Prepared in cooperation with the National Water Census Program

Evaluation and Comparison of Methods to Estimate Irrigation 

Withdrawal for the National Water Census Focus Area 

Study of the Apalachicola-Chattahoochee-Flint River Basin 

in Southwestern Georgia

Scientific Investigations Report 2015–5118

U.S. Department of the Interior

U.S. Geological Survey
A B C

D E F

Cover. Data inputs and techniques used in estimating irrigation withdrawal for the Apalachicola-Chattahoochee-Flint River Basin focus 

area study of the National Water Census: A, Landsat 5 satellite; B, conditional simulation of irrigation depth; C, weather station (photograph 

from University of Georgia); D, evapotranspiration computation (diagram from Allen and others, 1998); E, Landsat 5 false-color composite 

image (from U.S. Geological Survey); and F, water meter installed on irrigation system (photograph by Lynn J. Torak, U.S. Geological Survey). 

Background: Irrigation system in Terrell County, Ga. (photograph by Alan M. Cressler, U.S. Geological Survey).
Evaluation and Comparison of Methods 

to Estimate Irrigation Withdrawal for the 

National Water Census Focus Area Study 

of the Apalachicola-Chattahoochee-Flint 

River Basin in Southwestern Georgia

By Jaime A. Painter, Lynn J. Torak, and John W. Jones

Prepared in cooperation with the National Water Census Program

Scientific Investigations Report 2015–5118

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

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

Suggested citation:

Painter, J.A., Torak, L.J., and Jones, J.W., 2015, Evaluation and comparison of methods to estimate irrigation withdrawal 

for the National Water Census Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia, U.S. Geological Survey Scientific Investigations Report 2015–5118, 32 p., http://dx.doi.org/10.3133/

sir20155118.

ISSN 2328-0328 (online)
iii

Acknowledgments

The authors express appreciation for the efforts of those within the USGS and its contracted 

partners that contributed to the development of the methods evaluated in this report. Contractors to the USGS Eastern Geographic Science Center who helped in the development of the 

image analysis method included Ali Levent Yagci (George Mason University) as well as Angirah 

Baruah and Elitsa Penneva-Reed (Cherokee Nation Services, Inc.). J.W. Grubbs, retired USGS 

employee of the former Florida Water Science Center and Jason Bellino of the Caribbean-Florida 

Water Science Center lent their expertise in developing the crop-demand methodology.
iv

Contents

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

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

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

Apalachicola-Chattahoochee-Flint Focus Area ..............................................................................3

Georgia Agricultural Water Conservation and Metering Program ..............................................3

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

Study Objectives and Tasks ................................................................................................................3

Evaluation of Methods to Estimate Irrigation Withdrawal in the ACF–FAS.........................................6

Crop-Demand Method .........................................................................................................................6

Governing Equations ..................................................................................................................6

Parameterization and Data Limitations ...................................................................................7

Data Integration and Geographic Information System Processing ....................................9

Geostatistical Techniques ...................................................................................................................9

Description..................................................................................................................................10

Application Limitations .............................................................................................................12

Image Analysis....................................................................................................................................12

Process........................................................................................................................................12

Data Requirements....................................................................................................................12

Limitations ..................................................................................................................................13

Comparison of Methods to Estimate Irrigation Withdrawal.................................................................14

Assessments at a Regional Spatial Extent ............................................................................14

Assessment of Results with Data from Monthly Meter Sites ...........................................17

Enhancement of Data Requirements for Estimating Irrigation Withdrawal ......................................29

Summary and Conclusions.........................................................................................................................30

Crop-Demand Method .......................................................................................................................30

Geostatistical Techniques .................................................................................................................30

Image Analysis....................................................................................................................................31

References Cited..........................................................................................................................................31
v

Figures

1. Map of study area for Apalachicola-Chattahoochee-Flint River Basin ..............................2

2. Map showing the metering program in the Chattahoochee-Flint River Basins 

of Georgia, A, annually read and daily telemetry network, 2007–11 growing 

seasons; B, annually read and monthly meter network, 2012 growing season.................4

3. Schematic diagram illustrating geographic information system data processing 

steps for the crop-demand method to combine A, PRISM monthly precipitation 

and temperature data, B, CDL crop acreage, and C, SSURGO soils data...........................9

4. Schematic of geostatistical techniques used to estimate irrigation depth......................11

5. Schematic of image analysis process ....................................................................................13

6. Bar chart showing irrigation withdrawal estimates for each month of the 

study period .................................................................................................................................15

7. Bar chart showing monthly meter network irrigation withdrawals for the 

study period .................................................................................................................................15

8. Bar chart showing irrigation-withdrawal estimates using irrigated acres identified 

by the Georgia Department of Natural Resources Agricultural Lands dataset and 

image analysis associated with identical monthly irrigation-depth distributions 

derived from geostatistical techniques during months of available meter data 

and Landsat 5 imagery...............................................................................................................16

9. Bar graph showing estimates of irrigated acres from the crop-demand method, 

and from the Georgia Department of Natural Resources Agricultural Lands 

dataset and image analysis, associated with identical monthly irrigation-depth 

distributions derived from geostatistical techniques during months of available 

meter data and Landsat 5 imagery ..........................................................................................17

10. Maps showing sub-region of National Water Census focus area study of the 

Apalachicola-Chattahoochee-Flint River Basin depicting conflicting irrigated 

lands by comparing A, image analysis and the Georgia Department of Natural 

Resources Agricultural Lands dataset; B, crop-demand method and the GaDNR 

Agricultural Lands dataset; and C, crop-demand method and image analysis ...............18

11. Maps showing A, estimation of zero irrigation depth along the perimeter of the 

National Water Census focus area study of the Apalachicola-Chattahoochee-Flint 

River Basin region compared to the crop-demand estimates of nonzero irrigation 

for 2010 and B, 2012 estimates of irrigation depth showing nonzero estimates 

of irrigation depth along the ACF–FAS boundary..................................................................21

12. Maps showing irrigated land identified by image analysis for A, under-identified 

irrigated acres where meter data recorded irrigation, and B, comparison with 

constant assignment of irrigated acres to metered withdrawal ........................................24

13. Scatter-plots of crop-demand estimates of irrigation withdrawal in million 

gallons per day compared to corresponding monthly metering site irrigation 

withdrawal ...................................................................................................................................26

14. Map showing identified irrigated acres and crop type by the crop-demand 

method used in the GID calculation at meter sites...............................................................28
vi

Tables

1. Data parameters and sources for crop-demand method governing equations ................8

2. Irrigated lands identified exclusively by the crop-demand and image analysis 

methods........................................................................................................................................23
vii

Conversion Factors

[Inch/Pound to International System of Units[

Multiply By To obtain

Area

acre 4,047 square meter (m2

)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2

) 

acre 0.004047 square kilometer (km2

)

square foot (ft2

) 929.0 square centimeter (cm2)

square foot (ft2

) 0.09290 square meter (m2)

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Volume

gallon (gal) 3.785 liter (L) 

gallon (gal) 0.003785 cubic meter (m3) 

gallon (gal) 3.785 cubic decimeter (dm3

) 

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

)

billion gallons (Bgal) 3,785,000 cubic meter (m3

) 

cubic mile (mi3

) 4.168 cubic kilometer (km3) 

acre-foot (acre-ft) 1,233 cubic meter (m3

)

acre-foot (acre-ft) 0.001233 cubic hectometer (hm3

) 

Datum

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

(NAVD 88).

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

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

Abbreviations

ACF-FAS Apalachicola-Chattahoochee-Flint River Basin Focus Area Study of the National 

Water Census Program

CDL Cropland Data Layer

GaDNR Georgia Department of Natural Resources

GSWCC Georgia Soil and Water Conservation Commission

NLCD National Landcover Dataset

USGS U.S. Geological Survey

Evaluation and Comparison of Methods to Estimate 

Irrigation Withdrawal for the National Water Census 

Focus Area Study of the Apalachicola-ChattahoocheeFlint River Basin in Southwestern Georgia

By Jaime A. Painter, Lynn J. Torak, and John W. Jones

Abstract

Methods to estimate irrigation withdrawal using nationally available datasets and techniques that are transferable to 

other agricultural regions were evaluated by the U.S. Geological Survey as part of the Apalachicola-Chattahoochee-Flint 

(ACF) River Basin focus area study of the National Water 

Census (ACF–FAS). These methods investigated the spatial, 

temporal, and quantitative distributions of water withdrawal 

for irrigation in the southwestern Georgia region of the ACF–

FAS, filling a vital need to inform science-based decisions 

regarding resource management and conservation. The crop–

demand method assumed that only enough water is pumped 

onto a crop to satisfy the deficit between evapotranspiration 

and precipitation. A second method applied a geostatistical 

regimen of variography and conditional simulation to monthly 

metered irrigation withdrawal to estimate irrigation withdrawal where data do not exist. A third method analyzed Landsat satellite imagery using an automated approach to generate 

monthly estimates of irrigated lands. These methods were 

evaluated independently and compared collectively with measured water withdrawal information available in the Georgia 

part of the ACF–FAS, principally in the Chattahoochee-Flint 

River Basin. An assessment of each method’s contribution to 

the National Water Census program was also made to identify 

transfer value of the methods to the national program and 

other water census studies. None of the three methods evaluated represent a turnkey process to estimate irrigation withdrawal on any spatial (local or regional) or temporal (monthly 

or annual) extent. Each method requires additional information 

on agricultural practices during the growing season to complete the withdrawal estimation process. Spatial and temporal 

limitations inherent in identifying irrigated acres during the 

growing season, and in designing spatially and temporally 

representative monitor (meter) networks, can belie the ability of the methods to produce accurate irrigation-withdrawal 

estimates that can be used to produce dependable and consistent assessments of water availability and use for the National 

Water Census. Emerging satellite-data products and techniques 

for data analysis can generate high spatial-resolution estimates 

of irrigated-acres distributions with near-term temporal frequencies compatible with the needs of the ACF–FAS and the 

National Water Census.

Introduction

The U.S. Geological Survey (USGS) established the 

National Water Census, a “national water availability and 

use assessment program,” authorized by section 9508 of the 

Secure Water Act of 2009 (42 U.S. C. 10368) to address the 

following needs: 

• “to fund locally cost-shared water management 

improvements that save significant amounts of water;

• to plan for and reduce the impacts of drought; and, 

• to gather and analyze water supply and use information 

in a consistent manner, which would create a uniform, 

dependable national assessment of water availability 

and use” (United States Senate Report of the 113th 

Congress web site accessed at http://thomas.loc.gov/

cgi-bin/cpquery/T?&report=sr230&dbname=113& on 

February 13, 2015). 

Research to develop methodologies that inform water 

management decisions and address National Water Census 

needs has been centered on assessing irrigation withdrawal 

because of its high impact on water resources and susceptibility to climatic extremes. Knowing how much water is withdrawn, consumed, lost, transferred, and disposed of is necessary for effective resource management (Fanning, 2007). 

Three methods to estimate when, where, and how much 

water was applied for irrigation during the 2008–12 growing seasons were developed by the USGS as part of the 

National Water Census focus area study of the ApalachicolaChattahoochee-Flint River Basin (ACF–FAS) (fig. 1).The 
2 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

GEORGIA

GEORGIA

ALABAMA

FLORIDA

FLORIDA

ALABAMA

85° 84°

32°

31°

KinchafooneeMuckalee

Creeks

Middle Chattahoochee–

Walter F George

Reservoir

Ichawaynochaway

Creek

Middle FlintLake Blackshear

Lower

Spring Flint

Creek

Apalachicola

River

Lower

Chattahoochee

EXPLANATION

8-digit Hydrologic Unit Code boundary

Study area

Base modified from U.S. Geological Survey 0

1:2,000,000-scale digital data

Albers Equal-Area Conic projection

North American Datum of 1983

5 10 15 20 MILES

0 10 15 20 KILOMETERS 5

ApalachicolaChattahoochee-Flint 

River Basin

Lake Seminole

 Walter F

 George

Reservoir

Lake

Blackshear

Figure 1. Study area for Apalachicola-Chattahoochee-Flint River Basin.
Introduction 3

Apalachicola-Chattahoochee-Flint (ACF) Basin in Alabama, 

Florida, and Georgia (fig. 1) was selected as one of the initial 

focus areas for the National Water Census in 2010 because 

of its extensive development of and competition for water 

resources by municipal and industrial needs, power generation, and agriculture activities within its boundaries. The temporal extent selected for the irrigation-withdrawal estimates 

matched the monthly simulation horizons used in groundwater 

availability modeling activities also developed for the ACF–

FAS. One method, termed “gross irrigation” demand, estimated values for the water needed to satisfy crop growth after 

accounting for evapotranspiration, precipitation, and irrigation 

inefficiency. A second method applied geostatistical techniques to the region of the ACF–FAS that contained spatially 

correlated irrigation meter data, and estimated the volume of 

irrigation water applied per acre, termed “irrigation depth,” at 

metered and nonmetered locations of irrigation withdrawal. A 

third method analyzed Landsat satellite remote-sensed imagery to generate monthly estimates of irrigated acres. 

Apalachicola-Chattahoochee-Flint Focus Area

The ACF River Basin encompasses about 19,256 square 

miles (mi2), mostly in western Georgia and partly in southeastern Alabama and northwestern Florida (Jones and Torak, 

2006). The principal rivers and tributaries in the lower ACF 

River Basin in Georgia drain karstic and fluvial plains, which 

are hydraulically connected to the Upper Floridan aquifer, 

one of the most productive aquifers in the United States. 

Irrigation withdrawal is the major use of groundwater in this 

heavily agricultural region. Nearly 500,000 acres are irrigated 

with groundwater from the Upper Floridan aquifer (Torak 

and Painter, 2006). The spatial extent of the ACF–FAS for 

this discussion consists of the region in southwestern Georgia 

defined by parts of the lower Chattahoochee and Flint River 

Basin, approximately 4,080 mi2 (shaded area in fig. 1), which 

corresponds to the spatial extent of the groundwater availability modeling activities also used for the ACF–FAS. 

Georgia Agricultural Water Conservation and 

Metering Program

The need to document irrigation withdrawal information prompted the Georgia General Assembly to enact House 

Bill 579 on June 4, 2003, granting jurisdiction to the Georgia Soil and Water Conservation Commission (GSWCC) to 

“...[implement] a program of measuring farm uses of water 

in order to obtain clear and accurate information on the patterns and amounts of such use, which information is essential 

to proper management of water resources by the state and 

useful to farms for improving the efficiency and effectiveness 

of their use of water ... and [for] improving water conservation” (Georgia General Assembly, 2003). This bill created the 

State Agricultural Water Conservation and Metering Program 

(hereafter referred to as the metering program) allowing the 

GSWCC to install more than 11,000 water meters on wells, 

surface-water intakes, and irrigation systems statewide. 

Installation of the metering network began in 2004 and was 

completed in the ACF–FAS in time to monitor irrigation withdrawal during the 2007 growing season. 

About 4,400 meters initially were installed in the middle 

and lower parts of the Chattahoochee and Flint River Basins 

in southwestern Georgia, which coincides geographically 

with the concentration of agricultural irrigation. These meters 

were read annually to measure irrigation withdrawal for the 

region; a subset of about 60 meters reported daily irrigation 

withdrawal by satellite (daily telemetry). The telemetry network was replaced for the 2012 growing season with 90–100 

manual measurements from a monthly meter network that 

enhanced spatial representation and measurement of irrigation 

withdrawal (fig. 2). 

Purpose and Scope

This report evaluates three methods used in the ACF–

FAS to estimate irrigation withdrawal and describes for 

each method,

• Limitations that would affect the ability to provide a 

consistent and dependable estimate of irrigation withdrawal; and

• Advancements or emerging technology or both that 

became available since the onset of this study that 

would enhance the accuracy of the irrigation-withdrawal estimates.

Application of methods to estimate irrigation withdrawals 

are discussed in the context of the amount, type, and availability of data required by each method, and with regard to spatial 

and temporal limitations that would affect irrigation-withdrawal estimates. These estimates were compared collectively 

among the methods and with metered irrigation withdrawal to 

assess the relative accuracy of each method. 

Study Objectives and Tasks

A primary research objective for the ACF–FAS was to 

develop new tools that would support water management decisions and “create a uniform, dependable national assessment 

of water availability and use” (United States Senate Report of 

the 113th Congress Web site accessed at http://thomas.loc.gov/

cgi-bin/cpquery/T?&report=sr230&dbname=113& on February 13, 2015). These tools were evaluated herein according to 

the spatial, temporal, and quantitative accuracy of irrigationwithdrawal estimates as they were applied during the growing 

seasons (March–October) of the years 2008–12. Irrigationmeter data were used to ground-truth, compare, and assess the 

accuracy and consistency of computed irrigation-withdrawal 

estimates derived from these new tools.
4 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

LEE

EARLY

WORTH

GRADY THOMAS DECATUR

SUMTER

BAKER

STEWART

DOOLY

CLAY

CRISP

RANDOLPH

TERRELL

MILLER COLQUITT

CALHOUN

DOUGHERTY

SEMINOLE

WEBSTER

QUITMAN

MARION SCHLEY

TURNER

MACON

CHATTAHOOCHEE

BROOKS

MITCHELL

85° 84°

32°

31°

Annually read

Daily telemetry

0 5 10 15 20 MILES

0 10 15 20 KILOMETERS 5

EXPLANATION

Study area

Meter network for 2007–11 

 growing seasons 

Base modified from U.S. Geological Survey

1:2,000,000-scale digital data

Albers Equal-Area Conic projection

North American Datum of 1983

A

Figure 2. The metering program in the Chattahoochee-Flint River Basins of Georgia, A, annually read and daily telemetry 

network, 2007–11 growing seasons; B, annually read and monthly meter network, 2012 growing season.
Introduction 5

LEE

EARLY

WORTH

GRADY THOMAS DECATUR

SUMTER

BAKER

STEWART

MITCHELL

DOOLY

CLAY

CRISP

RANDOLPH

TERRELL

MILLER COLQUITT

CALHOUN

DOUGHERTY

SEMINOLE

WEBSTER

QUITMAN

MARION SCHLEY

TURNER

MACON

CHATTAHOOCHEE

BROOKS

85° 84°

32°

31°

0 5 10 15 20 MILES

0 10 15 20 KILOMETERS 5

Base modified from U.S. Geological Survey

1:2,000,000-scale digital data

Albers Equal-Area Conic projection

North American Datum of 1983

Annually read

Daily telemetry

EXPLANATION

Study area

Meter network in 2012

B

Figure 2. The metering program in the Chattahoochee-Flint River Basins of Georgia, A, annually read and daily telemetry 

network, 2007–11 growing seasons; B, annually read and monthly meter network, 2012 growing season.—Continued
6 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

The following tasks describe the USGS effort to evaluate irrigation-withdrawal estimates in support of the National 

Water Census, as contained in this report,

• Describe the crop-demand method for estimating water 

withdrawal;

• Evaluate geostatistical techniques applied to metered 

irrigation withdrawal to estimate irrigation withdrawal 

at non-metered locations;

• Analyze the process of using space-based Landsat 

images to identify where and when water is being 

used;

• Discuss attributes, contributions, and limitations of 

each estimation method;

• Compare irrigation-withdrawal estimates collectively 

among the three methods and with monthly meter data 

within the spatial extent of the study region; and

• Discuss emerging technology that would enhance the 

accuracy of estimation tools.

Evaluation of Methods to Estimate 

Irrigation Withdrawal in the ACF–FAS

Three methods were evaluated that address a critical 

need for the ACF–FAS to assess water availability and use 

by attempting to identify where, when, and how much water 

the agricultural community withdraws from the basin’s water 

resources. The crop-demand method intended to answer these 

metrics by applying standard methods with reference parameter values and nationally available datasets to calculate irrigation requirements. Geostatistical techniques developed spatial 

models that interpolate between measured irrigation withdrawal values to estimate irrigation withdrawal in areas where 

irrigation withdrawal data do not exist. The image analysis 

method generated a dataset of irrigated land as a subset of 

agricultural land identified by Landsat imagery.

The geostatistical and image analysis methods combine 

to estimate irrigation withdrawal for the near term depending 

on the availability of irrigation withdrawal (monthly meter) 

data and Landsat imagery (every 16 days, unobstructed by 

cloud cover). The crop-demand method provides “a consistent 

and transparent basis for a globally valid standard for crop 

water requirement calculations” with minimal inputs for geographic specificity (Allen and others, 1998). 

Crop-Demand Method

The crop-demand method assumes that only enough 

water is applied to a crop to satisfy the gross irrigation demand 

(GID); that is, the deficit between evapotranspiration and precipitation, plus a small amount of water lost to irrigation inefficiency because of the physical structure of the water delivery 

systems. This assumption relies on the economic disincentive 

for farmers to pump more water than required to turn a profitable harvest, thereby conserving energy (fuel) and reducing 

maintenance costs. It also assumes that each farmer knows the 

crop water deficit at any given time and irrigates accordingly. 

This method uses readily available, spatially distributed, reference and standardized data for weather conditions, and soil 

and crop characteristics. 

Governing Equations 

Gross irrigation demand is calculated using the following 

equation (Jason Bellino, U.S. Geological Survey, written commun., January 2014):

GID = ETcrop − Pe

Ieff

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥

× Acrop , (1)

where

GID is gross irrigation demand [L3/T];

ETcrop is evapotranspiration for a given crop [L/T];

Pe is effective precipitation [L/T];

Ieff is the irrigation efficiency, 0.9 for all systems 

[dimensionless]; and

Acrop is the area over which the crop is being grown 

[L2]; 

and where

L is length; and 

T is time.

Crop evapotranspiration (ETcrop ) incorporates into its calculation a modified Food and Agriculture Organization (FAO) 

Penman-Monteith standard method (FAO56; Allen and others, 

1998) for reference evapotranspiration (ETo ) that provides 

values that are more consistent with actual crop water-use data 

worldwide than previous methods and creates “a consistent 

and transparent basis for a globally valid standard for crop 

water requirement calculations” (Allen and others, 1998). The 

crop evapotranspiration [ETcrop ] differs distinctly from the 

reference evapotranspiration (ETo ) [calculated for a standard, 

uniform, grass surface, or reference surface] as the ground 

cover, canopy properties and aerodynamic resistance of the 
Evaluation of Methods to Estimate Irrigation Withdrawal in the ACF–FAS 7

crop are different from grass. The effects of characteristics that 

distinguish field crops from grass are integrated into the crop 

coefficient (Kc ). In the crop coefficient approach, crop evapotranspiration is calculated by multiplying ETo

 by Kc (p. 89, 

Allen and others, 1998), 

ETcrop = ETo × Kc , (2)

where 

ETcrop is crop evapotranspiration [L/T];

Kc is crop coefficient [dimensionless];

ETo is reference evapotranspiration [L/T], 

expressed as

ETo =

0.408Δ(Rn − G)+γ

900

T + 273

u2 (es − ea )

Δ +γ (1+ 0.34u2 ) ,

 where parameters and data sources for ETo are 

defined in table 1.

Effective precipitation (Pe) in equation 1 is that part of 

rainfall available to meet consumptive needs of crops, average monthly values of which are calculated as (Martin and 

Gilley, 1993),

Pe = SF(0.710917Pt

0.82416 )(100.02426ETcrop ) , (3)

where

SF is the soil water storage factor 

[dimensionless];

Pt is average monthly precipitation as rainfall 

[L]; and, 

ETcrop is average monthly crop evapotranspiration 

[L].

The soil water storage factor, SF, is the total amount of 

water that is stored in the soil within a plant’s root zone, and is 

described by the polynomial function,

SF = 0.531747 + 0.295164D – 0.057697D2 + 0.003804D3, (4)

where

D is the usable soil water storage [L].

The crop-demand method requires parameterization of 

meteorological, crop, and soil type conditions for spatial or 

temporal horizons related to all irrigated fields to produce 

accurate estimates of irrigation withdrawal, expressed as 

the gross irrigation demand (GID). Data requirements and 

limitations for this parameterization are discussed in the 

following section.

Parameterization and Data Limitations 

Data that parameterize crop, soil, and meteorological characteristics were obtained from a variety of sources 

(table 1), which contain distinct levels of precision that (1) 

affect their application to irrigated fields in the ACF–FAS; 

and, (2) impose limitations that negatively impact estimation 

accuracy of the crop-demand method. Locations and types of 

crops being grown, thereby indicating potential areas of irrigation withdrawal (Acrop ), were identified with raster datasets 

from the U.S. Department of Agriculture (USDA) National 

Agricultural Statistics Service (NASS) Cropland Data Layer 

(CDL) hereafter CDL (U.S. Department of Agriculture, 

National CDL’s 2008–12). The CDL datasets merge areas of 

distinct crop type, and all acres assigned to land associated 

with these specific crop types might not have been irrigated. 

Crop patterns generated from individual center-pivot-irrigation systems, such as irrigated by monthly meter sites, could 

not be discerned from the general agricultural-land classification associated with the CDL. 

Non-site-specific values for agronomic and meteorological parameters introduce limitations when used in the 

crop-demand method to compute GID. Crop coefficients (Kc ) 

obtained from publicly available sources (Allen and others, 

1998; Smajstrla, 1990), and planting dates, maturation times, 

and rooting depths supplied from data on file at the thenUSGS Florida Water Science Center Office, Tallahassee, Fla. 

(Jason Bellino, U.S. Geological Survey, written commun., 

2014) were used to generally evaluate agricultural conditions 

for GID calculations. Likewise, soil water storage, D, was 

obtained from the National Resources Conservation Service 

(NRCS) Soil Survey Geographic database (SSURGO) (http://

sdmdataaccess.nrcs.usda.gov/, accessed February 24, 2015) 

and used in calculating effective precipitation, Pe.

Adjustments to the above standard reference meteorological and agronomic characteristics during the growing 

season were made with regard to fixed crop planting and 

harvesting dates only; further adjustments were made using 

monthly precipitation and temperature data from the PRISM 

Climate Group (http://www.prism.oregonstate.edu/, Daly and 

Gibson, 2002; Daly and others, 2011) but did not account for 

season-specific changes in soil and meteorological conditions and crop-specific growth cycles. Meteorological and 

vegetation conditions are computed in PRISM using a variable that represents the coverage and health of the vegetation termed the “Greenness Vegetation Fraction” (GVF; 

Bell and others, 2012). Values for the GVF are computed 

based on a nearly 20-year-old climatology dataset obtained 

from the National Centers for Environmental Prediction 

(NCEP; Bell and others, 2012), termed “NCEP-GVF,” 

which “remains static from year to year... [which] may 

not properly represent the vegetation patterns of the United 
8 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

States in regions that have changed over the last two decades 

through urbanization, or experienced substantial deviations 

from climatology (e.g. droughts excessive rainfall, late/early 

season freezes or blooms.” The NCEP–GVF dataset does not 

distinguish or resolve large variations in vegetation over small 

distances because of its coarse resolution of 0.144° (about 

15 kilometers [km]). These issues affect updates to vegetation components derived from PRISM modeling that are used 

to calculate evapotranspiration, ETo, and effective precipitation, Pe , in turn, used by the crop-demand method to estimate 

irrigation withdrawal.

Average daily values of minimum and maximum air 

temperature (T), dew-point temperature (used to calculate 

psychrometric constant, γ), and precipitation data (Pt ) were 

obtained from PRISM data (table 1) to compute the monthly 

meteorological parameters for the ETo and Pe . Further, a value 

of 3.3 meters per second was used for the average monthly 

windspeed—associated with low humidity regions, represents 

a constant moderate to strong wind (Allen and others; 1998)—

during all months of the 2008–12 growing seasons, which is 

uncharacteristic of the ACF–FAS region. 

The irrigation efficiency term, (I

eff ) (Howell, 2003), 

accounts for the loss of water pumped from the irrigation 

source (groundwater or surface water) to the crop. Water 

losses are attributed to the type of irrigation system used such 

as traveler, portable pipe, drip, solid set, and center-pivot. 

Depending on the type of system used, irrigation efficiency 

ranges between 75 and 98 percent of water withdrawn (Howell, 2003). Center-pivot irrigation comprises at least 75 percent 

of the total irrigation systems in the ACF–FAS; most of these 

pivots had improved energy and application efficiency sprinkler packages (Harrison and Hook, 2005); therefore, a constant 

irrigation efficiency of 90 percent (0.9) was used for I

eff in the 

ACF–FAS. 

Table 1. Data parameters and sources for crop-demand method governing equations.

Parameter Definition

National Cropland data layer1

Acrop Area over which the crop is being grown 

Publicly available sources2

Kc Crop coefficient (incorporates planting dates, maturation times, and rooting depths)

ETo

 Functional Dependence FAO56 standard reference values

Rn Net radiation

G Soil heat flux density

es Saturation vapor pressure

ea Saturation vapor pressure deficit

Δ Slope vapor pressure curve

γ Psychrometric constant

PRISM data3

Pt Average precipitation

T Air temperature, minimum and maximum

SSURGO4

D Usable soil water storage 

Assumed constants

I

eff Irrigation efficiency, 0.9 for all systems

U2 Mean monthly windspeed, 3.3 meters per second

1 http://www.nass.usda.gov/research/Cropland/Release/index.htm.

2

Allen and others, 1998; Smajstrla, 1990; Jason Bellino, U.S. Geological Survey, written commun., 2014.

3 http://www.prism.oregonstate.edu/.

4 https://gdg.sc.egov.usda.gov/.
Evaluation of Methods to Estimate Irrigation Withdrawal in the ACF–FAS 9

Data Integration and Geographic Information 

System Processing

Estimation of irrigation withdrawal occurred within 

a geographic information system (GIS) through a series of 

Python scripts (Van Rossum, 2009) to solve equations 1–4. 

Identification of where irrigation occurred was determined 

from the CDL data using an algorithm that grouped together 

adjacent pixels having the same crop code and at least 5 acres 

in cumulative area. These grouped pixels were converted to 

a vector polygon feature class representing potential areas of 

irrigation withdrawal. The feature class was intersected with 

the PRISM and SSURGO raster datasets (fig. 3) to extract 

the meteorological and soil attributes at each area of potential 

irrigation withdrawal for subsequent GID calculations.

Geostatistical Techniques

Spatial correlation and estimation of monthly irrigation 

depth was calculated in a framework of geostatistical techniques involving structural analysis, variogram development 

(or variography), interpolation (kriging), and conditional 

simulation (Journel and Huijbregts, 1989). These techniques 

represent a second method of estimating irrigation evaluated 

for the ACF–FAS. 

Geostatistical techniques used monthly recorded wateruse data from a metering program in southwestern Georgia 

to estimate monthly irrigation at annually reported (metered) 

and unmetered irrigation sites. Values of irrigation depth 

(in inches), or the volume of irrigated water applied per 

acre (in acre-inches), were estimated for the ACF–FAS with 

A

D

B C

Figure 3. Geographic information system data processing steps for the crop-demand method to combine A, PRISM monthly 

precipitation and temperature data B, CDL crop acreage, and C, SSURGO soils data. The resulting polygon features in diagram 

D, potential areas of irrigation withdrawal for which gross irrigation demand were calculated. (U.S. Department of Agriculture, 

National CDL’s 2008–12; http://www.prism.oregonstate.edu/; http://sdmdataaccess.nrcs.usda.gov/)
10 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

geostatistical modeling. The GSWCC provided information on 

irrigated acres associated with meter data for each month of 

the growing season (March–October) and for the annually read 

meters (one value for total withdrawal during growing season) 

to facilitate irrigation-depth calculations. 

Description

Geostatistical techniques to estimate irrigation depth 

used the sequential, or staged approach outlined by Torak and 

Painter (2011, 2013), culminating with the development of 

a spatially correlated distribution of irrigation depth for the 

ACF–FAS region. Monthly metered irrigation withdrawal was 

normalized to irrigation depth to eliminate from the irrigation estimation any spatial bias associated with the size of 

each agricultural field. The initial stage of structural analysis 

evaluated the first two statistical moments of the data, namely 

the mean and covariance (semivariance, or the semivariogram) 

(American Society of Civil Engineers Task Committee on 

Geostatistical Techniques in Geohydrology, 1990) of irrigation 

depth. Subsequent stages of the analysis identified spatial correlations among annual and monthly meter data. 

Semivariance, γ(h), provides a measure of spatial correlation based on the difference in meter values between data 

pairs located within specific distance-class intervals and is 

expressed as 

γ (h) =

(zi − zi+h )

2

i=1

n

∑

2N(h) , (5)

where

h is the separation distance for the distanceclass interval [L];

N(h) is total data pairs; and,

zi , zi+h are metered irrigation values [L3/T].

An experimental variogram (see symbols on “Structural 

Analysis” graph, plotted in fig. 4) graphically represents the 

spatial correlation of one month’s meter data with distance. 

Structural analysis was completed by fitting a simple mathematical function (exponential curve on “Variography” graph 

in fig. 4) to each monthly experimental semivariogram to 

produce variogram models for the next stage of the analysis. 

Variogram modeling (or variography) (Journel and Huijbregts, 

1989) associates a mathematical function (such as exponential, 

Gaussian, or linear functions, for example) with the correlation 

structure inherent to the irrigation-depth data, and integrates 

the geospatial characteristics of meter-data locations into an 

estimation process for irrigation depth that involves interpolation at locations where data do not exist. 

Monthly variogram models of irrigation depth were used 

in a coupled linear-interpolation and modeling process consisting of kriging and conditional simulation to estimate irrigation 

depth at unmetered agricultural fields in the ACF–FAS. Kriging provided unbiased estimates for the expected values of 

irrigation depth, expressed as a weighted sum of the monthly 

meter data having minimum estimation variance (American 

Society of Civil Engineers Task Committee on Geostatistical 

Techniques in Geohydrology, 1990). Conditional simulation used the spatial correlation structure expressed in the 

variogram models and honored (or preserved) the monthly 

meter data at each site in the interpolation process to estimate 

irrigation depth for unmetered irrigated fields. Unlike kriging, 

which smooths out local variations in irrigation depth by calculating the average, or expected values, conditional simulation incorporates actual meter data and its spatial correlation 

structure into the estimation process, thereby using the spatial 

complexity and heterogeneity of the irrigation-depth data in 

estimates of irrigation withdrawal across multiple scales of 

correlation distances, as defined by the variogram (fig. 4).

Conditional simulation of irrigation depth based on 

variogram models of monthly meter data from sparse meter 

networks yielded large estimation variances, or errors, that 

proved inadequate for representing the spatial variability of 

irrigation withdrawal at unmetered agricultural fields during 

most months of the growing season. To improve the geostatistical estimation process, a method was developed that 

associated the monthly irrigation characteristics defined by the 

sparse monthly meter data to the larger distribution of annual 

meter sites by considering the temporal distribution of irrigation defined by the monthly meter data. 

Geostatistical techniques of structural analysis, variogram 

development, kriging, and conditional simulation were applied 

to spatial distributions of monthly percentages of total annual 

irrigation derived from the monthly meter data and were used 

to represent monthly percentages of total annual irrigation at 

the annual meter sites. Monthly percentages of total annual 

irrigation estimated from conditional simulation of monthly 

meter data were assigned to annual meter sites based on their 

spatial correlation with the monthly meter data. Monthly 

irrigation volume and depth were then calculated for annual 

meter sites by applying the assigned monthly percentages to 

the metered values of total annual irrigation. The resulting variogram models of monthly irrigation depth developed for the 

annual meter sites indicated high spatial correlation and few 

outliers, and produced kriging estimation variances that were 

consistent with estimation variances associated with variogram models of monthly percentages of total annual irrigation 

derived from monthly meter data. That is, the validity of using 

monthly meter data as a representative sample of monthly 

irrigation at the annual meter sites was confirmed by the consistency of the resulting spatial correlations of geostatistical 

models that estimated monthly irrigation depth at the annual 

meter sites from similar spatial correlations of independent 

geostatistical models that estimated percentages of total annual 

irrigation with the monthly meter data. 

Estimates of monthly irrigation depth at the annual meter 

sites obtained from geostatistical models representing percentages of total annual irrigation withdrawal were derived from 

monthly meter data. These data constitute a sample size of 

more than 4,000 annual meter sites from which variogram 
Evaluation of Methods to Estimate Irrigation Withdrawal in the ACF–FAS 11

 

Geostatistical modeling 

Estimate water use

Assign irrigation depth estimates

to agricultural acres 

Calculate monthly 

percentage of 

annual total for 

each monthly 

network site 

Variography

Mathematical representation

of spatial correlation structure 

Structural analysis

Evaluate spatial correlation

(direction and distance) 

Kriging

Estimate expected values

(interpolation) 

Conditional simulation

Estimate values at unmetered

locations honoring meter data 

Assign monthly 

percentages to 

annual meter 

network sites 

Calculate monthly 

irrigation depths 

at annual meter 

network sites 

1

2

3 4

5

6

Figure 4. Geostatistical techniques used to estimate irrigation depth.
12 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

models have identified spatial correlation structures for the 

final stage of geostatistical applications to estimate irrigation 

depth at unmetered irrigated fields. Monthly variogram 

models of irrigation depth at the annual meter sites were then 

used in a coupled kriging-conditional simulation process to 

estimate irrigation depth at unmetered sites in the ACF–FAS 

(see “Conditional Simulation” graph and “Kriging” graph 

on fig. 4).

Geostatistical techniques provided irrigation-depth estimates for the ACF–FAS that can be applied indiscriminately 

to any distribution of irrigated agricultural land (or non-agricultural land); therefore, accurate identification of spatial and 

temporal distributions of irrigated acres during the growing 

season is critical for accurately estimating irrigation withdrawal from the geostatistically derived distributions of irrigation depth. Irrigated acres not assigned to a meter represented 

areas where geostatistical models were required to estimate 

irrigation depth, values of which were then combined with 

monthly distributions of irrigated acres to estimate irrigation 

volume for the withdrawal evaluation. 

Distributions of agricultural land, from which irrigation 

depths were estimated using geostatistical techniques, were 

provided by a dataset from the Georgia Department of Natural 

Resources, termed “GaDNR Agricultural Lands,” (Danna 

Betts, Georgia Department of Natural Resources, written 

commun., May 2012). This dataset represents a distribution 

of metered and unmetered agricultural acres in southwestern 

Georgia that is associated with water-use permits and that 

identifies potential irrigated acres catalogued previously by the 

GSWCC to support the metering program. The GaDNR Agricultural Lands dataset is a static representation of agricultural 

acres developed from previous growing season data. 

Application Limitations

Limitations in the application of geostatistical techniques 

to estimate irrigation withdrawal in the ACF–FAS center on 

the ability of meter-network data to document monthly spatial 

correlations for (1) evaluation by variogram models; and, (2) 

estimation and simulation processes to fulfil water assessment 

and use objectives. Structural analysis, variography, and the 

coupled kriging-conditional simulation modeling approach 

work in concert to estimate values for regionalized variables 

of interest (irrigation volume, irrigation depth, and percent of 

total annual irrigation) using advanced, directional-interpolation methodology. The inability to obtain accurate spatial and 

temporal distributions of irrigated acres or metered irrigation 

volume for analysis using geostatistical techniques presents 

severe limitations on estimation of irrigation withdrawal at 

metered and unmetered irrigation sites using these techniques.

A critical limitation of these geostatistical techniques 

and of interpolation methods in general exists where meternetwork data do not completely envelop the area where 

interpolation is required to estimate the regionalized variable. During conditional simulation, sparse data at the edge 

of the ACF–FAS from the meter network limited the amount 

of, and direction from which, data were used in interpolations to estimate irrigation depth. Conditional simulation uses 

a multiscale and multidirectional interpolation process that 

integrates neighboring data and the variogram structure into a 

form of stochastic simulation in which measured data values 

are honored at their locations (Robertson, 2008). The sparse 

data along the edges of the ACF–FAS can cause spatial bias, 

relatively large estimation variances, and poor estimates. 

Image Analysis

A third method to estimate irrigation withdrawal for 

the ACF–FAS analyzed moderate spatial-resolution satellite imagery and commonly available meteorological data to 

identify irrigated land at specific times during the growing 

season. Resulting datasets of irrigated land were prepared 

on near-term horizons that correspond with satellite data 

repeat frequencies of imagery to provide water managers 

with meaningful datasets to assess then-current conditions of 

irrigation withdrawal.

Process

The overall process integrated two types of mathematical 

models into an imagery analysis to develop an irrigated-land 

dataset (fig. 5). The first model identified pixels in Landsat 5 

satellite imagery that indicated unusually cool land-surface 

temperature because of high evapotranspiration (ET) relative 

to pixels that represented a relatively warm land surface associated with known non-irrigated land. The resulting imagepixel modeling produced estimates of three image classes: 

(1) “Irrigated Emergent Crop”; (2) “Irrigated Soil”; and, (3) 

“Non-irrigated cropland” (fig. 5). The second model combined 

monthly maps generated from multiple Landsat 5 satellite 

images into a single image, from which monthly estimates 

were developed for the three irrigation classes identified by the 

first model. Spatial models portraying these irrigation classes 

were created for each month during the growing season in 

a GIS environment that used ArcMap (Esri, Inc., 2013) to 

account for differences in availability across Landsat 5 image 

paths/rows.

Data Requirements

Public domain, spatially referenced imagery and datasets 

were input to the image analysis process. Meteorological data 

required to compute evapotranspiration was obtained from 

National Oceanic and Atmospheric Administration climatological stations (National Oceanic and Atmospheric Administration, 2015) and the Georgia Environmental Monitoring 

Network (Georgia Automated Environmental Monitoring Network, 2015). Data identifying agricultural and non-agricultural 

land was supplied by the 2011 National Land Cover Database 
Evaluation of Methods to Estimate Irrigation Withdrawal in the ACF–FAS 13

(NLCD) (Homer and others, 2015). The Landsat 5 satellite 

provided thermal data in the form of apparent temperature, 

calibrated to a “top of atmosphere” value, and the provisional 

Landsat Surface Reflectance Product, termed “L5LSRP,” for 

Landsat 5 image paths and rows that cover the area of interest 

(http://landsat.usgs.gov/documents/cdr_sr_product_guide.pdf). 

Imagery from the newer Landsat 7 satellite suffered a partial 

loss of imaging capacity during May 2003, making it unsuitable for the study. Satellite imagery from Landsat 8, launched 

on February 11, 2013, post-dated the study period, 2008–12. 

Separation of image pixels into “Irrigated Emergent Crop” 

and “Irrigated Soil” classes was based on classification of a 

vegetation index derived from the satellite data (fig. 5). 

Limitations 

Data availability of satellite imagery presents a major 

limitation for the image analysis method to accurately estimate 

locations where irrigation occurred in the ACF–FAS during 

the growing season. Haze, clouds, and cloud shadows created 

non-clear sky conditions and confounded optical satelliteimage processing and analysis. Weather and satellite-system 

limitations resulted in a nominal scene-area repeat period of 

16 days. The lack of clear sky over some regions caused a 

longer repeat period of 32 days or longer, which rendered the 

image analysis method unreliable for accurately estimating 

locations of irrigation withdrawal on a monthly time horizon. 

EXPLANATION

Irrigated emergent crop

Irrigated soil

Non-irrigated cropland

Non-cropland

Inputs

Model (Python)

Outputs

Weather station data NLCD data Landsat 5 thermal band Landsat 5 false color

composite (RGB:432)

Photograph from Georgia Automated Environmental MonitoringNetwork, 2015, Camilla, at http://georgiaweather.net.

Figure 5. Image analysis process. (National Oceanic and Atmospheric Administration, 2015; Georgia Automated Environmental 

Monitoring Network, 2015; National Land Cover Database (NLCD), Homer and others, 2015)
14 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

Also, thermal infrared data used for this analysis were corrected only for sensor calibration drift, not atmospheric effects. 

Temperature differences caused by clouds and cloud shadows 

were largely removed through the cloud-shadow masking process; however, it is possible that some within-scene variations 

in apparent surface temperature were attributed to differences 

in atmospheric conditions rather than evapotranspiration of 

irrigation water. These temperature variations could lead to 

over- or underestimation of irrigated acres. 

Another limitation of image analysis centered on using 

agricultural land from the 2011 NLCD as the population of 

potentially irrigated land from which image analysis identified 

irrigation patterns for the study period of 2008–12. This process excluded actual irrigated land during 2008–10 and 2012 

that was not designated as agricultural in the 2011 NLCD; 

these “missing agricultural acres” were identified as irrigated 

through meter data. That is, areas where land cover was designated as agricultural before and after 2011 were excluded from 

the image analysis process even though the meter data identified these acres as agricultural and irrigated.

Comparison of Methods to Estimate 

Irrigation Withdrawal

 This comparison collectively assesses the estimation 

methods in the context of data availability, which affected 

the resulting time and spatial horizons for which the estimations were produced. Each method required meteorological, 

agronomic, remotely sensed, or physically metered or observed data by varying degrees throughout the ACF–FAS and 

for varying time horizons coincident with data acquisition. 

The inability to obtain complete parameterization because of 

incompleteness in these unique datasets posed limitations in 

the estimation process by each method, as previously discussed in the Parameterization and Data Limitations, Application Limitations, and Limitations sections. These limitations 

also affected the extent of method-by-method comparisons of 

estimation accuracy and completeness of a singular estimation 

process. Additional comparisons were done at meter sites during the 2010 and 2012 growing seasons between (1) metered 

irrigation withdrawal and estimates provided by the crop-demand method; and, (2) between irrigated acres associated with 

meters and irrigated acres estimated through image analysis.

Of the three estimation methods developed for the 

ACF–FAS, only the crop-demand method provided estimates 

of where irrigation was applied, when it was being used, and 

how much was used during each month of the study period 

(2008–12). By comparison, monthly meter data were absent 

during the 2009 growing season and the first 3 months of the 

2010 growing season (March–May), preventing use of geostatistical techniques to estimate irrigation depth for this period 

as described in the geostatistical techniques section. Additionally, the image analysis method was unable to identify irrigated acres during those months of the growing season when 

atmospheric effects (haze, clouds, and cloud shadows) obscured 

land surface and prevented the collection of usable imagery. 

Assessments at a Regional Spatial Extent

Estimates of irrigation withdrawal (million gallons per 

day) and irrigated areas (acres) were compared collectively at a 

study area, or regional, spatial extent (fig. 1) to assess similarities and differences in results produced by each estimation 

method. Irrigation-withdrawal estimates from the crop-demand 

method and geostatistical techniques were comparable for 

26 months of the 40-month study period because of the limitations presented previously in the Parameterization and Data 

Limitations, Application Limitations, and Limitations sections 

(fig. 6). The crop-demand method produced higher estimates 

of irrigation withdrawal than geostatistical techniques for 17 of 

the 26 months used in the comparison. The largest difference 

in monthly irrigation withdrawal estimated by these methods 

occurred for October 2012, when the crop-demand method estimated irrigation withdrawal that was about 12 times higher than 

withdrawal estimated using geostatistical techniques. Irrigation 

water use of nearly 1.2 billion gallons a day estimated during 

October 2012 using the crop-demand method conflicts drastically with irrigation withdrawal determined with geostatistical 

techniques on monthly meter data and is uncharacteristic of 

irrigation during the month of October in this region (fig. 7). 

According to monthly meter-network irrigation data, the least 

amount of water withdrawn for irrigation typically occurs during October (fig. 7) of each year; however, the crop-demand 

method for October 2012 produced the third largest irrigation 

withdrawal estimate during the study period (fig. 6). In contrast, 

the crop-demand method computed zero water use in October 

2008 (fig. 6), when the metered data indicated irrigation (fig. 7).

In general, the primary growing season months of May–

July exhibited the largest discrepancy between irrigation-withdrawal estimated with the crop-demand method and geostatistical techniques, discounting from this comparison the October 

2012 irrigation-estimation abnormality (fig. 6). The month with 

the highest water-use estimate varied by growing season; however, the two methods never coincided to estimate maximum 

irrigation withdrawal in the same month.

The large disparity between irrigation withdrawal estimated with the crop-demand method and irrigation withdrawal 

documented by monthly meter data demonstrated a major limitation of the crop-demand method related to evaluation of meteorological and agronomic parameters used to compute GID. 

That is, the use of standard reference values in the governing 

equations for GID did not accurately account for site-specific 

meteorological and agronomic conditions of irrigated areas. As 

previously mentioned in the Parameterization and Limitations 

section, maintaining a constant wind speed and constant crop 

coefficients, and the use of average meteorological conditions 

in the GID equations that do not match actual field conditions, 

can result in gross overestimation of values for GID and in 

equally large estimates of the water needed to irrigate a crop, 

as evidenced by the large (nearly 12-fold) discrepancy between 
Comparison of Methods to Estimate Irrigation Withdrawal 15

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

Estimated irrigation withdrawal,

in million gallons per day 

2008 2010 2011

Crop demand 

Geostatistical techniques 

Apr. May June July Aug. Sept Oct. June July Aug. Sept. Oct. Apr. May June July Aug. Sept. Oct.

2012

Apr. May June July Aug. Sept. Oct.

Estimation method

EXPLANATION

Figure 6. Irrigation withdrawal estimates for each month of the study period. (If a month is not shown, the Georgia Soil and 

Water Conservation Commission metering program data are missing.) Observed irrigation withdrawal, in million gallons per day 

2008 2010 2011

Apr. May June July Aug. Sept. Oct. June July Aug. Sept. Oct. Apr. May June July Aug. Sept. Oct.

2012

Apr. May June July Aug. Sept. Oct.

0 

5 

10 

15 

20 

25 

30 

35 

Monthly meter network 

Figure 7. Monthly meter network irrigation withdrawals for the study period.

irrigation withdrawal estimated by the crop-demand method 

and metered irrigation during October 2012.

Another assessment used monthly irrigation-depth distributions estimated with geostatistical techniques to identify the 

sensitivity of irrigation-withdrawal estimates to the distributions of irrigated acres defined by (1) the GaDNR Agricultural 

Lands dataset and, (2) image analysis (fig. 8). The irrigationdepth distributions for the ACF–FAS obtained from geostatistical techniques provided the control for this assessment; 

that is, irrigated acres from the above datasets (1 and 2) were 

associated with identical monthly irrigation-depth distributions 

derived from geostatistical techniques, and the effects on irrigation-withdrawal estimates of each dataset were compared. 

Monthly differences between total irrigation-withdrawal 

estimates derived from the two irrigated acres datasets (fig. 8) 

were more consistent during 2008 than during 2010. Total irrigation withdrawal during May–July 2008 estimated using the 

GaDNR Agricultural Lands dataset exceeded that using image 

analysis by approximately 53 percent. During June 2010, 

irrigation-withdrawal estimates derived from the image analysis dataset of irrigated acres constituted only 31 percent of that 

estimated using the GaDNR Agricultural Lands dataset. In the 

following month, however, irrigation-withdrawal estimates 

derived from image analysis dataset contributed 83 percent of 

the estimated irrigation withdrawal derived from the GaDNR 

Agricultural Lands dataset, demonstrating the critical importance of accurately identifying spatial and temporal distributions of irrigated acres to irrigation-withdrawal estimates.

Another assessment of each method to estimate monthly 

irrigation compared the amount and location of irrigated acres 
16 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

2008 2010

GaDNR Agricultural Lands

Irrigated acres dataset

EXPLANATION

Image analysis

0 

100 

200 

300 

400 

500 

600 

700 

800 

Apr. May June July Aug. Sept. Oct. June July Aug. Sept. Oct.

Estimated irrigation withdrawal,

in million gallons per day 

Figure 8. Irrigation-withdrawal estimates using irrigated acres identified by the Georgia Department of Natural Resources 

Agricultural Lands dataset (Danna Betts, Georgia Department of Natural Resources, written commun., May 2012) and image 

analysis associated with identical monthly irrigation-depth distributions derived from geostatistical techniques during months 

of available meter data and Landsat 5 imagery. (Zero irrigation withdrawal for image analysis indicates that Landsat 5 imagery 

were missing or unusable.)

with acres associated with identical monthly irrigation-depth 

distributions derived from geostatistical techniques. Irrigatedacres distributions from the GaDNR Agricultural Lands 

dataset and image analysis were compared with irrigated 

acres defined by the crop-demand method (fig. 9). The total 

number of acres identified as irrigated varied from month to 

month, and no one method consistently estimated the largest 

number of irrigated acres among the three methods. Likewise, 

no one method consistently over or under-reported irrigated 

acres, compared with the irrigated acres associated with the 

monthly meter data. A large discrepancy among irrigated 

acres represented by each estimation method existed during August 2008, where irrigated acres represented by the 

crop-demand method estimated 94 percent less than the 

irrigated acres represented by geostatistical techniques. Zero 

irrigated acres were estimated by the crop-demand method 

during October 2008; whereas irrigated acres identified by 

image analysis and geostatistical techniques (monthly meter 

data) documented the occurrence of irrigation. The closest 

agreement of irrigated acres identified with the estimation 

methods occurred during July 2010, compared with any other 

month during the quantitative assessment of irrigation for the 

ACF–FAS. The crop-demand method and geostatistical techniques yielded irrigated acres estimates of 578,649 acres and 

445,263 acres from the GaDNR Agricultural Lands dataset, 

respectively; irrigated land identified with image analysis 

totaled 448,567 acres.

The crop-demand method tended to underestimate irrigated acres during the last four months of the growing season 

(July–October) and overestimate irrigated acres during May 

and June, compared with irrigated acres derived from monthly 

meter data and used by geostatistical techniques (fig. 9). 

This finding indicates that farmers do not irrigate completely 

according to estimated crop planting, maturity, and harvesting 

dates, which are used in the crop-demand method, and that 

these dates do not necessarily coincide with irrigation schedules documented by the monthly meter data. In addition, meter 

data also indicate that water is applied to fields at the beginning and end of the growing season to prepare for planting 

and cultivating the crops, and to prepare the fields for the next 

season. Irrigated acres derived from monthly meter data and 

associated with geostatistical techniques exceed irrigated acres 

estimates used in the crop-demand method at the beginning 

and end of the growing season. 

In a spatial context, the occurrence, identification, and 

coincidence of irrigated acres used by each method were 

evaluated to compare the consistency of information used to 

estimate irrigation withdrawal. In general, none of the methods accounted for all of the irrigated acres in any given month. 

For example, a representative sub-region of the study area 

indicated that not all acres identified by image analysis or the 

crop-demand method were listed in the GaDNR Agricultural 

Lands dataset (figs. 10A, B). Additionally, the crop-demand 

method and image analysis representation of irrigated acres 

have minimal agreement for any given month (fig. 10C).

A summary of irrigated acres identified as irrigated land 

by image analysis and the crop-demand method during the 

2010 growing season indicates that the GaDNR Agricultural 

Lands dataset could be under-reporting agricultural acres by 

22,508 to 253,797 acres, depending on the growing season 

month (table 2). The acres identified as irrigated land by image 

analysis and the crop-demand method that do not intersect 

agricultural field polygons in the GaDNR Agricultural Lands 

dataset (figs. 10A, B), would add to the GaDNR dataset to 

give a more complete assessment of agricultural land. This 

is evidenced by comparing the potential agricultural fields 

identified by the GaDNR (544,235 acres) with agricultural 

fields identified by the CDL dataset (crop-demand method) 
Comparison of Methods to Estimate Irrigation Withdrawal 17

Crop-demand method

GaDNR Agricultural Lands

Image analysis

2008 2010 2011

Apr. May June July Aug. Sept Oct. June July Aug. Sept. Oct. Apr. May June July Aug. Sept. Oct.

2012

Apr. May June July Aug. Sept. Oct.

0 

100 

200 

300 

400 

500 

600 

700 

800 

Irrigated acres, in thousands 

Dataset

EXPLANATION

Figure 9. Estimates of irrigated acres from the crop-demand method, and from the Georgia Department of Natural 

Resources Agricultural Lands dataset (Danna Betts, Georgia Department of Natural Resources, written commun., May 2012) 

and image analysis, associated with identical monthly irrigation-depth distributions derived from geostatistical techniques 

during months of available meter data and Landsat 5 imagery. (Zero irrigation withdrawal for image analysis indicates that 

Landsat 5 imagery were missing or unusable.)

(718,848 acres) and cultivated cropland identified by the 2006 

NLCD (image analysis) (825,317 acres). 

Spatial comparisons between irrigated acres obtained 

from the crop-demand method and geostatistical techniques, 

and the previous discussion of limitations of the latter estimation method, indicate inaccuracies in irrigation-depth estimates 

at the geographic limits of the meter data (fig. 11A). Geostatistical techniques yielded erroneous zero-valued estimates of 

irrigation depth along the edges of the ACF–FAS region during 2008–11, but correctly and automatically estimated irrigation depth at unmetered fields encompassed by monthly meter 

data as part of the interpolation process. Appropriate redesign 

of the meter network, such as the network used for the 2012 

growing season, eliminated interpolation errors by encompassing the ACF–FAS region with data, which allowed geostatistical techniques to provide accurate estimates of irrigation 

depth along the ACF–FAS boundary (fig. 11B). In contrast, 

the crop-demand method estimated non-zero values of irrigation depth at fields located along the edges of the ACF–FAS 

region. The standard reference and assumed constant conditions used to evaluate meteorological and agronomic parameters contained in crop-demand calculations, however, could 

compromise the accuracy of irrigation-withdrawal estimates 

at all fields located in the ACF–FAS regardless of proximity 

to region boundaries. The ability of geostatistical techniques 

to provide complete spatial distributions of irrigation depth in 

the ACF–FAS demonstrates the robustness of geostatistical 

techniques to estimate irrigation depth at non-network (unmetered) fields, without additional calculations, data collection, 

or parameterization.

Assessment of Results with Data from Monthly 

Meter Sites 

Monthly meter-network data provided a sample of known 

irrigation withdrawal for comparison with estimates derived 

from the crop-demand method and for comparison with the 

image-analysis designation of irrigated acres. Irrigation-depth 

estimates at the monthly meter-data sites derived from geostatistical techniques were not compared because conditional 

simulation honors the monthly meter data and interpolates 

at unmetered locations in the ACF–FAS. That is, by design, 

conditional simulation (geostatistical techniques) identically 

“estimates” the monthly meter data at the meter sites. In addition, a subset of meter sites where crop identification information was available provided the basis for an assessment of crop 

identification accuracy, a driving factor for determining cropdemand estimates. The 2010 and 2012 growing seasons were 

selected for use in these comparisons because (1) the distribution (network) of monthly meters was redesigned for the 2012 

growing season from the network used during the 2007–11 

growing seasons, which allowed irrigation-depth estimates 
18 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

84°48'00" 84°37'30"

31°27'00"

31°16'30"

EXPLANATION

Agricultural lands

Image analysis

Irrigated soil

Irrigated emergent crops

Non-irrigated cropland

Non-agricultural land

0 2 3 4 5 MILES 1

0 1 2 3 4 5 KILOMETERS

Study area

GA

AL

FL

Map

area

A

Figure 10. Sub-region of National Water Census focus area study of the Apalachicola-Chattahoochee-Flint River Basin (ACF–

FAS) depicting conflicting irrigated lands by comparing A, image analysis and the Georgia Department of Natural Resources

(GaDNR) Agricultural Lands dataset; B, crop-demand method and the GaDNR Agricultural Lands dataset; and C, crop-demand 

method and image analysis. (Georgia Department of Natural Resources Agricultural Lands dataset from Danna Betts, Georgia 

Department of Natural Resources, written commun., May 2012.)
Comparison of Methods to Estimate Irrigation Withdrawal 19

84°48'00" 84°37'30"

31°27'00"

31°16'30"

Irrigated lands-crop demand method

EXPLANATION

Agricultural lands

Non-agricultural land

0 2 3 4 5 MILES 1

0 1 2 3 4 5 KILOMETERS

Study area

GA

AL

FL

Map

area

B

Figure 10. Sub-region of National Water Census focus area study of the Apalachicola-Chattahoochee-Flint River Basin (ACF–

FAS) depicting conflicting irrigated lands by comparing A, image analysis and the Georgia Department of Natural Resources 

(GaDNR) Agricultural Lands dataset; B, crop-demand method and the GaDNR Agricultural Lands dataset; and C, crop-demand 

method and image analysis. (Georgia Department of Natural Resources Agricultural Lands dataset from Danna Betts, Georgia 

Department of Natural Resources, written commun., May 2012.)—Continued
20 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

84°48'00" 84°37'30"

31°27'00"

31°16'30"

Irrigated—both estimates

Irrigated lands—crop 

 demand method

Image analysis

Irrigated soil

Irrigated emergent crops

Non-irrigated cropland

EXPLANATION

Non-agricultural land

0 2 3 4 5 MILES 1

0 1 2 3 4 5 KILOMETERS

Study area

GA

AL

FL

Map

area

C

Figure 10. Sub-region of National Water Census focus area study of the Apalachicola-Chattahoochee-Flint River Basin (ACF–

FAS) depicting conflicting irrigated lands by comparing A, image analysis and the Georgia Department of Natural Resources 

(GaDNR) Agricultural Lands dataset; B, crop-demand method and the GaDNR Agricultural Lands dataset; and C, crop-demand 

method and image analysis. (Georgia Department of Natural Resources Agricultural Lands dataset from Danna Betts, Georgia 

Department of Natural Resources, written commun., May 2012.)—Continued
Comparison of Methods to Estimate Irrigation Withdrawal 21

LEE

EARLY

WORTH

GRADY THOMAS DECATUR

SUMTER

BAKER

STEWART

DOOLY

CLAY

CRISP

RANDOLPH

TERRELL

MILLER

COLQUITT

CALHOUN

DOUGHERTY

SEMINOLE

WEBSTER

QUITMAN

MARION SCHLEY

TURNER

MACON

CHATTAHOOCHEE

BROOKS

MITCHELL

85° 84°

32°

31°

0 5 10 15 20 MILES

0 10 15 20 KILOMETERS 5

EXPLANATION

Study area

Geostatistical techniques, 

 June 2010

Non-zero water-use estimate

Zero water-use estimate

Base modified from U.S. Geological Survey

1:2,000,000-scale digital data

Albers Equal-Area Conic projection

North American Datum of 1983

A

Figure 11. A, Estimation of zero irrigation depth (geostatistical techniques) along the perimeter of the National Water Census 

focus area study of the Apalachicola-Chattahoochee-Flint River Basin (ACF–FAS) region compared to the crop-demand 

estimates of nonzero irrigation for 2010 and B, 2012 estimates of irrigation depth (geostatistical techniques) showing nonzero 

estimates of irrigation depth along the ACF–FAS boundary. 
22 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

LEE

EARLY

WORTH

GRADY THOMAS DECATUR

SUMTER

BAKER

STEWART

DOOLY

CLAY

CRISP

RANDOLPH

TERRELL

MILLER

COLQUITT

CALHOUN

DOUGHERTY

SEMINOLE

WEBSTER

QUITMAN

MARION SCHLEY

TURNER

MACON

CHATTAHOOCHEE

BROOKS

MITCHELL

85° 84°

32°

31°

0 5 10 15 20 MILES

0 10 15 20 KILOMETERS 5

EXPLANATION

Study area

Geostatistical techniques, 

 June 2012

Non-zero water-use estimate

Zero water-use estimate

Base modified from U.S. Geological Survey

1:2,000,000-scale digital data

Albers Equal-Area Conic projection

North American Datum of 1983

B

Figure 11. A, Estimation of zero irrigation depth (geostatistical techniques) along the perimeter of the National Water Census 

focus area study of the Apalachicola-Chattahoochee-Flint River Basin (ACF–FAS) region compared to the crop-demand 

estimates of nonzero irrigation for 2010 and B, 2012 estimates of irrigation depth (geostatistical techniques) showing nonzero 

estimates of irrigation depth along the ACF–FAS boundary.—Continued
Comparison of Methods to Estimate Irrigation Withdrawal 23

Table 2. Irrigated lands identified exclusively by the crop-demand and image analysis methods. These estimates are not 

represented in the GaDNR Agricultural Lands dataset, which includes 544,235 acres for every month of the 2010 growing 

season.

[Georgia Department of Natural Resources (GaDNR) Agricultural Lands dataset (Danna Betts, Georgia Department of Natural Resources, 

written commun., May 2012); —, no data]

Estimation

method

Identified irrigated acres

June 2010 July 2010 August 2010 September 2010 October 2010

Crop demand 253,797 242,263 91,680 103,881 104,472

Image analysis 54,393 139,499 — 69,731 22,508

to be compared against two distinct sets of known irrigation 

withdrawal represented by the monthly meter networks; (2) 

the monthly meter data for these years contained crop identification information; and, (3) the image-analysis estimates were 

available for 2010. 

The distinction between the monthly meter networks used 

for the 2010 and 2012 growing seasons resulted in varying 

amounts of meter data used to compare with estimates of irrigation withdrawal by the crop-demand method and geostatistical techniques. During the 2010 growing season, the monthly 

meter network yielded actual withdrawal data from 30 sites 

during October; 40 sites each during June and September; and 

48 sites each during July and August. Meter data were not 

available during March–May 2010, as described previously in 

the Comparison of Methods to Estimate Irrigation Withdrawal 

section. During the 2012 growing season, the new monthly 

meter-network design resulted in a larger and more consistent 

number of monthly meter data by month (number of meter 

data in parentheses): March–May (90), June (86), July (87), 

August (88), September (84), and October (85). 

Direct comparison of irrigation-withdrawal estimates 

to meter data was made in a GIS environment where field 

areas associated with meter sites were spatially joined to 

overlapping (coincident) irrigated areas identified with the 

crop-demand method and image analysis. The exact number 

of irrigated acres associated with monthly meter sites could 

not be compared with the spatially joined irrigated acres 

associated with the crop-demand method and image analysis 

method because of the limitations discussed previously in the 

Parameterization and Limitations and the Limitations sections. 

Specifically, the limitations related to the identification of 

agricultural lands using the NLCD and CDL datasets. Therefore, only designated fields from the crop-demand method and 

image analysis that overlap with the meter-identified fields 

were used as comparable acres in the analysis.

Comparison of irrigated acres associated with metered 

sites and image analysis indicates that image analysis underidentified irrigated fields that were defined with meter data 

as being irrigated (see example in fig. 12A). The previously 

discussed limitations of the image analysis method in the 

Limitations section, namely the irregular and long 16-day 

repeat interval for imagery and the meteorological interference 

of clear-sky conditions, adversely affected the ability of satellite imagery to identify irrigated land on a monthly basis. 

Assignment of constant values of irrigated acres to 

specific meters during the growing season could contribute 

toward differences in the identification of irrigated acres by 

image analysis and meter data (fig. 12B). Image analysis 

provided a temporal distribution of irrigated acres during the 

growing season, whereas acres assigned to specific meters 

were held constant throughout the growing season, although 

crop patterns and irrigated acres could change during the 

growing season. For example, double-cropping of corn followed by cotton or peanut on some of the previously irrigated 

acres (for corn) would not be represented in the irrigated acres 

assigned to the meter data. 

Comparisons of irrigation-withdrawal estimates from the 

crop-demand method with the monthly meter data for selected 

months during the 2010 and 2012 growing seasons show 

marginal similarities (fig. 13). For example, in October 2012, 

the GID calculated using the crop-demand method was a poor 

match for the metered amount of water applied to the fields. 

Limitations related to using standard reference and constant 

values for agronomic and meteorological parameters in GID 

calculations most likely resulted in withdrawal estimates from 

the crop-demand method showing little or no relation to meter 

values over the entire range of values for metered withdrawal. 

Irrigated acres used to calculate GID differ from irrigated 

acres assigned to meter data, limiting comparisons of irrigation withdrawal calculations by the crop-demand method with 

that documented by monthly meter data (fig. 14). Irrigated 

acres estimated by the crop-demand method encompass multiple meters; the acres of these additional meters may or may 

not be incorporated in the CDL datasets for calculations of 

GID. These meters were not part of the monthly meter dataset, 

but were read annually. 

Incongruities in agricultural acres designations by the 

CDL datasets and meter data contribute toward over- or 

under-estimation of GID by the crop-demand method. Additional acres contained in the CDL datasets that are neither 

identified by annual nor monthly meters contribute toward 

over-estimation of GID. The resulting disagreement of actual 

irrigation withdrawal (and acres) represented by the monthly 

meter data and that which should be used for comparison with 
24 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

84°34' 84°33' 84°32' 84°31'

31°15'

31°14'

31°13'

31°12'

31°11'

Monthly meter

Monthly meter field

Irrigated land-image analysis

0 0.5 1 MILE

0 0.5 1 KILOMETER

EXPLANATION Study area

GA

AL

FL

Map

area

A

Figure 12. Irrigated land identified by image analysis for A, under-identified irrigated acres where meter 

data recorded irrigation, and B, comparison with constant assignment of irrigated acres to metered 

withdrawal.
Comparison of Methods to Estimate Irrigation Withdrawal 25

84°35' 84°34'

31°26'

31°25'

0 0.25 0.5 MILE

0 0.25 0.5 KILOMETER

Monthly meter

Monthly meter field

Irrigated land-image analysis

Study area EXPLANATION

GA

AL

FL

Map

area

B

Figure 12. Irrigated land identified by image analysis for A, under-identified irrigated acres where meter 

data recorded irrigation, and B, comparison with constant assignment of irrigated acres to metered 

withdrawal.—Continued
26 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

0 

0.5 

1.0

1.5 

2.0

2.5 

3.0

3.5

4.0

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 

Crop demad estimates of irrigation withdrawal, in million gallons per day

June 2010 Mgal/d 

6

5

4

3

2

1

0

0 1 2 3 4 5 6 

July 2010 Mgal/d 

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 

August 2010 Mgal/d 

0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

3.5 

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 

September 2010 Mgal/d 

0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

1.6 

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 

October 2010 Mgal/d 

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 

April 2012 Mgal/d 

Monthly metering site irrigation withdrawal, in million gallons per day (Mgal/d)

Figure 13. Crop-demand estimates of irrigation withdrawal in million gallons per day compared to corresponding monthly metering site 

irrigation withdrawal.
Comparison of Methods to Estimate Irrigation Withdrawal 27

0 

1 

2 

3 

4 

5 

6 

0 1 2 3 4 5 6 

May 2012 Mgal/d 

0 

1 

2 

3 

4 

5 

6 

0 1 2 3 4 5 6 

June 2012 Mgal/d 

0 

1 

2 

3 

4 

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 

Monthly metering site irrigation withdrawal, in million gallons per day (Mgal/d)

Crop demad estimates of irrigation withdrawal, in million gallons per day

July 2012 Mgal/d 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0 0.1 0.2 0.3 0.4 0.5 0.6 

August 2012 Mgal/d 

0 

0.1 

0.2 

0.3 

0.4 

0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 

September 2012 Mgal/d 

0 

0.05 

0.10 

0.15 

0.20 

0.25 

0.30 

0 0.05 0.10 0.15 0.20 0.25 0.30 

October 2012 Mgal/d 

Figure 13. Crop-demand estimates of irrigation withdrawal in million gallons per day compared to corresponding monthly metering site 

irrigation withdrawal.—Continued
28 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

84°20' 84°19' 84°18'

31°14'

31°13'

EXPLANATION

Annually read meter

Monthly meter

Monthly meter field

Annually read meter or unmetered field

Crop demand irrigated field and crop type

Cotton

Peanut

0 0.25 0.5 MILE

0 0.25 0.5 KILOMETER

Study area

GA

AL

FL

Map

area

Figure 14. Identified irrigated acres and crop type by the crop-demand method used in the GID calculation 

at meter sites.
Enhancement of Data Requirements for Estimating Irrigation Withdrawal 29

GID calculations (which could include acres associated with 

annually read meters) with the crop-demand method prohibits 

direct comparison of the accuracy of the GID at monthly meter 

sites (fig. 13). 

Crop types from the CDL datasets that were used in the 

development of the crop-demand estimates share little commonality with crop types identified in the meter-network data. 

Many annual meter sites listed crop-identification information for 2010 and 2012, which was compared with spatially 

coincident crop information identified by CDL datasets, upon 

which the crop-demand method relied for crop identification 

and evaluation of associated parameters for GID calculations. 

Not all of the meters provided a crop designation; therefore, 

only a subset of monthly meters containing crop-identification 

information was included in the assessment of estimation 

methods. Furthermore, crop-identification information associated with annual meter sites introduced uncertainty into the 

associations of crop(s) with the monthly meter data, as double 

or triple cropping during the growing season could not be 

discerned from crop information at the annual meter sites. 

Image analysis and meter data confirmed irrigation at some 

metered fields where crop identification is missing; however, 

the specific crop or purpose of the irrigation (such as preparation to cultivate or to test irrigation equipment) was unknown. 

Likewise, the CDL dataset does not provide information on 

crop changes throughout the year, such as multiple cropping, 

whereas the crop-demand method relies on crop identification to apply standard crop planting, maturity, and harvesting 

schedules to determine when to irrigate during the growing 

season, independent of actual soil or meteorological attributes 

associated with the field and independent of additional dates 

for double- or triple-crop planting and harvesting. 

Enhancement of Data Requirements for 

Estimating Irrigation Withdrawal

Enhanced data collection that accurately represents 

site-specific characteristics of crops, soil, weather, field 

size, and irrigation systems could improve the accuracy of 

irrigation-withdrawal estimates by each method. Though these 

enhancements can improve the consistency and dependability of irrigation-withdrawal estimates by each method, they 

should be weighed against the data collection effort required 

by each method to support meaningful water availability 

and use assessments. Gauging enhanced data collection in 

the context of the effort required by each method to produce 

equally meaningful water availability and use assessments will 

culminate in the selection of methods that provide dependable 

results with less parameterization (and data requirements) than 

other methods.

Each of the three methods to estimate irrigation withdrawal in the ACF–FAS described herein depended on accurate identification of one critical parameter—irrigated acres—

to estimate irrigation withdrawal. Accurate identification 

of irrigated acres would, in turn, improve the accuracy of 

irrigation-withdrawal estimates using the crop-demand method 

and geostatistical techniques. Accurate estimation of irrigated acres using image analysis could identically and solely 

enhance estimates of monthly irrigation withdrawal by either 

geostatistical techniques or the crop-demand method; therefore, enhanced identification of irrigated acres using image 

analysis would also improve the accuracy of irrigation-withdrawal estimates. 

Enhanced identification of irrigated acres through image 

analysis of National Aeronautical and Space Administration 

(NASA) Earth observation satellite data, available since this 

study began in 2011, would provide the single-most-valuable 

improvement to parameterization of irrigation-withdrawal 

estimates. Techniques to infill areas obscured by clouds using 

combinations of Landsat datasets (for example, Landsat 5 and 

Landsat 7, for retrospective study, and Landsat 7 and Landsat 

8, launched on February 11, 2013) could improve understanding of irrigation dynamics and better identify irrigated acres 

than current image analysis that uses only Landsat 5 data. 

The Landsat 8 operational land imager (OLI) includes a cirrus 

band (band 9: 1.360–1.390 micrometers [μm]) to provide better detection and subsequent filtering of high-altitude clouds 

that could enhance image analysis and the identification of 

irrigated acres, compared with cloud detection and filtering 

used on Landsat 5 imagery. 

Landsat 8 contains instrumentation for detecting land 

surface temperature to provide important information about 

irrigation withdrawal that can inform irrigated acres identification. The Thermal Infrared Sensor (TIRS) measures radiation 

emitted in two thermal bands of the electromagnetic spectrum. Similar to Landsat 5 120-meter resolution and Landsat 

7 60-meter resolution thermal data, the TIRS’s 100-meter 

resolution allows monitoring on a field-by-field basis for agriculture, which is vital for water managers (National Aeronautical and Space Administration Features Web site, accessed at 

http://www.nasa.gov/topics/technology/features/tirs-thermal.

html, on June 18, 2015). A well-callibrated Landsat 8-derived 

land-surface temperature product, similar to surface reflectance available from Landsat 5, would improve evapotranspiration calculations by reducing error caused by atmospheric 

effects on the thermal bands. 

Enhanced meteorological data to support improved 

estimation of irrigation withdrawal requires spatial and 

temporal resolution of crop patterns and climatology that 

affects evapotranspiration calculations as used in the cropdemand method and image analysis. The NASA Short-term 

Prediction Research and Transition (SPoRT) Center (National 

Aeronautic and Space Administration Short-term Prediction 

Research and Transition Center web site accessed at http://

weather.msfc.nasa.gov/sport/, on June 18, 2015) provides 

a real-time, high-resolution, daily version of the greenness 

vegetation fraction (GVF), termed “SPoRT-GVF” (Bell and 

others, 2012), which has the capability to reduce spatial and 

temporal bias and improve the evaluation of GVF for PRISM 

modeling. Daily SPoRT-GVF datasets have been produced 
30 Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin

since June 2010, and are projected on a 0.01° (approximately 

1-km) grid, compared with the monthly estimates used in 

PRISM modeling that are derived from 20-year old climatology at a 15-km resolution. Although SPoRT-GVF can improve 

evapotranspiration calculations, additional data enhancements to crop- and soil-specific characteristics would further 

reduce spatial and temporal bias, which affects the accuracy of 

irrigation withdrawal.

Integration of results from NASA’s soil moisture active 

passive (SMAP) satellite mission with Landsat 8 OLI and 

TIRS data products could provide detailed analysis of irrigated 

acres in the ACF–FAS, essential for consistent and dependable 

assessment of agricultural water availability and use, while 

informing farmers on the efficient use of water and energy 

to enhance agricultural production and increase the Nation’s 

and world’s food supply (NASA SMAP Mission Description, accessed on March 12, 2015, at http://smap.jpl.nasa.gov/

mission/description/). The SMAP satellite mission, launched 

on January 30, 2015, can provide soil moisture data within 

30 centimeters of land surface every 2–3 days on a 3-km grid 

that can be used to identify irrigated acres. 

Detailed analysis of SMAP data through a downscaling 

process involving thermal infrared (TIR) imagery acquired 

from low-altitude flights by unmanned aircraft systems (UAS) 

holds the potential to identify irrigated acres in the ACF–FAS. 

Research on methods to downscale SMAP soil moisture data 

to the agricultural field level by using geostatistical techniques 

applied to UAS-TIR data is expected to identify over- or 

under-irrigated agricultural land in the lower ChattahoocheeFlint River Basin as part of NASA’s SMAP Early Adopter 

Program to analyze pre-launch SMAP data (Moran and 

others, 2015).

Summary and Conclusions

The summary and conclusions address the findings of the 

evaluation and comparison of methods to estimate irrigation 

withdrawal for the National Water Census focus area study of 

the Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia. The focus area study identified methods for 

the consistent collection and analysis of water supply and use 

information, which is essential for providing a uniform and 

dependable national assessment of water availability and use. 

In this study, gathering and analyzing water supply and use 

information centered on describing three methods to estimate 

irrigation withdrawal as prototypes for future adaptation to the 

National Water Census. Each of the estimation methods were 

evaluated individually, and compared collectively, in the context of data availability and parameterization, and in the level 

of technical complexity and scope of data required to meet 

the goals of the National Water Census program. The evaluation identified time and spatial horizons for data collection 

that would influence the feasibility of incorporating any of the 

methods into the national program.

In general, none of the three methods described herein—

the crop-demand method, geostatistical techniques, or image 

analysis—represent a complete turnkey procedure for estimating irrigation withdrawal on any spatial (local or regional) or 

temporal (monthly or annual) extent. That is, each method 

would require additional information on agricultural practices 

during the growing season to constitute a complete process to 

assess agricultural water availability and use. The evaluation 

and comparison of these methods for the ACF–FAS are summarized in the following sections and resulted in the conclusions as described herein. 

Crop-Demand Method

Accurate estimates of irrigation withdrawal, expressed as 

the gross irrigation demand (GID) by the crop-demand method 

requires parameterization of meteorological, crop, and soil 

type conditions for current or anticipated spatial or temporal horizons that are uniquely suited to each irrigated field. 

Acquiring such site-specific data represents a time-, labor-, 

and cost-intensive effort. To alleviate this intractable task of 

data collection, standard reference values for meteorological 

and agronomic parameters were used to produce only general 

estimates of GID. Accurate estimates of irrigation withdrawal 

would require site-specific evaluation of these parameters 

throughout the growing season. 

Standard reference conditions for calculating GIDrarely 

exist in nature; however, estimation of GID by using standard 

methods was assessed for the National Water Census to determine the value of irrigation-withdrawal estimates calculated 

by the crop-demand method with readily available, non-sitespecific meteorological and agronomic data. Moreover, it is 

because of the extreme meteorological conditions and agronomic diversity in soils and crop patterns present in the ACF–

FAS that this pilot study for the National Water Census was 

completed; that is, to further identify methods that accurately 

estimate crop water demand and irrigation withdrawal beyond 

the general estimates provided by standard reference methods. 

Use of standard reference values for parameters that 

govern GID calculations prevents the crop-demand method 

from providing a dependable assessment of agricultural water 

availability and use in the ACF–FAS or in the National Water 

Census. The value of these general estimates of GID lies in 

their ability to be compared universally with GID estimates 

that are calculated for similar agricultural practices worldwide. 

The crop-demand method is best applied during a postagricultural, seasonal determination of irrigation withdrawal, 

compared with potential applications of the method that would 

provide near-term (real-time) estimation of irrigation withdrawal throughout the growing season. 

Geostatistical Techniques

Geostatistical techniques were used to estimate irrigation withdrawal with minimal parameterization—only data 
References Cited 31

defining the volume of applied irrigation and corresponding 

irrigated acres were required for estimating irrigation withdrawal. The ability of geostatistical techniques to generate 

consistent and dependable agricultural water availability 

and use in the ACF-FAS depended largely on accurate and 

complete temporal distributions of metered irrigation volume 

and irrigated acres associated with metered and non-metered 

agricultural fields. Judicious selection of irrigation sites for 

a monitor (or meter) network, based on the application of 

geostatistical techniques, would yield accurate, spatially and 

temporally relevant irrigation withdrawal data, and provide 

the basis for geospatial models to estimate irrigation withdrawal in a dependable national assessment of agricultural 

water availability and use for the ACF–FAS and the National 

Water Census. 

Knowledge of the patterning and amount of irrigation 

withdrawal from a spatially and temporally representative monitor network, made possible through geostatistical 

analysis, can contribute valuable insight toward creating a 

uniform and dependable national assessment of agricultural 

water availability and use, and can assist water managers and 

scientists alike in developing future resource development and 

conservation strategies. Geostatistical techniques contain the 

analytics to design spatially and temporally relevant monitor networks that integrate correlation structures of data sites 

into a network for collecting unbiased, irrigation volume and 

acres information. Similarly, network data are analyzed in a 

geostatistical framework consistent with the techniques used 

in network design; that is, conditional simulation and variance 

analysis, thereby producing a consistent and uniform process 

to assess water availability and use, and to evaluate estimation 

accuracy in a spatial and temporal context. 

Image Analysis

Image analysis integrated public domain, spatially and 

temporally referenced, Landsat 5 imagery and meteorological 

data into a multistep process of mathematical modeling that 

yielded representations of irrigated land for assessing irrigation withdrawal for the ACF-FAS. Inconsistencies in spatial 

and temporal Landsat 5 imagery, caused by atmospheric 

interference and limitations related to satellite image acquisition, limited the image analysis method to produce accurate 

and dependable estimates of irrigation withdrawal to meet the 

goals established for the National Water Census. 

Emerging satellite-data products, such as Landsat 8 and 

SMAP, and techniques for data analysis, such as NASA’s 

SPoRT-GVF, have the potential to generate high spatialresolution distributions of irrigated-acres at temporal frequencies compatible with the goals of the National Water Census. 

Advancements in image analysis such as these have the potential to alleviate current hindrances to optical imaging caused 

by haze and clouds, and to provide valuable insight regarding 

detailed recognition of the spatial variability of irrigation in 

the midst of varying climatic conditions. 

References Cited

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Manuscript approved August 25, 2015

Prepared by the USGS Science Publishing Network

Raleigh Publishing Service Center

Edited by Teri Losano

Illustrations by Caryl J. Wipperfurth

Layout by Denis Sun

For more information concerning this report, contact:

Director, South Atlantic Water Science Center

North Carolina–South Carolina–Georgia

720 Gracern Road, Suite 129

Columbia, SC 29210

Phone: (803) 750-6100

http://ga.water.usgs.gov/
Painter and others—Methods to Estimate Withdrawal for the Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin—Scientific Investigations Report 2015–5118

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155118