.0001 0.025 <0.0001 <0.0001

Goodness-of-fit metrics of the best-fit model

AIC 202.49 705.29 191.85 194.89Hosmer-Lemeshow statistic 4.25 5.52 3.39 7.81Degrees of freedom 8 8 8 8p-value of Hosmer-Lemeshow test statistic 0.8338 0.701 0.9076 0.4527

Predictive accuracy of the best-fit model

Number of data points (total number of 

weeks for all irrigation sites)

167 588 174 174

Probability cutpoint 0.42 0.41 0.56 0.46Percent of correct predictions 70.1 66.5 72.4 71.8Percent of false positives 15.6 16.5 13.2 14.4Percent of false negatives 14.4 17.0 14.4 13.8
Irrigation Estimation Methods 15

in Georgia.

Crop

Median growing-season irrigation rate (May to October), 

in inches per week

May June July August September October

Corn 0.40 0.97 0.77 0.07 0.03 0.02

Cotton 0.08 0.35 0.57 0.40 0.16 0.05

Peanuts 0.66 0.72 1.03 0.71 0.72 0.45

Soybeans 0.11 1.16 1.32 0.76 1.18 0.90

Table 4. Seasonal median irrigation (May to October) during 2009 and 2010 for 6 corn, 18 cotton, 6 peanut, and 6 soybean sites 

25

20

15

10

5

0

Irrigation water use, in inches per year

Corn Cotton Peanuts Soybeans

Crop type

Crop-water-demand model 

Mean

Reported data

EXPLANATION

Outlier

75th percentile plus 

1.5 times the IQR

Median

75th percentile plus 

1.5 times the IQR

Interquartile

range (IQR)

Logistic regression model

75th percentile

25th percentile

Figure 6. Reported data and model estimates of 2009 and 2010 seasonal irrigation (May through October) for corn, 

cotton, peanut, and soybean crops in Georgia.

Despite large differences in total seasonal precipitation 

between sites (growing-season precipitation totals ranged 

from 19.5 to 39.1 in.), logistic regression estimates of total 

irrigation were not highly variable, likely as a result of the 

weekly time step used in the model. Equations at a weekly 

time scale can only produce a limited number of values for a 

growing season. For example, in any month, the model can 

produce one of five results—no irrigation or irrigation for 1, 

2, 3, or 4 weeks, and because the application rate is constant 

each week, the final predicted irrigation volume for the month 

can be only one of five values, despite potentially large 

differences in antecedent climate variables. Thus, the temporal 

scale of the equations inherently limits the sensitivity of the 

model to variations in climate. Additionally, reported weekly 

application rates are highly variable, ranging from 0.01 to 

1.05 in. per week, because irrigation can be used from between 

1 to 7 days during the week. The use of a median application 

rate adds uncertainty to the weekly volume estimate because 

it does not account for the number of irrigated days per week. 

Equations built on a daily time step are more sensitive to 

short-term weather patterns and are better able to account for 

site-to-site variability in irrigation due to local precipitation 

patterns. Reported daily application rates are also less variable 

than weekly application rates because the total volume per 

acre of water that can be applied in 1 day is much less than 

can be applied in 1 week. The collection of additional daily 

irrigation water-use data could help refine the temporal scale 

and application rates of logistic-regression-based irrigation 

models in future studies.
16 Estimating Irrigation Water Use in the Humid Eastern United States

Crop-Water-Demand Models

Crop-water-demand models are deterministic models 

that use mathematical representations of physical processes 

to estimate the state of the system during a given time period. 

These models are fundamentally different from statistical 

models such as logistic regression that use the correlation 

patterns in the observed data to derive empirical relations 

between explanatory variables and observed outcomes. Cropwater-demand models for irrigation keep track of soil moisture 

by accounting for daily soil water inputs, such as precipitation 

and irrigation, and daily soil water outflows from evapotranspiration. When soil water is depleted such that the crop water 

demand cannot be fulfilled, the model calculates the amount of 

irrigation needed to satisfy the deficit.

A daily crop-water-demand model was set up following 

guidelines from the University of Georgia Cooperative 

Extension (Harrison, 2009a) and Colorado State University 

Cooperative Extension (Andales and others, 2011) to estimate 

crop irrigation volumes in Georgia and turf irrigation volumes 

in Rhode Island. The crop-water-demand model has three 

steps. First, the daily soil water deficit is calculated as the 

difference between the maximum soil water content and 

the current soil water content. Next, the soil water deficit is 

compared to the maximum allowable deficit for a crop, which 

determines whether irrigation is needed. Finally, if the water 

deficit is greater than the maximum allowable deficit for a 

crop, irrigation is applied at a rate proportional to the water 

deficit. The daily soil-water content is computed as

Sint = St–1 + Pt – PETt × cft, (6)

where

Sint = soil water content prior to irrigation for day 

t, in inches;

St–1 = soil water content after irrigation for the 

previous day, in inches;

Pt = precipitation for day t, in inches;

PETt = potential evapotranspiration for day t, in 

inches; and

cft = crop coefficient.

The maximum soil water (Smax) is the product of the soil 

water capacity and the effective root depth. Soil water capacity is determined from soil surveys and was set at 0.12 in. 

of water per inch of soil. This is the value recommended by 

the University of Georgia Cooperative Extension (Harrison, 

2009a). The root depth was set at 5 in. at planting date and 

increased to a maximum of 24 in. over the growing period as 

suggested in the guidance document. For each day, the water 

deficit is computed as the difference between the Smax and the 

soil water content (Sint)

WDt = Smaxt – Sint, (7)

where

WDt = water deficit for day t, in inches;

Smaxt = maximum available soil water for day t, 

in inches; and

Sint = soil water content prior to irrigation for day 

t, in inches.

Sint accounts for the water content from the irrigation for the 

previous day.

Plants can withstand small water deficits (draught 

tolerance) without yield reduction so keeping soils at 

maximum saturation is usually not necessary. However, 

excessive depletion of soil water reduces crop growth and 

yield and at some point will eventually destroy the crop. 

Management allowed depletion (MAD) is the percentage of 

the Smax that can be depleted before irrigation is needed. 

MAD values are typically set to 50 percent of Smax, but cropspecific values and varying monthly values are found in cropproduction guidelines by university cooperative extensions, 

USDA, or other agricultural agencies (Al-Kaisi and Bromer, 

2009; U.S. Bureau of Reclamation, 2012). MAD is multiplied 

by the Smax to calculate the maximum allowable water deficit 

(WDMAD). Irrigation is needed whenever the daily water deficit 

exceeds the maximum allowable deficit. In this study, crop 

MAD values were set at a constant value of 50 percent.

WDMAD = MAD × Smaxt, (8)

where

WDMAD = maximum allowable water deficit, in inches;

MAD = managed allowable depletion, in percent; 

and

Smaxt = maximum available soil water, in inches.

The volume of irrigation water applied is proportional to 

the water deficit and can range from a minimum required to 

increase the soil water to the maximum allowable water deficit 

(WDMAD – Sint) to a maximum that completely replenishes 

soil water to saturation (WDt). Use of the maximum allowable 

water deficit as the application volume in the model results in 

high irrigation volumes but less frequent applications. Actual 

application rates are determined by many things including 

the pumping capacity of the irrigation system, the farmer’s 

expectation of future rainfall, and the perceived water deficit. 

This study used the minimum application rate (WDMAD – Sint) 

because this rate most closely matched reported daily application rates in Georgia. The crop water models with application 

rates equal to the minimum application rate (WDMAD – Sint) 

accurately predicted the use of irrigation for a given week 60, 

65, 78, and 64 percent of the time for corn, cotton, soybeans, 

and peanuts, respectively. Estimates of average seasonal 

irrigation by the crop-water-demand model in 2009 and 2010 

had RMSEs of 1.4, 2.7, 3.9, and 1.4 in., respectively (table 2). 

Over the 2009 and 2010 growing seasons, the crop-waterdemand method was generally less accurate than the logistic 

method, but the crop-water-demand model estimates of seasonal irrigation better represented the spatial variability than 
Model Validation and Comparison 17

the logistic regression estimates. The higher variability in the 

crop-water-demand model estimates is believed to result from 

the daily time step of the model compared to the weekly time 

step used in the logistic models.

A crop-water-demand model was developed to predict 

turf irrigation in Rhode Island by using published values for 

the model variables. Crop coefficients (cft) ranged from 0.4 in 

March to 1.0 from June through October (fig. 5) (Aronson 

and others, 1987; Carrow, 1995). The depth of the effective 

rooting zone ranged from 5 in. at planting to a maximum 

depth of 12 in. The frequency and magnitude of irrigation 

on Rhode Island turf farms best matched reported irrigation 

when the irrigation application rate specified in the model 

was equal to the maximum allowable deficit (WDMAD – Sint), 

as was used for Georgia row crops. The crop-water-demand 

model for turf was able to accurately predict the occurrence 

of daily irrigation 80 percent of the time in Rhode Island. 

Estimates of average total irrigation for the years 2000 to 2004 

were slightly less accurate than logistic regression estimates; 

the RMSE was 1.6 in. for the crop-water-demand model and 

1.0 in. for the logistic model (table 2).

Model Validation and Comparison

Performance of the two modeling methods was evaluated by using independent sets of reported irrigation data from 

Georgia and Virginia. Predicted annual irrigation from the 

logistic regression equations and crop-water-demand models 

were compared to each other and to reported data. In general, 

the evaluation of model performance was encumbered by 

the high variability in reported irrigation rates between sites, 

despite the fact that climate conditions were similar. High 

spatial variability is a common characteristic of irrigation 

water use. Site-to-site variation in irrigation rate can be caused 

by many factors not related to climate, such as differences in 

planting dates, water source, irrigation costs, and the risk tolerance of individual farmers. Climate-based irrigation models 

such as the ones developed for this study are therefore most 

appropriate for estimating average irrigation water use over a 

homogeneous climate area such as a county, rather than sitespecific irrigation water use.

Georgia Irrigation

Metered data reported annually in southwestern 

Georgia (fig. 7) in 2010 were available at 4,304 sites, of 

which 3,500 sites also reported irrigated acreage. The data 

span 18 counties, of which 8 counties were chosen (fig. 7) 

for model validation on the basis of centrally located climate 

stations and a range of geographic areas. Crop type was 

determined by matching the metered locations with a particular field from the Georgia irrigated areas data layer and 

overlaying the 2010 CDL, as described previously. Only sites 

that had a single crop type within the irrigated area were used 

in the evaluation. However, double-cropped sites cannot be 

determined from the CDL, and some of these sites likely had 

a second crop planted in the field during the growing season. 

Data screening removed sites with unreasonably high irrigation volumes per unit area (greater than 35 in/yr) that may 

reflect double cropping or incorrectly assigned crop acreage. 

After determining crop type and removing outliers, the validation dataset consisted of 846 sites including 169 corn, 408 

cotton, 263 peanut, and 6 soybean fields. Soybean crops are 

more common in northern Georgia counties and were limited 

in the validation data set.

The logistic regression and crop-water-demand models 

were applied from March 1 to November 30, 2010, to compute 

annual irrigation volumes to compare to the reported annual 

volumes (fig. 8). RMSEs for logistic regression models were 

6.0, 5.5, 7.0, and 1.0 for corn, cotton, peanut, and soybean 

models, respectively, and the RMSEs for crop-water-demand 

models were 5.8, 2.1, 2.6, and 2.4, respectively, for the same 

crops (table 5). Logistic regression models underestimated 

mean countywide irrigation water use in most counties for 

corn, cotton, and peanut crops. In general, the crop-waterdemand model estimates matched mean reported irrigation 

volumes better than the logistic regression estimates for cotton 

and peanuts; however, the crop-water-demand model underestimated corn irrigation volumes. Soybean irrigation was 

adequately predicted by both models; however, the low number of reported soybean irrigation sites make this comparison 

less certain. Underestimation of irrigation volume, particularly 

with corn crops, is likely because of the practice of double 

cropping. For example, cotton or peanut crops are commonly 

grown after corn crops in southern Georgia; however, these 

sites may only be classified as corn or peanuts in the CDL. 

Although this region practices double cropping, little information is available regarding the location or crop composition 

of double-cropped fields in the currently available data. The 

logistic regression and crop-water-demand model assume only 

one crop is planted during the summer growing season. To better estimate irrigation with multiple cropping, more information is needed to characterize the types of crops planted and 

the frequency of multiple croppings in a region.

Virginia Irrigation

Monthly irrigation data reported in Virginia from 1999 

through 2007 provided an independent data set outside of 

Georgia and Rhode Island to evaluate the model performance. 

The data spanned 9 years, of which 2 were relatively wet years 

(2003 and 2004) and 3 were relatively dry years (2002, 2005, 

and 2007). Virginia requires irrigators to report the cumulative 

water volume for each month that exceeds 1 Mgal. However, 

because Virginia does not require metering, few sites likely 

use meters to determine their water use. Most of the reported 

volumes were estimated by multiplying the pumping capacity 

by the time the pump was running. Many sites do not report 

the method used to determine the irrigation volume. In 

addition to irrigation volume, some sites also report the 

irrigated acreage, crop type, and water source (groundwater or 

surface water).
18 Estimating Irrigation Water Use in the Humid Eastern United States

GEORGIA

ALABAMA

FLORIDA

SOUTH

CAROLINA

TENNESSEE NORTH CAROLINA

Enlarged

area

85° 84°

32°

31°

0 5 10 20 MILES

0 5 10 20 KILOMETERS

92738

EXPLANATION

#

National Climatic Data Center station and 

cooperative station number 

Counties used in model validation

Metered irrigation sites reporting annual 

2010 data

LEE

MITCHELL

MACON

CRISP

MILLER

RANDOLPH

DOUGHERTY

SEMINOLE

#

# #

#

#

#

#

# 95979

95061

92738

92450

92266

92153 91500

90140

Base maps from USGS digital coverages,

State Plane Coordinate System, NAD 83

Figure 7. Metered irrigation sites with 2010 annual data and National Climatic Data Center climate stations in 

southwestern Georgia.
Model Validation and Comparison 19

A. Corn B. Cotton

C. Peanuts D. Soybeans

2 8 43 2 22 40 16 36 49 14 41 3 101 6 70

14 9 21 2 73 60 18 66 4 2

124 35

30

25

20

15

10

5

0

25

20

15

10

5

0

County County

Crisp Dougherty Lee Macon Miller Mitchell Randolph Seminole

Crisp Dougherty Lee Macon Miller Mitchell Randolph Seminole Crisp Dougherty Lee Macon Miller Mitchell Randolph Seminole

Macon Randolph

Annual irrigation, in inches Annual irrigation, in inches

35

30

25

20

15

10

5

0

35

30

25

20

15

10

5

0

Logistic regression

Crop water balance

25th percentile minus

 1.5 times the IQR

Median

75th percentile

25th percentile

 1.5 times the IQR

Outlier

75th percentile plus

20 Number of data points

Mean

EXPLANATION

Counties with fewer than five observations show individual data 

 points instead of IQR box

Inter-quartile

 range (IQR)

Observed data Model estimates

Figure 8. Reported data and model estimates of 2010 annual irrigation for A, corn; B, cotton; C, peanut; and D, soybean fields for 

selected counties in southwestern Georgia.
20 Estimating Irrigation Water Use in the Humid Eastern United States

Table 5. Reported data and model estimates of seasonal 2010 annual irrigation for corn, cotton, peanut, and soybean fields for 

selected counties in southwestern Georgia.

[RMSE, root mean squared error]

County Number of sites

Average irrigation, 

in inches per year

Reported data Crop-water-demand model Logistic regression model

Corn

Crisp 2 5.8 9.3 8.6

Dougherty 8 11.5 6.2 8.6

Lee 43 14.1 9.2 8.6

Macon 2 16.5 6.9 8.2

Miller 22 14.6 11.1 8.7

Mitchell 40 14.9 7.1 8.6

Randolph 16 13.8 9.8 8.2

Seminole 36 17.1 11.8 8.6

Mean 15.4 9.3 8.5

RMSE 5.8 6.0

Cotton

Crisp 49 8.7 7.5 3.9

Dougherty 14 8.2 5.1 3.5

Lee 41 11.3 7.6 4.5

Macon 3 6.1 7.1 4.1

Miller 101 9.9 9.9 3.7

Mitchell 124 8.2 6.4 3.1

Randolph 6 10.6 9.3 4.1

Seminole 70 10.2 8.3 4.2

Mean 9.2 7.6 3.9

RMSE 2.1 5.5

Peanut

Crisp 14 9.5 11.8 6.3

Dougherty 9 13.4 9.0 3.5

Lee 21 12.9 12.4 5.6

Macon 2 10.7 10.8 4.5

Miller 73 11.4 14.1 5.2

Mitchell 60 12.2 8.6 2.8

Randolph 18 11.2 13.1 5.6

Seminole 66 11.7 13.8 6.3

Mean 11.6 11.7 5.0

RMSE 2.6 7.0

Soybean

Macon 4 13.3 10.5 12.6

Randolph 2 13.8 11.9 12.6

Mean 13.6 11.2 12.6

RMSE 2.4 1.0
Model Validation and Comparison 21

The Virginia data are not metered and are considered less 

precise than metered data from Georgia and Rhode Island. For 

this reason and because of its temporal scale (monthly values), 

Virginia data were not used for model development. Potential 

data inaccuracies confound the evaluation of model performance, so additional screening was used to remove erroneous 

data to the extent possible. This included removing sites that 

reported applying 20 or more inches of water in a given month 

and sites that reported identical irrigation patterns in multiple 

years. After screening, 67 corn, 7 cotton, 30 soybean, and 

11 turf sites with periods of record ranging from 1 to 7 years 

during 1999 to 2007 were available for analysis. No irrigation 

data were reported for peanut crops in Virginia.

Average annual irrigation rates in Virginia ranged from 

2.0 to 8.3 in. for corn, 0.4 to 5.6 in. for cotton, and 1.6 to 

6.9 in. for soybeans (table 6). In general, annual irrigation 

rates in Virginia for these crops were in the same range or 

lower than irrigation rates reported in Georgia. This is likely 

not only because of climatic differences but also because of 

the fact that the data from Georgia include some sites that 

were double cropped during the growing season, a practice 

that is not typical in Virginia.

Average turf irrigation in Virginia ranged from 5.2 to 

14.2 in/yr, which is roughly twice as much as average annual 

turf irrigation reported in Rhode Island. Monthly data indicate 

turf-grass irrigation in Virginia begins in March or April and 

continues through November, whereas in Rhode Island, turfgrass irrigation usually begins in April and continues through 

October. Higher irrigation rates in Virginia are likely because 

of the longer growing season and increased PET during the 

growing season.

The crop-water-demand models used in Virginia were 

modified slightly to reflect regional conditions. Soil water 

capacity was estimated from soil surveys and was set uniformly at 0.14 in. of water per inch of soil, and climate data 

were obtained from local NOAA climate stations. Crop coefficients (used to compute CPET) for the crop-water-demand 

model and the regression equations were kept the same for 

the row crops, except for corn, which is typically planted 

from mid-April to mid-May in Virginia and from mid-March 

to mid-April in Georgia (U.S. Department of Agriculture, 

National Agricultural Statistics Service, 1997). Corn crop 

coefficients were adjusted accordingly by offsetting the planting date by 30 days for Virginia. Soybean and cotton crops 

are planted at roughly the same time in Virginia as they are in 

Georgia, so no adjustment of the planting date was made to 

these crop coefficients. Weekly irrigation data were not available in Virginia to estimate specific irrigation application rates 

for use with the logistic regression equations; therefore, the 

same irrigation rates used for row crops in Georgia and turf in 

Rhode Island were used to estimate irrigation use in Virginia.

The RMSEs for crop-water-demand model estimates of 

statewide annual average irrigation compared to the reported 

volumes were 2.0, 2.9, 3.9, and 3.4 in. for corn, cotton, soybeans, and turf, respectively; the logistic regression RMSEs 

were 3.0, 1.8, 4.9, and 3.1 in., respectively, for the same crops 

(table 6). Annual irrigation rates can vary considerably across 

the state in a given year because of geographic and climatic 

differences. Therefore, annual irrigation predictions by both 

models were also compared for each county that reported 

data (fig. 9). The number of sites within a county was low; 

many counties have only one or two reported data points per 

year. The low sample size combined with the high degree of 

variability between sites and the large uncertainties associated with the reported data complicate the interpretation of the 

model results. Results are presented with the caveat that mean 

reported countywide irrigation for a particular year may be 

highly uncertain and affected by these factors.

Differences between estimated and reported mean annual 

irrigation were high with both models, but the crop-waterdemand models were better at capturing irrigation variability 

(fig. 9). Irrigation volume estimates by the crop-water-demand 

model for corn were mostly underestimated and estimates for 

soybeans were overestimated. In general, counties with high 

reported irrigation rates had high predicted irrigation rates and 

counties with reported low irrigation rates had low predicted 

values. Adjustments in model parameters, such as crop coefficients or application rates, that are based on local knowledge, 

could potentially reduce the bias and improve model results 

for these crops. Irrigation volume estimates by the logistic 

regression model for annual corn and soybean irrigation rates 

show little variability between sites and years, likely because 

precipitation is not included in the equation as an explanatory 

variable for these two crops. Both models performed similarly 

for turf-farm irrigation, overestimating irrigation in some years 

and underestimating in other years, but overall, the model 

estimates are well correlated with reported values. The logistic 

regression performed marginally better at predicting cotton 

irrigation than the crop–water-demand model, which overestimated irrigation. Additionally reported and predicted values 

were not correlated for either method, indicating an inability 

of either model to adequately describe cotton irrigation patterns in Virginia.

Mean monthly reported irrigation volumes were 

compared to predicted volumes from the crop-water-demand 

and logistic models (fig. 10) to determine the ability of the 

models to predict monthly patterns of irrigation water use. In 

general, the daily crop-water-demand models and the daily turf 

logistic models were better able to match the complex monthto-month water-use patterns of the reported data, particularly 

for turf. As mentioned previously, weekly logistic models have 

a limited number of outcomes that can be produced during a 

given month, regardless of the explanatory variables in the 

equation. This results in an oversimplified, peaked wateruse curve that does not always match reported water-use 

volumes. Additionally, because the corn and soybean logistic 

regression equations do not include antecedent precipitation as 

an explanatory variable, modeled irrigation has little variation 

from year to year despite differences in annual precipitation 

(fig. 10; table 6).
22 Estimating Irrigation Water Use in the Humid Eastern United States Table 6. Reported and simulated mean annual irrigation from 1999 to 2007 for corn, cotton, soybean, and turf farms in Virginia. [RMSE, root mean squared error; --, no available data] Year Irrigation, in inches per year Corn Cotton Soybeans Turf Number of sites Reported data Water- demand model Logistic regression model Number of sites Reported data Water- demand model Logistic regression model Number of sites Reported data Water- demand model Logistic regression model Number of sites Reported data Water- demand model Logistic regression model 1999 21 7.5 5.4 7.9 3 5.6 3.0 2.1 7 6.2 8.8 9.5 0 -- -- -- 2000 14 3.0 1.0 7.4 3 1.8 0.4 1.2 2 4.0 1.8 7.6 2 6.3 11.0 10.2 2001 21 4.7 3.7 7.6 1 1.1 1.7 2.2 5 3.6 8.5 9.5 2 14.2 9.8 9.7 2002 15 8.3 6.4 8.2 7 2.4 3.3 2.1 6 6.5 12.7 8.8 2 10.3 11.6 10.4 2003 12 2.0 1.9 7.9 0 -- -- -- 5 1.6 4.5 9.1 1 9.3 12.1 12.1 2004 20 4.0 1.9 7.3 0 -- -- -- 3 1.6 3.9 9.3 5 11.9 9.4 11.1 2005 33 6.1 3.2 7.7 2 2.4 3.9 2.3 7 5.0 10.2 10.1 11 5.2 10.0 10.2 2006 25 6.0 2.9 5.8 6 1.5 5.9 2.8 19 6.2 8.1 8.2 1 10.9 13.1 10.6 2007 43 6.0 5.1 8.1 1 0.4 4.0 2.9 14 6.9 11.5 9.3 16 6.9 9.6 9.7 Mean 3.5 7.5 3.2 2.2 7.8 9.1 9.4 10.8 10.5 RMSE 2.0 3.0 2.9 1.8 3.9 4.9 3.4 3.1
Model Validation and Comparison 23

A. Corn B. Cotton

C. Soybeans D. Turf

Logistic regression model estimates

Crop-water-demand model estimates

EXPLANATION

Estimated annual irrigation, in inches Estimated annual irrigation, in inches

Reported annual irrigation, in inches Reported annual irrigation, in inches

0

3

6

9

12

15

0 3 6 9 12 15 0 2 4 6 8

0

1

2

3

4

5

6

7

8

0

3

6

9

12

15

18

21

0 3 6 9 12 15 18 21

0 3 6 9 12 15 18

0

3

6

9

12

15

18

Figure 9. Reported data and model estimates of annual irrigation by county, for A, corn; B, cotton; C, soybeans; and 

D, turf crops in Virginia from 1999 to 2007.
24 Estimating Irrigation Water Use in the Humid Eastern United States

1

3

4

5

0

2

4

5

6

2

3

4

0

1

2

3

4

5

1999 2000 2001 2002 2003 2004 2005 2006 2007

3

1

5

1

0

2

0

A. Corn

B. Cotton

D. Turf 

EXPLANATION

Belownormal

precipitation

Abovenormal

precipitation 

Crop-water-demand model Logistic regression model

C. Soybeans

Mean monthly irrigation, in inches Mean monthly irrigation, in inches Mean monthly irrigation, in inches Mean monthly irrigation, in inches

Reported irrigation

Figure 10. Reported data and model estimates of mean monthly irrigation from 1999 to 2007 for A, corn; B, cotton; 

C, soybean; and D, turf farms in Virginia.
Study Limitations and Areas of Further Study 25

Irrigation Model Transferability

Logistic regression and crop-water-demand models 

developed for this study can be used to estimate irrigation 

in other eastern states in the study area. Crop-water-demand 

models are easily transferred to other regions and different crop types and can be customized to local agricultural 

conditions such as usual planting and harvesting dates, soil 

type, and crop coefficients. These models can be particularly 

useful at estimating irrigation water use in areas with little 

or no irrigation data because model parameters such as soil 

type, climate, and crop coefficients can be obtained from 

other sources. Logistic regression equations are based on 

actual water-use patterns and are inherently calibrated to the 

data used for model development. Equations may accurately 

predict irrigation within the geographic range where they were 

developed; however, they may not perform well in other areas 

where irrigation practices differ. Given sufficient quality data, 

it is possible to develop regional logistic regression equations to more accurately estimate irrigation water use in other 

eastern states.

Crop-water-demand and logistic regression models may 

not be appropriate to estimate irrigation water-use practices 

for some specialty crops grown in eastern states. Nursery 

and greenhouse horticultural crops are among the top three 

irrigated crops by acreage in many eastern states (table 7) and 

can require two to three times the amount of water used for 

field crops per unit area (Harrison, 2009b). The water needs of 

nursery plants depend largely on the container size, the spacing of plants, and the size of the inventory, which may change 

daily (Burger and others, 1987). These factors are unrelated 

to climate conditions making it unsuitable for a crop-waterdemand modeling approach. In addition, many greenhouse 

nurseries capture water that drains through potted plants and 

reuse the water.

Irrigation practices for berries and fruit trees also are 

unsuitable for estimation with crop-water-demand methods. 

Cranberries are an important crop in Massachusetts (table 7), 

but water use for this crop is primarily to flood fields during 

harvest or to prevent frost damage and is eventually returned 

to the lake or stream; thus, this is considered a nonconsumptive use and the irrigation-use methods in this study are not 

applicable. Water also is used to prevent frost or freeze damage to fruit trees and bushes such as apple trees and blueberry 

bushes. Because these water uses are unrelated to the crop 

water needs, crop-water-demand models are not appropriate 

for estimating this use.

Study Limitations and Areas of Further 

Study

Logistic regression and crop-water-demand models 

show promise for estimating regional irrigation water use at 

a monthly or annual time scale. Although models reproduced 

the general patterns of spatially aggregated, annual irrigation 

water use for independent datasets for corn, cotton, soybeans, 

peanuts, and turf, error rates for predicted irrigation at a 

specific site were high, and the models failed to capture the 

large site-to-site variability in the reported data. Inaccuracies 

in model predictions may arise from uncertainty in the data 

used to develop, calibrate, and validate the models and from 

uncertainty due to imprecise or unknown model parameter 

values such as crop coefficients or maximum allowable deficit 

values, particularly because these vary over the humid eastern 

United States. Factors not accounted for in the model, such 

as individual irrigation-system types and efficiencies, water 

availability, and economic factors, which influence decisions 

made by individual farmers to irrigate or not, also vary and 

affect the confidence of the model estimates.

The lack of quality irrigation data and ancillary information in the mid-Atlantic and New England States was the 

greatest limitation of the study; only through additional data 

collection and further study can the accuracy of irrigation 

water-use estimation be improved in these regions. Aside 

from the need for additional irrigation data, areas of further 

study that could improve the estimates of irrigation water use 

include better estimates of crop coefficients, application rates, 

and irrigated areas.

Crop Coefficients

This study used crop coefficients for the crop-waterdemand and the logistic regression models for agricultural 

crops in Georgia to specify the crop water requirement as a 

function of PET over the growth cycle of the crop. The coefficients are specified for a particular crop and are sensitive to 

many different factors such as soil type, humidity, average 

wind speed, crop variety, planting time, and the method of 

PET computation. Many of these factors are not reported with 

published estimates of crop coefficients. Jagtap and Jones 

(1989) found that crop coefficients required adjustments of 

±30 percent in order to accurately estimate CPET under varying wind speeds, soil types, planting dates, PET estimation 

method, and humidity. Using crop coefficients in conditions 

different from those in which they were developed could cause 

inaccuracies in irrigation estimates.

Crop-water-demand models are highly sensitive to 

changes in crop coefficients. Crop coefficients are the most 

sensitive of the crop-water-demand model parameters, including root depth and soil water capacity (Satti and others, 2004). 

The sensitivities of crop coefficients for corn, cotton, peanut, 

and soybean crops in Georgia were tested by increasing their 

values by 10 and 20 percent and evaluating the effects on the 

logistic regression and crop-water-demand models. In the 

crop-water-demand model, increases of 10 and 20 percent in 

the crop coefficient increased total seasonal irrigation an average of 21 and 43 percent, respectively; total seasonal irrigation 

in the estimates from the logistic regression model increased 

comparably to the increases in the crop coefficient, by about 
26 Estimating Irrigation Water Use in the Humid Eastern United States

Table 7. Primary irrigated crops in eastern states, by acreage, reported in the 2008 Farm and Ranch Irrigation Survey. Predominant 

fruit, vegetable, and berry crop types were determined using the 2007 Census of Agriculture.—Continued

[FRIS, Farm and Ranch Survey by the U.S. Department of Agriculture]

State Crop category from 2008 FRIS1 FRIS irrigated 

acreage2

Percent of 

total irrigated 

acreage

Primary crops types in category3

Connecticut All crops 5,028

Horticultural crops except sod 2,756 55

Land in vegetables 1,030 20 Sweet corn, pumpkins, snap beans, tomatoes

Tobacco 837 17

Delaware All crops 117,406

Corn4 50,131 43

Soybeans 33,770 29

Vegetables 16,401 14 Lima beans, sweet corn, peas, cucumbers

Georgia All crops 1,107,761

Cotton 246,790 22

Peanuts 227,591 21

Corn4 181,182 16

Maine All crops 15,178

Berries 8,082 53 Blueberries

Vegetables 4,934 33 Potatoes

Horticultural crops except sod 759 5

Sod 725 5

Maryland All crops 105,014

Corn4 39,077 37

Soybeans 26,119 25

Vegetables 16,953 16 Sweet corn, snap beans, potatoes, green peas, 

cucumbers

Massachusetts All crops 18,980

Berries 13,883 73 Cranberries

Vegetables 2,568 14 Sweet corn, potatoes, pumpkins, squash

Horticultural crops except sod 1,662 9

New Hampshire All crops 1,518

Horticultural crops except sod 806 53

Vegetables 474 31 Sweet corn, pumpkins

Berries 119 8 Blueberries

New Jersey All crops 83,998

Vegetables 31,545 38 Sweet corn, cucumbers, tomatoes, peppers, 

squash, snap beans, spinach, pumpkins, 

potatoes

Horticultural crops except sod 15,975 19

Berries 11,391 14 Blueberries

New York All crops 29,189

Vegetables 11,314 39 Sweet corn, snap beans, potatoes, green peas

Land in orchards, vineyards, and 

nut trees

5,413 19 Apples and grapes

Sod 4,852 17
Study Limitations and Areas of Further Study 27

Table 7. Primary irrigated crops in eastern states, by acreage, reported in the 2008 Farm and Ranch Irrigation Survey. Predominant 

fruit, vegetable, and berry crop types were determined using the 2007 Census of Agriculture.—Continued

[FRIS, Farm and Ranch Survey by the U.S. Department of Agriculture]

State Crop category from 2008 FRIS1 FRIS irrigated 

acreage2

Percent of 

total irrigated 

acreage

Primary crops types in category3

North Carolina All crops 182,026

All hay (dry hay, greenchop, and 

silage)

36,484 20

Corn4 26,568 15

Soybeans 14,839 8

Cotton 14,156 8

Pennsylvania All crops 23,906

Vegetables 10,538 44 Sweet corn, potatoes, snap beans, pumpkins

Horticultural crops except sod 5,296 22

Land in orchards, vineyards, and 

nut trees

3,251 14 Apples and grapes

Rhode Island All crops 3,082

Sod 1,966 64

Vegetables 353 11

Land in orchards, vineyards, and 

nut trees

158 5 Apples and grapes

South Carolina All crops 130,189

Corn4 39,930 31

Vegetables 13,336 10 Watermelons, tomatoes, sweet corn, collards, 

cucumbers, turnip greens, cantaloupes

Soybeans 12,741 10

Sod 12,538 10

Vermont All crops 748

Horticultural crops except sod 260 35

Berries 208 28 Blueberries and strawberries

Land in orchards, vineyards, and 

nut trees

159 21 Apples

Virginia All crops 55,478

Corn4 16,016 29

Vegetables 11,170 20 Snap beans, potatoes, tomatoes, sweet corn

Soybeans 8,796 16

West Virginia All crops 975

Horticultural crops except sod 383 39

Vegetables 320 33 Sweet corn, potatoes, pumpkins, tomatoes

Land in orchards, vineyards, and 

nut trees

118 12 Apples

Berries 113 12 Blueberries and blackberries

1

Crop category and dominant crop types that comprise a combined total of about 70 percent or more of the total irrigated acreage in a state.

2

Statewide irrigated acreage is the sum of the acreage of all irrigated crop categories in the 2008 FRIS. Acreage of fields that are double cropped are 

counted twice.

3

Crop categories and types were determined from a combination of the Farm and Ranch Survey and Agricultural Census by the U.S. Department of Agriculture.

4

Corn used for grain, seed, silage, or greenchop.
28 Estimating Irrigation Water Use in the Humid Eastern United States

9 and 16 percent, respectively. Thus, the crop-water-demand 

model is about twice as sensitive to the crop coefficient as the 

logistic regression model.

Crop coefficients vary from twofold to fivefold relative to 

the number of days after planting. Even when crop coefficients 

are well known, model uncertainty exists because planting 

dates can vary by a month or more in the same region, affecting the potential crop evapotranspiration. Crops planted later 

in the spring will generally require less water in June and July 

and more water later in the summer relative to crops planted 

in the early spring. Irrigation water use for cotton in Georgia 

estimated with the logistic regression and crop-water-demand 

models decreased by up to 2.5 in. when planting dates were 

delayed by 1 month. However, the effects of the planting date 

and the effects of the crop coefficient in general on estimated 

irrigation water use largely depend on precipitation during the 

growing season.

Application Rate

Irrigation water use estimated by the logistic regression 

equations is highly sensitive to the monthly application rate. 

Weekly irrigation depths computed in Georgia varied widely, 

ranging from 0.1 to more than 3.0 in. per week. Hence, the use 

of average monthly application values does not represent the 

highly variable rates in many cases. Percent errors in monthly 

application rates will cause a proportional percent error in the 

resulting total irrigation volume. Therefore, even though a 

logistic regression equation may be accurate at predicting the 

occurrence of irrigation on a given day or week, the resulting 

estimate of water volume used will be uncertain if the application rate is not well known.

The need for accurate monthly application rates is a 

potential limitation of the logistic regression method. Application rates vary not only by crop but also can be affected by 

soil type, climate conditions, water source, pumping capacity, 

and other factors. Estimates of total irrigation determined by 

logistic regression models could be improved by developing 

predictive models for application rates from factors that affect 

application rates or developing localized logistic regression 

models that inherently incorporate these factors.

Irrigation in the crop-water-demand model is determined by the computed water deficit needed to maintain 

the crop, and the application rate can vary depending on the 

maximum allowed deficit, which is determined by the userspecified MAD. In the present study, MAD was assumed 

to be 50 percent, but the value likely varies temporally and 

spatially. Uncertainty in MAD values has relatively minor 

effects on total irrigation predicted by the crop-water-demand 

model. Varying MAD by ±20 percent (40 and 60 percent 

of the maximum allowed deficit) in the crop-water-demand 

model resulted in relatively minor changes of 3 to 7 percent 

in total irrigation in corn, cotton, peanut, and soybean models. 

Specifying the maximum MAD value of 100 percent resulted 

in increases in total seasonal irrigation by 15 to 40 percent, 

which is equal to about 1 to 3 in/yr, respectively.

Irrigated Area

In addition to accurately estimating when irrigation 

is used and at what rate, model estimates of total irrigation 

volume over a state or region also require accurate estimates 

of irrigated area by crop type. Published estimates of irrigated 

land vary widely. For example, in 2007, the farm and ranch 

survey (U.S. Department of Agriculture, National Agricultural 

Statistics Service, 2009a) reported 1.0 million acres of irrigated cropland in Georgia; however, the University of Georgia 

irrigation survey (Harrison, 2009b) reported 1.5 million acres 

of irrigated cropland for that year. Hence, statewide estimates 

of total annual irrigation water use can vary by hundreds of 

millions of gallons depending on which estimate of total irrigated area is used.

Remote sensing is frequently used to map irrigated 

acreage by comparing spatial patterns of vegetative indices 

such as the Normalized Difference Vegetation Index (NDVI) 

throughout the growing season combined with ancillary 

climate, topography, or other ground-based data (Masoner 

and others, 2003, Ozdogan and Gutman, 2008, Pervez and 

Brown, 2010, Qi and others, 2002). Although satellite imagery has been successful at distinguishing between irrigated 

and non-irrigated land in the arid or semiarid western United 

States, the contrast between spectral signatures from irrigated 

and non-irrigated lands is less discernible in the humid east 

(Ozdogan and others, 2010) but is an area of active research 

by the USGS Eastern Geographic Science Center (John Jones, 

U.S. Geological Survey, written commun., November 2011). 

The prospect of improved detection of irrigated lands together 

with the improved estimates of when irrigation is used can 

greatly improve estimates of irrigation water use in the eastern 

United States.

Data Limitations

The extent and quality of existing irrigation water-use 

data in the eastern United States is the biggest impediment 

to developing comprehensive accurate models for estimating 

irrigation water use in the region. Although efforts were made 

to collect all available irrigation data in the eastern United 

States, only statewide metered data from Georgia could be 

used with reasonable certainty. However, data from Georgia 

still required extensive screening because meter error and 

ancillary data required for model development, such as 

irrigated acreage and crop type, were incomplete. Attribute 

information was commonly determined by combining the 

irrigation data with other spatial information, which is another 

source of uncertainty. Irregularities also were found in the 

Georgia data, such as the fact that weekly total irrigation 

volumes did not always sum to the total annual reported 

irrigation volume for the site. These data limitations greatly 
Summary and Conclusions 29

reduced the size of the irrigation data set available for model 

development in Georgia, which correspondingly increases the 

uncertainty of the model, particularly the logistic regression 

model that is wholly dependent on a representative sample 

of the entire population. Hence, irrigation models developed 

from the Georgia data might not represent irrigation water use 

throughout the eastern United States.

Irrigation water-use data from Virginia were imprecise 

and highly uncertain because the data were largely estimated 

from pumping rates and the estimated time the pump was 

running. As a result, there were many inconsistencies in these 

data including irrigation volumes that were unreasonably high 

and irrigation rates that were identical from year to year or 

constant for each month. Although the data were screened to 

eliminate questionable data to the extent possible, screening 

methods were subjective, leaving the possibility that erroneous 

data are in the data set used in the analysis. The use of highly 

uncertain data complicates the interpretation of the validation 

results. Poor agreement between model predictions and the 

reported data could be a result of uncertainty in the model or 

uncertainty in the data. Conversely, good agreement between 

predicted and reported values could merely reflect similar 

biases in the model and reported data and not a true measure 

of the accuracy of model.

Quality irrigation data is needed in the eastern United 

States to develop tools to estimate irrigation water use over 

the region. These data need to be of known accuracy and need 

to be accompanied by other attribute information such as crop 

type, acreage irrigated, irrigation methods, and other characteristics, otherwise the data are of limited value for assessing and managing irrigation water use. Although widespread 

metering of irrigation in the eastern United States is unlikely 

in the near future, a stratified sample of metered irrigation 

water use in the region could help fill the gap in irrigation 

water-use data. The stratified sampling could be based on 

principal irrigation water use in each state, with as few as 6 to 

10 sites to represent a unique crop type, irrigation method, and 

similar geomorphic and climatic area. Although the number 

of unique groups could potentially be large, the groups could 

be prioritized by crops with the highest irrigation water use 

and by the regions with the most stressed water resources. 

High-quality irrigation water-use data are essential for further 

development of the tools needed to help make informed watermanagement decisions for sustainable water resources.

Summary and Conclusions

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use 

Information Program and the WaterSMART initiative to 

help develop and maintain sustainable water resources in the 

Nation. Despite the importance of irrigation data for evaluating the water resources of the Nation, reliable estimates of 

irrigation water use are missing in most eastern states because 

of a lack of reporting requirements and estimation methods. 

Methods of estimating irrigation water use are further complicated by the high spatial and temporal variability of irrigation 

due to natural rainfall variability in the region.

Irrigation data from eastern states were compiled in 

order to develop models of irrigation water use in the region. 

The availability and quality of irrigation water-use data vary 

from state to state. About half the eastern states have no 

reporting requirement, and states that do require reporting 

of irrigation water use vary widely in the type of information that is required, the reporting time scale, and whether the 

data was metered, estimated from average pumping times, or 

determined by other means. With the exception of Georgia, 

States in New England and the mid-Atlantic regions do not 

presently (2012) require reported irrigation water-use data to 

be metered, and where irrigation data do exist, the data are 

typically based on estimated pumping rates and run times. 

These nonmetered data are subject to many uncertainties, and 

data screening revealed many inconsistencies. Quality issues 

associated with reported irrigation water use in the eastern 

United States, including some metered data in Georgia, precluded their use for model development. In addition, ancillary 

data such as crop type, irrigated acreage, irrigation method, or 

water source are commonly incomplete or missing.

Georgia required irrigation metering in 2003, resulting 

in statewide availability of quality controlled irrigation data. 

In addition to the annually reported metered data at over 

4,000 sites, daily irrigation data from 55 sites in 2009 and 

weekly irrigation data from 76 sites in 2010 provided the basis 

for the irrigation models developed during this study. Irrigated 

acreage was reported with approximately half the daily and 

weekly data, which had to be supplemented with the National 

Cropland Data Layer and the Georgia irrigated lands data layer 

to determine crop type and irrigated acreage associated with 

the metered data. Sites for which crop type or acreage could 

not be determined were removed from the analysis.

Two methods of estimating irrigation water use were 

tested and compared. Logistic regression models and cropwater-demand models for corn, cotton, peanut, and soybean 

crops were developed from 2009 and 2010 metered data from 

Georgia. In addition, a previously developed daily logistic 

regression model for turf farms in Rhode Island was compared 

to a crop-water-demand model. Logistic regression models 

predict the use of irrigation based on antecedent climate and 

the temporal patterns of reported irrigation. The results of the 

regression model are combined with an applicable irrigation 

rate over the area irrigated to obtain an irrigation volume. 

Logistic regression equations accurately predicted when irrigation occurred 70 percent of the time for corn, 67 percent of 

the time for cotton, and 72 percent of the time for peanuts and 

soybeans. False negative and positive predictions were about 

equally balanced so the overall model performance for predicting irrigation over the 2009 and 2010 growing seasons was 

considered adequate. The use of a mean monthly application 

rate and a weekly time scale limited the ability of these models 

to adequately represent the high spatial variability seen in the 
30 Estimating Irrigation Water Use in the Humid Eastern United States

reported data and were inconsistent at estimating irrigation 

on a site-to-site basis. However, models were able to estimate 

mean annual irrigation for each crop type within about ±2 in. 

of the mean reported irrigation water use.

Crop-water-demand models simulate the daily crop water 

deficit based on daily precipitation, evapotranspiration, and 

soil moisture. Unlike the logistic regression model, the cropwater-demand model does not require an applicable irrigation 

rate because this is determined by the water deficit computed 

by the model. Both methods, however, still require information on the crop type and area irrigated. The crop-waterdemand model did not perform as well as the logistic 

regression model when compared to the 2009 and 2010 

Georgia irrigation data. Mean crop model irrigation estimates 

were within ±4 in. of the reported mean; however, the high 

variability between sites was better represented by the estimates from the crop-water-demand model than the estimates 

from the logistic regression model.

The logistic regression and crop-water-demand models 

were validated by using independent metered data reported 

annually at 846 sites in 8 counties in southwestern Georgia for 

2010 and reported monthly at 132 sites in Virginia from 1999 

to 2007. Model error was high for both modeling approaches; 

however, in general, the estimates from the crop-waterdemand model had lower RMSEs than the estimates from the 

logistic regression model for most crops. Crop-water-demand 

model estimates of monthly irrigation volumes for corn and 

soybeans matched reported patterns in Virginia better than the 

logistic model but were about the same as the logistic regression model for cotton and turf. Corn and soybean logistic 

regression equations do not include antecedent precipitation as 

an explanatory variable. Consequently, there was little variation from year to year in irrigation estimates despite differences in annual precipitation.

The logistic regression and crop-water-demand models developed for this study show promise for estimating 

regional irrigation water use at a monthly or annual time 

scale, although large uncertainties exist in model estimates. In 

general, the crop-water-demand model is more applicable to 

areas that lack adequate irrigation data, whereas the logistic 

regression equations work better when developed in areas that 

have reliable data available. Unfortunately, many areas in the 

eastern United States do not have the required level of irrigation data to develop logistic regression equations, underscoring an important limitation of this method.

Uncertainty in irrigation estimates from these methods 

exists because of imprecise or unknown model parameters 

such as crop coefficients or average application rates. In 

addition, factors such as irrigation-system efficiencies, water 

availability, and economic factors that influence irrigation 

decisions cause variability in irrigation data that are not 

accounted for in the models. Moreover, crop type and irrigated 

acreage must be known with more certainty in order to 

estimate total irrigation water use at a regional or statewide 

scale. Finally, comparisons between models and data could 

only reflect similar biases in both the model and the reported 

data and not the accuracy of model results. Only additional 

data collection and further study can improve the accuracy 

of irrigation water-use estimates in the humid eastern United 

States. Current statewide reporting programs could improve 

their data by requiring ancillary data such as the crop type, 

irrigated acreage, and method associated with reported water 

use and by encouraging the installation of water meters. Even 

though widespread water metering is infeasible in many 

states, the development of a small, stratified sample of quality 

metered irrigation sites across different regions of the east 

could help fill the data gap and provide sufficient information 

to develop a more accurate predictive models of irrigation 

water use. The stratified sampling could be based on principal 

irrigation water use in each state, with as few as 6 to 10 sites 

to represent a unique crop type, irrigation method, and similar 

geomorphic and climatic area.

References Cited

Al-Kaisi, M.M., and Bromer, I., 2009, Crop water use and 

growth stages: Colorado State University Extension, 

Fact Sheet No. 4.715, 3 p., accessed March 2011, at 

http://www.ext.colostate.edu/pubs/crops/04715.pdf.

Andales, A.A., Chavez, J.L., and Bauder, T.A., 2011, Irrigation 

scheduling—The water balance approach: Colorado State 

University Extension Fact Sheet 4.707, 6 p., accessed 

February 2011, at http://www.ext.colostate.edu/pubs/

crops/04707.html.

Aronson, L.J., Gold, A.J., Hull, R.J., and Cisar, J.L., 1987, 

Evapotranspiration of cool-season turfgrasses in the humid 

northeast: Agronomy Journal, v. 79, no. 5, p. 901–905.

Bent, G.C., Zarriello, P.J., Granato, G.E., Masterson, J.P., 

Walter, D.A., Waite, A.M., and Church, P.E., 2009, 

Simulated effects of water withdrawals and land-use 

changes on streamflows and groundwater levels in the 

Pawcatuck River basin, southwestern Rhode Island and 

southeastern Connecticut: U.S. Geological Survey Scientific 

Investigations Report 2009–5127, 254 p.

Burger, D.W., Hartin, Janet, Hodel, D.R., Lukaszewski, T.A., 

Tjosvold, S.A., and Wagner, S.A., 1987, Water use in 

California’s ornamental nurseries: California Agriculture, 

v. 41, no. 9, 2 p.

Carrow, R.N., 1995, Drought resistance aspects of turfgrasses 

in the southeast—Evapotranspiration and crop coefficients: 

Crop Science, v. 35, no. 6, p. 1685–1690.

Fanning, J.L., Schwarz., G.E., and Lewis, W.C., 2001, A field 

and statistical modeling study to estimate irrigation water 

use at benchmark farms study sites in southwestern Georgia, 

1995–96: U.S. Geological Survey Water-Resources 

Investigations Report 00–4292, 32 p.
References Cited 31

Food and Agriculture Organization of the United Nations, 

Water Development and Management Unit, 2011, Crop 

water information: Natural Resources and Environment 

Department, accessed March 2011, at http://www.fao.org/

nr/water/cropinfo.html.

Georgia General Assembly, 2003, 2003–2004 regular 

session—HB 579—Water resources; farm uses; watermeasuring device: Georgia General Assembly, accessed 

March 23, 2010, at http://www.legis.ga.gov/Legislation/

en-US/display/20032004/HB/579.

Guerra, L.C., Garcia, A.G., Hook, J.E., Harrison, K.A., 

Thomas, D.L., Stooksbury, D.E., and Hoogenboom, 

G., 2007, Irrigation water use estimates based on crop 

simulation models and kriging: Agricultural Water 

Management, v. 89, no. 3, p. 199–207. (Also available at 

http://dx.doi.org/10.1016/j.agwat.2007.01.010.)

Hamon, W.R., 1961, Estimation of potential 

evapotranspiration: Journal of the Hydraulics Division, 

Proceedings of the American Society of Civil Engineers, 

v. 87, p. 107–120.

Harrison, Kerry, 2009a, Irrigation scheduling methods: 

University of Georgia, College of Agricultural and 

Environmental Sciences, Cooperative Extension Bulletin 

974, 8 p., accessed March 2011, at http://www.caes.uga.edu/

publications/pubDetail.cfm?pk_id=6116.

Harrison, Kerry, 2009b, 2008 irrigation survey: University 

of Georgia, College of Agricultural and Environmental 

Sciences, 2 p., accessed May 2012, at http://www.nespal.

org/SIRP/agwateruse/facts/2009.0122.IrrSurvey08_misc_

pub.pdf.

Helsel, D.R., and Hirsch, R.M., 1992, Statistical methods 

in water resources: New York, Elsevier, Studies in 

Environmental Science 49, 522 p.

Hook, J.E., Hoogenboom, Gerrit, Paz, Joel, Risse, Mark, 

Bergstrom, John, and Mullen, Jeffrey, 2009, Assessing 

agricultural groundwater needs for the future—Identifying 

irrigated area and sources: Proceedings of the 2009 Georgia 

Water Resources Conference, accessed January 10, 2010, 

at http://www.nespal.org/SIRP/waterinfo/State/AWD/

AgWaterDemand_IrrArea.htm.

Hosmer, D.W., and Lemeshow, Stanley, 2000, Applied logistic 

regression (2d ed.): New York, John Wiley and Sons, 375 p.

Jagtap, S.S., and Jones, J.W., 1989, Stability of crop 

coefficients under different climate and irrigation 

management practices: Irrigation Science, v. 10, no. 3, 

p. 231–244.

Kenny, J.F., Barber, N.L., Hutson, S.S., Linsey, K.S., 

Lovelace, J.K., and Maupin, M.A., 2009, Estimated use of 

water in the United States in 2005: U.S. Geological Survey 

Circular 1344, 52 p.

Lee, Dewey, ed., 2012, A guide to corn production in 

Georgia—2012: University of Georgia College of 

Agriculture and Environmental Sciences Cooperative 

Extension, 106 p.

Masoner, J.R., Mladinich, C.S., Konduris, A.M., and Smith, 

S.J., 2003, Comparison of irrigation water use estimates 

calculated from remotely sensed irrigated acres and 

state reported irrigated acres in the Lake Altus drainage 

basin, Oklahoma and Texas, 2000 growing season: U.S. 

Geological Survey Water-Resources Investigations 

Report 03–4155, 39 p.

Ozdogan, Mutlu, and Gutman, Garik, 2008, A new 

methodology to map irrigated areas using multi-temporal 

MODIS and ancillary data—An application example in the 

continental U.S.: Remote Sensing of Environment, v. 112, 

p. 3520–3537.

Ozdogan, Mutlu, Yang, Yang, Allez, George, and Cervantes, 

Chelsea, 2010, Remote sensing of irrigated agriculture—

Opportunities and challenges: Remote Sensing, v. 2, no. 9, 

p. 2274–2304. (Also available at http://dx.doi.org/ 10.3390/

rs2092274.)

Peart, R.M., Curry, R.B., Rosenzweig, Cynthia, Jones, J.W., 

Boote, K.J., and Allen, L.H., Jr., 1995, Energy and irrigation 

in south eastern U.S. agriculture under climate change: 

Journal of Biogeography, v. 22, nos. 4/5, p. 635–642. (Also 

available at http://www.jstor.org/stable/2845964.)

Pervez, M.S., and Brown, J.F., 2010, Mapping irrigated lands 

at 250-m scale by merging MODIS data and national 

agricultural statistics: Remote Sensing, v. 2, p. 2388–2412. 

(Also available at http://dx.doi.org/ 10.3390/rs2102388.)

Qi, S.L., Konduris, Alexandria, Litke, D.W., and Dupree, Jean, 

2002, Classification of irrigated land using satellite imagery, 

the High Plains aquifer, nominal date 1992: U.S. Geological 

Survey Water-Resources Investigations Report 02–4236, 

31 p.

SAS Institute, Inc., 1995, Logistic regression examples using 

the SAS system (1st ed.): Cary, N.C., SAS Institute, Inc., 

version 6, 163 p.

Satti, S.R., Jacobs, J.M., and Irmak, Suat, 2004, Agricultural 

water management in a humid region—Sensitivity to 

climate, soil and crop parameters: Agricultural Water 

Management, v. 70, p. 51–65.

Solly, W.B., Pierce, R.R., and Perlman, H.A., 1998, Estimated 

use of water in the United States in 1995: U.S. Geological 

Survey Circular 1200, 71 p.
32 Estimating Irrigation Water Use in the Humid Eastern United States

Torak, L.J., and Painter, J.A., 2011, Summary of the Georgia 

agricultural water conservation and metering program and 

evaluation of methods used to collect and analyze irrigation 

data in the middle and lower Chattahoochee and Flint River 

basins, 2004–2010: U.S. Geological Survey Scientific 

Investigations Report 2011–5126, 25 p.

U.S. Bureau of Reclamation, 2012, AgriMet irrigation guide: 

U.S. Bureau of Reclamation, accessed March 2012 at 

http://www.usbr.gov/pn/agrimet/irrigation.html.

U.S. Department of Agriculture, National Agricultural 

Statistics Service, 1997, Usual planting and harvesting 

dates for U.S. field crops: Agricultural Handbook 628, 

51 p., accessed March 2012, at http://www.nass.usda.gov/

Publications/Usual_Planting_and_Harvesting_Dates/

uph97.pdf.

U.S. Department of Agriculture, National Agricultural 

Statistics Service, 2009a, Farm and ranch irrigation 

survey (2008), v. 3 of Special studies: U.S. Department of 

Agriculture 2007 Census of Agriculture, pt. 1, 209 p.

U.S. Department of Agriculture, National Agricultural 

Statistics Service, 2009b, Cropland data layer metadata: 

U.S. Department of Agriculture, accessed January 10, 2011, 

at http://www.nass.usda.gov/research/Cropland/metadata/

meta.htm.

U.S. Department of Agriculture, National Agricultural 

Statistics Service, 2010, Cropland data layer metadata: U.S. 

Department of Agriculture, accessed January 10, 2011, at 

http://www.nass.usda.gov/research/Cropland/metadata/

meta.htm.

Westenbroek, S.M., Kelson, V.A., Dripps, W.R., Hunt, 

R.J., and Bradbury, K.R., 2010, SWB—A modified 

Thornthwaite-Mather soil-water-balance code for estimating 

groundwater recharge: U.S. Geological Survey Techniques 

and Methods, book 6, chap. A31, 60 p.

Zarriello, P.J., and Bent, G.C., 2004, A precipitation-runoff 

model for the analysis of the effects of water withdrawals 

and land-use change on streamflow in the UsquepaugQueen River basin, Rhode Island: U.S. Geological Survey 

Scientific Investigations Report 2004–5139, 75 p.
Prepared by the Pembroke Publishing Service Center.

For more information concerning this report, contact:

Office Chief

U.S. Geological Survey

New England Water Science Center

Massachusetts-Rhode Island Office

10 Bearfoot Road

Northborough, MA 01532

dc_ma@usgs.gov

or visit our Web site at:

http://ma.water.usgs.gov
Levin and Zarriello—Estimating Irrigation Water Use in the Humid Eastern United States—Scientific Investigations Report 2013–5066